Sequence mismatch between gene-drive and target-site flanking regions significantly impairs homing efficiency in Culex quinquefasciatus

Sequence mismatch between gene-drive and target-site flanking regions significantly impairs homing efficiency in Culex quinquefasciatus | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Sequence mismatch between gene-drive and target-site flanking regions significantly impairs homing efficiency in Culex quinquefasciatus View ORCID Profile T. Harvey-Samuel , R. Kaur , View ORCID Profile P.T. Leftwich , X. Feng , V. Gantz , L. Alphey doi: https://doi.org/10.1101/2025.08.01.668174 T. Harvey-Samuel 1 School of Life Sciences, Keele University , Keele, Staffordshire, ST5 5GB, UK 2 Arthropod Genetics, The Pirbright Institute , Woking, UK, GU24 0NF, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for T. Harvey-Samuel R. Kaur 2 Arthropod Genetics, The Pirbright Institute , Woking, UK, GU24 0NF, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site P.T. Leftwich 3 School of Biological Sciences, University of East Anglia, Norwich Research Park , Norwich, NR4 7TJ, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for P.T. Leftwich X. Feng 4 Key Laboratory of Biology of Crop Pathogens and Insects of Zhejiang Province, College of Agriculture and Biotechnology, Zhejiang University , Hangzhou, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site V. Gantz 5 Section of Cell and Developmental Biology, University of California San Diego , California, 92093, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site L. Alphey 6 Department of Biology, University of York , Wentworth Way, York, YO10 5DD, UK 7 York Biomedical Research Institute, University of York , Heslington, YO10 5DD, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: luke.alphey{at}york.ac.uk Abstract Full Text Info/History Metrics Preview PDF Abstract CRISPR/Cas9-based homing gene-drives (homing-drives) hold enormous potential as control tools for mosquito disease-vectors. These genomically-encoded technologies spread themselves through target populations by creating double-stranded DNA breaks on homologous chromosomes, into which the homing-drives are copied (‘homed’). Homing is dependent on sequence homology between the genomic regions flanking the transgene insertion and the break site. Homing efficiency (i.e. copying rate) substantially impacts the power of these systems: less efficient homing-drives spread slower, have fewer applications and are more resistance-prone. Understanding what influences homing-drive efficiency is therefore vital to the successful use of these technologies. Here we report a novel mechanism by which a homing-drive’s efficiency can be significantly impaired by natural sequence variation within a population into which it is spreading. Using a kmo -targeting ‘split’ homing-drive in the West Nile virus mosquito Culex quinquefasciatus , we found that target-site heterology (sequence mismatch between the genomic regions flanking the target cut-site and the homing-drive transgene) of less than 10% reduced homing efficiency by up to 54%. While substantial research effort has been dedicated to increasing homing-drive efficiency through optimisation of within-construct components, our results highlight that the real-world efficacy of these systems may in part depend on variation beyond these controllable factors. Introduction CRISPR/Cas9 ‘homing’ gene-drives (henceforth homing-drives) are genomically encoded technologies which hold immense promise for controlling intractable pests, with notable development in disease-vectoring mosquitoes( 1 – 7 ). A homing-drive increases its allele frequency from one generation to the next by copying (‘homing’) itself from one homologous chromosome to another in germline cells ( 8 ). For pest management, a homing-drive can be designed to simultaneously spread traits beneficial for control e.g. pathogen refractoriness or a genetic load for population suppression/eradication ( 9 ). The efficiency of the homing reaction substantially influences the population-level efficacy of a homing-drive: higher homing rates enable faster spread and higher tolerance of drive-associated fitness costs ( 10 ). At a molecular level, homing occurs when Cas9 expressed by a homing-drive transgene creates a double-stranded DNA break (henceforth ‘cut-site’) at a specific sequence on the target chromosome, into which the homing-drive is homed ( 11 ) ( Fig 1 .). Homing is mediated by the homology-directed-repair (HDR) pathway, requiring the genomic sequences flanking the homing-drive transgene insertion-site and the target allele cut-site to show homology. While substantial effort has been devoted to exploring how ‘within-drive’ components (e.g. regulatory elements for expressing Cas9 ( 12 , 13 )) affect homing rates, comparatively little attention has been paid to the role of these genomic flanking regions in influencing homing efficiency. This is surprising as the impact of even minor sequence divergence between loci in substantially reducing homologous recombination rates is well established ( 14 ). In the real-world, homing-drives will be required to function in genetically diverse ‘wild’ populations, which will likely also include pre-existing heterogeneity around the cut-site. Whether sequence mismatch (henceforth ‘heterology’) between a homing-drive’s flanking genomic regions and those flanking its target allele cut-site influences homing rates is thus of considerable importance. Download figure Open in new tab Figure 1: Schematic representations of CRISPR-Cas9 based homing gene-drive technologies. A) A ‘global’ homing gene-drive in which all the molecular components required to initiate the homing reaction are linked and integrated at a single locus in the genome which conforms to the target site of the integrated gRNA. Expression of the Cas9 enzyme and gRNA thus mediates the creation of a double-stranded break (DSB) at the insertion locus on the homologous chromosome. This DSB can be repaired through the Homology-Dependent Repair (HDR) pathway which results in the copying of the gene-drive transgene onto the homologous chromosome. If this process occurs in germline cells, it will result in super-mendelian inheritance of the gene-drive. The efficiency of HDR is dependent on homology between the sequence used as the repair template and the sequences flanking the DSB. These sequences, however, are not always perfectly matched, as shown in the schematic (specifically, γ and θ flanking sequences show heterology). B) Schematic representation of a ‘split’ homing gene-drive, here where the gRNA-cassette is located within the homing locus (Locus B) as with a global gene-drive design, but the Cas9-cassette is now located at an unlinked, non-homing, location (Locus A). C) The mosquito line crossing scheme for combining the Cas9-transgene and the gRNA-transgene in a split-drive system. In the ‘grandparental’ F0 generation, the two lines are crossed. In the ‘parental’ F1 generation, individuals carrying both transgenes (trans-heterozygotes) are isolated and crossed to wild type, producing the F2 generation where transgene fluorescence inheritance ratios are recorded in order to estimate homing rates. In this representation, Locus B (i.e. that containing the gRNA cassette, aka the homing element) is coloured in blue, while Locus A (i.e. that containing the Cas9 cassette) is coloured in green. We explored this question using our homing-drive system in the West-Nile-virus vector mosquito Culex quinquefasciatus ( 5 ). This system consists of a ‘split-drive’ design where the two homing-drive components (germline-expressing Cas9 and gRNA-expressing cassettes) are integrated at independent loci - one of which (here the gRNA-cassette) is in the target allele - and maintained in separate lines ( 11 ). The two lines can then be crossed together to initiate the homing reaction in trans-heterozygotes. By using the kynurinine 3-monooxygenase ( kmo ) gene as our target allele (where homozygous null mutations give an easily visualized ‘white-eye’ phenotype ( 15 )) and conducting all comparison crosses in a ‘controlled’ genetic background, we were able to unambiguously distinguish Cas9-mediated cutting and homing, and attribute variation in these to individual kmo target alleles which displayed varying levels of heterology. This experimental design provided unparalleled power to assess this fundamentally important question. Results and Discussion Our aim was to investigate the effect of varying the heterology of sequences flanking the cut-site of a target allele on a homing-drive’s homing efficiency. We assessed this at two distinct levels, each specifically designed to give insight into the behavior of this potential effect. Firstly, we took an ‘allele-by-allele’ approach – assessing a homing-drive’s efficiency as it was paired against four different kmo target alleles in a controlled genetic background. Secondly, we extended this analysis to a broader range of cross schemes and sexes to assess the generality of findings. 1: Allele-by-allele approach In ‘split’ homing-drives, one of the expression cassettes (here, that expressing the gRNA) is integrated into the target allele at the precise cut-site, forming the ‘homing-element’. This can then be combined with the other element, here encoding Cas9, to provide trans-heterozygotes (aka “double heterozygotes”) which have one copy each of Cas9 and homing-element and so can catalyse the homing reaction. Furthermore, and crucially for our purpose, in this set-up, the target allele into which a homing-element attempts to home will always come from the line (and therefore the genetic background) which contributed the Cas9 transgene. Preliminary analysis of our highly inbred wild-type (WT) line (in which the kmo -gRNA line was generated and is outcrossed to each generation – TPRI genetic background – see methods) and the Vasa -Cas9 line (which was generated separately in the CA genetic background – see methods) showed that these two lines possessed highly divergent kmo allele sequences. Those carrying the TPRI genetic background contained only a single kmo sequence (allele A) which perfectly matched the sequences flanking the integrated kmo -gRNA homing-element, while the Vasa -Cas9 line contained three different kmo allele sequences (alleles B, C and D) (See figure 2A ). We took advantage of this to design an assay in which a Cas9-bearing individual could contribute a variety of target alleles to a homing cross, some but not all of which would match the homing-element flanking sequences but, critically, all of which would end up in the same mixed genetic background (see figure 2B ). These female trans-heterozygotes ( kmo -gRNA/+ A,B,C or D , Vasa -Cas9/+) were then crossed to male kmo -/- homozygotes to allow us to detect both homing (deviation in inheritance rate of homing-element from Mendelian expectation of 50%) and the rate of end-joining (individuals inheriting loss-of-function kmo alleles from their mother, generated via Cas9 cutting followed by error-prone repair, will have white eyes but lack the fluorescent marker of the homing-element) in their progeny. This assay showed a highly significant effect of the kmo target allele (i.e. A,B,C or D) on homing rate (LRT: χ 2 3 = 16.17, p = 0.001), with a 54% difference in estimated homing rates associated with the highest (A = 0.37 [95% CI; 0.31 – 0.42]) and the lowest (D = 0.17 [0.04 – 0.29]) kmo target alleles ( Figure 3 , Supplementary Figure 1, Supplementary Table 1, Supplementary Table 2). Preliminary sequencing confirmed that the gRNA target sequence was present in all four kmo alleles and this was confirmed by our comparison of estimated cutting rates (i.e. estimated homing + observed end-joining) between the kmo alleles, which did not significantly differ (LRT: χ 2 3 = 5.27, p = 0.15; Supplementary Figure 1, Supplementary Table 1, Supplementary Table 2). This suggests that the differences in homing we observed between the four target kmo alleles were not simply driven by Cas9 being able to cut some alleles more efficiently than others. Instead, taken together, these data strongly suggest that the mechanism underlying the observed reduction in homing is impairment of homology-directed repair through drive-target allele flanking sequence mismatch. Download figure Open in new tab Figure 2: Details of the kmo locus sequence targeted and genetic background of lines crossed in Assay 1 (allele-by-allele approach). A) Representations of the four kmo alleles identified through Sanger sequencing. Allele A was the only allele observed in the ‘TPRI’ genetic background and showed 100% homology over the sequenced region to the kmo -gRNA integrated homing element (0 mismatches over 776bp). Alleles B, C and D were the only alleles observed in the ‘CA’ genetic background and showed varying levels of heterology over the sequenced region to the kmo -gRNA integrated homing element. B) Schematic showing how introgression of the two genetic backgrounds allowed investigation of the influence of target allele identity on homing rates. In brief, in the F0 generation, individuals carrying the Vasa -Cas9 transgene (which resides in the CA genetic background – represented in orange) were first crossed to wild-type individuals possessing a TPRI genetic background (represented in blue). Vasa -Cas9 heterozygotes, now in a hybrid CA/TPRI genetic background were then crossed to individuals from the kmo -gRNA line (which possesses a TPRI genetic background) (F1 generation). This produced the F2 generation where the kmo-gRNA cassette was present opposite all four identified kmo alleles, in proportion to their frequencies in the F1 Vasa -Cas9 heterozygotes. This allowed the homing reaction to take place into four separate target alleles, at the same locus, in a controlled genetic background (shown by curved arrows). In this generation, trans-heterozygotes were crossed to individuals from a kmo -/- strain (CA genetic background). This produced the F3 generation where, for each egg raft, fluorescence ratios and eye phenotypes were recorded and the target kmo allele present In the F2 trans-heterozygote which produced that egg raft identified through PCR. Download figure Open in new tab Figure 3: Results from Assay 1 homing crosses: A) ‘Cutting rates’ estimated for the four identified kmo target alleles in the F2 Vasa -Cas9/ kmo -gRNA trans-heterozygotes. Here cutting was calculated as the sum of NHEJ (signified by white-eye phenotype individuals) and HDR (derived from kmo -gRNA inheritance >50%) events observed in the F3 progeny. B) ‘Homing rates’ estimated for the four identified kmo target alleles in the F2 Vasa -Cas9/ kmo -gRNA trans-heterozygotes. Here homing rate was calculated as the % of wild-type alleles converted to bear the homing-drive cassette in the trans-heterozygote germline (derived from level of kmo -gRNA inheritance >50%). As such, a homing rate of 50% would represent half the wild-type alleles in the F2 trans-heterozygote germline being converted to homing-element alleles, and an overall inheritance rate of the kmo -gRNA transgene of 75%. For both A) and B) Large symbols and error bars (vertical lines) represent mean and 95% confidence intervals calculated by a generalized linear mixed model with a binomial (‘logit’ link) error distribution, individual points to the right of each estimated mean represent pools of offspring derived from a single female parent. Relative size of the small points is in proportion to the number of individuals recorded for that data point (batch size). C) Raw data values of recorded ‘Homing rates’ as a percentage of the pooled total of ‘Cutting rates’ for each of the four identified kmo target alleles. Raw data proportions (C) represent unadjusted observations, while model-estimated means (A &B) account for covariates and random effects. 2: Generalised approach In the real world, homing-drive transgenes could be inherited from, and be active in, either sex. Previous work has shown that the efficiencies and dynamics of homing-drives can vary dramatically dependent on direction of inheritance (paternal/maternal) in parents and grandparents (e.g. ( 4 , 16 , 17 )). As such, we were interested in investigating the extent to which the observed influence of heterology on homing rates was maintained when a wider variety of cross combinations were considered. To achieve this, we first introgressed the Vasa -Cas9 transgene into the TPRI genetic background by outcrossing Vasa -Cas9 individuals to our TPRI wild-type line for three consecutive generations. This TPRI introgressed Vasa -Cas9 line (henceforth Vasa -Cas9 i ) was then crossed to the kmo -gRNA line in a full factorial homing-drive cross (i.e. where the Vasa -Cas9 and kmo -gRNA transgenes were inherited from both grandparental (F0) sexes and the trans-heterozygous (F1) individuals were of either sex) and results compared to our previously published homing analysis of these two transgenes - in that case where the Vasa -Cas9 transgene was in a pure CA genetic background ( 5 ). We found that in all like-for-like comparisons the homing rates in this study were higher than in our previous study (aggregate difference 57.8% - 69.3%), only in the male-male crosses was this increase not statistically significant ( Figure 4 , Supplementary Table 3). These results further support our conclusion that target-site/homing-element flanking-sequence heterology can significantly affect homing efficiency. Download figure Open in new tab Figure 4: Comparison of estimated homing rates from ( 5 ) ‘Original’ (i.e. Harvey-Samuel & Feng, 2023) and Assay 2 (i.e. Generalised approach - this study). In each case, a full factorial split-drive cross setup was applied (i.e. testing all combinations of grandparental and parental sex on estimated homing rates). Facets separate offspring according to the grandparent and parent from which they inherited the Vasa -Cas9 transgene. Large symbols and error bars represent mean and 95% confidence intervals, small points represent raw data. Relative size of the small points is in proportion to the number of individuals recorded for this data point (batch size). Annotated asterisks represent pairwise significant difference tests at (n.s. = non-significant, * <0.05, **<0.01, ***1%) flanking-sequence heterology on homing-drive performance (in Anopheles gambiae ) ( 18 ). In contrast to the results presented here, that study observed no significant influence of target-site heterology on homing-efficacy, despite similar levels of sequence divergence (5.3-6.6% over c.690bp). What might explain the differences between this finding and ours? We propose three non-exclusive possibilities. The first involves the genetic backgrounds in which these crosses were conducted. Pescod et al . ( 18 )crossed their homing-drive directly into different geographically distinct An. gambiae strains, each of which contained divergent target alleles. As such, each of the different homing-drive/target allele assessments occurred in a distinct genetic background. As it has been shown that (as with ‘natural’ homologous recombination( 19 )) many loci unlinked to a homing-drive may exert significant influence over homing efficiencies ( 20 ), this experimental design may have introduced additional variation, potentially obscuring the effects as observed here. We were able to control for these potential epistatic effects by crossing, and therefore partially introgressing, our Vasa -Cas9 line into the TPRI background prior to homing assays, ensuring each of our kmo target allele comparisons occurred within (on average) the same, mixed CA/TPRI genetic background. Secondly, there are potentially species-specific explanations for these contrasting results. Homing-drive efficiencies in Anopheline mosquitoes are uniquely high (often approaching or reaching 100%) (e.g. ( 21 )). The precise reason for this is unknown but could include less stringent requirements on engaging the HDR pathway e.g. higher tolerance to flanking-sequence heterology, or smaller flanking sequences required to initiate homing. Indeed, analysis of homing conversion tract lengths in An. gambiae (i.e the degree to which the flanking regions of a target allele are converted to carry heterologous sequences contained within the homing-drive flanking regions) showed that (beyond the homing-drive cassette itself) only small sequences are transferred onto the target chromosome (>80% of conversion tracts <50bp)( 22 ). This is substantially shorter than reported for studies considering ‘natural’ homologous recombination in other species (typically c. 200bp ( 19 )) and may signify shorter required sequences for RAD51-dependent complementary strand recognition/invasion in Anophelines . In contrast, conversion tract lengths in Aedes aegypti , another Culicine mosquito, following CRISPR-Cas9 plasmid-based knock-in (also dependent on HDR, though likely occurring in somatic rather than germline cells) were substantially longer (mean of 160bp) ( 23 ). Interestingly, and in concurrence with our results, that study in Ae. aegypti also identified significant negative effects of plasmid/target-site heterology on the efficiency of knock-in. Taken together, these results could suggest that the HDR-pathway in An. gambiae is simply less affected by sequence heterology than in other species, including Culicine mosquitoes. Finally, the near-100% homing rates displayed by the drives tested in Pescod et al may have made it difficult to observe anything other than extremely large changes (i.e. reductions) in homing, potentially underestimating or missing ‘subtler’ modifiers of homing-efficiency – e.g. those caused by target alleles less amenable to homing. This is because in a homing-drive, inheritance-bias (i.e. level of homing) is described by odds (p/(1-p)) – where p = wild-type allele conversion probability - and the effect of a change in odds on the change in homing rate differs substantially depending on the initial level of homing (( 10 ), Figure 5 ). In essence, for homing-drives which display very high homing-rates, even large changes in odds will have only small effects on the observed levels of homing. This effect is much less pronounced in less efficient homing-drives, like that demonstrated here. The diminishing return on changes in odds means that, near 100% efficiency, subtle effects on homing may go unnoticed, complicating efforts to accurately evaluate or identify modifiers of highly effective homing-drives. Download figure Open in new tab Figure 5: Variation in observed probability change (e.g. change in homing) based on starting probability and Log-Odds effect size (e.g. change in odds of alle conversion) This heatmap illustrates how a standardized change in log-odds affects observed probability differences, depending on the initial probability level. The y-axis represents the starting probability of an event (here the level of homing), while the x-axis indicates the standardized effect size of a change in log-odds. The color gradient reflects the magnitude of the resulting probability difference, with lighter regions signifying larger changes. The effect of the same log-odds shift is most pronounced when initial probabilities of an event are in the mid-range (e.g. around 50%, i.e in the context of inheritance-bias - where homing is calculated at 0%) and diminishes as probabilities approach the extremes (near 100% inheritance/homing). This highlights the non-linear nature of probability changes in a logit model, where the same predictor shift has varying impacts depending on the starting likelihood of an event. In conclusion, our results demonstrate a novel factor affecting homing-drive efficiency. We show that heterology between a homing-drive’s flanking regions and those sequences flanking the target allele cut-site can significantly reduce that drive’s performance. A critical question is to what extent this effect could impact homing-drive performance in the real-world. In most cases it should be assumed that homing-drives could encounter significant levels of sequence variation upon release, particularly in continental areas where most of their deployments will likely occur. In our system, even the most divergent kmo allele tested was still able to be targeted for Cas9 cleavage and successfully converted to carry the homing-drive, albeit at a significantly reduced rate. If the percentage reduction in homing efficiency observed here were to translate to released strains, however, this could indeed substantially impede their spread through a target population (Supplementary Figure 2). While naturally high homing rates may mitigate this effect, for the vast majority of species which have been tested so far, reported homing rates are modest ( 5 , 7 , 24 – 26 ) and thus they may be, to a degree, affected by this mechanism. Prior knowledge of these effects for a given species, homing-drive locus and target population may allow future release programs to preempt adverse consequences on project outcomes, e.g. by increasing release rates accordingly to counteract predicted lower drive efficiency, or by introgressing the homing-drive into a particularly homing-recalcitrant allele ahead of releases. Additionally, our findings highlight the utility of exploring factors which affect homing-drive dynamics and performance in species/model-systems which do not display near-perfect performance. While ‘high-performance’ homing-drives may be the first in line for field-release, it is precisely their extreme efficiency which may make them less useful for investigating some of the underlying factors which affect the drive mechanism itself. Our results are particularly significant as gene-drives edge ever closer to field application. They suggest that, at least in some cases, homing-drive efficiencies estimated in genetically homogenous lab populations may be unrepresentative of those which would occur in the real-world. While lab-field discrepancies are well known for other parameters relevant to genetic biocontrol, e.g. mating success, longevity or other factors relating to fitness, this is the first study we are aware of which identifies such a disparity related to the homing mechanism itself. This information will be critical in helping to set realistic expectations of these potentially game-changing systems, if and when they enter the field-testing phase. Materials and methods Ethics Work followed procedures/protocols approved by The Pirbright Institute Biological Agents & Genetic Modification Safety Committee. All homing assays were conducted at The Pirbright Institute IS4L arthropod containment facility under the necessary safety regulations for gene-drive research. Mosquito lines used, rearing and maintenance The kmo- gRNA line was generated previously by integrating a kmo -gRNA expression cassette via CRISPR/Cas9 HDR into the kmo gene in the TPRI (Tropical Pesticides Research Institute) genetic background. The kmo -/- line was generated previously by CRISPR/Cas9 knockout of the kmo gene in the CA (California) genetic background. The Vasa -Cas9 line was generated previously by integrating a Cas9 ORF under the transcriptional control of Cx. quinquefasciatus Vasa regulatory elements into the Cardinal gene in the CA genetic background via CRISPR/Cas9 HDR. The Wild-type (WT) line is the unmodified TPRI line. Rearing/maintenance followed procedures previously reported. Mosquito experiments Assay 1: Allele-by-allele approach Homozygous Vasa -Cas9 females were first pool-crossed to WT males, producing heterozygous Vasa -Cas9 F1 progeny in a 50:50 TPRI/CA genetic background. Female F1 were then pool-crossed to heterozygous kmo -gRNA males to give the F2 generation. Trans-heterozygous Vasa -Cas9, kmo -gRNA female F2 progeny were then pool-crossed to kmo -/- males and F3 egg-rafts produced individually isolated, with each egg-raft being from an individual female trans-heterozygote. L3-4 stage progeny from each egg-raft were scored for presence of transgenes/eye-pigmentation (white-eyes), as previously demonstrated. Genotyping of target allele (Assay 1 only) After scoring, gDNA was extracted from a single WT or Vasa -Cas9 individual from each F3 egg-raft (Machery-Nagel Nucleospin Tissue kit, Düren, Germany). This was used to PCR-genotype the maternally-contributed kmo allele (i.e. the allele into which the kmo -gRNA had attempted to home in the previous generation). The paternally-contributed kmo allele was excluded by designing the reverse primer (PL506) to lie across a region deleted during the generation of the kmo -/- line. The primers were designed to bind to all 4 kmo alleles identified in a preliminary analysis of the strains utilized (A,B,C,D). PCR conditions as follows 98C-1min, (98C-30s, 72C-15s, 72C-1min) x 35, 72C-2min. 200ng of gDNA used per 50ul Q5 PCR Reaction (New England Biolabs). Primers = F(PL303): CCAACATTCACCTTCACTTCAACCACAAGC and R(PL506): GTGAGCGTCCTCCCACCGAG. Amplicons extended c.320/540bp upstream/downstream of the cut site – sufficient distance to distinguish the 4 alleles. Reactions were run on a 1% agarose gel and bands extracted using the Machery-nagel Nucleospin Gel and PCR clean-up kit. Purified bands were Sanger sequenced using the forward primer PL303 and sequenced amplicons aligned to the TPRI kmo sequence. Calculation of homing and cutting rates (Assay 1 only) Homing rates are defined as the percentage of homologous wild-type chromosomes converted to carry the kmo -gRNA transgene (i.e. the percentage of WT alleles which have been affected by homing). It was estimated for each egg-raft as (200 x (no. kmo -gRNA/total progeny – 0.5). Cutting rate is defined as the percentage of homologous wild-type chromosomes observed as having been cut by Cas9. This includes DSBs that were repaired via HDR (homing), NHEJ (or other error-prone mechanisms) but excludes repairs which resulted in no observable change to the WT allele (perfect repair). It was estimated for each egg-raft as the previously estimated homing rate + (200 x (no. non- kmo -gRNA progeny which showed white-eyes/total progeny – 0.5). That is, the HDR + error-prone pathway rates. Assay 2: Generalising findings Homozygous Vasa -Cas9 females were first pool-crossed to WT males, producing heterozygous Vasa -Cas9 F1 progeny in a 50:50 TPRI/CA genetic background. This was repeated twice to give F3 Vasa -Cas9 progeny in a predominantly TPRI genetic background (c. 87.5% TPRI alleles). Male and female Vasa -Cas9 heterozygote F3s were pool-crossed to female and male kmo -gRNA line heterozygotes, respectively, giving two cohorts in the F4 generation (one from each of the two F3 crosses). Trans-heterozygous male and female F4 progeny from each of these two crosses were pool-crossed to female and male kmo -/- individuals, respectively (4 crosses total), and the egg rafts produced individually isolated and scored as for Assay 1. Statistical analysis Homing and cutting rates were both analysed with generalized linear mixed models with a binomial (logit) error distribution. Fixed effects for Assay 1 were the genotyped maternally contributed kmo allele with individual female egg-batch as a random effect. Minor overdispersion in the cutting analysis models meant all 95% confidence intervals were checked by semi-parametric bootstrapping (1000 iterations). Close agreement between the two sets of intervals indicated minimal overdispersion, suggesting that the model assumptions were adequately met and both summaries are presented. For Assay 2 fixed effects included the sex of the Cas9-bearing parent and grandparent as well as whether the data came from the original (i.e. previously published) experiment or post-introgression (i.e. Assay 2). Individual female egg-batches were also included as a random effect. All models and simulations were constructed in R version 4.3.3 with the package lmerTest, model fits were checked with the package DHARMa and model predictions generated with the package emmeans. Data processing and visualization was performed with the tidyverse packages. Competing interests VG is a founder of and has equity interests in Symbol, Inc. and Agragene, Inc., companies that may potentially benefit from the research results described in this manuscript. VG also serves on both the company’s Scientific Advisory Board and the Board of Directors of Synbal, Inc. The terms of this arrangement have been reviewed and approved by the University of California, San Diego in accordance with its conflict of interest policies. LA is an advisor to, and has financial or equity interest in, Synvect Inc. and Biocentis Ltd., companies operating in the area of genetic control of pest insects. The other authors declare that they have no competing interests. Author contributions THS, XF, VG and LA conceived the project. RK, THS and PL contributed to the design of the experiments. THS, RK and PL performed the experiments and contributed to the collection and analysis of data. THS and PL wrote the manuscript. All authors edited the manuscript. Study Funding This work was supported by strategic funding from the UK Biotechnology and Biological Sciences Research Council (BBSRC) to The Pirbright Institute (BBS/E/I/00007033, BBS/E/I/00007038, and BBS/E/I/00007039). Data Availability The authors affirm that all data necessary for confirming the conclusions of the article are present within the article, figures, and tables. Strains and plasmids are available upon request. References 1. ↵ Alphey L. Genetic control of mosquitoes . Annu Rev Entomol . 2014 ; 59 : 205 – 24 . OpenUrl CrossRef PubMed Web of Science 2. Sinkins SP , Gould F. Gene drive systems for insect disease vectors . Nat Rev Genet . 2006 Jun 9; 7 ( 6 ): 427 – 35 . OpenUrl CrossRef PubMed Web of Science 3. Gantz VM , Jasinskiene N , Tatarenkova O , Fazekas A , Macias VM , Bier E , et al. Highly efficient Cas9-mediated gene drive for population modification of the malaria vector mosquito Anopheles stephensi . Proceedings of the National Academy of Sciences . 2015 Dec 8; 112 ( 49 ). 4. ↵ Anderson MAE , Gonzalez E , Edgington MP , Ang JXD , Purusothaman DK , Shackleford L , et al. A multiplexed, confinable CRISPR/Cas9 gene drive can propagate in caged Aedes aegypti populations . Nat Commun . 2024 Jan 25; 15 ( 1 ): 729 . OpenUrl PubMed 5. ↵ Harvey-Samuel T , Feng X , Okamoto EM , Purusothaman DK , Leftwich PT , Alphey L , et al. CRISPR-based gene drives generate super-Mendelian inheritance in the disease vector Culex quinquefasciatus . Nat Commun . 2023 Nov 20; 14 ( 1 ): 7561 . OpenUrl PubMed 6. Hammond A , Galizi R , Kyrou K , Simoni A , Siniscalchi C , Katsanos D , et al. A CRISPR-Cas9 gene drive system targeting female reproduction in the malaria mosquito vector Anopheles gambiae . Nat Biotechnol . 2016 Jan 1; 34 ( 1 ): 78 – 83 . OpenUrl CrossRef PubMed 7. ↵ Li M , Yang T , Kandul NP , Bui M , Gamez S , Raban R , et al. Development of a confinable gene drive system in the human disease vector Aedes aegypti . Elife . 2020 Jan 21;9. 8. ↵ Burt A. Site-specific selfish genes as tools for the control and genetic engineering of natural populations . Proc R Soc Lond B Biol Sci . 2003 May 7; 270 ( 1518 ): 921 – 8 . OpenUrl CrossRef PubMed Web of Science 9. ↵ Champer J , Buchman A , Akbari OS . Cheating evolution: engineering gene drives to manipulate the fate of wild populations . Nat Rev Genet . 2016 Mar 15; 17 ( 3 ): 146 – 59 . OpenUrl CrossRef PubMed 10. ↵ Deredec A , Burt A , Godfray HCJ . The Population Genetics of Using Homing Endonuclease Genes in Vector and Pest Management . Genetics . 2008 Aug 1; 179 ( 4 ): 2013 – 26 . OpenUrl Abstract / FREE Full Text 11. ↵ Esvelt KM , Smidler AL , Catteruccia F , Church GM . Concerning RNA-guided gene drives for the alteration of wild populations . Elife . 2014 Jul 17;3. 12. ↵ Hammond A , Karlsson X , Morianou I , Kyrou K , Beaghton A , Gribble M , et al. Regulating the expression of gene drives is key to increasing their invasive potential and the mitigation of resistance . PLoS Genet . 2021 Jan 29; 17 ( 1 ): e1009321 . OpenUrl CrossRef PubMed 13. ↵ Anderson MAE , Gonzalez E , Ang JXD , Shackleford L , Nevard K , Verkuijl SAN , et al. Closing the gap to effective gene drive in Aedes aegypti by exploiting germline regulatory elements . Nat Commun . 2023 Jan 20; 14 ( 1 ): 338 . OpenUrl CrossRef PubMed 14. ↵ Elliott B , Richardson C , Winderbaum J , Nickoloff JA , Jasin M. Gene Conversion Tracts from Double-Strand Break Repair in Mammalian Cells . Mol Cell Biol . 1998 Jan 1; 18 ( 1 ): 93 – 101 . OpenUrl CrossRef PubMed Web of Science 15. ↵ Purusothaman DK , Shackleford L , Anderson MAE , Harvey-Samuel T , Alphey L. CRISPR/Cas-9 mediated knock-in by homology dependent repair in the West Nile Virus vector Culex quinquefasciatus Say . Sci Rep . 2021 Jul 22; 11 ( 1 ): 14964 . OpenUrl CrossRef PubMed 16. ↵ López Del Amo V , Bishop AL , Sánchez C. HM , Bennett JB , Feng X , Marshall JM , et al. A transcomplementing gene drive provides a flexible platform for laboratory investigation and potential field deployment . Nat Commun . 2020 Jan 17; 11 ( 1 ): 352 . OpenUrl CrossRef PubMed 17. ↵ Terradas G , Buchman AB , Bennett JB , Shriner I , Marshall JM , Akbari OS , et al. Inherently confinable split-drive systems in Drosophila . Nat Commun . 2021 Mar 5; 12 ( 1 ): 1480 . OpenUrl PubMed 18. ↵ Pescod P , Bevivino G , Anthousi A , Shelton R , Shepherd J , Lombardo F , et al. Measuring the Impact of Genetic Heterogeneity and Chromosomal Inversions on the Efficacy of CRISPR-Cas9 Gene Drives in Different Strains of Anopheles gambiae . CRISPR J . 2023 Oct 1; 6 ( 5 ): 419 – 29 . OpenUrl PubMed 19. ↵ Mansai SP , Kado T , Innan H. The Rate and Tract Length of Gene Conversion between Duplicated Genes . Genes (Basel) . 2011 Mar 25; 2 ( 2 ): 313 – 31 . OpenUrl PubMed 20. ↵ Champer J , Wen Z , Luthra A , Reeves R , Chung J , Liu C , et al. CRISPR Gene Drive Efficiency and Resistance Rate Is Highly Heritable with No Common Genetic Loci of Large Effect . Genetics . 2019 May 1; 212 ( 1 ): 333 – 41 . OpenUrl Abstract / FREE Full Text 21. ↵ Carballar-Lejarazú R , Dong Y , Pham TB , Tushar T , Corder RM , Mondal A , et al. Dual effector population modification gene-drive strains of the African malaria mosquitoes, Anopheles gambiae and Anopheles coluzzii . Proceedings of the National Academy of Sciences . 2023 Jul 18; 120 ( 29 ). 22. ↵ Pescod P , Bevivino G , Anthousi A , Shepherd J , Shelton R , Lombardo F , et al. Homing gene drives can transfer rapidly between Anopheles gambiae strains with minimal carryover of flanking sequences . Nat Commun . 2024 Aug 10; 15 ( 1 ): 6846 . OpenUrl CrossRef PubMed 23. ↵ Ang JX De , Nevard K , Ireland R , Purusothaman DK , Verkuijl SAN , Shackleford L , et al. Considerations for homology-based DNA repair in mosquitoes: Impact of sequence heterology and donor template source . PLoS Genet . 2022 Feb 18; 18 ( 2 ): e1010060 . OpenUrl PubMed 24. ↵ Grunwald HA , Gantz VM , Poplawski G , Xu XRS , Bier E , Cooper KL . Super-Mendelian inheritance mediated by CRISPR–Cas9 in the female mouse germline . Nature . 2019 Feb 7; 566 ( 7742 ): 105 – 9 . OpenUrl CrossRef PubMed 25. Yadav AK , Butler C , Yamamoto A , Patil AA , Lloyd AL , Scott MJ . CRISPR/Cas9-based split homing gene drive targeting doublesex for population suppression of the global fruit pest Drosophila suzukii . Proceedings of the National Academy of Sciences . 2023 Jun 20; 120 ( 25 ). 26. ↵ Meccariello A , Hou S , Davydova S , Fawcett JD , Siddall A , Leftwich PT , et al. Gene drive and genetic sex conversion in the global agricultural pest Ceratitis capitata . Nat Commun . 2024 Jan 8; 15 ( 1 ): 372 . OpenUrl CrossRef PubMed View the discussion thread. Back to top Previous Next Posted August 01, 2025. Download PDF Email Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Sequence mismatch between gene-drive and target-site flanking regions significantly impairs homing efficiency in Culex quinquefasciatus Message Subject (Your Name) has forwarded a page to you from bioRxiv Message Body (Your Name) thought you would like to see this page from the bioRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share Sequence mismatch between gene-drive and target-site flanking regions significantly impairs homing efficiency in Culex quinquefasciatus T. Harvey-Samuel , R. Kaur , P.T. Leftwich , X. Feng , V. Gantz , L. Alphey bioRxiv 2025.08.01.668174; doi: https://doi.org/10.1101/2025.08.01.668174 Share This Article: Copy Citation Tools Sequence mismatch between gene-drive and target-site flanking regions significantly impairs homing efficiency in Culex quinquefasciatus T. Harvey-Samuel , R. Kaur , P.T. Leftwich , X. Feng , V. Gantz , L. Alphey bioRxiv 2025.08.01.668174; doi: https://doi.org/10.1101/2025.08.01.668174 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Synthetic Biology Subject Areas All Articles Animal Behavior and Cognition (7642) Biochemistry (17708) Bioengineering (13904) Bioinformatics (41992) Biophysics (21466) Cancer Biology (18618) Cell Biology (25531) Clinical Trials (138) Developmental Biology (13387) Ecology (19924) Epidemiology (2067) Evolutionary Biology (24337) Genetics (15615) Genomics (22521) Immunology (17749) Microbiology (40424) Molecular Biology (17194) Neuroscience (88673) Paleontology (667) Pathology (2839) Pharmacology and Toxicology (4827) Physiology (7650) Plant Biology (15160) Scientific Communication and Education (2046) Synthetic Biology (4302) Systems Biology (9826) Zoology (2271)