Genome shuffling enables quantitative trait locus mapping in Bacillus subtilis

Genome shuffling enables quantitative trait locus mapping in Bacillus subtilis | 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 Genome shuffling enables quantitative trait locus mapping in Bacillus subtilis View ORCID Profile Delyana P. Vasileva , View ORCID Profile Hari B. Chhetri , Leah H. Hochanadel , View ORCID Profile Jared C. Streich , View ORCID Profile John H. Lagergren , View ORCID Profile Matthew J. Lane , View ORCID Profile Edanur Oksuz , View ORCID Profile Dana L. Carper , View ORCID Profile Benjamin Rubino , View ORCID Profile Sadie Chitwood , View ORCID Profile Zachary D. Schmitz , View ORCID Profile Dawn M. Klingeman , View ORCID Profile Paul E. Abraham , View ORCID Profile Richard J. Giannone , View ORCID Profile Daniel A. Jacobson , View ORCID Profile Joshua K. Michener doi: https://doi.org/10.1101/2025.07.14.664384 Delyana P. Vasileva 1 Biosciences Division, Oak Ridge National Laboratory ; Oak Ridge, TN, 37830, USA 2 Center for Bioenergy Innovation, Oak Ridge National Laboratory ; Oak Ridge, TN, 37830, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Delyana P. Vasileva Hari B. Chhetri 1 Biosciences Division, Oak Ridge National Laboratory ; Oak Ridge, TN, 37830, USA 2 Center for Bioenergy Innovation, Oak Ridge National Laboratory ; Oak Ridge, TN, 37830, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Hari B. Chhetri Leah H. Hochanadel 1 Biosciences Division, Oak Ridge National Laboratory ; Oak Ridge, TN, 37830, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jared C. Streich 1 Biosciences Division, Oak Ridge National Laboratory ; Oak Ridge, TN, 37830, USA 2 Center for Bioenergy Innovation, Oak Ridge National Laboratory ; Oak Ridge, TN, 37830, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jared C. Streich John H. Lagergren 1 Biosciences Division, Oak Ridge National Laboratory ; Oak Ridge, TN, 37830, USA 2 Center for Bioenergy Innovation, Oak Ridge National Laboratory ; Oak Ridge, TN, 37830, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for John H. Lagergren Matthew J. Lane 1 Biosciences Division, Oak Ridge National Laboratory ; Oak Ridge, TN, 37830, USA 3 Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee, Knoxville ; Knoxville, TN, 37996, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Matthew J. Lane Edanur Oksuz 1 Biosciences Division, Oak Ridge National Laboratory ; Oak Ridge, TN, 37830, USA 3 Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee, Knoxville ; Knoxville, TN, 37996, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Edanur Oksuz Dana L. Carper 1 Biosciences Division, Oak Ridge National Laboratory ; Oak Ridge, TN, 37830, USA 2 Center for Bioenergy Innovation, Oak Ridge National Laboratory ; Oak Ridge, TN, 37830, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Dana L. Carper Benjamin Rubino 1 Biosciences Division, Oak Ridge National Laboratory ; Oak Ridge, TN, 37830, USA 3 Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee, Knoxville ; Knoxville, TN, 37996, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Benjamin Rubino Sadie Chitwood 1 Biosciences Division, Oak Ridge National Laboratory ; Oak Ridge, TN, 37830, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Sadie Chitwood Zachary D. Schmitz 1 Biosciences Division, Oak Ridge National Laboratory ; Oak Ridge, TN, 37830, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Zachary D. Schmitz Dawn M. Klingeman 1 Biosciences Division, Oak Ridge National Laboratory ; Oak Ridge, TN, 37830, USA 2 Center for Bioenergy Innovation, Oak Ridge National Laboratory ; Oak Ridge, TN, 37830, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Dawn M. Klingeman Paul E. Abraham 1 Biosciences Division, Oak Ridge National Laboratory ; Oak Ridge, TN, 37830, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Paul E. Abraham Richard J. Giannone 1 Biosciences Division, Oak Ridge National Laboratory ; Oak Ridge, TN, 37830, USA 2 Center for Bioenergy Innovation, Oak Ridge National Laboratory ; Oak Ridge, TN, 37830, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Richard J. Giannone Daniel A. Jacobson 1 Biosciences Division, Oak Ridge National Laboratory ; Oak Ridge, TN, 37830, USA 2 Center for Bioenergy Innovation, Oak Ridge National Laboratory ; Oak Ridge, TN, 37830, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Daniel A. Jacobson For correspondence: jacobsonda{at}ornl.gov michenerjk{at}ornl.gov Joshua K. Michener 1 Biosciences Division, Oak Ridge National Laboratory ; Oak Ridge, TN, 37830, USA 2 Center for Bioenergy Innovation, Oak Ridge National Laboratory ; Oak Ridge, TN, 37830, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Joshua K. Michener For correspondence: jacobsonda{at}ornl.gov michenerjk{at}ornl.gov Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract Genetic mapping is a powerful tool for eukaryotic genetics that has only been applied to bacteria in limited circumstances. Quantitative trait locus (QTL) mapping generally relies on sexual recombination to break linkages between genes, yet bacteria rarely undergo sufficient homologous recombination to generate suitable mapping populations. In this work, we used iterative biparental genome shuffling by protoplast fusion in Bacillus subtilis to generate a population of bacteria with substantial random recombination throughout their genomes. Individual shuffled progeny were arrayed in well plates, resequenced, and characterized for a range of complex phenotypes including spore germination and swarming motility. Genetic mapping of the resulting phenotypes identified high-confidence QTLs of moderate size (∼10 kb), and these associations were validated through targeted genetic swaps. This B. subtilis QTL population can easily be used to map additional phenotypes, and the general approach for QTL mapping is applicable in a wide range of bacteria. One-Sentence Summary Bacterial genome shuffling mimics sexual recombination and enables genetic mapping of complex phenotypes to causal alleles. Main text Advances in next-generation DNA sequencing technologies have provided an unprecedented view of the vast genetic landscape of bacteria. However, linking DNA sequences to observable physical traits remains challenging. Even in the best-studied model bacteria, many genes have unknown functions ( 1 ), and little is known about the genetic networks underlying complex phenotypes or the functional effects of natural sequence variation in bacterial genes and regulatory elements ( 2 ). Arrayed and pooled genetic screens have revolutionized bacterial functional genomics and identified genetic determinants underlying multiple phenotypes ( 3 – 8 ) . However, these approaches are limited to inferring gene function based on loss-of-function or gain-of-function experiments and fail to account for more subtle genetic variation or to capture the complex genetic architecture of quantitative polygenic traits. Genome-wide association studies (GWAS) and QTL mapping are highly effective techniques in eukaryotic genetics that link genetic variation to phenotypic traits across natural and experimental segregating populations, respectively. Most commonly applied in macroscopic sexually-recombining eukaryotes such as humans and plants, these methods have provided substantial insight into the genetic basis of complex phenotypes and uncovered multiple novel biological mechanisms ( 9 , 10 ). Similar approaches have been developed in yeast to enable fine-scale genetic mapping and analysis of epistatic interactions ( 11 , 12 ). Unfortunately, the use of quantitative genetic mapping techniques in bacteria has been limited by the lack of native systems for sexual recombination and the generally low recombination rates within bacterial populations ( 13 ). While the GWAS approach has been successfully applied to extant bacterial populations in several instances, these studies have predominantly focused on pathogenic species with large collections of clinical isolates that enabled the assembly of suitably diverse mapping populations ( 14 – 16 ). Even then, the mapping approach was most successful when applied to genetic variants that arise under strong selection pressure in recombination hotspots, including variants linked to antibiotic resistance ( 15 ) and virulence ( 17 ). Since isolating non-pathogens in sufficient numbers is difficult and most bacteria do not undergo sufficient recombination for GWAS, QTL mapping would be more broadly applicable. Genetic mapping with experimentally generated inter-strain recombinants has been attempted on a small scale in some specific cases, where chromosome regions can be transferred between strains through unconventional conjugal mechanisms ( 18 ). However, a comprehensive experimental framework for QTL mapping is lacking in bacteria. We have previously shown that genome shuffling by protoplast fusion between genetically diverse Bacillus strains generates frequent, unbiased, genome-wide recombination that mimics the effects of sexual recombination ( 19 ). In this study, we leveraged protoplast fusion to establish a bacterial QTL mapping platform ( Fig. 1A-F ). We demonstrated the utility of this approach by identifying causal genetic loci with high statistical power, as well as its potential to reveal the genetic basis of a broad range of complex phenotypes. Download figure Open in new tab Fig. 1. Overview of bacterial QTL mapping. ( A ) Replacing amino acid biosynthesis genes with antibiotic resistance markers in genetically divergent parents allows for selection of recombinant hybrid strains containing different marker allele combinations following genome shuffling by protoplast fusion (fig. S1). ( B ) Repeated rounds of shuffling increase genetic diversity generating mapping populations of highly recombinant progeny. ( C ) The progeny are sequenced to identify genetic contributions from each parent and ( D ) phenotyped. ( E ) Statistical mapping methods are then used to identify genetic loci that correlate with a phenotype of interest. ( F ) These correlations are experimentally validated through CRISPR-mediated markerless genetic swaps. ( G ) The fractions of recombined genome after one round of shuffling between B. subtilis RO-NN-1 and 168 ( 19 ) (white bar) and after two and three rounds of shuffling between strains RO-NN-1 and DK1042 (gray bars) are shown. The number of genetic variants was determined in 24 randomly selected recombinant strains from each shuffle. Error bars represent standard deviation. ( H ) A fine-scale recombination map of the B. subtilis QTL population. Each row represents a sequenced individual strain from the 350-strain mapping panel, with blue bars indicating recombined fragments originating from strain DK1042 and black regions representing the genomic portions inherited from strain RO-NN-1. Arrows in panel H indicate locations of the selection markers metE :: erm and hisB :: kanR . Development of a highly-recombinant bacterial QTL mapping panel Our previous work showed that a single round of genome shuffling between pairs of Bacillus strains generates recombinant strains with genomes primarily derived from one of the parents but containing multiple unselected DNA fragments originating from the second parent that are randomly recombined throughout the chromosome ( 19 ). For example, when B. subtilis strains 168 and RO-NN-1 were shuffled, DNA fragments from B. subtilis 168 replaced, on average, 2.6% of the genome of B. subtilis RO-NN-1 ( Fig. 1G ) and the sizes of the recombined regions were broadly distributed including short inserts ( 60 kb) ( 19 ). These two strains vary phenotypically ( 20 ) and differ in their core genomes at 63,641 single nucleotide polymorphisms (SNPs) (∼98% nucleotide identity), averaging ∼1 SNP every 50 bp. We therefore reasoned that a high frequency of recombination between the chromosomes of strains with this level of genetic divergence would generate in the progeny sufficient admixture of both core genes and accessory pathways to potentially drive mappable variation in phenotypes of interest. To construct a mapping population, we selected B. subtilis RO-NN-1 as the first parent due to its success at genome shuffling in our previous work, and DK1042 as the second parent, a genetically competent strain that exhibits phenotypes lost in the domesticated strain 168 ( 21 ). The statistical confidence to identify causal loci is determined by the degree of recombination and the resulting decrease in linkage between genetic variants in the mapping population. To reduce linkage between genetic loci, we aimed to maximize recombination through repeated cycles of genome shuffling using a backcrossing approach (fig. S1). We used three and four rounds of shuffling to increase genome-wide recombination ( Fig. 1G and fig. S3) and determined the DNA inheritance patterns of 623 recombinant strains from these shuffles. After removing nearly-identical strains and performing quality control, we retained a total of 350 strains with 60,937 SNPs as a mapping population ( Fig. 1H and fig. S4). While our mapping population used only two parents, multiparental crosses have been applied to improve the power and resolution of QTL studies in eukaryotic genetics by introducing a broader spectrum of genetic and phenotypic variation ( 22 ). To test whether a similar multiparental approach could be applied in bacterial QTL mapping populations, we shuffled three genetically-divergent Bacillus strains, B. subtilis RO-NN-1, B. subtilis DK1042, and B. spizizenii TU-B-10 (fig. S5). The resulting progeny showed diverse inheritance patterns with multiple recombination events from all three parents, demonstrating that a more complex mapping population could be constructed if desired. Bacterial QTL mapping identifies causal genetic variants in coding regions and regulatory elements We next tested whether the mapping population could be used to identify the causal alleles responsible for complex phenotypes. Spore germination is an important developmental phenotype with direct implications for the food industry and human disease ( 23 ). Previous studies have identified variation in spore germination between B. subtilis RO-NN-1 and 168 ( 20 ), prompting us to use this trait as an initial test case for QTL mapping. To analyze the heritability of spore germination, we generated spores from the two QTL parents and the full panel of shuffled strains and measured their response to L-alanine as the sole germinant. As previously described, we observed significant differences in the spore germination abilities of the two parents ( Fig. 2A and fig. S7B). The recombinant strains exhibited a high degree of phenotypic variability with a bimodal distribution ( Fig. 2B and fig. S8). We therefore performed QTL mapping using spore germination as a binary phenotype. This analysis identified a ∼10 kb high-confidence locus of strong effect ( H 2 = 0.94) and the most significant genetic variants were located within a three-gene operon encoding the germinant receptor GerA ( Fig. 2C and fig. S9) ( 24 , 25 ). The gerA operons of the two parental strains contained 166 sequence variants, among which 46 were non-synonymous (figs. S10 and S11). Analysis of the gerA region in the recombinant progeny showed that DK1042 variants were more prevalent in shuffled strains that germinate with L-alanine, whereas RO-NN-1 variants were more common in those that did not germinate, demonstrating the significance of the genotype-trait association (fig. S9). Download figure Open in new tab Fig. 2. Bacterial QTL mapping identifies genetic variants associated with spore germination. ( A ) B. subtilis DK1042 and RO-NN-1 exhibit different spore germination abilities with L-Alanine. Spores of the two strains were incubated with 1 mM L-Alanine and the decrease in optical density was monitored as phase-bright spores transitioned to dark-phase germinated spores. Similar results were obtained with 50 mM L-Alanine (fig. S7B). ( B ) Distribution of spore germination phenotypes across the QTL mapping population. A histogram of optical densities after incubation of spores from each strain with 1 mM L-Alanine for 120 min is shown (fig. S8). Black and blue dashed lines indicate the spore germination data from RO-NN-1 and DK1042, respectively. The solid black line indicates the threshold we defined for spore germination. ( C ) Manhattan plot summarizing the statistical significance of associations between each genetic variant along the genome and spore germination across the B. subtilis QTL mapping population. The inset shows a zoomed-in view of the region containing the spore germination QTL. Candidate causal genetic loci from the gerA operon are highlighted in green. ( D ) Genetic swaps validate causal genetic loci. Gene segments within the gerA operon in strain DK1042 were replaced with corresponding segments from strain RO-NN-1 using a CRISPR-based approach and their spore germination abilities were evaluated. The bar plot shows optical densities measured after incubation of spores of each strain with 1 mM L-Alanine for 120 min. Data from the full library of gene-swapped strains is shown in fig. S12. Black regions indicate genetic fragments originating from strain RO-NN-1 and blue regions indicate segments from strain DK1042. The data represents the mean results of three biological replicates. Error bars represent standard deviation. To validate our QTL inference, we performed allele replacement experiments by swapping regions within the DK1042 gerA operon with corresponding genetic fragments from strain RO-NN-1 and evaluated spore germination of the resulting strains in response to L-alanine ( Fig. 2D , and figs. S12 and S13). Spores derived from a strain where the entire DK1042 gerA operon was replaced with the corresponding RO-NN-1 allele failed to germinate under these conditions, in a manner similar to RO-NN-1. Strains harboring different gerA regions derived from RO-NN-1 showed diverse quantitative phenotypes, including germination rates similar to the QTL parents as well as intermediate phenotypes. Specifically, genetic variants located in the genes encoding the GerAA and GerAC subunits of the germination receptor, as well as interactions between these variants, were associated with different spore germination rates (fig. S12). In contrast to the response with L-alanine, spores of strain RO-NN-1 germinated in response to L-valine in a GerA-dependent manner (figs. S14 and S15), indicating that both strains produce active GerA but with differing sensitivity to common germinants. By identifying SNPs within functional genes that affect complex phenotypes, we demonstrated that QTL mapping provides a framework to characterize the complex biological effects of natural genetic variation in bacteria beyond simple loss-of-function mutations. Targeted genetic swaps can be applied to narrow down the range of candidate genetic variants in a QTL region and distinguish the effects of SNPs within causal genes. Although the majority of the shuffled strains in our QTL population contained either DK1042 or RO-NN-1 variants in the gerA locus, seven strains harbored single variants originating from strain DK1042 or different combinations of variants from the two parents. With a larger sample size, the density of recombination events achieved with our cross design has the potential to facilitate fine-scale mapping without the need for additional experimentation to resolve composite QTLs. We next tested the performance of our QTL platform using an additional phenotype, swarming motility -a model multicellular trait that varies substantially among different Bacillus isolates (fig. S16) ( 26 ). Our analysis revealed significant differences in the swarming motility of the two QTL parents ( Fig. 3A and B ), and the QTL population displayed a broad continuous distribution of this phenotype ( Fig. 3C ). Consistent with the spore germination test case, we mapped this trait and identified a highly significant QTL ( H 2 = 0.66) of moderate size (∼10 kb) associated with swarming motility ( Fig. 3D and fig. S19). We then systematically screened loci within the QTL using our allele replacement approach ( Fig. 3E and fig. S20). These experiments narrowed the candidate causal variants down to two SNPs: one located in the regulatory region of the transcriptional regulator rghR , and the other a synonymous mutation within the coding sequence of rghR . A modified DK1042 strain containing the two candidate causal SNPs showed reduced swarming motility, similar to that of the RO-NN-1 strain ( Fig. 3E ). RghR is known to repress the expression of rapG and rapH , which encode inhibitors of the surfactin biosynthetic cluster srfA ( 27 ). Both wild-type RO-NN-1 and the DK1042 strain containing the two candidate causal SNPs exhibited significantly lower expression of the RghR and SrfA proteins, as well as reduced surfactin production, compared to strain DK1042 (fig. S21, A and B). Furthermore, addition of surfactin to the growth medium enabled swarming motility in strain RO-NN-1 (fig. S21, C and D), suggesting that variation in surfactin levels is responsible for the phenotypic differences. Download figure Open in new tab Fig. 3. Regulatory genetic variants drive phenotypic differences in swarming motility. ( A ) Comparison of swarming motility of B. subtilis DK1042 and RO-NN-1 over the course of 5h and 24h. Representative data from three biological experiments is shown. ( B ) Areas covered by the DK1042 and RO-NN-1 swarms measured after 5h of incubation. The data were analyzed by means of two-tailed Student’s t test and represents the average results of three biological replicates. Error bars indicate standard deviation. p value < 0.001 (***) is indicated. ( C ) Distribution of swarming motility phenotypes across the QTL population. A histogram of swarm areas after incubation for 5h is shown. ( D ) Inset of a Manhattan plot (fig. S19) shows a 50-kb genomic region including the QTL for swarming motility. Candidate causal genetic loci are highlighted in green. ( E ) Genetic swaps within the QTL validate the association and identify two candidate causal genetic variants. Black regions indicate genetic fragments originating from strain RO-NN-1 and blue regions indicate segments from strain DK1042. These results highlight that our QTL mapping approach can uncover subtle regulatory mechanisms and genetic factors contributing to phenotypes related to public goods production, which would likely be overlooked in traditional pooled genetic screens. Genome shuffling creates mappable variation across a wide range of bacterial traits To explore further the applicability of our platform for mapping a variety of traits, we selected the 23 most genetically diverse shuffled strains from the QTL population and evaluated phenotypic variability across this panel ( Fig. 4 ). We tested both phenotypes relevant to microbial ecology and traits of interest for bacterial engineering. Since none of the shuffled strains in the QTL population carried the DK1042-derived plasmid pBS32 and the effects of this plasmid on some of the tested phenotypes is not known, we used a cured strain as the parental control in these experiments. Download figure Open in new tab Fig. 4. Bacterial QTL mapping is applicable to a wide range of complex phenotypes. Phenotypic variability was tested across the 23 most genetically diverse strains from the QTL population for a variety of traits. ( A ) Distribution of colony morphology phenotypes under biofilm-promoting conditions. A histogram summarizing colony morphology category frequencies (fig. S22) is shown. ( B ) Distribution of extracellular xylanase activity phenotypes. A histogram of xylanase activities (fig. S23) is shown. ( C ) Distribution of pulcherrimin biosynthesis phenotypes. A histogram of pulcherrimin production (fig. S24) is shown. All strains except RO-NN-1 and one shuffled strain indicated by a dark gray bar carried the pulcherrimin biosynthesis cluster. ( D ) Distribution of transformation efficiency phenotypes. A histogram of transformation efficiencies via natural competency (fig. S25) is shown. Dark blue dashed lines indicate phenotypic data from RO-NN-1 and the cured DK1042 strain JM300 (Materials and Methods), respectively. Biofilm formation is a complex phenotype that involves multiple genes and genetic interactions ( 28 ). We tested biofilm architectures across the 23-strain panel and the parental controls and categorized the colonies based on their morphology ( Fig. 4A and fig. S22). Our analysis revealed that genome shuffling generated extensive variability in colony patterns. In addition, we evaluated enzyme activity and secondary metabolite production phenotypes, including extracellular xylanase activity ( Fig. 4B and fig. S23A) and biosynthesis of pulcherrimin, an iron-binding reddish pigment ( Fig. 4C and fig. S24). Finally, we assessed phenotypes relevant to genetic engineering, including promoter leakiness (fig. S23B) and transformation efficiency ( Fig. 4D and fig. S24). All of these assays also revealed substantial phenotypic variability among the 23 shuffled strains. Across the different assays, we frequently identified shuffled strains exhibiting phenotypes that are not intermediate of the parental strains, but rather represent quantitatively or qualitatively new phenotypes. Collectively, these findings indicated that the tested traits are complex and that the progeny represent a new mixture of alleles, enabling the discovery and mapping of novel phenotypes. Conclusions The power of phenotype-genotype association studies in bacteria is limited by the relatively low recombination rates in natural bacterial populations. To overcome these limitations, we developed an approach to generate highly recombinant panels, enabling high-confidence genetic mapping. We detected QTLs spanning on average ∼10 kb, though our data suggest that genetic mapping resolution in bacteria can be improved either by increasing the number of recombinant strains in the QTL population or the overall recombination frequency across the genome. Our findings demonstrate that, beyond gene presence or absence, natural genetic variation in both coding and non-coding regions significantly influences bacterial phenotypes. Bacterial QTL mapping, therefore, provides a more complete picture of the genetic architecture underlying complex bacterial traits by systematically exploring the functional impacts of the standing genetic diversity within natural microbial populations. The iterative protoplast fusion recombination method developed in this study can be extended to a wide range of Gram-positive bacteria. However, its use in Gram-negative bacteria can be potentially challenging due to the complexity of their cell walls, which makes protoplasting more difficult. Large-scale interstrain genetic exchange in Gram-negative bacteria can be achieved through alternative approaches like chromosomal conjugation and natural transformation ( 29 – 32) . These approaches can be leveraged in future efforts to create libraries with sufficient genome-wide recombination for genetic mapping. We envision that bacterial QTL mapping, in combination with the traditional deletion and pooled genetic screens, would enable a powerful platform technology for comprehensive functional bacterial genomics. Funding Initial development of the mapping population was supported by ORNL Laboratory Directed Research and Development (LHB, JCS, DAJ, and JKM). Phenotyping, genetic mapping, and validation were supported by the Center for Bioenergy Innovation, U.S. Department of Energy, Office of Science, Biological and Environmental Research Program under Award Number ERKP886 (DPV, HPC, JCS, JHL, MJL, DLC, RJG, DAJ, and JKM). Lipopeptide analysis was supported by the Secure Ecosystem Engineering and Design Science Focus Area, funded by the U.S. Department of Energy, Office of Science, Biological and Environmental Research under Award Number ERKPA17 (EO and PEA). Genome sequencing of the mapping population was conducted by the Joint Genome Institute, a DOE Office of Science User Facility, supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231. Work by SC and ZDS was supported by the U.S. Department of Energy, Office of Science, Office of Workforce Development for Teachers and Scientists (WDTS) under the Science Undergraduate Laboratory Internships (SULI) program. Author contributions Investigation: DPV, LHH, JCS, EO, DLC, BR, SC, ZDS, DMK Methodology: DPV, HPC, JCS, JHL, MJL, BR, PEA Software: HPC, JCS, JHL, MJL, PEA Visualization: DPV, HPC, JCS, JHL, MJL Conceptualization: DAJ, JKM Funding acquisition: DAJ, JKM Project administration: DAJ, JKM Supervision: DAJ, JKM, PEA, RJG Writing – original draft: DPV, LHH, HBC, JCS, JHL, MJL, EO, DLC, DAJ, JKM Writing – review & editing: SC, ZDS, DMK, PEA, RJG, Competing interests D.P.V., J.C.S., D.A.J. and J.K.M. are inventors on a patent that has been filed based, in part, on the work reported in this manuscript. Acknowledgments We thank Dr. Ilenne Del Valle for providing us with the plasmid pIDV40 used for the transformation efficiency assays. Funder Information Declared Oak Ridge National Laboratory, https://ror.org/01qz5mb56 United States Department of Energy, https://ror.org/01bj3aw27 , ERKP886 , ERKPA17 References and Notes 1. ↵ C. Vanni , M. S. Schechter , S. G. Acinas , A. Barberán , P. L. Buttigieg , E. O. Casamayor , T. O. Delmont , C. M. Duarte , A. M. Eren , R. D. Finn , R. Kottmann , A. Mitchell , P. Sánchez , K. Siren , M. Steinegger , F. O. Gloeckner , A. Fernàndez-Guerra , Unifying the known and unknown microbial coding sequence space . Elife 11 ( 2022 ). 2. ↵ Y. Oren , M. B. Smith , N. I. Johns , M. Kaplan Zeevi , D. Biran , E. Z. Ron , J. Corander , H. H. Wang , E. J. Alm , T. Pupko , Transfer of noncoding DNA drives regulatory rewiring in bacteria . Proc Natl Acad Sci U S A 111 , 16112 – 16117 ( 2014 ). OpenUrl Abstract / FREE Full Text 3. ↵ A. K. Cain , L. Barquist , A. L. Goodman , I. T. Paulsen , J. Parkhill , T. van Opijnen , A decade of advances in transposon-insertion sequencing . Nat Rev Genet 21 , 526 – 540 ( 2020 ). OpenUrl CrossRef PubMed 4. K. M. Wetmore , M. N. Price , R. J. Waters , J. S. Lamson , J. He , C. A. Hoover , M. J. Blow , J. Bristow , G. Butland , A. P. Arkin , A. Deutschbauer , Rapid quantification of mutant fitness in diverse bacteria by sequencing randomly bar-coded transposons . MBio 6 , e00306 – 15 ( 2015 ). OpenUrl CrossRef PubMed 5. H. Todor , M. R. Silvis , H. Osadnik , C. A. Gross , Bacterial CRISPR screens for gene function . Curr Opin Microbiol 59 , 102 – 109 ( 2021 ). OpenUrl CrossRef PubMed 6. V. K. Mutalik , P. S. Novichkov , M. N. Price , T. K. Owens , M. Callaghan , S. Carim , A. M. Deutschbauer , A. P. Arkin , Dual-barcoded shotgun expression library sequencing for high-throughput characterization of functional traits in bacteria . Nat Commun 10 , 308 ( 2019 ). OpenUrl CrossRef PubMed 7. B.-M. Koo , G. Kritikos , J. D. Farelli , H. Todor , K. Tong , H. Kimsey , I. Wapinski , M. Galardini , A. Cabal , J. M. Peters , A.-B. Hachmann , D. Z. Rudner , K. N. Allen , A. Typas , C. Gross , Construction and Analysis of Two Genome-Scale Deletion Libraries for Bacillus subtilis . Cell Syst 4 , 291 – 305 .e7 ( 2017 ). OpenUrl CrossRef PubMed 8. ↵ T. Baba , T. Ara , M. Hasegawa , Y. Takai , Y. Okumura , M. Baba , K. A. Datsenko , M. Tomita , B. L. Wanner , H. Mori , Construction of Escherichia coli K-12 in-frame, single-gene knockout mutants: the Keio collection . Mol Syst Biol 2 , 2006 .0008 ( 2006 ). OpenUrl CrossRef PubMed 9. ↵ D. Altshuler , M. J. Daly , E. S. Lander , Genetic mapping in human disease . Science 322 , 881 – 888 ( 2008 ). OpenUrl Abstract / FREE Full Text 10. ↵ J. Labbé , W. Muchero , O. Czarnecki , J. Wang , X. Wang , A. C. Bryan , K. Zheng , Y. Yang , M. Xie , J. Zhang , D. Wang , P. Meidl , H. Wang , J. L. Morrell-Falvey , K. R. Cope , L. G. S. Maia , J.-M. Ané , R. Mewalal , S. S. Jawdy , L. E. Gunter , W. Schackwitz , J. Martin , F. Le Tacon , T. Li , Z. Zhang , P. Ranjan , E. Lindquist , X. Yang , D. A. Jacobson , T. J. Tschaplinski , K. Barry , J. Schmutz , J.-G. Chen , G. A. Tuskan , Mediation of plant-mycorrhizal interaction by a lectin receptor-like kinase . Nat. Plants 5 , 676 – 680 ( 2019 ). OpenUrl PubMed 11. ↵ R. She , D. F. Jarosz , Mapping Causal Variants with Single-Nucleotide Resolution Reveals Biochemical Drivers of Phenotypic Change . Cell 172 , 478 – 490 .e15 ( 2018 ). OpenUrl CrossRef PubMed 12. ↵ A. N. Nguyen Ba , K. R. Lawrence , A. Rego-Costa , S. Gopalakrishnan , D. Temko , F. Michor , M. M. Desai , Barcoded bulk QTL mapping reveals highly polygenic and epistatic architecture of complex traits in yeast . Elife 11 ( 2022 ). 13. ↵ R. A. Power , J. Parkhill , T. de Oliveira , Microbial genome-wide association studies: lessons from human GWAS . Nat Rev Genet 18 , 41 – 50 ( 2017 ). OpenUrl CrossRef PubMed 14. ↵ S. K. Sheppard , X. Didelot , G. Meric , A. Torralbo , K. A. Jolley , D. J. Kelly , S. D. Bentley , M. C. J. Maiden , J. Parkhill , D. Falush , Genome-wide association study identifies vitamin B5 biosynthesis as a host specificity factor in Campylobacter . Proc Natl Acad Sci U S A 110 , 11923 – 11927 ( 2013 ). OpenUrl Abstract / FREE Full Text 15. ↵ C. Chewapreecha , P. Marttinen , N. J. Croucher , S. J. Salter , S. R. Harris , A. E. Mather , W. P. Hanage , D. Goldblatt , F. H. Nosten , C. Turner , P. Turner , S. D. Bentley , J. Parkhill , Comprehensive identification of single nucleotide polymorphisms associated with beta-lactam resistance within pneumococcal mosaic genes . PLoS Genet 10 , e1004547 ( 2014 ). OpenUrl CrossRef PubMed 16. ↵ C. A. Desjardins , K. A. Cohen , V. Munsamy , T. Abeel , K. Maharaj , B. J. Walker , T. P. Shea , D. V. Almeida , A. L. Manson , A. Salazar , N. Padayatchi , M. R. O’Donnell , K. P. Mlisana , J. Wortman , B. W. Birren , J. Grosset , A. M. Earl , A. S. Pym , Genomic and functional analyses of Mycobacterium tuberculosis strains implicate ald in D-cycloserine resistance . Nat Genet 48 , 544 – 551 ( 2016 ). OpenUrl CrossRef PubMed 17. ↵ M. M. Maury , Y.-H. Tsai , C. Charlier , M. Touchon , V. Chenal-Francisque , A. Leclercq , A. Criscuolo , C. Gaultier , S. Roussel , A. Brisabois , O. Disson , E. P. C. Rocha , S. Brisse , M. Lecuit , Uncovering Listeria monocytogenes hypervirulence by harnessing its biodiversity . Nat Genet 48 , 308 – 313 ( 2016 ). OpenUrl CrossRef PubMed 18. ↵ T. A. Gray , J. A. Krywy , J. Harold , M. J. Palumbo , K. M. Derbyshire , Distributive conjugal transfer in mycobacteria generates progeny with meiotic-like genome-wide mosaicism, allowing mapping of a mating identity locus . PLoS Biol 11 , e1001602 ( 2013 ). OpenUrl CrossRef PubMed 19. ↵ D. P. Vasileva , J. C. Streich , L. H. Burdick , D. M. Klingeman , H. B. Chhetri , C. M. Brelsford , J. C. Ellis , D. M. Close , D. A. Jacobson , J. K. Michener , Protoplast fusion in Bacillus species produces frequent, unbiased, genome-wide homologous recombination . Nucleic Acids Res . 50 , 6211 – 6223 ( 2022 ). OpenUrl PubMed 20. ↵ O. M. Alzahrani , A. Moir , Spore germination and germinant receptor genes in wild strains of Bacillus subtilis . J Appl Microbiol 117 , 741 – 749 ( 2014 ). OpenUrl CrossRef 21. ↵ M. A. Konkol , K. M. Blair , D. B. Kearns , Plasmid-encoded ComI inhibits competence in the ancestral 3610 strain of Bacillus subtilis . J Bacteriol 195 , 4085 – 4093 ( 2013 ). OpenUrl Abstract / FREE Full Text 22. ↵ F. A. Cubillos , L. Parts , F. Salinas , A. Bergström , E. Scovacricchi , A. Zia , C. J. R. Illingworth , V. Mustonen , S. Ibstedt , J. Warringer , E. J. Louis , R. Durbin , G. Liti , High-resolution mapping of complex traits with a four-parent advanced intercross yeast population . Genetics 195 , 1141 – 1155 ( 2013 ). OpenUrl Abstract / FREE Full Text 23. ↵ G. Christie , P. Setlow , Bacillus spore germination: Knowns, unknowns and what we need to learn . Cell Signal 74 , 109729 ( 2020 ). OpenUrl CrossRef PubMed 24. ↵ L. Artzi , A. Alon , K. P. Brock , A. G. Green , A. Tam , F. H. Ramírez-Guadiana , D. Marks , Kruse, D. Z. Rudner , Dormant spores sense amino acids through the B subunits of their germination receptors . Nat Commun 12 , 6842 ( 2021 ). OpenUrl CrossRef PubMed 25. ↵ Y. Gao , J. D. Amon , L. Artzi , F. H. Ramírez-Guadiana , K. P. Brock , J. C. Cofsky , D. S. Marks , A. C. Kruse , D. Z. Rudner , Bacterial spore germination receptors are nutrient-gated ion channels . Science 380 , 387 – 391 ( 2023 ). OpenUrl CrossRef PubMed 26. ↵ D. B. Kearns , A field guide to bacterial swarming motility . Nat Rev Microbiol 8 , 634 – 644 ( 2010 ). OpenUrl CrossRef PubMed Web of Science 27. ↵ K. Hayashi , T. Kensuke , K. Kobayashi , N. Ogasawara , M. Ogura , Bacillus subtilis RghR (YvaN) represses rapG and rapH, which encode inhibitors of expression of the srfA operon . Mol Microbiol 59 , 1714 – 1729 ( 2006 ). OpenUrl CrossRef PubMed 28. ↵ S. Arnaouteli , N. C. Bamford , N. R. Stanley-Wall , Á. T. Kovács , Bacillus subtilis biofilm formation and social interactions . Nat Rev Microbiol 19 , 600 – 614 ( 2021 ). OpenUrl CrossRef PubMed 29. J. Silbert , V. de Lorenzo , T. Aparicio , Refactoring the Conjugation Machinery of Promiscuous Plasmid RP4 into a Device for Conversion of Gram-Negative Isolates to Hfr Strains . ACS Synth Biol 10 , 690 – 697 ( 2021 ). OpenUrl PubMed 30. T. Corneloup , J. Bellengier , M. Magnan , A. Chavan , B. Gachet , Z. Dixit , C. Pintard , A. Baron , D. Toko , A. Lambert , A. Choudhury , O. Tenaillon , High-throughput conjugation reveals strain specific recombination patterns enabling precise trait mapping in Escherichia coli , bioRxiv ( 2025 ) p. 2025.02.26.640286. 31. T. N. Dalia , C. A. Hayes , S. Stolyar , C. J. Marx , J. B. McKinlay , A. B. Dalia , Multiplex Genome Editing by Natural Transformation (MuGENT) for Synthetic Biology in Vibrio natriegens . ACS Synth Biol 6 , 1650 – 1655 ( 2017 ). OpenUrl CrossRef PubMed 32. M. Vaneechoutte , D. M. Young , L. N. Ornston , T. De Baere , A. Nemec , T. Van Der Reijden , E. Carr , I. Tjernberg , L. Dijkshoorn , Naturally transformable Acinetobacter sp. strain ADP1 belongs to the newly described species Acinetobacter baylyi . Appl Environ Microbiol 72 , 932 – 936 ( 2006 ). OpenUrl Abstract / FREE Full Text View the discussion thread. Back to top Previous Next Posted July 14, 2025. Download PDF Supplementary Material 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 Genome shuffling enables quantitative trait locus mapping in Bacillus subtilis 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 Genome shuffling enables quantitative trait locus mapping in Bacillus subtilis Delyana P. Vasileva , Hari B. Chhetri , Leah H. Hochanadel , Jared C. Streich , John H. Lagergren , Matthew J. Lane , Edanur Oksuz , Dana L. Carper , Benjamin Rubino , Sadie Chitwood , Zachary D. Schmitz , Dawn M. Klingeman , Paul E. Abraham , Richard J. Giannone , Daniel A. Jacobson , Joshua K. Michener bioRxiv 2025.07.14.664384; doi: https://doi.org/10.1101/2025.07.14.664384 Share This Article: Copy Citation Tools Genome shuffling enables quantitative trait locus mapping in Bacillus subtilis Delyana P. Vasileva , Hari B. Chhetri , Leah H. Hochanadel , Jared C. Streich , John H. Lagergren , Matthew J. Lane , Edanur Oksuz , Dana L. Carper , Benjamin Rubino , Sadie Chitwood , Zachary D. Schmitz , Dawn M. Klingeman , Paul E. Abraham , Richard J. Giannone , Daniel A. Jacobson , Joshua K. Michener bioRxiv 2025.07.14.664384; doi: https://doi.org/10.1101/2025.07.14.664384 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 Microbiology Subject Areas All Articles Animal Behavior and Cognition (7637) Biochemistry (17705) Bioengineering (13899) Bioinformatics (41968) Biophysics (21460) Cancer Biology (18603) Cell Biology (25526) Clinical Trials (138) Developmental Biology (13385) Ecology (19910) Epidemiology (2067) Evolutionary Biology (24327) Genetics (15614) Genomics (22513) Immunology (17741) Microbiology (40423) Molecular Biology (17193) Neuroscience (88646) Paleontology (667) Pathology (2835) Pharmacology and Toxicology (4825) Physiology (7647) Plant Biology (15160) Scientific Communication and Education (2046) Synthetic Biology (4302) Systems Biology (9825) Zoology (2271)