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Mutagenomics: A Rapid, High-Throughput Method toIdentify Causative Mutations from a Genetic Screen1[OPEN]

Charles Hodgens,a,b Nicole Chang,a G. Eric Schaller,c and Joseph J. Kiebera,b,2,3

aDepartment of Biology, University of North Carolina at Chapel Hill, North Carolina 27599bCurriculum in Genetics and Molecular Biology, University of North Carolina at Chapel Hill, Chapel Hill,North Carolina 27599cDepartment of Biological Sciences, Dartmouth College, Hanover, New Hampshire 03755

ORCID IDs: 0000-0002-0384-4458 (C.H.); 0000-0003-4032-2437 (G.E.S.); 0000-0002-5766-812X (J.J.K.).

Genetic screens are powerful tools to dissect complex biological processes, but a rate-limiting step is often the cloning of targetedgenes. Here, we present a strategy, “mutagenomics,” to identify causal mutations from a screen in a high throughput fashion inthe absence of backcrossing. Mutagenomics is initiated by sequencing the genomes of the mutants identified, which are thensubjected to a three-stage pipeline. The first stage identifies sequence changes in genes previously linked to the targetedpathway. The second stage uses heuristics derived from a simulation strategy to identify genes that are represented bymultiple independent alleles more often than expected by chance. The third stage identifies candidate genes for theremaining lines by sequencing multiple lines of common descent. Our simulations indicate that sequencing as few as three tofour sibling lines generally results in fewer than five candidate genes. We applied mutagenomics to a screen for Arabidopsis(Arabidopsis thaliana) mutants involved in the response to the phytohormone cytokinin. Mutagenomics identified likely causativegenes for many of the mutant lines analyzed from this screen, including 13 alleles of the gene encoding the ARABIDOPSIS HISKINASE4 cytokinin receptor. The screen also identified 1-AMINOCYCLOPROPANE-1-CARBOXYLATE (ACC) SYNTHASE7, anACC synthase homolog involved in ethylene biosynthesis, and ELONGATED HYPOCOTYL5 (HY5), a master transcriptionalregulator of photomorphogenesis. HY5 was found to mediate a subset of the transcriptional response to cytokinin.Mutagenomics has the potential to accelerate the pace and utility of genetic screens in Arabidopsis.

A wide variety of genetic screens have been used todissect numerous biological processes, helping to de-fine the elements involved as well as their roles invarious pathways (Jorgensen and Mango, 2002; Pageand Grossniklaus, 2002; St Johnston, 2002; Candelaand Hake, 2008). The most common approach utilizedis to screen a population of randomly mutagenizedlines for a phenotype of interest and to subsequentlyidentify the genes corresponding to these mutations.Generating transfer DNA (T-DNA) or other inser-tional alleles simplifies subsequent cloning of the mu-tated genes but limits the breadth of allelic diversityobtained, and it is tedious to generate such populationsin desired genetic backgrounds. Chemical mutagens

(such as ethyl methanesulfonate [EMS]), which inducepoint mutations, enable the facile generation of largemutagenized populations in nearly any genetic back-ground and create a large diversity of alleles; however,cloning the causative genes from such alleles is muchmore technically challenging. Diverse methods exist forthese purposes, but most rely on marker-based map-ping procedures. To this end, an outcross is performedbetween a mutant line of interest with a second lineharboring numerous genomic variants, most com-monly small nucleotide polymorphisms (SNPs). The F2population from the cross is collected, and the cose-gregation of the phenotype of interest with molecularmarkers is determined. With sufficient marker density,an interval defining the genomic region containing thetargeted mutation can be deduced and the causativegene confirmed by genetic complementation or analysisof independent alleles.

The use of next-generation, high-throughput se-quencing technologies and experimental approachesthat leverage bulked segregant mapping has greatlyfacilitated the process of cloning genes correspondingto point mutations (Michelmore et al., 1991). Variationsof this include NGM (Austin et al., 2011), SIMPLE(Wachsman et al., 2017), CloudMap (Minevich et al.,2012), the SHOREmap pipeline (Schneeberger et al.,2009a), MutMap1 (Fekih et al., 2013), and NIKS(Nordström et al., 2013). Typically, these pipelines relyon segregation of SNPs derived from sequence variations

1This work was funded by grants from the National Science Foun-dation (grant nos. IOS–1238051 to G.E.S. and J.J.K., and IOS–1856431and MCB–1856248 to J.J.K.).

2Author for contact: [email protected] author.The author responsible for distribution of materials integral to the

findings presented in this article in accordance with the policy de-scribed in the Instructions for Authors (www.plantphysiol.org) is:Joseph J. Kieber ([email protected]).

C.H., G.E.S., and J.J.K. conceived the project; J.J.K. supervised theexperiments; C.H. performed the experiments; C.H. and J.J.K. wrotethe article with contributions from G.E.S.

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1658 Plant Physiology�, December 2020, Vol. 184, pp. 1658–1673, www.plantphysiol.org � 2020 American Society of Plant Biologists. All Rights Reserved.

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https://orcid.org/0000-0002-0384-4458https://orcid.org/0000-0002-0384-4458https://orcid.org/0000-0003-4032-2437https://orcid.org/0000-0003-4032-2437https://orcid.org/0000-0002-5766-812Xhttps://orcid.org/0000-0002-5766-812Xhttps://orcid.org/0000-0002-0384-4458https://orcid.org/0000-0003-4032-2437https://orcid.org/0000-0002-5766-812Xhttp://crossmark.crossref.org/dialog/?doi=10.1104/pp.20.00609&domain=pdf&date_stamp=2020-12-01http://dx.doi.org/10.13039/100000001http://dx.doi.org/10.13039/100000001mailto:[email protected]://www.plantphysiol.orgmailto:[email protected]://www.plantphysiol.org/cgi/doi/10.1104/pp.20.00609

of a distinct parental ecotype or the numerous randomSNPs induced by EMSmutagenesis. Comparisons of SNPfrequencies between homozygous mutant lines and ref-erence lines reveal regions of the genome linked to thecausative mutation. Simulations for the effect of increas-ing the number of F2 segregants pooled from this analysisindicate that at least 40 segregants may be required togenerate fewer than 20 candidate genes (James et al.,2013). While powerful, these methods are laborious and,consequently, only a small subset of lines are analyzedfrom a screen.The plant hormone cytokinin regulates a diverse set

of biological processes in plants, including shoot for-mation, meristem activity, nutrient uptake, variousabiotic and biotic interactions, and multiple develop-mental pathways (Kieber and Schaller, 2014). Cytokininbinds to Arabidopsis (Arabidopsis thaliana) His kinasereceptors (AHKs; Inoue et al., 2001; Ueguchi et al., 2001;Yamada et al., 2001; Caesar et al., 2011; Wulfetangeet al., 2011), which autophosphorylate on a His withinthe His kinase domain and then shuttle the phosphateto an Asp residue within the fused receiver domain ofthe AHK. This phosphate is subsequently passed toan Arabidopsis His phosphotransfer protein (AHPs;Miyata et al., 1998; Suzuki et al., 1998; Imamura et al.,1999; Hutchison et al., 2006; Schaller et al., 2008), whichin turn transfers it to type A- or type B-Response Reg-ulators (ARRs; To et al., 2004; Mason et al., 2005;To et al., 2007). Phosphorylation of the type-B RRs ac-tivates these transcription factors, which bind to theirgenomic targets to regulate the primarywave of cytokinin-regulated transcriptional changes (Mason et al., 2005;Argyros et al., 2008; Zubo et al., 2017), which includesthe type A-ARRs (Brandstatter and Kieber, 1998;Taniguchi et al., 1998; D’Agostino et al., 2000;Taniguchi et al., 2007). Type-A ARRs negativelyregulate the signaling pathway, acting as feedbackregulators to dampen the response to cytokinin (Kibaet al., 2003; To et al., 2004; Leibfried et al., 2005; Leeet al., 2007a; To et al., 2007).Here, we performed a genetic screen on an Arabi-

dopsis line genetically sensitized to perturbation ofcytokinin responsiveness to search for novel elementsinvolved in the response to cytokinin. To analyze theoutput from this screen in an unbiased way, we de-veloped a pipeline to identify causative mutations in aparallel manner, which we call “mutagenomics.”Genesidentified in this screen include multiple alleles of theAHK4 receptor, the ethylene signaling element ETHYL-ENE-INSENSITIVE2 (EIN2), the ethylene biosynthesisgene 1-AMINOCYCLOPROPANE-1-CARBOXYLATESYNTHASE7 (ACS7), the auxin efflux carrier AUXINRESISTANT1 (AUX1), and the transcription factorELONGATED HYPOCOTYL5 (HY5). Further analysisrevealed that HY5 was necessary for a subset of thetranscriptional response to cytokinin. The mutagenomicsstrategy described here is a powerful tool for researchersperforming mutant screens. It is species-agnostic, al-though it is especially useful in self-fertilizing model or-ganisms.Mutagenomics has the potential to accelerate the

pace of discovery of novel genes by greatly reducing thetime and effort required to identify causative mutations.

RESULTS

Identification of Mutants Affected in the Response toCytokinin Using a Sensitized Genetic Screen

While prior analysis has defined a cytokinin responsepathway from perception of cytokinin by the AHK re-ceptors through phosphorylation and activation oftranscription factors, it is likely that additional elementsinvolved in the response to this phytohormone remainto be identified. One challenge with identifying muta-tions in genetic screens is that the effects of these al-terations on the process being analyzed may be subtle,due to genetic redundancy; or alternatively, null allelesmay be lethal for some genes due to pleiotropy, andthus, only hypomorphic alleles may be recoverable.This is particularly true for a well-characterized path-way such as cytokinin signaling, in which the moststraightforward genes have already been identified(Kieber and Schaller, 2014). To exhaustively screen forgenes involved in the response to cytokinin, we muta-genized an Arabidopsis line that is partially compro-mised for cytokinin signaling due to loss-of-functionmutations in ahp2 and ahp3 with the alkylating agentEMS. This double ahp2,3 mutant has comparable cyto-kinin sensitivity to the wild type in a root elonga-tion assay, as does the single ahp1 mutant (Fig. 1A;Hutchison et al., 2006). In contrast, the ahp1,2,3 triplemutant is partially resistant to cytokinin, suggesting itis genetically sensitized to modest perturbations of cy-tokinin responsiveness. Using a root elongation assay,we determined that 0.1 mM of 6-benzylaminopurine(BA), a synthetic cytokinin, resulted in a substantial,easily scored difference in root length between theahp2,3 and ahp1,2,3 lines, but was not saturating for theresponse, indicating that this is a suitable condition todetect subtle changes in the response to cytokinin(Fig. 1, A and B).We mutagenized an ahp2,3 line and screened for al-

tered sensitivity to cytokinin using these conditions.Approximately 75,000 Arabidopsis seeds were muta-genized with EMS, the resultant M1 plants were grownto maturity, and the M2 seeds were harvested in 111pools, with a mean pool size of 140 fertile, viable plantsper pool (Fig. 1C). An initial 1,200 putative mutantswere identified from this population, which were re-fined to 272 strongly hyposensitive mutants from 78independent pools after rescreening M3 seedlings. Arepresentative mutant is shown in Figure 1B. We referto these mutations as enhancer of AHPs (eah). Mutantsdisrupted in ethylene signalingwould also be identifiedin this screen as exogenous cytokinin inhibits rootelongation via elevated ethylene biosynthesis (Vogelet al., 1998a). To identify these ethylene-hyposensitivelines, we assessed the sensitivity of the eah lines toethylene using a triple response screen (Table 1;

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Rapid Cloning of Genes from a Genetic Screen

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Guzmán and Ecker, 1990). We removed most of thestrongly ethylene-insensitive eah lines from furtherconsideration, although we continued to analyze eightas a proof-of-concept for the subsequent analysis.

To identify the causative mutations in the eah lines,we developed themutagenomics pipeline, summarizedin Figure 2. For this approach, the genomic sequences ofa large subset of the mutant lines were determined,with the goal of identifying all the predicted protein-altering (PA) SNPs in each line. We identified M3progeny that were homozygous for the eah phenotypeand determined the sequence of their genomes usinghigh-throughput Illumina sequencing. We sequencedeach mutant line to a target-read depth of at least 203coverage. In addition, we sequenced the parental ahp2,3plants used formutagenesis to identify any backgroundSNPs. Cataloguing the background mutations permitsfiltering those SNPs from final SNP calls for each mu-tant, restricting the SNPs in consideration to only thoseintroduced by the mutagenesis process.

Due to the random sampling procedure of whole-genome–library preparation, some regions of the

genome may have low or no coverage. The size of low-coverage regions will shrink as average genome cov-erage rises. Empirically, at 203 coverage in our librar-ies,;10% of the genome had coverage of,103. Nearlyall libraries had at least 53 coverage at the 2.5% per-centile level. Thus, while it is formally possible that acausative SNP might fall in unsequenced regions of thegenome in this experiment, it is highly unlikely.

A total of 1,825 background SNPs (relative to TheArabidopsis Information Resource 10) were observedin the ahp2,3 background. In addition to backgroundSNPs, we found that there was an average of 144 PA-SNPs per mutant line (both homozygous and hetero-zygous), ranging from as few as 20 to as many as 223(Fig. 1D; Table 1). When one considers only homozy-gous PA-SNPs, there are on average 57 per line, rangingfrom 11 to 125 per line. This is consistent with previousanalyses of EMS-mutagenized Arabidopsis that found100 to 200 PA-SNPs per line (Thole et al., 2014). In thefirst step, lines with PA-SNPs in genes known to beinvolved in the process of interest and likely to becausative, are identified. For example, in the case of the

Figure 1. Genetic screen for cytokininhyposensitivity.We employed an ahp2,3double mutant as a sensitized parent fora genetic screen of mutants altered inthe response to cytokinin. A, Root elon-gation assay to determine the optimalconcentration of BA touse for the screen.Seedlings of the indicated genotypeswere grown for 3 d on controlmedia andthen moved to media containing the in-dicated level of BA. The growth between3 and 8 d after germination (DAG) wasquantified. Bars are SE. n $ 11 for allgroups except ahp2,3 control (7), 0.01(6), and 10 mM (4) of BA. B, Representa-tive images of wild type (Col) and theindicated mutants, including a repre-sentative eah line (eah22), grown as inA. Scale bar 5 1 cm. C, Flow chart de-scribing the details of the genetic screen.D, Histogram of number of PA-SNPdensities in sequenced mutant lines.PA-SNPs are inferred using the pro-gram SnpEff and consist of all muta-tions causing missense, nonsense, orsplice-site mutations.

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eah screen, we screened for genes previously identifiedthat might result in an elongated root in the presence ofexogenous cytokinin, such as AHK4 (Inoue et al., 2001;Higuchi et al., 2004). Lines that do not harbor mutationsin the screened known targets then progress to step twoof mutagenomics. In this second step, genes that arefound inmultiple lines with PA-SNPs are identified. Theidea is that if a gene is found mutated in multiple inde-pendent lines beyondwhat is expected by chance, then itis likely to be causative for the phenotype being screenedfor. In the third step, to identify the causative mutationsin the remaining lines, multiple lines from the same pool(likely siblings) are analyzed for overlapping homozy-gous PA-SNPs to narrow the candidate gene list to asmall number. As described below, we successfully

applied mutagenomics to the eah screen as a proof-of-concept for this methodology. A user guide to per-forming mutagenomics is presented in SupplementalData S1.

Mutagenomics Applied to the eah Screen

Step 1: Screening for Mutations in Genes Known to AffectCytokinin Responsiveness

After retesting and removing most ethylene-insensitive lines, we distilled our eah screen to 51 linesthat we subjected to mutagenomics (Table 1). Theselines were sequenced to a target coverage of 203 using

Table 1. eah mutant lines analyzed by mutagenomics

I, Insensitive, nd, not determined; S, sensitive; W, weakly insensitive. Asterisk indicates stop codon.

Mutant All EMS SNPs All PA-SNPsa Homozygous PA-SNPs Candidate Geneb MutationACC

Response

eah1 400 112 99 AHK4 Splice acceptor variant c.1708-1G . A Seah12c 653 172 86 AHK4 Thr-1034Ile Seah14-1c,d 153 20 14 AHK4 Gln-928* Seah14-2d 125 27 11 AHK4 Gln-928* Seah15c 565 137 79 AHK4 Asp-1036Asn weah17c 200 34 26 AHK4 Ser-142Asn Seah25c 586 171 109 AHK4 Gly-139Arg weah32 427 106 55 AHK4 Ser-142Asn Seah34 168 46 42 AHK4 Trp-753* ndeah45 779 223 118 AHK4 Thr-486Ile Seah46 142 30 24 AHK4 Gln-927* weah41 662 171 94 AHK4 Asp-996Asn S

ARR13 Asp-66Asneah13 479 139 75 AHK4 Trp-134* Seah22-1e 129 21 10 HY5 c.439-1G . A Seah22-2e 130 25 16 HY5 c.439-1G . A Seah2 529 137 107 EIN2 Cys-1141Tyr Ieah3c 364 98 55 AHK4 Arg-124Gln w

EIN2 Pro-1185Leueah4 385 89 47 EIN2 Pro-460Leu Ieah6 335 70 41 EIN2 Gly-247Arg Ieah10 307 68 45 EIN2 Leu-375Phe Ieah18 531 132 35 EIN2 Trp-1012* Ieah21 429 103 76 EIN2 Trp-1158* Ieah47 401 105 51 EIN2 Gly-882Glu weah16 409 94 44 AUX1 Thr-245Ile Seah20 318 59 43 AUX1 36311G . A Splice donor variant weah30 511 140 59 CKI1 Pro-289Ser Seah39 372 83 62 ARR12 Pro-538Ser Seah24 268 63 33 AHK3 Trp-801* ndeah9 526 120 71 ACS7 Trp-417* Seah44 448 117 68 ACS7 Ser-249Phe ndeah48 297 90 57 ACS7 Trp-83* Seah5 563 163 99 — — weah7 300 72 34 — — Ieah28 697 173 112 — — IeahX 224–748 39–201 17–125 Fourteen independent eah lines with no change in

Supplemental Table S1 genesS

eahY 115–615 24–182 12–107 Five independent eah lines with no change inSupplemental Table S1 genes

nd

aIndicates all PA-SNPs called by GATK and SnpEff, regardless of zygosity. bLikely caused by the nature of the mutation and role ofthe gene. cF1 failure to complement in cross with cre1-12. dSibling group for eah14. eSibling group for eah22.



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150-bp paired-end reads (;11 million DNA fragmentsper library). In the first step of the analysis, we sought toidentify lines harboring mutations in the key genes thathave been previously linked to cytokinin sensitivity(Supplemental Table S1) as well as genes linked toethylene signaling (Merchante and Stepanova, 2017).We screened the genomic sequences of the eah lines forPA-SNPs in these genes. We identified 13 alleles of ahk4in the eah screen (Table 1). AHK4 encodes a cytokininreceptor and is the predominant member acting in theroot response to cytokinin of the three Arabidopsiscytokinin receptors (Higuchi et al., 2004; Nishimuraet al., 2004; Riefler et al., 2006). AHK4 has a cytoplas-mic N-terminal domain, an endoplasmic reticulum-localized cytokinin-binding CHASE domain, and cy-tosolic kinase and receiver domains.We identified eightmissense alleles in ahk4, four nonsense alleles, and oneallele predicted to alter a splice site (Fig. 3A). We con-firmed that the causative mutations in a subset of theselines were indeed ahk4 alleles by complementation testswith the cre1-4 allele of AHK4 (Table 1). Three of the eahmissense mutations are predicted to alter residueswithin the first transmembrane domain of AHK4, andone (S142N) is adjacent to it. Remarkably, this includestwo identical mis-sense mutations (S142N) that wereisolated from independent pools. These lines (eah17 andeah32) were confirmed to be independent alleles as they

did not share any SNPs throughout the genome otherthan S142N. Two mis-sense mutations were predictedto alter residues within the His kinase domain, consis-tent with the observation that His kinase activity is re-quired for cytokinin signaling (Mähönen et al., 2006).Finally, three of the mutations are predicted to alterresidues within the receiver domain (Fig. 3A). Two ofthese (D996N and T1034I) are annotated as part of theactive site (Lin et al., 1999). A third mis-sense mutation,D1036N, is two residues adjacent to an active site resi-due on the AHK4 primary amino acid sequence. TheAsp at position 996 is, in fact, the target Asp that acceptsthe phosphate group from theHK domain and has beenpreviously demonstrated to be essential for AHK4function (Inoue et al., 2001; Mähönen et al., 2006). Toexamine the predicted effect of these mis-sense alleles,the AHK4 receiver domain was modeled using thehhpred software suite (Zimmermann et al., 2018) andmodeled in the program PyMol (https://pymol.org/2/; Fig. 3B). Examination of the predicted 3D structureshows that the D1036 residue is physically near theactive site residues, similar to the D996 and T1034 res-idues, but further toward the outer surface of thedomain.

We also identified mutations in other two-component signaling elements in the eah screen. Theeah24 mutant harbors a nonsense allele in the codingregion of AHK3, which encodes a second cytokinin re-ceptor that plays a more minor role in cytokinin re-sponses in the root (Higuchi et al., 2004; Nishimuraet al., 2004). Single ahk3 mutations do not significantlyaffect the response to exogenous cytokinin in rootelongations assays (Higuchi et al., 2004; Nishimuraet al., 2004), and so it is likely that the cytokinin hypo-sensitivity in this eah24 line reflects the sensitized natureof the ahp2,3 parental line. We also identified mutationsin CYTOKININ INDEPENDENT1 (CKI1), which en-codes a His kinase that lacks the cytokinin-bindingCHASE domain (Kakimoto, 1996). Various lines ofevidence suggest that CKI1 can activate downstreamcytokinin signaling to regulate vascular and game-tophytic development, although it lacks a CHASE do-main and thus its activity is not regulated by cytokininbinding (Kakimoto, 1996; Hwang and Sheen, 2001;Pischke et al., 2002; Hejátko et al., 2009; Yuan et al.,2016). Null alleles of cki1 are lethal (Pischke et al.,2002). It is possible that this eah30 allele of cki1 de-creases input into cytokinin signaling sufficiently in anahp2,3 background to confer cytokinin hyposensitivity,although it is also plausible that this is not the causativemutation for the cytokinin response phenotype in thisline. Finally, we identified two lines with mutations intype-B ARRs. In eah39, there is a mutation in ARR12;single arr12 mutants display a modest hyposensitivityto cytokinin in root elongation assays (Mason et al.,2005), and this may act additively with the ahp2,3 mu-tations. In line eah41 there is a mutation in ARR13, aswell as a second mutation in AHK4; it is likely that theahk4 mutation is the primary driver of cytokinin hy-posensitivity in this line.

Figure 2. Overview of the mutagenomics process. Top, Implementingmutagenomics is a multistep process that starts with a collection ofmutant lines with unknown causative mutations. Middle, A number oflines are resequenced to detect mutations in known genes. Lines withno mutations in known genes can be prioritized in later stages. Bottomright, Independent lines are examined to determine whether any genesare mutated in multiple independent lines more frequently than wouldbe expected by chance alone. Bottom left), Additional mutants from thesame pool as previously sequencedmutants can be resequenced to findlines of common descent (sibling lines). Sibling lines will share a subsetof mutations between them, and this subset is examined to determinewhich genes are mutated in every related line. The causative mutationfor the phenotype of interest will be in that set.


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Of the eah lines that we analyzed, eight of them dis-played a strong EIN phenotype as measured by a tripleresponse assay (Table 1). Six of these have mutations inthe previously identified EIN2 gene. Two lines (eah3and eah47) with an ein2 mutation had a weak EINphenotype; eah3 also had an ahk4 mutation that wasconfirmed as a loss of function by a complementationtest with the cre1-4 mutations Intriguingly, two of theeah lines that displayed a strong EIN phenotype did notdisplay PA-SNPs in any of the screened ethylene-related candidate genes (Supplemental Table S1), sug-gesting that they may affect novel elements in the re-sponse to ethylene. However, we cannot rule out thepossibility that mutations in known genes were misseddue to low sequence coverage, although, as describedabove, it is unlikely at this genome-sequencing depth.

Step 2: Screening for Potential Causative MutationsIdentified by Multiple Independent Alleles

The second step in the mutagenomics pipeline in-volves identifying genes that are found to bemutated inmultiple independent lines, above what one wouldpredict by chance. To ascertain the probability of find-ing any gene mutated in multiple lines randomly, weperformed simulation modeling to define the proba-bility of finding independently derived mutations withan increasing number of lines analyzed (Fig. 4). Thesimulations were performed using the average densityof homozygous PA-SNPs observed in the eah screen.The actual rate at which any gene is mutated is de-

pendent on multiple factors, including target size (in-cluding both gene size and the number of criticalresidues) and its chromatin environment. To isolate theeffect of gene size on the likelihood of finding inde-pendent mutations, we analyzed subsets of genes in thesimulations defined by the quartiles of Arabidopsis

gene sizes (Fig. 4). The probability of finding a genemutated multiple times is a function of both its size andthe number of lines examined. For the smallest quartileof genes (defined as having a coding length of,590 bp),the probability of observing three independent allelesby chance alone in 50 independent lines is ,0.1(Fig. 4A), while for the largest quartile (defined ashaving a coding length of.1,555 bp), the probability is;95% (Fig. 4D). For three-quarters of all genes in theArabidopsis genome, the probability of observing fourindependent alleles by chance in 50 independent lines is,0.1.Consistent with our simulation studies, we find

many genes that are independently mutated at leasttwo to four times from our pool of 51 eah lines(Supplemental Fig. S1). As there is a reasonable prob-ability (;25%) that a large gene would be indepen-dentlymutated five times by chance (Fig. 4), we initiallysearched for genes identified as being mutated in atleast six independent lines, as these are highly likely tobe causative mutations. The only two genes identifiedwith at least six lines harboring homozygous PA-SNPsin the same gene (i.e. independent alleles) are ein2 (eightalleles) and ahk4 (13 alleles). In screens for ethylene-insensitivity, ein2 is the most frequently identifiedmutation, presumably reflecting its large size (encodedprotein is 1,294 amino acids), nonredundant nature,and strong phenotype (Alonso et al., 1999). Likewise,AHK4 is a large gene (encoded protein is 1,080 aminoacids) and ahk4 loss-of-functionmutations are known toconfer a strong cytokinin-hyposensitive phenotype inroot elongation assays (Higuchi et al., 2004; Nishimuraet al., 2004; Riefler et al., 2006). As noted, the eah3 lineharbors both ein2 and ahk4 mutations. As this line hasboth a weak EIN phenotype and fails to complementthe cre1-4mutation, it is likely a double loss-of-functionein2 ahk4 mutant.

Figure 3. Disruptive and missense mutationswere observed in AHK4. A, Gene model of AHK4showing the position and character of mutationsin AHK4 identified in this screen. Transmembranedomains 1 and 2 (brown); CHASE receiver-likeand receiver domains (green), His kinase domain(blue), REC-like domain (orange), REC domain(purple); and UTRs (gray). The red D996N muta-tion is the second D in the conserved DDK motif(Mähönen et al., 2006), which is the conservedphosphorylation site for the receiver domain. B,Computationally predicted model of the AHK4receiver domain. The three mutated residues inthis domain are shown in green.



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There are 22 lines remaining that are not ethylene-insensitive (or were not tested), and which do notcontain mutations in obvious cytokinin targets. Wegenerated a network diagram consisting of thesemutant lines connected to any genes with PA-SNPs inat least two lines (Fig. 5A). Three genes were foundmutated in three independent lines; two of these arein genes that fall into the fourth quartile of Arabi-dopsis genes by the size of their coding region(CATION/H1 EXCHANGER2, 2.3 kb; emb1507, 6.5kb). Finding three mutations by chance is not unlikelyfor genes in this size range (P . 0.9; Fig. 4). The thirdgene that is mutated in three lines (eah9, eah44, andeah48) is ACS7, which falls into the third quartile ofgenes (1.44-kb coding region). The probability offinding three hits in a gene of this quartile in 22 in-dependent lines is ,0.2, suggesting that it may becausative.

ACS7 encodes an ACC synthase protein that cata-lyzes the generally rate-limiting step in ethylene bi-osynthesis (Yang and Hoffman, 1984). Cytokinininhibits root growth in Arabidopsis primarily by in-creasing the production of ethylene (Vogel et al.,1998a), which is why ein mutants are identified inthe eah screen. Previous genetic screens for cytokininhyposensitivity using etiolated seedlings identified adifferent ACS gene, ACS5, as the target of cytokinin-induced ethylene production (Vogel et al., 1998b).There are eight genes in the Arabidopsis genome thatencode active ACS enzymes (Yamagami et al., 2003),and these fall into three classes based on theirC-terminal domains (Harpaz-Saad et al., 2012).

Cytokinin has been shown to regulate the stability ofboth class 1 and class 2 ACS proteins in etiolatedseedlings, but did not affect ACS7 turnover, which isthe sole class 3 ACS in Arabidopsis (Hansen et al.,2009; Lee et al., 2017). Further, cytokinin was foundto not affect the level of ACS7 mRNA, and disruptionof ACS7 did not reduce the level of ethylene made inresponse to exogenous cytokinin in etiolated seed-lings (Lee et al., 2017). Thus, ACS7 was not includedin the list of known targets for our screen.

Themutation in eah44 is a mis-sense mutation in theACS7 gene and is predicted to change Ser-249 to a Pheresidue. This residue is adjacent to a residue (Tyr-248)strongly conserved in plant aminotransferases(Rottmann et al., 1991). If this acs7 mutation is caus-ative, it may act by interfering with the active siterequired for the Asp aminotransferase activity ofACS7. The mutation in eah9 is a nonsense allelecausing a predicted premature translational stop atTrp-417. This mutation is near the end of the proteinand is predicted to truncate a conserved domain ofthe ACS proteins (Yamagami et al., 2003). The mu-tation in eah48 is a nonsense allele affecting an earlycodon (83).

To test if disruption of ACS7 does result in reducedsensitivity to cytokinin in a root elongation assay, weexamined the response of the acs7-1 mutant, whichharbors a T-DNA insertion mutation in the ACS7 gene(Tsuchisaka et al., 2009). The acs7-1 mutant displaysstrong insensitivity to cytokinin, similar to the responseobserved in lines eah9 and eah44 (Fig. 5, B and C). Thisconfirms that the acs7 mutations are causative for

Figure 4. Probability of identifying multiple ran-dom independent mutations from increasingnumbers of lines analyzed. Simulation-derivedprobability of at least one occurrence of three tofive independent alleles (red 5 3, green 5 4, andblue5 5 occurrences) in any gene in a given sizerange in n independent mutant lines. Each graph(A–D) represents a different quartile of Arabi-dopsis genes based on coding region length.


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cytokinin hyposensitivity in the eah9, eah44, and eah48lines. Consistent with observations from etiolatedseedlings (Lee et al., 2017), our analysis indicates thatthe level of ACS7mRNA is not significantly induced inresponse to cytokinin in Arabidopsis roots (see below,“RNA-Seq Analysis”: 760 versus 781 normalized ACS7counts for vehicle control and 0.1 mM BA-treated roots,respectively), suggesting that the effect of cytokinin onACS7 levels is post-transcriptional, similar to the sta-bilization of ACS5 and other type 1 and type 2 ACSproteins by cytokinin in etiolated seedlings (Hansenet al., 2009; Lee et al., 2017). In other studies, ACS7protein levels were shown to be regulated by theXBAT32 RING E3 ligase (Lyzenga et al., 2012), and theN-terminal domain of ACS7 plays a role in the regula-tion of its turnover (Xiong et al., 2014). We concludethat, in contrast to etiolated seedlings, ACS7 is the pri-mary target of cytokinin-induced ethylene biosynthesis

in light-grown Arabidopsis roots, and this likely occursvia an increased stability of the ACS7 protein.

Step 3: Analysis of Lines of Common Descent toNarrow-Down Candidate Genes

In the first two steps of mutagenomics, we identifiedgenes that were previously implicated in cytokininsignaling and/or which are represented by more thansix independent alleles in ourmutant pool. Twenty-twolines remain (out of the original 51 analyzed) for whicha causative gene was not yet identified. For these, wehave developed a third step in the mutagenomicspipeline to substantially narrow down the number ofpotential candidate genes. This step involves sequenc-ing additional lines that are derived from the same poolas the targeted mutant; these will generally be siblingmutants harboring the same causative mutation (if the

Figure 5. acs7 is causative for three eahlines. A, Network diagram of eah lineswith ahk4, ein2, aux1, hy5, and un-known sibling sets removed. Threegenes are mutated three times: CAT-ION/H1 EXCHANGER20, emb1057,and ACS7. These genes are color-codedby their size. Red denotes the thirdquartile of Arabidopsis gene lengthsand green indicates the fourth quartile.B, Root elongation assay for acs7, eah9,and Columbia plants grown on 0.05and 0.1 mM of BA media from 5 to 9DAG. Error bars indicate SE. Differentlowercase letters indicate statisticallysignificant differences by a Tukey posthoc test. C, Eleven-day–old Arabidopsisseedlings grown on control and 0.1 mMof BA. WT, wild type.



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pool size is sufficiently small, ,100 lines). The basicidea is that, as you sequence increasing numbers ofhomozygous sibling lines, you continually enrich forthe homozygous causative mutation as the other non-relevant PA-SNPs randomly segregate.

To further develop the theoretical underpinnings ofthis step, we performed a series of simulation experi-ments to estimate the number of overlapping homo-zygous PA-SNPs expected in comparisons of two to sixM3 siblings. The M3 generation was simulated ratherthan the M2 to emulate a typical screen in which M2individuals are identified and retested in the M3 gen-eration to confirm the phenotype of interest. For thisanalysis, we used PA-SNP densities ranging from 10 to285 per line, consistent with the observed numbers perline in our screen (Fig. 1D). The PA-SNPs were ran-domly allocated across the genome and allowed tosegregate independently. If a mutation was identifiedas homozygous in all lines, this was added to thenumber of candidate genes for that trial. This processwas repeated for 10,000 trials to estimate the degree ofoverlap observed between siblings. The simulation ex-periments are summarized in Figure 6. A striking pre-diction from this analysis is that even for a line with285 PA-SNPs, sequence analysis of as few as four ad-ditional siblings reduces the number of candidate genesto fewer than five, which is a very manageable numberfor subsequent analysis. With lines containing fewerPA-SNPs, this is reduced to even fewer candidates.

As a proof-of-concept, we sequenced a pair of sib-lings from the eah14 mutant line (eah14-1 and eah14-2).Note that AHK4 was already identified as a causativemutation in the line from both steps one and two of themutagenomics analysis. This pair of siblings had asmall mean number of PA-SNPs (�X 5 23.5 if bothhomo- and heterozygous PA-SNPs are included; �X 512.5 homozygous; Table 1). When the second siblingline was sequenced, it was found to only share a singlehomozygous PA-SNP with the eah14-1 line, which wasin theAHK4 gene (Fig. 7F), which is consistent with this

being the causative mutation. Given the observednumber of PA-SNPs, this fits with what our simulationmodel would predict (Fig. 6). To confirm that these twolines were true siblings, the network of shared identicalSNPs, including those not predicted to affect proteincoding, was examined; the large number of othershared mutations confirms that these two lines are in-deed siblings (Supplemental Fig. S3).

We also analyzed the eah22 line, which had a com-paratively small mean number (13) of homozygous PA-SNPs (Table 1). We sequenced a second eah line (eah22-2) derived from the same M1 pool that eah22-1 wasidentified from. There were only six genes that sharedhomozygous PA-SNPs in these two lines (Fig. 7A). Oneof these, HY5, has in fact been suggested to affect cy-tokinin sensitivity (Cluis et al., 2004), but was not in-cluded in our original set of “known” genes due to itspredominant role in light signaling. Consistent with theoverlap of hy5 PA-SNPs, both eah22 siblings displayedcharacteristic hy5 phenotypes, such as elongated hy-pocotyls and elongated, horizontal lateral roots(Oyama et al., 1997). To confirm that hy5 was the mu-tation responsible for cytokinin hyposensitivity in thisline, we examined an independent hy5 mutant line.Similar to eah22, a hy5 T-DNA allele also conferred cy-tokinin hyposensitivity as measured using a root elon-gation assay (Fig. 7, B and C). We conclude that hy5 isthe causative mutation in eah22, and further character-ized the role of HY5 in cytokinin responsiveness.

Mutations in HY5 Alter the Transcriptional Responseto Cytokinin

HY5 is a transcription factor involved in regulatingthe output of blue-light–responsive cryptochromes andtheir downstream processes, such as photomorpho-genesis and anthocyanin production (Wang et al., 2001;Yang et al., 2001). In the dark, HY5 is degraded byCONSTITUTIVELY PHOTOMORPHOGENIC1, an E3ubiquitin ligase (Ma et al., 2002; Bauer et al., 2004).When plant cells are exposed to light, CONSTITU-TIVELY PHOTOMORPHOGENIC1 production de-clines and HY5 accumulates. We hypothesized thatHY5 may play a role in the transcriptional response tocytokinin. To test this, we first determined if the HY5binding sites in the genome correlated to the bindingsites for the type-B ARRs. We compared the chromatinimmunoprecipitation (ChIP)-chip (Lee et al., 2007b)peaks determined for HY5 with the peaks derived froma ChIP-seq analysis of several type-B ARRs (ARR1,ARR10, and ARR12; Zubo et al., 2017; Xie et al., 2018;Fig. 7D). There was highly significant overlap betweengenes with adjacent type-B ARR and HY5 binding sitesacross the genome (the P values for the overlap of HY5with ARR10 [Zubo et al., 2017] and ARR1, ARR10, andARR12 [Xie et al., 2018] by a hypergeometric test are6.81e-257, 2.4e-267, 5.6e-283, and 1.9e-252, respec-tively). For example, 45% of the genes identified ashaving adjacent HY5 binding sites also are targeted by

Figure 6. Projected numbers of shared alleles between M3 siblings.Error bars indicate the mean and SD of simulation-derived distributionsof mutations shared between sibling lines. The dashed gray line indi-cates five overlapping genes. The numbers above the graph indicate theaverage number of homozygous PA-SNPs observed in a set of siblings.The range of M3 PA-SNP densities is representative of the number of PA-SNPs observed in the eah screen.


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ARR12. This suggests that these transcription factorsshare many common targets.We next examined the role of HY5 in modulating

cytokinin-mediated changes in gene expression. Wild-type and hy5 (T-DNA allele) mutant roots were treatedwith a vehicle control or 0.1 mM BA, and RNA transcriptlevels were determined by RNA sequencing (RNA-seq;Fig. 7E). A total of 347 genes were upregulated aftercytokinin treatment in the wild type in these conditions(false discovery rate of 0.02 and log2 fold-change$ 0.5;Supplemental Table S3). Of those, 53% (185 genes) werenot upregulated in the hy5mutant (Fig. 7E; SupplementalTable S4). Likewise, 177 genes were downregulated inresponse to cytokinin in wild-type roots, and 38%of these were not downregulated in the hy5 mutant(Fig. 7E; Supplemental Table S4). Interestingly, therewere a substantial number of genes upregulated (49)and downregulated (477) in response to cytokininspecifically in the hy5 mutant (Fig. 7E).

We also examined if changes in basal gene expressionin the hy5 mutant affected cytokinin-regulated genes.There were 1,277 genes that showed increased and 954decreased, expression in the hy5 mutant as comparedto wild-type roots (Supplemental Table S5). Thesegenes overlapped significantly with cytokinin differ-entially expressed genes (DEGs) in the wild-type roots(Supplemental Fig. S2), suggesting that disruption ofHY5 alters the expression of a subset of cytokinin-regulated genes in the absence of exogenous cytoki-nin. Together with the ChIP data, these data are con-sistent with HY5 playing an important role inregulating the transcriptional response to cytokinin.

DISCUSSION

Genetic screens are remarkably powerful tools todissect biological processes, but the identification of

Figure 7. Sibling analysis used toidentify a hy5 mutation in two siblinglines. A, Network of shared and un-shared PA-SNPs in eah22-1 and eah22-2, sibling lines containing a mutation inhy5. B, Representative images of rootelongation at 8 DAG for Col-0, ahp2,3,ahp1,2,3, hy5-SALK_096651C, eah22,and ahk2,4, grown on control and BA-treated media. Bar 5 1 cm. C, Quanti-fied root elongation data from 3 to 8DAG for Col-0, ahp2,3, ahp1,2,3, hy5-SALK_096651C, eah22, and ahk2,4,grown on control and BA-treated me-dia. Error bars are SE. Lowercase lettersindicate statistical significance after aTukey post hoc test. D, Comparison ofHY5 binding sites with BA-treated type-B binding sites. Type-B binding sites forARR10 (Zubo et al., 2017), and sepa-rately for ARR1, ARR10, and ARR12(Xie et al., 2018) were compared withbinding sites for HY5 (Lee et al.,2007b). E, Comparison of DEGs be-tween BA and control-treated wild-type(WT) plants and between BA andcontrol-treated hy5 mutant plants.DEGs are at least 0.5-fold different (on alog2 scale) with Benjamini-Hochberg-adjusted P value , 0.02. F, Networkdiagram of PA-SNPs of eah14.



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causal genes can be a challenging task in Arabidopsisand other eukaryotic organisms. EMS and other mu-tagens that induce point mutations are extraordinarilyuseful in that a wide range of different alleles can begenerated, including those that reduce but not do noteliminate the function of the encoded protein (i.e. hy-pomorphic alleles), thereby overcoming potential le-thality or infertility of null alleles, or mutations thatmodify protein function that can overcome functionalredundancy, such as dominant negative (antimorphic)or activating (neomorphic or hypermorphic) changes.Further, EMS can be used to rapidly develop muta-genized populations in any genetic background,allowing screens to be performed in sensitized back-grounds, enabling suppressor and enhancer screens.Such screens can be useful in many ways, includingallowing the identification of genes with relativelyweak effects on a pathway of interest, potentiallyovercoming partial functional redundancy, and/oridentifying elements involved in signaling crosstalk.While extraordinarily useful, cloning the genes targetedby EMS mutagenesis remains challenging.

The advent of high-throughput sequencing hasprovided tools to facilitate the cloning of EMS-generated and other mis-sense alleles. The mostcommon approach in Arabidopsis and other se-quenced organisms is to use “mapping-by-sequenc-ing” (Schneeberger et al., 2009b). This approach hasgreatly facilitated cloning of genes in Arabidopsisand other model organisms. While these approachesare quite powerful and reduce the time and effortrequired for identifying causative mutations of in-terest, they still are focused on analyzing a singlemutant line at a time and require multiple genera-tions to develop a mapping population. The muta-genomics pipeline described here provides a meansto identify causative mutations in a relatively rapid,parallel manner, facilitating the cloning of genescorresponding to many identified mutants. Muta-genomics has the capacity to accelerate the analysis ofmutant screens, enabling exhaustive screening inArabidopsis and other model organisms.

The foundational data for themutagenomics pipelineis determining the genomic sequencing of the identifiedmutants as well as the parental line. Although, in the-ory, this could be done using M2 plants, in general it ishighly advantageous to confirm the phenotypes of theisolated mutants after a rescreen of M3 seedlings, andthis has the important advantage of identifying lineshomozygous for the mutant phenotype. While reces-sive mutations will be necessarily homozygous in theM2 plants, dominant mutations will be homozygous inthe M2 generation only ;33% of the time, although ifsemi-dominant there would likely be a bias towardhomozygous lines as they would have a stronger phe-notype. To include dominant lines in mutagenomics,one could either identify a homozygous M2 siblingfrom the same pool (as evidenced by lack of segregationof wild-type phenotypes in its progeny) or screen theprogeny of various M3 plants for homozygosity.

The first step in the mutagenomics pipeline is theidentification of lines with mutations in known genes.In the case of a well-characterized pathway such ascytokinin signaling, there can bemany potential knowntargets, but in more naïve screens, there may be few orno presumptive candidate genes, rendering this stepunnecessary. In the cytokinin screen analyzed here, weincluded 23 genes related to cytokinin (including aux1,which is known to give cytokinin-insensitivity) and 77previously identified in EIN screens (Merchante andStepanova, 2017) that we anticipated might be identi-fied in the eah screen (Supplemental Table S1). As an-ticipated, mutations in several of these were indeedidentified in the eah screen. However, identifying amutation in a known gene does not guarantee thatmutation is causative for the altered phenotype, so caremust be taken to examine the specific mutation and toassess the plausibility of that mutation deleteriouslyaltering the protein. Complementation tests can also beperformed to confirm the causative nature of mutationsin known genes. In any case, this step helps to prioritizewhich lines to focus on in a screen for novel elements ina pathway, as one would likely not pursue lines withmutations in known genes, whether complementationwas tested or not.

The second step of mutagenomics is the comparisonof independent mutations across all mutant lines toidentify any genes mutated more times than expectedby chance. Our simulation modeling indicates that thesame gene can be found mutated in multiple linesrandomly, which is not necessarily an intuitive pre-diction. In our case, in which we analyzed 51 mutantlines, there was a high likelihood (.90%) that a genewill be mutated in up to four independent lines bychance when one includes genes of all sizes. As thefrequency at which a gene is mutated is directly pro-portional to the length of its coding region (amongother factors), we stratified our analysis by the size ofthe gene by considering quartiles of the genome binnedby the size of the coding region. As expected, thisanalysis demonstrates that smaller genes are less likelyto be mutated multiple times by chance. Further, thelikelihood of multiple random mutations decreaseswith smaller numbers of mutant lines. We used thesefactors to reanalyze the unassigned eah lines, and thisrevealed three mutations in acs7 as the potentiallycausative mutation, which was subsequently con-firmed. This suggests that ACS7 is the primary target bywhich cytokinin elevates ethylene biosynthesis in rootsof light-grown Arabidopsis seedlings, in contrast toetiolated seedlings in which ACS5 is the primary target(Vogel et al., 1998b). Interestingly, ACS7 has also beenlinked to root gravitropism, lateral root formation, andABA responsiveness (Prasad et al., 2010; Dong et al.,2011; Huang et al., 2013).

In addition to the size of the coding region, differentgenes are likely more or less sensitive to mutations af-fecting their function due to differences in the numberof critical residues. Further, some genes will result in aweak phenotype even with a complete loss-of-function


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as they, for example, may only modify a pathwayrather than being a central component, and thesewouldbe under-represented in the identified mutant pools.These differences likely underlie the low number oftimes some known genes were identified in the eahscreen. For example, AHP1 was surprisingly not foundin our screen, even though it results in strong cytokinin-insensitivity when combined with the ahp2,3mutations(Hutchison et al., 2006); this may reflect its small size(154 amino acids) and/or low mutability. An ahk3mutation was only found in a single line and the ahk2mutation was not found at all, which may reflect theirrelative minor contributions to cytokinin signaling inthe root, necessitating a strong/null allele to yield aphenotype that could be identified in the primaryscreen.In cases where the number of mutations does not

show clear enrichment, the exact nature of observedmutations can be considered when deciding whether agene is worth further study. However, caution must betaken. In the eah screen, 12 alleles of ahk4were identified(Fig. 3; Table 1), but only four nonsense alleles (three inthe last third of the gene) and one splice mutation(about halfway through the gene) were observed. Stopmutations and splice errors are easy to classify as del-eterious, mis-sense mutations less so. The most carefulapproach would be that the nature of observed muta-tions should be treated as reasons to include a genewhich otherwise might be excluded, rather than to ex-clude any gene from further analysis.The third step of the mutagenomics pipeline relies on

the ability to identify mutant lines of common descent(i.e. siblings). The ease with which this is accomplisheddepends to a large extent on the size of the M1 poolsgenerated. At one extreme, an M1 pool size of onewould greatly facilitate the identification of siblings butwould make the primary screen more tedious. At theother extreme, very large pool sizes would simplify theprimary screen, but would make isolation of siblingsmore challenging. For example, the original pool size inour eah screen was slightly larger (140 per pool) thanwhat is optimal for rapid sibling identification. Con-firming that two mutant lines are siblings is clear-cutfrom genome sequencing data because siblings willshare many common mutations, including both PA-SNPs and non-PA-SNPs, but independent linesshould share no or extremely few common mutations.The idea behind the sibling analysis is that in the

mutagenesis process, the M1 plant acquires randomlyallocated heterozygous mutations. Upon selfing, thiswill produce M2 offspring segregating for all muta-tions, including the mutation of interest. The screenselects M2 lines homozygous for the mutation of in-terest if recessive. Alternatively, homozygotes can beselected from M3 rescreens for dominant mutations.Thus, as one sequences additional sibling lines, theywill all be homozygous for the mutation of interest (asthe phenotype selected for this), with other randommutations segregating. Our simulation study suggeststhat sequencing just a few additional individuals

rapidly reduces the candidate pool list down to a small,manageable number. For example, for mutant lineswith fewer than 60 M1 homozygous SNPs (the averagethat we found in our screen) comparing only two ad-ditional sibling M2 plants (a total of three siblings) canresult in an overlap list of close to five genes. Thisprocess can be carried out in the M3 generation as well,although the overlapping gene sets will be larger asheterozygous M2 SNPs have an opportunity to fix inthe M3 generation. Additional siblings can be analyzedby whole genome resequencing, but if the number ofcandidate genes is small (,10), it may be more feasibleto analyze remaining SNPs using a targeted PCR-basedapproach rather than generate a whole genome se-quencing library. One complication in this analysis isthat random mutations closely linked to the causativemutation will be difficult to separate if they are in cis tothe causative mutation in the M1 plant, but this shouldrepresent a small minority of the overall PA-SNPspresent. Subsequent tests (genetic complementation,isolation of T-DNA, or clustered regularly interspacedshort palindromic repeats alleles) are required in anycase to definitively identify the causative gene.While mutagenomics has the considerable advan-

tages of being rapid, fairly easy to use, and capable ofparallel analysis of many mutant lines, it may not bestraightforward to identify the causative gene for everymutant line identified. For those mutant lines remain-ing after the mutagenomics pipeline, a more tradi-tional bulked segregant mapping population could beemployed.We used a screen for reduced cytokinin response in a

root elongation assay to test the mutagenomic pipeline.Highlighting the power of the mutagenomics process,we observed a line (eah14) for which the causative genewas identified in all three stages of the mutagenomicsprocess (detection of known gene mutations; over-enriched mutations; and mutations shared by sib-lings). Themost frequentlymutated gene to come out ofthis screen was AHK4, and this allelic series shedlight on the residues essential for AHK4 function.We also further characterized the role of HY5 in theresponse to cytokinin. HY5 has been implicated as alink between cytokinin and cryptochrome pathways(Vandenbussche et al., 2007), and hy5 mutants werefound to be resistant to exogenous cytokinin in root andhypocotyl assays (Cluis et al., 2004).We found that HY5binds to an overlapping set of genomic targets withtype-B ARRs, which mediate the primary transcrip-tional response to cytokinin (Zubo et al., 2017; Xie et al.,2018). Consistent with this,HY5 is required for a subsetof cytokinin-regulated gene expression changes.There are 22 lines from our eah screen that do not

harbor mutations in any of the known targets wescreened for. These should provide rich fodder forfurther application of sibling analysis and perhaps bulksegregant mapping to identify new elements involvedin the response to cytokinin. Mutagenomics shouldenable the parallel processing of mutant screens inArabidopsis and other model organisms and should



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further enhance the awesome power of genetics todissect biological processes.

CONCLUSIONS

Mutagenomics is a powerful approach to facilitateparallel processing of multiple mutants identified fromgenetic screens. This should empower all manner ofscreens in diverse genetic backgrounds in Arabidopsis.Further, mutagenomics is species-agnostic and can beapplied to most model organisms.

MATERIALS AND METHODS

Plant Materials and Growth Conditions

TheArabidopsis (Arabidopsis thaliana) ahp2,3doublemutant, the ahp1 controlin the Ws-0 background, and the ahp1,2,3 mutant have been described inHutchison et al. (2006). The hy5 allele is from the SALK T-DNA collection(SALK_096651C). The ahk2-ahk4 line has been described as ahk2-7 cre1-12 in theliterature (Inoue et al., 2001; Yamada et al., 2001; Cheng et al., 2013). The acs7line has been described as acs7-1 in Yamagami et al. (2003). Seed were sterilizedby incubating in 95% (v/v) ethanol (1 min) followed by a 15-min incubation in50% (v/v) bleach (8.25% [w/v] sodium hypochlorite; Clorox) and 0.5% (v/v)TWEEN 20. After sterilization, the seeds were washed five times with sterilewater and then stratified for 3 d at 4°C. Root elongation assays were performedon one-half strength Murashige and Skoog medium with 0.1 mM of BA with 8 gL21 Phytagel (Sigma-Aldrich). Seedlings were germinated on retest media tomatch the initial screen conditions. Retest plates were grown vertically inconstant light conditions at 21°C.

EMS Mutagenesis and Screen forCytokinin Hyposensitivity

EMS mutagenesis was performed by incubating 1.5 g of dry ahp2,3 seeds in100 mM of potassium phosphate buffer at pH 7.5. EMS was added to a con-centration of 0.4% (v/v) and the seeds were mixed on a rocking shaker for 8 h.The seeds were washed 20 times with water and sown directly on soil. The M1plants were grown to maturity and the M2 seeds were harvested in pools of;140 M1 plants.

For the primary assay,;100mL ofM2 seedwas sterilized and plated denselyon media containing 0.1 mM of BA. Pooled M2 seeds were plated in dense rowson BA plates and grown for 5 to 8 d. Seedlings with roots longer than thesurrounding seedlings were picked and transplanted to soil. Transplantedseedlings were grown to maturity, and M3 seed was harvested and storedat 220°C. Retesting of M3 seed was performed on BA media to verify cyto-kinin hyposensitivity. M3 seed was sterilized and plated on media containing0.1 mM of BA at low density. A wild-type control was used on each plate.Three DAG, the position of the primary root tips for each seedling wasmarked. Roots were scanned at 8 DAG and the distance from the mark to theprimary root tip was measured using the software ImageJ (Abràmoff, 2004).The control and treatment root elongation values were compared using asingle-tailed Welch’s t test with a 5 0.975.

The lines were screened for ethylene responsiveness by plating on Murashigeand Skoog medium containing 10 mM of ACC. After 3-d growth in the dark, thelineswere scored for their triple responsemorphology (Guzmán and Ecker, 1990).

Library Preparation for Genome Resequencing

Foreachmutant lineof interest, one to fourM3plantsweregrownonselectivemedia and inflorescences were collected from each plant. A modified cetyl-trimethylammonium bromide extraction protocol (Doyle and Doyle, 1987) wasperformed to extract genomic DNA for genome resequencing. Frozen tissuewas ground using a SPEX SamplePrep 2010 Geno/Grinder (Thermo FisherScientific). Ground tissue was mixed with 23 cetyltrimethylammonium bro-mide buffer. The homogenate was washed twice with chloroform. DNA wasprecipitated with 0.53 volume 5 M of NaCl and 2 volumes of 100% (v/v)

ethanol and washed with 70% (v/v) ethanol with 10 mM of ammonium acetate.The DNA was then resuspended in 250 mL of water with 10 mg of mL21 RNaseA. The DNA was reprecipitated and washed with 70% (v/v) ethanol, thenresuspended in 50 mL of water.

Genomic libraries were then prepared using a TruSeq PCR-Free DNA Li-brary Kit (cat. no. FC-121-3003; Illumina) for the first two phases, and a KAPAHyperPrepDNALibrary Kit (Roche) for the third phase. Library concentrationswere determined using a KAPA Library Quantification Kit (cat. no. 7960140001;Sigma-Aldrich/Roche). Mutant resequencingwas performed in three rounds—twice on aHiSeq 4000 (first and third phases; Illumina) and once on aHiSeq X10platform (second phase; Illumina).

SNP Detection

Several processing steps are necessary after identifying SNPs in sequencingdata. SNPs are filtered out of this set based on quality scores, presence inbackground mutants, and consistency with an EMS-derived mutation. Afterfiltering, SNPs are analyzed using the program SnpEff (Cingolani et al., 2012) todetermine whether they are PA mutations. The SnpEff output is then parsed tocreate final output tables and figures. Currently, these functions are performedby a collection of batch scripts calling R scripts and SnpEff. At publication, thecollection of scripts will be packaged together in a GitHub repository such thatadvanced users could run analyses locally.

Sequencing

Genome resequencing was performed in three rounds. The first round ofsequencing was performed with 33 libraries on a single lane of a HiSeq 4000instrument (23150 bp) by the High Throughput1 Sequencing Facility (HTSF)at University of North Carolina (UNC) at Chapel Hill. The second round wasperformed on a HiSeq 310 platform (23150 bp; BGI). The third round wassequenced on a HiSeq 4000 (2375 bp) by the HTSF at UNC Chapel Hill. TheRNA-seq experiment was sequenced on a HiSeq 4000 in high output mode(2375 bp) by the HTSF at UNC Chapel Hill.

Mutagenomics Data Processing

Sequenced libraries were aligned to The Arabidopsis Information Resource10 genome (https://www.arabidopsis.org/) using the software BowTie2(Langmead and Salzberg, 2012). Variants were inferred using the GenomeAnalysis Tool Kit (GATK; McKenna et al., 2010; DePristo et al., 2011; Van derAuwera et al., 2013). Detected variants were filtered based on their call qualitiesbyGATKwith the filterstring “QD,2.0jjFS.60.0jjMQ,40.0jjMQRankSum,212.5jjReadPosRankSum,28.0.” Variants passing all GATK filters were furtherfiltered by the read depth of the detected variant and by the predicted effect asdetermined by the program SnpEff (Cingolani et al., 2012). Only PA mutationswere considered.

AHK4 Receiver Domain Structure

The receiver domain structure wasmodeled using the HHpred andModelerpackages available at https://toolkit.tuebingen.mpg.de/ (Webb and Sali, 2016;Zimmermann et al., 2018). The response regulator domain (residues 946 to1,071) was submitted to the HHpred search and the top 100 matches werechosen to model the structure of the domain using Modeler.

Library Preparation and Analysis for RNA-Seq

The hy5 gene expression experiment was performed using RNA extractedfrom flash-frozen tissue using an RNEasy kit (cat. no. 74106; Qiagen). Samplesof mRNA were isolated using Sera-Mag oligo dT beads (Thermo Fisher Sci-entific) in the presence of RNase Out (Enzymatics) and heat-fragmented. First-strand synthesis was performed with Enzscript (Enzymatics), and second-strand synthesis with DNA PolI (Enzymatics) and RNase H (Enzymatics).End repair was performed with T4 DNA Polymerase (Enzymatics), Klenowpolymerase (Enzymatics), and T4 PNK (Enzymatics). A-tailing was performedwith Klenow exo2 (Enzymatics). Adapter ligationwas performedwith T4DNAligase (Enzymatics). The libraries were PCR-amplified using KAPA HiFi Hot-Start ReadyMix (Kapa Biosystems). All wash steps were performed withAMPure XP beads (Beckman Coulter). Read alignment was performed usingthe tool Star (Dobin et al., 2013), and count data were normalized using the


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program Salmon (Patro et al., 2017). Differential gene expression was analyzedusing the program DESeq2 (Love et al., 2014) in R. The Benjamini-Hochberg-adjusted P-value cutoff (Benjamini and Hochberg, 1995) used for determiningsignificancewas lowered to 0.02 to account for only having two replicates, and alog2 fold-change cutoff of 0.5 was applied.

Independent Allele Simulations

The algorithm for the independent allele simulations is as follows. First, adecision ismadeonhowmanygenomes to simulate andhowmanymutations toallocate. In this study, the allocated mutations are assumed to represent ho-mozygous recessive mutations. Mutations are randomly allocated across thegenomeusinggene coding lengthas aweighting factor,with the assumption thatno gene will be mutated more than once. Once all simulated genomes have hadmutations allocated to them, a network of mutated genes is generated andanalyzed to determine whether any genes were independently mutated x ormore times. This simulation process is repeated until a population of simulatedscreens is collected. Then, the population of screens can be assessed to deter-mine the proportion n of all simulated screens in which a gene was indepen-dently mutated x times. This process can be repeated for different numbers ofgenomes and different PA-SNP densities.

Sibling Allele Simulations

Empirical crossover frequencies were taken from Salomé et al. (2012), al-though the frequencies of no-crossovers and single-crossovers were combinedinto a single value for single-crossovers. The algorithm for the simulation is asfollows. For any given trial, a decision is made on how many siblings to sim-ulate and how many PA mutations to simulate. The chosen number of simu-lations are allocated to genes randomly in a heterozygous fashion. Mutationsare allocated across the whole genome using individual gene coding regionlengths as weighting factors. Two rounds of simulated meiosis are performed.In each, crossovers to be performed are randomly sampled from the suppliedempirical distribution. The positions of crossovers are randomly allocated butassumed to always occur between genes. Sister chromatids are generated, andcrossovers occur at the predetermined positions between randomly chosennonsister chromatids. One of the four chromatids is randomly chosen to beeither the pollen or ovule gamete. The recombination process is then repeated togenerate the second gamete. The two simulated gametes are combined to formtheM2 generation. The meiosis process described above is repeated to generatethe M3 generation, and the whole process is repeated independently for each ofthe siblings in the trial. The number of genes in a homozygous state shared byall siblings in the trial is then recorded. This process can be repeated for differentnumbers of siblings and different PA-SNP densities.

User Requirements to Perform Mutagenomics

Severalprogramsare required foruseof theMutagenomicspipeline. Theusershould have access to a high-throughput computing platform to perform readalignment and variant calling. Once variant calling is completed, the user canmove from a server to a personal computer. The scripts as written use thesoftware BowTie2 and the Genome Analysis Toolkit.

The pipeline has been set up to be compatible with either a Mac/Linux orWindows operating system. Different versions of the scripts are provideddepending on the operating system in use. The user is required to have R in-stalled, either through the base RGui or the RStudio project, and to have theRscript front-end command available from the command line (e.g. set in thePATH variable on Windows). The user should also have a Bash/zsh shell in-stalled. Macs have one by default, and Windows users can use Git Bash(available at https://gitforwindows.org/). The scripts rely on several utilities(sed, find) that are available natively on Macs and available on Windowsthrough projects like Cygwin (available at https://www.cygwin.com/) orGNU On Windows (available at https://github.com/bmatzelle/gow/wiki).These scripts were developed on aWindows platform using Git Bash and GNUOn Windows. The user should also have a copy of the program SnpEffdownloaded (available at snpeff.sourceforge.net).

Code and Data Availability

Statistical and data analyses were performed using R and Python. Code isavailable at https://github.com/KieberLab/Mutagenomics.

Accession Numbers

Locusdata for the genes from this article canbe found in theGenBank/EMBLdata libraries or at https://www.arabidopsis.org/ under accession numbers:AHP1 (AT3G21510); AHP2 (AT3G29350); AHP3 (AT5G39340); AHK2(AT5G35750); AHK3 (AT1G27320); AHK4 (AT2G01830); HY5 (AT5G11260);ACS7 (AT4G26200); ARR1 (AT3G16857); ARR10 (AT4G31920); ARR12(AT2G25180); ARR13 (AT2G27070); EIN2 (AT5G03280); AUX1 (AT2G38120);and CKI1 (AT2G47430). The RNA-seq Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo/) accession number is GSE149641. The Muta-genomics sequence read archive (https://www.ncbi.nlm.nih.gov/sra) dataaccess number is PRJNA631403.

Supplemental Data

The following supplemental materials are available.

Supplemental Figure S1. Full network diagram of PA-SNPs.

Supplemental Figure S2. Comparison of DEGs between BA and control-treated wild-type plants.

Supplemental Figure S3. Exact shared SNPs between eah14-1 and eah14-2.

Supplemental Table S1. List of genes previously linked to cytokinin re-sponse in roots.

Supplemental Table S2. Genes mutated in at least two sequenced librariesof the eah screen.

Supplemental Table S3. Genes differentially expressed in response to BAin wild-type roots.

Supplemental Table S4. Genes differentially expressed in response to BAin hy5 roots.

Supplemental Table S5. Genes differentially expressed between wild-typeand hy5 roots in the absence of BA.

Supplemental Data S1. User guide to mutagenomics.

ACKNOWLEDGMENTS

We thank Jamie Winshell for technical support, Gyeong Mee Yoon for theacs7 line, and Caroline Sjogren for helpful discussions. The authors also thankYan Zubo for performing initial genetic analyses that confirmed a role for HY5in mediating the root response to cytokinin.

Received May 12, 2020; accepted September 2, 2020; published September 4,2020.

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