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High-throughput capturing and characterization of mutations in essential genes of Caenorhabditis elegans Chu, Jeffrey S; Chua, Shu-Yi; Wong, Kathy; Davison, Ann M; Johnsen, Robert; Baillie, David L; Rose, Ann M May 12, 2014

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RESEARCH ARTICLE Open AccessHigh-throughput capturing and characterizationof mutations in essential genes of CaenorhabditiselegansJeffrey Shih-Chieh Chu1*, Shu-Yi Chua2, Kathy Wong1, Ann Marie Davison3, Robert Johnsen2, David L Baillie2and Ann M Rose1AbstractBackground: Essential genes are critical for the development of all organisms and are associated with manyhuman diseases. These genes have been a difficult category to study prior to the availability of balanced lethalstrains. Despite the power of targeted mutagenesis, there are limitations in identifying mutations in essential genes.In this paper, we describe the identification of coding regions for essential genes mutated using forward geneticscreens in Caenorhabditis elegans. The lethal mutations described here were isolated and maintained by a wild-typeallele on a rescuing duplication.Results: We applied whole genome sequencing to identify the causative molecular lesion resulting in lethality inexisting C. elegans mutant strains. These strains are balanced and can be easily maintained for subsequentcharacterization. Our method can be effectively used to analyze mutations in a large number of essential genes.We describe here the identification of 64 essential genes in a region of chromosome I covered by the duplicationsDp2. Of these, 42 are nonsense mutations, six are splice signal mutations, one deletion, and 15 are non-synonymousmutations. Many of the essential genes in this region function in cell cycle, transcriptional regulation, and RNA processing.Conclusions: The essential genes identified here are represented by mutant strains, many of which have more thanone mutant allele. The genetic resource can be utilized to further our understanding of essential gene function and willbe applicable to the study of C. elegans development, conserved cellular function, and ultimately lead to improvedhuman health.Keywords: Whole genome sequencing, EMS, Mutagenesis, Essential genes, Balanced mutation, Lethal mutation,C. elegans, Cell cycleBackgroundThe proper development and viability of an organism isdependent on a group of genes called essential genes. Inhumans, gene essentiality has been long associated withmany diseases such as miscarriages [1,2], heritable dis-eases, and cancer [3]. Recent studies have shown thatover-expression of some essential genes promotes cellproliferation in cancer [4]. Due to its importance for sur-vival, essential genes have been targets for new therapeu-tics or antimicrobials [5]. To effectively study essentialgenes, generating lethal alleles in model systems isrequired. In the nematode Caenorhabditis elegans, theessential gene set is the largest set of genes and is esti-mated to contain 25% of all the genes [6-9]. Using RNAi,about 3500 genes have been annotated as essential (datacollected from WormBase [10,11]). Inparanoid, a se-quence based orthology inference tool, detects about40% of the C. elegans genes are orthologous to the hu-man genes. But approximately 60% of the essential genesshow clear human orthologs, showing high conservationof essential genes, which makes C. elegans an excellentplatform for examination of essential gene functions thatare relevant to human health. Many important genes,such as let-60/Ras [12] and let-740/dcr-1 [13,14], were* Correspondence: jeff.sc.chu@gmail.com1Department of Medical Genetics, University of British Columbia, Vancouver,CanadaFull list of author information is available at the end of the article© 2014 Chu et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the CreativeCommons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, andreproduction in any medium, provided the original work is properly credited. The Creative Commons Public DomainDedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article,unless otherwise stated.Chu et al. BMC Genomics 2014, 15:361http://www.biomedcentral.com/1471-2164/15/361first discovered through C. elegans genetics. However, thegenetic resource for studying these genes is severely lack-ing. Even with the concerted community effort such as theC. elegans Deletion Mutant Consortium [15], mutations inmany essential genes are still lacking in the knock-out col-lection. The consortium have generated close to 6000knock-out strains since 1998, but only 1436 essentialgenes are in the current collection [16,17]. In addition tothe considerable time and effort required to generate asingle knock-out allele, an outstanding disadvantage of thetargeted deletion approach is that extra effort is needed tobalance the lethal mutation [18]. Recently, the Consortiumhas adopted a procedure of random mutagenesis followedby whole genome sequencing (WGS) to generate andidentify a large number of mutations [15]. Although thisproject can generate more mutations in shorter time, theirmethod does not capture mutations that exhibit lethalphenotypes, and thus, essential genes are selected against.This outcome indicates thousands of essential genes donot have knockout alleles.To complement the effort of the C. elegans community,we took advantage of the balancer system, which wasdeveloped 30 years ago for capturing and maintaining le-thal mutations, with the next-generation DNA sequencingtechnologies. Almost 70% of the C. elegans genome havebeen successfully balanced by large genomic rearrange-ments [18]. By mutagenizing a pre-balanced strain removesthe need to perform additional genetic crosses to balance alethal mutation. The balancer system, designed specificallyto capture and maintain lethal mutations, is the system ofchoice for generating mutations in essential genes. Suchscreens have been carried out for regions in chromosome I[19,20], II [21], III [22], IV [23-26], V [27], and X [28,29].In our laboratories, we have generated over 1350 lethalmutations that fall into 486 complementation groups.The next hurdle in the analysis of essential genes is themolecular identification of the genomic lesion, which todate has involved an enormous effort. Traditional methodsof gene cloning that rely upon candidate identification ofmapped mutations can take months or years. This gene-by-gene approach was only able to characterize 30 essen-tial genes from our library to date. This problem has beendifficult to solve until the recent advances in sequencingtechnology. To address the problem of coding regionidentification, we have recently developed a fast and scal-able pipeline that takes advantage of whole genome se-quencing and bioinformatics analysis to identify the causalmutation responsible for the lethal phenotype [30]. Recentstudies, including our initial analysis of let-504 [30], haveshown that whole genome sequencing is an efficient andcost-effective approach to identifying the encoded geneproduct especially when there are additional alleles thatcan be sequenced to provide confirmation [30-34]. Inthis paper, we describe our approach of combining anestablished mutagenesis technique with the latest sequen-cing technology in order to close the gap in the essentialgene knock-out collection.Results and discussionChromosome I left has a high percentage of essential genesThe leftmost 7.3 Mbp of chromosome I has the highestpercentage of mapped essential genes and closest to satur-ation with 237 essential genes isolated and mapped [19].The mutant strains were derived by mutagenizing KR235[dpy-5 (e61), +, unc-13 (e450)/dpy-5(e61), unc-15(e73), +;sDp2] with a low dose of EMS and isolating let-x dpy-5unc-13 homozygotes rescued with a third wild-type alleleof dpy-5 and let-x balanced by free duplication sDp2 [35](see schematic in Additional file 1). In order to positionthe genes, mutations were mapped into 60 zones using acombination of three-factor mapping and complementa-tion to a series of duplications and deficiencies [19].Within zones, lethal mutants were inter-complementationtested. The earliest developmental arrest stages were de-termined for each complementation group [19]. The can-didate lesions are present in two copies and rescued by athird wild-type allele on sDp2. Thus, our high throughputidentification method focused on finding heterozygousmutations that exhibit an allelic ratio between the range of40% to 90% [30]. In order to assess the accuracy of our re-cently developed high throughput method [30], we se-lected 81 genes from this set with the criteria that theyformed a complementation group having more than oneallele (Additional file 2). The extra alleles provide an addedresource for validation. We sequenced 10 indexed gen-omic DNA samples per Illumina HiSeq lane and obtaineda total of 385 Gbp of sequence. The sequencing readswere aligned using BWA [36] to the WS200 C. elegans ref-erence sequence. We achieved 30X coverage on averageacross the whole genome and an average of 35X coveragein coding elements. In the case of two strains, only 6Xcoverage was obtained: let-369(h125) and let-594(h407).Genomes from these two strains were removed from sub-sequent analysis.The mutational landscape provided a quality checkOur first analytical step, as a quality check, was to con-firm the presence of the dpy-5 (e61) and unc-13 (e450)mutations in each genome. For unc-13, the expectedvariant ratio should be 100% because the duplicationdoes not extend far enough to provide an additionalwild-type allele. For dpy-5 however, there is a wild-typeallele on sDp2, and thus we would expect to see a 66%variant ratio. We found the expected ratios in 76 of the79 genomes. Three genomes deviated from the norm:let-516(h144) is missing both e450 and e61 (all the readssupported the reference sequence); let-388(h88) is miss-ing e61; let-393(h225) has e61, but with a 33% ratioChu et al. BMC Genomics 2014, 15:361 Page 2 of 13http://www.biomedcentral.com/1471-2164/15/361rather than the expected 66%. We examined thesestrains for the presence of the duplication sDp2. Whenthe duplication is present, the read depth is 33% greaterin the first 7Mbp of chromosome I than for rest of thechromosome. Our analysis showed that none of thesethree genomes showed any depth difference (Additionalfile 3). It is likely these strains do not carry sDp2. Al-though sDp2 does not crossover with the normal homo-logs at a readily detectable frequency, rare exchangeevents can occur resulting in subsequent loss of the du-plicated fragment [37]. These three strains were not ana-lyzed further.Coding sequence correlated with high confidenceWe analyzed the parental strain KR235 and identified571 SNVs and 167 small indels that show >40% readsupport on Chromosome I when compared to the C. ele-gans WS200 reference using VarScan (see Methods).These mutations represent the background mutations inwhich the lethal mutations were maintained. For theremaining 76 genomes, we filtered out the backgroundmutations and found on average 44 SNVs that show >40%allelic ratio in the sDp2 region. Most of the SNVs areG > A or C > T changes as expected and previouslyobserved after EMS treatment [30,38]. We also foundan average of 7 small indels of 1–2 bps. We categorizedeach mutation as either nonsense, missense, synonym-ous, splice signal disruption, frame shifting indel, framepreserving indel, or noncoding mutation. Noncodingmutations were defined as any mutation located outsideof coding regions. A full list of SNVs and indels, for eachstrain, is available on our server at http://lethal.mbb.sfu.ca/jschu/essential_genes.We identified candidate mutations for the 76 genomesusing our bioinformatics pipeline that we developed pre-viously [30] (see also Methods) and validated a subset ofour candidates by sequencing a second allele or by com-plementation testing (Table 1). Nine of our candidate le-sions were in genes that had been previously identifiedand published. In a few cases, candidates expected to bein separate genes were located in the same coding re-gion. These observations were confirmed by further gen-etic complementation tests (Additional file 4). Previouslyidentified let-631 and let-103 were found to be allelic tolet-363. As a result, let-363 gains three new sDp2-balanced alleles (h216, h451, h502) in addition to thenine existing ones. let-519 and let-104 are allelic to let-526 and thus let-526 gains four new alleles: h799, h373,h405 and h526. let-630 fails to complement let-596 andnow has five alleles: h355, h702, h432, h782, and h258.Thirty-five candidates were tested by sequencing a sec-ond allele using previously published complementationdata [19]. Of these, we confirmed 29 identities. All inall, we tested 48 candidates and confirmed 42 (87.5%).For the remaining 28 genomes, we have high confidencein the identity of 22 genes based on their map position.Thus, including previously described let-504, we nowhave coding region assignments for 64 let- genes in thesDp2 region. Because the genes in this study all havemultiple alleles, thus by inference, we have confidentlyidentified the coding regions affected in a total of 259mutant strains (Additional file 2).Seven of these genes have been molecularly identifiedand phenotypically described. let-603, an aurora kinase[46], and let-605, the cyclin E, had severe gonadal defects[56]. let-355, a DEAD box helicase, and let-384, an inte-grator subunit, failed to develop gametes [56]. let-370, let-599, and let-604 produced malformed embryos that werenot laid or hatched [56]. let-370 encodes a hexaprenylpyrophosphate synthetase that is associated with Parkin-son’s disease [51]. let-599 encodes the N-acetyl transferasenath-10. let-604 encodes mdt-18, a mediator subunit. Acomprehensive summary of the let- encoded products isgiven in Table 1.Novel knock-out alleles provide new genetic resourcesWe have generated new alleles for 13 genes that currentlyhave no knock-out alleles available: let-595 (imb-1), let-362 (Y71G12B.8), rnp-6 (let-147), aars-2 (let-366), let-598(F27C1.6), let-355 (T05E8.3), let-384 (C06A5.1), fars-1(let-396), let-611 (C48E7.2), mdt-18 (let-604), acdh-5 (let-383), rpb-5 (let-397), and let-630 (Y110A7A.19). Eight ofthese genes are predicted to have roles in essential basicfunctions such as transcription or translation. This is notsurprising, because we expect genes that function in basiccellular processes to be essential and are best capturedusing balancer systems. Besides these novel alleles, wehave provided additional loss of function alleles for manycharacterized genes (Table 1). Additional alleles affect-ing different parts of the gene may disrupt differentdomains providing an allelic series correlating with dif-ferent phenotypes.Genetic strains carrying heritable mutational changesprovide a lasting resource that can be used in a varietyof experimental conditions and compared to informationgained from RNAi knock-down experiments. We cross-checked our high confidence list with the RNAi data an-notated in WormBase to see if the lethal phenotype wasobserved in at least two RNAi experiments. Althoughfor the most part, RNAi data agrees with our mutationaldata, not every gene was supported by RNAi. We foundnine genes showing no lethal phenotype with RNAi andthree genes showing lethal phenotype of variablepenetrance (Table 1). Of the nine genes that show noRNAi lethal phenotype, six (inx-12, coq-1, lim-7, tag-146, let-381, and let-503) have additional knock-outalleles that are lethal, suggesting RNAi did not reveal thenull phenotype of these genes. The additional informationChu et al. BMC Genomics 2014, 15:361 Page 3 of 13http://www.biomedcentral.com/1471-2164/15/361Table 1 Coding DNA Sequence (CDS) identifications of let- genesGene Allele Allele mutation Molecular identity Support Confirmation status Human ortholog Associated human conditions Referenceslin-6/mcm-4 h92 C > * Mini chromosome maintenance RNAi Confirmed1 MCM4 Natural killer cell and glucocorticoiddeficiency with DNA repair defect[39]let-354/dhc-1 h79 Q > * Dynein heavy chain Both Confirmed1 DYNC1H1 Charcot-Marie-Tooth disease, Mentalretardation, Spinal muscular atrophy[40-42]let-502/rock h392 Q > * Rho associated kinase RNAi Confirmed1 ROCK1 [43]let-363/tor h98 Splice variant Tor kinase Both Confirmed1 MTOR pancreatic neuroendocrine tumors [44,45]h420a Q > * Confirmed3h502a Splice variant Confirmed3let-603/air-2 h289 W > * Aurora-related serine/threoninekinaseBoth Confirmed1 AURKA Susceptibility to colon cancer [46]let-512/vps-34 h797 P > S phosphoinositide 3-kinase Confirmed1 PIK3C3 [47]let-381/foxf h107 splice variant Forkhead transcription factor F K.O. Confirmed1 FOXF2 [48]let-607/bZip h402 Q > * Leucine zipper transcription factor Both Confirmed1 CREB3L3 [49]let-504/E01A2.4 h448 M > I NFkB activating protein Both Confirmed1 NKAPlet-152/ccb-1 h685 W > * Calcium channel subunit Confirmed2 CACNB2 Brugada syndrome 4let-355/hel/T05E8.3h81 Y > * DEAD/H helicase RNAi Confirmed2 DHX33let-362/rhel/Y71G12B.8h86 R > * DEAD/H RNA helicase RNAi Confirmed2 DDX27let-366/aars-2 h112 Q > * Alanine tRNA synthetase RNAi Confirmed2 AARS Charcot-Marie-Tooth disease [50]let-368/inx-12 h121 W > * Innexin gap junction K.O. Confirmed2let-370/coq-1 h128 G > E hexaprenyl pyrophosphatesynthetaseK.O. Confirmed2 PDSS1 Coenzyme Q10 deficiency, Parkinson’sdisease[51]let-389/nars-1 h680 G > E Asparagine tRNA synthetase Both Confirmed2 NARSlet-396/fars-1 h217 Q > * Phenylalanine tRNA synthetase RNAi Confirmed2 FARSAlet-522/hlh-2 h735 W > * Helix loop helix transcription factor Both Confirmed2 TCF3 Acute lymphoblastic leukemialet-529/asd-2 h238 Q > * KH domain containing RNAbinding proteinRNAi Confirmed2 QKI Mental retardationlet-575/ptr-2 h345 W > * Sterol sensing domain protein RNAi Confirmed2 PTCHD1 Autism spectrum disorders [52-54]let-585/inx-13 h784 W > * Innexin gap junction RNAi Confirmed2let-595/imb-1 h353 R > * Importin RNAi Confirmed2 KPNB1let-598/F27C1.6 h213 Q > * U3 small nucleolarribonucleoproteinRNAi Confirmed2 UTP14Clet-599/nath-10 h290 L > F N-acetyl transferase Both Confirmed2 NAT10Chuetal.BMCGenomics2014,15:361Page4of13http://www.biomedcentral.com/1471-2164/15/361Table 1 Coding DNA Sequence (CDS) identifications of let- genes (Continued)let-608/ncbp-1 h706 Q > * Nuclear cap binding protein RNAi Confirmed2 NCBP1let-611/C48E7.2 h756 Q > * RNA polymerase III subunit RNAi Confirmed2 POLR3Clet-612/apm-1 h466 splice variant Adaptin subunit RNAi Confirmed2 AP1M1let-365/sep-1 h108 W > * Separase Both Confirmed2 ESPL1 Breast cancer oncogenelet-364/mat-1 h104 S > F Anaphase promoting complexsubunitRNAi Confirmed2 CDC27let-101/npp-6 h242 W > * Nuclear pore complex protein Both Confirmed2 NUP160let-106/hcp-6 h787 C > Y Condensin subunit Both Confirmed2 NCAPD3let-379/tag-345 h127 W > * Nucleolar protein complexmemberRNAi Confirmed2 WDR12let-503/R12E2.2 h313 Q > * Protein of unknown function K.O. Confirmed2 SUCOlet-517/spg-7 h264 G > E Metalloprotease Both Confirmed2 AFG3L2 Spastic ataxia, Spinocerebellar ataxialet-597/hcp-4 h349 E > * Holocentromeric protein RNAi Confirmed2 CENPClet-630/Y110A7A.19h355b R > * Pentatricopeptide repeatcontaining proteinRNAi Confirmed2 PTCD3h782b W > * Confirmed2let-646/pat-10 h233 G > E Troponin C RNAi Confirmed2 TNNC1 Cardiomyopathylet-526 h799c Q > * SWI/SNF complex subunit Both Confirmed3 ARID1A Mental retardationh405c W > * Confirmed3let-129/zfh-2 h379 Q > * zinc finger homeobox protein Both Prediction ZFHX3, ZFHX4 Susceptibility to prostate cancer,Ptosislet-147/rnp-6 h463 G > E RNA splicing factor RNAirangePrediction PUF60 Verheij syndromelet-373/unc-73 h234 Del Guanine nucleotide exchange factor Both Prediction TRIOlet-377/lim-7 h110 W > * LIM homeodomain protein K.O. Prediction ISL2let-378/dnj-21 h124 G > E DnaJ domain containing protein RNAi Prediction DNAJC15let-380/knl-2 h80 W > * Centromeric protein Both Predictionlet-382/nuo-2 h82 Q > * Mitochondria complex I subunit Both Prediction NDUFS3 Leigh syndrome, Mitochondrialcomplex I deficiencylet-383/T21G5.6 h115 W > * Protein of unknown function Predictionlet-384/C06A5.1 h84 Q > * Integrator subunit RNAi Prediction INTS1let-385/teg-4 h85 splice variant splicing factor RNAi Prediction SF3B3let-386/dbr-1 h117 G > E RNA lariat-debranching enzyme RNAirangePrediction DBR1let-391/tag-146 h91 Q > * Uncharacterized zinc finger protein K.O. PredictionChuetal.BMCGenomics2014,15:361Page5of13http://www.biomedcentral.com/1471-2164/15/361Table 1 Coding DNA Sequence (CDS) identifications of let- genes (Continued)let-397/rpb-5 h228 Q > * RNA polymerase II subunit RNAi Prediction POLR2Elet-400/prpf-4 h269 D > G Pre-mRNA processing factor RNAi Prediction PRPF4B [55]let-509/unc-73 h142 W > * Guanine nucleotide exchangefactorBoth Prediction TRIOlet-527/nhr-23 h207 R > Q Nuclear hormone receptor Both Prediction RORClet-534/ahcy-1 h260 Q > * S-adenosylhomocysteine hydrolase Both Prediction AHCY Hypermethioninemialet-581/unc-11 h725 A > V clathrin adaptor protein RNAi;RangePrediction PICALM Acute lymphoblastic leukemia, AcuteT-cell lymphoblastic leukemialet-601/cuti-1 h281 Q > * Cuticle regulatory protein Both Predictionlet-602/T09B4.9 h283 W > * translocase RNAi Prediction TIMM44let-604/mdt-18 h293 splice variant Mediator subunit RNAi Prediction MED18let-605/cye-1 h312 W > * E-type cyclin Both Prediction CCNE1let-614 h138 Tested against F27C1.3 but didnot confirmlet-376 h130 Tested against F55F8.3 but didnot confirmlet-375 h241 Tested against imb-1 but did notconfirmlet-387 h87 Tested against pnk-1 but did notconfirmlet-515 h730 Tested against rpl-13 but did notconfirmlet-501 h714 Tested against rpl-4 but did notconfirmlet-361 h97 no candidatelet-531 h733 no candidatelet-576 h816 no candidatelet-518 h316 no candidatelet-523 h751 no candidatelet-525 h874 no candidatelet-584 h743 no candidateThe asterisk (*) signify a stop codon. Support column describes whether the CDS are lethal when treated with RNAi or a knock-out (K.O.) allele, or both. RNAi Range signifies RNAi lethal phenotype show varying degreeof penetrance. Confirmation status notes: 1Confirmed by previous publication. 2Confirmed by sequencing 2nd allele. 3Confirmed by complementation testing. Annotation of human orthologs and associated humanconditions are from the literature and public databases such as WormBase and OMIM. The genes are sorted first by confirmation status and then by genomic coordinates.alet-103 (h420) and let-631 (h502) have collapsed into let-363.blet-596 (h782) and let-630 (h355) both confirmed by sequencing a second allele and failed to complement each other. Thus, these two are collapsed into let-630.clet-104 (h799) and let-519 (h405) have collapsed into let-526.Chuetal.BMCGenomics2014,15:361Page6of13http://www.biomedcentral.com/1471-2164/15/361provided by genetic mutation highlights the importance ofour collection.Essential genes in sDp2 function in cell cycle andcytokinesis, transcriptional regulation, and RNA processingTo identify the processes that are essential, we investigatedthe function of our high confidence gene set along withtheir orthologs in D. melanogaster (fly), S. cerevisiae (yeast),and H. sapiens (humans). Essential genes are often con-served due to their important biological roles. Fifty-fourof our identified essential genes have readily identifiableorthologs in humans [57] (Table 1). We further catego-rized each gene into at least one of eight functionalgroups based on their GO annotations (Figure 1). Tohave a better picture of the roles of different essentialgenes, multi-functional genes were categorized into morethan one functional group. The cell cycle & cytokinesis,transcriptional regulation, transport, RNA processing, andtranscription categories contained more genes than didthe groups representing translation, signal transduction,and the other groups that includes metabolic and struc-tural processes.Of these eight functional groups, we found three groupsthat were significantly enriched in the sDp2 region whencompared to the non-essential genes in sDp2: cell cycle &cytokinesis (p = 3.61e−9, χ2 test), regulation of transcription(p = 6.21e−8, χ2 test), and RNA processing (p = 6.35e−12, χ2test). Our analysis indicates that members of these pro-cesses are enriched in essential genes. We have previouslyshown that components of the spindle assembly check-point are essential for survival [58]. Here we showed thatgenes in the sDp2 region function in various phases of thecell cycle. For instance, let-380 (knl-2) is critical for load-ing hcp-3 (CENP-A) to chromatin and forming the kineto-chore [59]. let-603 (air-2), let-597 (hcp-4), and let-106(hcp-6) remove cohesions for proper resolution of cen-tromeric connections and segregation of homologouschromosomes during meiosis [60-62]. let-365 (sep-1) is es-sential for chromatid separation and proper anaphase. Inaddition, let-364 (mat-1), a member of the anaphase pro-moting complex (APC), is crucial for the transition frommetaphase to anaphase [63]. lin-6 (mcm-4) is required forDNA replication and activates a checkpoint when enteringinto M phase [39]. let-599 (nath-10) and let-354 (dhc-1)are crucial for cytokinesis during cell division [64,65]. let-385 (teg-4) is a component of splicing complex A thatfunctions in the meiosis entry decision [66,67]. Our dataindicate that disrupting any phase of the cell cycle processcan lead to lethality.Are functions of the essential genes identified in thisstudy representative of all essential genes? Randomsampling simulation from 3500 essential genes indi-cated by RNAi shows a very different GO term distribu-tion (Figure 1). In the larger set samples, we observedthat cell cycle and cytokinesis (p = 1.02e−22, χ2 test), re-gulation of transcription (p = 2.48e−20, χ2 test), and RNAprocessing (p = 5.43e−10, χ2 test) are under-representedcompared to our sequenced set. Although we acknow-ledge that comparing lethal mutants to RNAi phenocopiesis not fully equivalent, at the present time there is not alarge enough mutant essential gene collection to do thiscomparison. It is intriquing nevertheless to raise the ques-tion of regional differences in essential gene functions andwe look forward to having a more complete dataset thatcan be used to address this issue.Essential gene transcripts are supplied maternallyFrom the set of 59 essential genes, 34 of them arrest devel-opment as embryos or early larvae, indicating that theyare important early in development. To test this hypoth-esis, we analyzed the temporal expression of these genesusing RNA-seq divided into 23 separate 30-minute embry-onic stages, 4 larval stages, pre-gravid young adult stage,and the young adult stage. The normalized RNA-seq datawas obtained from the modENCODE project [68,69].Seven distinct patterns were seen from the heatmap(Figure 2). Five genes (colored red) express highly duringmid-embryonic stage (300 min – 600 min), six genes (col-ored blue) express highly during late-embryonic stage(600 min – hatch), and seven genes (colored green) ex-press highly in both mid-embryonic and late-embryonicstages. Eighteen genes (colored purple) show elevatedFigure 1 Functional categorization of essential genes identified in this study using GO terms. The Y-axis indicates the GO term categories.The X-axis represents the number of genes in each category. Random sampling of 1000 iterations was done by selecting equal number of genesfrom either all sDp2 genes or the set of all essential genes identified by RNAi. Error bars represent standard error.Chu et al. BMC Genomics 2014, 15:361 Page 7 of 13http://www.biomedcentral.com/1471-2164/15/361expression very early in embryonic development (0 min –300 min). Most of these genes, however, had a dramaticdrop in expression level at 150 min, which is when gas-trulation occurs [70]. Observing that many of these genesalso show strong expression in young adults but not in lar-val stages suggests that these messages are highly tran-scribed in the germline and are likely maternally derivedin the embryo. On the other hand, nine genes (coloredbrown) show some early embryonic expression but havetheir strongest expression during mid-embryonic stages. Agroup of four genes (colored orange) show specific expres-sion during gastrulation. Lastly, eight genes (colored black)have elevated expression during specific larval stages.From the RNAseq data, we observed 18 genes with ex-pression patterns that indicated maternal contributionduring early embryogenesis. This ratio is not significantlydifferent from the set of all essential genes. However, whencompared with the set of non-essential genes, our essentialgene list is significantly enriched for genes with strong ma-ternal contribution (1.24e−5, χ2 test). These data indicatethat many essential genes important for early embryonicdevelopment have maternal contribution.ConclusionsThe function of essential genes is poorly understood.Having a combination of genetic strains for which theFigure 2 This figure represents the normalized transcript level (read number per coding length per million reads) for each gene acrossthe developmental stages including 23 embryo stages separated by 30 minute interval, four larval stages (L1-L4), pre-gravid youngadult, and gravid young adult. For comparing germline expression, we’ve included the transcript level from JK1107 carrying a mutation inglp-1, which is essential for mitotic germ cell proliferation [71]. The heatmap represents normalized transcript level from high (yellow) to low(blue). Seven distinct clusters that are based on their expression pattern are shown by colored branches. Purple: early-embryonic; Brown:early- and mid-embryonic; Red: mid-embryonic; Blue: late-embryonic; Green: mid- and late-embryonic; Orange: gastrulation; Black: larval.Chu et al. BMC Genomics 2014, 15:361 Page 8 of 13http://www.biomedcentral.com/1471-2164/15/361molecular identity is known would provide a powerful re-source for their study. However, even in the model systemC. elegans, only about 25% of the essential genes have aknockout alleles. RNAi has also been used to identify essen-tial genes [72,73]. Despite the success of these studies, onlya small subset (~800 genes) have been profiled phenotypic-ally [72]. We have a large collection of mutant strains, butonly now has it been technically feasible to easily identifytheir corresponding coding regions. Our library currentlyconsists of 1350 lethal mutations maintained by balancersin chromosomes I, III, IV, and V, of which chromosome Iis the closest to saturation [19]. Recent whole genomescreening experiments using the CRISPR/Cas9 system haveopened up the possibility of identifying essential genesusing this targeted approach. However, targeted approachesdirected towards identifying essential genes in an intactmulticellular organism are still limited in terms of recoveryand maintenance of lethal mutations and impractical forlarge scale screens. The relative ease of capturing andmaintaining lethal mutations makes balancer systems themethod of choice for essential gene studies. However, usingrandom mutagenesis is not possible to achieve 100% sa-turation (finding all essential genes). Small targets have asmaller chance of being mutated and are likely missed inmutagenesis experiments. Also, finding new essential genesin subsequent screenings becomes more and more difficultbecause the screens follow (approximately) a Poisson distri-bution giving diminishing returns. Thus, a combination oftargeted and forward mutational approaches is best.We previously developed a pipeline and applied it tothe identification of let-504 [30]. In the analysis pre-sented here, we applied the pipeline to further analyze76 essential genes on Chromosome I and produced highconfidence identification for 64 genes. Some of the con-firmed candidates were found outside the mapped re-gion suggesting that the boundaries of the geneticallyidentified zones can be further refined. We have shownthat our approach is much more efficient and cost-effective than the traditional method. Assessments fromthis study will help us improve our identification pipe-line and give us the confidence to apply this techniqueto the rest of our collection of essential genes.Our results here provide additional alleles to knowngenes as well as provide new alleles. The added alleleswill be valuable for establishing allelic series that mayexhibit different phenotypes. For instance let-147/rnp-6has 4 alleles each showing a different arrest stage [19],suggesting different protein domains are being disrupted.More importantly, our results provided 13 new alleles inessential genes where no alleles existed. The genetic re-sources provided with our method will be beneficial tothe field of essential gene research.We have demonstrated here that Let mutants can beused, not only individually to study the gene’s function,but analyzed as a group to better understand the functionsa living multi-cellular animal needs for survival. Under-standing the function of individual essential genes has ap-plications for medicine. Essential genes in bacteria havebeen exploited to develop new antimicrobials [5]. An un-derstanding of essential genes can be exploited for newmedical uses. For example, the human ortholog of let-400/prpf-4, has been found to induce G1/S arrest and mayfunction as a cancer suppressor [55]. Therefore, a resourcesuch as described here for identifying and studying essen-tial genes in model organisms has direct benefit.We have shown that essential genes in the left half ofchromosome I in C. elegans function in cell cycle control,transcriptional regulation, and RNA processing. Previousreports studying other genomic regions have shown differ-ent gene classes such as those regulated by the GATA tran-scription factor [74] and the sex-regulated genes [75] arenon-randomly distributed in the genome. Thus, we believethe organization of these genes within the genome is alsonon-random. With our method, it is now possible to gener-ate genetic resources to capture the majority of the essen-tial genes. The study of which will provide us with a globalpicture of the minimum set of genes and pathways that isneeded for the survival of a multi-cellular organism, andtheir organization in the genome. An increased under-standing of the nature of essential genes is relevant not onlyto our knowledge of the biological survival of the organismbut also has the potential for better medical procedures.MethodsStrainsThe strains used in this study are listed in Table 1. Wehave listed all the other available alleles for each let-gene in Additional file 2. The strains were grown andmaintained on nematode growth medium streaked withE. coli OP50 [76]. The strains used in this study weregenerated by mutagenizing KR235 [dpy-5 (e61), +, unc-13 (e450)/dpy-5(e61), unc-15(e73), +; sDp2] with 12 mMEMS [35]. Briefly, the treated gravid wildtypes were indi-vidually plated on 5 cm plates and wildtype gravid F1swere also individually plated 5 days later. Their progeny(F2s) were screened for the absence of Dpy-5 Unc-13 in-dividuals (Additional file 1). A single Unc-13 animal wastransferred to confirm the existence of a lethal mutation.A balanced lethal would exhibit Unc-13 and develop-mentally arrested Dpy-5 Unc-13 [35]. All the strainswere maintained at 20°C and by selecting Unc animals.Each strain was grown from one hermaphrodite and ex-panded to 20 2-inch plates. The worms were collectedby rinsing the plates with M9 (6 g Na2HPO4, 3 gKH2PO4, 5 g NaCl, 0.2 g MgSO4 in 1 L of H20). Theworms were washed with 12 ml of M9 three times andincubated at room temperature for 2 hours. The finalpellet was frozen in −80°C.Chu et al. BMC Genomics 2014, 15:361 Page 9 of 13http://www.biomedcentral.com/1471-2164/15/361Genomic DNA extraction and sequencingGenomic DNA was extracted by phenol/chloroform asdescribed previously [30]. Briefly, the worm pellet waslysed in 0.5% SDS and 100ug of Proteinase K in 50°C fortwo hours. DNA was extracted with phenol/chloroformthree times and precipitated with 100% ethanol. 20 ug ofRNase A was added to the eluted sample to removeRNA contaminants and this was followed by three morerounds of phenol/chloroform extraction and ethanolprecipitation. 10 ug of purified genomic DNA was se-quenced at the BC Cancer Agency Genome SciencesCentre using Illumina PET HiSeq technology.Mutation identification procedureSequencing reads were aligned to the WS200 C. elegansgenome using BWA [36] under default settings. Dupli-cated reads were filtered with GATK [77]. Further realign-ment around indels was also done with GATK. The BAMfiles were analyzed for SNV and small indels using Varscan[78]. The SNVs or indels returned by Varscan were filteredby 1) mutations in the parental strain KR235 mutation,2) variant ratio (90% > x > 40%), and 3) genomic location(in coding sequences only). Allelic ratio was calculated asthe ratio of mutant allele:reference allele. The effect foreach CDS from the accumulate effect of the mutations inthe genome was analyzed using Coovar [79]. Mutationallandscape analysis was done using SNVs exhibiting G >Aor C > T transitions as described previously [30]. Eachgenes in the sDp2 carrying a non-synonymous mutationwas considered and ranked according to the severity ofthe mutation. Mapping information from [19] was used asa guide to find the most likely mutation. The mutationsfor each strain can be downloaded from http://lethal.mbb.sfu.ca/jschu/essential_genes.Sequencing of a second allele was done with Sangersequencing or WGS. PCR primers were designed usingPrimer3 [80,81] spaced 250 bp apart with staggeredorientation. This allowed sufficient overlap so that eachposition was covered at least twice. The Sanger readswere aligned to the wildtype transcript sequence usingClustal [82]. The alignments from each Sanger read weremerged and analyzed with Bioedit. A mutation was con-firmed if it was supported by all the Sanger reads andthe sequencing traces show a clear double peak. A pre-diction was also confirmed when WGS of a second allelehas a different mutation in the same gene.Confirmation by complementation testingAllelic combinations were established previously by com-plementation testing as described in [19] with the followingexceptions. In a few cases, candidate SNVs were found formutations, which were previously described as mapping toseparate zones, in a single coding region. In these casescomplementation testing was done between mutationspredicted to be in the candidate coding region and con-firmed that they did form a single complementation groupas shown in Additional file 3.Strains carrying a lethal mutation were selected for com-plementation testing with other lethal-carrying strainsbased on the identification of candidate mutations in thesame gene. In order to determine allelism, let-x dpy-5 unc-13/let-x dpy-5 unc-13; sDp2 hermaphrodites were matedto wild-type males. F1 males (let-x dpy-5 unc-13/ + + +)were crossed to hermaphrodites carrying a second lethal(let-y dpy-5 unc-13/let-y dpy-5 unc-13; sDp2). The diag-nostic phenotype indicating complementation in theprogeny of the cross was Dpy Unc males and fertile her-maphrodites (let-x dpy-5 unc-13/let-y dpy-5 unc-13). Aminimum of ten wild-type males on one plate was con-sidered sufficient to conclude that the absence of DpyUnc animals was not due to poor mating.Gene ontology analysisOrthologs were predicted by a set of programs consistingof Inparanoid [83], OrthoMCL [84], and Ensembl-Compara[85] with methods as previously described [57]. The proteinsets used were: C. elegans (WS230), S. cerevisiae (64-1-1),D. melanogaster (r5.46), and H. sapiens (GRCh37.66). GOannotation was done using Blast2GO [86]. GO profile com-parison was done using all the genes under sDp2 and allthe essential genes as identified by RNAi collected fromWormBase WS230.RNA-seq expression analysisNormalized RNA-seq data were downloaded from themodEncode website (www.modencode.org). The averagenormalized read count for each CDS was calculated asthe total normalized read count of all coding base-pairsdivided by the length of CDS. The expression profileclustering was done using agnes clustering in R.Additional filesAdditional file 1: This figure describes how lethal mutations arebalanced with sDp2 [35]. KR235 is mutagenized with 12 mM EMS.The treated gravid wildtypes were individually plated on 5 cm plates andwildtype gravid F1s were also individually plated 5 days later. Theirprogeny (F2s) were screened for the absence of Dpy-5 Unc-13 individuals.A single Unc-13 animal was transferred to confirm the existence of alethal mutation. A balanced lethal would exhibit Unc-13 and developmentallyarrested Dpy-5 Unc-13. The asterisk (*) denotes an EMS mutation. In theF1 generation, the mutation could be on either homolog but not both.Additional file 2: List of genes studied and their associated alleles.The alleles used for WGS are listed in the 2nd column. The alleles used forconfirmation are noted by an asterisk (*).Additional file 3: Comparison of genomes missing dpy-5 and/orunc-13 markers. The average read depth per 10Kbp of coding elementis plotted along the length of chromosome I. The x-axis shows thecoordinate in 10 K units. The y-axis shows the number of reads.The control genome show 33% more reads in the first 7 Mbp while thegenome with missing markers shows a flat distribution.Chu et al. BMC Genomics 2014, 15:361 Page 10 of 13http://www.biomedcentral.com/1471-2164/15/361Additional file 4: Complementation table for let-363 (h98), let-130(h216), let-130 (h451), let-631 (h502), let-630 (h355), let-596 (h782),let-526 (h185), let-104 (h799), and let-519 (h405). (−) indicates twomutations fail to complement and (+) indicates two mutationscomplement each other. N.D. indicates the particular combination wasnot done.AbbreviationsWGS: Whole genome sequencing; SNV: Single nucleotide variation;EMS: Ethyl methanesulfonate; GO: Gene ontology.Competing interestsThe authors declare that they have no competing interests.Authors’ contributionsAMR and DLB conceived the project. JSCC, SYC, KW, AMD, and AMRperformed the experiments. JSCC, RJ, DLB, and AMR analyzed the data.JSCC, DLB, and AMR wrote the paper. All authors read and approved thefinal manuscript.AcknowledgementsWe thank Shir Hazir for his technical support. We also thank Dr. NanshengChen and members of the Rose lab for their comments and editing of themanuscript. JSCC is supported by CIHR Fanconi Anemia. AR and DB aresupported by CIHR and NSERC.Author details1Department of Medical Genetics, University of British Columbia, Vancouver,Canada. 2Department of Molecular Biology and Biochemistry, Simon FraserUniversity, Burnaby, Canada. 3Department of Biology, Kwantlen PolytechnicUniversity, Surrey, Canada.Received: 5 December 2013 Accepted: 6 May 2014Published: 12 May 2014References1. Goh KI, Cusick ME, Valle D, Childs B, Vidal M, Barabasi AL: The humandisease network. Proc Natl Acad Sci U S A 2007, 104:8685–8690.2. Park D, Park J, Park SG, Park T, Choi SS: Analysis of human disease genes inthe context of gene essentiality. Genomics 2008, 92:414–418.3. Dickerson JE, Zhu A, Robertson DL, Hentges KE: Defining the role ofessential genes in human disease. PLoS One 2011, 6:e27368.4. 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