Genome architecture and stability in the Saccharomyces cerevisiae knockout collection


Despite major progress in defining the functional roles of genes, a complete understanding of their influences is far from being realized, even in relatively simple organisms. A major milestone in this direction arose via the completion of the yeast Saccharomyces cerevisiae gene-knockout collection (YKOC), which has enabled high-throughput reverse genetics, phenotypic screenings and analyses of synthetic-genetic interactions1,2,3. Ensuing experimental work has also highlighted some inconsistencies and mistakes in the YKOC, or genome instability events that rebalance the effects of specific knockouts4,5,6, but a complete overview of these is lacking. The identification and analysis of genes that are required for maintaining genomic stability have traditionally relied on reporter assays and on the study of deletions of individual genes, but whole-genome-sequencing technologies now enable—in principle—the direct observation of genome instability globally and at scale. To exploit this opportunity, we sequenced the whole genomes of nearly all of the 4,732 strains comprising the homozygous diploid YKOC. Here, by extracting information on copy-number variation of tandem and interspersed repetitive DNA elements, we describe—for almost every single non-essential gene—the genomic alterations that are induced by its loss. Analysis of this dataset reveals genes that affect the maintenance of various genomic elements, highlights cross-talks between nuclear and mitochondrial genome stability, and shows how strains have genetically adapted to life in the absence of individual non-essential genes.

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Fig. 1: Assessment of repetitive-DNA alterations in the YKOC.
Fig. 2: Identification of knockout strains with aberrant karyotypes.
Fig. 3: mtDNA copy number and its links to genome instability and increased RNR expression.
Fig. 4: Defects in homologous recombination yield an SNV signature.

Data availability

The primary sequencing data have been deposited in the European Nucleotide Archive in the study ERP109205, under the accession numbers detailed in Supplementary Table 1. Gene-specific information is available at

Code availability

The custom code used for the analysis of primary sequencing data is available at (v.0.5) and it relies on Slurm workload manager (v.15.08.13); Samtools v.1.3.1 (using htslib 1.3.1); VCFtools (v.0.1.13); Bcftools v.1.3.1 (using htslib 1.3.1); BWA v.0.7.12-r1039; Python 2.7.12; Perl (v.5.22.1); Gnuplot (v.5.0 patchlevel 3). Code for secondary analyses is available on request.


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We thank J. Warringer for providing the YKOC and discussing results; E. Alonso-Perez for help with YKOC management; the Cancer Genome Project and DNA sequencing pipelines at the Wellcome Sanger Institute for help with sample submission, tracking, library preparation and sequencing; C. Bradshaw for help with HPC cluster and nanopore sequencing; R. Wellinger for providing mutant strains; V. Mustonen, I. Vazquez-Garcia, M. Stratton, D. Adams, S. Nik-Zainal and members of the laboratory of S.P.J. for advice. This research was supported by: Wellcome Strategic Award 101126/Z/13/Z (COMSIG); Wellcome Investigator Award 206388/Z/17/Z; Wellcome PhD Fellowship 098051 to M.H.; Cancer Research UK Programme Grant C6/A18796; Cancer Research UK C6946/A24843 and Wellcome WT203144 Institute Core Funding; and the National Institutes of Health R35 GM118172 to R.L.

Author information




The project was conceived by F.P. and S.P.J. YKOC duplication was carried out by I.S. and F.P. Single-colony isolation and DNA extractions were carried out by A.S. and S.W. Data analyses were carried out by F.P., M.H. and I.A. Logistic regression and telomere length analysis were carried out by R.S., R.M., S.K.-L. and M.K. Chromosome instability measurements were performed by J.Z. and M.G. under the supervision of R.L. Validation experiments were designed by F.P., and carried out by F.P. and G.M.-Z. The manuscript was written by F.P. and S.P.J., with contributions from G.M.-Z., M.H., M.K., I.S., J.Z., I.A. and R.L.

Corresponding authors

Correspondence to Fabio Puddu or Stephen P. Jackson.

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The authors declare no competing interests.

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Peer review information Nature thanks Grant Brown, Sergei Mirkin and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Extended data figures and tables

Extended Data Fig. 1 Statistics of YKOC analyses, and distribution of estimates of repetitive DNA across the YKOC.

a, Colonies that did not carry the expected deletion (red) were re-assigned by reading the barcode inserted with the deletion marker; deletion of an alternative gene was then confirmed by loss of sequencing coverage. b, Number of strains proceeding through the steps of the pipeline for data generation, and analysis used to create the dataset on which this work is based. c, Distribution of estimates of copy numbers for the indicated repeats across the YKOC. Strains are sorted by the average across the colonies sequenced, and the estimate of each colony is shown. Red zones represent values >3 s.d. of the wild-type distribution. n = 8 biologically independent samples for wild-type strains; n = 1 sample for 258 of the knockout strains; n = 2 biologically independent samples for 4,097 of the knockout strains; n = 3 biologically independent samples for 30 of the knockout strains; n = 4 biologically independent samples for 72 of the knockout strains; and n = 5 biologically independent samples for 1 of the knockout strains. d, Correlations between changes in relative copy numbers at rDNA and CUP1 tandem-repeat loci. Average correlations in all colonies of each knockout strain are shown. n = 8 biologically independent samples for wild-type strains; n = 1 sample for 258 of the knockout strains; n = 2 biologically independent samples for 4,097 of the knockout strains; n = 3 biologically independent samples for 30 of the knockout strains; n = 4 biologically independent samples for 72 of the knockout strains; and n = 5 biologically independent samples for 1 of the knockout strains. e, No overall correlation for estimates of rDNA and CUP1 copy numbers across all colonies sequenced. f, Distribution of estimates of copy number for the 2μ plasmid across the YKOC, and Gene Ontology analysis of the hits. The 2μ copy numbers did not follow a normal distribution. Instead, the maximum s.d. increases linearly with the mean copy number; this is consistent with the mode of 2μ amplification, which is activated by expression of the in cis gene FLP1 when the copy number crosses a lower threshold (we estimate this to be at 20–25 copies). Different durations of FLP1 expression will result in different increases in copy number. We detected an enrichment in gene knockouts connected to gene silencing in strains with high 2μ copy numbers, which is also consistent with this 2μ amplification mechanism.

Extended Data Fig. 2 Development of tools to study the instability of repetitive DNA.

a, Left, schematic of yeast chromosome XII. The apparent increase in sequencing coverage maps to rDNA repeats. Right, similar apparent coverage increases mapped to CUP1 and Ty transposon loci. b, WGS estimates of copy numbers of rDNA repeats linearly correlate with estimates obtained by pulsed-field gel electrophoresis. n = 3 or 4 biologically independent samples per strain. c, Per cent deviation of two sequencing technical replicates from their average. n = 2 technical replicates derived from n = 89 biologically independent samples. Median, yellow line; quartiles, blue line. Measurements were within 5% of their average. Notable exceptions were Ty5 and CUP1, probably owing to their relatively low repeat numbers. d, Relative estimated content of telomeric repeats in indicated strains (normalized to the estimated content of one wild-type colony) is plotted as a function of the minimum number of telomeric repeats in a sequencing read required to classify that read as telomeric. n = 2 biologically independent samples per strain. e, Estimates of telomere length for wild-type, tel1∆ and rif1∆ strains obtained by calculating the relative abundance of telomeric reads. Mean from n = 4–8 biologically independent samples per strain. f, Estimates of rDNA, CUP1, Ty1, Ty2 and mtDNA copy numbers, and telomeric DNA content for MATa, MATα and diploid strains in W303 and BY4743 backgrounds. Median from n = 8 biologically independent samples per strain. g, A long read spanning the CUP1 locus, derived from Oxford Nanopore Technology sequencing of a W303 (K699) genomic library. h, Comparison of CUP1 copy number estimated by qPCR or by WGS. The same DNA samples (as indicated by the labels) were analysed. Two estimates were extracted from WGS data: ‘from CUP1’ indicates estimation using a large region of the CUP1 locus and the genome-wide median for reference (the same method used for the entire YKOC); ‘from qPCR amplicon’ indicates a small region of CUP1 and a small region of GAL1 for reference (the same regions used for qPCR).

Extended Data Fig. 3 Southern blot analysis and functional connections between TLM genes.

a, Gel electrophoresis and Southern blot analysis of telomeres for 14 newly identified predicted TLM strains (‘hits’). Hits denote strains with two or more colonies with measures >3× the s.d. of the wild-type distribution. Twenty-one strains that failed these stringent hit-selection criteria (non-hits), but which displayed relatively high or low estimates of telomere length, are also shown. Representative images from two independent experiments. Purple lines, location of molecular mass markers; orange line, average telomere length for wild-type samples; green dashes, average telomere lengths for strains predicted to have longer telomeres; white dashes, average telomere lengths for strains predicted to have shorter telomeres. b, Validation of gene knockouts that failed the TLM selection criteria but that displayed relatively high or low telomere counts. c, Network-graph analysis of gene knockouts that affect telomere length, highlighting the newly identified genes that were validated by Southern blotting.

Extended Data Fig. 4 Examples of aneuploidies and chromosomal rearrangements in the YKOC.

a, Example of a strain with fractional aneuploidy of chromosome XII, which probably reflects clonal heterogeneity. b, Distribution of fractional and non-fractional aneuploidies per chromosome. n = 8,843 biologically independent samples. c, Knockout of genes that encode subunits of ribosomal proteins frequently leads to gain of the chromosome that carries the paralogue gene. d, Ploidy plots of chromosome II for two different colonies of the hta1∆, swi4∆ and spt10∆ knockout strains. hta1∆ cells (in which one of the two genes that encodes histone H2A is deleted) accumulate a specific amplification of a genome region that contains the paralogue HTA2, a centromere and two origins of replication. This is most probably transmitted as a circular genetic element formed by recombination between two adjacent transposon sequences. Only two other YKOC strains were found to carry the same genetic element: these were spt10∆ and swi4∆, which encode factors that control the transcription of cell-cycle-regulated genes (including histones).

Extended Data Fig. 5 Calculation of mtDNA copy number.

a, Sequencing coverage across the mitochondrial genome of a wild-type haploid strain (BY4741; accession ERS616991). Shaded areas indicate regions (that loosely correspond to COX1 and COX3 genes) that were used to estimate total mtDNA content. b, mtDNA regions of low sequence coverage correspond to regions with strongly reduced GC content. c, Comparison of mtDNA content estimated by qPCR and by WGS. The same DNA samples (as indicated by the labels) were analysed by qPCR and WGS. Two estimates were extracted from WGS data: ‘from COX1’ indicates estimates using a large region of the COX1 gene and the genome-wide median for reference (the same method used for the entire YKOC); ‘from qPCR amplicon’ indicates a small region of COX1 and a small region of GAL1 for reference (the same regions used for qPCR). d, Correlation between estimates of mtDNA content using COX1 or COX3 region on all sequenced strains belonging to the YKOC. Pearson R2 = 0.7596.

Extended Data Fig. 6 Connections between mtDNA and nuclear genome alterations.

a, Venn diagram showing the overlap between genes identified as rho0 by our sequencing, genes that encode mitochondria proteins (ref. 48) and gene knockouts for which respiratory growth was annotated as ‘absent’ (in the Saccharomyces Genome Database (SGD), b, Gene Ontology of rho0 strains (estimated mtDNA copy number < 1) and rho++ strains (estimated mtDNA copy number > 20.3). Bonferroni-corrected P values. c, Sixteen gene knockouts from the top end of the mtDNA distribution were assessed for spontaneous activation of the DNA-damage response by Rad53 and histone H2A phosphorylation (representative images from two technical replicates; source data in Supplementary Fig. 1) and RNR expression (average from three technical replicates, one biological sample per strain). Strains with increased RNR expression (violet) or increased RNR expression and Rad53 hyperphosphorylation (yellow) are highlighted. Serial dilutions of the same cultures were also tested for HU sensitivity. d, Comparisons of mtDNA estimates with systematic analysis of HU sensitivity. HU-sensitive strains are highlighted in different colours depending on the study. ‘Parsons, 2004’ denotes ref. 30, n = 62 biologically independent samples; ‘Woolstencroft, 2006’ denotes ref. 31, n = 33 biologically independent samples. e, Comparison of predicted mtDNA copy number and RNR3 expression levels. Gene knockouts with increased levels of Rnr3 protein (blue, Z-score > 2); gene knockouts with increased mtDNA (yellow, mtDNA > 22.2); gene knockouts with both measures increased (green). n = 4,436 by knockout averages, of n = 8,843 biologically independent samples.

Extended Data Fig. 7 mtDNA content in knockout strains for genes that encode enzymes involved in tryptophan metabolism.

Pathway for tryptophan biosynthesis from phosphoenolpyruvate, tryptophan import and NAD biosynthesis from tryptophan are depicted along with estimates of mtDNA copy number for strains that lack each of the enzymes in the pathways. Mean from n = 8 (wild type) or 2 (knockout) biologically independent samples.

Extended Data Fig. 8 Genes that frequently carry mutations contain repetitive regions.

Self dot plots that highlight degenerate repetitive regions in the DNA sequence of genes that are found to be frequently mutated in the YKOC. Plots were obtained using FlexiDot.

Extended Data Fig. 9 The most-frequent YKOC mutations, and their distributions between different source laboratories.

The most-frequent mutations, with predicted effects on genes, detected in the YKOC (top 200) and their distribution among different source laboratories. a, Left, the mutation is indicated by its predicted effect, and the background indicates whether it is a mutation in a gene with degenerate repeats (grey), a mutation that comes from a founder effect (yellow) or a frequently mutated site (green). Genes in bold indicate a homozygous mutation. Centre, heat map of the distribution of the most-frequent mutations by the laboratory in which the strain that carries that mutation was produced; 100% indicates that all the strains with a particular mutation were generated in the same laboratory. Right, number of strains that carry the mutation. b, Not all strains derived from each laboratory share founder mutations. As in a, but a value of 100% in the heat map indicates that all the strains generated by a particular laboratory have the mutation.

Extended Data Fig. 10 Overview of genomic instability caused by knockouts of non-essential genes.

a, Overview of results from our genome instability screens. Strains with an abnormal copy number for different genomic features, aneuploidies and chromosomal rearrangements (CR) are represented by coloured boxes. b, Gene Ontology analysis for 151 genome instability genes, defined as knockouts that show 3 or more abnormal features. The numbers of genes in each Gene Ontology category, as well as Holm–Bonferroni-corrected P values, are reported. c, Genome instability genes were manually sorted into classes on the basis of their function, inferred from annotations in the SGD.

Supplementary information

Supplementary Figure 1

Spontaneous checkpoint activation in different KO strains. Source data for western blot in Extended Data Figure 6C.

Reporting Summary

Supplementary Tables 1-6

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Puddu, F., Herzog, M., Selivanova, A. et al. Genome architecture and stability in the Saccharomyces cerevisiae knockout collection. Nature 573, 416–420 (2019).

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