Gene Essentiality: From Bacteria to Human Cells

Prévia do material em texto

Some genomes may contain >100,000 protein-coding 
genes1, but which are really indispensable for life? The 
answer to this fundamental — yet deceptively simple 
— question depends on the definition of gene essen-
tiality. Working on Mendelian segregation patterns 
in mice, Castle and Little2 were the first to define the 
‘lethal’ phenotype in genetic terms. Since then, several 
definitions have been used to describe this seemingly 
intuitive concept, but herein we refer to essential genes 
as those indispensable for reproductive success. To assess 
the ‘essentiality’ of a gene, researchers therefore need 
to assess the phenotype of a living system that either 
entirely lacks that gene or in which the expression or 
function of that gene has been substantially impaired. 
In the case of single-celled organisms or of single cells 
derived from multicellular organisms, this would trans-
late into identifying genes required for the proliferation 
of individual cells (cellular gene essentiality). By contrast, 
in the case of multicellular organisms, this would mean 
finding genes required for growth and development 
from a zygote into a fertile adult (organismal gene essen-
tiality). Here, we focus mostly on cellular gene essen-
tiality and in particular on genes whose inactivation 
or loss causes either severe growth impairment (looser 
definition) or irreversible growth arrest or cell death 
(stricter definition). Owing to a lack of space, we do 
not cover viral gene essentiality, and even though several 
non-coding RNAs are known to be essential in vari-
ous contexts3,4, the scope of this Review is focused on 
protein-coding genes.
Classical genetic approaches, such as transposon 
mutagenesis or targeted single-gene knockout (KO) 
studies, have enabled the first systematic screens of gene 
essentiality in a variety of microorganisms, thus un -
veiling the first essentialomes in bacteria and yeasts. The 
recent availability of high-throughput genetic resources, 
such as genome-wide RNA interference (RNAi) and 
genome editing technologies, enabled these screens to 
be extended to non-model organisms and to increas-
ingly more complex systems, such as mice and human 
cells. As discussed in greater detail later, the availability 
of essentialomes in a wide range of species has proved 
critical for a variety of applications, including the defi-
nition of a minimal genome for synthetic biology, the 
eluci dation of design principles of cellular networks, 
the understanding of genome organization and evolu-
tion and the development of chemical genomics tools 
for drug target identification and prioritization.
In this Review, we first provide a historical perspec-
tive on how key technological breakthroughs in genetics 
and genomics have contributed to spurring the study of 
gene essentiality towards increasingly complex biological 
systems, from bacteria to human cells. Next, we build 
a consensus around common properties of essential 
genes, drawn from findings in a wide range of species, 
and discuss how these properties have been exploited for 
a multitude of synthetic or therapeutic purposes. Then, 
we review important classical and recent findings that 
challenge the view of gene essentiality as an absolute 
and static property and instead shed light on its context- 
dependent and evolvable nature. Finally, we discuss the 
consequences of incorporating these novel concepts of 
gene essentiality into both basic and applied biomedical 
sciences and in particular how they could be exploited to 
improve current antimicrobial and anticancer treatment 
strategies.
Evolution of gene essentiality research
Distinguishing essential genes from non-essential genes 
has been a long-standing question in genetics. Although 
numerous efforts have been put forward to attempt to 
predict gene essentiality in silico based on evolutionary 
1Institute of Medical Biology, 
Agency of Science, 
Technology and Research 
(A*STAR), 8A Biomedical 
Grove, Immunos #05, 
Singapore 138648, 
Singapore.
2Donnelly Centre, University 
of Toronto, Toronto, Ontario 
M5S3E1, Canada.
3Canadian Institute for 
Advanced Research, Toronto, 
Ontario M5G1Z8, 
Canada.
4European Molecular Biology 
Laboratory (EMBL), Genome 
Biology, Meyerhofstrasse 1, 
69117 Heidelberg, 
Germany.
5Singapore Immunology 
Network (SIgN), A*STAR, 
8A Biomedical Grove, 
Immunos #04, Singapore 
138648, Singapore.
Correspondence to N.P. 
norman_pavelka@
immunol.a‑star.edu.sg
doi:10.1038/nrg.2017.74
Published online 16 Oct 2017
Gene essentiality
The extent to which a gene is 
required for the reproductive 
success of a living system, for 
example, a virus, a single-celled 
organism, a cell line or a 
multicellular organism.
Emerging and evolving concepts in 
gene essentiality
Giulia Rancati1, Jason Moffat2,3, Athanasios Typas4 and Norman Pavelka5
Abstract | Gene essentiality is a founding concept of genetics with important implications in both 
fundamental and applied research. Multiple screens have been performed over the years in 
bacteria, yeasts, animals and more recently in human cells to identify essential genes. A mounting 
body of evidence suggests that gene essentiality, rather than being a static and binary property, 
is both context dependent and evolvable in all kingdoms of life. This concept of a non-absolute 
nature of gene essentiality changes our fundamental understanding of essential biological 
processes and could directly affect future treatment strategies for cancer and infectious diseases.
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Reproductive success
The ability of a living system to 
generate fertile progeny, that 
is, viable offspring that can 
themselves generate further 
viable offspring.
Cellular gene essentiality
The extent to which a gene is 
required for the reproductive 
success of a single-celled 
organism or of a cell line 
derived from a multicellular 
organism.
Organismal gene 
essentiality
The extent to which a gene is 
required for the reproductive 
success of a multicellular 
organism, that is, for it to grow 
and develop from a zygote into 
a fertile adult.
Viral gene essentiality
The extent to which a gene is 
required for the reproductive 
success of a virus.
conservation, expression levels and/or systems-level 
properties5–10, here we focus on experimental approaches 
for defining essentialomes (TABLE 1).
The pre-genomic era. Attempts to determine the pro-
portion of a genome that is required for life date back 
to the early days of molecular biology research. In 1951, 
Horowitz and Leupold11 proposed that the major-
ity of proteins are likely to be indispensable for life. 
They isolated a large number of temperature-sensitive 
Escherichia coli and Neurospora crassa mutants grown 
in minimal medium and observed that only a quarter of 
the E. coli mutants and half of the N. crassa mutants did 
not grow at the restrictive temperature in rich medium, 
implying that the majority of genes are non-essential 
in both organisms. More than two decades later, sat-
urating random mutagenesis by chemicals, followed 
by analysis of offspring viability, enabled more accu-
rate estimations of essentiality in diploid organisms: 
~50%, ~15% and ~12% of the genome was reported 
as essential in Drosophila melanogaster, Caenorhabditis 
elegans and Saccharomyces cerevisiae, respectively12–15. 
However, mapping the identity of essential genetic 
elements was cumbersome at the time and could be 
performed only on a case-by-case basis. Transposon 
mutagenesis16, shotgun sequencing17, restriction enzymes 
and PCR all made mutant mapping easier in the sub-
sequent decades, but it was not until the genomic era 
that the first repertoires of essentialgenes within an 
organism (essentialomes) were defined (FIG. 1).
From complete genomes to essentialomes. Regardless 
of the methods used to systematically inactivate genes 
or their products, the complete genome sequence of an 
organism is a prerequisite for compiling the full list of 
genes as well as for designing and interpreting targeted 
or random mutagenesis screens. Owing to the invention 
of shotgun sequencing and improvements in genome 
assembly software18, the complete genome sequences of 
the first free-living organisms, Haemophilus influenzae 
and Mycoplasma genitalium, were published in 1995 
(REFS 19,20). Although the complete genomes of model 
organisms such as E. coli, Bacillus subtilis and S. cerevisiae 
were published in the subsequent 2 years21–23, it was not 
until 1999 that the first essentialome screen was reported 
for M. genitalium, by use of an inventive combination 
Table 1 | Tools to determine essentialomes
Method 
category
General 
advantages of 
the method 
category
General 
disadvantages 
of the method 
category
Method Specific advantage of the 
method
Specific disadvantage of the 
method
Random 
mutagenesis
The fastest way 
to generate 
mutants
Does not 
guarantee loss 
of function of 
mutated gene; 
applicable only 
to haploid cells or 
organisms
Chemical 
mutagenesis
Simple and low cost to implement Low-throughput and high-cost 
mutant mapping; several genes 
mutated per genome
Transposon 
mutagenesis
When coupled with next-generation 
sequencing, it allows maximal 
rapidity and throughput; when 
coupled with barcoding, it allows 
bulk analysis of pools of mutants in 
different conditions
Mutation site bias; when in 
pools, trans-complementation of 
essential properties (for example, 
metabolic requirements) can occur
Gene trapping Reports the expression of the 
trapped gene; allows for rapid 
identification of the disrupted gene
Applicable only to species with 
intron-containing genomes
Targeted 
mutagenesis
Complete KO 
achievable; few 
off-target effects
Labour-intensive 
generation of 
mutants
Homologous 
recombination
Can be used to achieve complete 
KO
Does not work efficiently enough 
in all species
Gene editing Greatest flexibility in terms of 
the type of genetic modification 
that can be introduced; CRISPR is 
scalable to genome-wide pooled 
screens
High cost and longer timelines 
for generating ZFN and TALEN 
enzymes; cutting sites that are 
highly amplified in a particular 
cell will trigger cell proliferation 
defects
Knockdown 
approaches
Amenable for 
high-throughput 
screening in a 
wide range of 
organisms
Does not always 
lead to complete 
suppression
Repressible 
promoters
Most specific repression of gene of 
interest
As with targeted mutagenesis, 
mutants have to be individually 
generated
RNAi Inducible, tuneable and reversible 
suppression of gene expression
More off-target effects than for 
gene editing, as short 6–8 base 
seed regions can trigger RNAi
dCas9-based 
gene 
repression 
(CRISPRi)
Highly specific targeting of the gene 
of interest
Polar effects on polycistron-en-
coded mRNAs; limited to genes 
containing protospacer adjacent 
motifs flanking the transcription 
start site
CRISPRi, CRISPR-based transcriptional inhibition; dCas9, a nuclease-dead Cas9 mutant; KO, knockout; RNAi, RNA interference; TALEN, transcription activator-like 
effector nuclease; ZFN, zinc-finger nuclease.
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Technology Biology
Sanger 
sequencing 
starts189 
Transposition 
and mobile 
genetic elements 
discovered188 
Microarray 
technologies 
emerge
DNA 
oligonucleotide 
synthesis starts 
on a large scale190 
Human Genome 
Project draft 
sequence72 
RNAi 
demonstrated in 
worms61 
Arrayed siRNA, 
esiRNA and 
lentivirus-based 
shRNA 
screens194–196 
First-generation 
RNAi genetic 
screens192,193 
Gene editing with 
CRISPR–Cas9 
introduced197 
Next-generation 
sequencing 
technologies 
emerge69 
Low-complexity 
CRISPR–Cas9 
genetic 
screens198,199 
Efficient gene 
editing in human 
cells197 
High-complexity 
CRISPR–Cas9 
genetic 
screens91,92 
W. E. Castle and C. C. 
Little replicate Cuenot’s 
observations and define 
the ‘lethal’ phenotype in 
genetic terms2 
Lucien Cuenot in Nancy, 
France, introduces multiple 
allelism at a genetic locus 
and non-Mendelian 
inheritance patterns 
working in M. musculus
M. genitalium 
essentialome24,97 
Sydney Farber reports first 
remissions in acute 
leukaemia using the 
inhibitor of dihydrofolate 
reductase aminopterin — 
possibly the first cancer 
essential gene200 
S. cerevisiae33, 
H. influenzae27 
and S. pneumoniae202 
essentialomes 
S. aureus essentialome201 
S. Typhimurium203, 
H. pylori29 and D. rerio204 
essentialomes 
E. coli34, P. aeruginosa28 and 
C. elegans62 essentialomes 
S. pombe essentialome40
D. melanogaster organism 
essentialome205 
Multiple groups define 
human cell line 
essentialomes using 
CRISPR and insertional 
mutagenesis67,91,92 
Partial M. musculus 
essentialome defined 
through large-scale 
knockout study55 
RNA interference 
demonstrated in 
mammalian cells191 
Introduction of 
CRISPRi and 
CRISPRa screens87 
1950
1975
1982
1995
1998
2001
2004
2007
2006
2012
2013
2014
2015
1905
1910
1947
1999
2001
2002
2003
2004
2007
2010
2013
2015
Essentialomes
Comprehensive sets of 
essential genes in genomes.
RNA interference
(RNAi). A technique to inhibit 
the production of a protein by 
destabilizing a target mRNA 
molecule.
Minimal genome
A genome consisting solely of a 
minimal set of genes that are 
required and sufficient to 
sustain cellular life.
Evolvable
Able to change via a process of 
adaptive evolution, that is, via 
acquisition and fixation of 
genetic mutations that confer 
selective advantages.
of transposon mutagenesis and shotgun sequencing24. 
As high-throughput Sanger sequencing was not univer-
sally accessible, alternative techniques were developed to 
map transposon insertion sites. Microarray technology 
and PCR-based genetic footprinting enabled individual 
laboratories to determine the essentialome of numerous 
bacteria, including H. influenzae, Mycobacterium tubercu-
losis, Pseudomonas aeruginosa and Helicobacter pylori25–29. 
Whereas whole-genome sequences of multicellular eukar-
yotic model organisms, including C. elegans, D. melano-
gaster and Arabidopsis thaliana30–32, became available by 
the turn of the century, systematic catalogues of essential 
genes lagged behind: it took a few years more for the 
first essentialomes of genetic workhorse models such as 
S. cerevisiae and E. coli to be published33,34.
From random to targeted mutagenesis. Random 
mutagenesis approaches, such as transposon muta-
genesis, catalysed early attempts at defining essentia-
lomes in bacteria and eukaryotic microorganisms and 
still represent the main approach to identifying essential 
genes in microorganisms to date35,36. However, although 
these methods have been optimized over the years and 
some of their drawbacks have been mitigated, they suffer 
Figure 1 | Milestones of technological and biological breakthroughs in gene essentiality research. The figure 
illustrates some of the most important technological advancements61,72,188–199 (left panel) that were conducive to some key 
biological discoveries2,24,27–29,33,34,40,55,62,67,91,92,97,200–205 (right panel), which have shaped our current understanding of gene 
essentiality. C. elegans, Caenorhabditis elegans; CRISPRa, CRISPR-based transcriptional activation; CRISPRi, CRISPR-based 
transcriptional inhibition; D. rerio, Danio rerio; D. melanogaster, Drosophila melanogaster; E. coli, Escherichia coli; 
esiRNA, endoribonuclease-preparedsmall interfering RNA; H. influenzae, Haemophilus influenzae; H. pylori, Helicobacter 
pylori; M. genitalium, Mycoplasma genitalium; M. musculus, Mus musculus; RNAi, RNA interference; P. aeruginosa, 
Pseudomonas aeruginosa; S. aureus, Staphylococcus aureus; S. cerevisiae, Saccharomyces cerevisiae; S. pneumoniae, 
Streptococcus pneumoniae; S. pombe, Schizosaccharomyces pombe; S. Typhimurium, Salmonella enterica subsp. 
enterica serovar Typhimurium; shRNA, short hairpin RNA; siRNA, small interfering RNA.
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Transposon mutagenesis
A gene disruption strategy 
based on random insertion of 
transposable genetic elements 
into a host genome.
Shotgun sequencing
A sequencing method based 
on sequencing several random 
fragments from a long DNA 
molecule followed by 
bioinformatic assembly of the 
fragments based on similarity 
of overlapping ends.
Gene traps
High-throughput approaches 
to introduce genome-wide 
insertional mutations in 
mammalian genomes. They 
inactivate the trapped gene by 
introducing a premature 
polyadenylation site.
Next-generation sequencing
A term used to describe 
different high-throughput 
sequencing technologies that 
became available over the past 
decade.
Genetic interactions
(GIs). Phenomena by which 
concomitant mutations in two 
genes result in a phenotype 
that is not readily predictable 
from the phenotype of the two 
individual mutations.
Signature mutagenesis
A genetic technique based on 
transposon mutagenesis, 
where each transposable 
element contains a different 
molecular tag that uniquely 
identifies it. This allows the 
phenotype of pools of mutants 
to be analysed en masse.
from a few limitations. First, there is no guarantee that 
all genes in the genome will be disrupted, even when 
working under saturating conditions. This is especially 
important in higher organisms, in which only a small 
fraction of the genome is coding. Second, not all muta-
tions necessarily completely disrupt the expression or the 
function of a gene, leaving room for misidentification of 
essential genes. Targeted approaches in which the entire 
open-reading frame (ORF) of a single gene is completely 
deleted from the genome can more accurately determine 
its essentiality. A PCR-based method to simply and relia-
bly achieve this goal by homologous recombination was 
developed for S. cerevisiae37, but in order to apply this 
technology genome-wide, the concerted effort of sev-
eral laboratories was required. An international consor-
tium, the Saccharomyces Genome Deletion project, was 
founded in 1998 with the goal of deleting every single 
ORF in the budding yeast genome. The first draft of the 
S. cerevisiae essentialome arising from this consortium 
was published in 1999 (REF. 38), and the first complete 
yeast single-gene KO collection was published in 2002 
(REF. 33). A similar approach was used to generate the 
first KO library in bacteria, the E. coli Keio collection39. 
Despite automation of many steps of the process, targeted 
approaches are still labour-intensive, and such libraries 
are available only for a few model bacteria and yeasts40–43. 
For some fungal organisms, such as Candida albicans, 
targeted gene inactivation is further hindered by their 
obligate diploid nature, and only partial homozygous KO 
libraries exist to date44. However, the discovery of haploid 
C. albicans cells45 might accelerate the generation of the 
first genome-scale gene KO collection in this important 
human pathogen.
From microorganisms to animals. Initial efforts to per-
form targeted gene disruption in animals were hindered 
because homologous recombination is typically in efficient 
in metazoan tissue culture46. The first breakthrough came 
with the discovery that homologous recombination works 
surprisingly well in embryonic stem (ES) cells derived 
from mouse blastocysts47–49, which led to the develop-
ment of the first KO mice in 1989 (REFS 50,51). Several 
international consortia have been established since then 
with the long-term goal of systematically inactivating all 
individual genes in the mouse genome and scoring the 
phenotypes of the resultant strains52–57. When completed, 
these efforts will provide the first organismal essentialome 
of a mammalian species.
Another breakthrough came with the serendipitous 
discovery of ‘co-suppression’ in plants in 1990 (REF. 58), 
which was later dubbed RNAi. This phenomenon, which 
is part of a plant’s natural immunity against pathogens, 
was co-opted as an effective way to knock down the 
expression of a targeted gene59–61. Taking advantage 
of this new technology, the first genome-wide RNAi 
screen to systematically define mutant phenotypes was 
performed in C. elegans in 2003 (REF. 62). This tech-
nology was then rapidly adapted for use in mamma-
lian cells63, and several groups generated human and 
mouse genome-scale RNAi libraries, which were widely 
adopted by the community not only to screen for gene 
essentiality but also to study the function of both essen-
tial and non-essential genes in metazoan genomes64.
Finally, the identification of haploid cell lines of dip-
loid organisms enabled insertional mutagenesis meth-
ods, such as gene traps, to be applied for the first time in 
higher eukaryotes65. Although in its first iterations, the 
method was used more for classical forward genetics, 
later improvements allowed the construction of compre-
hensive KO libraries66, mapping of essentialomes in cell 
lines67 and more quantitative reverse genetics68, in which 
the phenotype of each gene KO could be measured.
The next-generation sequencing revolution. Thanks 
to next-generation sequencing69, the past two decades 
witnessed progression from a few sequenced bacterial 
genomes, each requiring years of work by many groups, 
to the processing of thousands of bacterial genomes 
in single studies70,71 and from the human genome pro-
ject72,73 to the 1,000 and 100,000 genomes projects74,75. 
This ease in acquiring sequencing data further fuelled 
other methodo logical leaps to introduce tractable 
genetic variation. The use of transposons with restric-
tion sites recognized by enzymes cutting within flanking 
chromosomal regions enabled transposon sequencing 
(Tn-seq) to map transposon insertion sites by sequenc-
ing these flanking regions76. Coupled with the power of 
next-generation sequencing, these types of transposon 
library have been used to identify the essentialomes of 
dozens of different microorganisms and to probe con-
ditional essentiality and genetic interactions (GIs)35. A 
further advance came from an earlier concept of intro-
ducing barcodes next to transposons in order to track 
the abundance of all mutants within a pool. This pio-
neering idea was first introduced in Salmonella enterica 
subsp. enterica serovar Typhimurium for identifying 
virulence genes required for mouse infection and was 
dubbed signature mutagenesis77. This concept was later 
improved for the construction of the yeast KO collec-
tion and subsequently used in almost all single-gene KO 
libraries78, radically accelerating the mapping of trans-
poson insertion sites36 and the ability to link genes to 
phenotypes and to probe for conditional essentiality79,80.
The genome editing revolution. Advances in genome 
sequencing capabilities were paralleled by major inno-
vations in our ability to edit genome sequences, includ-
ing the development of zinc-finger nucleases (ZFNs), 
transcription activator-like effector nucleases (TALENs) 
and CRISPR–Cas9 RNA-guided technologies. ZFNs, 
TALENs and Cas9 nucleases can be programmed to 
recognize and cut specific DNA sequences, thereby 
enabling a broad range of genetic modifications. In par-
ticular, CRISPR–Cas9,which was discovered as part of 
the bacterial immune system against phages81, enables 
cost-effective and straightforward genome editing in 
yeasts, plants and animals82,83. Further development of 
the technology enabled single and multiplex gene editing 
in both mouse and human cells84,85. An engineered ‘dead’ 
Cas9 (dCas9) variant with inactivating mutations in the 
endonuclease domains can be guided to bind specific 
DNA locations without cutting86, providing a platform 
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Nature Reviews | Genetics
100
1,000
10,000
a
b
100 1,000 10,000
R2=0.5368
100,000
Es
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 g
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 p
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Total number of protein-coding genes 
R2=0.2429
 
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1
10
100
100 1,000 10,000 100,000
Total number of protein-coding genes 
Eukaryotes (whole organisms)
Eukaryotes (single cells)
Bacteria
Eukaryotes (whole organisms)
Eukaryotes (single cells)
Bacteria
for recruiting different functional moieties to target sites. 
Among others, the dCas9 system has been used for tran-
scriptional activation and repression (known as CRISPRa 
and CRISPRi, respectively), allowing genome-wide 
gain-of-function and loss-of-function screens to function-
ally annotate genomes87,88 or to study the role of essential 
genes89. Notably, both Cas9 and dCas9 have been used 
to map essentialomes in human cell lines with much 
improved results over RNAi, including less data variation, 
more functional constructs and fewer off-target effects90 
(TABLE 1).
As a result of the above-described technological 
leaps, the landscape of essential genes in human cells is 
now being explored using the same conceptual frame-
works established in yeast67,91–93, a reality that was in -
conceivable less than 5 years ago. Moreover, the marriage 
of new and old genetic engineering tools with high- 
throughput and ‘omics’ approaches has paved the way 
for studying the function and interconnection of essen-
tial genes with unprecedented ease from bacteria to 
human cells.
Emerging properties of essential genes
The existence today of essentialomes for a variety of 
species enables comparative analyses of gene essentiality 
across the tree of life94. As reviewed below, essential genes 
share several common features, which have also been 
used by in silico prediction tools to infer the essentiality 
of uncharacterized genes95. These properties of essen-
tial genes, emerging from a vast body of literature, have 
repercussions in a variety of fields, from evolutionary and 
systems biology to drug development.
How many genes are required for life? Genome sizes 
vary greatly across species, offering an opportunity to 
detect emerging trends in the number of essential genes 
in genomes96. Focusing on a subset of species for which 
near-complete essentialomes have been reported, we note 
that genomes harbouring larger numbers of ORFs tend to 
have higher total numbers but lower percentages of essen-
tial genes, suggesting an approximate power-law scaling 
(FIG. 2; Supplementary information S1 (table)). On one 
end of the spectrum, the obligate intracellular parasite 
M. genitalium has one of the smallest genomes known, 
and 382 (~80%) of its 482 genes were reported to be 
essential for growth under laboratory conditions97. This 
might not be surprising, as extreme genome reduction 
is a common feature of parasites98, making them poten-
tial outliers in this analysis. Considering other bacteria, 
~22% of ~1,600 genes are essential in H. influenzae99, 
whereas only ~7% of ~4,000 are essential in E. coli39,100, 
but essentialomes of bacteria with much larger genomes 
(such as certain cyanobacteria with >12,000 predicted 
ORFs101) have not yet been determined. Among eukar-
yotes, ~20% of ~6,000 genes are essential in budding 
yeast, whereas only ~10% of ~20,000 are so in cultured 
human cells (Supplementary information S1 (table)); 
however, eukaryotic essentialomes are still very limited, 
and methodo logical biases might exist between studies. 
More essentialomes from both eukaryotes and large- 
genome-bearing bacteria will be required to verify the 
power-law scaling hypothesis, as well as to understand 
the origin and potential consequences of this relationship.
What functions do essential genes encode? A clear com-
mon property of essential genes is that they tend to encode 
ancient functions that are fundamental for the very 
Figure 2 | Scaling of essential gene number with genome size. The relationship between 
the number or percentage of essential genes in a genome and the size of the genome was 
investigated based on data compiled in Supplementary information S1 (table). 
a | The total number of essential genes is plotted against the number of protein-coding 
genes. b | The percentage of essential genes is plotted against the number of protein-coding 
genes. Taken together, these analyses show that whereas the total number of essential genes 
in a genome increases with total gene content, the percentage of essential genes decreases 
as a function of the same quantity. Mathematically, this type of scaling can be approximated 
by a power-law relationship between the number of essential genes and the total number of 
genes in a genome, in which the power coefficient lies between 0 and 1. Biologically, this 
result suggests the existence of a type of economy of scale, by which larger genomes require 
a proportionately smaller number of essential genes to support survival of the species. These 
analyses have been restricted to species for which a near-complete essentialome has been 
experimentally determined and to the most up-to-date publications. When available, the 
genome assembly of the same strain used for the essentialome screen was extracted from 
the Ensembl database to obtain the number of protein-coding genes. Otherwise, the 
genome assembly of the reference strain of the corresponding species was used.
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http://www.ensembl.org
Purifying selection
Negative selection against 
deleterious alleles.
Phyletic retention
The tendency of genes to be 
retained in genomes along 
phylogenetic lineages.
Orthologue
Orthologues are genes found in 
different species that have 
evolved from a common 
ancestor.
Interactomes
Complete sets of genetic, 
protein–protein, metabolic or 
other types of molecular 
interaction within a given 
genome.
Degree
Within the context of biological 
networks, the number of 
genetic or physical interactions 
a gene or protein is involved in.
Pleiotropy
The production of two or more 
apparently unrelated 
phenotypes or traits by a single 
gene.
Epistasis
A form of genetic interaction, 
whereby an allele of one gene 
influences non-additively the 
phenotype associated with the 
allele of another gene. Positive 
or negative epistasis exists 
when a double mutant is fitter 
or less fit than the sum of the 
fitness effects of the two single 
mutants, respectively.
existence of cellular life itself. From bacteria to eukaryotes, 
essentialomes are enriched in genes required for DNA, 
RNA and protein synthesis40,95. Interestingly,while essen-
tial cellular functions can be highly conserved between 
species, one, more or even all components of a pathway or 
protein complex can be replaced by functional equivalents 
with an independent evolutionary origin. This process is 
known as ‘non-orthologous gene displacement’ (REF. 102) 
and occurs frequently when comparing evolutionarily 
distant organisms, such as the Gram-positive and Gram-
negative model bacteria, B. subtilis and E. coli43. In fact, 
only ~60 of the 500–600 genes that are estimated to be 
present in the last universal common ancestor of all forms 
of cellular life are found in all modern-day genomes103. A 
showcase example of this phenomenon is the kinetochore, 
which is a protein complex that mediates chromosome–
microtubule interactions during mitosis and that is essen-
tial in all eukaryotes104. Despite its universally essential 
function, the majority of kinetochore subunits found in 
kinetoplastids, a group of flagellated protists, are phyloge-
netically unrelated to those of yeasts and meta zoans105,106. 
Overall, this concept implies that it is likely that essenti-
ality is a property more of functions than of genes and 
thus has important implications for our understanding 
of genetics, evolution and systems biology.
How conserved are essential genes? As genes that are 
essential for cellular life cannot be easily lost or mod-
ified, an intuitive prediction is that they should evolve 
more slowly than non-essential genes. This prediction 
has been tested many times, revealing a more complex 
reality107. In support of the hypothesis, many essential 
proteins are infrequently lost during evolution and dis-
play quasi-invariant amino acid sequences. For instance, 
70 kDa heat shock proteins (Hsp70s) are present in vir-
tually all living organisms and serve essential chaperone 
functions; remarkably, 567 amino acids of the Hsp70 
protein sequence have remained identical from bac-
teria to eukaryotes for the past ~2 billion years108. At 
the nucleotide level, lower non-synonymous to synony-
mous substitution ratios, which are indicative of purifying 
selection, have been reported in essential versus non-es-
sential bacterial genes109,110 (FIG. 3a). The same trend was 
also observed in mice but was shown to be driven by 
a biased overrepresentation among the non-essential 
immune-related genes, which are thought to be under 
positive selection due to co-evolution with pathogens111. 
Moreover, the evolutionary distance between yeast and 
worm essential genes was similar to that of non-essential 
genes112. This discrepancy is potentially explained by the 
fact that purifying selection acts more strongly in bacteria 
than in eukaryotes owing to their vastly different effective 
population sizes113 but could also be due to fundamental 
issues in the dichotomous classification of gene essen-
tiality (see the section below on the non-absolute nature 
of gene essentiality).
When analysing phyletic retention, a somewhat clearer 
picture emerges across the tree of life. Counting the 
number of organisms a gene orthologue is present in 
proved highly predictive of gene essentiality in E. coli, 
B. subtilis and S. cerevisiae7,114,115. Bergmiller et al. found 
that the level of conservation of essential genes across 
bacterial taxa could be predicted by the extent to which 
their loss could be compensated by overexpression of 
non-homologous genes116. These data suggest that 
essential gene loss does not necessarily lead towards an 
evolutionary dead end. Conversely, many non-essential 
bacterial genes are retained at high frequency, or persist, 
across bacterial genomes114, suggesting that their loss 
has a substantial fitness impact that can lead to lineage 
extinction, even though it does not cause immediate 
cell death. Consistent with this hypothesis, condition-
ally essential genes are on average nearly as conserved 
as essential genes in S. Typhimurium or E. coli (A.T., 
unpublished observations). Moreover, the majority of 
C. elegans genes, although not essential for the imme-
diate survival of individual organisms, are nonetheless 
required for the multigenerational fitness of worm popu-
lations117. Together, these observations challenge the per-
vasive concept that essential genes are more important 
in evolution than non-essential genes and underline the 
necessity for a new definition of essentiality.
How connected are essential genes? Integrating 
essential omes with other genome-wide information is 
leading towards a deeper understanding of how cells 
work at the systems level. For instance, the availability 
of high-quality and comprehensive interactomes for some 
model organisms, ranging from genome-wide protein–
protein interactions (PPIs) to GI maps, has led to the 
initial observation that essential genes tend to act as hubs 
in molecular networks5,118,119. This is generally known as 
the ‘centrality–lethality’ rule, whereby genes and proteins 
with a high degree of connectivity, that is, playing a more 
‘central’ role in molecular networks, are hypothesized 
to be essential because their inactivation is more likely to 
disrupt overall network architecture (FIG. 3b).
Subsequent reports showed that essential genes 
encode proteins that are more likely to be involved 
in densely connected functional modules and in pro-
tein complexes120–123. Therefore, the use of pull-down 
approaches, which do not discriminate between direct 
and indirect interactions, might have inflated the 
degree of essential proteins in PPI networks. In fact, a 
reanalysis of the yeast PPI network focusing on direct 
binary interactions did not provide support for the 
centrality– lethality rule124. Rather, protein connectivity 
was more related to genetic pleiotropy than to gene essen-
tiality. Similarly, no relationship between centrality and 
essentiality was found in metabolic networks125.
A recent study described the use of automated yeast 
genetics to generate >23 million double mutants and 
reported ~850,000 GIs126. This near-complete GI net-
work in budding yeast confirmed the earlier hypo thesis 
that essential genes are network hubs, displaying on 
average five times as many interactions as non- essential 
genes118,126. Consistent with earlier analyses of the PPI 
network119,127, genes associated with higher fitness 
effects tended to be more pleiotropic. Hence, essential 
genes are expected to be in epistasis with a larger num-
ber of pathways, explaining their higher degree in GI 
networks. Thus, whereas the centrality–lethality rule 
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Nature Reviews | Genetics
0.80 0.1 0.2 0.3 0.4 0.5 0.6 0.7
0 10 20 30 40 50 60 70 80
Eukaryotes
Bacteria
Essential Non-essential All
Essential Non-essential All
Actinobacteria
Tenericutes
Firmicutes
Gammaproteobacteria
Epsilonproteobacteria
Plant
Worm
Bacteroidetes
Firmicutes
Tenericutes
Betaproteobacteria
Alphaproteobacteria
Gammaproteobacteria
Epsilonproteobacteria
Mycobacterium tuberculosis (H37Rv)
Bacteroides fragilis (638R)
Bacteroides thetaiotaomicron (VPI-5482)
Porphyromonas gingivalis (ATCC 33277)
Bacillus subtilis (168)
Staphylococcus aureus (N315)
Staphylococcus aureus (NCTC 8325)
Streptococcus sanguinis (SK36)
Mycoplasma genitalium (G37)
Caulobacter crescentus (NA1000)
Sphingomonas wittichii (RW1)
Burkholderia pseudomallei (K96243)
Burkholderia thailandensis (E264)
Acinetobacter baylyi (ADP1)
Escherichia coli (MG1655)
Francisella novicida (U112)
Haemophilus influenzae (Rd KW20)
Helicobacter pylori (26695)
Pseudomonas aeruginosa (PAO1)
Pseudomonas aeruginosa (UCBPP-PA14)
Salmonella enterica serovar Typhi (Ty2)
Vibrio cholerae (N16961)
Campylobacter jejuni (NCTC 11168)
a Actinobacteria
Yeast
Mycobacteriumtuberculosis (H37Rv)
Bacillus subtilis (168)
Staphylococcus aureus (NCTC 8325)
Staphylococcus aureus (N315)
Streptococcus pneumoniae (TIGR4)
Streptococcus sanguinis (SK36)
Mycoplasma genitalium (G37)
Mycoplasma pulmonis (UAB CTIP)
Acinetobacter baylyi (ADP1)
Escherichia coli (MG1655)
Francisella novicida (U112)
Haemophilus influenzae (Rd KW20)
Pseudomonas aeruginosa (UCBPP-PA14)
Salmonella enterica serovar Typhi (Ty2)
Salmonella enterica serovar Typhimurium (LT2)
Vibrio cholerae (N16961)
Helicobacter pylori (26695)
Arabidopsis thaliana
Caenorhabditis elegans
Saccharomyces cerevisiae
b
Mean Ka/Ks
Mean protein–protein interaction degree
appears to hold well for GI but not for other networks, the 
‘centrality– pleiotropy’ rule appears to be a more general 
property of biological networks.
The non-absolute nature of gene essentiality
Despite its deceptively simple definition, gene essential-
ity is neither binary nor static in its nature. In this sec-
tion, we review key research milestones that redefined 
gene essentiality as a property that is dependent on both 
environmental and genetic contexts and that is subject 
to evolutionary change.
The context-dependent nature of gene essentiality. 
Classic genetics has been based on a strict classification 
of genes as either essential or non-essential for cellular 
life. Yet, this binary classification was soon proved to be 
Figure 3 | Emerging properties of essential genes. Representative parameters for evolutionary conservation 
(the ratio of non-synonymous to synonymous sites (Ka/Ks); part a) and of connectivity in protein–protein interaction networks 
(degree; part b) are plotted for essential and non-essential genes in the indicated species. These graphs show that the amino 
acid sequence of essential genes tends to be more conserved than that of non-essential genes and that essential genes tend 
to participate in larger numbers of protein–protein interactions than non-essential genes. The former can be explained by a 
more stringent purifying (negative) selection on essential genes due to their critical function for species fitness, as any amino 
acid change that negatively affects protein function would not be well tolerated by the organism and would probably be 
pruned by evolution. The latter can be explained by the fact that, relative to non-essential genes, essential genes more often 
participate in large, multisubunit protein complexes, such as ribosomes, many of which play key roles in essential cellular 
functions, such as translation. Data have been extracted and replotted from REF. 110 (part a) and REF. 206 (part b).
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Synthetic lethality
An extreme form of negative 
epistasis, whereby the 
combination of mutations in 
two or more genes causes cell 
death, whereas none of the 
single mutations are lethal in 
isolation.
Ploidy
The number of sets (that is, full 
complements) of chromosomes 
in a genome.
too simplistic, and gene essentiality is now well accepted 
to be a conditional trait128 (FIG. 4). First, the essentiality of 
a gene often depends on the environmental context in 
which it is probed129–131. Classical examples include the 
concept of auxotrophy, whereby metabolic genes that are 
required for the synthesis of certain building blocks of 
life (for example, amino acids or nucleotides) are essen-
tial only if the same building blocks are absent from the 
growth medium or natural environment (FIG. 4a). By 
carefully modelling the yeast metabolic flux network, 
Papp et al. extended this concept, demonstrating that 
the majority of dispensable genes for growth in rich 
media are actually important for fitness in other growth 
conditions132. This was later confirmed experimentally 
by screening libraries of KO deletion strains in hundreds 
of conditions, as a plethora of non-essential genes were 
found to be required for optimal fitness in at least one 
of several tested growth conditions130,131. Qian et al. even 
found hundreds of non-essential gene deletions that 
were actually beneficial in some environments, while 
being deleterious in others133, a phenomenon known as 
antagonistic pleiotropy.
Gene essentiality also depends on the genetic context. 
In the case of synthetic lethality, a gene becomes essen-
tial only when the function of a second gene is lost134 
(FIG. 4b). On the other hand, some essential genes can 
become dispensable upon the loss of another gene; this 
is often because the former encodes protective functions 
towards the toxic effects of the latter135,136 (FIG. 4c). Due 
to differences in genetic or epigenetic backgrounds, the 
essentiality of some genes could differ between individ-
uals of the same species or cell types of the same indi-
vidual, respectively (FIG. 4d). The presence of protective 
alleles in some healthy humans can even completely 
mask the effects of loss-of-function mutations associated 
with highly penetrant Mendelian childhood diseases137 
or with sterility138. Furthermore, in two widely used 
laboratory strains of S. cerevisiae, 44 genes are uniquely 
essential in the Sigma1278b strain, whereas 13 are essen-
tial only in the S288c strain139. In bacterial species, where 
strains of the same species can differ by as much as half 
of their genomic content (owing to rampant horizontal 
gene transfer), changes in essentiality within species are 
presumably more dramatic, although this remains to 
be measured. Other genetic contexts that can influence 
gene essentiality include ploidy: ~1% of genes known to 
be non-essential in haploid budding yeast were essen-
tial in tetraploid cells, where the presence of a larger 
number of chromosomes increases the burden on the 
mitotic machinery and the requirement for genome 
stability genes140.
More interestingly, environmental and genetic 
context dependency seem to be linked. For exam-
ple, synthetic lethality can be largely dependent on 
the environ ment141–143. Moreover, yeast genes with more 
environment-dependent phenotypes or chemical–genetic 
interactions tend to have larger numbers of GIs119,141. In 
accordance with the centrality–pleiotropy rule, this could 
again be explained by the fact that essential genes are asso-
ciated with a higher level of functional pleiotropy than 
non-essential genes.
Comparing essentialomes across different species 
and contexts has led to some re-evaluation of earlier 
notions. For instance, the previously mentioned signif-
icant difference in evolutionary conservation between 
bacterial essential and non-essential genes appears to 
be both species-dependent and media- dependent110,144. 
Nevertheless, we expect that the invariant set of core 
essential genes across eukaryotic and prokaryotic 
organisms could eventually be identified, and these 
genes are likely to underlie the fundamental processes 
that drive the reproductive success of cellular organ-
isms145. Cataloguing core and context- dependent 
essential genes is particularly relevant for studying 
disease in multi cellular organisms, where de lineating 
tissue- specific essential genes has the potential to 
reveal both the genetic roots of and candidate targets 
for tissue- specific diseases. Similarly, understanding 
the set of genes and GIs that are required for cellular 
and/or organismal reproductive success is the first step 
for developing cancer- specific or pathogen-specific 
therapies and for precision medicine91,93,146 (see the 
subsection ‘Implications for therapeutic applications’).
The evolvable nature of gene essentiality. The essentiality 
of a gene does not only depend on the context in which 
it is probed; it can also change in the course of evolution. 
Indeed, as genomes evolve, genetic backgrounds canbe 
altered in such a way that changes the essentiality of a 
gene. For instance, roughly one-third of the genes found 
to be essential in E. coli are non-essential in B. subtilis 
Figure 4 | Context-dependent gene essentiality. 
Schematic representations illustrating different examples 
of context-dependent gene essentiality. a | A hypothetical 
gene X encodes enzyme X, which is required for the 
production of the essential metabolite B. In an environment 
where metabolite B is present, gene X is dispensable. When 
metabolite B is absent, gene X becomes essential. This 
phenomenon is also known as auxotrophy. b | Hypothetical 
genes X and Y encode enzymes performing redundant 
biochemical reactions. Whereas inactivation of either gene 
alone leads to viable cells, the simultaneous deletion of 
both genes causes cell death. This is an example of synthetic 
lethality. c | Hypothetical gene X encodes an inhibitor of 
toxin Y. In the absence of toxin Y, gene X is dispensable, but 
its activity is required for viability in the presence of the 
toxin. Gene X is an example of a protective essential gene. 
d | Hypothetical genes X and Xʹ encode mutually exclusive 
and redundant subunits of an essential protein complex 
with subunit Y. In cells in which the expression of gene Xʹ is 
epigenetically silenced, gene X becomes essential. This 
could form the basis of cell type-specific essentiality in 
multicellular eukaryotes. e | Hypothetical gene X encodes a 
protein that promotes essential process X. At a normal level 
of expression, the product of gene Y does not contribute to 
process X. Upon upregulation of protein Y (for example, due 
to aneuploidy of the chromosome encoding gene Y), 
a hidden function of protein Y is unmasked, leading to its 
promotion of process X. Therefore, the essentiality of gene 
X could be bypassed by the acquisition of mutations that 
upregulate gene Y. This is the basis of high copy number 
suppression screens and occurs frequently during adaptive 
evolution of yeast species.
▶
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Nature Reviews | Genetics
Enzyme X
c Protective essential genes d Cell-type-specific essentiality
e Karyotype-dependent essentiality
Environment 1: Metabolite B present Gene X non-essential 
Environment 2: Metabolite B absent Gene X essential
Toxin Y
Antitoxin X
Permissive epigenetic mark
Inhibitory epigenetic mark
Euploid cell: Gene Y expressed at normal levels Gene X essential
Aneuploid cell: Gene Y over-expressed Gene X non-essential
Cell type A: Gene X′ expressed Gene X non-essential 
Cell type B: Gene X′ not expressed Gene X essential 
Strain A: Gene Y present 
Strain B: Gene Y absent
Gene X
Gene X′
Viable Viable
Viable
Viable
Viable
Viable
Viable
Viable
Viable
Viable Viable
Viable
Viable
Viable
Viable
Δ
Δ
Δ
Δ
Δ
Δ
Subunit X
Subunit Y
Subunit X′
Subunit Y
Δ
Δ
 Gene X non-essential
 Gene X essential
Strain A: Gene Y present 
Strain B: Gene Y absent
 Gene X essential
 Gene X non-essential
Δ
Δ
Process X Process X
Process X Process X
GX
GX
GY GY
EYEX
MA MA′GX
Δ
GX
GX
GY GY
GY
GX′
GY
GX′
GY
GX′
GX′
GX
GY
GX′
GX
GX
Δ Δ
Δ
MBMB MB MBMB MB
MB
MB
MB
MB
MB
MB
MB
MB
MB
EYEX
MA MA′
MB
EYEX
MA MA′
MB
EYEX
MA MA′
MB
MBMBMB MBMBMB
MB
MA
MB
MA
MA
EX
MB
MA
EX
EX
EX
MB
Toxin Y
Antitoxin X
Toxin Y
Antitoxin X
Toxin Y
Antitoxin X
Subunit X
Subunit Y
Subunit X′
Subunit Y
Subunit X
Subunit Y
Subunit X′
Subunit Y
Subunit X
Subunit Y
Subunit X′
Subunit Y
PY
PY
PY
PYPY
PY
PY
PY
PX
PXGX
GY
GY
GY GY
GY
GY
GX
PX
PX
Enzyme Y
Gene Y
a Auxotrophy b Synthetic lethality
Metabolite B
Metabolite A
Metabolite A′
GX
EX
MA
MA′
GY
EY
MB
Protein X
Protein Y
PX
PY
Inviable
Inviable
Inviable
Inviable
Inviable
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and vice versa39,43, whereas ~27% of essential genes in 
S. pombe fission yeast are non-essential in S. cerevisiae 
budding yeast, and ~17% of essential budding yeast genes 
are non-essential in fission yeast40, confirming that the 
essentiality of genes often changes during evolution. This 
change in essentiality can be due to various reasons: genes 
or functions could arise separately or be lost or replaced 
by others during evolution (distinct biology of the spe-
cies); the cellular network could become more robust 
(for example, by acquiring separate pathways or pro-
tein complexes performing the same function); or the 
network could be rewired to bypass the essentiality43. 
Overall, protein complexes typically behave uniformly 
in this evolutionary switch of essentiality, with all sub-
units being either non-essential or essential in one 
species120,123,147 and by switching essentiality states in a 
coherent manner between species148,149. This suggests 
that essentiality is largely a property of entire molecu-
lar machines and/or functional modules rather than of 
individual genes.
If changes in genetic background could render an 
essential gene dispensable, could essential gene loss be 
compensated by short-term adaptive evolution, thus pre-
venting lineage extinction? One potential mechanism is 
by spontaneous genetic suppression. Suppression screens 
have been used by geneticists for decades to infer the 
function of uncharacterized genes and are still success-
fully utilized today to systematically identify connections 
between and within biological pathways150. The pheno-
menon arises when a secondary mutation suppresses 
the phenotype of a primary mutation, and spontaneous 
genetic suppression by means of natural acquisition of 
compensatory mutations can be readily observed in 
cells that harbour a deletion of a non-essential gene151. 
By definition, complete deletion of an essential gene is 
incompatible with life; therefore, researchers wishing 
to isolate suppressors of an essential gene typically have 
to either mutagenize a conditional KO strain before 
inactivation of the essential gene or resort to a less severe 
alteration of the essential gene, for example, by employing 
temperature-sensitive or hypomorphic alleles.
However, cells can spontaneously circumvent the 
lethality associated with the complete deletion of at least 
some essential genes. Following genetic inactivation of 
the essential type II myosin-encoding MYO1 gene, which 
is required for cytokinesis in budding yeast, although 
the majority of cells succumbed after a few failed cell 
divisions, some myo1 cells survived after prolonged 
incubation and improved their fitness and cytokinesis 
proficiency upon serial passaging. Interestingly, these 
evolved myo1 cells performed cytokinesis by mecha-
nisms that were fundamentally different from wild-type 
cells152. Specifically, a subset of evolved myo1 strains 
acquired extra copies of chromosome XVI, which led to 
increased expression of RLM1 and MKK2, which encode 
a transcription factor and a signalling molecule, respec-
tively, that play key roles in the cell wall integrity path-
way153. As a result, these evolved myo1 strains achieved 
cytokinesis not by pulling the plasma membrane from 
the inside via the actomyosin ring, but by pushing it 
from the outside via thickening of the cell wall around 
the bud neck. As type II myosins drive cytokinesis from 
yeast to human cells154, these results suggest that short-
term evolutionary processes, such as the acquisition of 
aneuploid chromosomes, are sufficient to overcome even 
the lossof a highly conserved essential gene (FIG. 4e).
More recently, some of us undertook a genome-
wide effort to establish the generality of this finding. 
Liu et al.155 tested the extent to which S. cerevisiae cells 
could withstand the individual deletion of each of 
~1,100 essential genes and designated 88 (~9%) of these 
as evolvable essential genes. We define evolvable essen-
tial genes as essential genes that can be acutely removed 
from the genome without causing stereotypical cell 
death; instead, a subset of cells with these genes deleted 
can undergo short-term adaptive evolution and spon-
taneously acquire compensatory mutations that sup-
press the lethal phenotype. Interestingly, evolved strains 
in which different essential subunits of the nuclear pore 
complex were deleted increased the gene dosage of 
BRL1 (which encodes an integral membrane protein) 
and restored nuclear–cytoplasmic transport by altering 
membrane fluidity. Consistent with other reports in 
bacteria and yeast116,152,156, this indicates that adap tation 
to essential gene loss often occurs by tinkering with 
seemingly unrelated biological functions rather than by 
fixing the broken molecular machinery. Efforts to sys-
tematically map the interconnections between essential 
functional units89,118 might help us understand when and 
how networks can be rescued from such deep valleys in 
fitness landscapes.
Importantly, evolutionary responses to genetic per-
turbations are not restricted to essential genes. In fact, 
yeasts and bacteria can also acquire adaptive mutations 
in response to the deletion of non-essential genes151,157,158, 
indicating that non-essential genes might encode func-
tions that are still essential for overall cellular fitness and 
that their loss can thus act as a powerful selective pres-
sure. Together, these observations suggest the existence 
of a gradient of gene essentiality, with some essential 
genes being less essential than others (meaning that they 
can become dispensable via a change in the environment 
or via short-term or long-term evolutionary processes) 
and some non-essential genes being less dispensable 
than others (meaning that they encode functions that 
are nevertheless required for full fitness). In support of 
the existence of such an essentiality gradient, proper-
ties previously found to be differentially associated with 
essential and non-essential genes, such as sequence con-
servation, phyletic retention and centrality in molecular 
networks, were found to display intermediate values in 
evolvable essential genes155.
Overall, these observations support a paradigm shift 
from a qualitative to a quantitative definition of gene 
essentiality, which must take into account not only the 
viability of the corresponding mutant cells but also 
the environmental and genetic context in which it is 
probed, as well as the ability of the mutant cells to evolve 
compensatory mechanisms via spontaneous acquisition 
of suppressor mutations (TABLE 2). Moving forward, it is 
likely that as gene essentiality was found to be context- 
dependent, the evolvability of gene essentiality will also 
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be dependent on both environmental and genetic con-
texts. Consistent with this idea, compensatory evolution 
of S. cerevisiae cells in response to a genetic alteration 
modelling a human disease was found to be dependent 
on the genetic background, the environmental condition 
and a combination of the two159.
Broader implications across various fields
Implications for metabolic engineering and synthetic 
biology. Knowledge of which genes are essential or 
non-essential in a genome in a given context is of crucial 
importance for metabolic engineering and the growing 
field of synthetic biology. The optimal production of 
chemicals, nutrients, pharmaceuticals and biofuels via 
genetic engineering of microbial cell factories relies on 
the addition and deletion of key metabolic genes to re-
direct metabolic fluxes towards desired end products160. 
To this end, understanding the context-dependent essen-
tiality of individual cellular reactions is key for devel-
oping and optimizing relevant pathways, as genes that 
are non- essential under standard laboratory conditions 
might become essential under industrial conditions and 
vice versa.
One of the main goals of synthetic biology is the 
rational design and complete synthesis of a living organ-
ism161–163, and minimal genomes are seen as promising 
starting points for such endeavours164,165. Whereas the 
essentialome represents the set of genes that are required 
for reproductive success, the minimal genome repre-
sents those genes that are sufficient to sustain cellular 
life166. By use of a top-down approach, genomes have 
been successfully minimized by progressively deleting 
increasing numbers of non-essential genes167–169. These 
efforts are, however, time consuming, as genes are typ-
ically deleted in a stepwise manner and therefore can 
follow only specific design trajectories. This may lead 
to unexpected dead ends, as at each step of the process, 
the genetic context is modified in such a way that could 
alter the essentiality of other genes. The introduction of 
synthetic chromosome rearrangement and modification 
by loxP-mediated evolution (SCRaMbLE)162 promises 
to accelerate the generation of strains carrying multiple 
random gene deletions. Multiple SCRaMbLE sites will 
be incorporated in the first synthetic eukaryotic genome, 
Sc2.0 (REF. 164). Once completed, the Sc2.0 genome could 
then be subjected to multiple rounds of ‘SCRaMbLEing’, 
which could potentially bypass some of the fitness val-
leys caused by context-dependent gene essentiality, until 
a eukaryotic minimal genome is achieved.
Bottom-up approaches based on the synthesis of 
minimal genomes consisting only of essential genes, on 
the other hand, have proved much less straightforward. 
In silico modelling of bacterial metabolic networks demon-
strated that minimal genomes require more than all the 
essential genes170. Experimental efforts to build a mini-
mal genome consisting of only the 375 genes found to be 
individually essential in M. genitalium24 have consistently 
failed to yield viable cells165. An approximately minimal 
bacterial genome, Syn3.0, was eventually built based on 
several rounds of rational design and random muta genesis 
data on progressively reduced genomes and contained 98 
more genes than the initially predicted set of individu-
ally essential genes. This observation was attributed to 
non- essential genes in the original genome becoming 
essential or quasi-essential in the context of the reduced 
genome due to synthetic lethality165. This result high-
lights the difficulty in predicting minimal genomes 
from single-gene deletion or knockdown (KD) studies 
alone and the importance of epistasis in determining 
the essentiality of a gene. Genome-wide GI maps will 
be required to improve these predictions, but due to 
the combinatorial nature of the problem, mapping even 
only all binary interactions within an organism is cur-
rently a colossal effort119,126,150. However, as more ‘non- 
essential’ genes are removed from a genome, knowledge 
of higher-order GIs becomes important, rendering such 
predictions almost impossible. On the other hand, as 
more efficient technologies become available for gener-
ating completely synthetic genomes by replacing long 
fragments of genomic DNA with synthetic ones171, 
bottom-up approaches are bound to increasingly yield 
greatly reduced, if not minimal, microbial genomes even 
following a trial-and-error process.
Synthetic microbial communities and the emerg-
ing field of synthetic ecology172 offer another potentialavenue for applying the concept of context- dependent 
gene essentiality. Although multi-species microbial 
communities often carry out complex bio conversions 
more efficiently than single-strain microbial cul-
tures, it is challenging to maintain them long term172. 
Table 2 | Quantitative definitions of gene essentiality
Definition based on Extent of essentiality
No essentiality Low essentiality High essentiality Complete essentiality
Context dependency Dispensable in all 
environmental and 
genetic contexts
Dispensable in most environmental 
and genetic contexts
Indispensable in most 
environmental and genetic 
contexts
Indispensable in all 
environmental and 
genetic contexts
Evolvability following 
gene inactivation
No compensatory 
mutations required for 
survival
Compensatory mutations are 
required for survival. For these 
compensatory mutations, multiple 
independent compensatory 
mechanisms exist and/or the 
mutations occur at high frequency 
and/or they are easily selected and 
fixed in the population
Compensatory mutations are 
required for survival. For these 
compensatory mutations, only a 
few compensatory mechanisms 
exist and/or the mutations occur 
at low frequency and/or they are 
not easily selected and fixed in 
the population
No compensatory 
mechanism exists
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Syntrophic relationships
A phenomenon by which one 
species requires the product of 
another species to survive.
Cooperative interactions could be predicted by in sil-
ico modelling173 or by engineering mutual syntrophic 
relationships in a synthetic community. These relationships 
can be achieved by introducing mutual viability depend-
encies between community members by taking advantage 
of what could be termed ‘ecology-dependent gene essen-
tiality’, that is, by deleting genes in one species that are 
essential only if another species is absent from the com-
munity. This strategy has been successfully implemented 
for synthetic communities of up to 14 members and 
effectively prevented some members from dominating the 
community and others from going extinct174.
Implications for therapeutic applications. Given the 
fundamental role played by essential genes, it is un -
surprising that they represent current and potential 
novel targets of many antimicrobial and anticancer 
compounds91,146,175,176. Yet the concept of core versus 
context-dependent gene essentiality could be exploited 
in different ways to eliminate pathogens or cancer cells.
For antimicrobial therapy, the goal would be to target a 
cellular function that is universally essential for the micro-
organism under most, if not all, environments and genetic 
backgrounds, so that it will be effective against different 
clinical isolates of the pathogen and in any possible body 
part of the host (FIG. 5a). This strategy can translate to both 
broad-spectrum and narrow-spectrum compounds, with 
the former focusing on conserved essential genes across 
species and the latter on conserved essential genes within 
a specific pathogenic species. Pathogen-specific drugs, 
which could also include drugs targeting non-essential 
virulence genes177, may also mitigate the collateral dam-
age on the commensal microbiome and the dysbiosis or 
secondary infections associated178.
By contrast, for cancer therapies, the focus would be 
on genes that are specifically essential in tumour cells but 
not in normal cells; this context-dependent essentiality 
would provide therapeutic efficacy without having exces-
sive toxic effects on the whole body (FIG. 5b). An example 
of this strategy has been implemented in the case of can-
cers that have mutations in the BRCA1 or BRCA2 breast 
and ovarian cancer susceptibility genes. Tumour cells with 
homozygous BRCA1 or BRCA2 loss of function (obtained 
through germline acquisition of a heterozygous mutant 
state, followed by cancer-associated somatic inactivation 
of the remaining functional allele) become dependent on 
poly(ADP-ribose) polymerase (PARP) activity179. This 
observation has been exploited for developing PARP 
inhibitors, such as olaparib, which is now clinically 
approved for the treatment of ovarian cancer180 (FIG. 5c). 
Indeed, the concept of synthetic lethality, whereby the 
presence of cancer-specific mutations in certain genes 
renders other genes essential for the proliferation and sur-
vival of cancer cells181, has matured into a drug-targeting 
strategy for oncology programmes in both industry and 
academia. Patient-tailored therapy based on individual-
ized cancer drug susceptibility profiles is already yielding 
promising results for precision medicine93,182 (FIG. 5d).
Finally, the fact that gene essentiality is an evolvable 
property has tremendous implications for our under-
standing of how drug resistance arises and our ability 
to curb its alarmingly progressive incidence183. Selection 
of drug-resistant pathogens or cancer cells by anti-
microbials or chemotherapy, respectively, is an inherent 
evolutionary process184. Whether to fight off an infection 
or to beat cancer, anti-proliferative drugs typically target 
essential functions that are required for cell growth and 
survival. If the essentiality of cellular function truly lies 
on a gradient of evolvability, then genes associated with 
maximum essentiality (and hence, least evolvability) 
would make better drug targets. A way to address this is 
by screening KD or conditional KO libraries of essential 
genes in pathogens or cancer cells for gene mutations 
associated with the least propensity to acquire spon-
taneous suppressors. Such genes would represent supe-
rior targets for further drug screening and development, 
as they would probably be associated with a lower inci-
dence of drug resistance. However, if the evolv ability of 
gene essentiality is as context-dependent as gene essen-
tiality itself, in vitro evolution of mutant cells may not 
always translate to in vivo emergence of drug resistance. 
Thus, more research is required to fully define gene 
essentiality across different contexts and timescales.
Conclusions
In conclusion, recent technological advances have 
enabled massive genome-wide screening efforts that 
are uncovering the complex and multifaceted nature 
of essential genes. From the several examples herein 
reviewed, it is evident that gene essentiality is not a 
fixed property of a gene but strongly depends on the 
environmental and genetic context and can be altered 
in the course of both short-term and long-term evo-
lution. Thus, the essentiality of a gene is a quantitative 
rather than a binary trait and should be measured on a 
continuous scale. This idea could be further extended 
by claiming that no gene is absolutely essential — only 
functions can be so. These emerging concepts are open-
ing up exciting avenues for fundamental research into 
the basic requirements for life as well as illuminating 
new paths towards therapeutic exploitation against 
diseases spanning from cancer to infectious diseases.
Some of the key next steps include re-assessing 
gene essentiality in the light of its context- dependent 
and quantitative nature not only in the few model 
organisms but also in non-model organisms across 
the tree of life80. This assessment will be instrumen-
tal not only for understanding the evolutionary 
plasticity of essential cellular functions but also for 
gaining more knowledge of medically and industri-
ally relevant microorganisms. Once systematic quan-
tifications of gene essentiality are available for a large 
number of species, the next leap will be to understand 
how these genes are interconnected within the cell. 
GI maps need to become truly ‘genome-wide’ and 
not only focus onnon-essential genes, which could 
be accomplished by employing hypomorphic, tem-
perature-sensitive or repressible alleles of essential 
genes126,185. By comprehensively mapping connections 
within and between cellular pathways across various 
species and environmental conditions, these studies 
will facilitate our understanding of archetypal network 
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Nature Reviews | Genetics
 
a
b
c
 a Blood
b Spinal fluid
c Respiratory tract
d Gastrointestinal tract
e Kidneys
f Liver
g Brain
h Spleen 
a Colon cancer cells
b Normal colonocytes
c Hepatocytes
d Bone marrow cells
e Neurons
f Hair follicles
g Cardiomyocytes
h Splenocytes
 
Strategy to target colon cancer
Target context-dependent essential genes specifically
required for growth and survival of cancer cells
a
c
b
DNA damage
Single-strand break 
Double-strand break
Base-excision 
repair
PARP inhibitor
Homologous 
recombination
Normal, BRCA1–/– or BRCA2–/– cells Normal cells BRCA1–/– or BRCA2–/– cells 
No repair
Cell survival Cell death
Patient with AML
 
 
 
 
 
d Essential genes in AML cell lines
Without
oncogenic RAS
With
oncogenic RAS Blood sample
Treatment
 
* *
*
Strategy to target a pathogenic microbe causing a
systemic infection
Target core essential genes required for growth and survival
in all body sites
 
PARP 
NRAS
KRAS
Oncogenic RAS? 
PREX1
inhibitor?
• RCE1
• ICMT
• RAF1
• SHOC2
• PREX1
d
e
f
g
h
a
b
c
d
e
f
g
h
Figure 5 | Exploiting the non-absolute nature of gene essentiality for drug targeting. The figure illustrates examples of 
how the concept of the core versus context-dependent nature of gene essentiality could be exploited to define drug targets. 
a | In the case of infectious diseases, the goal is to eradicate from the human body a microbial pathogen that might have spread 
to various body sites. In this case, an ideal drug target for antimicrobial therapy should be chosen from the core set of context- 
independent essential genes of that microbial species. This will ensure that the therapy will work in any part of the human body. 
b | In the case of cancer, the aim is to eradicate tumour cells while sparing healthy tissues. In this case, an ideal drug target 
should be chosen from the set of cancer-specific essential genes, which should be dispensable in normal tissues. c | In the 
presence of poly(ADP-ribose) polymerase (PARP), single-strand DNA breaks are effectively repaired by the base-excision repair 
pathway regardless of BRCA1 or BRCA2 functionality. In the absence of PARP, cells become dependent on BRCA1 and BRCA2 
to repair their DNA by homologous recombination. Therefore, PARP is essential only in BRCA1-deficient or BRCA2-deficient 
cells, providing a therapeutic avenue to selectively eliminate cancer cells. d | Acute myeloid leukaemia (AML) cells were found 
to be dependent on PREX1 for survival but only if they were carrying oncogenic RAS mutations93. This could be a strategy to 
stratify patients with AML for treatment: a sample of blood could be tested for KRAS or NRAS mutations (indicated by the 
asterisks), which, if detected, could guide the use of hypothetical future anti-PREX1 treatments. ICMT, isoprenylcysteine 
carboxyl methyltransferase; RAF1, Raf-1 proto-oncogene, serine/threonine kinase; RCE1, Ras converting CAAX 
endopeptidase 1; SHOC2, SHOC2, leucine rich repeat scaffold protein. Part c is from REF. 207, Macmillan Publishers Limited.
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