Subscriber access provided by Northern Illinois University
Article
CRISPR perturbation of gene expression alters bacterial fitness under stress and reveals underlying epistatic constraints Peter Britton Otoupal, Keesha E Erickson, Antoni Escalas Bordoy, and Anushree Chatterjee ACS Synth. Biol., Just Accepted Manuscript • DOI: 10.1021/acssynbio.6b00050 • Publication Date (Web): 16 Aug 2016 Downloaded from http://pubs.acs.org on August 17, 2016
Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a free service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are accessible to all readers and citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.
ACS Synthetic Biology is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.
Page 1 of 46
ACS Synthetic Biology
1 2 3
CRISPR perturbation of gene expression alters bacterial fitness under stress 4 5 6
and reveals underlying epistatic constraints 7 8 9 10 1 12
Peter B. Otoupal1, Keesha E. Erickson1, Antoni Escalas-Bordoy1 and Anushree Chatterjee1,2* 13 14 15 16 17 18
1
19 20
Department of Chemical and Biological Engineering, University of Colorado at Boulder, Boulder, CO,
USA. 21 2 23
2
24
BioFrontiers Institute, University of Colorado at Boulder, Boulder, Colorado, USA.
25 26 27 28 29 30
Classification: Synthetic Biology & Biotechnology, Transcription, Evolution 31 32 3
Key words: CRISPR / Transcriptome / Adaptive Evolution / Epistasis / Antibiotic Resistance 34 35 36 37 38 39
*
Corresponding author:
40 41 42 43
Anushree Chatterjee 4 45
3415 Colorado Avenue, 596 UCB, University of Colorado Boulder, CO 80303 46 47
Email:
[email protected] 48 49 50 51
Phone: (303) 735-6586 Fax: (303) 492-8425
52 53 54 5 56 57 58 59 60
ACS Paragon Plus Environment
ACS Synthetic Biology
Page 2 of 46
1 2 3
1 4 5 6 7 8 9 10
ABSTRACT
2
The evolution of antibiotic resistance has engendered an impending global health crisis that
3
necessitates a greater understanding of how resistance emerges. The impact of non-genetic factors and
4
how they influence the evolution of resistance is a largely unexplored area of research. Here we present a
5
novel application of CRISPR-Cas9 technology for investigating how gene expression governs the
6
adaptive pathways available to bacteria during the evolution of resistance. We examine the impact of gene
7
expression changes on bacterial adaptation by constructing a library of deactivated CRISPR-Cas9
8
synthetic devices to tune the expression of a set of stress-response genes in Escherichia coli. We show
9
that artificially inducing perturbations in gene expression imparts significant synthetic control over fitness
10
and growth during stress exposure. We present evidence that these impacts are reversible; strains with
11
synthetically perturbed gene expression regained wild-type growth phenotypes upon stress removal, while
12
maintaining divergent growth characteristics under stress. Furthermore, we demonstrate a prevailing trend
13
towards negative epistatic interactions when multiple gene perturbations are combined simultaneously,
14
thereby posing an intrinsic constraint on gene expression underlying adaptive trajectories. Together, these
15
results emphasize how CRISPR-Cas9 can be employed to engineer gene expression changes which shape
16
bacterial adaptation, and present a novel approach to synthetically control the evolution of antimicrobial
17
resistance.
1 12 13 14 15 16 17 18 19 20 21 2 23 24 25 26 27 28 29 30 31 32 3 34 35 36 37 38 39 40 41 42 43 4 45 46 47 48 49 50 51 52 53 54 5 56 57 58 59
1
60 ACS Paragon Plus Environment
Page 3 of 46
ACS Synthetic Biology
1 2 3
18 4 5 6 7 8 9 10
INTRODUCTION
19
As bacteria continue to demonstrate their ability to adapt to a broad range of antibiotics1 and other
20
antimicrobials2, a dearth of effective treatments for life-threatening pathogenic infections has become a
21
prominent concern3. Although genomic divergences (i.e. mutations) have been the focus of conventional
22
adaptive evolutionary research, the impact of variations in gene expression on microbial evolution during
23
stress exposure4–6 is a relatively unexplored field. Heterogeneous gene expression has been shown to
24
enable bacterial bet-hedging7 strategies to create diversity in order to dynamically respond to sudden
25
environmental stressors6. This mutation-independent process, known as adaptive resistance8, could
26
expedite the evolution of antimicrobial resistance. Supporting this notion is the observance of distinct
27
changes in bacterial transcriptomes during exposure to antibiotics9 and disinfectants10, as well as
28
significant heterogeneity in inter-population gene expression during the first hundred or so generations of
29
adapting bacterial populations11–13.
1 12 13 14 15 16 17 18 19 20 21 2 23 24 25 26 27 28 29 30 31 32 3 34
30
In this study, we take inspiration from these adaptive strategies found in nature, and hypothesize
31
that synthetically inducing small perturbations in gene expression can enable artificial control over both
32
positive and negative fitness phenotypes in adapting strains. Assuming that gene expression is normally
33
distributed around basal levels in a bacterial population, we hypothesize small changes in the distribution
34
of gene expression could exacerbate the pre-existing growth and fitness phenotypes of sub-populations
35
with altered gene expression (Fig 1A). Further, we hypothesize that the simultaneous perturbation of
36
multiple genes can induce unique phenotypic responses via epistatic interactions. Negative epistatic
37
interactions, where the combined fitness benefits of simultaneous mutations are less than expected, have
38
been shown to either overshadow positive epistasis during adaptation14,15 to environmental conditions or
39
impact long-term evolvability16. While it has been suggested that the epigenetic epistatic interactions of
40
gene expression ultimately constrain long-term evolution17, very little is understood regarding how these
41
interactions might impact the early stages of adaptive resistance.
35 36 37 38 39 40 41 42 43 4 45 46 47 48 49 50 51 52 53 54 5 56 57 58
42
To investigate our hypotheses, we engineered deactivated CRISPR (Clustered Regularly
43
Interspaced Short Palindromic Repeats)-associated protein 9 (dCas9) based genomic devices to
59
2
60 ACS Paragon Plus Environment
ACS Synthetic Biology
Page 4 of 46
1 2 3 4 5 6 7 8 9 10
44
synthetically induce small perturbations in the transcriptome of Escherichia coli (E. coli). This presents a
45
novel application of CRISPR technology as we employ it to explore the impact of subtle gene expression
46
changes on bacterial fitness in the presence of sub lethal levels of stressors, and to the best of our
47
knowledge is the first of its kind18. dCas9 and dCas9 constructs fused with the ω-subunit of RNA
48
polymerase (dCas9-ω) have been shown to controllably inhibit19 or activate20 gene expression
49
respectively. When combined in vivo with small guide RNAs (sgRNAs), these devices exhibit highly
50
specific and localized control over the transcription rates of individual genes. Moreover, these CRISPR
51
devices are able to perturb expression of multiple genes simultaneously, thereby allowing for the
52
investigation of combinatorial effects19 of targeted gene control and the subsequent interactions this
53
induces.
1 12 13 14 15 16 17 18 19 20 21 2 23 24 25
54
We chose to investigate seven stress-response genes, whose functions are outlined in
55
Supplementary Table S1. These include the global transcriptional regulators marA, soxS and recA. MarA
56
(multiple antibiotic resistance) activates expression of the mar operon to increase efflux activity, decrease
57
porin expression, and regulate other biochemical processes to confer tolerance to solvents and drugs21.
58
SoxS (superoxide stress response) shares 49% homology in binding sites with MarA, and regulates
59
similar genes to promote antimicrobial tolerance22. RecA activates the SOS response, wherein DNA
60
repair occurs and cell growth is arrested23. The remaining four genes we chose to examine were
61
downstream genes of these global regulators: mutS, dinB, acrA and tolC. MutS functions in DNA
62
mismatch repair pathways (thereby decreasing mutation rates)24, while DinB acts as an error-prone
63
polymerase lacking proofreading capacity (thereby increasing mutation rates)25. Finally, TolC and AcrA
64
work in tandem to construct an efflux pump to channel toxic materials outside of the cell26.
65
engineered CRISPR devices to systematically inhibit and activate the expression of these stress-response
66
genes in E. coli during short-term (72 hour) exposure to various stress conditions, including antibiotics
67
(tetracycline and rifampicin), disinfectants (bleach and hydrogen peroxide) and glucose limitation. We
68
monitored the resulting growth and fitness impacts during the early stages of adaptation, as well as the
69
epistatic interactions induced by simultaneous gene perturbation.
26 27 28 29 30 31 32 3 34 35 36 37 38 39 40 41 42 43 4 45 46 47
We
48 49 50 51 52 53 54 5 56 57 58 59
3
60 ACS Paragon Plus Environment
Page 5 of 46
ACS Synthetic Biology
1 2 3 4 5 6 7 8 9 10
70
Corroborating our hypothesis, we observe that CRISPR-Cas9 based synthetic devices enable
71
small perturbations in gene expression that are sufficient to significantly influence native bacterial
72
adaptive responses to stress by altering growth rates, lag times, and overall fitness. We show that these
73
impacts are reversible upon stress removal, indicating their non-genetic nature. We demonstrate that
74
simultaneous perturbations predominately induce negative epistasis, extending mutation-based epistasis
75
concepts to the gene expression landscape. This work builds upon landmark gene knockout27, plasmid
76
over-expression28, network rewiring29 and long-term evolution30 studies by outlining a novel synthetic
77
biology approach for engineering control over bacterial adaptation via exogenously regulating gene
78
expression profiles. Our study also helps to elucidate the early adaptive response preceding genome
79
modifications, and serves as a paradigm shift in the field of antibiotic resistance research away from
80
investigating downstream adaptations and towards pathways bacteria utilize for adaptation.
1 12 13 14 15 16 17 18 19 20 21 2 23 24 25 26 27
81 28 29 30 31 32 3 34
82
Results
83
Design and characterization of single target gene perturbation devices
84
To accomplish controlled gene expression perturbation, we designed and synthesized (see
85
Methods) a set of 14 Type II CRISPR sgRNA plasmid constructs to inhibit or activate transcription of
86
seven stress-response genes in E. coli, chosen for their known influence on adaptation21–26 (Fig 1B and
87
Supplementary Fig S1). The sgRNA constructs were named pPO-genei or pPO-genea for inhibition and
88
activation respectively of each given gene (Supplementary Table S2), and were co-transformed alongside
89
a separate plasmid containing anhydrotetracycline (aTc) inducible dCas9 or dCas9-ω into E. coli strain
90
MG1655. This produced 14 unique experimental perturbation strains, designated MG1655-genei or
91
MG1655-genea. Two control strains harboring dCas9 or dCas9-ω plasmids, as well as the control sgRNA
92
construct sgRNA-RFPi (targeting the rfp coding sequence not present in MG1655) were also created
93
(Supplementary Table S3). All sgRNAs utilized common promoter and scaffolding elements, but differed
94
in their unique 20 nucleotide (nt) sequence-specific DNA-binding domain. Inhibition and activation
95
sgRNAs were coupled in vivo with dCas9 or dCas9-ω respectively to form the final protein-RNA hybrid
35 36 37 38 39 40 41 42 43 4 45 46 47 48 49 50 51 52 53 54 5 56 57 58 59
4
60 ACS Paragon Plus Environment
ACS Synthetic Biology
Page 6 of 46
1 2 3 4 5 6 7 8 9 10
96
construct with inherent DNA-binding affinity for the 20 nt sequences of each sgRNA, allowing for
97
specific control of gene expression (Fig 1C). Activation sgRNAs targeting ≈80-110 nt upstream of the +1
98
transcription start site of each gene provided optimal spacing for RNA polymerase to bind to the promoter
99
and increase gene expression20. Inhibition sgRNAs targeted within the first ≈50 nt of the genes’ open
100
reading frame (ORF) to inhibit transcriptional read-through via a roadblock mechanism19. Each CRISPR
101
target sequence was flanked by an “NGG” Protospacer-Adjacent-Motif (PAM) on the 3’end for proper
102
binding of the protein-RNA complex with the target DNA19. The impact of a subset of these constructs on
103
neighboring genes’ expression was quantified and was found to be either absent or minimal
104
(Supplementary Fig S2). It is expected that perturbing each of these genes may induce changes in
105
expression of downstream genes as governed by the connections through respective gene regulatory
106
networks within E. coli.
1 12 13 14 15 16 17 18 19 20 21 2 23 24 25 26 27 28 29 30 31 32 3 34
107
The ability of bacteria to evolve resistance depends on the accessibility of higher-fitness states
108
within a hypothetical “adaptive landscape”, which can be visualized as a multi-dimensional space
109
comprised of the variable expression states of n-by-n genes (analogous to similar adaptive landscapes
110
based on gene mutations)31–33 (Fig 1A). Cloning our library of synthetic CRISPR devices into MG1655
111
enabled us to engineer a set of strains in which this adaptive landscape was perturbed. By inhibiting or
112
activating individual genes, these strains enabled exploration of the impact that gene expression has on
113
stress response. An advantage of using CRISPR devices is that this approach does not directly modify the
114
wild-type genome, allowing for investigation of adaptive pathways in their natural state without the need
115
to create a unique genome for each gene studied as done in canonical gene knockout studies, and thereby
116
provides a unique insight.
35 36 37 38 39 40 41 42 43 4 45 46 47 48 49
117
To measure the effects of gene perturbation, we utilized RT-qPCR to quantify the gene
118
expression of each of these strains relative to wild-type MG1655. Our results indicate that the strains’
119
expression profiles were indeed perturbed as intended, with a range of 32-fold reduction to 8-fold increase
120
in gene expression (Fig 1D). Optimization of expression perturbation was influenced by native gene
121
orientation; for instance, binding of dCas9-ω upstream of the +1 soxS transcription start site necessitated
50 51 52 53 54 5 56 57 58 59
5
60 ACS Paragon Plus Environment
Page 7 of 46
ACS Synthetic Biology
1 2 3 4 5 6 7 8 9 10
122
overlap with the ORF of soxR, an activator of soxS. Growth tests were also performed to analyze the
123
viability of these strains. No loss of viability that is not intrinsic to growth with two plasmids was
124
observed (Supplementary Fig S3). Since MG1655-rfpi and MG1655-rfpa strains demonstrated similar
125
growth characteristics, we used MG1655-rfpa as the control strain in subsequent stress-exposure
126
experiments (referred to hereafter as the MG1655-Control).
1 12 13 14
127 15 16
128 17 18 19 20 21
Perturbation of gene expression influences bacterial growth characteristics during stress exposure
129
We sought to examine the growth of strains harboring the CRISPR constructs under various
130
environmental stresses to which infectious bacteria are commonly exposed, to determine whether
131
artificial perturbation of gene expression enabled control over bacterial growth (and thus adaptive
132
potential). To achieve this, five stress conditions were selected representing oxidizing agents (household
133
bleach34 and hydrogen peroxide35), antibiotics (tetracycline36 and rifampicin37), and nutrient limitation
134
(M9 minimal media supplemented with 0.4% glucose). The Minimum Inhibitory Concentration (MIC)
135
was determined using MG1655-Control to estimate the appropriate starting concentrations for growth
136
under each stress condition (Supplementary Fig S4). The sub-MIC levels were used as starting points for
137
stress exposure experiments (Fig 2A, see Methods). We exposed E. coli strains harboring the CRISPR
138
constructs to each stress over a course of 72 hours (Fig 2B), transferring biological triplicates every 24
139
hours into fresh media supplemented with aTc and antibiotics to maintain plasmid selection (see
140
Methods). During this time, optical densities were monitored to track changes in growth rate (µ) and
141
inverse lag phase (τ-1) on each day of the experiment (Extended Dataset). These data was normalized to
142
MG1655-Control by dividing µ and τ-1 by the average performance of biological triplicates of MG1655-
143
Control from the experimental day (creating µnorm and τ-1norm). Normalized data was averaged over three
144
experimental days. Adapting bacterial populations have been shown to exhibit significant heterogeneity in
145
growth rates38 and lag times39, and thus these serve as useful metrics to quantitatively compare adaptive
146
trends between strains. We chose to keep lag times in their reciprocal format, as larger lag times (smaller
147
inverse lag time, τ-1 norm
