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. 2021 Nov 5;4(1):1267.
doi: 10.1038/s42003-021-02783-x.

Potentiating antibiotic efficacy via perturbation of non-essential gene expression

Peter B Otoupal #  1   2   3 , Kristen A Eller #  1 , Keesha E Erickson  1 , Jocelyn Campos  1 , Thomas R Aunins  1 , Anushree Chatterjee  4   5   6   7
Affiliations

Potentiating antibiotic efficacy via perturbation of non-essential gene expression

Peter B Otoupal et al. Commun Biol. .

Abstract

Proliferation of multidrug-resistant (MDR) bacteria poses a threat to human health, requiring new strategies. Here we propose using fitness neutral gene expression perturbations to potentiate antibiotics. We systematically explored 270 gene knockout-antibiotic combinations in Escherichia coli, identifying 90 synergistic interactions. Identified gene targets were subsequently tested for antibiotic synergy on the transcriptomic level via multiplexed CRISPR-dCas9 and showed successful sensitization of E. coli without a separate fitness cost. These fitness neutral gene perturbations worked as co-therapies in reducing a Salmonella enterica intracellular infection in HeLa. Finally, these results informed the design of four antisense peptide nucleic acid (PNA) co-therapies, csgD, fnr, recA and acrA, against four MDR, clinically isolated bacteria. PNA combined with sub-minimal inhibitory concentrations of trimethoprim against two isolates of Klebsiella pneumoniae and E. coli showed three cases of re-sensitization with minimal fitness impacts. Our results highlight a promising approach for extending the utility of current antibiotics.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1. Synergistic interactions between E. coli BW25113 gene knockouts and drug treatment.
a An example of how synergy values were calculated. Strain fitness (W) was calculated as the maximum optical density reached during 16 h of growth, normalized to the maximum optical density of wildtype BW25113 with no antibiotic exposure during the same 16 h growth period. Fitness was calculated in the presence of antibiotic (WX), gene knockout (WY), or antibiotic treatment of a gene knockout strain (WXY). Synergy (S) was calculated as WX * WY – WXY, with positive values indicating synergy and negative values indicating antagonism. This example shows that deletion of acrA potentiates antibiotic activity of ampicillin against BW25113. b This process was performed for all 270 gene–drug combinations. Interactions that proved significantly synergistic (or antagonistic) are color-coded red and have an “S” (or green and an “A”). Non-significant interactions are classed as additive (blue and contain no letter distinction). All bar graphs’ y-axes use the same scale (from 0.0 to 1.5) used in Fig. 1a. Error bars represent standard deviation of at least three biological replicates.
Fig. 2
Fig. 2. Correlations in gene–drug synergy.
Degree of synergy between gene knockouts and antibiotic treatments, grouped by biological mechanisms. Gene knockouts are separated into their specific cellular processes on the y-axis, with corresponding synergy plotted on the x-axis, going from antagonistic (green, left) to synergistic (red, right). Antibiotics are further grouped based on the mechanism of action, such as targeting cell wall synthesis. The top three synergistic interactions and top three antagonistic interactions are specifically labeled in each graph. In the bottom left of each graph is listed the average synergy of all thirty gene knockouts with the specific antibiotic. Error bars represent standard deviation of at least three biological replicates propagated from fitness values.
Fig. 3
Fig. 3. Applying CRISPRi to potentiate antibiotic treatment.
a dCas9 is targeted to promoter or open reading frame elements of specific genes, preventing RNA polymerase from transcribing DNA into mRNA. Constructs were created to block transcription of six genes for which deletion resulted in significant synergy with a specific antibiotic. b Each of these CRISPRi strains were tested for their synergy with antibiotic treatment. Strain ODs’ after 16 h of growth in M9 minimal media were quantified and these values were used to calculate fitness. The growth of the control rfp perturbation strain during exposure to the listed antibiotic, the growth of the gene perturbation with no antibiotic, and the growth of the gene perturbation in the presence of the listed antibiotic were all normalized to the growth of the control strain without exposure to antibiotic, giving WX, WY, and WXY respectively. Statistically significant synergy or antagonism is indicated by a red background and an “S” or green background and an “A” respectively, with additive interactions shown in blue. Synergy values are listed below each graph with their associated significance. Error bars represent standard deviation of at least 20 biological replicates. Growth curves of each associated bar graph are shown below. Dark green lines indicate the control, light green lines indicate antibiotic only exposure, dark blue lines indicate CRISPRi perturbation (Pert in legend), and light blue lines indicate combination. Error bars represent standard deviation of at least 20 biological replicates and gray circles represent individual biological replicates.
Fig. 4
Fig. 4. Multiplexed CRISPRi perturbations further potentiate antibiotic treatment.
a The six individual gene perturbations designed for each antibiotic were multiplexed into one strain, and synergy was again screened for (right column). A control strain with six nonsense rfp perturbations was also created to show that harboring multiple targets did not influence these results (left column). Strain ODs’ after 16 h of growth were quantified, and these values were used to calculate fitness. For the left column under each antibiotic, growth of the control single rfp perturbation strain during exposure to the listed antibiotic, growth of the six rfp perturbation strain with no antibiotic, growth of the six rfp perturbation strain in the presence of the listed antibiotic were all normalized to the growth of the single rfp control strain without exposure to antibiotic, giving WX, WY, and WXY, respectively. The same occurred on the right column, except the control single rfp perturbation strain was replaced with the six rfp perturbation strain, and the six rfp perturbation strain was replaced with the six multiplexed gene perturbation strains designed for each antibiotic. Statistically significant synergy is indicated by a red background and the letter “S” and additive by a blue background. Error bars represent standard deviation of 22 biological replicates and gray circles represent individual biological replicates. Synergy values are listed above each graph with significance. b Growth curves of these multiplexed CRISPRi strains in the presence of each antibiotic. Error bars represent standard deviation of three biological replicates. A more thorough fitness assay using competition was applied to more precisely estimate the fitness impacts of multiplexed perturbations for c TET and d TMP. Competition was performed for these strains against a fluorescent control strain harboring one nonsense CRISPRi perturbation in either the presence or absence of antibiotic treatment. A control competition of the 6× rfp perturbation strain against the fluorescent control was also performed in the presence of antibiotic. Fitness was calculated using the standard Malthusian fitness equation (see “Methods” section). Error bars represent standard deviation of eight biological replicates.
Fig. 5
Fig. 5. CRISPRi potentiation of antibiotic treatment of intracellular Salmonella infections.
a CRISPRi treatments that were demonstrated to be effective in E. coli and maintained significant homology to the genome of Salmonella were applied to Salmonella SL1344 cells. These perturbed SL1344 cells were used to infect HeLa epithelial cells to observe their ability to potentiate antibiotic treatment in a clinically relevant setting. b The exact 20 nt target sequences of six CRISPRi constructs are listed, with the native PAM (protospacer adjacent motif) sequence listed in capitals at the end of each sequence. Underlined red sequences indicate a mismatch in the sgRNA sequence with the native sequence of Salmonella enterica serovar Typhimurium SL1344. On the right is shown how these gene knockouts (Δ) or CRISPRi knockdowns (i) interacted with the corresponding antibiotic. c, d Two CRISPRi constructs targeting the genes with perfect homology (acrA, cyoA, and fnr, c) or all six genes (d) were created and screened for their ability to potentiate antibiotic treatment of SL1344. Growth of the control six rfp perturbation strain during exposure to the listed antibiotic, growth of the multiplexed CRISPRi strains, and growth of the multiplexed CRISPRi strains in the presence of the listed antibiotic were all normalized to the growth of the six rfp control strain without exposure to antibiotic, giving WX, WY, and WXY respectively. Significant synergy or antagonism is indicated by a red background and the letter “S” or a green background with the letter “A”, with blue representing additive interactions. Synergy values are listed below each graph with significance. Error bars represent standard deviation of 22 biological replicates and dark gray circles represent individual biological replicates. e Growth curves of CRISPRi SL1344 strains in the presence or absence of antibiotic treatment. Error bars represent standard deviation of at least five biological replicates. f, g Survival of CRISPRi SL1344 strains in intracellular HeLa infections after 18 h of 0.5 µg/mL TET (f) or 0.5 µg/mL TMP (g) treatment, relative to survival with no antibiotic treatment. P values are given in relation to the control strain. Error bars represent standard deviation of three biological replicates and two technical duplicates; gray circles represent individual biological replicates.
Fig. 6
Fig. 6. PNA gene knockdown treatment resensitizes MDR clinical isolates to antibiotics.
a Chemical structures of DNA and PNA show how the negatively charged phosphate backbone of DNA is replaced with a neutrally charged peptide backbone in PNA. These PNAs are conjugated to a CPP to enable penetration of bacterial membranes. Upon entry to the cell, PNAs complex with complementary mRNA to inhibit protein translation. b Resistance of MDR, clinically isolated bacteria to TMP above CLSI breakpoint levels of resistance, as demonstrated by growth curves unaffected by TMP concentration. Error bars represent standard deviation of four biological replicates. c MDR bacteria growth after exposure to 2 µg/mL TMP (red bars, WX), 10 µM PNA (blue bars, WY), or both (green bars, WXY). Bar plots (top) show growth in each condition normalized to blank wells and starting OD580 values and are subsequently normalized to the maximum growth in the absence of treatment. Growth curves (bottom) show OD580 values of individual biological replicates (3) over time with the minimum value subtracted. d Synergy values of PNA with TMP, grouped by specific PNA targets. Error bars represent standard deviation of biological triplicates and black circles represent individual biological replicates.
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