DNA is a universal and programmable signal of living organisms. Here we develop cell-based DNA sensors by engineering the naturally competent bacterium Bacillus subtilis (B. subtilis) to detect specific DNA sequences in the environment. The DNA sensor strains can identify diverse bacterial species including major human pathogens with high specificity. Multiplexed detection of genomic DNA from different species in complex samples can be achieved by coupling the sensing mechanism to orthogonal fluorescent reporters. We also demonstrate that the DNA sensors can detect the presence of species in the complex samples without requiring DNA extraction. The modularity of the living cell-based DNA-sensing mechanism and simple detection procedure could enable programmable DNA sensing for a wide range of applications.
Next-generation engineered bacteria hold tremendous promise for a wide range of applications in human health, environment and agriculture. These bacteria can sense key environmental signals and perform computation on these signals to regulate a response that modulates specific environmental parameters1. Developing specific and selective sensors of key environmental signals is a critical feature of next-generation engineered bacteria. For example, bacteria have been engineered to detect physical and chemical signals such as light, ultrasound, and quorum-sensing molecules2,3,4. These signals could be exploited to control the collective growth or gene expression of the bacterial population or mediate interactions between constituent community members4,5,6. In addition, we could exploit their sensing ability to achieve real-time monitoring of natural environments. For example, synthetic genetic circuits have been designed in bacteria to sense signals produced by pathogens and use this information to regulate the production of antimicrobials that inhibit the specific pathogen7,8. However, there are limited well-characterized and orthogonal signals that can be exploited to sense different bacterial species in a microbial community9,10.
DNA provides the blueprint for living organisms and is prevalent in natural environments11. Therefore, extracellular DNA (eDNA) could be exploited as a biomarker for identifying different species. Naturally competent bacteria have the capability to take up DNA from the environment and integrate these imported sequences into the genome based on sequence homology requirements. Horizontal gene transfer (HGT) via natural transformation can facilitate nutrient utilization, DNA repair, or the acquisition of new genes12. Since homologous recombination of imported DNA requires sequences of sufficient length and homology13, natural transformation could be exploited to build a selective cell-based DNA sensor.
We constructed a living cell-based DNA sensor by designing a novel synthetic genetic circuit in the naturally competent bacterium B. subtilis. This circuit controls B. subtilis growth and fluorescence reporter genes in response to specific input DNA sequences. We demonstrate that the cell-based DNA sensor is highly specific to species harboring the target DNA sequence. In addition, we show that cell-based DNA sensors can perform multiplexed DNA detection in complex samples. The cell-based DNA sensors can also detect DNA released from pre-treated target cells. Our detailed characterization of the cell-based DNA sensors in vitro provides a foundation for future DNA-sensing applications.
Construction of a living DNA sensor strain
To build the living cell-based DNA sensor, we exploited the natural competence ability of the well-characterized soil bacterium B. subtilis14. The natural competence ability of B. subtilis enables uptake of environmental DNA and integration of specific sequences with sufficient homology onto the genome via homologous recombination15. The efficiency of homologous recombination depends stringently on the sequence percent identity and length13,16, which can be exploited to build a highly specific DNA sensor.
To detect eDNA sequences in a programmable fashion, we constructed a synthetic genetic circuit in B. subtilis that implements a growth selection function based on the presence of target DNA sequence in the environment. The circuit consists of a xylose-inducible master regulator of competence comK17 and an isopropyl β-d-1 thiogalactopyranoside (IPTG)-inducible toxin-antitoxin system txpA-ratA18 and green fluorescent protein (GFP) regulated by the repressor LacI (Fig. 1a; Supplementary Fig. 1). The flanking regions (upstream and downstream) of txpA-ratA and lacI consist of the target sequences and are referred to as landing pads for homologous recombination.
In the presence of xylose, ComK activates competence genes for DNA uptake and homologous recombination (Fig. 1a). Bistability and stochastic processes in the regulation of natural competence can yield a sub-population that can be transformed with extracellular DNA19. This naturally competent sub-population forms competence pili and binds to double stranded DNA outside the cell. The DNA is degraded into single-stranded DNA (ssDNA) outside the cell membrane, and transported into the cell12. Inside the cell, RecA binds the ssDNA and searches the B. subtilis genome for a region with sufficient homology. If the target DNA sequence is present, homologous recombination removes the toxin-antitoxin txpA-ratA and repressor lacI. To select for the transformed subpopulation, the introduction of IPTG induces the expression of the toxin TxpA, which in turn inhibits the growth of the non-transformed subpopulation by blocking cell wall synthesis18. The transformed sub-population can grow and express GFP (Fig. 1a).
We constructed a sensor for Escherichia coli (E. coli) (EC sensor) by introducing the xdhABC operon onto the B. subtilis genome (landing pad region), which encodes genes for purine catabolism20. We analyzed the degree of conservation of the target sequence by evaluating sequences of different E. coli strains using the BLAST search in the NCBI Nucleotide Collection Database. This sequence is highly conserved such that 99% of 5000 E. coli genomes in the NCBI database contain this sequence with >95% coverage (the degree of alignment of the query sequence with a reference sequence) and >95% identity similarity (the percentage of bases that are identical to the target sequence within the aligned region). Therefore, the xdhABC operon is a representative sequence that can detect a wide range of E. coli strains.
To characterize the homology length needed for robust DNA sensing, we varied the homology length of the xdhABC operon in each landing pad (0.5–2.5 kb). We performed time-series measurements of transformation efficiency with 100 ng/mL E. coli genomic DNA (gDNA). The transformation efficiency is defined as the ratio of the number of transformed B. subtilis to the total number of B. subtilis based on colony forming units (CFU). Transformation efficiency plateaued at ~10 h and the colonies expressed GFP (Fig. 1b; Supplementary Fig. 2a). In addition, transformation efficiency at 10 h increased with homology length in the landing pads (Fig. 1c). A homology length of 1 kb or greater was required to robustly sense the target sequence over the background frequency of escape mutants (10−7−10−6 frequency) that displayed heterogenous GFP expression in the absence of gDNA (Supplementary Fig. 2b). To achieve high performance of the DNA sensor (>102 increase in transformation efficiency above background), we used a landing pad homology length of 2.5 kb (transformation efficiency of 10−5−10−4). In the transformed sub-population, homologous recombination occurred at the expected location with the elimination of txpA-ratA and lacI based on sequencing (Supplementary Fig. 2c, d). The moderate number of observed escape mutants that displayed growth in the absence of gDNA had mutations in txpA or lacI, which reduced the growth inhibitory activity of TxpA (Supplementary Fig. 2e, f). In sum, the synthetic genetic circuit enabled B. subtilis to sense a specific E. coli DNA sequence present in the environment.
Building living DNA sensors to sense human pathogens
Exploiting the modularity of the DNA sensing circuit, we replaced the landing pad region with specific sequences targeting different bacterial strains (Supplementary Fig. 1c). To this end, we constructed DNA sensors to detect sequences harbored in human gut pathogens Salmonella typhimurium21 (S. typhimurium), Clostridioides difficile22 (C. difficile), and the skin and respiratory tract pathogen Staphylococcus aureus23 (S. aureus). We selected two 2.5 kb sequences in the pathogenicity island sipBCDA in S. typhimurium (ST sensor), the heme biosynthesis pathway hemEH in S. aureus (SA sensor), and the phenylalanyl-tRNA synthetase pheST in C. difficile (CD sensor)24,25,26.
The selected set of target DNA sequences are highly conserved within a given species (ST sensor: 94%, SA sensor: 96%, and CD sensor: 96% all with >95% coverage and >95% identity similarity). In addition, some of the sequences are linked to virulence activities of the pathogen or encode enzymes that are critical for fitness24,25,26. To further explore the conservation of the target sequences across different strains, we performed nucleotide BLAST using the NCBI Database to quantify the homology coverage and sequence similarity across species. The pathogenicity island sipBCDA in S. typhimurium was found only in genomes of Salmonella enterica and infrequently observed in other species (Supplementary Fig. 3a). Homologs in other species have low coverage and identity similarity, suggesting that the pathogenicity island could be a good target sequence for this species (Supplementary Fig. 3a). The heme biosynthesis pathway hemEH in S. aureus and phenylalanyl-tRNA synthetase pheST in C. difficile are conserved in some closely related strains with varying degrees of similarity and coverage (Fig. 2i, k). The E. coli xdhABC purine catabolism operon is found in other closely related bacteria such as Shigella with high coverage and identity similarity (Supplementary Fig. 3b). Although the target sequences for building the different DNA sensor strains varied in the degree of specificity based on bioinformatic analyses, a detailed characterization of DNA sensor performance could nevertheless guide the design of optimized cell-based DNA sensors for future applications.
The four sensors robustly detected the presence of 100 ng/mL target gDNA over background (no gDNA) based on transformation efficiency (Fig. 2a). We evaluated the sensitivity of each DNA sensor strain by performing time-series fluorescence measurements in selective liquid media after being transformed with a wide range of gDNA concentrations (0–1500 ng/mL gDNA) (Fig. 2b, c; Supplementary Fig. 4). We evaluated the time required for each culture to display a fluorescence level higher than a threshold (i.e. detection time) as a metric for DNA sensor performance (Fig. 2c; Supplementary Fig. 4). The sensitivity of the sensor strain was defined as the lowest gDNA concentration that yielded a statistically significant difference in the detection time in the presence versus absence of gDNA (Fig. 2d–g). The EC, SA, and CD DNA sensor strains displayed high sensitivity of 1–16 ng/mL (105−106 chromosome copy number per mL), whereas the ST sensor displayed a lower sensitivity (62.5 ng/mL, 107 chromosome copy number per mL).
The relationship between the log transformed gDNA concentration and detection time is linear due to the exponential growth of the fluorescent B. subtilis sub-population in the selective liquid medium (Supplementary Fig. 5). Therefore, to assess the range of gDNA concentrations that was accurately sensed, we inferred the parameters of a linear function fit to the log transformed gDNA concentration versus detection time (Fig. 2d–g). The inferred slope of the linear function is determined by the cell doubling time (~0.5 h) and intercept is determined by the background mutation frequency (Fig. 2d–g). The observed goodness of fit of the data to a linear function indicates that DNA sensing is quantitative across a wide range of gDNA concentrations.
To characterize the specificity of each DNA sensor strain to the target sequence, we performed time-series fluorescence measurements in liquid culture in response to the four individual species’ gDNA. The fluorescence signal was observed at a substantially earlier time (6.1–7.1 h) in the presence of the corresponding species’ gDNA than in the presence of a non-target species’ gDNA or in the absence of DNA (9.1–10.7 h) (Fig. 2h; Supplementary Fig. 6). To further examine the specificity, we evaluated the ability of the DNA sensors to distinguish between closely related species with similar target sequences. To this end, we measured the transformation efficiency of the SA sensor in the presence of gDNA (100 ng/mL) derived from S. epidermidis. S. epidermidis is a closely related human skin commensal bacterium that harbors a hemEH homolog (89% coverage and 77% identity similarity) (Fig. 2i). To simplify the procedure, we quantified the number of transformed colonies as opposed to transformation efficiency since the number of total B. subtilis colonies was not substantially affected by the presence or absence of gDNA (Fig. 2a; Supplementary Fig. 7). The number of transformed SA sensor colonies in the presence of S. epidermidis gDNA (SE) was similar to the absence of DNA and substantially lower than in the presence of S. aureus gDNA (SA) (Fig. 2j). Hence, the SA sensor can distinguish the target pathogen S. aureus DNA from a closely related commensal S. epidermidis DNA. Similarly, the CD sensor can distinguish the target pathogen C. difficile DNA from a closely related commensal species C. hiranonis DNA harboring a pheST homolog (87% coverage and 75% identity similarity) (Fig. 2k, l). These data demonstrate that the cell-based DNA sensors were highly specific to the target sequence.
Multiplexed detection of pathogen DNA in complex samples
Since certain future applications may require sensing of more than one organism, we tested the ability of the DNA sensors to detect more than one species within mixed DNA samples. To this end, we constructed individual sensors with orthogonal fluorescent reporters to achieve multiplexed DNA detection. Exploiting the modularity of the circuit, we constructed an RFP-labeled ST sensor (ST-R) and a BFP-labeled SA sensor (SA-B), in addition to the GFP-labeled EC sensor (EC-G) (Supplementary Fig. 1c). We introduced gDNA (200 ng/mL) extracted from each of the three target strains into a mixture containing EC-G, ST-R, and SA-B sensors and determined the number of fluorescent colonies for each reporter (Fig. 3a). The sensors accurately reported the presence/absence of species’ gDNA reliably for all combinations (Fig. 3b; Supplementary Fig. 8). Therefore, a mixture of DNA sensor strains enabled multiplexed detection of gDNA with high specificity.
To investigate if multiplexed detection can be achieved for samples derived from a complex microbial community, we constructed a four-member human gut community composed of diverse commensal bacteria from three major phyla in human gut—Anaerostipes caccae (AC, Firmicutes), Bacteroides thetaiotaomicron (BT, Bacteroidetes), Bifidobacterium longum (BL, Actinobacteria), and Clostridium asparagiforme (CG, Firmicutes) (Fig. 3c). This community also contained the target pathogens S. typhimurium (ST) and S. aureus (SA). We tested whether the DNA sensors could accurately report the relative abundance of the two pathogens during community assembly. The 6-member community was inoculated in equal initial species proportions based on absorbance at 600 nm (OD600, Day 0) and cultured anaerobically for 24 h (Day 1). An aliquot of the community was transferred to fresh media and community composition was characterized following an additional 24 h (Day 2). Based on 16 S rRNA gene sequencing, the abundance of S. typhimurium was similar as a function of time whereas the abundance of S. aureus decreased over time (Fig. 3c).
We characterized the ability of the DNA sensors to accurately track the temporal trends in species abundance by introducing purified community gDNA collected at different times into a mixed culture of the ST-R and SA-G sensors (Fig. 3d). Due to the low abundance of target species in the sample, a higher amount of DNA (1 µg/mL) was used for transformation. Consistent with the trends based on 16 S rRNA gene sequencing, the number of green fluorescent colonies of the SA-G sensor displayed a decreasing trend, whereas the number of red fluorescent colonies of ST-R sensor were similar over time (Fig. 3d–f). The SA sensor displayed better performance in mirroring the trend based on 16S rRNA gene sequencing than the ST sensor, consistent with its higher sensitivity than other sensors (Figs. 2a, 3e, f). For the community lacking S. typhimurium and S. aureus, a much smaller number of background mutant colonies was detected than in the presence of the 6-member community gDNA (Supplementary Fig. 9). This implies that the ST and SA sensors were specific to the target species gDNA and did not recognize the other constituent community member gDNA. In sum, our results show that accurate multiplexed DNA detection can be achieved in DNA samples derived from multi-species microbial communities.
Detection of target species without DNA extraction
Specific bacterial species have been shown to release eDNA in response to environmental stimuli11, suggesting that the DNA sensor could detect species without requiring prior gDNA purification. To test this possibility, we co-cultured individual DNA sensor strains with the corresponding target species with an initial OD600 0.1 of the target strain (1.22 × 108 CFU/mL, 1.07 × 108 CFU/mL, 3.2 × 108 CFU/mL, and 1.1 × 107 CFU/mL for E. coli, S. typhimurium, S. aureus, and C. difficile, respectively) (Fig. 4a). Since the other species could compete with B. subtilis, we introduced antibiotics (ABX) to inhibit the growth of the target cells and enhance their eDNA release. The DNA sensor strains harbored antibiotic resistance genes and were thus resistant to the antibiotics. In the presence of 100 µg/mL spectinomycin, the DNA sensors displayed robust detection of E. coli, S. typhimurium, and S. aureus (Fig. 4b). The addition of spectinomycin was not required for C. difficile detection since the growth of C. difficile was negatively impacted by the presence of oxygen27. To confirm the transformation was mediated by the eDNA released from the target strain, DNase I (1 unit/mL) was added into the co-culture. The number of transformed cells was substantially lower in the presence of DNase I, indicating that DNA detection occurred via natural transformation in the co-cultures (Fig. 4b). Antibiotic resistance is prevalent in microbiomes and may not be used universally as a treatment for the target cells. Therefore, we tested if heat treatment could be used to inhibit the growth of the target strain and enhance eDNA release. Incubation of E. coli at 90 °C for 10 min substantially enhanced the EC sensor detection limit in the co-culture (5 × 106 cells per mL) compared to spectinomycin treatment (Fig. 4c). Therefore, heat treatment enabled efficient species detection without DNA purification.
To evaluate the robustness of the DNA sensing function for complex samples, we characterized the performance of the DNA sensors for multiplexed detection of spike-in bacteria in the presence of cecal contents derived from germ-free mice that were orally gavaged with a defined bacterial consortium (Methods). Cecal contents derived from mice contain diverse bacterial species, host cells, viruses and other chemical compounds (e.g. dietary factors), and thus can be used to evaluate the robustness of the DNA sensor to these factors. To this end, we introduced varying amounts of E. coli and S. typhimurium into 10 mg of mouse ceca. Samples were incubated at 90 °C for 10 min and transferred into a mixed culture of the EC-G and ST-R sensors (Methods; Fig. 4d). Our results demonstrated that both sensors detected spike-in E. coli and S. typhimurium cells in 10 mg mouse ceca without DNA extraction. In particular, the EC and ST sensors displayed a detection limit of 107 cells per mL (109 cells per gram) (Fig. 4d). In samples containing a single target species, high density of E. coli or S. typhimurium (108 cells per mL) yielded higher false positives for the multiplexed DNA detection (Supplementary Fig. 10). This suggests that further optimization of the DNA sensors may be needed in complex samples containing high target cell densities. In sum, these data demonstrate that the DNA sensor can perform multiplexed detection of target cells in a complex sample containing other biological factors (e.g. host cells and other bacteria).
Here we engineered the naturally competent bacterium B. subtilis to sense and respond to specific DNA sequences. DNA sensing can be achieved for purified DNA or eDNA released from pre-treated cells. We demonstrate that DNA sensing is specific and multiplexed sensing can be achieved in complex samples. Detection of species using a living cell-based DNA sensor strain enables versatile and programmable sensing of bacteria that does not rely on small molecule chemical or physical signals28,29. Since our circuit design is modular, customized sensors could be constructed in the future for the detection of sequences derived from diverse organisms including viruses, fungi and mammalian cells.
The living DNA sensors have potential for a wide range of DNA detection applications. The cell-based DNA detection is relatively simple and cost-effective. In comparison, other DNA detection methods such as next-generation sequencing and quantitative polymerase chain reaction require specialized instruments. In addition, B. subtilis spores are robust for potential long-term storage30. However, to achieve environmentally relevant concentrations, the sensitivity of the DNA sensor needs to be substantially enhanced. The sensitivity of the DNA sensor is determined by the transformation efficiency and background mutation rate of the negative growth selection module of the circuit. The DNA sensor sensitivity could be improved by modifications to the genetic circuit design31,32, engineering of the strain33, directed evolution17, or selection of an alternative chassis with a higher transformation efficiency34. In B. subtilis, rational strain engineering33 and directed evolution17 could be performed to enhance transformation efficiency. To reduce the background mutation rate, the circuit design could be modified to include more efficient counter-selectable markers31,32. For example, if the background mutation rate was reduced from 10−6 (Fig. 2a) to 10−8 using a more efficient counter-selectable marker31,32, pathogens with lower abundance such as C. difficile could be detected in fecal samples (107 cells per gram)35. Nevertheless, our current detection limit is close to the sensitivity range required for DNA detection during certain pathogen infection processes. For example, certain pathogens such as Salmonella in contaminated yolk (1010 cells per gram)36 or in ceca of infected chicken (109 cells per gram)37 can achieve high densities close to the detection range of the DNA sensor (109 cells per gram for Salmonella, Fig. 4d). In addition, some naturally competent bacteria such as Streptococcus pneumoniae can achieve 10−1 transformation efficiency34, which is substantially higher than our constructed DNA sensors (10−5−10−4, Fig. 2a). Using a suitable chassis with a high transformation efficiency, GFP expression could be observed at an earlier time, enabling a faster detection time34.
We focused on the detection of diverse bacteria (i.e. different species) as opposed to characterizing DNA sensing performance of strain-level differences. Based on our results, we predict that the DNA sensors could differentiate target strains harboring conserved sequence with less than 77% nucleotide sequence similarity based on the stringent requirements of homologous recombination in B. subtilis (Fig. 2i–l). To achieve higher strain-level specificity, the landing pads could be composed of unique sequences with minimal conservation to other strains. For example, to differentiate E. coli MG1655 and E. coli DH10B, the landing pads could be designed using the lac operon or leuLABCD operon in MG1655, which is not present in DH10B (∆lacX74, ∆(ara leu) 7697)38. To even achieve single nucleotide-level specificity, CRISPR could be incorporated into the genetic circuit design39.
The power of a living cell-based sensor is illustrated by early detection of pathogens in situ prior to infection. B. subtilis has been shown to colonize or reside temporarily in diverse environments including soil and the mammalian gastrointestinal tract40,41, enabling in situ DNA monitoring. For example, living DNA sensors could be introduced into the gastrointestinal tract or associated with plants to monitor microbiome dynamics by sensing and recording in real time42. The sensing mechanisms could also be coupled to the release of antimicrobials to target specific pathogens43. A recent study demonstrated that the naturally competent bacterium Acinetobacter baylyi can be engineered to detect tumor DNA in the mouse colon39, demonstrating an additional potential application of in situ DNA detection. Since the cell-based DNA sensor can identify long homologous DNA sequences, the DNA sensor could also be repurposed to mine biosynthetic gene clusters from environmental DNA by targeting genes in particular pathways44. In sum, engineering DNA-sensing bacteria could open new avenues for both in vitro and in situ applications in the future.
Plasmid and strain construction
All DNA sensor strains were derived from B. subtilis PY79. Plasmids used or constructed in this work are listed in Supplementary Table 1. The pAX01-comK plasmid was purchased from the Bacillus Genetic Stock Center (BGSC ID: ECE222) to introduce PxylA-comK to the lacA locus in B. subtilis PY79 by the MLS selection (1 µg/mL erythromycin from Sigma-Aldrich and 25 µg/mL lincomycin from Thermo Fisher Scientific), which can enhance the transformation efficiency in LB in the presence of xylose17,45. Fluorescent protein genes GFP(Sp), mCherry, and mTagBFP were cloned from plasmids pDR111_GFP(Sp)46 (BGSC ID: ECE278), mCherry_Bsu14 (BGSC ID: ECE756), and mTagBFP_Bsu14 (BGSC ID: ECE745) to construct fluorescent reporter plasmids pOSV00170, pOSV00455 and pOSV00456, respectively. The fluorescent reporter was introduced into ycgO locus by selection in the presence of 5 µg/mL chloramphenicol (MilliporeSigma) and expressed from the IPTG-inducible promoter Phyperspank. The kill switch plasmid pOSV00157 was composed of the repressor lacI and an IPTG-inducible toxin-antitoxin system Phyperspank-txpA-ratA and can be introduced into the amyE locus by selection in the presence of 100 µg/mL spectinomycin (Dot Scientific). The toxin-antitoxin system txpA-ratA was amplified from B. subtilis 168 gDNA by polymerase chain reaction (PCR) and cloned onto plasmid pOSV00157.
B. subtilis, E. coli, S. typhimurium, S. aureus and S. epidermidis were all cultured at 37 °C in Lennox LB medium (MilliporeSigma). C. difficile and human gut species A. caccae, B. thetaiotaomicron, C. asparagiforme, C. hiranonis and B. longum were cultured at 37 °C in YBHI medium in an anaerobic chamber (Coy Laboratory). YBHI medium is Brain-Heart Infusion Medium (Acumedia Lab) supplemented with 0.5% Bacto Yeast Extract (Thermo Fisher Scientific), 1 mg/mL D-Cellobiose (MilliporeSigma), 1 mg/mL D-maltose (MilliporeSigma), and 0.5 mg/mL L-cysteine (MilliporeSigma). The gDNA of each species was extracted using DNeasy Blood & Tissue Kit (Qiagen). For S. aureus gDNA extraction, 0.1 mg/mL Lysostaphin (MilliporeSigma) was added in the pre-treatment step in combination with enzymatic lysis buffer (Qiagen). Bacterial strains are listed in Supplementary Table 2.
The target sequences xdhABC were PCR amplified from E. coli MG1655 gDNA (NCBI Reference Sequence: NC_000913.3, location: 3001505-3004004 and 3004005-3006504), sipBCDA from Salmonella enterica serovar Typhimurium LT2 ATCC 700720 (NCBI Reference Sequence: NC_003197.2, Location: 3025979-3028478 and 3028479-3030978), hemEH from S. aureus DSM 2569 (GenBank: LHUS02000002.1, Location: 553-2770 and 2864-5638), and pheST from C. difficile DSM 27147 (GenBank: FN545816.1, Location: 770923-773144 and 773157-775686) to construct a set of plasmids (pOSV00169, pOSV00205, pOSV00206, pOSV00207, pOS00208, pOSV00292, pOSV00459 and pOSV00475) using restriction enzymes BamHI-HF (New England Biolabs) and EcoRI-HF (New England Biolabs) or Golden Gate Assembly Mix (New England Biolabs). These plasmids were derived from the kill switch plasmid pOSV00157 where the target sequences were introduced to the upstream and downstream of repressor lacI and toxin-antitoxin system Phyperspank-txpA-ratA. DNA sequences of genetic parts are listed in Supplementary Table 3.
All plasmids were constructed using E. coli DH5α and transformed into B. subtilis using MC medium47. MC medium is composed of 10.7 g/L potassium phosphate dibasic (Chem-Impex International), 5.2 g/L potassium phosphate monobasic (MilliporeSigma), 20 g/L glucose (MilliporeSigma), 0.88 g/L sodium citrate dihydrate (MilliporeSigma), 0.022 g/L ferric ammonium citrate (MilliporeSigma), 1 g/L Oxoid casein hydrolysate (Thermo Fisher Scientific), 2.2 g/L potassium L-glutamate (MilliporeSigma), and 20 mM magnesium sulfate (MilliporeSigma). Plasmid DNA was extracted from E. coli DH5α using Plasmid Miniprep Kit (Qiagen). B. subtilis was inoculated into MC medium and incubated at 37 °C for 4 h. Extracted plasmids were added into the B. subtilis culture and incubated at 37 °C for another 2 h before plating. Transformed B. subtilis were selected on LB agar plates with selective antibiotics (spectinomycin, MLS and kanamycin). Double crossover was verified for single colonies by the replacement of a different antibiotic resistance gene at the integration locus (lacA::specR, ycgO::erm or amyE::kan).
DNA detection using cell-based sensors
DNA sensor strain was inoculated from the −80 °C glycerol stock into LB medium and incubated at 37 °C with shaking (250 rpm) for 14 h. On the next day, the OD600 of overnight culture was measured by NanoDrop One (Thermo Fisher Scientific) and diluted to OD600 0.1 in 1 mL LB in a 14 mL Falcon™ Round-Bottom Tube (Thermo Fisher Scientific) supplemented with 50 mM xylose (Thermo Fisher Scientific) and 100 µg/mL spectinomycin (Dot Scientific). gDNA was quantified by the Quant-iT dsDNA Assay Kit (Thermo Fisher Scientific) and introduced into the DNA sensor culture. The DNA sensor culture was incubated at 37 °C with shaking (250 rpm) for 10 h for transformation. Transformed sensors (5 µL) was plated onto a 12-well plate (Thermo Fisher Scientific) for selection. Each well contained 1 mL LB agar supplemented with 2 mM IPTG (Bioline), 5 µg/mL chloramphenicol (MilliporeSigma), and MLS (1 µg/mL erythromycin from Sigma-Aldrich and 25 µg/mL lincomycin from Thermo Fisher Scientific). GFP-expressing colonies were imaged using an Azure Imaging System 300 (Azure Biosystems) by Epi Blue LED Light Imaging with 50 millisecond exposure time. Colony forming unit (CFU) was counted manually. Transformation efficiency is defined as the ratio of CFU on selective plates (transformed B. subtilis with GFP expression) to the CFU on non-selective plate (total B. subtilis). To count CFU of total B. subtilis, cell culture was serially diluted in phosphate-buffered saline (PBS) (Dot Scientific) and plated onto LB agar plates containing 5 µg/mL chloramphenicol and MLS. Since the total B. subtilis CFU was similar with or without DNA supplement (Supplementary Fig. 7b), CFU of transformed cells can be simply used to indicate transformation efficiency of individual sensor strains. To confirm that the DNA detection was via the synthetic genetic circuit, gDNA of fluorescent transformed colonies was extracted by DNeasy Blood & Tissue Kit (Qiagen). Primers targeting the landing pads of E. coli sensor (EC_FW and EC_RV) were used to PCR amplify and sequence the landing pads from the gDNA of the transformed E. coli sensor to confirm the loss of the repressor lacI and toxin-antitoxin system txpA-ratA. The sequence of the PCR product was confirmed by Sanger sequencing. Primers targeting the repressor lacI and toxin-antitoxin system txpA-ratA (lacI_txpA_FW and lacI_txpA_RV) were used to confirm the mutations in the escape mutants. Primer sequences are listed in Supplementary Table 4.
To determine the sensitivity of sensors, serially diluted DNA from 1500 ng/mL to 1 ng/mL was supplemented to the sensor culture for 10-h transformation, and transformed cell culture was transferred into liquid LB medium with a 1:20 dilution ratio in a 96-well black and clear-bottom CELLSTAR® microplate (Greiner Bio-One). To test the specificity towards gDNA from different strains, 100 ng/mL purified DNA was used for transformation. Liquid LB medium was supplemented with 2 mM IPTG (Bioline), 5 µg/mL chloramphenicol (MilliporeSigma), and MLS. Plate was sealed with Breathe-Easy Adhesive Microplate Seals (Thermo Fisher Scientific) and incubated in the SPARK Multimode Microplate Reader (TECAN) at 37 °C with shaking for time-series OD600 and GFP fluorescence measurements. A threshold of GFP fluorescence 400 was used to determine the detection time for different DNA or different concentrations. The threshold was selected in the linear region of the fluorescence curve where the difference in the detection time in response to different gDNA concentrations or absence of gDNA was not substantially affected by the choice of threshold. An unpaired t-test was used to determine if the detection time of specific DNA concentration was different from the condition without DNA with 4 technical replicates. The detection limit is the lowest DNA concentration with a detection time statistically different from the background mutations. A linear function was fit to the log transformed gDNA concentration versus mean detection time to determine significant differences. The DNA mass per mL in detection limit (1 ng, 62.5 ng, 4 ng, and 16 ng) and genome size (4639675 bp, 4857450 bp, 2827820 bp, and 4153430 bp) were converted to the chromosome copy number by NEBioCalculator for E. coli (2.10 × 105), S. typhimurium (1.25 × 107), S. aureus (1.38 × 106), and C. difficile (3.75 × 106), respectively. Matlab R2019a (184.108.40.2062779) were used to generate plots of OD600 and fluorescence and perform unpaired t-tests.
Bioinformatic analysis of target DNA sequences
To analyze if the target sequence is conserved for different strains in the same species, we searched the 5000 bp of E. coli xdhABC, S. typhimurium sipBCDA, S. aureus hemEH, and C. difficile pheST DNA sequence (Supplemental Table 3) in the NCBI Nucleotide Collection Database. Nucleotide BLAST was optimized for somewhat similar sequences (blastn). The search was specified to taxid 561 for E. coli, taxid 28901 for S. enterica, taxid 1280 for S. aureus, and taxid 1496 for C. difficile. The percentage of strains with conserved sequences (more than 95% identity similarity and 95% coverage) is 99% (N = 3462), 94.3% (N = 2078), 95.6% (N = 1488), and 96.4% (N = 139) for E. coli, S. typhimurium, S. aureus, and C. difficile, respectively. To analyze if the target sequence is conserved in other species, the same search was performed excluding the same species. Homologs with varying identity similarity and coverage were found in different species for the target sequences E. coli xdhABC (N = 5000), S. typhimurium sipBCDA (N = 117), S. aureus hemEH (N = 2993), and C. difficile pheST (N = 5000). Scatter plots of homology coverage and identity similarity for each strain other than the target species was displayed (Fig. 2i, k; Supplemental Fig. 3). Accession date of data is 2022-08-19.
Multiplexed DNA detection using cell-based sensors
Overnight cultures of DNA sensor strains EC-G, ST-R and SA-B were diluted to OD600 0.1, 0.1, and 0.01, respectively, in one single culture containing 1 mL LB supplemented with 50 mM xylose (Thermo Fisher Scientific) and 100 µg/mL spectinomycin (Dot Scientific). Different combinations of gDNA extracted from E. coli, S. typhimurium, and S. aureus (200 ng/mL each) were added into the mixed cultures containing the three DNA sensor strains in 14 mL Falcon tubes (Thermo Fisher Scientific). The DNA sensor strains were incubated at 37 °C with shaking (250 rpm) for 10 h and 5 µL of transformed cell culture was plated onto 12-well plates (Thermo Fisher Scientific). In these plates, each well contained 1 mL LB agar supplemented with 2 mM IPTG, 5 µg/mL chloramphenicol, and MLS. Plates were incubated at 37 °C overnight. On the next day, individual wells of 12-well plate were imaged using Nikon Eclipse Ti-E Microscope. Brightfield images were collected at 4X magnification using the built-in transilluminator of the microscope. Fluorescence images of colonies were collected with the epifluorescence light source X-Cite 120 (Excelitas) and standard band filter cubes including Texas Red (Excitation: 560/40 nm, Emission: 630/70 nm, Nikon), GFP (Excitation: 470/40 nm, Emission: 525/50 nm, Nikon), and BFP (Excitation: 395/25 nm, Emission: 460/50 nm, Chroma) to image RFP, GFP, and BFP, respectively. Images were processed with 8 × 8 binning for each image. The exposure times for RFP, GFP, and BFP were 7 ms, 1.5 ms, and 1.5 ms, respectively. Complete images of each well were generated from multipoint images after scanning a 24 × 24 mm area. Once the full images were assembled, each of the four channels were mapped to unity by the minimum and maximum pixel values of all images for that channel using ImageJ (v1.53c). Colonies of different colors were counted manually to determine transformation efficiency.
To assemble a multi-species microbial community for evaluating multiplexed detection, bacterial species A. caccae, B. thetaiotaomicron, B. longum, C. asparagiforme, S. typhimurium, and S. aureus were inoculated from the −80 °C glycerol stock into YBHI medium and incubated at 37 °C overnight in an anaerobic chamber (Coy Laboratory). On the next day, cell culture of each strain was diluted to OD600 0.01 into YBHI in one single culture (Passage 0) and incubated for 24 h (Passage 1). The overnight cell culture containing 6 species was diluted to OD600 0.1 into a fresh YBHI and incubated for another 24 h (Passage 2). At each passage, a cell pellet was collected for DNA extraction using a DNeasy Blood & Tissue Kit (Qiagen). To extract S. aureus gDNA, 0.1 mg/mL Lysostaphin (MilliporeSigma) was added in the pre-treatment step in combination with enzymatic lysis buffer (Qiagen). Purified DNA was stored at −20 °C for B. subtilis transformation or NGS analysis.
To characterize microbial community compositions by NGS, the V3-V4 region of the 16 S rRNA gene was PCR amplified from extracted DNA using custom dual-indexed primers on a 96-well PCR plate (detailed method described in Clark et al. Nat. Comm., 202148). PCR products from each well were pooled and purified using a DNA Clean & Concentrator kit (Zymo). The resulting library was sequenced on an Illumina MiSeq using a MiSeq Reagent Kit v2 (500-cycle) to generate 2 × 250 paired-end reads. Sequencing data were demultiplexed using Basespace Sequencing Hub’s FastQ Generation program. Sequences of each strain were mapped to the 16 S rRNA reference database using custom Python scripts. The reference database of the V3–V4 16 S rRNA gene sequences was created using consensus sequences from Sanger sequencing data of monospecies cultures. Relative abundance was calculated as the read count mapped to each species divided by the total number of reads of all species.
To detect S. typhimurium and S. aureus gDNA in the community DNA by cell-based DNA sensors, extracted community DNA (1000 ng/mL) was added to a mixed culture of SA-G (OD600 0.1) and ST-R sensors (OD600 0.1). The cell culture was incubated at 37 °C with shaking (250 rpm) for 10 h and 5 µL of cell culture was plated onto 12-well plates (Thermo Fisher Scientific) containing 1 mL LB agar supplemented with 2 mM IPTG, 5 µg/mL chloramphenicol, and MLS. Plates were incubated at 37 °C overnight. On the next day, each well was imaged using a Nikon Eclipse Ti-E Microscope. Brightfield and fluorescence images of GFP and RFP were collected at 4X magnification using the same procedure described above for multiplexed detection. Colony numbers of different colors were counted manually. The average colony number of 3 technical replicates at Day 1 and Day 2 was divided by the average colony number at Day 0 to calculate the fold change of S. typhimurium and S. aureus abundance over time, which was compared to the 16S rRNA gene next-generation sequencing results.
Direct detection of target species using cell-based sensors
The target strains E. coli, S. typhimurium, and S. aureus and DNA sensor strains were inoculated from the −80 °C glycerol stock into LB medium and incubated at 37 °C with shaking (250 rpm) for 14 h. C. difficile was inoculated in YBHI medium and incubated in an anaerobic chamber (Coy Laboratory). On the next day, cell cultures of sensor and target strain were diluted (OD600 0.1 each) into a single culture containing 1 mL LB supplemented with 50 mM xylose with or without 100 µg/mL spectinomycin in 14 mL Falcon tubes (Thermo Fisher Scientific). To determine the initial CFU of target bacteria in the mixed culture, overnight culture of target strains was diluted in PBS (Dot Scientific) and plated onto LB agar plates or YBHI agar plates. The target strain abundance was 1.22 × 108 CFU/mL, 1.07 × 108 CFU/mL, 3.2 × 108 CFU/mL, and 1.1 × 107 CFU/mL for E. coli, S. typhimurium, S. aureus, and C. difficile, respectively. The sensor and target strains were co-cultured at 37 °C with shaking (250 rpm) for 10 h, and 5 µL of cell culture was plated onto 12-well plates (Thermo Fisher Scientific) containing 1 mL LB agar, 2 mM IPTG, 5 µg/mL chloramphenicol, and MLS. The 12-well plates were incubated overnight at 37 °C. On the next day, fluorescent colonies were imaged using an Azure Imaging System c300 (Azure Biosystems) by the Epi Blue LED Light Imaging with 50 millisecond exposure time. Colonies with GFP fluorescence expression were counted manually. To determine if the appearance of colonies was due to natural transformation, 1 µL (1 unit) DNase I (Thermo Fisher Scientific) was added to the 1 mL co-culture, which can completely degrade 1 µg of plasmid DNA in 10 min at 37 °C according to the manufacturer’s specification.
To improve the detection efficiency, overnight culture of E. coli was incubated at 90 °C in digital dry baths/block heaters (Thermo Fisher Scientific) for 10 min, placed on ice for 3 min, and diluted to the sensor culture containing 1 mL LB, 50 mM xylose, and sensor strain (OD600 0.1) without spectinomycin for detection. To perform multiplexed detection for spike-in E. coli and S. typhimurium in mice ceca, 10 mg ceca was resuspended in 100 µL LB supplemented with 50 mM xylose in 1.7 mL Eppendorf tubes (Dot Scientific). A low amount of mouse cecal contents (10 mg per mL) was used to minimize a negative effect of the cecal contents on B. subtilis growth and transformation efficiency. Varying amounts of E. coli and S. typhimurium were spiked into resuspended liquid ceca content. The samples were incubated at 90 °C for 10 min, placed on ice for 3 min, and transferred to the mixed culture of EC-G and ST-R sensors (1 mL LB, 50 mM xylose, and OD0.1 for each sensor strain) for multiplexed detection. Cecal contents were collected from germ-free mice following protocols approved by the University of Wisconsin-Madison Animal Care and Use Committee. Briefly, 8-week-old C57BL/6 gnotobiotic male mice (wild-type) were orally gavaged with 8 human gut bacteria - Dorea formicigenerans, Coprococcus comes, Anaerostipes caccae, Bifidobacterium longum, Bifidobacterium adolescentis, Bacteroides vulgatus, Bacteroides caccae, and Bacteroides thetaiotaomicron via oral gavage. After 4 weeks of colonization, mice were euthanized for cecal collection. The mouse cecal contents were stored at −80 °C prior to the introduction of spike-in bacteria.
Statistics and reproducibility
A sample size of 3 was chosen for all characterizations of the cell-based DNA sensors in biological replicates (three independent transformations) or technical replicates (one transformation with three separate colony forming unit measurements on the same sample). Transformation results were highly similar across either 3 biological replicates or 3 technical replicates. A sample size of 4 was chosen for plate reader (absorbance at 600 nm and fluorescence measurements) measurements specifically due to the observed variability across technical replicates. An unpaired, two-tailed Student t-test was performed using Matlab R2019a (220.127.116.112779) to calculate p-values to determine if the detection time in the presence of varying concentrations of DNA was significantly different from the detection time in the absence of DNA based on the technical replicates (N = 4). Using a sample size of 4, we observed a linear relationship between the observed detection times of cell-based DNA sensors and the log transformed gDNA concentrations (Fig. 2d–g).
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Source data for Figs. 1–4 are provided and can also be downloaded from the BioStudies database (https://www.ebi.ac.uk/biostudies/studies/S-BSST1044). Source data are provided with this paper.
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We would like to thank Dr. Scott Coyle and Zhejing Xu for the assistance of microscopic imaging. We also thanks Yiyi Liu in the Venturelli lab for providing the mice ceca with defined microbial communitues. This work was supported by the Defense Advanced Research Projects Agency (DARPA) Grant HR0011-19-2-0002 and National Institutes of Health Biomedical Imaging and Bioengineering R01EB030340.
O.S.V., Y.-Y.C., and Z.C. have filed a nonprovisional application with the US Patent and Trademark Office on the cell-based DNA detection method (U.S. Nonprovisional Patent No. 18/067,194). The other authors declare that they have no competing interests.
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Cheng, YY., Chen, Z., Cao, X. et al. Programming bacteria for multiplexed DNA detection. Nat Commun 14, 2001 (2023). https://doi.org/10.1038/s41467-023-37582-x
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