Modified Taq DNA Polymerase for Allele-Specific Ultra-Sensitive Detection of Genetic Variants

Youngshin Lim; Il-Hyun Park; Huy-Ho Lee; Kyuwon Baek; Byung-Chul Lee; Ginam Cho

doi:10.1016/j.jmoldx.2022.08.002

. 2022 Nov;24(11):1128–1142. doi: 10.1016/j.jmoldx.2022.08.002

Modified Taq DNA Polymerase for Allele-Specific Ultra-Sensitive Detection of Genetic Variants

Youngshin Lim ^∗, Il-Hyun Park ^†, Huy-Ho Lee ^†, Kyuwon Baek ^†, Byung-Chul Lee ^†, Ginam Cho ^∗,^∗

PMCID: PMC9746316 PMID: 36058471

Abstract

Allele-specific PCR (AS-PCR) has been used as a simple, cost-effective method for genotyping and gene mapping in research and clinical settings. AS-PCR permits the detection of single nucleotide variants and insertion or deletion variants owing to the selective extension of a perfectly matched primer (to the template DNA) over a mismatched primer. Thus, the mismatch discrimination power of the DNA polymerase is critical. Unfortunately, currently available polymerases often amplify some mismatched primer–template complexes as well as matched ones, obscuring AS detection. To increase mismatch discrimination, mutations were generated in the Thermus aquaticus (Taq) DNA polymerase, the most efficient variant was selected, and its performance evaluated in single nucleotide polymorphism and cancer mutation genotyping. In addition, the primer design and reaction buffer conditions were optimized for AS amplification. Our highly selective AS-PCR, which is based on an allele-discriminating priming system that leverages a Taq DNA polymerase variant with optimized primers and reaction buffer, can detect mutations with a mutant allele frequency as low as 0.01% in genomic DNA and 0.0001% in plasmid DNA. This method serves as a simple, fast, cost-effective, and ultra-sensitive way to detect single nucleotide variants and insertion or deletion mutations with low abundance.

Allele-specific PCR (AS-PCR), also known as allele-discriminating PCR, is commonly used to detect natural or experimentally introduced genetic variations.¹^,² Originally developed in 1989 as an amplification-refractory mutation system,² the premise of AS-PCR is a lower amplification efficiency of a mismatched primer–template complex compared with that of a matched one. Specifically, a more efficient (selective) extension of the primer by DNA polymerase occurs when the 3′ end of the primer is perfectly complemented (matched) to the template. For a matched primer–template complex, accurate Watson-Crick base pairing occurs at the 3′ end of the primer, while a mismatched primer–template complex results in non-canonical base pairing. If the selectivity of the DNA polymerase is high, only the primer with accurate base pairing to the template will be extended but not the primer with non-canonical base pairing, which is critical for accurate AS amplification.

Given its ease and simplicity, AS-PCR/allele-discriminating PCR has been used in many areas, including pharmacogenetics, genetic disorders, and cancer.³^,⁴ For example, AS-PCR can be used to detect single nucleotide polymorphisms (SNPs) that can predict an individual’s susceptibility to disease and response to drugs5, 6, 7, 8 and can also trace the inheritance of specific genes and traits of interest.⁹^,¹⁰ In addition to germline variants, detection of somatic variants has become increasingly valuable, notably in cancer as well as in hematologic, neurodevelopmental, and neurologic disorders.11, 12, 13, 14, 15 Furthermore, somatic variant detection in healthy individuals also provides important clues into normal development and aging of complex organs such as the brain.16, 17, 18 However, despite the power of this method in both clinical and research settings, the mismatch discrimination power of the currently available DNA polymerases is limited for making confident mutation calls.¹⁹

In addition to the selectivity, the sensitivity of the amplification is a critical factor for AS-PCR/allele-discriminating PCR, especially in early-stage disease detection. This is exemplified in the ability to discriminate and detect very small amounts of circulating tumor DNA (ctDNA) in blood, especially among the high abundance of other circulating cell-free DNA. The detection of ctDNA is particularly important for the early diagnosis of cancer, estimates of tumor volume, tracking recurrence, and monitoring therapy.20, 21, 22 Therefore, a rapid, sensitive, and accurate detection of somatic variants would enable better clinical care of patients with cancer.

To improve the specificity and sensitivity of AS-PCR/allele-discriminating PCR, Thermus aquaticus (Taq) DNA polymerase was altered by mutating amino acids that form close contacts with the phosphate backbone of the primer strand and identified a highly efficient triple mutant (TM)-Taq DNA polymerase. Using this engineered Taq DNA polymerase, the reaction buffer conditions as well as primer lengths were optimized. The efficiency of the improved Taq DNA polymerase was evaluated by using real-time quantitative PCR (qPCR) with SNP markers and cancer-related genes. Our results present a significantly improved polymerase and methodology that will enhance both investigative and clinical applications.

Materials and Methods

Mutagenesis of Taq DNA Polymerase Variants

Mutant Taq DNA polymerase clones were generated by overlap extension PCR.²³ First, intermediate PCR products (overlapping fragments of the entire product) were generated by using mutagenic primers and flanking primers; sequence information is presented in Table 1. The target PCR products were purified and used in a second PCR with Nco-F and Not-R primers to generate the full-length products. The amplified products were purified by gel electrophoresis and cloned into pET-28a(+) vector (Novagen, Gibbstown, NJ) using the EZ-Fusion HT Cloning Kit (Enzynomics, Daejeon, Republic of Korea). The ligation mixture was transformed into Escherichia coli DH5α, and the sequence was confirmed by Sanger sequencing. Wild-type (WT) and mutant Taq DNA polymerase structure modeling (Figure 1A) was conducted with the program PyMOL version 1.5 (Schrödinger, New York, NY) using the crystal structure of an active ternary complex of the Taq DNA polymerase I (Protein Data Bank code: 3KTQ).

Table 1.

Oligonucleotide Sequences Used in This Study

Subset	Name	Sequences	Modification (5′/3′)
Mutagenesis	Nco-F	5′-AACTTTAAGAAGGAGATATACCATGCTGCCCCTCTTTGAGCC-3′	–
	E507K-R	5′-CTTGCCGGTCTTTTTCGTCTTGCCGAT-3′	–
	E507K-F	5′-ATCGGCAAGACGAAAAAGACCGGCAAG-3′	–
	R536K-R	5′-CTTGGTGAGCTCCTTGTACTGCAGGAT-3′	–
	R536K-F	5′-ATCCTGCAGTACAAGGAGCTCACCAAG-3′	–
	R660V-R	5′-GATGGTCTTGGCCGCCACGCGCATCAGGGG-3′	–
	R660V-F	5′-CCCCTGATGCGCGTGGCGGCCAAGACCATC-3′	–
	R536L-R	5′-CTTGGTGAGCTCCAGGTACTGCAGGAT-3′	–
	R536L-F	5′-ATCCTGCAGTACCTGGAGCTCACCAAG-3′	–
	Not-R	5′-TGGTGGTGCTCGAGTGCGGCCGCTCACTCCTTGGCGGAGAGCCAGT-3′	–
rs1015362	1015362 OF1	5′-TGAAGAGCAGGAAAGTTCTTCA-3′	–
	1015362 RC	5′-CTGTGTGTCTGAAACAGTG-3′	–
	1015362 RT	5′-CTGTGTGTCTGAAACAGTA-3′	–
	1015362 FAM	5′-TGCTGAACAAATAGTCCCGACCAG-3′	FAM/BHQ1
rs1408799	1408799 F2	5′-CCAGTGTTAGGTTATTTCTAACTTG-3′	–
	1408799 RT	5′-CTCGGAGCACATGGTCAA-3′	–
	1408799 RC	5′-CTCGGAGCACATGGTCAG-3′	–
	1408799 FAM	5′-AGATATTTGTAAGGTATTCTGGCCT-3′	FAM/BHQ1
rs4911414	4911414 R1	5′-AGTATCCAGGGTTAATGTGAAAG-3′	–
	4911414 FG	5′-GTAAGTCTTTGCTGAGAAATTCATTG-3′	–
	4911414 FT	5′-GTAAGTCTTTGCTGAGAAATTCATTT-3′	–
	4911414 FAM	5′-TGATGCTTTTCTCTAGTTGCCTTTAAGA-3′	FAM/BHQ1
BRAF V600E	V600E Fmt18	5′-TTTTGGTCTAGCTACAGA-3′	–
	V600E SR1	5′-GATCCAGACAACTGTTCAAACTG-3′	–
	V600E FAM	5′-AAATCTCGATGGAGTGGGTCCCATCA-3′	FAM/BHQ1
JAK2 V617F	V617F Fmt22	5′-GTTTTAAATTATGGAGTATGTT-3′	–
	V617F OR1	5′-AGTCCTACAGTGTTTTCAGTTTC-3′	–
	V617F FAM	5′-TCAGTTTCAGGATCACAGCTAGGT-3′	FAM/BHQ1
EGFR L858R	L858R SF1	5′-CGTACTGGTGAAAACACCG-3′	–
	L858R Rmt14	5′-CAGCAGTTTGGCCC-3′	–
	L858R CFO560_R	5′-CAGCATGTCAAGATCACAGATTTTGGGC-3′	CFO560/BHQ1
	L858R Fmt19	5′-AGATCACAGATTTTGGGCG-3′	–
	L858R OR1	5′-TTGCCTCCTTCTGCATGGTATTC-3′	–
	L858R FAM_F	5′-CCAAACTGCTGGGTGCGGAAGAG-3′	FAM/BHQ1
EGFR Ex19Del	Ex19del SF2	5′-TCCTTCTCTCTCTGTCATAGGG-3′	–
	Ex19Del C1 Rmt19	5′-GTTGGCTTTCGGAGATGCC-3′	–
	Ex19Del CFO560_R	5′-CTCTGGATCCCAGAAGGTGAGAAAG-3′	CFO560/BHQ1
EGFR Ex20Ins	Ex20Ins SF4	5′-GAAGCCACACTGACGTGCC-3′	–
	Ex20Ins C3 Rmt18	5′-GGCACACGTGGGGGTTAC-3′	–
	Ex20Ins FAM_R	5′-TCACGTAGGCTTCCTGGAGGGA-3′	FAM/BHQ1
GNAS R844C	R844C FWT16	5′-GACCTGCTTCGCTGCC-3′	–
	R844C Fmt16	5′-GACCTGCTTCGCTGCT-3′	–
	R844X SR1	5′-GTTGACTTTGTCCACCTGGAA-3′	–
	R844X FAM_F	5′-CCTGACTTCTGGAATCTTTGAGACC-3′	FAM/BHQ1
GNAS R844S	R844S FWT16	5′-GACCTGCTTCGCTGCC-3′	–
	R844S Fmt16	5′-GACCTGCTTCGCTGCA-3′	–
	R844X SR1	5′-GTTGACTTTGTC-CAC-CTG-GAA-3′	–
	R844X FAM_F	5′-CCTGACTTCTGGAATCTTTGAGACC-3′	FAM/BHQ1
GNAS R844H	R844H FWT17	5′-GACCTGCTTCGCTGCCG-3′	–
	R844H Fmt17	5′-GACCTGCTTCGCTGCCA-3′	–
	R844X SR1	5′-GTTGACTTTGTCCACCTGGAA-3′	–
	R844X FAM_F	5′-CCTGACTTCTGGAATCTTTGAGACC-3′	FAM/BHQ1
SMAD4 D351H	D351H RWT19	5′-AAGGGTCCACGTATCCATC-3′	–
	D351H Rmt19	5′-AAGGGTCCACGTATCCATG-3′	–
	D351H SF1	5′-AGGTAGGAGAGACATTTAAGGTTC-3′	–
	D351H FAM_R	5′-ACAGTAACAATAGGGCAGCTTGAAG-3′	FAM/BHQ1
KIT D816V	D816V FWT17	5′-TTTGGTCTAGCCAGAGA-3′	–
	D816V Fmt17	5′-TTTGGTCTAGCCAGAGT-3′	–
	D816V SR1	5′-CTTTGCAGGACTGTCAAGCA-3′	–
	D816V FAM_F	5′-AGGAAACGTGAGTACCCATTCTCTG-3′	FAM/BHQ1
TP53 I195T	I195T RWT18	5′-AATTTCCTTCCACTCGGA-3′	–
	I195T Rmt18	5′-AATTTCCTTCCACTCGGG-3′	–
	I195-R196 SF1	5′-TGATTCCTCACTGATTGCTCT-3′	–
	I195-R196 FAM_R	5′-TAAGATGCTGAGGAGGGGCCAGA-3′	FAM/BHQ1
PIK3CA N345K	N345K RWT21	5′-TCAATGTCTCGAATATTTACA-3′	–
	N345K Rmt21	5′-TCAATGTCTCGAATATTTACC-3′	–
	N345K SF1	5′-GGGTTATAAATAGTGCACTCAGAATAA-3′	–
	N345K FAM_R	5′-AAATTCTTTGTGCAACCTACGTGAA-3′	FAM/BHQ1
KRAS G12V	G12V FWT17	5′-GTGGTAGTTGGAGCTGG-3′	–
	G12V Fmt17	5′-GTGGTAGTTGGAGCTGT-3′	–
	G12X SR5	5′-GTTGGATCATATTCGTCCACAAA-3′	–
	G12X FAM_F2	5′-AGGCAAGAGTGCCTTGACGATACA-3′	FAM/BHQ1
KRAS Q61R	Q61R RWT19	5′-ATTGCACTGTACTCCTCTT-3′	–
	Q61R Rmt19	5′-ATTGCACTGTACTCCTCTC-3′	–
	Q61R SF3	5′-AGTAGTAATTGATGGAGAAACCTG-3′	–
	Q61R FAM_R	5′-TGCTGTGTCGAGAATATCCAAGAGA-3′	FAM/BHQ1
EGFR C797S	C797S FWT21	5′-GCAGCTCATGCCCTTCGGCTG-3′	–
	C797S Fmt21	5′-GCAGCTCATGCCCTTCGGCTC-3′	–
	C797S SR2	5′-CAGGTACTGGGAGCCAATATTGTC-3′	–
	C797S FAM_F	5′-CTCCTGGACTATGTCCGGGAACAC-3′	FAM/BHQ1
CDKN2A L130Q	L130Q FWT16	5′-ATGTCGCACGGTACCT-3′	–
	L130Q Fmt16	5′-ATGTCGCACGGTACCA-3′	–
	L130Q SR1	5′-CCTTCCGCGGCATCTATG-3′	–
	L130Q FAM_F	5′-CACCAGAGGCAGTAACCATGCC-3′	FAM/BHQ1
KRAS K117N	K117N RWT18	5′-CTAGAAGGCAAATCACAT-3′	–
	K117N Rmt18	5′-CTAGAAGGCAAATCACAG-3′	–
	K117N SF1	5′-CCCAGAGAACAAATTAAAAGAGTTAA-3′	–
	K117N FAM_R	5′-ATTTCCTACTAGGACCATAGGTACATCTTC-3′	FAM/BHQ1
KRAS G13D	G13D SF1	5′-ATAAGGCCTGCTGAAAATGAC-3′	–
	G13D Rmt16	5′-GCACTCTTGCCTACGT-3′	–
	G13D Rmt15	5′-CACTCTTGCCTACGT-3′	–
	G13D Rmt14	5′-ACTCTTGCCTACGT-3′	–
	G13D FAM_R	5′-AGCTCCAACTACCACAAGTTTATATTCAGT-3′	FAM/BHQ1

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Engineering and single nucleotide polymorphism (SNP) genotyping test of *Taq* DNA polymerase for higher mismatch discrimination. A: Enzyme active site close-up illustrating the contacts between *Taq* DNA polymerase and primer–template DNA complex. Each indicated side chain of target residues of the wild-type (WT) (cyan) and mutant (purple) polymerases form different level of contacts with the backbone of the primer (yellow) bound to the template (green). **Dotted red lines:** hydrogen bonds between amino acid side chains of the enzyme and phosphate backbone of the primer; **circled red x:** no binding. B: Real-time quantitative PCR results for SNP detection by WT-, E507K-, and E507K/R536K/R660V-mutant *Taq* DNA polymerases. Template: genomic DNA (50 ng per reaction) from buccal swab. SNPs: *rs1015362* (CC genotype) and *rs1408799* (TT genotype). ΔC_T: difference of C_T (cycle threshold; number of cycles required to reach 10% of maximum fluorescence) values between mismatched (blue) versus matched (red). ΔRn: difference of Rn (fluorescence signal of reporter probe normalized to that of reference dye) between the experimental versus baseline signal.

Expression and Purification of Taq DNA Polymerase Variants

Liquid culture of E. coli BL21(DE3) transformed with each recombinant DNA encoding Taq DNA polymerase variants and WT (200 mL, LB/kanamycin) was induced by 1 mmol/L of isopropyl-β-d-thiogalactopyranoside (at OD₆₀₀: 0.4 to 0.5) for protein expression, and cells were harvested (after 12 hours) and resuspended in phosphate-buffered saline, 50 μL of which was removed for expression confirmation (SDS-PAGE, not shown). The remaining cells were harvested, resuspended in lysis buffer [20 mmol/L Tris-HCl (pH 8.0), 50 mmol/L potassium chloride (KCl), 1 mmol/L EDTA, 0.01% NP-40, 0.1% Tween-20, 1 mg/mL lysozyme, 1 mmol/L phenylmethylsulfonyl fluoride], sonicated, and incubated at 75°C for 1 hour with gentle shaking every 10 minutes at 4°C to remove the cell debris. After centrifugation, clarified lysates were loaded onto a low-pressure chromatography column (2 mL, Poly-Prep Columns; Bio-Rad Laboratories, Hercules, CA) packed with Q Sepharose XL (1 mL; Cytiva, Marlborough, MA). The column was then washed with 5 column volumes of the washing buffer [20 mmol/L Tris-HCl (pH 8.0), 50 mmol/L KCl, 1 mmol/L EDTA, 0.01% NP-40, 0.1% Tween-20], and the Taq DNA polymerase was eluted in a single step with 3 column volumes of the elution buffer [20 mmol/L Tris-HCl (pH 8.0), 500 mmol/L KCl, 1 mmol/L EDTA, 0.01% NP-40, 0.1% Tween-20].

The purified Taq DNA polymerase was diluted 10-fold with the dilution buffer [20 mmol/L Tris-HCl (pH 8.0), 1 mmol/L EDTA, 0.01% NP-40, 0.1% Tween-20] and loaded onto a second column, in the same manner described in the previous paragraph. The column was washed with 5 column volumes of the washing buffer, and the Taq DNA polymerase was eluted with 3 column volumes of the elution buffer. Dialysis (50 kDa; Spectra/Por 6 Dialysis Tubing, Spectrum Chemical, New Brunswick, NJ) was performed with a storage buffer [20 mmol/L Tris-HCl (pH 8.0), 100 mmol/L KCl, 0.1 mmol/L EDTA, 0.5% NP-40, 0.5% Tween-20, 50% glycerol] for stabilization of the eluted proteins. The E507K/R536K/R660V TM-Taq DNA polymerase is available through collaboration with the authors (GENECAST, Seoul, Republic of Korea).

Primer and Probe Design

Each AS primer was designed according to its corresponding mutation or allele sequence. In the case of indels, two to four mismatches were designed at the 3′ end to distinguish the primer sequences according to the sequence difference before and after insertion or deletion. AS primers were designed with melting temperatures <60°C to increase selectivity due to mismatch at 3′ ends. To achieve sufficient binding efficiency, the primer designed on the opposite side of the AS primer was at, or slightly higher than, 60°C. Melting temperatures of oligonucleotides were calculated by using OligoAnalyzer 3.1 (Integrated DNA Technologies, Coralville, IA). Of note, in the TM-Taq PCR reaction, when the primer melting temperature was selected to be 2°C to 4°C lower than the annealing temperature, the polymerase reaction resulted in the greatest discrimination between the mutant and WT templates. The oligonucleotide sequences used in this study are listed in Table 1. Oligonucleotides were synthesized either by Integrated DNA Technologies (primers) or Biosearch Technologies (dual-labeled probes; Hoddesdon, UK).

Template Preparation for qPCR

For SNP detection (rs1015362, rs1408799, and rs4911414), buccal swabs of volunteers (authors) were used to extract genomic DNA (Plain Dry Swab, Noble Bio, Hwaseong, Republic of Korea; QIAamp DNA Mini Kit, QIAGEN, Hilden, Germany). The genotype of each SNP was confirmed through Sanger sequencing. For cancer mutation detection with plasmid DNA, DNA fragments harboring each mutant and WT sequences were synthesized (Bio Basic, Markham, Ontario, Canada) and cloned into the pBluescript II SK(+) plasmid. Each template plasmid was linearized and diluted to 10⁹ copies/μL with TE buffer containing 2 ng/μL of sheared salmon sperm DNA (Invitrogen, Waltham, MA) through molecular weight calculation. The specific mutations used in this study included: BRAF V600E (c.1799T>A), EGFR C797S (c.2390G>C), EGFR L858R (c.2573T>G), EGFR Ex19Del (p.E746_T751delinsA, c.2237_2251del), EGFR Ex20Ins (p.D770_N771insG, c.2310_2311insGGT), GNAS R844C (c.2530C>T), GNAS R844H (c.2531G>A), GNAS R844S (c.2530C>A), TP53 I195T (c.584T>C), SMAD4 D351H (c.1051G>C), KIT D816V (c.2447A>T), CDKN2A L130Q (c.389T>A), KRAS G12V (c.35G>T), KRAS G13D (c.38G>A), KRAS Q61H (c.183A>C), KRAS K117N (c.351A>C), PIK3CA N345K (c.1035T>A), PIK3CA Q546L (c.1637A>T), and PIK3CA H1047R (c.3140A>G). For cancer mutation detection with genomic DNA, three human cell lines [HEK293T (ATCC, Manassas, VA), HEL 92.1.7 (ATCC), and A375SM (Korean Cell Line Bank, Seoul, Republic of Korea)] were grown in RPMI 1640 media supplemented with 10% fetal bovine serum (1% penicillin/streptomycin), and their genomic DNA samples were prepared by using the Blood & Cell Culture DNA Maxi Kit (QIAGEN). DNA concentrations were measured by using an Epoch microplate spectrophotometer (BioTek Instruments, Winooski, VT), and genomic DNA copy numbers were calculated by using the conversion factor of one copy of the haploid genome having a mass of 3.3 pg.

qPCR Conditions

The reaction buffer contained 50 mmol/L Tris-HCl (pH 8.8), 75 mmol/L KCl, 5 mmol/L ammonium sulfate [(NH₄)₂SO₄], 2.5 mmol/L magnesium chloride, 0.1% Tween 20, and 0.01% bovine serum albumin. Buffer optimization was achieved by starting with a buffer containing no monovalent ions [KCl, (NH₄)₂SO₄] and variable amounts of KCl, (NH₄)₂SO₄, or/and tetramethylammonium chloride with different combinations as indicated. qPCR was performed with 0.25 mmol/L of each dNTP, 200 nmol/L forward primer, 200 nmol/L reverse primer, 400 nmol/L dual-labeled fluorescent probe (Table 1), 2 μL of DNA template of desired copy number, and 15 ng of Taq DNA polymerase variants, in a total of 20 μL. The reactions were performed in a 7500 Fast Real-Time PCR System (Applied Biosystems, Waltham, MA) with an initial denaturation for 5 minutes at 95°C followed by the thermal cycles as given here: denaturation at 95°C for 10 seconds, annealing at 60°C for 30 seconds (fluorescence acquisition at this step), and elongation at 72°C for 10 seconds.

All qPCR data were analyzed in 7500 software (Applied Biosystems). In the qPCR reactions that were performed during earlier stages of the study (eg, Figure 1B), the reference dye carboxyrhodamine was not used, which resulted in higher ΔRn (difference of Rn values, where Rn is the fluorescence signal of the reporter probe normalized to that of the reference dye) values (more than thousand folds) than those in the reactions in which carboxyrhodamine was used. Of note, the presence or absence of carboxyrhodamine, however, is generally accepted to have no significant effects on qPCR reactions [ie, did not change threshold cycle (C_T) values].

Results

Engineering Taq DNA Polymerase for Improved Mismatch Discrimination and Polymerization Activity

There have been previous efforts to improve the function of DNA polymerases by changing amino acids that affect the interaction between the polymerase and the primer–template complex.¹⁹^,24, 25, 26 For example, substitution of the positively charged amino acids (R536, R587, or R660) of the Taq DNA polymerase Klenow fragment, which are directly in contact with the phosphate backbone of the primer in the closed conformation, has been shown to increase primer selectivity.¹⁹ However, when the single point mutations at these locations were combined to improve the selectivity, a drop in polymerase activity was observed.¹⁹ An E507K mutation reportedly stabilizes the Taq polymerase–DNA binary complex and improves polymerization speed, as it forms a strong interaction with a distal portion (location distant from the active site) of the primed template, dramatically reducing K_D (a dissociation constant).²⁷^,²⁸ This mutation also enhances resistance to PCR inhibitors.²⁸

To maximize mismatch discrimination and yet retain or improve polymerization activity of the Taq DNA polymerase, targeted mutagenesis was performed at amino acids E507, R536, and R660 to generate multiple variants in various combinations (Supplemental Table S1). Our model, based on X-ray crystallography structure of the active site of the enzyme, predicts that the substitution of the negatively charged amino acid (E, glutamate) at 507 to a positively charged one (K, lysine) would make a stronger contact with the primer; R536K (positive to positive; two hydrogen bonds to one) and R660V (positive to neutral; two hydrogen bonds to none) would generate a weaker and no contact, respectively (Figure 1A).¹⁹^,²⁸^,²⁹

E507K/R536K/R660V TM-Taq DNA Polymerase Shows Excellent Selectivity in SNP Genotyping and Cancer DNA Mutation Detection

To evaluate the mismatch discrimination efficiency of the variant Taq DNA polymerases, they were first tested with a SNP genotyping assay. The performance of the WT- and mutant (E507 and E507K/R536K/R660V)-Taq DNA polymerases were compared in qPCR on two human SNPs (rs1015362 and rs1408799) using genomic DNA from buccal swabs with two different sets of primers: matched and mismatched primers for each SNP (Figure 1B). The mismatch discrimination power of these enzymes was assessed by measuring ΔC_T values. ΔC_T is the difference in cycle threshold (C_T, the number of cycles required for the fluorescence signal to exceed approximately 10% of the maximum fluorescence value) between the two reactions; for example, the reactions with matched versus mismatched primers (higher ΔC_T means higher discrimination) (Figure 1B). The single mutant Taq (E507K) showed a negligible level of increase in ΔC_T (6.7 and 3.3 for rs1015362 and rs1408799, respectively) compared with the WT (6.3 and 2.2 for rs1015362 and rs1408799). In contrast, the TM (E507K/R536K/R660V) enzyme showed a dramatic increase in ΔC_T (20.4 and 16.8 for rs1015362 and rs1408799) compared with WT and single mutant, indicating that this variant enzyme amplifies DNA with the highest selectivity for the matched primer. The two double mutant Taq DNA polymerases, E507K/R536K and E507K/R660V, exhibited intermediate selectivity compared with WT (and the E507K single mutant) and the TM-Taq DNA polymerase (Supplemental Figure S1, A and B). Hereafter, the E507K/R536K/R660V TM-Taq DNA polymerase will be referred as TM-Taq DNA polymerase.

To further evaluate the mismatch discrimination of TM-Taq DNA polymerase, qPCR was performed with cancer genes. In these assays, two different cancer genes, BRAF and EGFR, were examined by using WT and cancer mutant plasmid DNA templates [single nucleotide (BRAF V600E and EGFR L858R) as well as deletion/insertion (EGFR Ex19Del and EGFR Ex20Ins) variants] and primers specific to cancer mutations (Figure 2). The performance of TM-Taq (E507K/R536K/R660V) was compared with that of a single mutant Taq DNA polymerase (E507K) (Figure 2). When E507K-Taq was used, it showed a modest discrimination between the matched versus mismatched primer–template complex (ΔC_T = 12.2, 11.5, 15.4, and 14.5 for four cancer mutations, respectively) (Figure 2). In contrast, when the TM-Taq was used, only the cancer mutant DNA templates (matched) were amplified, whereas the same number of copies (1,000,000 copies for BRAF; 30,000 copies for EGFR) of the WT templates (mismatched) were not detectable (ΔC_T >25.1, 20.2, 21.3, and 20.3 for four cancer mutations) (Figure 2). Together, these results show that the TM-Taq DNA polymerase displays highly selective amplification of the cancer allele over the WT allele, independent as to whether the cancer mutation is a single nucleotide variant or a deletion/insertion variant.

Triple mutant (TM)-*Taq* DNA polymerase shows excellent mismatch discrimination in cancer mutation detection. Real-time quantitative PCR results comparing the performance between E507K- and E507K/R536K/R660V-*Taq* polymerase. Templates: plasmid DNA harboring wild-type or mutant sequence of the *BRAF* and *EGFR* genes (1 × 10⁶ copies for *BRAF*, and 3 × 10⁴ copies for *EGFR* DNA) [*BRAF* V600E (c.1799T>A) and *EGFR* L858R (c.2573T>G), single nucleotide mutations; *EGFR* Ex19Del (c.2237_2251del), 15 bp in-frame deletion; *EGFR* Ex20Ins (c.2310_2311insGGT), 3 bp in-frame insertion]. Primers: each mutation-specific primer. ΔC_T: the difference of C_T values between the wild-type (blue, mismatched) and mutant (red, matched) templates. ΔRn: difference of Rn (fluorescence signal of reporter probe normalized to that of reference dye) between the experimental versus baseline signal.

Of note, the R536K/R660V double mutant enzyme exhibited compromised polymerase activity even with the matched primer (only slow linear amplification without exponential amplification) (Supplemental Figure S2), as expected.¹⁹ In addition, the R536K single mutant enzyme showed a modest mismatch discrimination (ΔC_T = 10.5) for EGFR L858R detection, whereas the R536L mutant lost polymerase activity (Supplemental Figure S3). Another TM-Taq enzyme, E507K/R536L/R660V, with an R536L substitution instead of R536K had no polymerase activity (Supplemental Figure S3).

TM-Taq DNA Polymerase Shows a Higher Mismatch Selectivity with all Possible Mismatch Types

The performance of the engineered TM-Taq enzyme was next tested with all 12 possible primer–template mismatch types (A/C, A/G, A/T, C/A, C/G, C/T, G/A, G/C, G/T, T/A, T/C, and T/G). The WT plasmid DNA templates for the selected cancer genes, and the two different primer sets for each gene, matched (WT primers) and mismatched (mutant primers), were used for qPCR runs with the WT-, E507K-, or TM-Taq DNA polymerases (Table 2). The C_T values were measured for each reaction, and the ΔC_T values (difference in C_T values between the matched versus mismatched) were compared among the WT-, E507K, and TM-Taq DNA polymerases in each mismatch case (Table 2).

Table 2.

Performance Evaluation of the TM-Taq Enzyme with all 12 Possible Mismatch Types

Case	Type	Target mutation	Mutation at CDS (direction^∗)	3′ end of WT primer (matched)	3′ end of MT primer (mismatched)	WT-Taq (C_T)		E507K-Taq (C_T)		TM-Taq (C_T)		ΔC_T value
Case	Type	Target mutation	Mutation at CDS (direction^∗)	3′ end of WT primer (matched)	3′ end of MT primer (mismatched)	Matched	Mismatched	Matched	Mismatched	Matched	Mismatched	WT-Taq	E507K-Taq	TM-Taq
C/T	Transition	GNAS R844C	C>T (Forward)	C	T	26.2	27.5	26.2	27.8	26.2	38.4	1.3	1.6	12.2
C/A	Transversion	GNAS R844S	C>A (Forward)	C	A	26.2	39.0	26.2	40.6	26.2	49.7	12.8	14.4	23.5
C/G	Transversion	SMAD4 D351H	G>C (Reverse)	C	G	26.7	36.3	26.7	36.7	26.4	41.9	9.6	10.0	15.5
A/G	Transition	TP53 I195T	T>C (Reverse)	A	G	26.5	35.8	26.9	36.1	26.5	42.0	9.3	9.2	15.5
A/T	Transversion	KIT D816V	A>T (Forward)	A	T	25.8	37.7	26.3	38.1	25.8	>50^†	11.9	11.8	>24.2^‡
A/C	Transversion	PIK3CA N345K	T>G (Reverse)	A	C	28.6	36.2	28.9	37.0	27.7	>50^†	7.6	8.1	>22.3^‡
T/C	Transition	KRAS Q61R	A/G (Reverse)	T	C	26.3	31.5	26.3	32.3	25.4	42.0	5.2	6.0	16.6
T/G	Transversion	KRAS K117N	A/C (Reverse)	T	G	26.9	38.4	27.2	38.7	26.8	46.7	11.5	11.5	19.9
T/A	Transversion	CDKN2A L130Q	T>A (Forward)	T	A	27.6	39.0	27.7	40.0	27.1	>50^†	11.4	12.3	>22.9^‡
G/A	Transition	GNAS R844H	G/A (Forward)	G	A	26.3	31.0	26.3	32.4	26.0	40.6	4.7	6.1	14.6
G/T	Transversion	KRAS G12V	G/T (Forward)	G	T	27.0	32.2	27.0	33.0	26.6	46.4	5.2	6.0	19.8
G/C	Transversion	EGFR C797S	G/C (Forward)	G	C	27.6	40.4	27.6	42.0	27.1	>50^†	12.8	14.4	>22.9^‡

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CDS, coding sequence; MT, mutant; Taq, Taq DNA polymerase; TM, triple mutant; WT, wild-type.

^∗

Direction is defined as “forward” if the mutation case is taken from the coding strand and “reverse” if the mutation case is taken from the complementary strand.

^†

The mismatch product was not detected until the end of the cycles (50 cycles).

^‡

C_T was assigned with the value of 50 as the highest cycle number assayed to calculate ΔC_T: since the mismatch product was not detected until 50 cycles.

The TM-Taq DNA polymerase showed improved mismatch discrimination in all possible mismatch cases, compared with the WT- or E507K-Taq DNA polymerase, although with differing degrees of allele discrimination depending on the mismatch type (Figure 3 and Supplemental Figure S4). For example, the TM-Taq DNA polymerase successfully distinguished C to T changes (pyrimidine to pyrimidine, a “transition” case), whereas WT- and E507K-Taq DNA polymerase did not (ΔC_T = 12.2 for TM-Taq, 1.3 for WT-Taq, and 1.6 for E507K-Taq DNA polymerase) (Figure 3A). Similarly, C to A and C to G changes (pyrimidine to purines, “transversion” cases) were also distinguished better by TM-Taq DNA polymerase (ΔC_T = 23.6) than by WT- or E507K-Taq (ΔC_T = 12.8 and 14.4, respectively), although WT- or E507K-Taq DNA polymerase also performed better with the transversion (ΔC_T = 12.8 and 14.4) than transition (ΔC_T = 1.3 and 1.6) cases (Figure 3A). Likewise, the other nine of the mismatch cases were all tested and produced similar results (Supplemental Figure S4 and Table 2). As expected, overall mismatch discrimination by all three enzymes was better in transversion cases than in transition cases (Figure 3B).

Triple mutant (TM)-*Taq* DNA polymerase shows improved mismatch selectivity for all 12 possible mismatch types. A: Real-time quantitative PCR results of the three mismatch types of the primer containing C at the 3′ end primer sequence (C to T, A, and G). Wild-type (WT) genomic DNA was used as a template with either WT-specific (red, match) or mutant-specific (blue, mismatch) primer in each reaction by WT-, E507K-, or TM-*Taq* DNA polymerase. B: Comparison of ΔC_T values (between the matched versus mismatched) among WT-, E507K-, and TM-*Taq* DNA polymerases for all 12 mismatch types. Details are listed in Table 2. The results of the other nine mismatch types are presented in Supplemental Figure S4. ΔC_T: the difference of C_T values between the matched (red, WT) and mismatched (blue, mutant) template–primer complex. **Asterisks** indicate where ΔC_T values were calculated, not with the C_T obtained from the experiments, with the assigned C_T value of 50, because the PCR product from the mismatched primer–template complex was not detected until the end of the cycles (50 is the highest cycle number assayed).

Primer Design and Buffer Condition Can Affect the Mismatch Selectivity of TM-Taq DNA Polymerase

To optimize qPCR reaction for TM-Taq maximum mismatch discrimination,³⁰ the effect of primer length was first evaluated on the selectivity of extension by TM-Taq DNA polymerase. For this, KRAS G13D mutant and WT plasmid DNAs were used as templates, with three different lengths of primers (14-, 15-, and 16-mers) specific (matched) to KRAS G13D (Supplemental Figure S5A). Although a C_T delay of about 2 was observed in the matched primer (compare C_T = 28 in 15- or 16-mer vs C_T = 30 in 14-mer), the 14-mer primer exhibited optimal discrimination. These data suggest that optimization of the primer length is critical for a successful mismatch discrimination in TM-Taq DNA polymerase–mediated AS-PCR. Similar results were observed for PIK3CA Q546L and H1047R mutant detection; 19-mer (PIK3CA Q546L) or 18-mer (PIK3CA H1047R) primers resulted in the most effective mismatch discrimination by TM-Taq enzyme (Supplemental Figure S5A).

Next, the ideal PCR reaction buffer conditions were determined. Starting concentrations were 50 mmol/L Tris-HCl (pH 8.8), 0.1% Tween 20, 0.01% bovine serum albumin, and 2.5 mmol/L magnesium chloride as a base and the concentration and combination of KCl, (NH₄)₂SO₄, or tetramethylammonium chloride, which are known to affect the specificity and sensitivity of the reaction, were formulated.³¹ The titration curves indicate that 80 mmol/L KCl or 80 mmol/L tetramethylammonium chloride resulted in the optimal mismatch discrimination for KRAS Q61H and WT templates (Supplemental Figure S5B). Through a further optimization process with other template/primer pairs, it was found that the combination of 75 mmol/L KCl and 5 mmol/L (NH₄)₂SO₄ worked the best overall (Supplemental Figure S5C) and thus was used for all the TM-Taq PCR reactions.

Taken together, the AS-PCR condition for TM-Taq DNA polymerase to detect rare mutations (eg, cancer-specific somatic mutations widely used as biomarkers) was optimized (primer/probe, buffer condition, and TM-Taq enzyme) and referred to as an allele-discriminating priming system (ADPS) (Supplemental Figure S6).

Ultra-sensitive Detection of Rare Somatic Mutations by TM-Taq DNA Polymerase–Based ADPS

To evaluate the sensitivity of the TM-Taq enzyme in ADPS, the maximum abundance of the DNA template for the mutant enzyme was first determined to appropriately return a negative result with a mismatched primer. For this, the DNA amplification was monitored in multiple reactions with different input copy numbers of the WT BRAF plasmid DNA template, ranging from 1 × 10⁶ to 8 × 10⁶, while the BRAF V600E-specific primer (mismatched to template) was used (Figure 4A). With up to 3 × 10⁶ copies of the WT template, no amplification was detected with a mutant-specific primer, although 4 × 10⁶ and 8 × 10⁶ copies resulted in a negligible level of amplification [one reaction of three with 4 × 10⁶ (C_T = 44.5) and all three reactions with 8 × 10⁶ (average C_T = 41.2)] (Figure 4A). Thus, the maximum input copy number of the WT template without generating false-positive amplicons is 3 × 10⁶. As a positive control, 1 × 10⁵ copies of the BRAF V600E mutant plasmid DNA template (matched) produced successful amplification (average C_T = 21.8) (Figure 4A). These results establish that no false-positive amplicons are detected, when up to 3 × 10⁶ copies of WT plasmid DNA are used as the only source of the template.

Ultra-sensitive detection of the rare mutations by the triple mutant-*Taq* DNA polymerase–based allele-discriminating priming system. A: Evaluation of specificity [real-time quantitative PCR (qPCR) results]. Templates: mutant (1 × 10⁵ copies) or increasing amount of wild-type (WT) (1 × 10⁶ to 8 × 10⁶ copies) plasmid DNA templates. B: Sensitivity test with plasmid DNA templates (qPCR results). Templates: plasmid blend samples (WT and mutant *BRAF*) with mutant allele fraction (MAF) ranging from 0.0001% to 50%. In the case of the 0.0001% sample, only two wells were detected out of the triplicated test (as indicated by the **asterisk**). C: Sensitivity test with genomic DNA (gDNA) templates (qPCR results). Templates: gDNA blend (WT and mutant *BRAF*) from HEL92.1.7 (*BRAF* WT) and A375SM (homozygous *BRAF* V600E), with MAF ranging from 0.01% to 100%. D: (**Left**) The mean C_T value plotted against the copy number (logarithm) of the mutant plasmid DNA template present in each sample from Figure 4B. Error bar: standard deviation. (**Right**) Measured MAF plotted against the expected MAF of the mutant template (gDNA). For measured MAF, copy number of the mutant template (*BRAF* V600E) (genomic DNA) was first calculated from the C_T value obtained in each reaction (using the linear regression equation in the standard curve on the left) and then converted to MAF. Primer: *BRAF* V600E-specific primer for A, B, and C. N/D, not detected.

The sensitivity of TM-Taq enzyme was next assessed to detect BRAF V600E template when an excess amount of WT template was present in the same reaction. A serial dilution of the plasmid DNA template was prepared by adjusting the proportion of the cancer mutant DNA relative to the WT DNA, with the mutant (minor) allele fraction (MAF) ranging from 100% to 0.0001% (Figure 4B). When 3 × 10⁶ copies of the WT plasmids were present in the sample, the mutant template was amplified by TM-Taq DNA polymerase, down to 0.0001% MAF (Figure 4B). Reassuringly, the C_T value and the amount of the BRAF V600E template present in each reaction showed an inverse linear relationship (Figure 4D).

Furthermore, the results from plasmid DNA were confirmed with genomic DNA extracted from cells (Figure 4C). The genomic DNA from the HEL92.1.7 cell line in which the BRAF V600 region is WT and from the A375SM cell line containing the BRAF V600E mutation (homozygous) were mixed. Even in the presence of 3 × 10⁴ copies of the WT genomic DNA templates, as few as 0.01% of the mutant templates were successfully amplified, indicating a highly sensitive detection of the mutations with ultra-low MAF, by TM-Taq enzyme-mediated AS-PCR (Figure 4C).

Finally, the limit of detection (sensitivity) between the WT- and TM-Taq DNA polymerases was compared with all 12 possible mismatch examples (Table 3). For this, qPCR was performed with either enzyme in the presence of: i) the selected cancer mutant plasmid DNA templates (Table 3) mixed with each WT genomic DNA (from HEK293T cells), with the MAF ranging from 100% (ie, mutant DNA only) to 10%, 1%, 0.1%, 0.01%, and 0% (ie, WT DNA only); and ii) the mutant primers. The results were compared for each MAF between each enzyme (Figure 5A and Supplemental Figure S7). Given that even the WT DNA templates (0% MAF) can be also amplified with the mutant primers if allowed with enough cycle number, the selective amplification was called successful only if the difference between the C_T value of the reaction with a certain MAF (X% MAF) and that with the 0% MAF (thus only WT DNA present) is bigger than 2 (ΔC_T >2; ΔC_T = C_{T0% MAF} – C_{Tx% MAF}). Using this criterion, the lowest distinguishable MAF (%) was determined for each mismatch case in each enzyme (Figure 5B). The lowest distinguishable MAF for the TM-Taq DNA polymerase was 0.01% in 10 of 12 mismatch cases and 0.1% for the other two mismatch cases (C>T in GNAS R844C and G>A in GNAS R844H), suggesting consistently higher sensitivity, regardless of the mismatch types. On the contrary, WT-Taq DNA polymerase showed a lower and variable degree of sensitivity depending on the mismatch types, compared with the TM-Taq enzyme.

Table 3.

Limit-of-Detection Comparison between WT- and TM-Taq with all Mismatch Cases

Case	Target	Taq DNA polymerase	C_T for each reaction with the indicated MAF
Case	Target	Taq DNA polymerase	100%	10%	1%	0.10%	0.01%	0% (WT only)
C/T	GNAS R844C	WT-Taq	23.5	26.1	27.3	27.4	27.5	27.5
C/T	GNAS R844C	TM-Taq	23.6	27.0	30.5	33.6	36.5	38.4
C/A	GNAS R844S	WT-Taq	24.3	27.7	31.1	34.7	37.9	39.8
C/A	GNAS R844S	TM-Taq	24.3	27.7	31.3	34.5	38.8	N/D^∗
C/G	SMAD4 D351H	WT-Taq	24.4	27.8	30.9	34.0	35.1	35.7
C/G	SMAD4 D351H	TM-Taq	24.3	27.7	30.9	34.3	37.7	43.5
A/G	TP53 I195T	WT-Taq	24.6	27.9	31.3	34.4	36.5	36.9
A/G	TP53 I195T	TM-Taq	24.7	28.1	31.5	35.0	38.2	43.2
A/T	KIT D816V	WT-Taq	24.5	27.8	31.3	34.5	38.4	38.8
A/T	KIT D816V	TM-Taq	24.9	27.7	31.3	34.3	37.8	N/D^∗
A/C	PIK3CA N345K	WT-Taq	25.6	28.9	32.6	35.4	36.7	36.9
A/C	PIK3CA N345K	TM-Taq	25.6	29.0	32.4	35.7	39.2	N/D^∗
T/C	KRAS Q61R	WT-Taq	24.7	27.9	31.1	32.4	33.3	33.2
T/C	KRAS Q61R	TM-Taq	24.7	28.2	31.8	35.0	37.9	42.8
T/G	KRAS K117N	WT-Taq	25.0	28.3	31.7	34.6	37.5	37.6
T/G	KRAS K117N	TM-Taq	24.8	28.2	31.3	34.6	38.0	43.0
T/A	CDKN2A L130Q	WT-Taq	24.3	27.6	31.0	34.4	37.3	38.9
T/A	CDKN2A L130Q	TM-Taq	23.9	27.4	30.7	34.0	38.0	N/D^∗
G/A	GNAS R844H	WT-Taq	25.5	28.4	29.9	30.2	30.2	30.2
G/A	GNAS R844H	TM-Taq	25.4	28.9	32.5	36.2	39.2	40.8
G/T	KRAS G12V	WT-Taq	24.6	27.9	30.9	32.3	32.7	32.7
G/T	KRAS G12V	TM-Taq	24.4	27.8	31.1	34.6	37.5	42.2
G/C	EGFR C797S	WT-Taq	25.2	28.6	32.0	35.4	38.3	39.5
G/C	EGFR C797S	TM-Taq	24.6	28.0	31.5	35.1	38.2	N/D^∗

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The bold numbers indicate where the enzyme was able to distinguish the mutant allele from the wild-type (WT) [we defined if ΔC_T >2 (ΔC_T = C_{T0% MAF} - C_Teach%MAF), then distinguishable]. Note 0% mutant (or minor) allele fraction (MAF) indicates WT template only. TM, triple mutant.

^∗

Not detected (N/D); ie, the fluorescent signal-PCR product is not detected when checked up to 50 cycles of reactions.

Comparison of the limit-of-detection (sensitivity) between the wild-type (WT)- and triple-mutant (TM)-*Taq* DNA polymerases with all possible mismatched types. A: Real-time quantitative PCR results of the three representative mismatch types (C to T, A, and G) using the selected cancer mutant DNA templates (plasmid DNA) mixed with WT templates (genomic DNA) at different proportion [mutant allele fraction (MAF) from 100% to 0%] and the mutant primers, by WT- or TM-*Taq* DNA polymerase. B: Two different plots summarizing the lowest distinguishable MAF of the WT- and TM-*Taq* DNA polymerases with all 12 possible mismatch types.

Taken together, these data show a highly selective and ultra-sensitive detection of rare somatic mutations in cancer genes using the TM-Taq enzyme-based ADPS established in this study.

Discussion

Among the various methods used to detect and analyze SNPs/single nucleotide variants, primer–template complex mismatch discrimination-based qPCR is fast, simple, and cost-effective. Despite this benefit, the low selectivity of the polymerase has been a limiting factor. Here we report an engineered Taq DNA polymerase that exhibits high selectivity and sensitivity for single nucleotide mismatch detection.

The crystal structure of the Taq DNA polymerase²⁹^,³² has shown that the phosphate backbone of the nine nucleotides at the 5′ end of the DNA template interacts with the DNA polymerase. The seven nucleotides at the 3′ end of the primer are also in contact with the DNA polymerase, at residues R660, R587, R536, K508, and R487 (all positively charged amino acids), as well as E507 (a negatively charged amino acid). The substitution of the positively charged amino acids directly in contact with phosphate backbone of the primer in the closed conformation (R536, R587, or R660) has been reported to increase primer selectivity.¹⁹ However, when these mutations were combined to maximize selectivity, the polymerase activity of the resulting variants turned out to be compromised. This issue was resolved by introducing an additional mutation at E507, which interacts with the phosphate backbone of the sixth and seventh nucleotides of the primer’s 3′ ends. E507K amino acid substitution is known to stabilize the Taq polymerase–DNA binary complex, dramatically reduce the K_D by a factor of 90, improve polymerization responsiveness by a factor of approximately 1.7 during fast cycling PCR, and increase the resistance to PCR inhibitors.²⁷^,²⁸ Accordingly, different combinations of E507, R536, and R660 were tested, and ultimately the E507K/R536K/R660V triple mutation was used for optimum AS amplification.

The improved performance by the TM-Taq DNA polymerase, therefore, is the combination of the reduced affinity of the enzyme to a mismatched primer–template complex achieved by R536K and R660V and the improved binding to a matched primer–template complex due to E507K mutation. The changes in these amino acids may hamper the primer extension, by releasing the mismatched primer from the active site, when the 3′ end of the primer has a single mismatch to the template, while successfully extending the matched primer (Figure 6). In contrast, the WT-Taq DNA polymerase still extends the primer even when the 3′ end of the primer has a mismatch, likely owing to the binding strength not being weak enough to release the mismatched primer from the template (Figure 6).

Triple-mutant (TM)-*Taq* DNA polymerase–based, allele discriminating priming system for higher mismatch discrimination. Simplified schematics showing the difference between wild-type (WT)- and TM-*Taq* DNA polymerase. WT-*Taq* enzyme extends even mismatched primer (although less efficiently), whereas TM-*Taq* enzyme extends only perfectly matched primer.

The results presented here are of particular significance when applied to early diagnosis of cancers. ctDNA refers to cell-free DNA fragments released from cancer cells, present in body fluids such as blood and cerebrospinal fluid. It has been used with increasing frequency to identify genomic alterations, to detect disease progression before clinical confirmation, and to monitor treatment response or resistance.²¹^,²²^,³³^,³⁴ The detectability of ctDNA is limited by tumor size, and thus sensitivity and specificity of the detection method are critical in early diagnosis when the tumor is small. Our TM-Taq DNA polymerase–based ADPS can improve ctDNA detection in a patient’s blood sample, as it provides high sensitivity of detecting cancer mutations even when they are present in only as low as 0.01% of 3 × 10⁴ genomic DNA copies (Figure 4C). Furthermore, its high selectivity/specificity for the matched primer–template complex against the mismatched reduces the potential for false-positive diagnosis. The application of our ADPS with TM-Taq DNA polymerase to clinical samples from patients with cancer has shown considerable promise and will be reported elsewhere. In addition, our system was also successfully applied to tissue-based genotyping for genetically engineered mice: Pik3r2 knock-in³⁵ and Flag-Arx knock-in mice generated with the CRISPR/Cas9 system (data not shown).³⁶

A limitation of the ADPS system is that its performance is highly dependent on primer design, which can be solved by manipulating the length of the primer with a trial-and-error approach. Also, ADPS can be used only when the variant sequence of the template DNA is known, and thus it is not suitable for the discovery of emerging or novel cancer mutations during disease progression.

In summary, we have developed a TM-Taq DNA polymerase–based ADPS for ultra-rare genetic variant detection that is highly selective and sensitive. Our system can detect all types of 3′ end mismatch with a consistently higher sensitivity than the WT-Taq enzyme. This system may be useful for multiple applications such as SNP/single nucleotide variant genotyping, cancer diagnostics and monitoring, and rapid genotyping for genetically engineered cells or animals generated by CRISPR/Cas 9 genome editing.

Acknowledgments

We thank Drs. Diane Shao (Boston Children’s Hospital), Michael Miller (Boston Children’s Hospital, Brigham and Women's Hospital), and Jeffrey Golden (Cedars-Sinai Medical Center) for their critical review and editing of the manuscript.

Footnotes

Supported in part by the NIH National Institute of Neurological Disorders and Stroke (R01NS100007) (Y.L.).

Y.L. and I.-H.P. contributed equally to this work.

Disclosures: Aspects of techniques or materials used in this work are covered by issued patents (10-1958659; 10-1958660; I733038; I688656) and pending patents (US20200149018A1), owned by GENECAST. Patents describing the allele-discriminating priming system have been filed and registered by the following authors listed as inventors: I.-H.P., H.-H.L., and B.-C.L.

Current address of Y.L. and G.C., Department of Pathology and Laboratory Medicine, Cedars-Sinai Medical Center, Los Angeles, CA.

Supplemental material for this article can be found at http://doi.org/10.1016/j.jmoldx.2022.08.002.

Author Contributions

K.B., B.-C.L., and G.C. conceived and designed the study; I.-H.P., H.-H.L., and G.C. performed experiments; Y.L., I.-H.P., K.B., B.-C.L., and G.C. collected and analyzed the data; and Y.L. wrote the manuscript. All authors edited the manuscript.

Supplemental Data

Supplemental Figure S1

Real-time quantitative PCR results for single nucleotide polymorphism detection by E507K/R536K and E507K/R660V mutant Taq DNA polymerases. Template: genomic DNA (50 ng per reaction) from buccal swab. Single nucleotide polymorphisms: rs1015362 (CC genotype) (A) and rs1408799 (TT genotype) (B). ΔC_T: the difference of C_T (cycle threshold) values between mismatched (blue) versus matched primers (red). ΔRn, the difference of Rn (the fluorescence signal of the reporter probe normalized to that of the reference dye) values between the experimental versus the baseline signal.

mmc1.pdf^{(137.5KB, pdf)}

Supplemental Figure S2

Real-time quantitative PCR results for cancer DNA mutation detection by R536K/R660V mutant Taq DNA polymerase. Templates: plasmid DNA harboring wild-type or mutant sequence of the BRAF and JAK2 genes (1 × 10⁶ copies for BRAF, and 1 × 10⁶ copies for JAK2 DNA) (BRAF V600E and JAK2 V617F). Primers: each mutation-specific primer. ΔC_T: the difference of C_T values between the wild-type (blue, mismatched) and mutant (red, matched) templates.

mmc2.pdf^{(126.4KB, pdf)}

Supplemental Figure S3

Real-time quantitative PCR results for cancer DNA mutation detection by R536K, R536L, E507K/R536L/R660V, and E507K/R536K/R660V mutant Taq DNA polymerases. Templates: plasmid DNA harboring wild-type (EGFR) or mutant (EGFR L858R) sequence of the EGFR gene (3 × 10⁴ copies each). Primers: cancer mutant DNA-specific primer. ΔC_T: the difference of C_T values between the wild-type (blue, mismatched) and mutant (red, matched) templates.

mmc3.pdf^{(316.4KB, pdf)}

Supplemental Figure S4

Triple mutant (TM)-Taq DNA polymerase shows improved mismatch selectivity irrespective of mismatch types. Real-time quantitative PCR results of all three possible mismatch types of each primer containing T, G, or A at the 3′ end. Wild-type (WT) genomic DNA (genomic DNA) was used as a template and either WT-specific (red, match) or mutant-specific (blue, mismatch) primer was used in each reaction, by WT-, E507K-, or TM-Taq DNA polymerase. ΔC_T: the difference of C_T (cycle threshold) values between mismatched (blue) versus matched primers (red). ΔRn: the difference of Rn (the fluorescence signal of the reporter probe normalized to that of the reference dye) values between the experimental versus the baseline signal.

mmc4.pdf^{(2.1MB, pdf)}

Supplemental Figure S5

Optimization of triple mutant-Taq DNA polymerase (TM-Taq Pol) reaction. A: Primer length optimization: real-time quantitative PCR (qPCR) results comparing the performance of TM-Taq Pol with different lengths of primers. Template DNAs: plasmid DNAs (10,000 copies per reaction) harboring wild-type (WT) or each mutation sequence [KRAS G13D (c.38G>A), PIK3CA Q546L (c.1637A>T), and PIK3CA H1047R (c.3140A>G)] of the KRAS and PIK3CA genes. Primers: each mutation-specific primer with indicated lengths (the same 3′ end and different lengths at the 5′ direction). B: qPCR results comparing the performance of TM-Taq Pol with different concentrations of potassium chloride (KCl) and tetramethylammonium chloride (TMAC). Template DNAs: plasmid DNA (100,000 copies per reaction) harboring WT or mutation sequence [(KRAS Q61H (c.183A>C)] of the KRAS gene. C: qPCR results comparing the performance of TM-Taq Pol with different concentrations of ammonium sulfate [(NH₄)₂SO₄]. The reaction buffer contained 50 mmol/L Tris-HCl (pH 8.8), 75 mmol/L KCl, 2.5 mmol/L magnesium chloride, 0.1% Tween 20 and 0.01% bovine serum albumin. Template: genomic DNA (50 ng per reaction) from buccal swab. Single nucleotide polymorphism: rs4911414 (GG genotype). ΔC_T: the difference of C_T values between the WT template (blue, mismatched) and the mutant template (red, matched).

mmc5.pdf^{(1.1MB, pdf)}

Supplemental Figure S6

Schematic diagram illustrating triple mutant Taq DNA polymerase (TM-Taq DNA Pol)–based allele-discriminating priming system (ADPS) for cancer-related single nucleotide variant (SNV) detection. A: ADPS consists of three components: E507K/R536K/R660V TM-Taq DNA Pol, optimized primer–probe, and optimized buffer system. B: Only matched target sequence (eg, cancer mutation) is specifically amplified but not mismatched target (eg, wild type) by TM-Taq DNA Pol–mediated allele-specific PCR.

mmc6.pdf^{(119.6KB, pdf)}

Supplemental Figure S7

Comparison of the detection sensitivity between the wild-type (WT)- and triple mutant (TM)-Taq DNA polymerase. Real-time quantitative PCR results by WT- and TM-Taq enzyme using the selected cancer mutant plasmid DNA templates mixed with the corresponding WT genomic DNA template at a different proportion [mutant (minor) allele fraction from 100% to 0%], and the mutant-specific primers. All three possible cases of the mismatch type for each base sequence (eg, T to C, G, and A) were evaluated by using the selected cancer genes indicated (eg, KRAS Q61R for T to C mismatch). ΔRn: the difference of Rn (the fluorescence signal of the reporter probe normalized to that of the reference dye) values between the experimental versus the baseline signal.

mmc7.pdf^{(1.5MB, pdf)}

Supplemental Table S1

mmc8.docx^{(41.5KB, docx)}

References

1.Okayama H., Curiel D.T., Brantly M.L., Holmes M.D., Crystal R.G. Rapid, nonradioactive detection of mutations in the human genome by allele-specific amplification. J Lab Clin Med. 1989;2:105–113. [PubMed] [Google Scholar]
2.Newton C.R., Graham A., Heptinstall L.E., Powell S.J., Summers C., Kalsheker N., Smith J.C., Markham A.F. Analysis of any point mutation in DNA. The amplification refractory mutation system (ARMS) Nucleic Acids Res. 1989;17:2503–2516. doi: 10.1093/nar/17.7.2503. [DOI] [PMC free article] [PubMed] [Google Scholar]
3.Matsuda K. PCR-based detection methods for single-nucleotide polymorphism or mutation: real-time PCR and its substantial contribution toward technological refinement. Adv Clin Chem. 2017;80:45–72. doi: 10.1016/bs.acc.2016.11.002. [DOI] [PubMed] [Google Scholar]
4.Starostik P. Clinical mutation assay of tumors. Anticancer Drugs. 2017;28:1–10. doi: 10.1097/CAD.0000000000000427. [DOI] [PubMed] [Google Scholar]
5.Kwok P.-Y., Chen X. Detection of single nucleotide polymorphisms. Curr Issues Mol Biol. 2003;5:43–60. [PubMed] [Google Scholar]
6.Roses A.D. Pharmacogenetics and the practice of medicine. Nature. 2000;405:857–865. doi: 10.1038/35015728. [DOI] [PubMed] [Google Scholar]
7.Ridge P.G., Hoyt K.B., Boehme K., Mukherjee S., Crane P.K., Haines J.L., Mayeux R., Farrer L.A., Pericak-Vance M.A., Schellenberg G.D., Kauwe J.S.K., Alzheimer’s Disease Genetics Consortium (ADGC) Assessment of the genetic variance of late-onset Alzheimer’s disease. Neurobiol Aging. 2016;41:200.e13–200.e20. doi: 10.1016/j.neurobiolaging.2016.02.024. [DOI] [PMC free article] [PubMed] [Google Scholar]
8.Zhang Q., Sidorenko J., Couvy-Duchesne B., Marioni R.E., Wright M.J., Goate A.M., Marcora E., Huang K.-L., Porter T., Laws S.M., Australian Imaging Biomarkers and Lifestyle (AIBL) Study, Sachdev P.S., Mather K.A., Armstrong N.J., Thalamuthu A., Brodaty H., Yengo L., Jian Y., Wray N.R., McRae A.F., Visscher P.M. Risk prediction of late-onset Alzheimer’s disease implies an oligogenic architecture. Nat Commun. 2020;11:4799. doi: 10.1038/s41467-020-18534-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
9.Hacia J.G., Fan J.B., Ryder O., Jin L., Edgemon K., Ghandour G., Mayer R.A., Sun B., Hsie L., Robbins C.M., Brody L.C., Wang D., Lander E.S., Lipshutz R., Fodor S.P., Collins F.S. Determination of ancestral alleles for human single-nucleotide polymorphisms using high-density oligonucleotide arrays. Nat Genet. 1999;22:164–167. doi: 10.1038/9674. [DOI] [PubMed] [Google Scholar]
10.Jorde L.B., Watkins W.S., Bamshad M.J., Dixon M.E., Ricker C.E., Seielstad M.T., Batzer M.A. The distribution of human genetic diversity: a comparison of mitochondrial, autosomal, and Y-chromosome data. Am J Hum Genet. 2000;66:979–988. doi: 10.1086/302825. [DOI] [PMC free article] [PubMed] [Google Scholar]
11.D’Gama A.M., Walsh C.A. Somatic mosaicism and neurodevelopmental disease. Nat Neurosci. 2018;21:1504–1514. doi: 10.1038/s41593-018-0257-3. [DOI] [PubMed] [Google Scholar]
12.Biesecker L.G., Spinner N.B. A genomic view of mosaicism and human disease. Nat Rev Genet. 2013;14:307–320. doi: 10.1038/nrg3424. [DOI] [PubMed] [Google Scholar]
13.Miller M.B., Reed H.C., Walsh C.A. Brain somatic mutation in aging and Alzheimer’s disease. Annu Rev Genomics Hum Genet. 2021;22:239–256. doi: 10.1146/annurev-genom-121520-081242. [DOI] [PMC free article] [PubMed] [Google Scholar]
14.Ye Z., McQuillan L., Poduri A., Green T.E., Matsumoto N., Mefford H.C., Scheffer I.E., Berkovic S.F., Hildebrand M.S. Somatic mutation: the hidden genetics of brain malformations and focal epilepsies. Epilepsy Res. 2019;155:106161. doi: 10.1016/j.eplepsyres.2019.106161. [DOI] [PubMed] [Google Scholar]
15.Sim N.S., Ko A., Kim W.K., Kim S.H., Kim J.S., Shim K.-W., Aronica E., Mijnsbergen C., Spliet W.G.M., Koh H.Y., Kim H.D., Lee J.S., Kim D.S., Kang H.-C., Lee J.H. Precise detection of low-level somatic mutation in resected epilepsy brain tissue. Acta Neuropathol. 2019;138:901–912. doi: 10.1007/s00401-019-02052-6. [DOI] [PubMed] [Google Scholar]
16.Lodato M.A., Walsh CA Genome aging: somatic mutation in the brain links age-related decline with disease and nominates pathogenic mechanisms. Hum Mol Genet. 2019;28:R197–R206. doi: 10.1093/hmg/ddz191. [DOI] [PMC free article] [PubMed] [Google Scholar]
17.Paquola A.C.M., Erwin J.A., Gage F.H. Insights into the role of somatic mosaicism in the brain. Curr Opin Syst Biol. 2017;1:90–94. doi: 10.1016/j.coisb.2016.12.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
18.Bizzotto S., Dou Y., Ganz J., Doan R.N., Kwon M., Bohrson C.L., Kim S.N., Bae T., Abyzov A., NIMH Brain Somatic Mosaicism Network, Park P.J., Walsh C.A. Landmarks of human embryonic development inscribed in somatic mutations. Science. 2021;371:1249–1253. doi: 10.1126/science.abe1544. [DOI] [PMC free article] [PubMed] [Google Scholar]
19.Drum M., Kranaster R., Ewald C., Blasczyk R., Marx A. Variants of a Thermus aquaticus DNA polymerase with increased selectivity for applications in allele- and methylation-specific amplification. PLoS One. 2014;9:e96640. doi: 10.1371/journal.pone.0096640. [DOI] [PMC free article] [PubMed] [Google Scholar]
20.Fiala C., Diamandis E.P. New approaches for detecting cancer with circulating cell-free DNA. BMC Med. 2019;17:159. doi: 10.1186/s12916-019-1400-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
21.Ignatiadis M., Lee M., Jeffrey S.S. Circulating tumor cells and circulating tumor DNA: challenges and opportunities on the path to clinical utility. Clin Cancer Res. 2015;21:4786–4800. doi: 10.1158/1078-0432.CCR-14-1190. [DOI] [PubMed] [Google Scholar]
22.Heitzer E., Ulz P., Geigl J.B. Circulating tumor DNA as a liquid biopsy for cancer. Clin Chem. 2015;61:112–123. doi: 10.1373/clinchem.2014.222679. [DOI] [PubMed] [Google Scholar]
23.Heckman K.L., Pease L.R. Gene splicing and mutagenesis by PCR-driven overlap extension. Nat Protoc. 2007;2:924–932. doi: 10.1038/nprot.2007.132. [DOI] [PubMed] [Google Scholar]
24.Coulther T.A., Stern H.R., Beuning P.J. Engineering polymerases for new functions. Trends Biotechnol. 2019;37:1091–1103. doi: 10.1016/j.tibtech.2019.03.011. [DOI] [PMC free article] [PubMed] [Google Scholar]
25.Doublié S., Tabor S., Long A.M., Richardson C.C., Ellenberger T. Crystal structure of a bacteriophage T7 DNA replication complex at 2.2 A resolution. Nature. 1998;391:251–258. doi: 10.1038/34593. [DOI] [PubMed] [Google Scholar]
26.Huber C., Marx A. Variants of sequence family B Thermococcus kodakaraensis DNA polymerase with increased mismatch extension selectivity. PLoS One. 2017;12:e0183623. doi: 10.1371/journal.pone.0183623. [DOI] [PMC free article] [PubMed] [Google Scholar]
27.Raghunathan G., Marx A. Identification of Thermus aquaticus DNA polymerase variants with increased mismatch discrimination and reverse transcriptase activity from a smart enzyme mutant library. Sci Rep. 2019;9:590. doi: 10.1038/s41598-018-37233-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
28.Arezi B., McKinney N., Hansen C., Cayouette M., Fox J., Chen K., Lapira J., Hamilton S., Hogrefe H. Compartmentalized self-replication under fast PCR cycling conditions yields Taq DNA polymerase mutants with increased DNA-binding affinity and blood resistance. Front Microbiol. 2014;5:408. doi: 10.3389/fmicb.2014.00408. [DOI] [PMC free article] [PubMed] [Google Scholar]
29.Li Y., Korolev S., Waksman G. Crystal structures of open and closed forms of binary and ternary complexes of the large fragment of Thermus aquaticus DNA polymerase I: structural basis for nucleotide incorporation. EMBO J. 1998;17:7514–7525. doi: 10.1093/emboj/17.24.7514. [DOI] [PMC free article] [PubMed] [Google Scholar]
30.Allawi H.T., SantaLucia J., Jr. Thermodynamics and NMR of internal G.T mismatches in DNA. Biochemistry. 1997;36:10581–10594. doi: 10.1021/bi962590c. [DOI] [PubMed] [Google Scholar]
31.Mok E., Wee E., Wang Y., Trau M. Comprehensive evaluation of molecular enhancers of the isothermal exponential amplification reaction. Sci Rep. 2016;6:37837. doi: 10.1038/srep37837. [DOI] [PMC free article] [PubMed] [Google Scholar]
32.Kim Y., Eom S.H., Wang J., Lee D.S., Suh S.W., Steitz T.A. Crystal structure of Thermus aquaticus DNA polymerase. Nature. 1995;376:612–616. doi: 10.1038/376612a0. [DOI] [PubMed] [Google Scholar]
33.Yan Y.-Y., Guo Q.-R., Wang F.-H., Adhikari R., Zhu Z.-Y., Zhang H.-Y., Zhou W.-M., Yu H., Li J.-Q., Zhang J.-Y. Cell-free DNA: hope and potential application in cancer. Front Cell Dev Biol. 2021;9:639233. doi: 10.3389/fcell.2021.639233. [DOI] [PMC free article] [PubMed] [Google Scholar]
34.Li H., Jing C., Wu J., Ni J., Sha H., Xu X., Du Y., Lou R., Dong S., Feng J. Circulating tumor DNA detection: a potential tool for colorectal cancer management. Oncol Lett. 2019;17:1409–1416. doi: 10.3892/ol.2018.9794. [DOI] [PMC free article] [PubMed] [Google Scholar]
35.Shi X., Lim Y., Myers A.K., Stallings B.L., Mccoy A., Zeiger J., Scheck J., Cho G., Marsh E.D., Mirzaa G.M., Tao T., Golden J.A. PIK3R2/Pik3r2 Activating mutations result in brain overgrowth and EEG changes. Ann Neurol. 2020;88:1077–1094. doi: 10.1002/ana.25890. [DOI] [PMC free article] [PubMed] [Google Scholar]
36.Lim Y., Cho I.-T., Golden J.A., Cho G. Generation of FLAG-tagged Arx knock-in mouse model. Genesis. 2022;60:e23479. doi: 10.1002/dvg.23479. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplemental Figure S1

mmc1.pdf^{(137.5KB, pdf)}

Supplemental Figure S2

mmc2.pdf^{(126.4KB, pdf)}

Supplemental Figure S3

mmc3.pdf^{(316.4KB, pdf)}

Supplemental Figure S4

mmc4.pdf^{(2.1MB, pdf)}

Supplemental Figure S5

mmc5.pdf^{(1.1MB, pdf)}

Supplemental Figure S6

mmc6.pdf^{(119.6KB, pdf)}

Supplemental Figure S7

mmc7.pdf^{(1.5MB, pdf)}

Supplemental Table S1

mmc8.docx^{(41.5KB, docx)}

[bib1] 1.Okayama H., Curiel D.T., Brantly M.L., Holmes M.D., Crystal R.G. Rapid, nonradioactive detection of mutations in the human genome by allele-specific amplification. J Lab Clin Med. 1989;2:105–113. [PubMed] [Google Scholar]

[bib2] 2.Newton C.R., Graham A., Heptinstall L.E., Powell S.J., Summers C., Kalsheker N., Smith J.C., Markham A.F. Analysis of any point mutation in DNA. The amplification refractory mutation system (ARMS) Nucleic Acids Res. 1989;17:2503–2516. doi: 10.1093/nar/17.7.2503. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib3] 3.Matsuda K. PCR-based detection methods for single-nucleotide polymorphism or mutation: real-time PCR and its substantial contribution toward technological refinement. Adv Clin Chem. 2017;80:45–72. doi: 10.1016/bs.acc.2016.11.002. [DOI] [PubMed] [Google Scholar]

[bib4] 4.Starostik P. Clinical mutation assay of tumors. Anticancer Drugs. 2017;28:1–10. doi: 10.1097/CAD.0000000000000427. [DOI] [PubMed] [Google Scholar]

[bib5] 5.Kwok P.-Y., Chen X. Detection of single nucleotide polymorphisms. Curr Issues Mol Biol. 2003;5:43–60. [PubMed] [Google Scholar]

[bib6] 6.Roses A.D. Pharmacogenetics and the practice of medicine. Nature. 2000;405:857–865. doi: 10.1038/35015728. [DOI] [PubMed] [Google Scholar]

[bib7] 7.Ridge P.G., Hoyt K.B., Boehme K., Mukherjee S., Crane P.K., Haines J.L., Mayeux R., Farrer L.A., Pericak-Vance M.A., Schellenberg G.D., Kauwe J.S.K., Alzheimer’s Disease Genetics Consortium (ADGC) Assessment of the genetic variance of late-onset Alzheimer’s disease. Neurobiol Aging. 2016;41:200.e13–200.e20. doi: 10.1016/j.neurobiolaging.2016.02.024. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib8] 8.Zhang Q., Sidorenko J., Couvy-Duchesne B., Marioni R.E., Wright M.J., Goate A.M., Marcora E., Huang K.-L., Porter T., Laws S.M., Australian Imaging Biomarkers and Lifestyle (AIBL) Study, Sachdev P.S., Mather K.A., Armstrong N.J., Thalamuthu A., Brodaty H., Yengo L., Jian Y., Wray N.R., McRae A.F., Visscher P.M. Risk prediction of late-onset Alzheimer’s disease implies an oligogenic architecture. Nat Commun. 2020;11:4799. doi: 10.1038/s41467-020-18534-1. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib9] 9.Hacia J.G., Fan J.B., Ryder O., Jin L., Edgemon K., Ghandour G., Mayer R.A., Sun B., Hsie L., Robbins C.M., Brody L.C., Wang D., Lander E.S., Lipshutz R., Fodor S.P., Collins F.S. Determination of ancestral alleles for human single-nucleotide polymorphisms using high-density oligonucleotide arrays. Nat Genet. 1999;22:164–167. doi: 10.1038/9674. [DOI] [PubMed] [Google Scholar]

[bib10] 10.Jorde L.B., Watkins W.S., Bamshad M.J., Dixon M.E., Ricker C.E., Seielstad M.T., Batzer M.A. The distribution of human genetic diversity: a comparison of mitochondrial, autosomal, and Y-chromosome data. Am J Hum Genet. 2000;66:979–988. doi: 10.1086/302825. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib11] 11.D’Gama A.M., Walsh C.A. Somatic mosaicism and neurodevelopmental disease. Nat Neurosci. 2018;21:1504–1514. doi: 10.1038/s41593-018-0257-3. [DOI] [PubMed] [Google Scholar]

[bib12] 12.Biesecker L.G., Spinner N.B. A genomic view of mosaicism and human disease. Nat Rev Genet. 2013;14:307–320. doi: 10.1038/nrg3424. [DOI] [PubMed] [Google Scholar]

[bib13] 13.Miller M.B., Reed H.C., Walsh C.A. Brain somatic mutation in aging and Alzheimer’s disease. Annu Rev Genomics Hum Genet. 2021;22:239–256. doi: 10.1146/annurev-genom-121520-081242. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib14] 14.Ye Z., McQuillan L., Poduri A., Green T.E., Matsumoto N., Mefford H.C., Scheffer I.E., Berkovic S.F., Hildebrand M.S. Somatic mutation: the hidden genetics of brain malformations and focal epilepsies. Epilepsy Res. 2019;155:106161. doi: 10.1016/j.eplepsyres.2019.106161. [DOI] [PubMed] [Google Scholar]

[bib15] 15.Sim N.S., Ko A., Kim W.K., Kim S.H., Kim J.S., Shim K.-W., Aronica E., Mijnsbergen C., Spliet W.G.M., Koh H.Y., Kim H.D., Lee J.S., Kim D.S., Kang H.-C., Lee J.H. Precise detection of low-level somatic mutation in resected epilepsy brain tissue. Acta Neuropathol. 2019;138:901–912. doi: 10.1007/s00401-019-02052-6. [DOI] [PubMed] [Google Scholar]

[bib16] 16.Lodato M.A., Walsh CA Genome aging: somatic mutation in the brain links age-related decline with disease and nominates pathogenic mechanisms. Hum Mol Genet. 2019;28:R197–R206. doi: 10.1093/hmg/ddz191. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib17] 17.Paquola A.C.M., Erwin J.A., Gage F.H. Insights into the role of somatic mosaicism in the brain. Curr Opin Syst Biol. 2017;1:90–94. doi: 10.1016/j.coisb.2016.12.004. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib18] 18.Bizzotto S., Dou Y., Ganz J., Doan R.N., Kwon M., Bohrson C.L., Kim S.N., Bae T., Abyzov A., NIMH Brain Somatic Mosaicism Network, Park P.J., Walsh C.A. Landmarks of human embryonic development inscribed in somatic mutations. Science. 2021;371:1249–1253. doi: 10.1126/science.abe1544. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib19] 19.Drum M., Kranaster R., Ewald C., Blasczyk R., Marx A. Variants of a Thermus aquaticus DNA polymerase with increased selectivity for applications in allele- and methylation-specific amplification. PLoS One. 2014;9:e96640. doi: 10.1371/journal.pone.0096640. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib20] 20.Fiala C., Diamandis E.P. New approaches for detecting cancer with circulating cell-free DNA. BMC Med. 2019;17:159. doi: 10.1186/s12916-019-1400-z. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib21] 21.Ignatiadis M., Lee M., Jeffrey S.S. Circulating tumor cells and circulating tumor DNA: challenges and opportunities on the path to clinical utility. Clin Cancer Res. 2015;21:4786–4800. doi: 10.1158/1078-0432.CCR-14-1190. [DOI] [PubMed] [Google Scholar]

[bib22] 22.Heitzer E., Ulz P., Geigl J.B. Circulating tumor DNA as a liquid biopsy for cancer. Clin Chem. 2015;61:112–123. doi: 10.1373/clinchem.2014.222679. [DOI] [PubMed] [Google Scholar]

[bib23] 23.Heckman K.L., Pease L.R. Gene splicing and mutagenesis by PCR-driven overlap extension. Nat Protoc. 2007;2:924–932. doi: 10.1038/nprot.2007.132. [DOI] [PubMed] [Google Scholar]

[bib24] 24.Coulther T.A., Stern H.R., Beuning P.J. Engineering polymerases for new functions. Trends Biotechnol. 2019;37:1091–1103. doi: 10.1016/j.tibtech.2019.03.011. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib25] 25.Doublié S., Tabor S., Long A.M., Richardson C.C., Ellenberger T. Crystal structure of a bacteriophage T7 DNA replication complex at 2.2 A resolution. Nature. 1998;391:251–258. doi: 10.1038/34593. [DOI] [PubMed] [Google Scholar]

[bib26] 26.Huber C., Marx A. Variants of sequence family B Thermococcus kodakaraensis DNA polymerase with increased mismatch extension selectivity. PLoS One. 2017;12:e0183623. doi: 10.1371/journal.pone.0183623. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib27] 27.Raghunathan G., Marx A. Identification of Thermus aquaticus DNA polymerase variants with increased mismatch discrimination and reverse transcriptase activity from a smart enzyme mutant library. Sci Rep. 2019;9:590. doi: 10.1038/s41598-018-37233-y. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib28] 28.Arezi B., McKinney N., Hansen C., Cayouette M., Fox J., Chen K., Lapira J., Hamilton S., Hogrefe H. Compartmentalized self-replication under fast PCR cycling conditions yields Taq DNA polymerase mutants with increased DNA-binding affinity and blood resistance. Front Microbiol. 2014;5:408. doi: 10.3389/fmicb.2014.00408. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib29] 29.Li Y., Korolev S., Waksman G. Crystal structures of open and closed forms of binary and ternary complexes of the large fragment of Thermus aquaticus DNA polymerase I: structural basis for nucleotide incorporation. EMBO J. 1998;17:7514–7525. doi: 10.1093/emboj/17.24.7514. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib30] 30.Allawi H.T., SantaLucia J., Jr. Thermodynamics and NMR of internal G.T mismatches in DNA. Biochemistry. 1997;36:10581–10594. doi: 10.1021/bi962590c. [DOI] [PubMed] [Google Scholar]

[bib31] 31.Mok E., Wee E., Wang Y., Trau M. Comprehensive evaluation of molecular enhancers of the isothermal exponential amplification reaction. Sci Rep. 2016;6:37837. doi: 10.1038/srep37837. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib32] 32.Kim Y., Eom S.H., Wang J., Lee D.S., Suh S.W., Steitz T.A. Crystal structure of Thermus aquaticus DNA polymerase. Nature. 1995;376:612–616. doi: 10.1038/376612a0. [DOI] [PubMed] [Google Scholar]

[bib33] 33.Yan Y.-Y., Guo Q.-R., Wang F.-H., Adhikari R., Zhu Z.-Y., Zhang H.-Y., Zhou W.-M., Yu H., Li J.-Q., Zhang J.-Y. Cell-free DNA: hope and potential application in cancer. Front Cell Dev Biol. 2021;9:639233. doi: 10.3389/fcell.2021.639233. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib34] 34.Li H., Jing C., Wu J., Ni J., Sha H., Xu X., Du Y., Lou R., Dong S., Feng J. Circulating tumor DNA detection: a potential tool for colorectal cancer management. Oncol Lett. 2019;17:1409–1416. doi: 10.3892/ol.2018.9794. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib35] 35.Shi X., Lim Y., Myers A.K., Stallings B.L., Mccoy A., Zeiger J., Scheck J., Cho G., Marsh E.D., Mirzaa G.M., Tao T., Golden J.A. PIK3R2/Pik3r2 Activating mutations result in brain overgrowth and EEG changes. Ann Neurol. 2020;88:1077–1094. doi: 10.1002/ana.25890. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib36] 36.Lim Y., Cho I.-T., Golden J.A., Cho G. Generation of FLAG-tagged Arx knock-in mouse model. Genesis. 2022;60:e23479. doi: 10.1002/dvg.23479. [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

Modified Taq DNA Polymerase for Allele-Specific Ultra-Sensitive Detection of Genetic Variants

Youngshin Lim

Il-Hyun Park

Huy-Ho Lee

Kyuwon Baek

Byung-Chul Lee

Ginam Cho

Abstract

Materials and Methods

Mutagenesis of Taq DNA Polymerase Variants

Table 1.

Figure 1.

Expression and Purification of Taq DNA Polymerase Variants

Primer and Probe Design

Template Preparation for qPCR

qPCR Conditions

Results

Engineering Taq DNA Polymerase for Improved Mismatch Discrimination and Polymerization Activity

E507K/R536K/R660V TM-Taq DNA Polymerase Shows Excellent Selectivity in SNP Genotyping and Cancer DNA Mutation Detection

Figure 2.

TM-Taq DNA Polymerase Shows a Higher Mismatch Selectivity with all Possible Mismatch Types

Table 2.

Figure 3.

Primer Design and Buffer Condition Can Affect the Mismatch Selectivity of TM-Taq DNA Polymerase

Ultra-sensitive Detection of Rare Somatic Mutations by TM-Taq DNA Polymerase–Based ADPS

Figure 4.

Table 3.

Figure 5.

Discussion

Figure 6.

Acknowledgments

Footnotes

Author Contributions

Supplemental Data

References

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

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