Simultaneous Detection of Seven Human Coronaviruses

Simultaneous Detection of Seven Human Coronaviruses: Comparison

Please note this is a comparison between Version 1 by Wenwen Xin and Version 2 by Yvaine Wei.

Human coronaviruses (HCoVs) are associated with a range of respiratory symptoms. The discovery of severe acute respiratory syndrome (SARS)-CoV, Middle East respiratory syndrome, and SARS-CoV-2 pose a significant threat to human health. The HCoV-MS method is a sensitive assay that combines multiplex PCR with matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS), to detect and differentiate seven HCoVs simultaneously.

human coronavirus
MALDI-TOF MS
RT-PCR
high throughput

1. Introduction

Coronaviruses (CoVs) are large, enveloped, positive-sense RNA viruses that cause respiratory diseases in a range of animals, including humans [1]. CoVs are divided into four genera, namely δ-CoVs, γ-CoVs, β-CoVs, and α-CoVs, among which β-CoVs and α-CoVs can infect mammals [2]. Seven human CoVs (HCoVs) have been identified, including HCoV-NL63, HCoV-229E, HCoV-OC43, HCoV-HKU1, SARS-CoV, MERS-CoV, and SARS-CoV-2 ^[1][3][1,3]. Respiratory diseases caused by HCoV infection range from mild to severe. Approximately 15–30% of respiratory tract infections worldwide each year are caused by HCoV-229E, HCoV-OC43, HCoV-NL63, and HCoV-HKU1. They are mild and self-healing diseases that do not pose a major threat to public health [4].

Studies have shown that HCoV most probably originated in wild animal hosts such as bats. A rich gene pool of SARS-related CoVs was found in bats in a cave in Yunnan, China ^[5][6]. Related viruses may reappear at any time and possibly mutate to produce more pathogenic CoV variants. Effective treatment methods are lacking for SARS-CoV, MERS-CoV, and SARS-CoV-2. Moreover, the initial symptoms of HCoVs infection are similar, but the treatment methods are different. The rapid, accurate detection and diagnosis of HCoV will help treat and block the spread, minimizing the loss of life caused by an epidemic. Therefore, it is important to develop a sensitive, high-throughput detection method for HCoV.

The traditional method for detecting HCoVs involves cell culture isolating the virus from clinical specimens. However, this approach is time-consuming, and most of the common cell lines are not suitable for the growth of HCoV. In addition to being time-consuming, these contributing factors do not constitute a conventional diagnostic method ^[6][7]. Next-generation sequencing technology can obtain whole-genome information of HCoVs, which contributes to our knowledge of HCoVs and helps the discovery of unknown HCoVs. However, this technology requires sophisticated bioinformatic analysis and is expensive and time-consuming; hence, it is unsuitable for large-scale population screening ^[7][8]. In recent years, real-time multiplex PCR (RT-PCR) and mass spectrometry technologies have gradually been developed into established pathogen detection methods, which are widely used today ^[8][9][10][9,10,11]. Multiplex RT-PCR is highly specific and sensitive and short detection time, making it a rapid and reliable diagnostic tool. Unfortunately, this methodology has some drawbacks. The types of fluorescence and light sources are limited, and although it is highly sensitive, a correspondingly larger sample size is required ^[11][12]. Multiplex RT-PCR theoretically detects more than a dozen viruses ^[12][13]. RT-PCR has become the gold standard for HCoV detection ^[13][14]. However, when multiple RT-PCRs are run, the sensitivity decreases as the detection factor increases ^[12][14][13,15].

Nucleic acid mass spectrometry analysis, where multiplex PCR (mPCR) is combined with matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS), designs sequence targets based on single nucleotide polymorphism (SNP) sites to achieve multiple detections ^[15][16][16,17]. Nucleic acid mass spectrometry technology allows multi-target high-throughput screening and can, in principle, reach about 40 detections. This is not possible with other multiplex detection methods. Moreover, MALDI-TOF MS is known for its strong specificity and high sensitivity. At present, nucleic acid mass spectrometry has been widely used for multiple detection and typing of bacteria and viruses. For example, it has been used for the simultaneous detection of 10 duck viruses ^[17][18], multi-site typing of Mycoplasma pneumoniae, simultaneous detection of drug resistance ^[18][19], and simultaneous detection of 21 common respiratory viruses ^[19][20]. There are many detection methods for respiratory viruses, each with different detection specificities, sensitivities, and detection limits ^[20][21][21,22].

2. Performance of the Hcov-MS Method

The initial concentration of the mPCR primers for all HCoVs target genes was 0.5 μM. Mixing of the mass probes extension (MPE) primers was based on equalizing the mass spectrum signal intensity of each primer; hence, the amount added was slightly different according to the molecular weight. None of the primers and probes participated in an extension reaction when deionized water was used as the template for detection. When the mixed plasmid (10² copies/μL) was used as the template for detection, target genes of other plasmids could be detected to form specific product peaks by MPE, except NL63-RdRp, which had low amplification efficiency. In this case, the concentration of the NL63-RdRp mPCR primer in the mixed primer (reaction concentration was 4 μM) was used to optimize the system. After optimization, each primer was specifically amplified at 45 cycles.

3. Specificity of the HCoV-MS Method

Nine high-concentration (10⁵ copies/μL) plasmids containing target genes were used to verify the specificity of the system, with the number of mPCR cycles set at 30, 35, 40, or 45. The results showed that high-concentration plasmids amplified well in the detection system and that all target genes could be specifically amplified in 45 cycles. Respiratory samples containing high concentrations of ADV7, InfB, H1N1, and H3N2 viruses were used to evaluate the specificity of the HCoV-MS method. The results showed no cross-reactivity, suggesting that the specificity of the HCoV-MS method is good.

4. Sensitivity of the HCoV-MS Method

Serial plasmid dilutions were used to evaluate the sensitivity of the HCoV-MS method. The detection limits of part of the target genes are shown in Figure 12, and listed in Table 12. The detection limit of the HCoV-MS method was found to be 1–5 copies/reaction.

Figure 12. The detection limit of part of target genes: (a) 1 copy/reaction, (b) 2.5 copies/reaction, (c) 5 copies/reaction, and (d) 10 copies/reaction. The red arrow in each figure refers to extended or unextended primer.

Table 1. Detection limit of the human coronavirus-mass spectrometry (HCoV-MS) method.

Assays	Target	Detection Limit (Copies/Reaction)
5
SARS-CoV-2

	N1

2.5
N2
2.5
S	2.5
ORF1b	2.5

5. Sensitivity Comparisons of HCoV-MS and RT-PCR

A total of 26 SARS-CoV-2 clinical samples were serially diluted, and the HCoV-MS and RT-PCR methods were used for simultaneous detection to compare their detection sensitivities. The detection limit gradient of the 26 SARS-CoV-2 clinical samples is shown in Figure 2 3 and Table 24. Results for the majority of the clinical samples were the same for the two methods or differed by only 1–2 gradients. Furthermore, only a few samples showed a significantly different detection gradient in the experiment. Evidently, the detection sensitivity of the HCoV-MS method is almost the same as that of RT-PCR.

Figure 23. Comparison of the gradient of detection limit of 26 severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) clinical samples. The concentration of the gradient 1–5 is gradually increasing.

Table 2. The detection limit gradient of 26 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) clinical samples.

Sample	Gradient
	1		2		3		4		5
	RT-PCR *	HCoV-MS *	RT-PCR	HCoV-MS	RT-PCR	HCoV-MS	RT-PCR	HCoV-MS	RT-PCR	HCoV-MS
Human RNase P	Human RNase P	1
1	No Ct/No Ct	− ¹	No Ct/No Ct	−	40.74/No Ct	+ ²	36.87/No Ct	+	34.17/No Ct	HCoV-NL63	N	1
+										HCoV-NL63
2	No Ct/No Ct	−	No Ct/No Ct	−	40.65/No Ct	−	36.30/No Ct	+	34.36/No Ct	RdRp	1
+	HCoV-229E	N	2.5
3	HCoV-229E	35.94/No Ct	−	32.84/No Ct	−	33.35/No Ct	−	32.56/No Ct	+	30.16/No Ct	+
4	36.88/34.01	+	35.96/32.03	+	32.92/29.77	+	31.24/27.63	+	28.52/25.92	+	RdRp	2.5
5	39.64/33.68	+	37.01/32.01	+	34.79/30.05	+	33.03/27.92	+	30.6/25.83	+	HCoV-OC43	N	2.5
6	37.33/35.04	+	36.67/32.81	+	35.45/31.17	+	34.28/29.21	RdRp	2.5		HCoV-OC43
+	32.9/27.14	+	HCoV-HKU1	N
7	40.63/34.79	+	HCoV-HKU1	40.11/32.60	+	38.66/30.97	+	35.83/28.46	+	33.83/26.86	+	1
8	No Ct/No Ct	−	No Ct/39.02	−	39.35/36.45	−	36.3/34.71	+	35.15/32.30	+	RdRp	2.5
9	No Ct/No Ct	−	No Ct/No Ct	−	No Ct/No Ct	−	39.33/36.50	+	36.32/34.60	+	MERS-CoV	N	1
10	No Ct/No Ct	−	37.23/37.43	−	37.06/34.76	+	35.16/32.90	+	33.24/30.99	+		RdRp	2.5
11	39.02/36.78	−	36.35/33.84	+	34.35/31.44	+	32.36/30.16	+	30.66/28.16	+		E	2.5
12	No Ct/No Ct	+	No Ct/No Ct	+	No Ct/38.39	+	35.95/35.15	+	34.61/32.06	ORF1b		2.5
SARS-CoV	E	5
	+
	13	No Ct/No Ct	+	No Ct/No Ct	+	No Ct/No Ct	+	34.99/No Ct	+	33.94/38.01	+
14	No Ct/No Ct	−	No Ct/No Ct	−	No Ct/38.16	+	37.12/35.66	+	34.52/33.22	+	N	5
15	No Ct/No Ct	+	37.15/36.10	+	37.04/33.67	+	35.26/31.76	+	33.01/30.57	+	ORF1b
16	No Ct/No Ct	+	40.81/No Ct	+	No Ct/38.45	+	39.89/36.62	+	37.84/35.04	+
17	No Ct/No Ct	−	No Ct/39.73	−	39.31/37.82	+	37.87/36.36	+	36.43/34.64	+
18	No Ct/No Ct	−	No Ct/40.08	−	No Ct/39.48	−	42.18/38.98	+	40.41/36.18	+
19	No Ct/No Ct	+	37.22/36.00	+	36.90/34.46	+	34.81/33.69	+	32.92/31.26	+
20	37.80/32.63	+	36.22/30.56	+	33.91/28.28	+	32.24/26.46	+	29.53/23.98	+
21	39.93/34.69	+	36.10/32.38	+	32.28/30.01	+	30.51/27.60	+	27.69/25.37	+
22	No Ct/No Ct	−	No Ct/38.56	+	38.56/39.32	+	37.10/34.75	+	36.13/33.54	+
23	No Ct/No Ct	−	35.15/No Ct	−	34.93/No Ct	−	34.15/38.72	−	30.79/35.09	+
24	No Ct/No Ct	−	39.17/36.95	+	36.61/34.35	+	35.95/31.70	+	33.32/29.73	+
25	No Ct/No Ct	−	No Ct/No Ct	+	No Ct/38.88	+	36.66/37.20	+	33.94/35.70	+
26	No Ct/No Ct	−	36.46/No Ct	+	34.97/38.94	+	35.39/35.67	+	33.65/34.16	+

The first RT-PCR value represents RT-PCR-ROX-N, while the second represents RT-PCR-FAM-ORF1ab. The concentration of the gradient 1–5 is gradually increasing. ¹ “−” represents a negative HCoV-MS result. ² “+” represents a positive HCoV-MS result. * RT-PCR: real time PCR * HCoV-MS: human coronavirus-mass spectrometry.

6. Research Findings of the HCoV-MS Method

A HCoV-MS method was established to simultaneously detect seven HcoVs, which can be used as a detection system when new HCoVs appear. Notably, the detection sensitivity of HCoV-MS is 1–5 copies/reaction. Except for SARS-CoV, other HCoVs could be detected with 1–2.5 copies/reaction. HCoV-NL63 was even detected with 1 copy/reaction. This sensitivity of this method outperforms that of other detection methods ^{[20][21][22][23][24]}[21,22,23,24,25].

When testing 151 unknown clinical samples, the specificity and sensitivity of the HCoV-MS method reached 100%, surpassing other methods ^[25][26][27][26,27,28]. This could, however, be due to insufficient clinical samples. Samples of human/animal throat swabs or cell cultures were obtained in cooperation with UN-CoV-2020. Seven samples were positive, including four SARS-CoV-2 samples, two SARS-CoV samples, and one HCoV-NL63 sample. The results obtained with the HCoV-MS method were consistent with the official answers (Table S3). Moreover, the concentration of individual positive samples was low, but could still be accurately identified, which further highlights the detection ability of the HCoV-MS method.

The HCoV-MS method is high throughput, as reflected in the detection of multiple target genes and the requirement of small sample sizes. The HCoV-MS method could simultaneously detect 384 targets in one run spanning 30 min, with results automatically determined by the relevant software. Moreover, the reagent cost of the HCoV-MS method is relatively low, making this method ideal for large-scale population screening.

The HCoV-MS also has some limitations. This method is difficult to identify new HCoV because it is based on comparing and analyzing known HCoV sequences, selecting gene fragments with conserved intraspecies specificity. In conclusion, the HCoV-MS method has the characteristics of high throughput, speed, and sensitivity, only requiring a small number of samples. Therefore, it is expected to be a supplement to real-time PCR technology.