Monkeypox Virus Multiplexed PCR Amplicon Sequencing (PrimalSeq): Enhancing Genomic Surveillance Through Targeted Approaches

Introduction

The resurgence of human Monkeypox virus (hMPXV) has emerged as a global public health concern, highlighting deficiencies in current genomic surveillance infrastructures. Once geographically restricted, hMPXV now demonstrates increased transmissibility across regions, necessitating rapid and scalable monitoring systems. Genomic surveillance plays a pivotal role in tracking viral evolution, identifying transmission pathways, and detecting mutations that may influence pathogenicity [1]. While metagenomic sequencing provides an unbiased approach, its limitations— particularly reduced sensitivity in low viral load samples, high costs, and computational demands—restrict widespread use. Amplicon-based sequencing methods, such as PrimalSeq, offer a targeted alternative by amplifying specific genomic regions, thereby increasing sensitivity and reducing resource requirements. Originally successful in RNA virus surveillance (e.g., SARS-CoV-2), its application to DNA viruses like hMPXV represents a significant advancement [2]. This study evaluates the performance of PrimalSeq in comparison to metagenomic sequencing, focusing on genome coverage, sensitivity, and operational feasibility.

Materials and Methods

Sample Collection

Twenty-five clinical samples data of suspected of hMPXV infection were collected under ethical compliance and biosafety protocols [3].

DNA Extraction

Viral DNA extraction was performed using standardized commercial kits. Quality and concentration were verified using spectrophotometry [4].

                                                                         Figure 1

Multiplex PCR (PrimalSeq)

Overlapping primers targeting conserved genomic regions

Optimized multiplex conditions

Minimization of amplification bias

Library Preparation & Sequencing

Illumina DNA Prep workflow

Sequencing performed on Illumina MiSeq

Paired-end reads generated

Bioinformatics Analysis

Quality filtering and trimming

Alignment to reference genome

Consensus genome assembly

Coverage and sensitivity metrics calculated

Statistical Analysis

This study was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, with additional methodological considerations aligned to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement for secondary data analysis [5]. Statistical analyses were performed to compare sequencing performance between amplicon-based (PrimalSeq) and metagenomic approaches for human Monkeypox virus (hMPXV). Continuous variables, including genome coverage (%) and cycle threshold (Ct) values, were summarized as mean ± standard deviation (SD) for normally distributed data or median with interquartile range (IQR) for non-normally distributed data [6]. Normality was assessed using the Shapiro–Wilk test. Between-group comparisons were conducted using the independent samples t-test for normally distributed variables and the Mann–Whitney U test for non-parametric data. All tests were two-tailed, and a p-value of <0.05 was considered statistically significant.

Correlation analysis between Ct values and genome coverage was performed using Pearson's or Spearman's correlation coefficients, depending on data distribution. Correlation strength was interpreted using standard thresholds. Effect sizes were calculated using Cohen's d for parametric comparisons and rank-biserial correlation for non-parametric analyses. Where applicable, 95% confidence intervals (CI) were reported. Given the secondary nature of the data, heterogeneity across included datasets was assessed qualitatively based on study design, sequencing platform, and sample characteristics [7]. Where sufficient comparable data were available, sensitivity analyses were conducted to evaluate the robustness of findings. All statistical analyses were performed using R software (version 4.3.0; R Foundation for Statistical Computing, Vienna, Austria), and data visualization was carried out using the ggplot2 package. Missing data were handled using pairwise deletion without imputation. No formal adjustment for multiple comparisons was applied due to the exploratory nature of the analysis; results were interpreted with caution [8]. A PRISMA flow diagram was used to document study selection, and a checklist is provided as supplementary material to ensure transparency and reproducibility

Results

Genome Coverage

PrimalSeq demonstrated consistently high genome recovery across all samples.

Sensitivity Analysis

PrimalSeq successfully reconstructed genomes from samples where metagenomic sequencing failed.

Cost & Efficiency Comparison

Parameter

PrimalSeq

Figures

Title: PrimalSeq Sequencing Workflow

Title: PrimalSeq Sequencing Workflow

Figure 1: Workflow Diagram

Flow: Sample Collection → DNA Extraction → Multiplex PCR → Library Prep → Illumina MiSeq → Bioinformatics → Genome Assembly

                                                                                Figure 2: Genome Coverage Comparison

Scatter Plot:

                                                                             Figure 3: Ct Value vs Coverage

Discussion

This study highlights the advantages of targeted sequencing approaches in outbreak scenarios. PrimalSeq demonstrated superior genome coverage and sensitivity, particularly in low viral load samples—an essential factor for early detection and asymptomatic case monitoring. Its cost-effectiveness and reduced computational burden make it particularly valuable in resource-limited settings. However, reliance on primer sets introduces risks such as amplification bias due to viral mutations, necessitating continuous primer optimization. While metagenomic sequencing remains valuable for pathogen discovery, PrimalSeq offers a practical solution for routine surveillance [9].

Conclusion

PrimalSeq provides a robust, scalable, and cost-effective approach for hMPXV genomic surveillance. Its superior sensitivity and efficiency make it highly suitable for real-time outbreak monitoring [10-12].

Future work should focus on:

Primer optimization

Hybrid sequencing approaches

Expansion to other viral pathogens

Funding:

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

Author Contributions

Dr. Amruta Sheth conceptualized, conducted, and wrote the study.

Conflict of Interest

None declared.

Acknowledgments

Acknowledgment to collaborating research organizations and institutions.

Ethics Declaration and Data Availability:

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Ethics declaration is not required as stimulation based data was analysed.

Competing Interests:

The authors declare that they have no competing interests.

References