The events of the last decade have made clear that a single pathogenic microbe can bring modern life to a standstill, as SARS-CoV-2 did when it swept across the globe in 2020. The COVID-19 pandemic exposed critical gaps in global preparedness infrastructure and underscored the need for microbial surveillance systems that empower a rapid response.

The threats are significant, demanding vigilance in public health monitoring practices. Emerging viruses like Influenza and SARS-CoV-2, whether arising from genetic variation or zoonotic spillover events that transfer pathogens from animals to humans, can spread through immunologically naive populations with devastating speed. Meanwhile, declining vaccination rates1 are fueling the re-emergence of diseases (e.g., measles) that were once considered controlled. Looming in parallel is the antimicrobial resistance (AMR) crisis for which the World Health Organization has identified a priority pathogen list.2 For example, Carbapenem-resistant Acinetobacter baumannii has been identified as the highest priority level (i.e., critical) potentially leading into an era of untreatable infections. Without intervention, AMR is projected to cause 10 million deaths annually by 2050.3

Across all these threat categories is the urgent need for efficient, accurate, and scalable technologies to rapidly detect and genetically characterize microbial threats. These tools can inform containment strategies, guide treatment decisions, and protect public health before the next outbreak becomes the next pandemic.

 

Technologies enhancing microbial surveillance

Modern microbial surveillance requires a strategic shift from “finding everything” to “monitoring what matters” with high sensitivity. Broad metagenomic sequencing, while powerful, generates enormous volumes of data dominated by host and irrelevant environmental sequences, making it expensive and analytically burdensome for routine monitoring applications.

Targeted next-generation sequencing (NGS) with hybridization capture is a compelling alternative. By using sequence-specific RNA or DNA “baits” or probes, hybridization capture selectively enriches targets of interest directly from complex sample matrices like clinical specimens, wastewater, environmental swabs, and degraded archived material, while avoiding the inefficiency of sequencing host or non-target DNA. The result is deeper coverage of specific genomic sequences at a fraction of the cost, sequencing power, and analysis time. This makes large multi-pathogen panels practical for routine, high-throughput surveillance programs.

Daicel Arbor Biosciences’ myBaits® platform offers both predesigned and fully custom targeted NGS hybridization capture panels built for this purpose. The myBaits Expert Respiratory Virus Panel provides modular, mix-and-match virus coverage to meet specific surveillance mandates. Daicel Arbor Biosciences also offers myBaits predesigned community panels for antimicrobial resistance surveillance. For programs requiring custom designs (e.g., focused or expanded AMR gene panels, multi-pathogen panels, or region-specific surveillance targets) bait sets can be optimized for specific samples and scientific questions.

 

Leveraging hybridization capture in public health monitoring

Precise genetic characterization of microbes does more than confirm identity. It reveals the sequence-level changes that signal increased virulence, reduced vaccine efficacy, or the emergence of treatment resistance. This same data can be leveraged to identify new drug targets to prevent or protect against future infection. Across these three active areas of public health research, hybridization capture is proving its value. Additionally, unlike other targeted approaches (e.g., qPCR, PCR) hybridization bait capture can capture sequence divergence, allowing researchers to detect novel genes without changing their assay.

 

Tracking the resistome across environments

Finding and characterizing reservoirs of antimicrobial resistance genes (ARGs) is fundamental to understanding and combating the spread of superbugs. Beaudry et al.4 demonstrated the power of hybridization capture for exactly this purpose, deploying a set of 19,933 baits designed from the Comprehensive Antibiotic Resistance Database (CARD) v1.1.2 and Pathogenicity Island Database (PAIDB) v 2.0 to enrich ARGs from custom resistance mock communities and complex environmental samples. The enrichment increased the proportion of on-target reads by more than 200-fold compared to unenriched libraries, a dramatic efficiency gain that makes resistome surveillance feasible at scale.

The authors also addressed a key challenge inherent to AMR surveillance: novel resistance genes are continuously being discovered, and bait sets must be updated accordingly. Their framework for forecasting bait set performance and planning timely updates provides a practical roadmap for programs committed to staying ahead of an evolving resistome.

 

Wastewater as a window into circulating viral variants

Clinical testing is often incomplete or lagging, as such wastewater-based epidemiology (WBE) can fill the gap, but only if the sequencing approach is sensitive enough to detect minority variants in a complex environmental matrix. Li et al.5 demonstrated this capability by deploying a myBaits coronavirus-specific bait set to enrich and sequence SARS-CoV-2 from wastewater collected at water reclamation facilities serving a metropolitan area over an eight-month period.

Variant signatures detected in wastewater, including dominant circulating lineages like Delta (B.1.617.2) and Alpha (B.1.1.7), as well as lower-abundance variants, correlated directly with those identified through clinical specimen sequencing during the same periods. Critically, the wastewater data detected Alpha variant signatures as early as November 2020, when only a small number of clinical cases had been confirmed in the county. This kind of early pathogen detection capability, enabled by targeted enrichment sequencing, transforms wastewater-based epidemiology into a practical population-level genomic surveillance tool.

 

Detecting and characterizing zoonotic spillover

When respiratory disease broke out across three groups of human-habituated western lowland gorillas in the Sangha Trinational Protected Area Network in 2019, researchers needed to rapidly identify the causative agent and determine whether transmission was spreading between gorilla groups or originating repeatedly from humans. Jochum et al.6 used an RNA-bait hybridization capture kit targeting major human respiratory viruses, coupled with high-throughput sequencing, to reconstruct nearly complete viral genomes from non-invasive fecal samples. The genomic analyses revealed two distinct viral types, RSV A in one gorilla group and RSV B in two others at a site more than 30 km away, indicating independent human-to-gorilla transmission events rather than inter-group spread. This level of resolution enables the refinement of containment protocols to protect animal health and support conservation efforts.

In a parallel example of pathogen genomics supporting zoonotic surveillance, Ebinger et al.7 used a set of 17,858 myBaits targeting the full Bornaviridae genome to enrich and sequence Borna disease virus 1 (BoDV-1) from archived brain tissue and CSF samples collected from infected animals and humans across Germany, Austria, Switzerland, and Liechtenstein. In 90% of cases within 40 km of the patient’s residence, the resulting phylogeny linked human BoDV-1 sequences to nearby animal cases enabling the construction of high-resolution risk maps for zoonotic transmission. This approach demonstrates how targeted enrichment sequencing, even from degraded FFPE material, can generate the genomic data needed to define high-risk zones for zoonotic transmission.

 

Strengthening your surveillance toolkit

As microbial threats continue to evolve and emerge, surveillance programs that rely on broad or low-sensitivity methods will consistently lag behind. Across AMR, respiratory virus surveillance, and zoonotic monitoring, hybridization capture enables researchers and public health agencies to extract maximum genomic information from complex, low-titer, or degraded samples efficiently and cost-effectively.

Targeted NGS with myBaits panels provides a flexible solution. Whether your program can benefit from a predesigned panel or requires a fully custom multi-pathogen, viral genomics, or AMR panel, Daicel Arbor Biosciences has the expertise to support your work from design through deployment.

Explore the myBaits Expert Respiratory Virus Panel or contact us to discuss a custom panel designed around your surveillance priorities.

 

References

  1. Willams, E., Kates, J., and Michaud J. Kindergarten routine vaccination rates continue to decline. org, 5 Aug 2025. https://www.kff.org/medicaid/kindergarten-routine-vaccination-rates-continue-to-decline/
  2. World Health Organization. WHO bacterial priority pathogens list, 2024: Bacterial pathogens of public health importance to guide research, development and strategies to prevent and control antimicrobial resistance. 17 May 2024. https://www.who.int/publications/i/item/9789240093461
  3. Nazir, A., et al. The global challenge of antimicrobial resistance: mechanisms, case studies, and mitigation approaches. Health Sci Rep. 2025 Jul 23;8(7):e71077. https://doi.org/10.1002/hsr2.71077
  4. Beaudry MS, et al. Escaping the fate of Sisyphus: assessing resistome hybridization baits for antimicrobial resistance gene capture. Environ Microbiol. 2021;23(12):7523–7537. https://doi.org/10.1111/1462-2920.15767
  5. Li L, et al. Detecting SARS-CoV-2 variants in wastewater and their correlation with circulating variants in the communities. Sci Rep. 2022;12:16141. https://doi.org/10.1038/s41598-022-20219-2
  6. Jochum MJS, et al. Outbreaks of human respiratory syncytial virus in wild gorillas highlight the importance of prevention measures and integrated surveillance for risk mitigation. One Health. 2026;22:101376. https://doi.org/10.1016/j.onehlt.2026.101376
  7. Ebinger A, et al. Lethal Borna disease virus 1 infections of humans and animals – in-depth molecular epidemiology and phylogeography. Nat Commun. 2024;15:7908. https://doi.org/10.1038/s41467-024-52192-x

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