Polymerase Chain Reaction (PCR) for Avian Influenza Virus Detection

Introduction

Avian influenza virus (AIV) is a segmented negative-sense RNA virus belonging to the family Orthomyxoviridae that causes significant economic losses in poultry production and poses a persistent threat to wild bird populations [1, 2]. The virus is classified into low pathogenic (LPAI) and highly pathogenic (HPAI) forms based on the presence of a multibasic cleavage site in the hemagglutinin (HA) protein, with H5 and H7 subtypes being the most frequently associated with high pathogenicity [3, 4]. Rapid and accurate detection of AIV is essential for outbreak control, surveillance, and trade compliance [5, 6]. Polymerase chain reaction (PCR), specifically reverse transcription PCR (RT-PCR) and its real-time quantitative variant (RT-qPCR), has become the gold standard molecular diagnostic method for AIV detection due to its high sensitivity, specificity, and rapid turnaround time [7, 8, 9]. This article provides a detailed technical review of PCR-based methods for AIV detection, focusing on assay design, target genes, sample types, multiplexing strategies, digital PCR, and quality assurance.

Principles of RT-PCR for Avian Influenza Virus

AIV possesses a single-stranded RNA genome of approximately 13.5 kb organized into eight gene segments encoding at least 10 proteins [10, 11]. Detection of viral RNA requires an initial reverse transcription step to generate complementary DNA (cDNA), followed by PCR amplification of a conserved target region [12]. The most commonly targeted gene for universal AIV detection is the matrix (M) gene, which is highly conserved across all influenza A subtypes [13, 12]. Alternative targets include the nucleoprotein (NP) gene and the nonstructural (NS) gene, though the M gene remains the preferred target for screening assays [14, 15].

Conventional RT-PCR uses endpoint detection via agarose gel electrophoresis, while real-time RT-qPCR incorporates fluorescent probes (e.g., TaqMan, SYBR Green) to monitor amplification in real time [16, 17]. TaqMan probes, often with minor groove binder (MGB) modifications, provide enhanced specificity and are widely used for AIV subtyping [16, 17]. The cycle threshold (Ct) value is inversely proportional to the initial viral RNA copy number, allowing semi-quantitative or absolute quantification when a standard curve is included [12]. The analytical sensitivity of RT-qPCR for AIV typically ranges from 10 to 100 RNA copies per reaction, depending on the assay design and sample matrix [18, 9].

Assay Design and Target Genes

Primer and probe design for AIV detection must account for genetic diversity among subtypes and clades. The matrix gene assay developed by Spackman et al. (2002) remains a widely used reference, but continuous evolution of AIV necessitates periodic updates to primer and probe sequences [19, 9]. For H5 subtype detection, assays targeting the HA gene are designed to distinguish HPAI from LPAI by detecting the multibasic cleavage site [8, 17]. Similarly, H7 and H9 subtype-specific assays target the HA gene [20, 21]. Neuraminidase (NA) subtyping assays, such as those for N1, N2, and N8, are also available and can be combined with HA assays in multiplex formats [14, 17].

The following table summarizes common target genes and their applications in AIV PCR assays:

Multiplex RT-qPCR assays that simultaneously detect multiple targets in a single reaction are increasingly used to reduce cost and turnaround time [23, 20]. For example, a triplex assay targeting H5, H7, and H9 has been validated for field and clinical samples [20]. Quadruplex droplet digital PCR (ddPCR) assays have also been developed to detect multiple respiratory pathogens of chickens, including AIV, in a single reaction [23].

Sample Types and Processing

AIV can be detected from a variety of clinical and environmental samples. Oropharyngeal and cloacal swabs are the standard sample types for individual birds [24, 2]. For surveillance in wild birds, fecal samples and environmental water samples are commonly used [24, 6, 25]. In dairy cattle, milk has emerged as a relevant sample matrix following the detection of H5N1 in bovine milk [26, 27, 18]. Environmental samples, such as swabs from feeders, waterers, and surfaces in live bird markets, provide a sensitive and practical alternative to individual bird sampling, particularly in vaccinated flocks where viral shedding may be reduced [24, 28].

Sample pooling is a strategy to increase testing throughput while maintaining sensitivity. Pooling of up to five swabs per pool has been shown to have acceptable sensitivity for AIV detection in swine breeding herds, and similar approaches are used in poultry [29]. However, pooling reduces the effective viral concentration and may lead to false negatives in low-prevalence scenarios [29]. RNA extraction methods must be optimized for each sample type to remove inhibitors and concentrate viral RNA. Commercial extraction kits based on silica membrane or magnetic bead technology are widely used, but lyophilized RT-qPCR reagents have been validated for use in resource-limited settings [30].

Multiplex and Subtyping Approaches

Multiplex RT-PCR and RT-qPCR assays enable simultaneous detection and subtyping of AIV in a single reaction. A multiplex universal RT-PCR assay for HA and NA subtyping has been developed that can identify 16 HA and 9 NA subtypes [14]. Real-time multiplex assays targeting H1, H2, and H3 [15] or H5, H7, and H9 [20] have been validated for field samples. The use of different fluorophores (e.g., FAM, HEX, Cy5) allows discrimination of multiple targets in the same well [7, 20]. High-throughput RT-qPCR systems can process hundreds of samples per run and have been adapted for AIV subtyping panels [7].

Strand-specific quantitative RT-PCR methods can differentiate between viral RNA (vRNA), complementary RNA (cRNA), and messenger RNA (mRNA), providing insights into viral replication dynamics [31]. This approach is particularly useful for pathogenesis studies and vaccine efficacy evaluation [31].

Digital PCR and Emerging Variants

Droplet digital PCR (ddPCR) is an emerging technology that provides absolute quantification of nucleic acid targets without the need for standard curves [23, 19]. In ddPCR, the sample is partitioned into thousands of nanoliter-sized droplets, and PCR amplification is performed in each droplet. The fraction of positive droplets is used to calculate the target concentration using Poisson statistics [23]. Quadruplex ddPCR assays have been developed for simultaneous detection of AIV and other respiratory pathogens in chickens [23]. Digital PCR offers advantages in terms of precision and tolerance to inhibitors, making it suitable for complex matrices such as feces and environmental samples [19].

The continuous emergence of new AIV variants, such as H5N1 clade 2.3.4.4b genotypes, requires ongoing validation of existing PCR assays [1, 19]. Novel (d)PCR assays specifically designed for clade 2.3.4.4b surveillance have been developed and evaluated [19]. Assay validation must include testing against a panel of genetically diverse AIV strains to ensure broad reactivity [9, 17].

Quality Control and Validation

Interlaboratory validation is essential to ensure the reliability of PCR assays for AIV detection. Proficiency testing programs using fit-for-purpose samples, such as spiked milk or swab eluates, have been established to evaluate interlaboratory performance [27, 18]. Method-dependent sensitivity variability has been observed in interlaboratory comparisons, highlighting the need for standardized protocols and reagents [18]. Lyophilized RT-qPCR reagents have been validated for multi-site deployment in Sub-Saharan Africa, demonstrating stability under field conditions [30].

Internal controls, such as exogenous RNA (e.g., MS2 phage) or endogenous housekeeping genes (e.g., beta-actin), should be included in every reaction to monitor for inhibition and extraction efficiency [12]. Positive and negative controls must be run with each batch to validate assay performance.

Workflow Diagram

The following Mermaid diagram illustrates a typical workflow for AIV detection using RT-qPCR:

flowchart TD
 A[Sample Collection] --> B[RNA Extraction]
 B --> C[Reverse Transcription]
 C --> D[Real-Time PCR Amplification]
 D --> E[Data Analysis]
 E --> F{Interpretation}
 F -->|Ct < 40| G[Positive for AIV]
 F -->|Ct >= 40| H[Negative for AIV]
 G --> I[Subtyping Assays]
 I --> J[HA/NA Subtype Identification]
 H --> K[Report as Negative]

Conclusion

PCR-based methods, particularly RT-qPCR, remain the cornerstone of molecular diagnostics for avian influenza virus in poultry and wild birds. The choice of target gene, assay format, and sample type depends on the specific surveillance or diagnostic objective. Multiplex and digital PCR approaches offer enhanced throughput and quantification capabilities. Continuous monitoring of viral evolution and rigorous interlaboratory validation are necessary to maintain assay performance. As AIV continues to evolve, PCR assays must be regularly updated to ensure detection of emerging variants [19, 9].

References

[1] Shosha EAE, Mohamd MK, Shehata MAE et al. Genomic and evolutionary characterization of newly emerged highly pathogenic avian influenza H5N1 clade (2023-2025). Vet World. 2025. URL: https://pubmed.ncbi.nlm.nih.gov/41716167/

[2] Aldous EW, Seekings JM, McNally A et al. Infection dynamics of highly pathogenic avian influenza and virulent avian paramyxovirus type 1 viruses in chickens, turkeys and ducks. Avian Pathol. 2010. URL: https://pubmed.ncbi.nlm.nih.gov/20706882/

[3] Niel K, St Charles K, Elissa J et al. Highly pathogenic avian influenza (HPAI H5N1, clade 2.3.4.4b, genotype C2.1) in commercial ring-necked pheasants during the 2022 outbreak in the United States. Can J Microbiol. 2026. URL: https://pubmed.ncbi.nlm.nih.gov/41429004/

[4] EFSA Panel on Animal Health and Welfare (AHAW), Alvarez J, Boklund AE et al. Risk of infection of dairy cattle in the EU with highly pathogenic avian influenza virus affecting dairy cows in the United States of America (H5N1, Eurasian lineage goose/Guangdong clade 2.3.4.4b. genotype B3.13). EFSA J. 2025. URL: https://pubmed.ncbi.nlm.nih.gov/41425767/

[5] Yang H, Zhang Y, Wang X et al. Epidemiological and Genomic Characterization of H5 Subtype Avian Influenza Viruses in Jining City, 2024-2025. Pathogens. 2026. URL: https://pubmed.ncbi.nlm.nih.gov/42198646/

[6] Lee JK, Kim MB, Kim SH et al. Surveillance of avian influenza viruses in migratory wild birds in South Korea, 2019-2025. J Vet Sci. 2026. URL: https://pubmed.ncbi.nlm.nih.gov/41947677/

[7] Giersch K, Nörz D, Grunwald M et al. Adaptation and validation of an influenza a subtyping panel for detection of H1pdm09, H3 and H5 on a high-throughput RT-qPCR system. Sci Rep. 2026. URL: https://pubmed.ncbi.nlm.nih.gov/42009717/

[8] Bacsik DJ, Mills MG, Monroe LD et al. Validation of H5 influenza virus subtyping RT-qPCR assay and low prevalence of H5 detection in 2024-2025 influenza virus season. J Clin Microbiol. 2025. URL: https://pubmed.ncbi.nlm.nih.gov/41117594/

[9] Xue YC, Bertsch J, Monacy K et al. Enhancing diagnostic preparedness for H5N1: a validation study of H5 single-plex assay and detection across multiple platforms. J Clin Microbiol. 2025. URL: https://pubmed.ncbi.nlm.nih.gov/40679852/

[10] Chaves AJ, Vergara-Alert J, Busquets N et al. Neuroinvasion of the highly pathogenic influenza virus H7N1 is caused by disruption of the blood brain barrier in an avian model. PLoS One. 2014. URL: https://pubmed.ncbi.nlm.nih.gov/25506836/

[11] Huo C, Wu H, Xiao J et al. Genomic and Bioinformatic Characterization of Mouse Mast Cells (P815) Upon Different Influenza A Virus (H1N1, H5N1, and H7N2) Infections. Front Genet. 2019. URL: https://pubmed.ncbi.nlm.nih.gov/31281330/ *** Disclaimer: This article is for educational and informational purposes only. It is not intended to substitute for professional veterinary advice, diagnosis, treatment, or regulatory guidance. Always consult a licensed veterinarian or qualified specialist regarding animal health, disease diagnosis, and therapeutic decisions.

[12] Pereira LA, Lapinscki BA, Debur MC et al. Standardization of a high-performance RT-qPCR for viral load absolute quantification of influenza A. J Virol Methods. 2022. URL: https://pubmed.ncbi.nlm.nih.gov/34942203/

[13] Abdusalam M, Elbasir M, Ashteba M et al. Monitoring influenza A virus in wild migratory birds and waterfowl in Libya using RT-qPCR. Open Vet J. 2025. URL: https://pubmed.ncbi.nlm.nih.gov/41200293/

[14] An SH, Heo GB, Kang YM et al. Development of a multiplex and universal RT-PCR assay for hemagglutinin and neuraminidase subtyping of avian influenza virus. J Vet Sci. 2025. URL: https://pubmed.ncbi.nlm.nih.gov/41045234/

[15] Yan S, Yang F, Yao H et al. A [multiplex real-time RT-PCR](/knowledge/diagnostics/molecular/multiplex-rt-pcr-pedv-tgev-pdcov-fecal-environmental 2) assay for the detection of H1, H2 and H3 subtype avian influenza viruses. Virus Genes. 2023. URL: https://pubmed.ncbi.nlm.nih.gov/36515804/

[16] Li X, Tian J, Zhou W et al. Development and validation of a sensitive fluorescence RT-qPCR assay with TaqMan-MGB probe for detection of H6 subtype avian influenza A virus. J Vet Diagn Invest. 2026. URL: https://pubmed.ncbi.nlm.nih.gov/41562546/

[17] Yang F, Xu L, Liu F et al. Development and evaluation of a TaqMan MGB RT-PCR assay for detection of H5 and N8 subtype influenza virus. BMC Infect Dis. 2020. URL: https://pubmed.ncbi.nlm.nih.gov/32727378/

[18] Miller MR, Frost K, Smith EL et al. Evaluation of PCR-Based H5N1 Influenza Detection Methods in Milk from an Interlaboratory Comparison Study Demonstrating Method-Dependent Sensitivity Variability. J Food Prot. 2026. URL: https://pubmed.ncbi.nlm.nih.gov/42103287/

[19] Buttinger G, Petrillo M, Valastro V et al. Novel (d)PCR assays for influenza A(H5Nx) viruses clade 2.3.4.4b surveillance. Euro Surveill. 2025. URL: https://pubmed.ncbi.nlm.nih.gov/40843521/

[20] Yang F, Dong D, Wu D et al. A multiplex real-time RT-PCR method for detecting H5, H7 and H9 subtype avian influenza viruses in field and clinical samples. Virus Res. 2022. URL: https://pubmed.ncbi.nlm.nih.gov/34954007/

[21] Yang F, Xiao Y, Chen B et al. Development of a colloidal gold-based immunochromatographic strip test using two monoclonal antibodies to detect H7N9 avian influenza virus. Virus Genes. 2020. URL: https://pubmed.ncbi.nlm.nih.gov/32034616/

[22] Xiao Y, Yang F, Liu F et al. Development and application of a real-time RT-PCR assay to rapidly detect H2 subtype avian influenza A viruses. J Vet Diagn Invest. 2021. URL: https://pubmed.ncbi.nlm.nih.gov/33618630/

[23] Mu Y, Wang X, Dong T et al. Development and Evaluation of Quadruplex Droplet Digital PCR Method to Multiplex Detection of Different Respiratory Pathogens of Chickens. Animals (Basel). 2026. URL: https://pubmed.ncbi.nlm.nih.gov/41514825/

[24] Kalvelage L, Casteel M, Hartmann M et al. Environmental samples, a sensitive and practical alternative to individual bird sampling in the surveillance of H9N2 vaccinated turkey flocks. J Gen Virol. 2026. URL: https://pubmed.ncbi.nlm.nih.gov/42262844/

[25] Wang Y, Shen J, Liu G et al. Analysis of the surveillance results of avian influenza in the external environment of huzhou city from 2017 to 2023. PLoS One. 2025. URL: https://pubmed.ncbi.nlm.nih.gov/40608765/

[26] Urie N, Stenkamp-Strahm C, Wagner B et al. Associations between bovine viral diarrhea virus, bovine leukemia virus, and Mycobacterium avium subspecies paratuberculosis exposure on H5N1 clinical signs and shedding in US dairy cows. J Dairy Sci. 2026. URL: https://pubmed.ncbi.nlm.nih.gov/42264359/

[27] Smith EL, Jin Y, Miller MR et al. Development of Fit-for-Purpose, High Quality Proficiency Samples for Interlaboratory Evaluation of RT-PCR Detection of HPAI H5N1 in Milk. Food Environ Virol. 2026. URL: https://pubmed.ncbi.nlm.nih.gov/42258002/

[28] Jiang M, Wang J, Hou Y et al. Comparative and predictive analysis of avian influenza in live bird markets of the Poyang Lake region: a long-term monitoring study. Virol J. 2026. URL: https://pubmed.ncbi.nlm.nih.gov/41691273/

[29] Moraes DCA, Osemeke OH, Gauger PC et al. Probability of Influenza A virus RNA detection at different pooling levels for commonly used sample types in swine breeding herds. Prev Vet Med. 2025. URL: https://pubmed.ncbi.nlm.nih.gov/40912158/

[30] Drzewnioková P, Brian I, Mancin M et al. Validation and multi-site deployment of a lyophilized qRT-PCR reagent for the molecular diagnosis of avian influenza and rabies in Sub-Saharan African regions. J Clin Microbiol. 2025. URL: https://pubmed.ncbi.nlm.nih.gov/40590553/

[31] Wang B, Wang G, Wang YH et al. A Strand-Specific Quantitative RT-PCR Method for Detecting vRNA, cRNA, and mRNA of H7N9 Avian Influenza Virus in a Mouse Model. Viruses. 2025. URL: https://pubmed.ncbi.nlm.nih.gov/40733623/

[32] Kibiger L, Oltean HN, Leitz L et al. Fatal Human Case of Highly Pathogenic Avian Influenza A(H5N5) in a Backyard Flock Owner - Washington, November 2025. MMWR Morb Mortal Wkly Rep. 2026. URL: https://pubmed.ncbi.nlm.nih.gov/42096351/

[33] Vaughan A, Joyce A, Traub E et al. Serologic Evidence of Highly Pathogenic Avian Influenza A(H5N1) Virus Infection in a Veterinary Professional Exposed to an Infected Domestic Cat - Los Angeles County, California, December 2024-January 2025. MMWR Morb Mortal Wkly Rep. 2026. URL: https://pubmed.ncbi.nlm.nih.gov/42096344/

[34] Ye H, Dong Q, Qian M et al. Development and application of a VNAR-based detection nanobody for avian influenza virus H9N2. Front Immunol. 2026. URL: https://pubmed.ncbi.nlm.nih.gov/41953018/

[35] Wu H, Wang P, Fu J et al. A monoclonal antibody-based, time-resolved fluorescence microsphere immunochromatographic testing strip for rapid and sensitive detection of H7 avian influenza viruses. BMC Vet Res. 2026. URL: https://pubmed.ncbi.nlm.nih.gov/41943056/

[36] Bo H, Zhang Y, Dong J et al. Serological Surveillance of Avian Influenza Virus H9N2 Subtype in Occupational Populations Exposed to Poultry Environment in China During 2018-2023. Open Forum Infect Dis. 2026. URL: https://pubmed.ncbi.nlm.nih.gov/41923892/

[37] Zhao Y, Chen Y, Han X et al. Development of a MALDI-TOF nucleic acid mass spectrometry-based assay for the detection of 10 major avian diseases. Vet J. 2026. URL: https://pubmed.ncbi.nlm.nih.gov/41759769/

[38] Risalvato J, Sewid AH, Dalrymple DZ et al. Biosensor Technologies for Avian Influenza Detection: A New Frontier in Rapid Diagnostics for HPAI. Biosensors (Basel). 2026. URL: https://pubmed.ncbi.nlm.nih.gov/41744736/

[39] Chai M, Liu X, Liu J et al. Novel Ultrafast Sample-to-Answer Microfluidic Platform for Highly Sensitive On-Site Quantification of Avian Influenza Virus. Anal Chem. 2026. URL: https://pubmed.ncbi.nlm.nih.gov/41521898/

[40] Wang P, Wu H, Fu J et al. Rapid and sensitive time-resolved fluorescence immunochromatographic strip for detecting H10 subtype avian influenza virus. Poult Sci. 2026. URL: https://pubmed.ncbi.nlm.nih.gov/41252859/

[41] Dizayee BA, Khlef EL. Molecular characterization of a highly pathogenic avian influenza A (H5N4) virus isolated from domestic chickens in Iraq during the 2015 HPAI H5N1 outbreak. Open Vet J. 2025. URL: https://pubmed.ncbi.nlm.nih.gov/41246384/

[42] Puga-Torres B, Navarrete H, de la Torre D. Molecular surveillance confirms absence of avian influenza virus type A in Ecuadorian poultry: A retrospective reverse transcription quantitative polymerase chain reaction study from April 2023 to June 2024. Vet World. 2025. URL: https://pubmed.ncbi.nlm.nih.gov/41064827/

[43] Tobolowsky FA, Morris E, Castro L et al. Highly Pathogenic Avian Influenza A(H5N1) Virus Infection in a Child with No Known Exposure - San Francisco, California, December 2024-January 2025. MMWR Morb Mortal Wkly Rep. 2025. URL: https://pubmed.ncbi.nlm.nih.gov/40906597/

[44] Wang P, Yan L, Wang J et al. using multiplex reverse transcription-multiple cross displacement amplification-lateral flow biosensor technology for detecting H1N1, H3N2 and H7N9 influenza A virus subtypes. J Virol Methods. 2025. URL: https://pubmed.ncbi.nlm.nih.gov/40754008/

[45] Shafi M, Shabir S, Jan S et al. The role of artificial intelligence in detecting avian influenza virus outbreaks: A review. Open Vet J. 2025. URL: https://pubmed.ncbi.nlm.nih.gov/40557099/

[46] Costa-Hurtado M, Afonso CL, Miller PJ et al. Virus interference between H7N2 low pathogenic avian influenza virus and lentogenic Newcastle disease virus in experimental co-infections in chickens and turkeys. Vet Res. 2014. URL: https://pubmed.ncbi.nlm.nih.gov/24393488/

[47] Nili H, Mohammadi A, Habibi H et al. Pathogenesis of H9N2 virus in Chukar partridges. Avian Pathol. 2013. URL: https://pubmed.ncbi.nlm.nih.gov/23631735/

[48] Buchy P, Mardy S, Vong S et al. Influenza A/H5N1 virus infection in humans in Cambodia. J Clin Virol. 2007. URL: https://pubmed.ncbi.nlm.nih.gov/17526430/

[49] Wu XX, Tang SJ, Yao SH et al. The viral distribution and pathological characteristics of BALB/c mice infected with highly pathogenic Influenza H7N9 virus. Virol J. 2021. URL: https://pubmed.ncbi.nlm.nih.gov/34844617/

[50] Nakaya Y, Fukuda T, Ashiba H et al. Quick assessment of influenza a virus infectivity with a long-range reverse-transcription quantitative polymerase chain reaction assay. BMC Infect Dis. 2020. URL: https://pubmed.ncbi.nlm.nih.gov/32762666/

[51] Xiao Q, Yan L, Yao L et al. Development of oligonucleotide microarray for accurate and simultaneous detection of avian respiratory viral diseases. BMC Vet Res. 2019. URL: https://pubmed.ncbi.nlm.nih.gov/31324180/