# Assessing the integrity of SARS-CoV-2 and F-specific RNA bacteriophage RNA in raw wastewater
(ANRS0160)

Ahlam Chaqroun, Julie Challant, Hélène Jeulin, Julie Loutreul, Thomas

Ansmant, Stéphane Labialle, Zuzana Gerber, Cédric Fund, Christelle

Aigueperse, Jean-François Deleuze, et al.

► To cite this version:

Ahlam Chaqroun, Julie Challant, Hélène Jeulin, Julie Loutreul, Thomas Ansmant, et al. "Assessing the integrity of SARS-CoV-2 and F-specific RNA bacteriophage RNA in raw wastewater (ANRS0160)." Water Research, 2026, 289 (Part B), pp.125002. 10.1016/j.watres.2025.125002. hal-05382421

HAL Id: hal-05382421

https://hal.univ-lorraine.fr/hal-05382421v1

Submitted on 25 Nov 2025

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Distributed under a Creative Commons Attribution 4.0 International License
# Assessing the integrity of SARS-CoV-2 and F-specific RNA bacteriophage RNA in raw wastewater (ANRS0160)

Ahlam Chaqroun<sup>a,g, ID</sup>, Julie Challant<sup>a,g, ID</sup>, Hélène Jeulin<sup>a,b,g</sup>, Julie Loutreul<sup>c,g</sup>, Thomas Ansmant<sup>a,g, ID</sup>, Stéphane Labialle<sup>d</sup>, Zuzana Gerber<sup>e, ID</sup>, Cédric Fund<sup>e</sup>, Christelle Aigueperse<sup>d</sup>, Jean-François Deleuze<sup>e</sup>, Robert Olaso<sup>e</sup>, Nicolas Boudaud<sup>c,g</sup>, Vincent Maréchal<sup>f,g, ID</sup>, Christophe Gantzer<sup>a,g, ID</sup>, Isabelle Bertrand<sup>a,g,* ID</sup>

## HIGHLIGHTS

* An innovative digital RT-PCR approach was developed to assess viral RNA integrity.

* First study evaluating the integrity of viral RNAs naturally present in wastewater.

* Sequence length and intrinsic sequence characteristics influence RNA integrity.

* Integrity of complete viral RNA of 3500 nt was above 55 % in controlled conditions.

* ORF3a-S3 was a stable SARS-CoV-2 region in both controlled and wastewater samples.

## GRAPHICAL ABSTRACT

<figure><img src="image_2.png" /></figure>

## ARTICLE INFO

**Keywords:**

RNA integrity

Emerging viruses

SARS-CoV-2

Raw wastewater

Long-range RT-dPCR

## ABSTRACT

RNA integrity is an essential parameter for analyzing the nature of viral particles, especially in environmental samples where assessing virus infectivity is often difficult or impossible. It is also an important factor in the effectiveness of virus sequencing in environmental matrices containing mixed viral populations composed of variants that differ from one another by only a limited number of mutations, such as in the case of SARS-CoV-2. This study introduces a multiplex Reverse Transcription Digital PCR (RT-dPCR) method for evaluating the RNA integrity of SARS-CoV-2 and F-specific RNA phages belonging to subgroup I (FRNAPH-I) using synthetic RNA, viral stocks, and then raw wastewater (WW) in which SARS-CoV-2 and FRNAPH-I were naturally present. An initial approach using one-step multiplex digital Reverse Transcription PCR (dRT-PCR) demonstrated unequal detection across the genomic regions of both FRNAPH and SARS-CoV-2. To overcome this methodological bias, a two-step method called Long-Range Reverse Transcription digital PCR (LR-RT-dPCR) was developed. This

<footnote><sup>a</sup> Université de Lorraine, CNRS, LCPME, F-54000 Nancy, France</footnote>

<footnote><sup>b</sup> Laboratoire de Virologie, CHRU de Nancy Brabois, Vandauvre-lès-Nancy, France</footnote>

<footnote><sup>c</sup> ACTALIA, F-50000 Saint Lô, France</footnote>

<footnote><sup>d</sup> Université de Lorraine, CNRS, IMoPA, F-54000 Nancy, France</footnote>

<footnote><sup>e</sup> CEA, Centre National de Recherche en Génomique Humaine, Université Paris-Saclay, F-91057 Evry, France</footnote>

<footnote><sup>f</sup> Sorbonne Université, Inserm, Centre de Recherche Saint-Antoine, CRSA, F-75012 Paris, France</footnote>

<footnote><sup>g</sup> OBEPINE consortium, Paris, France</footnote>

<footnote>* Corresponding author.
E-mail address: isabelle.bertrand@univ-lorraine.fr (I. Bertrand).</footnote>
approach involves performing long-range reverse transcription at the 3' end using a single specific reverse primer to generate contiguous cDNA that spans multiple targets of interest. Following cDNA synthesis, the sample is partitioned, and a multiplex amplification is carried out on targets located at the 3' end, middle, and 5' end of the sequence. The LR-RT-dPCR method enabled uniform detection with enhanced sensitivity and was validated using capillary electrophoresis on synthetic RNA of MS2, a phage which belongs to the FRNAPH-I subgroup. LR-RT-dPCR was employed in both triplex and quintuplex formats to analyze the MS2 phage genome (3,569 nucleotides (nt)) and SARS-CoV-2 genome (~30,000 nt), respectively. Using this approach, viral RNA integrity was evaluated through the detection frequencies of genome fragments of the whole genome. The viral stocks of MS2 phages replicated in a laboratory and stored in phosphate-buffered saline (PBS) exhibited high RNA detection frequencies (> 50 %). In WW, RNA detection frequency was significantly lower, not exceeding 2 % even for the shortest fragment of the FRNAPH-I genome. On the other hand, SARS-CoV-2 RNA showed greater detection frequency than FRNAPH-I RNA in WW, with values exceeding 30 % for short fragments (<1,500 nt) and ranging from 0 % to 44 % for longer fragments (1,500 to 3,500 nt). The relationship between the detection frequency of a fragment and its length does not appear to be strictly linear, as factors other than length can influence genome integrity. These factors include the intrinsic properties of specific genomic regions. For example, the S3-ORF3a region of the SARS-CoV-2 genome appears particularly stable.

# 1. Introduction

Evaluating RNA integrity is essential in multiple domains, such as therapeutic development and vaccine production, as degraded RNA can adversely affect efficacy, safety, adherence to regulatory standards (Cheng et al., 2023), and the accuracy of viral epidemiological research (Ahmed et al., 2022). In the field of environmental epidemiological surveillance, RNA integrity is pivotal for accurate sequencing, quantification, and identification of RNA viruses in environmental samples, which are fundamental to the broad understanding of transmission dynamics, for tracking mutations, and for developing effective public health interventions (Feng et al., 2023; Otero et al., 2022; Wurtzer et al., 2024). However, viral RNA integrity may be inherently low due to the naturally inefficient process of virion formation, as even within permissive host cells infection leads to the release of only a small proportion of infectious particles (Bastin et al., 2021; Kalamvoki, 2025; Ward et al., 1984). In addition, harsh environmental conditions, especially in raw WW containing fecal matter, detergents, pharmaceuticals and personal care products among other numerous contaminants, further compromise viral RNA integrity. For example, Wigginton et al. (2012) showed that exposure to oxidants such as free chlorine can damage viral RNA, as shown by concurrent losses of RT-qPCR signal and infectivity in MS2 phage following disinfection. This presents a significant challenge for the accurate assessment and interpretation of RNA-based data from such complex environmental samples. This is notably the case for viral genome detection, as unstable genomic regions are generally unsuitable for use in reliable PCR assays or advanced sequencing technologies like direct RNA sequencing (DRS) using Nanopore platforms (Prawer et al., 2023). In the same study by Prawer et al., RNA samples from human cells were subjected to controlled degradation to evaluate its impact on DRS. They observed a marked reduction in the detection of long RNAs (> 1,000 nt) in degraded samples, where only about 5 % of reads exceeded 1,500 nt, compared to about 16 % in high-quality RNA samples. In contrast, shorter RNAs (< 1,000 nt) remained reliably detectable using DRS. Unlike DRS, short-read sequencing methods, such as Illumina, intentionally include RNA fragmentation. This approach allows the detection of incomplete RNA fragments, ensuring usable results even from degraded RNA. However, for DRS, damaged RNA molecules cannot be fully sequenced, leading to a loss of information for long RNAs and a bias toward shorter RNAs. This limitation underlines the importance of assessing RNA integrity for the reliable application of such advanced molecular technologies. This is particularly crucial for studying recombination events that require a comprehensive global view of viral sequences, such as the emergence of multiple recombinants of SARS-CoV-2 variants. Moreover, RNA integrity holds significant promise as one of the indirect methods for estimating viral infectivity, especially for viruses that are difficult to replicate using traditional cell-culture approaches. A notable example is

SARS-CoV-2, where efforts to detect infectious particles in environmental samples through cell culture methods have largely been unsuccessful, unlike in respiratory clinical samples (Atoui et al., 2023).

However, evaluating RNA integrity is not simple, as existing methods often lack either sensitivity or large genomic resolution. Conventional techniques, such as capillary electrophoresis, allow for the evaluation of RNA integrity by determining the size of separated molecules and calculating their RNA Integrity Number (RIN) (Fleige and Pfaffl, 2006). The RIN is a widely recognized metric for assessing RNA quality, measured on a scale from 0 to 10. For long-read sequencing, an optimal RIN is close or equal to 10. However, capillary electrophoresis requires relatively high nucleic acid concentrations—reaching 200 pg/µL for most common capillary electrophoresis protocols (Fleige and Pfaffl, 2006) and up to 2.5 pg/µL for ultrasensitive capillary electrophoresis protocols—meaning it is not suitable for environmental samples, where viral genome concentrations are often below 2.5 pg/µL. Advanced analytical methods, such as digital PCR (dPCR) or one-step dRT-PCR, have recently emerged as powerful tools for nucleic acid analysis, offering enhanced sensitivity. Digital PCR enables absolute quantification of specific DNA targets and provides a framework for the assessment of genome integrity by simultaneously targeting both ends of a DNA fragment (Furuta-Hanawa et al., 2019; Levy et al., 2021; Tereshko et al., 2023). Despite its promise, the few existing studies employing dPCR for evaluating genome integrity have predominantly focused on plasmid DNA or viral vectors, leaving an important gap in the analysis of other sample types such as viral stocks produced in laboratories or complex environmental samples like raw wastewater. Furthermore, these few studies frequently focus on only two specific genomic regions (DNA), which constrains their ability to provide a comprehensive evaluation of genome integrity across the entire viral sequence (Furuta-Hanawa et al., 2019; Tereshko et al., 2023a). Even when multiple genomic regions (RNA) were analyzed using one-step dRT-PCR, the focus remained on short sequences, typically no longer than 1,500 nt (Wurtzer et al., 2024). Expanding the number of targeted regions that cover long viral sequences could deepen our understanding of RNA genome integrity. There is a pressing need for complementary approaches that can address these limitations, particularly for RNA viruses like SARS-CoV-2, which has a genome length of about 30,000 nt. There is a need for comprehensive analyses of genome integrity to identify regions most susceptible to environmental damage, as well as how these regions contribute to viral persistence, infectivity, and detectability. Beyond pathogenic viruses like SARS-CoV-2, viral surrogates such as FRNAPH-I—commonly used as models for pathogenic enteric viruses such as noroviruses—have minimal biosafety requirements, making them valuable tools for studying the stability and behavior of RNA viruses (Wagner et al., 2022). FRNAPH-I are characterized by a single-stranded RNA genome (about 3,500 nt) and are excreted in the feces of warm-blooded animals, resulting in their natural occurrence in aquatic environments. Consequently,
FRNAPH-I phages show great potential as a model system for evaluating RNA genome integrity.

This study aims to evaluate RNA integrity across both short and long-genome fragments of SARS-CoV-2 and FRNAPH-I using different conditions, controlled and environmental ones. To do this, it was necessary to develop an advanced multiplex RT-dPCR strategy. The ultimate objective of this study is to integrate this approach into wastewater-based epidemiology to enhance environmental monitoring capabilities. While enveloped phages such as Phi6 could have been considered due to their structural similarity with SARS-CoV-2, they are much more sensitive to environmental degradation compared to non-enveloped viruses (Ye et al., 2016). Therefore, FRNAPH-I (MS2) was used in this study to minimize sensitivity-related issues and to develop the integrity assessment method under simple experimental conditions before its application to the enveloped SARS-CoV-2.

To our knowledge, this is the first study investigating the genome integrity of viruses naturally present in environmental water matrices, providing novel insights in real-world conditions.

## 2. Material and methods

### 2.1. Synthetic nucleic acids, viruses and raw wastewater samples

Several kinds of samples were used in the present study: synthetic DNA and RNA, viral stocks produced in laboratory-controlled conditions, and environmental wastewater samples.

#### 2.1.1. Plasmid DNA and synthetic RNA

Plasmid DNA of the MS2 phage (Table 1) was produced by GenScript. The plasmid pMS2T7_pUC57-Kan had a length of 6,225 nt and included an insert corresponding to the complete MS2 phage genome (GenBank accession number NC_001417.2, size: 3,569 nt). To linearize and isolate the insert, the plasmid was digested with the HindIII enzyme (Promega France, R6041) and the size of the MS2 phage insert was confirmed by agarose gel electrophoresis. Synthetic MS2 phage RNA (Table 1), corresponding to the complete MS2 phage genome was also provided by GenScript. Plasmid DNA of SARS-CoV-2 (Table 1) was designed at LCPME and produced by GenScript. The plasmid pCovIntJC_1_pUC57-Kan was 3,282 nt long with a 697 nt insert containing the five specific targets distributed within the entire SARS-CoV-2 genome (GenBank accession number NC_045512.2) used in this study. The plasmid was linearized and the insert isolated through digestion with the HindIII enzyme. The size of the SARS-CoV-2 insert was checked by agarose gel electrophoresis.

#### 2.1.2. Viruses

MS2, a well-characterized bacteriophage from the FRNAPH-I subgroup, recently renamed Emesvirus zinderi by the ICTV (International Committee on Taxonomy of Viruses), was used for experiments

<table><caption>**Table 1**
Viruses and their corresponding nucleic acid templates.</caption><thead><tr><th>Virus</th><th>Name of sample</th><th>Nature of nucleic acid template</th><th>Viral genome size</th></tr></thead><tbody><tr><td rowspan="4">MS2 phage</td><td>MS2 plasmid</td><td>DNA</td><td>3,569 nt (insert)</td></tr><tr><td>MS2 synthetic RNA</td><td>RNA</td><td>3,569 nt</td></tr><tr><td>MS2 stock 1</td><td>RNA</td><td rowspan="2">3,569 nt</td></tr><tr><td>MS2 stock 2</td><td></td></tr><tr><td rowspan="5">SARS-CoV-2</td><td>SARS-CoV-2 plasmid</td><td>DNA</td><td>697 nt (insert)</td></tr><tr><td>SARS-CoV-2 stock 1</td><td>RNA</td><td rowspan="3">~30,000 nt</td></tr><tr><td>SARS-CoV-2 stock 2</td><td></td></tr><tr><td>SARS-CoV-2 stock 3</td><td></td></tr></tbody></table>

performed with FRNAPH at lab scale. Regarding the MS2 bacteriophage (ATCC, 15597-B1), two viral stocks (Table 1) were produced by inoculating *Escherichia coli* Hfr K12 (ATCC, 29425). This was performed according to the standardized procedure ISO 10,705-1 (2001) without the chloroform lysis step to limit the presence of incomplete viral particles. The phages were then purified using cesium chloride density gradient and dialysis as previously described by Dika et al. (2013) with certain modifications. The phages were pelleted by ultracentrifugation (40,000 × g, 7 h, 4 °C, Thermo Scientific Sorvall WX, fixed rotor A-621) and resuspended in a cesium chloride solution with a density 1.4 g/mL. A density gradient was formed by isopycnic centrifugation at 110,000 × g, 15 °C for 18 h (Thermo Sorvall WX, swinging bucket rotor TH-641), allowing the phages to reach the position that matches their own density (1.4 g/mL). Finally, in order to remove cesium chloride, the phages were collected with a syringe and subjected to two successive dialyzes in 1 mM PBS (13.7 mM NaCl, 0.27 mM KCl, 1 mM Na2HPO4, 0.18 mM KH2PO4 at pH 7.4) using Float-A-Lyzer G2 with Biotech Cellulose Ester membranes (MWCO: 100 kDa, volume: 1 mL) (Spectrum Laboratories, 734-3576), and the solution was stirred gently at 4 °C for 12 h and stored at 4 °C, in the dark.

Regarding SARS-CoV-2, three viral stocks (Table 1) derived from supernatant samples were obtained by culturing the SARS-CoV-2 D614G strain from a positive respiratory sample (Covi-Lor collection, University hospital, Nancy, France) on Vero E6 cells (provided by L2CM Laboratory, Université de Lorraine, CNRS, Nancy, France) as previously described by Varbanov et al. (2021). These independent supernatants were harvested 48 h post-infection, at the onset of cytopathic effects, to recover virions with the most intact genome possible, as later harvesting would not increase yield but rather lead to degradation of infectious particles (Ogando et al., 2020; Bartolomeo et al., 2022).

### 2.2. Raw wastewater samples

Raw wastewater (WW) samples (N = 13) were also collected at the entry point of three French wastewater treatment plants (WWTPs): WWTP-A and -B are in the Nouvelle Aquitaine region (southwest France) and WWTP-C is in the Grand Est region (northeast France) (Table 2).

<table><caption>**Table 2**
Characteristics of the WWTPs and the raw WW samples.</caption><thead><tr><th>WWTP</th><th>Sample name</th><th>Sampling date</th><th>Location</th><th>Capacity (Inhabitant-equivalents)</th><th>Type of WW</th></tr></thead><tbody><tr><td rowspan="3">A</td><td rowspan="3">WW-A1</td><td>06/24/24</td><td>Nouvelle Aquitaine Region</td><td>150,000</td><td>Urban, Residential, <sup>*</sup>Separative sewer-system</td></tr><tr><td>WW-A2</td><td>06/26/24</td><td></td><td></td></tr><tr><td>WW-A3</td><td>07/01/24</td><td></td><td></td></tr><tr><td rowspan="2">B</td><td rowspan="2">WW-A4</td><td>07/03/24</td><td rowspan="2">Nouvelle Aquitaine Region</td><td rowspan="2">135,000</td><td rowspan="2">Urban, Residential, <sup>*</sup>Separative sewer-system</td></tr><tr><td>WW-B1</td><td>06/24/24</td></tr><tr><td rowspan="5">C</td><td rowspan="5">WW-B2</td><td>06/26/24</td><td rowspan="5">Grand Est region</td><td rowspan="5">400,000</td><td rowspan="5">Urban, Residential, <sup>*</sup>Combined sewer-system</td></tr><tr><td>WW-B3</td><td>07/01/24</td></tr><tr><td>WW-B4</td><td>07/03/24</td></tr><tr><td>WW-C1</td><td>07/14/24</td></tr><tr><td>WW-C2</td><td>09/22/24</td></tr><tr><td rowspan="4"></td><td rowspan="4">WW-C3</td><td>10/06/24</td><td rowspan="4"></td><td rowspan="4"></td><td rowspan="4"></td></tr><tr><td>WW-C4</td><td>10/20/24</td></tr><tr><td>WW-C5</td><td>11/03/24</td></tr></tbody></table>

<footnote>* Separative system: only residential/urban wastewater enters the plant (stormwater/rainwater goes separately)</footnote>

<footnote>† Combined system: residential/urban wastewater and stormwater/rainwater go together to the treatment plant.</footnote>
Sampling was conducted on several dates between June and November 2024. The WW samples were stored at 4 °C for between two days and one week after collection until the virus concentration step. Viral particles from wastewater were concentrated from 100-mL samples using an ultrafiltration method. This involved the use of a Centricon® Plus-70 centrifugal ultrafilter with a 100-kDa cut-off (Merck Millipore), as described by Bertrand et al. (2021). Ultrafiltration yielded a 5-mL concentrate, which was subsequently used for RNA extraction (detailed below).

## 2.3. RNA extraction

For both the MS2 and SARS-CoV-2 viral stocks, RNA was extracted using the NucliSENS® extraction kit (bioMérieux) without prior concentration of viral particles. The extraction procedure started with the addition of 2 mL of NucliSENS® lysis buffer to 1 mL of the sample, followed by vortexing and a 10-min incubation at room temperature. The lysate was then centrifuged at 2,500 × g for 5 min. The resulting supernatant was combined with 70 µL of magnetic silica beads, vortexed, and incubated at room temperature for 10 min to allow the beads to capture the nucleic acids. The captured nucleic acids were purified through sequential washing of the silica beads in a magnetization system. Elution was achieved by adding 50 µL of DNase/RNase-free water twice, with each addition followed by heating at 60 °C for 5 min. The eluate was subsequently recovered from the silica beads using the magnetization system (bioMérieux).

For raw WW concentrates, the NucliSENS® extraction kit (bioMérieux) was also used for RNA extraction, with two slight modifications: phenol-chloroform treatment was applied after the lysis step (Bertrand et al., 2021), and 140 µL of silica beads were used instead of 70 µL to improve RNA capture (Chaqroun et al., 2024). The extracted RNA underwent an additional re-concentration step using the RNA Clean & Concentrator™ kit (Zymo Research, ZR1016), and the final elution was performed in 10 or 30 µL of DNase/RNase-free water.

All extracted RNA samples were stored at -80 °C until further analysis.

## 2.4. Molecular amplification

Different amplification protocols using dPCR or RT-dPCR were evaluated in the first part of this work to define the most efficient protocol for studying viral RNA integrity (Fig. 1). The QIAcuity One, 5plex Platform device (Qiagen, 911032) was used for all experiments. This setup allows for up to five PCR assays to be performed simultaneously in each partition, targeting various regions of the genome. The nanoplates used in this study (QIAcuity Nanoplate 26k 8-well or 24-well formats, Qiagen, 250031 or 250001) make it possible to partition the template into up to 26,000 nanoliter-sized reactions per well, effectively isolating each genome copy into separate partitions before amplification (Fig. S1). Based on the recommendations of the manufacturer (Qiagen), the limit of quantification is defined as 10 partitions, corresponding to 3.0 × 10¹ genome copies (GC) /µL of nucleic acid extract. Additionally, to ensure reliable and accurate measurement of viral genome integrity, the optimal RNA concentration should range from 1.4 × 10² GC/µL to 2.7 × 10³ GC/µL of nucleic acid extract based on the SARS-CoV-2 plasmid quantification performed during the first trials of this work (Table 1) (data not shown). This range minimizes the probability of having partitions containing more than one genome copy to <10 %.

### 2.4.1. PCR systems

For FRNAPH-I we developed a 3PLEX system named FRNAPH-I, 3,296 nt. It featured three PCR assays targeting three specific regions of the genome: *UTRL*, *cp*, and *rep* (Table 3). The three assays were selected based on their comparable specificity to six FRNAPH-I genome sequences among the 17 complete FRNAPH-I sequences available in GenBank (Table S1). The comparable specificity of the three assays is

crucial for accurately evaluating environmental samples, such as raw WW samples, which contain a mixture of FRNAPH-I strains.

Following the same principle, we developed two 5PLEX systems for the SARS-CoV-2 genome. The first system, named SARS-CoV-2.3,669 nt (Table 3), was composed of five PCR assays within a 3,670 nt sequence of the SARS-CoV-2 genome, comparable in size to the FRNAPH-I genome. Among the five selected assays, three were in the spike (S) gene—named S1, S2, and S3—and the two other targets were located within the ORF3a and E genes, respectively. The second system, named SARS-CoV-2_30kb, targeted five specific regions within the entire SARS-CoV-2 genome (nsp2, ORF1a, ORF1b, S and N2) (Table 3). During the selection of the PCR systems, several SARS-CoV-2 variant sequences obtained from the GISAID database were aligned using MEGA (Molecular Evolutionary Genetics Analysis) software (Tables S2 and S3). This approach aimed to minimize the impact of genome variability on PCR specificity. To address this, a tolerance for a single nucleotide change within a PCR target was permitted, provided it was restricted to the 5' position and avoided both the 3' position and the central region of the primer or probe sequence. The selected PCR targets adhered to these criteria to ensure compatibility with the predominantly circulating variants at that time. However, the S2 PCR target for the SARS-CoV-2.3,669 nt system did not meet these criteria due to the presence of a variable nucleotide in the middle of the reverse primer sequence, which differed from the predominant variants that were circulating during the wastewater analysis period (Table S2). Consequently, the S2 target was excluded from further integrity analysis in the raw WW samples and was used only for synthetic samples and viral stocks.

### 2.4.2. dPCR

DNA templates were subjected to dPCR amplification using the QIAcuity Probe PCR Kit (Qiagen, 250102) and the Nanoplate-based digital PCR system (Qiagen, 911032). Amplification was performed in 3PLEX for FRNAPH-I and 5PLEX for SARS-CoV-2. When performing dPCR in 3PLEX (Fig. S1) for FRNAPH-I and in 5PLEX for SARS-CoV-2, each partition underwent amplification. The mixture contained 800 nM of each primer (forward and reverse), 400 nM of each probe, and 5 µL of DNA. The dPCR amplification was conducted with the following cycling program: an initial enzyme activation step at 95 °C for 5 min, followed by 45 cycles of denaturation at 95 °C for 15 s and annealing/ extension at 60 °C for 30 s. The presence or absence of specific genomic targets was indicated by fluorescent signals. Positive partitions reflected the successful detection of the target amplicon, whereas negative partitions lacked amplifiable template material. The quantities of DNA detected for each assay of a multiplex system were then compared.

### 2.4.3. One-step dRT-PCR

When performing one-step dRT-PCR in 3PLEX for FRNAPH-I (Table 3) and in 5PLEX for SARS-CoV-2 (Table 3), each sample was partitioned before reverse transcription (RT) (Fig. 2A). The one-step dRT-PCR was carried out using the QIAcuity OneStep Advance Probe kit (Qiagen, 250132). The total reaction volume per well was 42 µL, with 37 µL of the prepared reaction mix and 5 µL of the RNA sample. Each reaction mix contained 400 nM of each primer (forward and reverse), and 200 nM of each probe. dRT-PCR amplification was performed using the following conditions: an initial reverse transcription (RT) step at 50 °C for 40 min, followed by PCR enzyme activation at 95 °C for 2 min. This was followed by 40 cycles of denaturation at 95 °C for 5 s and annealing/extension at 58 °C for 30 s. As described above with dPCR, the presence or absence of specific genomic targets was indicated by fluorescent signals. In one-step dRT-PCR, the RT step was performed in multiplex, targeting three positions for FRNAPH-I (Table 3) and five positions for SARS-CoV-2 (Table 3). The resulting cDNA was subsequently amplified using the multiplex dPCR designed specifically for the virus of interest (Table 3). The size of a genome fragment detected within a partition is determined by the number of positions that are simultaneously positive in that partition. Consequently, a complete
<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Decision tree used to determine the most appropriate digital RT-PCR method for the study of viral RNA integrity. 2-step-LR-RT-dPCR: 2-step long-range-RT-dPCR.</figcaption></figure>
<table><caption>Table 3<br>Characteristics of FRNAPH-I and SARS-CoV-2 PCR assays of the 3PLEX or 5PLEX systems including primer and probe sequences, their genomic positions, and the size of the amplified products. The positions are given for GenBank sequence NC_001417.2 for FRNAPH-I and sequence NC_045512.2 for SARS-CoV-2.</caption><thead><tr><th>3PLEX or<br>5PLEX<br>systems</th><th>PCR<br>target</th><th>Start</th><th>End</th><th>Product<br>size (nt)</th><th>Forward primer</th><th>Probe</th><th>Reverse primer</th><th>Reference</th></tr></thead><tbody><tr><td>3PLEX<br>FRNAPH-<br>L_3.296 nt</td><td>UTR<sub>l</sub></td><td>90</td><td>171</td><td>81</td><td>CTGTAGGTAGCCGGAAATTCAT</td><td>TAAAAGCTCGCACAGGTTCAAAACCTCCTAGG_FAM-BHQ1</td><td>GAAGGTCTCGTTCTCCTATCAAG</td><td>Present study</td></tr><tr><td>5PLEX<br>SARS-CoV-<br>2.3.669 nt</td><td>cp<br>rep<br>E</td><td>1.543<br>3.312<br>26.269</td><td>1.625<br>3.386<br>26.381</td><td>82<br>104<br>113</td><td>CCAGAGACTGTTGGTGGTGTAGA<br>GGCGTGGCGGTTIATACG<br>ACAGGTACGTTAATAGTTAATAGCGT</td><td>CCTGTAGCCGCATGGCGTTTCGTAC_ROX-BHQ2<br>CTAACGCGGCGTTCCCACATCCCTHEX-BHQ1<br>ACACTAGCCATCCTTACTGCGCTTCG_FAM-BHQ1</td><td>AGCGAAAATTTGGAAATGGTTAGT<br>GCTGCTGGGCCACACT<br>ATATTGCAGCAGTACGCACACA</td><td>Corman et al.,<br>2020</td></tr><tr><td>5PLEX<br>SARS-CoV-<br>2.30kb</td><td>ORF3a<br>S3<br>S2<br>S1<br>nsP2</td><td>25.835<br>23.981<br>23.076<br>22.712<br>1.866</td><td>25.982<br>24.118<br>23.232<br>22.869<br>1.970</td><td>146<br>137<br>132<br>158<br>105</td><td>GGTGGCATACTAATTGTTAGGACTAT<br>CCAGATCCATCAAAACCAAGCA<br>ACCAACCATACAGAGTAGTAGT<br>CCTACTAAATTAATGATCTTGCTTTACT<br>ATGCATTTGCATCAGAGGCT</td><td>ACTTCAGGTGATGGCACAAGTCCCTA ATTO647N-BHQ3<br>AAGTGACACTTGCAGATGCTGGCTTCATHEX-BHQ1<br>TGCACCAGCAACTGTTTGTGGACCTAA_TAMRA-BHQ2<br>CGCTCCAGGGCAACTTGGAAAG_ROX-BHQ2<br>TCTCCCGCACTCTTGAAACTGC_FAM-BHQ1</td><td>ACTCCAGATTCCCATTTTTCAGT<br>TGCACAAATGAGGTCTCTAGCA<br>TGTTAGACTCAGTGAAGAACCTG<br>CAAAGTATAACGCAGCCTGTA<br>TGTTATAGGGCGCTTCTGT</td><td>Chan et al., 2020<br>Vip et al., 2020;<br>Probe designed<br>in the present<br>study</td></tr><tr><td>ORF1a</td><td></td><td>10.965</td><td>11.071</td><td>107</td><td>CTTTCCAAAGTGCAGTGAAAAGAACAAATCA</td><td>GGGTACACACCACTGGTTGTTACTCA_TAMRA-BHQ2</td><td>CAAAGACCATTGAGTACTCTGGACTA</td><td>Negrón et al.,<br>2022 (adapted<br>for the forward<br>primer)</td></tr><tr><td>ORF1b</td><td></td><td>18.974</td><td>19.082</td><td>109</td><td>TTCAACACATGGTTGTTAAGCTGCATTATTA</td><td>GCAGACAAATTCCAGTCTTCTTCACGACATTGG_ATTO647N-BHQ3</td><td>CATTCTACATCAGCTTGAGGTACACA</td><td>Negrón et al.,<br>2022</td></tr><tr><td>S<br>N2</td><td></td><td>22.712<br>29.164</td><td>22.869<br>29.230</td><td>158<br>67</td><td>CCTACTAAATTAATGATCTTGCTTTACT<br>TTACAAACATTGGCCGCAA</td><td>CGCTCCAGGGCAACTTGGAAAG_ROX-BHQ2<br>ACAAATTGCCCCAGCGCTTCAG_HEX-BHQ1</td><td>CAAAGTATAACGCAGCCTGTA<br>GGCGACATTCGGAAGAA</td><td>Chan et al., 2020<br>Lu et al., 2020</td></tr></tbody></table>

UTR<sub>l</sub> (leading untranslated region at the 5' end); cp (coat protein); rep (replicase); E (envelope gene); ORF (Open reading Frame gene); nsp2 (nonstructural protein gene); S (spike gene); N (Nucleocapsid gene); nt (nucleotides).
<figure><img src="image_4.png" /><figcaption>**Fig. 2.** The three protocols of the reverse transcription digital PCR (example of 3 targets). A) In the one-step dRT-PCR protocol, reverse transcription (RT) and PCR amplification are combined in a single step following the partitioning of the sample. The resulting cDNA is amplified in a multiplex reaction, targeting the A, B and C targets simultaneously using specific forward and reverse primers. B) In the two-step RT-dPCR protocol, reverse transcription (RT) is performed separately using specific reverse primers for each target (A, B, C), generating cDNA for each target individually. Partitioning is applied after cDNA synthesis, followed by separate digital PCR amplification for each target. This protocol aims to compare target concentrations and evaluate their similarity of detection. C) In the 2-step LR-RT-dPCR protocol, long-range reverse transcription generates contiguous cDNA covering the three targets (A, B, C) using a single specific reverse primer at one target position (C). Partitioning is conducted after cDNA synthesis, followed by a multiplex amplification of all targets (A, B, C) in a single reaction.</figcaption></figure>

**Table 4**

The various combinations of reverse transcription (RT) and digital PCR (dPCR) employed for amplifying different fragment sizes of MS2/FRNAPH-I genome using two-step LR-RT-dPCR and 3PLEX FRNAPH-I 3,296 nt system.

<table><thead><tr><th>MS2/FRNAPH-I</th><th>5'</th><th>3'</th></tr></thead><tbody><tr><td>RT position</td><td></td><td>Rep</td></tr><tr><td>dPCR</td><td>UTR<sub>L</sub></td><td>Rep</td></tr><tr><td>Fragment size (nt)</td><td>3,296</td><td></td></tr><tr><td></td><td>1,843</td><td></td></tr><tr><td>RT position</td><td></td><td>Cp</td></tr><tr><td>dPCR</td><td>UTR<sub>L</sub></td><td>Cp</td></tr><tr><td>Fragment size (nt)</td><td>1,535</td><td></td></tr></tbody></table>

electropherogram highlights a major peak, representing the predominant RNA fragment, along with minor peaks corresponding to less abundant fragments of viral RNA. The area under the curve (AUC) for each peak reflects the quantity of RNA within its respective size ranges. The total AUC (the sum of all individual peak AUCs) represents the overall RNA content. The detection frequency of the predominant fragment is assessed by calculating the ratio of the major peak AUC to the total AUC.

## 2.6. Statistical analysis

Statistical analyses were performed using GraphPad Prism software (version 9.5.0). The Wilcoxon test was applied to paired samples (n = 2

series) to compare detection levels obtained using two distinct dRT-PCR approaches: one-step dRT-PCR versus 2-step RT-dPCR. Additionally, this test was used to evaluate differences in detection levels and detection frequency between the 2-step RT-dPCR approach and the capillary electrophoresis method. For paired samples with more than two series (n > 2 series), the Friedman test was employed to assess differences in sample detection frequency across groups and to compare the equivalence of PCR target detection. Statistical significance is defined as a p-value of < 0.05. Significant differences are indicated with asterisks: * for p ≤ 0.05 and ** for p ≤ 0.01. Non-significant differences are denoted as 'ns'.
<caption>**Table 5**</caption>

The various combinations of reverse transcription (RT) and digital PCR (dPCR) employed for amplifying different fragment sizes of the SARS-CoV-2 genome using two-step LR-RT-dPCR and 5PLEX SARS-CoV-2_3,669 nt system.

<table><caption>**Table 6**</caption><thead><tr><th>SARS-CoV-2_3,690nt</th><th>5'</th><th>3'</th></tr></thead><tbody><tr><td>RT position</td><td></td><td>E</td></tr><tr><td>dPCR</td><td>S1 S2 S3 ORF3a</td><td>E</td></tr><tr><td>Fragment size (nt)</td><td>3,669 3,305 2,400 546</td><td></td></tr><tr><td>RT position</td><td></td><td>ORF3a</td></tr><tr><td>dPCR</td><td>S1 S2 S3</td><td>ORF3a</td></tr><tr><td>Fragment size (nt)</td><td>3,270 2,906 2,001</td><td></td></tr><tr><td>RT position</td><td></td><td>S3</td></tr><tr><td>dPCR</td><td>S1 S2 S3</td><td></td></tr><tr><td>Fragment size (nt)</td><td>1,406 1,402</td><td></td></tr><tr><td>RT position</td><td></td><td>S2</td></tr><tr><td>dPCR</td><td>S1 S2</td><td></td></tr><tr><td>Fragment size (nt)</td><td>520</td><td></td></tr></tbody></table>

The various combinations of reverse transcription (RT) and digital PCR (dPCR) employed for amplifying different fragment sizes of the SARS-CoV-2 genome using 2-step LR-RT-dPCR and 5PLEX SARS-CoV-2_30kb system. Fragment sizes colored in very light grey were excluded from the analyses as the reverse transcriptase used is limited to generating complementary DNA (cDNA) up to 12,000 nt in length, as specified by the manufacturer.

<table><thead><tr><th>SARS-CoV-2_30kb</th><th>5'</th><th>3'</th></tr></thead><tbody><tr><td>RT position</td><td></td><td>N2</td></tr><tr><td>dPCR</td><td>nsp2 ORF1a ORF1b S</td><td>N2</td></tr><tr><td>Fragment size (nt)</td><td>27,364 18,265 10,256 6,518</td><td></td></tr><tr><td>RT position</td><td></td><td>S</td></tr><tr><td>dPCR</td><td>nsp2 ORF1a ORF1b S</td><td></td></tr><tr><td>Fragment size (nt)</td><td>21,003 11,798 3,895</td><td></td></tr><tr><td>RT position</td><td></td><td>ORF1b</td></tr><tr><td>dPCR</td><td>nsp2 ORF1a ORF1b</td><td></td></tr><tr><td>Fragment size (nt)</td><td>17,216 8,117</td><td></td></tr><tr><td>RT position</td><td></td><td>ORF1a</td></tr><tr><td>dPCR</td><td>nsp2 ORF1a</td><td></td></tr><tr><td>Fragment size (nt)</td><td>9,205</td><td></td></tr></tbody></table>

# 3. Results

## 3.1. Validation of the LR-RT-dPCR method for the study of viral RNA integrity

The decision tree in Fig. 1 was used to identify the most appropriate RT-PCR method for assessing the viral RNA integrity of MS2 (or

FRNAPH-I in wastewater samples) and SARS-CoV-2. In the one-step dRT-PCR protocol was to use one-step dRT-PCR to obtain a method easier to apply than 2-step methods, but this approach strongly depended on the performance of the reverse transcription step for all five targets.

The first experiments aimed to confirm that dPCR provided equivalent quantification of all the targets of each multiplex system. Both MS2
and SARS-CoV-2 were consistently detected across the different targets using plasmid DNA (data not shown). For SARS-CoV-2, this was verified for both SARS-CoV-2_3,669 nt and SARS-CoV-2_30kb systems. This meant that both multiplex systems allowed similar quantification levels for the PCR assays that compose them, at least in the simplest conditions.

Then, synthetic MS2 RNA (hereinafter, “MS2 synthetic RNA”) was used for evaluating the one-step dRT-PCR method. The concentrations of UTR<sub>L</sub> and *cp* targets were between 4 and 15 times lower than those of the *rep* target (Fig. 3A). However, when 2-step RT-dPCR was performed independently for each assay, similar concentrations were obtained for UTR<sub>L</sub>, *cp*, and *rep* (Fig. 3A). The MS2 viral stock was included in this comparison and the same results were obtained (Fig. 3A). Moreover, the concentrations of each target were higher using two-step RT-dPCR than those obtained using one-step dRT-PCR, and the improvement was

particularly noticeable for UTR<sub>L</sub> and *cp* targets (Wilcoxon tests, p-value = 0.0039 for both UTR<sub>L</sub> and *cp*, and p-value = 0.0273 for *rep*). These results show that the use of the Induro<sup>®</sup> as reverse transcriptase significantly improves the detection of UTR<sub>L</sub> and *cp* targets. Given that our objective was to assess viral RNA integrity, the use of RT to target each sequence independently was not suitable, since it led to the production of three different cDNA products corresponding to the three assays of the multiplex prior to sample partitioning. Therefore, the most reliable approach was 1) to perform RT on the *rep* target, located at the 3' end of the viral genome using the Induro<sup>®</sup> reverse transcriptase, 2) to partition the cDNA, and 3) to carry out dPCR in 3PLEX format (Table 4). This two-step RT-dPCR characterized by long-range RT (two-step LR-RT-dPCR) was compared with the two-step RT-dPCR using the MS2 synthetic RNA (Fig. 3B). Again, similar concentrations were obtained for

<figure><img src="image_5.png" /><figcaption>**Fig. 3.** Comparative analysis of RNA quantity for three PCR targets of the MS2 phage genome (UTR<sub>L</sub>, *cp* and *rep*) using one-step dRT-PCR, two-step RT-dPCR and 2-step LR-RT-dPCR (long range-RT-dPCR) methods. A) Comparative analysis of genome quantity of RNA obtained by one-step dRT-PCR and two-step RT-dPCR for the three PCR targets of MS2 phage genome (UTR<sub>L</sub>, *cp* and *rep*) using the MS2 synthetic RNA in triplicate (*n* = 3) and two MS2 viral stocks in triplicate (*n* = 3). The one-step dRT-PCR method involves sample partitioning directly followed by a 3PLEX RT-PCR targeting UTR<sub>L</sub>, *cp*, and *rep* regions. The two-step RT-dPCR method involves a first RT step, performed on each target individually before partitioning, followed by 3PLEX PCR amplification of each target individually. B) Comparative analysis of genome quantity of RNA obtained by two-step RT-dPCR and two-step LR-RT-dPCR for the three PCR targets of the MS2 synthetic RNA (UTR<sub>L</sub>, *cp* and *rep*) in triplicate (*n* = 3). The two-step LR-RT-dPCR method involved a first RT step, performed on only one target (the *rep* target) before sample partitioning, followed by 3PLEX PCR amplification of UTR<sub>L</sub>, *cp*, and *rep* targets simultaneously.</figcaption></figure>
UTR<sub>L</sub>, cp, and *rep*, and the concentration values of the three targets were similar using both methods. This means that for MS2 synthetic RNA (Fig. 3A) and even for MS2 viral stocks (data not shown), Induro® reverse transcriptase was able to generate cDNA of 3,215 nt and then 3PLEX dPCR enabled equivalent quantification of all three targets distributed across the whole sequence.

To finalize the validation of the two-step LR-RT-dPCR method, MS2 synthetic RNA and the two MS2 viral stocks were both analyzed using the two-step LR-RT-dPCR and pulsed-field capillary electrophoresis (Femto Pulse, Agilent) to compare the RNA concentrations and the percentages of genome detection frequency (Table 7). For two-step LR-RT-dPCR, the percentage of complete genomes was equal to the number of positive partitions for the UTR<sub>L</sub> target among those positive for the *rep* target, with the RT beginning in the *rep* region. The comparison between capillary electrophoresis and 2-step LR-RT-dPCR showed similar RNA concentrations for the MS2 synthetic RNA (1.8 × 10<sup>8</sup> GC/µL versus 2.2 × 10<sup>8</sup> GC/µL, respectively), for the MS2 viral stock 1 (4.8 × 10<sup>7</sup> versus 4.6 × 10<sup>7</sup> GC/µL, respectively), and for the MS2 viral stock 2 (2.4 × 10<sup>7</sup> GC/µL for both of approaches) (Wilcoxon tests, p-values = 0.7969). The detection percentage of complete MS2 synthetic RNA was higher with capillary electrophoresis (an average of 80 % (Fig. S2A)) than with 2-step LR-RT-dPCR (65 %). For MS2 viral stock 1, capillary electrophoresis detected an average of 81 % of the complete genome (Fig. S2B), whereas 2-step LR-RT-dPCR detected an average of 57 %. For MS2 viral stock 2, both methods detected a similar percentage of the complete genome: 51 % for capillary electrophoresis (Table 7, Fig. 4) and 54 % for 2-step LR-RT-dPCR. Overall, these findings indicate that both methods provide consistent estimation of the RNA concentration across MS2 synthetic RNA and MS2 viral stocks, with capillary electrophoresis yielding a percentage of complete MS2 phage genomes that was not significantly different from that obtained using LR-RT-dPCR (Wilcoxon test, p-values = 0.0742). 2-step LR-RT-dPCR proves to be a reliable method for assessing RNA integrity, comparable to conventional capillary electrophoresis, and can be applicable for environmental samples with low viral RNA concentrations. Further experiments in this work were conducted with 2-step LR-RT-dPCR to study the genome integrity of FRNAPH-I and SARS-CoV-2 in controlled conditions, as well as in environmental raw wastewater samples.

## 3.2. Study of viral RNA integrity

### 3.2.1. Integrity of the MS2 genome in controlled conditions

The 2-step LR-RT-dPCR method and 3PLEX FRNAPH-I, 3,296 nt system were used to evaluate the genome integrity of MS2 (Table 4). This was performed in controlled conditions on various nucleic acid templates, including MS2 synthetic RNA and MS2 viral stocks. Reverse transcription was performed at different positions (Cp and *Rep*), allowing the evaluation of three fragments of various sizes: the whole MS2

genome (3,296 nt) and the two other fragments described in Table 4 (1,535 nt and 1,843 nt).

The results described in Fig. 5A and 5B showed that MS2 synthetic RNA demonstrated the highest detection frequency level across all fragment lengths, reflecting the significant integrity and stability of this MS2 synthetic RNA. More precisely, for the shorter fragments (1,535 and 1,843 nt), the detection frequency levels were 80 ± 3 % and 86 ± 3 %, respectively (Fig. 0.5B). However, a significant reduction in detection frequency was observed for the full-length fragment of the MS2 synthetic RNA (3,296 nt), dropping to 65 ± 4 % on average (Fig. 5A and 5B). For MS2 viral stocks, significantly lower detection frequencies were observed across all fragment length compared to the MS2 synthetic RNA (Wilcoxon test, p-value = 0.0078) (Fig. 5B). For MS2 viral stocks, detection frequencies for the two shorter fragments of 1,535 nt and 1,843 nt were 72 ± 3 % and 75 ± 5 %, respectively (Fig. 5B). The detection frequency of the whole MS2 genome (3,296 nt) in these samples was 56 ± 8 % on average (Fig. 5B).

To conclude, detection frequencies were higher for shorter fragments (1,535 and 1,843 nt) compared to the whole MS2 genome (3,296 nt) for both synthetic RNA and viral stocks (Wilcoxon test, *p* = 0.0078) (Fig. 5A). However, the detection frequency of the whole MS2 genome remained high (> 50 % on average), especially for synthetic RNA samples (Fig. 5B).

### 3.2.2. Integrity of SARS-CoV-2 genome in controlled conditions

The 2-step LR-RT-dPCR experiments were then conducted on short SARS-CoV-2 genome fragments using the 5PLEX SARS-CoV-2, 3,669 nt system (Table 5). Reverse transcription was performed at different target positions (E, ORF3a, S3, S2 and S1) using three SARS-CoV-2 viral stocks, allowing the evaluation of ten fragments of various sizes (363 to 3,512 nt). The detection levels of five of them were comparable (Friedman test, p-value = 0.1172).

The data presented in Fig. 6A indicates a progressive reduction of detection frequency, as the length of the targeted SARS-CoV-2 fragments increases. However, this trend is not consistent across all fragments since certain longer fragments displayed detection frequencies comparable to, or even exceeding, those of shorter fragments. For example, the S1-ORF3a fragment (3,270 nt), which was longer than the S3-E fragment (2,400 nt), showed a slightly higher frequency (57 ± 12 % versus 46 ± 10 %, respectively (Fig. 6B)). Furthermore, the S3-ORF3a fragment (2,001 nt) exhibited a similar detection frequency (86 ± 5 %) to that of much shorter fragments S1-S2 (520 nt) and ORF3a-E (546 nt) (86 ± 5 % and 83 ± 4 %, respectively) (Fig. 6B). These results suggest that factors other than fragment length, such as sequence-specific characteristics, may influence genome integrity. Additionally, all SARS-CoV-2 stocks showed slight variations in detection frequency across fragment lengths (Fig. 6A), suggesting that RNA integrity is also sample-dependent. However, this variation could also reflect differences in the efficacy of

<caption>**Table 7**</caption>

Comparative analysis of genome concentrations and detection frequencies of MS2 using capillary electrophoresis and two-step LR-RT-dPCR methods for synthetic RNA and the two MS2 viral stocks. nt = nucleotides, GC = genome copies.

<table><thead><tr><th rowspan="2">Sample</th><th colspan="3">Capillary electrophoresis</th><th colspan="3">2-step LR-RT-dPCR</th></tr><tr><th>Peak Size (nt)</th><th>Concentration (GC/µL)</th><th>% of complete genome</th><th>Size (nt)</th><th>Concentration (GC/µL)</th><th>% of complete genome</th></tr></thead><tbody><tr><td rowspan="4">MS2 synthetic RNA</td><td>3,818</td><td>1.8 × 10<sup>8</sup></td><td>84</td><td>3,296</td><td>1.9 × 10<sup>8</sup></td><td>69</td></tr><tr><td>3,800</td><td>1.9 × 10<sup>8</sup></td><td>77</td><td>3,296</td><td>3.6 × 10<sup>8</sup></td><td>65</td></tr><tr><td>3,800</td><td>1.6 × 10<sup>8</sup></td><td>78</td><td>3,296</td><td>1.1 × 10<sup>8</sup></td><td>61</td></tr><tr><td><strong>mean value</strong></td><td><strong>1.8 × 10<sup>8</sup></strong></td><td><strong>80</strong></td><td><strong>mean value</strong></td><td><strong>2.2 × 10<sup>8</sup></strong></td><td><strong>65</strong></td></tr><tr><td rowspan="4">MS2 stock 1</td><td>3,491</td><td>5.1 × 10<sup>7</sup></td><td>78</td><td>3,296</td><td>3.9 × 10<sup>7</sup></td><td>72</td></tr><tr><td>3,564</td><td>5.2 × 10<sup>7</sup></td><td>74</td><td>3,296</td><td>5.9 × 10<sup>7</sup></td><td>48</td></tr><tr><td>3,510</td><td>3.9 × 10<sup>7</sup></td><td>90</td><td>3,296</td><td>4.0 × 10<sup>7</sup></td><td>52</td></tr><tr><td><strong>mean value</strong></td><td><strong>4.8 × 10<sup>7</sup></strong></td><td><strong>81</strong></td><td><strong>mean value</strong></td><td><strong>4.6 × 10<sup>7</sup></strong></td><td><strong>57</strong></td></tr><tr><td rowspan="4">MS2 stock 2</td><td>3,500</td><td>3.0 × 10<sup>7</sup></td><td>64</td><td>3,296</td><td>8.9 × 10<sup>6</sup></td><td>54</td></tr><tr><td>3,482</td><td>2.8 × 10<sup>7</sup></td><td>58</td><td>3,296</td><td>4.0 × 10<sup>7</sup></td><td>55</td></tr><tr><td>3,556</td><td>1.3 × 10<sup>7</sup></td><td>32</td><td>3,296</td><td>2.3 × 10<sup>7</sup></td><td>54</td></tr><tr><td><strong>mean value</strong></td><td><strong>2.4 × 10<sup>7</sup></strong></td><td><strong>51</strong></td><td><strong>mean value</strong></td><td><strong>2.4 × 10<sup>7</sup></strong></td><td><strong>54</strong></td></tr></tbody></table>
<figure><img src="image_6.png" /><figcaption>**Fig. 4.** Electropherogram profile of genome obtained from the RNA extracted from MS2 viral stock 2. The purple peak indicates the size of the main RNA fragment in the sample, with fluorescence intensity measured in Relative Fluorescence Units (RFU). LM corresponds to lower marker that serves to calibrate the electrophoresis run.</figcaption></figure>

reverse transcription, and further experiments are necessary to distinguish true sample-specific variations from experimental variability. Regarding the three SARS-CoV-2 viral stocks, stock 1 showed higher integrity levels than stocks 2 and 3 (Friedman test, *p* < 0.0001).

Experiments were also conducted on extended SARS-CoV-2 genome fragments using the 5PLEX SARS-CoV-2_30 kb system (Table 6). Reverse transcription was carried out at different target positions (N2, S, ORF1a, ORF1b, and nsp2) for which equivalent detection levels were observed (Friedman test, *p*-value = 0.9137). This approach enabled the evaluation of six fragments of varying lengths, ranging from 3,895 to 11,904 nt, as described in Table 5. Because the reverse transcriptase enzyme Induro® can synthesize complementary DNA fragments of approximately 12,000 nt, as indicated by the manufacturer's specifications, six fragments (size ≤ 12,000 nt) were analyzed instead of all ten for the three SARS-CoV-2 stocks, the remaining four fragments exceeded 12,000 nt (Table 6).

As shown in Fig. 7A, detection frequencies were below 30 %, suggesting challenges in detecting intact fragments longer than 6,518 nt. Results also showed that the shortest fragment (S-ORF1b (3,895 nt)) had the highest average detection frequency (26 ± 2 %), while other genome fragments exhibited lower detection frequencies, ranging from 2 % to 10 %. Interestingly, as previously shown for the SARS-CoV-2_3,669 nt system (Fig. 7), detection frequencies did not always correlate with fragment length, since certain long fragments exhibited higher frequencies than shorter ones, as seen with the ORF1aS fragment (11,904 nt, 10 ± 7 %) compared to the nsp2-ORF1a fragment (9,205 nt, 3 ± 2 %) (Fig. 7B).

### 3.2.3. Integrity of FRNAPH-I and SARS-CoV-2 RNA isolated from raw WW samples

Under laboratory conditions, specifically when evaluating SARS-CoV-2 stocks, fragments longer than 6,361 nt were detected at frequencies not exceeding 10 %. Therefore, to assess the RNA integrity of FRNAPH-I and SARS-CoV-2 genomes in raw WW samples known to be complex matrices, two-step LR-RT-dPCR was performed on short genomic fragments. This included the FRNAPH-I and the SARS-CoV-2_3,669 nt systems.

The S2 assay was removed from the SARS-CoV-2_3,669 nt system due to its lower specificity for circulating variants compared with the four other assays (Table S2), meaning that the system monitored only six fragments of the SARS-CoV-2 genome.

Thirteen raw WW samples were analyzed in this study, but only six of them contained concentrations of FRNAPH-I and SARS-CoV-2 above the lowest concentration required to study genome integrity (1.4 × 10² GC/µL of RNA extract (Table S4)). The detection frequencies of the FRNAPH-I genome were very low across all six WW samples (noted A-1, A-2, A-4, C-3, C-4, and C-5): the average detection frequency of the 3,296 nt fragment was 2 ± 2 % and the detection frequencies of its constituent shorter fragments were 3 ± 3 % for the 1,843 nt fragment and 0 % for the 1,535 nt fragment (Fig. 8A and 8B). Only four WW samples had high enough SARS-CoV-2 concentrations to study RNA integrity (A-1, A-2, A-3, and C-1). Contrary to the results observed for the slightly shorter FRNAPH-I fragments, SARS-CoV-2 genome fragments of 2,001 nt and 2,400 nt in length exhibited higher detection frequencies (30 ± 14 % and 15 ± 8 %, respectively), for the same raw WW samples (Fig. 8C and 8D). Moreover, the shortest fragment (ORF3a-E (546 nt)) had the highest detection frequency in WW samples (55 ± 11 %), whereas the longest fragments (S1-E (3,669 nt) and S1-ORF3a (3,270 nt)) had the lowest detection frequencies (5 ± 10 %). However, as previously observed in controlled conditions, this trend was not only size-dependent. For instance, the S3-ORF3a fragment (2,001 nt) presented a similar detection frequency (30 ± 14 %) to a shorter fragment (S1-S3, 1,406 nt), which had an average detection frequency of 32 ± 2 % (Fig. 8D). These findings support the assumption that, in addition to fragment size, sequence characteristics and genomic position may influence the RNA integrity.

## 4. Discussion

In the context of environmental virology, assessing RNA integrity is essential to ensuring high-quality sequencing for the epidemiological surveillance of emerging viruses from complex environmental samples, such as raw WW. While it does not demonstrate infectivity, it may also
<figure><img src="image_7.png" /><figcaption>**Fig. 5.** Evaluation of MS2 genome integrity under controlled conditions using two-step LR-RT-dPCR. A) Detection frequency by fragment length for the near full-length MS2 genome (3,296 nt) and the two other fragments (1,535 and 1,843 nt) across the MS2 synthetic RNA and the two MS2 viral stocks. Lines represent the median values (n = 3). The Wilcoxon test was applied to compare the detection frequencies of the different fragments (\*: p < 0.05). B) Heatmap showing the average detection frequency by fragment length of the near full-length MS2 phage genome (3,296 nt) and its two other fragments (1,535 and 1,843 nt) across the MS2 synthetic RNA (n = 3) and the two MS2 viral stocks (n = 3 per stock). The 1,843 nt fragment is positioned on the 3' end, while the 1535 nt fragment is located on the 5' end. The Wilcoxon test was used to compare the detection frequency by fragment length between the MS2 synthetic RNA and the MS2 viral stocks, respectively, whatever the size of the fragments (n = 9 individual data per sample) (\*: p < 0.05; ns: non-significant).</figcaption></figure>

serve as an indirect proxy to avoid entirely dismissing potential infec-
tious risk in the absence of culture-based data, which is particularly
relevant for protecting operators in WWTPs. Despite its importance,
existing tools for assessing RNA integrity in WW, and in environmental
water matrices more generally, remain limited. Existing methods for
studying the integrity of nucleotide sequences, such as capillary elec-
trophoresis (Fleige and Pfaffl, 2006; Hiemenz et al., 2023; Mantri et al.,
2024; Sun et al., 2024) and dPCR (Furuta-Hanawa et al., 2019; Tereshko
et al., 2023), primarily focus on medical or therapeutic applications
targeting DNA. Capillary electrophoresis exhibits limitations in terms of
sensitivity, especially for complex samples such as raw WW. dPCR has
only recently been used to study viral DNA integrity (Furuta-Hanawa
et al., 2019; Levy et al., 2021; Tereshko et al., 2023) and is used even less
frequently to analyze environmental water samples and RNA genomes
(Wurtzer et al., 2024). In this study, we developed a comprehensive
analytical framework to evaluate the integrity of two viral RNA genomes
(FRNAPH and SARS-CoV-2) under both controlled and environmental
conditions.

In the first trials, we encountered difficulties in achieving equivalent
amplification of the targeted genomic regions of MS2 using 1-step dRT-
PCR. These challenges were likely due to limited accessibility of reverse
transcriptase to certain genomic regions in the 1-step setup, which
prompted us to shift to a 2-step LR-RT-dPCR strategy. Casmil et al.
(2023) reported contrasting results when assessing self-amplifying RNA
(saRNA) integrity using duplex one-step droplet dRT-PCR and two-step
RT-ddPCR. The one-step method yielded 50–60 % of intact saRNA,
whereas the two-step method, using random hexamer primers and
ProtoScript® reverse transcriptase, showed a 20 % reduction of intact
saRNA. In our study, we used specific primers targeting defined MS2
genome regions, instead of random hexamers, and we employed
Induro® reverse transcriptase. This likely explains why our two-step
method outperformed the one-step approach. Both the priming strat-
egy and the choice of enzyme are crucial. Induro® appears more effec-
tive at processing strong secondary RNA structures, as shown in a tRNA
profiling study (Nakano et al., 2025), which demonstrated improved
readthrough without increasing misincorporation rates. Additionally,
<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /><figcaption>**Fig. 6.** Evaluation of the genome integrity of SARS-CoV-2 from three independent viral stocks using the SARS-CoV-2_3,669 nt system and two-step LR-RT-dPCR. A) Detection frequency of ten fragments of the SARS-CoV-2 genome. B) Heatmap illustrating the average detection frequencies of ten fragments of the SARS-CoV-2 genome. The E gene is located at the 3' end, and the S1 gene at the 5' end.</figcaption></figure>

we validated our two-step LR-RT-dPCR results using capillary gel electrophoresis, by confirming its reliability for assessing genome integrity, especially thanks to the careful selection of specific primers and reverse transcription enzyme.

This reliable, validated two-step LR-RT-dPCR approach was used to evaluate the RNA integrity of FRNAPH-I and SARS-CoV-2, across samples of varying complexity (controlled versus environmental conditions) and genome sizes (short genome fragments up to 3,670 nt versus larger genome fragments of up to 12,000 nt). Regarding the genome size, under controlled conditions (synthetic RNA or viral stocks), the results revealed a high level of detection frequency (>70%) for short fragments (≤ 1,500 nt) for both viruses. Wurtzer et al. (2024) investigated RNA integrity for Coxsackievirus B3 (genome size ~7,400 nt) using one-step dRT-PCR across five genomic regions, enabling the evaluation of RNA fragments ranging from 271 to 1,376 nt in length. They calculated a

relative integrity (RI) value for these fragments, reflecting their detection frequency, and introduced the Fragment Size 50 (FS50) index, defined as the median fragment size for which 50 % of RNA molecules remain intact (RI = 50 %) (Wurtzer et al., 2024). Analysis of RI decay curves as a function of FS50 revealed that RNA fragmentation increases exponentially with increasing genome size, indicating that longer genome fragments are more prone to degradation (Wurtzer et al., 2024). Similarly, Mantri et al. (2024) determined the half-life of synthetic mRNA fragments of varying lengths using forced degradation experiments and fitting the data to a decay model. The genome integrity of synthetic mRNA was evaluated using capillary electrophoresis-based methods by these authors and they observed an inverse correlation between mRNA persistence and RNA fragment length. This correlation can be attributed to longer RNA having a higher number of nucleotide linkages, each acting as a potential site for hydrolysis. As a result, the
<figure><img src="image_10.png" /><figcaption>**Fig. 7.** Evaluation of the genome integrity of SARS-CoV-2 from three independent viral stocks using the SARS-CoV-2_30 kb system and two-step LR-RT-dPCR. A) Detection frequencies of six extended SARS-CoV-2 genome fragments. B) Heatmap illustrating the average detection frequencies of six extended SARS-CoV-2 genome fragments. The N2 gene is located at the 3' end, and the nsp2 gene at the 5' end.</figcaption></figure>

increased number of hydrolysis-prone sites in longer RNA leads to a higher probability of degradation (Mantri et al., 2024). However, this relationship should be interpreted with nuance, as noted in our study. Under controlled conditions (viral stocks from cell culture supernatant), a 2,001-nt fragment of the SARS-CoV-2 genome (the S3-ORF3a fragment) exhibited integrity detection frequency comparable to that of much shorter fragments: the S1-S2 fragment and the ORF3a-E fragment (520 nt and 546 nt, respectively). A similar pattern was also observed in environmental samples, where the larger S3-ORF3a fragment (2,001 nt) showed a detection frequency comparable to a shorter fragment (1,406 nt, S1-S3). This result may be attributed to the presence of complex secondary structures within the S3-ORF3a region, as suggested by a previous study (Simmonds, 2020). Such complex secondary structures have been shown to enhance the stability of viral RNA by protecting it against exoribonuclease activity at the 3' terminus, as observed in the Hepatitis C Virus (HCV) (Dickson and Wilusz., 2011). Additionally, viral RNAs can bind to proteins like HuR, which normally stabilize cellular transcripts, making it harder for the cell's decay machinery to break them down. This has been observed for the Sindbis virus and other alphaviruses (Dickson and Wilusz., 2011). These findings highlight the importance of considering not only RNA length but also intrinsic, sequence-specific features when evaluating genome integrity. This could explain why some regions of the SARS-CoV-2 genome showed higher detection frequency than others under the same conditions. However,

protective elements such as secondary structures could be an obstacle to viral RNA quantification and may require the use of specific reverse transcriptase such as the Induro® enzyme employed in our work.

Furthermore, variability in genome detection frequency was also observed between SARS-CoV-2 viral stocks derived from cell culture supernatants, with stock 1 exhibiting significantly higher integrity levels than stocks 2 and 3 (Friedman test, *p* < 0.0001; Fig. 6A). This finding suggests that, in addition to fragment length and intrinsic RNA properties, sample-specific factors may also influence detection outcomes.

As RNA fragments longer than 3,895 nt were poorly detected in viral stocks of SARS-CoV-2, we focused on shorter RNA fragments in WW samples. For this study, we analyzed the complete FRNAPH-I genome (measuring 3,569 nt) and fragments of the SARS-CoV-2 genome (up to 3,669 nt) in WW samples. The S3-ORF3a fragment (2,001 nt) of SARS-CoV-2 was also stable, as was the case for SARS-CoV-2 viral stocks, confirming that genome degradation is influenced not only by fragment size but also by specific sequence characteristics, notably the complexity of secondary structures in some regions, such as in the ORF3a gene (Simmonds, 2020). These findings align with those of a recent study by Williams et al. (2024), who used RT-qPCR to assess the sensitivity of different regions of the SARS-CoV-2 genome to degradation in WW samples stored at -20 °C and -80 °C for 10 days. The S gene, corresponding to the S1 region in our study, was particularly vulnerable to degradation, with a progressive decrease in genome copy numbers over
<figure><img src="image_11.png" /><figcaption>**Fig. 8.** Evaluation of FRNAPH-I and SARS-CoV-2 genome integrity in raw WW samples using two-step LR-RT-dPCR, specifically employing the FRNAPH-I system and the SARS-CoV-2_3,669 nt system, respectively. A) Detection frequencies of the FRNAPH-I genome as a function of the three fragments (1,843, 1,535, and 3,296 nt). B) Heatmap illustrating the average detection frequencies of the FRNAPH-I genome. The target *rep* is located at the 3' end, and the target *UTR* at the 5' end. C) Integrity levels of the SARS-CoV-2 genome as a function of the six fragments (546, 1,406, 2,001, 2,400, 3,305, and 3,669 nt). D) Heatmap illustrating the average detection frequencies of SARS-CoV-2 genome fragments. The E target is located at the 3' end, and the F target at the 5' end.</figcaption></figure>

time. In contrast, the E and N genes showed greater stability, corroborating earlier observations of higher concentrations of the N gene in wastewater compared to the S gene (Kumar et al., 2021; Williams et al., 2024). Overall, our work confirms that there is a correlation between sequence length and its detection frequency. This finding aligns with other studies showing that longer RNAs possess a greater number of sites vulnerable to hydrolysis, making them more prone to degradation (Geisberg et al., 2014; Mantri et al., 2024). These results emphasize the importance of considering all factors that can influence RNA integrity—such as sequence length, intrinsic sequence characteristics and water matrices’ complexity—for an accurate evaluation of RNA stability. Moreover, this study sheds light on challenges and future directions of research. For long viral genomes such as SARS-CoV-2, long-read sequencing from WW samples appears challenging. Degraded RNA poses a particularly important issue for advanced sequencing technologies, such as long-read sequencing (Prawer et al., 2023). Unlike long-read sequencing, short-read sequencing methods intentionally include RNA fragmentation in their protocol, yielding usable results even from degraded RNA (Prawer et al., 2023). However, with long-read sequencing, damaged RNA molecules cannot be fully sequenced, leading to a loss of information. Additionally, a relationship between genome integrity and the proportion of infectious viruses has been observed, particularly for the MS2 phage (Table S4). The detection

frequency of the entire genome (MS2) or fragments of around 3,500 nucleotides is higher in viral stocks of MS2 and SARS-CoV-2 than in FRNAPH-I and SARS-CoV-2 in WW. This difference can be attributed to the higher concentrations of infectious viruses in controlled conditions (viral stocks) than in complex conditions (WW samples). In WW samples, infectious FRNAPH particles represent only about 1 % of the total detected genome (Hartard et al., 2015), which likely explains the very low detection frequencies of FRNAPH-I in these samples. Moreover, viruses bound to WW particulates may retain infectivity while remaining non-culturable, which, in combination with the low proportion of infectious particles, likely explains the very low detection frequencies observed for FRNAPH-I in these samples. For SARS-CoV-2, genome fragments of similar size to those of FRNAPH-I exhibited higher integrity levels (>30 % for fragments ≤ 1,500 nt). This may be due to the stability of certain genomic regions targeted by our PCR systems, such as the S3-ORF3a region (2,001 nt), which contains complex secondary structures. By analogy, targeting other regions of the FRNAPH-I genome with similarly complex structures could also reveal higher integrity levels, highlighting the influence of sequence and structural features on fragment stability. One might consider that PCR inhibitors present in WW could also contribute to this difference. However, in our study, the extraction protocol included a phenol-chloroform step, which has been shown to effectively remove inhibitors in our previous work (Chaqroun
et al., 2024, 2025). In addition, the use of digital PCR, which is inherently less sensitive to inhibition due to sample partitioning, further minimized this effect.

Briefly, it is important to consider both the intrinsic characteristics of viral sequences when designing PCR systems, as well as the proportion of infectious viruses among total particles of a given virus, which is generally low in WW samples compared to other, less complex matrices such as clinical or laboratory samples. Therefore, developing specific methods for capturing and selecting infectious viral particles is crucial to isolating large portions of intact genomes for long-read sequencing from WW. Moreover, viral genome integrity assessment could represent a future approach to indirectly estimate infectious risk, while acknowledging that it cannot alone demonstrate infectivity. This may be particularly relevant for emerging viruses that are difficult to detect by traditional culture or that require high-level biosafety facilities. In addition, even when culture-based assays are feasible, molecular approaches such as PCR combined with genome integrity assessment provide complementary information by detecting strains that may remain undetected, since culture inherently captures only a subset of viral strains, those capable of replicating in the selected host system and producing lytic effects.

## 5. Conclusion

To conclude, the 2-step LR-RT-dPCR method developed in the present study has proven to be reliable and accurate for studying the viral RNA integrity of two viruses (FRNAPH-I and SARS-CoV-2). This 2-step LR-RT-dPCR method has been validated for fragments up to 3,500 nt in length, and it could be very useful for viral RNAs whose sequences lead to the formation of numerous secondary structures, as is the case for SARS-CoV-2 and FRNAPH. The analysis of RNA integrity can also be influenced by intrinsic characteristics of the genomic sequence, the complexity of matrices, and low concentrations of infectious viral particles. These limitations highlight the need to optimize protocols for capturing infectious particles (naked and enveloped viruses), particularly in complex matrices such as WW, to improve the relevance and reliability of genome integrity analysis. Moreover, these findings are invaluable for assessing RNA integrity, which may be useful not only for improving the sequencing of viruses in the environment, but also for therapeutic purposes, including for the assessment of RNA quality in the context of the design and delivery of RNA-based therapies and vaccines.

### CRediT authorship contribution statement

**Ahlam Chaqroun:** Writing – original draft, Visualization, Resources, Methodology, Investigation, Formal analysis. **Julie Challant:** Writing – review & editing, Resources, Methodology, Investigation. **Hélène Jeulin:** Writing – review & editing, Resources, Investigation. **Julie Loutreul:** Resources, Investigation. **Thomas Ansmant:** Investigation. **Stéphane Labialle:** Writing – review & editing, Methodology. **Zuzana Gerber:** Writing – review & editing, Visualization, Methodology, Investigation. **Cédric Fund:** Investigation. **Christelle Aigueperse:** Writing – review & editing, Resources, Methodology. **Jean-François Deleuze:** Writing – review & editing, Funding acquisition. **Robert Olaso:** Writing – review & editing, Funding acquisition. **Nicolas Boudaud:** Writing – review & editing, Funding acquisition. **Vincent Maréchal:** Writing – review & editing, Supervision, Funding acquisition, Conceptualization. **Christophe Gantzer:** Writing – review & editing, Validation, Supervision, Funding acquisition, Conceptualization. **Isabelle Bertrand:** Writing – review & editing, Visualization, Validation, Supervision, Resources, Project administration, Methodology, Funding acquisition, Conceptualization.

### Declaration of competing interest

The authors declare the following financial interests/personal

relationships which may be considered as potential competing interests:

Isabelle Bertrand reports financial support was provided by ANRS Emerging Infectious Diseases. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

### Acknowledgments

This work was carried out within the scope of the EmerEaUde project (ANRS0160), funded by ANRS-Maladies Infectieuses Emergentes, and also supported by the French scientific group of interest GIS-OBEPINE and the “MIE mesure 6 OBEPINE+ (ANR-24-MIEM-0004)” programme. This study was also supported by the UMT Actia VIROcontrol (Joint Technological Unit), a partnership tool shared by LCPME and Actalia, which was established and is supported by the French Ministry for Food. Additional support was obtained from the Zone Atelier Moselle (ZAM) and the Syndicat Intercommunal du Bassin d'Arcachon, which provided raw WW samples during the course of this study.

### Supplementary materials

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.watres.2025.125002.

### Data availability

Data will be made available on request.

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