Variant profiling after whole genome sequencing. Omicron sub-lineage BA.2 was predominant (>83%). BA.4 and BA.5 were emergent variants. Rare mutations of SARS-CoV-2 detected. The same representativeness of both sampling methods.

Wastewater-based epidemiology (WBE) has emerged as a powerful tool for monitoring population health, yet its quantitative reliability remains constrained by uncertainties in population estimation and sampling frequency. This study evaluates whether dynamic de facto population estimates can be derived from hydrochemical wastewater parameters and whether monthly wastewater monitoring can be analytically refined to a daily timescale through population normalization. A Bayesian smoothing and hierarchical outlier-detection framework was developed to denoise concentration and flow measurements of standard hydrochemical parameters. From these smoothed flows, a multidimensional inference model based on five hydrochemical parameters was constructed to estimate connected populations at daily resolution and validated using 2020 population benchmarks from two major Parisian wastewater treatment plants. The model achieved mean absolute percentage errors of approximately 6% across sites with contrasting catchment characteristics, outperforming or matching existing literature models and demonstrating robustness to operational disturbances. Applied to four years of data from the City’s wastewater Observatory, the method revealed population variations by a factor of three, substantially improving the interpretation of chemical and virological signals. After normalization, pharmaceutical trends derived from wastewater closely matched prescription data, enabling the detection of non-prescription usage. Conversely, monthly viral monitoring provided only limited correspondence with clinical data, confirming that such low-frequency sampling is insufficient for short-term epidemic tracking. Overall, this work demonstrates that hydrochemical-based population modeling enhances the interpretability, scalability, and operational value of long-term wastewater observatories, providing a practical route to integrate monthly monitoring into finer-scale WBE analyses.

The recent resurgence of measles during the post-COVID-19 period represents a major public health concern worldwide, particularly for low- and middle-income countries like Guinea which has experienced recurrent measles epidemics in recent years, characterized by limited clinical surveillance. In this context, environmental surveillance adapted to the constraints of decentralized wastewater management can represent an effective and cost-efficient tool for monitoring measles virus circulation. Following a pilot study conducted in the semi-enclosed wastewater drainage networks of the city center of Conakry (March 2022–April 2024), measles surveillance was expanded to the entire city, including informal networks and natural waterways. From August 2024 to October 2025, measles virus was detected in 32% of the wastewater samples analyzed (345/1072) with a clear peak during the early 2025 epidemic. This detection consistent with national clinical surveillance data demonstrates the value of wastewater monitoring as an early warning tool, helping to anticipate the resurgence of measles cases in the population. The quantification of the measles virus was normalized relative to the crAssphage signal, a quantitative indicator of the density of the populations being monitored. The modeling of our environmental surveillance adapted to a decentralized surveillance framework proposes an accurate characterization of the spatiotemporal dynamics of measles virus circulation in municipalities of Conakry and allows early alert about outbreaks before detection in health centers. This work demonstrates the feasibility of environmental surveillance in low-resource country settings and proves that it is a relevant tool during epidemic periods, helping to complement and strengthen the national surveillance system.

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 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.

BACKGROUND : Wastewater-based epidemiology has been investigated as a very effective way of monitoring SARS-CoV-2 variants. This can be achieved through accurate lineage deconvolution of wastewater sequencing data. Variants Ratios from Pooled Sequencing (VaRaPS) is a Python package designed for this purpose, utilizing pooled sequencing data and lineage mutation profiles to estimate their proportions. RESULTS : VaRaPS re-implements core algorithms from the literature, achieving significant improvements in computational speed and efficiency. Comparative analyzes with simulated and synthetic data sets demonstrate its superior performance in lineage prevalence estimation, underscored by its user-oriented design for broader accessibility. CONCLUSIONS : By improving speed and accuracy in SARS-CoV-2 variant analysis, VaRaPS offers valuable insights into viral evolution, supporting ongoing surveillance efforts in the post-pandemic landscape.

This study aimed to evaluate whether a nucleic acid extraction protocol specifically designed for raw wastewater (WW) provides a measurable advantage over protocols not originally intended for WW matrices. Three laboratories have independently compared two RNA extraction protocols, using paired WW samples. A common WW-designed protocol (Z), based on silica columns was tested across all labs. As comparators, three non-WW designed protocols were used: NS1 and NS2 both silica beads-based, with NS2 including an additional phenol-chloroform step; and SB a homemade protocol using also silica beads but differing in its formulation. As different samples were used across labs, direct statistical comparison was difficult; instead, paired comparisons within each lab were used to rank SARS-CoV-2 RNA detection and RT-qPCR inhibitor removal. NS1 yielded significantly higher SARS-CoV-2 RNA concentrations than Z (Logrank test, p ≤ 0.001), though with RT-qPCR inhibition in one sample. NS2 also showed higher SARS-CoV-2 RNA detection than Z (Wilcoxon test, p < 0.0001), with both protocols showing complete inhibitor removal. SB performed worse than Z for SARS-CoV-2 RNA detection (Logrank test, p ≤ 0.05), and showed inhibition in one sample. NS2 was the most effective option for both RNA detection and inhibitor removal.

Glucose metabolism plays a pivotal role in physiological processes and cancer growth. The final stage of glycolysis, converting phosphoenolpyruvate (PEP) into pyruvate, is catalyzed by the pyruvate kinase (PK) enzyme. Whereas PKM1 is mainly expressed in cells with high energy requirements, PKM2 is preferentially expressed in proliferating cells, including tumor cells. Structural analysis of PKM1 and PKM2 is essential to design new molecules with antitumoral activity. To understand their structural dynamics, we performed extensive high-resolution molecular dynamics (MD) simulations using adaptive sampling techniques coupled to the polarizable AMOEBA force field. Performing more than 6 μs of simulation, we considered all oligomerization states of PKM2 and propose structural insights for PKM1 to further study the PKM2-specific allostery. We focused on key sites including the active site and the natural substrate Fructose Bi-Phosphate (FBP) fixation pocket. Additionally, we present the first MD simulation of biologically active PKM1 and uncover important similarities with its PKM2 counterpart bound to FBP. We also analysed TEPP-46's fixation, a pharmacological activator binding a different pocket, on PKM2 and highlighted the structural differences and similarities compared to PKM2 bound to FBP. Finally, we determined potential new cryptic pockets specific to PKM2 for drug targeting.

Repeated waves of emerging variants during the SARS-CoV-2 pandemics have highlighted the urge of collecting longitudinal genomic data and developing statistical methods based on time series analyses for detecting new threatening lineages and estimating their fitness early in time. Most models study the evolution of the prevalence of particular lineages over time and require a prior classification of sequences into lineages which is prone to induce delays and biases. More recently, several authors studied the evolution of the prevalence of mutations over time with alternative clustering approaches, avoiding specific lineage classification. Most existing methods are either non parametric or unsuited to pooled data characterizing, for instance, wastewater samples. The analysis of wastewater samples has recently been pointed out as a valuable complementary approach to clinical sample analysis, however the pooled nature of the data involves specific statistical challenges. In this context, we propose an alternative unsupervised method for clustering mutations according to their frequency trajectory over time and estimating group fitness from time series of pooled mutation prevalence data. Our model is a mixture of observed count data and latent group assignment and we use the expectation-maximization algorithm for model selection and parameter estimation. The application of our method to time series of SARS-CoV-2 sequencing data collected from wastewater treatment plants in France from October 2020 to April 2021 shows its ability to agnostically group mutations in a consistent way with lineages B.1.160, Alpha, B.1.177, Beta, and with selection coefficient estimates per group in coherence with the viral dynamics in France reported by Nextstrain. Moreover, our method detected the Alpha variant as threatening as early as supervised methods (which track specific mutations over time) with the noticeable difference that, since unsupervised, it does not require any prior information on the set of mutations.

Monitoring the presence of RNA from emerging pathogenic viruses, such as SARS-CoV-2, in wastewater (WW) samples requires suitable methods to ensure an effective response. Genome sequencing of WW is one of the crucial methods, but it requires high-quality RNA in sufficient quantities, especially for monitoring emerging variants. Consequently, methods for viral concentration and RNA extraction from WW samples have to be optimized before sequencing. The purpose of this study was to achieve high coverage (> 90 %) and sequencing objective was to determine the range of SARS-CoV-2 RNA concentrations that allow high-quality sequencing, and the optimal sample volume for analysis. Ultrafiltration (UF) methods were used to concentrate viral particles from large influent samples (up to 500 mL). An RNA extraction protocol using silica beads, neutral phenolchloroform treatment, and a PCR inhibitor removal kit was chosen for its effectiveness in extracting RNA and eliminating PCR inhibitors, as well as its adaptability for use with large influent samples. Recovery rates ranged enough for UF concentration, as they showed high quality sequencing analyses with between 5 x 10(4) GC/L and 6 x 10(3) GC/L. Below 6 x 10(3) GC/L, high-quality sequencing was also achieved for similar to 40 % of the samples using 500 mL of WW. Sequencing analysis for variant detection was performed on 200 mL WW samples with coverage (66 %-100 %). The use of UF methods in combination with a suitable RNA extraction protocol appear promising for sequencing enveloped viruses in WW in a context of viral emergence.

In July 2023, the Center of Excellence in Respiratory Pathogens organized a two-day workshop on infectious diseases modelling and the lessons learnt from the Covid-19 pandemic. This report summarizes the rich discussions that occurred during the workshop.The workshop participants discussed multisource data integration and highlighted the benefits of combining traditional surveillance with more novel data sources like mobility data, social media, and wastewater monitoring. Significant advancements were noted in the development of predictive models, with examples from various countries showcasing the use of machine learning and artificial intelligence in detecting and monitoring disease trends. The role of open collaboration between various stakeholders in modelling was stressed, advocating for the continuation of such partnerships beyond the pandemic. A major gap identified was the absence of a common international framework for data sharing, which is crucial for global pandemic preparedness.Overall, the workshop underscored the need for robust, adaptable modelling frameworks and the integration of different data sources and collaboration across sectors, as key elements in enhancing future pandemic response and preparedness.

Wastewater-based epidemiology is experiencing exponential development. Despite undeniable advantages compared to patient-centered approaches (cost, anonymity, survey of large populations without bias, detection of asymptomatic infected peoples…), major technical limitations persist. Among them is the low sensitivity of the current methods used for quantifying and sequencing viral genomes from wastewater. In situations of low viral circulation, during initial stages of viral emergences, or in areas experiencing heavy rains, the extremely low concentrations of viruses in wastewater may fall below the limit of detection of the current methods. The availability during crisis and the cost of the commercial kits, as well as the requirement of expensive materials such as high-speed centrifuge, can also present major blocks to the development of wastewater-based epidemiological survey, specifically in low-income countries. Thereby, highly sensitive, low cost and standardized methods are still needed, to increase the predictability of the viral emergences, to survey low-circulating viruses and to make the results from different labs comparable. Here, we outline and characterize new protocols for concentrating and quantifying SARS-CoV-2 from large volumes (500 mL-1 L) of untreated wastewater. In addition, we report that the methods are applicable for monitoring and sequencing. Our nucleic acid extraction technique (the routine C: 5 mL method) does not require sophisticated equipment such as automatons and is not reliant on commercial kits, making it readily available to a broader range of laboratories for routine epidemiological survey. Furthermore, we demonstrate the efficiency, the repeatability, and the high sensitivity of a new membrane-based concentration method (MBC: 500 mL method) for enveloped (SARS-CoV-2) and non-enveloped (F-specific RNA phages of genogroup II / FRNAPH GGII) viruses. We show that the MBC method allows the quantification and the monitoring of viruses in wastewater with a significantly improved sensitivity compared to the routine C method. In contexts of low viral circulation, we report quantifications of SARS-CoV-2 in wastewater at concentrations as low as 40 genome copies per liter. In highly diluted samples collected in wastewater treatment plants of French Guiana, we confirmed the accuracy of the MBC method compared to the estimations done with the routine C method. Finally, we demonstrate that both the routine C method processing 5 mL and the MBC method processing 500 mL of untreated wastewater are both compatible with SARS-CoV-2 sequencing. We show that the quality of the sequence is correlated with the concentration of the extracted viral genome. Of note, the quality of the sequences obtained with some MBC processed wastewater was improved by dilutions or enzyme substitutions suggesting the presence of specific enzyme inhibitors in some wastewater. To the best of our knowledge, our MBC method is one of the first efficient, sensitive, and repeatable method characterized for SARS-CoV-2 quantification and sequencing from large volumes of wastewater.

The Obépine project (OBservatoire EPIdémiologique daNs les Eaux usées) has brought together research teams with a variety of skills (virology, mathematics, hydrology, infectiology) to evaluate the relevance of a quantification for SARS-CoV-2 genomes and its variants in wastewater, to provide a new element for monitoring the COVID-19 epidemic. We have demonstrated the relevance of this strategy, which aims at quantifying the viral genome at the entrance of wastewater treatment plants thanks to the use of sensitive, quantifiable, and reproducible molecular techniques associated with an original mathematical model. Obépine monitored up to 200 wastewater treatment plants, on a bi-weekly basis, in metropolitan and overseas France which corresponds to more than 33% of the French population. This article summarizes the key steps in the construction of the Obépine project.

Epstein-Barr virus (EBV) is a highly prevalent human herpesvirus that persists for life in more than 95% of the adult population. EBV usually establishes an asymptomatic life-long infection, but it is also associated with malignancies affecting B lymphocytes and epithelial cells mainly. The virus alternates between a latent phase and a lytic phase, both of which contribute to the initiation of the tumor process. So far, there is only a limited number of antiviral molecules against the lytic phase, most of them targeting viral replication. Recent studies provided evidence that EBV uses components of the NLRP3 inflammasome to enter the productive phase of its cycle following activation in response to various stimuli. In the present work, we demonstrate that shikonin, a natural molecule with low toxicity which is known to inhibit inflammasome, can efficiently repress EBV reactivation. Similar results were obtained with apigenin and OLT 1177, two other NLRP3 inflammasome inhibitors. It is shown herein that shikonin repressed the transcription of reactivation-induced NLRP3 thereby inhibiting inflammasome activation and EBV lytic phase induction.

The French Armed Forces Biomedical Research Institute (IRBA) deeply involved in research on SARS-COV-2, participated in the creation of the Obépine sentinel network in charge of detecting, qualifying and quantifying the virus genome in wastewater in France. During this pandemic, wastewater-based epidemiology has proven to be a first class public health tool for assessing viral dynamics in populations and environment. Obépine has also conducted research demonstrating the low infectivity of faeces and wastewater and allowed for early detection of epidemic waves linked to new variants. The IRBA has adapted this powerful tool to the monitoring of viral infections on board the aircraft carrier Charles-de-Gaulle in order to get an operational system for anticipation after the first local outbreak in 2020. The presence of this surveillance and anticipation tool has allowed a better management of SARS-CoV-2 contingent introductions on board during stopovers or crewmembers entries. The combination of a mandatory vaccination protocol and the surveillance of viral circulation in black waters has made it possible to identify and locate cases, and thus to continue the operational mission in the COVID-19 environment while limiting the spread and preserving the health of the crew. This innovative tool can easily be redirected to the search for any other pathogens in blackwater or even, in the long term, to ensure health surveillance of any military establishment, at sea or on land, in France or on overseas bases.

A sentinel network, Obépine , has been designed to monitor SARS-CoV-2 viral load in wastewaters arriving at wastewater treatment plants (WWTPs) in France as an indirect macro-epidemiological parameter. The sources of uncertainty in such a monitoring system are numerous, and the concentration measurements it provides are left-censored and contain outliers, which biases the results of usual smoothing methods. Hence, the need for an adapted pre-processing in order to evaluate the real daily amount of viruses arriving at each WWTP. We propose a method based on an auto-regressive model adapted to censored data with outliers. Inference and prediction are produced via a discretized smoother which makes it a very flexible tool. This method is both validated on simulations and real data from Obépine . The resulting smoothed signal shows a good correlation with other epidemiological indicators and is currently used by Obépine to provide an estimate of virus circulation over the watersheds corresponding to about 200 WWTPs.