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https://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC12822238&blobtype=pdf
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# Integrated Genomic and Epidemiological Surveillance to Monitor SARS-CoV-2 Variants in Italy: Insights From the JN.1 Case Study (2023-2024)
Mattia Manica, Emanuela Giombini, Martina Manso, Carla Grané, | Luigina, Antonino Bella, Angela Di Martino, Daniele Petrone, Flavia Riccardo, Piero Poletti, | Patrizio Pezzotti, Anna Palamara, Stefano Merler, Paola Stefanelli
## Abstract
The epidemiology of SARS-CoV-2 is marked by the continuous emergence of new lineages. Early detection and assessment of their transmissibility can be challenging for surveillance systems that rely solely on case time series data. Genomic surveillance, focusing on identifying and characterizing circulating variants, can provide early insights into their epidemiological impact. Phylogenetic and phylodynamic methods were applied to sequence data collected between October 2023 and January 2024 to study the transmission of the JN.1 variant in Italy. The genomic surveillance encompassed two data flows: flash surveys estimating variant prevalence and continuous sampling to identify emerging variants. We estimated the effective reproduction number (R e ) of JN.1 using a phylodynamic birth-death model. Results were compared with the daily net reproduction number (R t ) of SARS-CoV-2 estimated from time series of hospital admissions recorded through epidemiological surveillance. We traced back the appearance of JN.1 in Italy to October 2023, with subvariants emerging and co-circulating shortly thereafter. JN.1 became dominant nationwide by the end of 2023. According to phylodynamic analysis, the R e of JN.1 was 1.73 (95% CI: 1.36-2.28) in mid-November, and its transmissibility declined over the following months. This trend aligned with R t estimates from epidemiological surveillance, encompassing all co-circulating lineages. The high transmissibility of JN.1 anticipated the rise in its prevalence in the population and showed a temporal correlation with a transient increase in COVID-19 hospitalizations. Integrating genomic and epidemiological surveillance enhances pathogen monitoring and the assessment of new lineages' transmissibility, providing complementary evidence to patterns observed through standard surveillance.
## 1 | Introduction
The evolution of SARS-CoV-2 has become a highly dynamic process, marked by the relentless emergence of new variants and subvariants [1,2]. The selective pressure exerted on the circulating lineages can favor mutations enhancing the viral transmissibility and/or immune evasion, thereby promoting their spread [3][4][5]. The epidemiology of SARS-CoV-2 infections has been characterized by successive waves of COVID-19 cases of varying intensity and associated with potentially different morbidity rates, driven by the genetic characteristics of the spreading variants.
A standard approach for monitoring viral transmissibility relies on estimating the net reproduction number by applying the renewal equation to the time series of cases that test positive for the infection, exhibit symptoms, or are hospitalized [6,7]. However, intensive surveillance efforts are costly and may not be sustainable in the long term. As a result, under conditions of relatively low disease burden and stable pressure on the healthcare system, surveillance efforts are typically scaled back and adapted to monitor warning signs of changing conditions, rather than continuously tracking every detectable case. Additionally, estimates based on the time series of confirmed cases can be strongly affected by fluctuations in case reporting, often biased towards more severe cases, and are unable to provide separate transmissibility estimates for cocirculating variants due to the availability of genetic characterization only for a limited set of identified infections.
Since 2021, genomic surveillance has been successfully applied to monitor SARS-CoV-2 variants. Genetic, phylogenetic, and phylodynamic analyses have enabled public health systems to rapidly identify and characterize emerging variants [8][9][10], as well as trace their origins and patterns of spread [11]. These efforts have contributed to the development of effective mitigation and containment strategies to counter new epidemic waves [8,12]. In Italy, surveillance of SARS-CoV-2 is currently conducted through two complementary data flows: epidemiological and genomic.
In this study, we investigated how genetic and phylodynamic methods can successfully integrate the epidemiological analysis of new emerging SARS-CoV-2 variants. To this aim, as a proof of concept, we focused on the emergence and spread of the JN.1 variant in Italy, between 16 October 2023 and 31 January 2024. Internationally, the first confirmed case of JN.1 variant was identified in France in August 2023 [13]. The variant rapidly spread across American and European countries, eventually becoming prevalent globally at the beginning of 2024. JN.1 is a descendant of the Omicron lineage's BA.2.86 subvariant, characterized by more than 30 distinctive mutations in the spike protein [14]. JN.1 has subsequently given rise to multiple sublineages that dominate the current global circulation of .
During the study period, genomic surveillance of SARS-CoV-2 virus was conducted following two sequencing flows: 1) genomic surveys (hereafter denoted as "flash surveys") aimed at estimating variant prevalence by sequencing a representative sample of ascertained cases across the national territory on a given week of the month; and 2) continuous sequencing, applied to a subset of hospitalized patients with confirmed SARS-CoV-2 infection, enabling the early detection of variants or mutations. We analyzed the emergence of the JN.1 variant and its descendants, which together we refer to as JN.1*, as identified according to the Pango designation criteria as of 16 May 2024. The analysis of its temporal dynamics serves as an illustrative example of how the spread of new SARS-CoV-2 lineages can be assessed by integrating data from epidemiological and genomic surveillance.
## 2 | Materials and Methods
## 2.1 | Epidemiological Surveillance
Since February 2020, Italy notifies all laboratory-confirmed SARS-CoV-2 human infections to a national case-based surveillance system (hereby indicator-based surveillance) as previously described in Riccardo et al. [16]. The surveillance collects also the date of eventual hospitalization admissions. We analyzed the epidemiology of SARS-CoV-2 in Italy in the period September 2023-January 2024. We extracted epidemiological records, consolidated as of 27 May 2024, and we calculated the number of hospital admissions by day and week during the study period. We then estimated the net reproduction number (R t ) as previously described in [6] at national and at Region/ Autonomous Province (AP) levels. Estimates of R t assume that the generation time of newly emerging variants is comparable to that of pre-circulating variants and consistent across different lineages. During the study period, three flash surveys were conducted: 13-19 November 2023, 11-17 December 2023, and 15-21 January 2024. Out of 820, 913, and 433 sequences collected in the three surveys, 52 (6.3%), 372 (40.7%), and 333 (76.9%) were identified as belonging to the JN.1* lineage (including JN.1 and its subvariants), respectively. A generalized linear model with a binomial distribution was applied to estimate the increase in JN.1* prevalence over time (day of the year).
## 2.2 | Genomic Surveillance
Genomes collected through both flash surveys and continuous sequencing of COVID-19 hospitalized patients, covering all variants circulating between 16 October 2023 and 31 January 2024, were uploaded to GISAID [17]. From this dataset, we selected all sequences belonging to the JN.1* lineage that met minimal quality criteria, which included the absence of sporadic insertions or deletions and a coverage (i.e., the percentage of the genome sequenced) > 90%. The resulting dataset consisted of 1217 genomes, which were further analyzed using genomic modeling approaches.
## 2.3 | Genomic Diversity and Phylogenetic Analysis
To calculate the genetic diversity within and between JN.1 and JN.1 subvariants, the selected genomes were aligned using MAFFT V.7.520 [18]. Pairwise genetic distances were calculated using the p-distance model and 500 bootstrap repetitions with Mega 11. The distribution of JN.1 and JN.1 subvariants across the Italian regions were evaluated by means of a maximum likelihood phylogenetic tree constructed using IQ-TREE v1.6.9 [19]. The phylogenetic tree included all the JN.1* genomes that met our inclusion criteria and was rooted to reference 'BA.2.86' (using the Nextclade reference Wuhan-Hu-1 with BA.2.86 SNPs), with bootstrap support values calculated from 1,000 replicates. The tree was built using the best substitution model (GTR + F) as identified through ModelFinder and visualized using TVBOT [20].
## 2.4 | Phylodynamic Analysis
To provide quantitative epidemiological insights on the new emerging variant, we performed a Bayesian phylodynamic analysis of the JN.1 sequences, using a birth-death skyline model (BDSKY) [21]. We used the BEAST2 software (v2.7.5) for the phylodynamic analysis and the R package "beastio" to inspect the parameter posterior distributions and assess convergence and sufficient sampling (effective sample size > 200).
This modeling approach enabled us to estimate key epidemiological indicators, including the variant specific reproduction number (R e ), representing its transmissibility potential; the 'uninfectious rate', which directly relates to the variant-specific generation time; the 'time of most recent common ancestor', informing on the time of initial (potentially unobserved) variant emergence; and the 'sampling proportion', which provides insights into the overall number of individuals infected by the variant during the study period. The phylodynamic model assumes a general time reversible (GTR) + G4 nucleotide substitution model and an uncorrelated, lognormally distributed, relaxed molecular clock, implying that every branch in a phylogenetic tree may evolve at different evolutionary rates. To investigate temporal changes of the variant's transmissibility, we assumed a prior Gamma-distributed R e (shape = 2, scale = 1) to be piecewise constant over five intervals. For the uninfectious rate parameter, we assumed a lognormal prior distribution with a mean of 90 and a standard deviation of 0.4; for the origin parameter, we assumed a uniform prior distribution ranging from 0 to 5 years. Finally, for the sampling proportion, we assumed a Beta distributed prior (alpha = 1, beta = 3).
We applied the model to JN.1 sequences collected in Italy between 16 October 2023 and 31 January 2024. Specifically, based on the results of genomic diversity and phylogenetic analyses, JN.1 subvariant sequences were excluded from our baseline phylodynamic analysis to reduce computational time and complexity. To assess the model's robustness under this approach while accounting for computational constraints, we conducted a sensitivity analysis by comparing results from one illustrative Italian region using either all JN.1* sequences or only JN.1 sequences (i.e., with and without JN.1 subvariants).
To do this, we selected the Veneto region because it shared the highest number of sequences and accounted for approximately one third (31.2%, n = 380) of all JN.1* sequences collected in Italy (Figure 1). An additional sensitivity analysis was conducted by considering that every branch in a phylogenetic tree evolves according to the same evolutionary rate, assuming a strict molecular clock with uninformative uniform prior. For each analysis, we carried out 100 million independent MCMC runs, sampling every 1000 steps and discarding 10% of the initial iterations to account for the burn-in period. Finally, we compared the estimated reproductive number (R e ) of the JN.1 variant obtained from the phylodynamic model to the overall SARS-CoV-2 reproduction number (R t ), as inferred from the time series of hospitalized COVID-19 cases during the same period.
## 3 | Results
During the study period, 49089 hospitalizations were reported to the Italian National surveillance of SARS-CoV-2 infections. The median age was 79 (IQR: 67-86) and 49.2% (n = 24152) were women. Weekly cases almost steadily increased up to the middle of December 2023 and then continuously decreased at the minimum level in the last week of January 2024 (Figure 2). Figure S1 shows the temporal distribution of hospitalizations by Region/Autonomous province. In all cases, the peak was observed in December, with most of them showing a temporal trend similar to the one observed at the national level.
The JN.1 variant was first detected through the genomic flash survey in November 2023, with a point prevalence of 5.97% at the national level, when adjusted by the number of cases by region (see Figure 2). According to data collected through the flash surveys, JN.1 became the predominant SARS-CoV-2 variant circulating in Italy by the end of 2023, reaching a 77% national point-prevalence in the third week of January 2024 (see Figure 2).
Based on analyzed genomic data collected in Italy during the study period and shared on GISAID, according to the lineage assignment confirmed using Nextclade (accessed 16 May 2024) we retrospectively traced back the earliest JN.1 sequence to a case sampled on 16 October 2023 (Figure 1), before the official identification of the JN.1 variant through the national surveys. However, only later, during the week of 23-29 October 2023, distinct subvariants of JN.1 (i.e. with a limited number of mutations from JN.1 [22]) emerged and began to co-circulate with the original JN.1 (Table S1 for a complete list of JN.1 subvariants).
The proportion of JN.1 among JN.1* sequences remained approximately constant throughout the study period (Figure 3), resulting in 635 (52.1%) JN.1 sequences out of the 1217 JN.1* sequences analyzed.
All analyzed sequences reported the region of collection (Figure 1, Figure S2), while information regarding the age class and the admission in hospital of the sequenced cases was available for 75.8% (n = 922) and 43.2% (n = 526) of sequences, respectively. Similar percentages were observed between JN.1 and JN.1 subvariants (age class: 489 out of 635, 77.0% JN.1 vs 433 out of 582, 74.4% JN.1 subvariants; hospitalization: 276 out of 635, 43.5% JN.1 vs 250 out of 582, 43.0% JN.1 subvariants). Almost half of the sequenced cases for which the age class was reported were between 70 and 90 years of age; no differences were observed between JN.1 and JN.1 subvariants (Figure S3).
To evaluate the genetic distance between genomes belonging to JN.1 and JN.1 subvariants and to determine to what extent they should be considered as two distinct groups, inter and intragroup distances were calculated. The JN.1 group exhibited an internal distance with a standard deviation of 2.3 × 10 -4 ± 1.5 × 10 -5 , while the JN.1 subvariants group had a slightly higher internal distance of 3.3 × 10 -4 ± 3.5 × 10 -5 . The inter-group distance between JN.1 and JN.1 subvariants was calculated to be 3.0 × 10 -4 ± 2.8 × 10 -5 . This overall similarity among sequences is further supported by the phylogenetic tree, where only small clusters with significant bootstrap values (> 80) were identified. Additionally, Figure 4 shows how all sequences from different regions are interspersed and to what extent sequences from individual regions contributing a substantial number of sequences (e.g. the Veneto region corresponding to orange interconnections) are mixed with those of other regions without forming a distinct cluster.
Results from the phylodynamic model revealed a relatively high transmissibility of JN.1 during mid-November, with an estimated R e of . This finding aligns with evidence coming from flash surveys conducted at the same time, showing a progressive increase in the prevalence of JN.1 (see Figure 2). R t estimates obtained from epidemiological surveillance records collected in the same period show a downward trend lasting until October 2023, followed by a sharp upsurge in November (from 0.98 to 1.22). A similar dynamic was observed in the number of hospital admissions associated with SARS-CoV-2 infection (see Figure 2). R e estimates obtained from the phylodynamic model show a decline in transmissibility in December 2023, reaching estimated values below the epidemic threshold (0.95, 95% CI: 0.91-0.99) at the beginning of 2024. This pattern coincided with JN.1* becoming the predominant SARS-CoV-2 variant circulating in Italy by the end of 2023 (Figure 2). The increased prevalence of JN.1, coupled with an estimated decrease in its reproduction number, correlated with a decline in R t based on hospitalized cases. Specifically, R t dropped below the epidemic threshold towards the end of 2023 and ranged between 0.61 and 0.82 in January 2024 (Figure 2).
The phylodynamic model also yielded a mean estimated duration of infectiousness of 5.4 days (95% CI: 2.9-9.2 days), which is consistent with available estimates for previous Omicron lineages (mean estimates ranging between 5.7 and 8.6 days [23]). The time to the most recent common ancestor (tMRCA) was estimated to fall between 15 August and 24 September 2023, in line with the emergence of JN.1 in Europe [13]. Our analysis also suggests that analyzed sequences represented the 0.22% (95% CI: 0.06%-0.58%) of all JN.1 infections occurred in the country during the reconstructed epidemic, suggesting that as of 31 January 2024, the circulation of the JN.1 variant might have caused approximately 110-1095 thousand SARS-CoV-2 infections in Italy. Similar estimates were obtained under the assumption of a strict molecular clock, with a mean duration of infectiousness of 5.38 days, a tMRCA falling between 15 August and 24 September, and approximately 113-985 thousand JN.1 infections occurring during the study period (Table S2). The temporal dynamics of the JN.1 reproduction number were also consistent, showing a peak of approximately 1.72 in early December, followed by a decrease in transmissibility that led to Re estimates below 1 in early February (Figure S4). A considerable overlap in the estimated trajectory of transmissibility was found when restricting the analysis to the sequences obtained from the Veneto region, either including or not JN.1 subvariants (see Figure S5 and Table S1).
## 4 | Discussion
In Italy, JN.1 and its subvariants rapidly became predominant between December 2023 and January 2024, almost replacing previously circulating variants. Our analysis reveals a high degree of genetic homogeneity among all JN.1* genomes analyzed, including both JN.1 and its subvariants. Although, as expected, the JN.1 group exhibited slightly lower diversity compared to the JN.1 subvariants, the phylogenetic tree analysis did not reveal well-supported bootstrap clusters, likely due to the high sequence similarity. This suggests an ongoing evolutionary process marked by a high degree of genetic relatedness among circulating variants. These findings support the assumption that JN.1 can serve as a representative of the entire JN.1* group for phylodynamic analyses, thereby reducing computational constraints.
The rapid spread of JN.1* observed over a relatively short period -about 1 month and a half from emergence to dominanceunderscores its high transmissibility compared to pre-circulating strains. We found that the temporal expansion of JN.1* correlates with a swift, albeit brief, wave of hospital admissions detected by monitoring epidemic trends from epidemiological surveillance data. This surge was relatively short-lived, with the variant's specific transmissibility peaking in early November and subsiding within a couple of months. In principle, the increasing prevalence of JN.1* and the high transmissibility we estimated for JN.1 in early November could also be associated with the development of new immune escape mechanisms or an increased ability to infect specific niches of susceptible individuals [13,24,25]. During the 2023-2024 season, the Italian Ministry of Health recommended administration of the updated monovalent XBB.1.5 mRNA vaccine as a booster for individuals aged ≥ 60 years and for younger people with high frailty due to underlying medical conditions, with priority given to those aged ≥ 80 years, residents of long-term care facilities, and individuals with chronic diseases [26]. However, vaccination coverage among the elderly remained modest, with 11.7% of individuals aged 70-79 years and 15.8% of those aged ≥ 80 years receiving a booster between September 2023 and July 2024 [27]. Although vaccination may have partially influenced transmission dynamics, its overall impact was likely limited. During this period, no age-specific social distancing measures were in place, and most COVID-19-related restrictions in Italy had already been lifted. Given the complex epidemiological landscape associated with SARS-CoV-2, including uncertainties surrounding the current levels of both natural and vaccine-induced immunity within the considered population, it is not possible to draw definitive conclusions about the factors driving the temporary epidemiological success of JN.1 based only on the results presented here. Multiple lines of evidence -such as results from immune surveillance and severity data -would be required to provide a robust interpretation of the observed pattern.
Our estimates suggest that, following its rapid surge, the transmissibility of JN.1 began to decrease significantly. This pattern aligns well with the temporal qualitative trajectory of the (overall) SARS-CoV-2 net reproduction number, as estimated from the time series of hospitalized patients. However, our analysis reveals that transmissibility estimates differ significantly when comparing a phylogenetic approach applied to the spread of JN.1 (average R e ~1.7) with the assessment of overall SARS-CoV-2 transmissibility encompassing all co-circulating lineages based on epidemiological data (average R t ~1.2). This underscores the value of integrating diverse approaches to characterize the spread of new variants. The observed progressive decrease in the transmission potential of JN.1 could be due to several alternative or coexisting factors [28][29][30]. Firstly saturation, i.e. the reduction of susceptible because of acquired immunity. Secondly competition, i.e. the emergence or persistence of other circulating variants limiting the component of overall SARS-CoV-2 transmission due to JN.1. Thirdly, since we are measuring this decrease among hospitalized patients, the observed reduction in severity might reflect decreased JN.1 circulation only within hospitalized cases, and not necessarily in the general population or be a distinctive seasonal recurring pattern in SARS-CoV-2 epidemiology in Italy [31].
Moreover, limited evidence suggest that JN.1* exhibits either increased or reduced pathogenicity compared to other circulating variants [32]. Accurately characterizing specific SARS-CoV-2 variants in terms of transmissibility and attack rates remains a significant challenge due to the availability of samples eligible for the sequencing and the testing policy. However, phylodynamic analyses may effectively integrate and increase the granularity to data collected through epidemiological surveillance. Here we show that combining these two sources can provide valuable insights into transmission patterns by allowing the production of estimates that are associated with specific lineages significantly reducing uncertainties underlying complex epidemiological dynamics. For instance, by leveraging the estimated sampling proportion, we also derived an approximate estimate of the infection attack rate of JN.1 during the considered period. Despite the uncertainty of the obtained estimates, such a result is well beyond what can be estimated using data collected only through routine epidemiological surveillance [8]. More in general, when testing is predominantly limited to hospitalized patients -as in the case for SARS-COV-2 in Italy in 2024combining genetic and phylodynamic methods with standard analyses of epidemiological surveillance data may become essential for monitoring complex epidemiological patterns and epidemic trends occurring in the general population.
Nonetheless, several limitations need to be considered when interpreting our results. First, we neglected any geographical pattern in the spread of the infection and therefore considered the progressive expansion of JN.1 as an epidemic occurred at national scale. This may have introduced a bias in our analysis due to potentially different sampling efforts carried out across regions, different local transmission patterns, or the likely heterogeneous representativeness of collected samples of the viral diversity cocirculating during the study period. However, the phylogenetic tree of full-length genomes of JN.1 and JN.1 subvariants demonstrated that circulating variants were not geographically restricted but rather exhibited a uniform distribution across different regions. This supports the idea that individual regions contributing a substantial number of sequences may serve as arbitrary yet representative proxies of nationally circulating variants when applying phylodynamic approaches, which are computationally intensive and may become impractical when applied to very large sequence datasets. Specifically, as an illustrative example, we show that phylodynamic analyses conducted using samples exclusively from one region yielded estimates consistent with those obtained when including data from all regions. Second, we should acknowledge that genetic epidemiology is highly dynamic, with the classification of variants and their subvariants being continuously revised as new information becomes available. This means that estimates obtained from sequencing data should be progressively and carefully revised over time. Although our results highlight the potential of phylodynamic approaches in providing complementary evidence on the epidemiological patterns detected through standard surveillance of cases, non-negligible computational challenges may still arise from the analysis of large datasets of sequences gathered during large and widespread epidemics.
The approach described here will be valuable in illustrating the spread mechanisms of SARS-CoV-2 variants that may emerge in the future, during a time when the virus is no longer considered pandemic but endemic. Given the complex and everevolving immunity landscape associated with SARS-CoV-2, maintaining and enhancing the integration of different surveillance systems is required to equip public health systems with multiple and adaptive strategies to control and monitor new variants as they emerge.
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110. Ou, Yang, Zhang (2024) "Evolving Immune Evasion and Transmissibility of SARS-CoV-2: The Emergence of JN.1 Variant and Its Global Impact" *Drug Discoveries & Therapeutics*
111. Liu, Zhao, Shi (2024) "Lineage-Specific Pathogenicity, Immune Evasion, and Virological Features of SARS-CoV"
112. "Indicazioni e raccomandazioni per la campagna di vaccinazione autunnale/invernale 2023/2024 anti COVID-19"
113. (2024) "COVID-19 Vaccination Coverage in the EU/EEA During the 2023-24 Season Campaigns"
114. Del Rio, Omer, Malani (2022) "Winter of Omicron-The Evolving COVID-19 Pandemic" *Journal of the American Medical Association*
115. Rochman, Wolf, Faure et al. (2021) "Ongoing Global and Regional Adaptive Evolution of SARS-CoV-2" *Proceedings of the National Academy of Sciences*
116. Otto, Day, Arino (2021) "The Origins and Potential Future of SARS-CoV-2 Variants of Concern in the Evolving COVID-19 Pandemic" *Current Biology*
117. Marziano, Guzzetta, Menegale (2023) "Estimating SARS-CoV-2 Infections and Associated Changes in COVID-19 Severity and Fatality" *Influenza and Other Respiratory Viruses*
118. Levy, Chilunda, Davis (2024) "Reduced Likelihood of Hospitalization With the JN.1 or HV.1 Severe Acute Respiratory Syndrome Coronavirus 2 Variants Compared With the EG.5 Variant" *Journal of Infectious Diseases*
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# P-748. Chronic wounds and xylazine exposure among people who use drugs in Baltimore and Washington, DC: prevalence, preferences, and testing methods
Edward Traver, ; Onyinyechi Ogbumbadiugha-Weekes, Meredith Zoltick, Claire Tindula, Dnp, Fnp-C, Tina Liu, Sabina Ghale, Meghan Derenoncourt, Miriam Jones, Ashley Davis, Dorcas Salifu, Lydia Mitchell, Meghan Anderson, Rahwa Eyasu, Emade Ebah, Elana Rosenthal, Sarah Kattakuzhy
Background. Chronic wounds in people who use drugs (PWUD) are frequently infected and may progress to severe, life-threatening infections. Such wounds may be caused by xylazine, a non-opioid adulterant of illicit fentanyl and avoidance of xylazine may decrease wound incidence and infection. Xylazine test strips (XTS, BTNX Inc.) are commercially available to check illicit drugs for xylazine, but more data is needed on xylazine prevalence and knowledge among PWUD, and it is unknown if XTS can be used to detect xylazine in urine. Methods. We surveyed patients at two clinics in Baltimore and Washington, DC that provide multidisciplinary care to PWUD. Patients were included if they reported non-prescribed opioids, cocaine, or methamphetamine in the past 30 days. Urine was tested for xylazine with XTS and standard liquid chromatography-mass spectroscopy S572 • OFID 2026:13 (Suppl 1) • Poster Abstracts (LC-MS). We measured associations between wound prevalence, xylazine urine positivity, and other demographic and clinical factors with Fischer's exact test for categorical variables and Mann Whitney for continuous variables. We estimated the sensitivity and specificity of XTS to detect xylazine in urine compared to LC-MS and calculated 95% confidence intervals with the Wilson-Brown method. Results. 119 participants were included; 27 (23%) who had ever had a wound (Table 1). Xylazine was detected by LC-MS in urine from 56 (47%) participants. People with wounds were more likely to be recruited from Baltimore (p=.002) and White or Caucasian race (p< .001). Wounds were not associated with injection drug use or xylazine detection in urine. People with wounds were more likely to be knowledgeable about xylazine (Table 2). XTS had a low sensitivity but high specificity for urine xylazine detection compared to LC-MS (Figure 1).
Conclusion. Xylazine exposure at a single timepoint in PWUD in Baltimore and Washington was common but not associated with lifetime history of wounds, potentially due to variable exposure and detection over time. People with wounds were more familiar with xylazine. PWUD are interested in using XTS but the vast majority have not, suggesting residual structural barriers. XTS are have useful positive predictive value but low negative predictive value when used on urine.
Disclosures. All Authors: No reported disclosures
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# Retraction RETRACTION: Expression of Factor X in BHK-21 Cells Promotes Low Pathogenic Influenza Viruses Replication Advances in Virology
Te retraction has been agreed following an investigation of the concerns raised by Actinopolyspora biskrensis on PubPeer [1], which identifed several concerns related to Figure 3.
More specifcally, the images of BHK-21/FX and BHK-21/trypsin cells at 24 and 72-hour marks contain overlapping features, despite representing diferent experimental conditions.
As a result of the investigation, the data and conclusions of this article are considered unreliable.
Te authors disagree with this retraction.
## References
1. (2024) "Expression of Factor X in BHK-21 Cells Promotes Low Pathogenic Infuenza Viruses Replication, PubPeer"
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# Clinical Phenotypes of Critically Ill Patients with COVID-19 Infected with Omicron: A Nationwide Prospective Cohort Study
Etienne Audureau, Pierre Bay, Sébastien Préau, Raphaël Favory, Aurélie Guigon, Nicholas Heming, Elyanne Gault, Tài Pham, Amal Chaghouri, Matthieu Turpin, Laurence Morand-Joubert, Sébastien Jochmans, Aurélia Pitsch, Sylvie Meireles, Damien Contou, Amandine Henry, Damien Roux, Quentin Le Hingrat, Antoine Kimmoun, Cédric Hartard, Frédéric Pène, Anne-Sophie L'honneur, Antoine Guillon, Lynda Handala, Fabienne Tamion, Alice Moisan, Thomas Daix, Sébastien Hantz, Flora Delamaire, Vincent Thibault, Cédric Darreau, Jean Thomin, Jean-Michel Pawlotsky, Slim Fourati, A.-S L'honneur, J.-M Pawlotsky
## Abstract
Introduction: The clinical presentation of critically ill patients with coronavirus disease 2019 (COVID-19) has evolved significantly with the emergence of the Omicron variant. Current intensive care unit (ICU) admissions involve patients with diverse comorbidities and immune statuses, highlighting the need to redefine homogeneous phenotypic subgroups within this population. This study aimed to characterize distinct clinical phenotypes among critically ill patients with COVID-19 and acute respiratory failure. Methods: This multicenter prospective substudy of the SEVARVIR cohort included adult patients from 39 French ICUs between December 2021 and October 2024 with acute Etienne Audureau and Pierre Bay have contributed equally as first authors.clusters 5 and 7 had the highest requirements for organ support, with frequent use of invasive mechanical ventilation, vasopressors (cluster 6), and renal replacement therapy (cluster 7). Dexamethasone and tocilizumab were most commonly prescribed in cluster 4 (91.3% and 30.2%, respectively). Mortality at day 28 varied significantly across clusters, ranging from 13.1% in cluster 3 to 41.1% in cluster 6.
Conclusions:This clustering analysis highlights, for the first time, the clinical heterogeneity of critically ill patients infected with Omicron, identifying seven distinct clusters with varying clinical presentations, management strategies and outcomes. These findings underscore the relevance of a phenotype-driven approach to support personalized treatment strategies and guide future clinical trials. Trial Registration: Clinicaltrials.gov, NCT05162508.
respiratory failure and infected with the Omicron variant. Clustering analysis was conducted using Kohonen's self-organizing maps (SOMs) and validated with ClinTrajan, two unsupervised clustering methods, to identify homogeneous patient phenotypes. Results: During the study period, 777 patients with Omicron infection were included, and 7 distinct clinical clusters were identified. Clusters 1 and 2 included patients with metabolic and cardiovascular comorbidities. Cluster 3 featured younger, mildly ill patients with isolated chronic respiratory failure, while cluster 4 comprised older male patients with isolated respiratory failure. Cluster 5 included patients with isolated hematologic malignancies, cluster 6 patients with multiorgan failure, and cluster 7 organ transplant recipients, with high severity scores and impaired renal function. ICU management varied substantially across clusters. Patients in Clinical presentations of critically ill patients with COVID-19 have evolved substantially in the Omicron era. These patients present with a greater burden of comorbidities, including a higher prevalence of immunosuppression.
There are very limited data describing the clinical heterogeneity of critically ill patients infected with Omicron.
Most therapeutic evidence originates from earlier waves of the pandemic and may not be fully applicable to the population in the Omicron era.
This nationwide prospective study, including 777 critically ill patients infected with Omicron across 39 French intensive care units (ICU), aimed to explore phenotypic heterogeneity using unsupervised clustering analysis.
## What was learned from the study?
Seven distinct clinical phenotypes were identified, each characterized by markedly different demographic, comorbidity, and severity profiles.
Therapeutic management varied substantially between clusters, particularly regarding the use of dexamethasone, tocilizumab, and organ support strategies.
Clinical outcomes differed significantly, with 28-day mortality ranging from 13.1% to 41.1%.
These results support a phenotype-driven approach for personalized care and future trial design.
## INTRODUCTION
Although the period of overwhelming intensive care unit (ICU) admissions due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pneumonia appears to be over, coronavirus disease 2019 (COVID-19) remains a common reason for ICU admission, particularly among vulnerable patients with increasingly heterogeneous clinical profiles [1]. The course of successive epidemic waves, the emergence of new variants, and the development of natural and vaccine-induced immunity have profoundly changed the pandemic landscape [2].
The early waves of the COVID-19 pandemic were marked by a relatively uniform clinical presentation among patients in ICUs. The majority of these were male, with a median age of around 60 years, presenting with moderate cardiovascular comorbidities, including hypertension, diabetes, or obesity [3]. However, the emergence of the Omicron variant, which has been the dominant lineage since 2022, has led to significant changes in the clinical profile of critically ill patients. These individuals tend to be older, with a greater burden of comorbidities, including a higher prevalence of immunosuppression [4].
The clinical presentation of critically ill patients infected with Omicron has become increasingly heterogeneous, with evolving sublineages, the acquisition of vaccine-and infection-induced immunity, and novel therapeutic strategies. As with other respiratory viruses, severe SARS-CoV-2 infection is no longer merely a viral pneumonia, but can also precipitate an exacerbation of preexisting comorbidities such as chronic heart failure (CHF) or chronic obstructive pulmonary disease (COPD) [5,6]. Immunocompromised patients may experience prolonged respiratory symptoms that persist for several weeks due to impaired viral clearance [7]. In addition, accurately assessing the true impact of SARS-CoV-2 in some critically ill patients remains challenging, particularly in cases of multiorgan failure where the virus is detected but its exact contribution to the patient's clinical condition is unclear.
Notwithstanding these changes, pharmacological management remains principally based on immunomodulatory therapies (e.g., corticosteroids, anti-IL-6 receptor, baricitinib) that have been evaluated in the pre-Omicron era patient population [8]. A number of studies have sought to delineate subgroups of patients with homogeneous clinical phenotypes by employing a wide spectrum of methodologies [9][10][11][12]. However, the majority of these studies were conducted prior to the emergence of the Omicron variant, thereby missing the full spectrum of clinical heterogeneity currently observed among critically ill patients with COVID-19 infected with Omicron.
The objective of this study is to investigate the phenotypic heterogeneity of patients with acute hypoxemic respiratory failure due to SARS-CoV-2 infection requiring ICU admission by identifying homogeneous clinical phenotypes in the Omicron era.
## METHODS
## Patients and Clinical Data
This is a substudy of the prospective, multicenter observational SEVARVIR cohort study [4][5][6]13]. Patients admitted to one of the 39 participating ICUs (including 18 from the Greater Paris area, see Table S1 for the list of participating centers) between 7 December 2021 and 10 October 2024 were eligible for inclusion in the SEVARVIR cohort study (NCT05162508) if they met the following inclusion criteria: age ≥ 18 years, SARS-CoV-2 infection confirmed by a positive reverse transcriptase-polymerase chain reaction (RT-PCR) in nasopharyngeal swab samples, admission to the ICU for acute respiratory failure (i.e., peripheral oxygen saturation (SpO 2 ) ≤ 90% and need for supplemental oxygen or any kind of ventilator support), patient or next of kin informed of study inclusion. Patients with SARS-CoV-2 infection but no acute respiratory failure, or whose nasopharyngeal swabs had an RT-PCR cycle threshold (Ct) value > 32, were not included. For this substudy focused on the Omicron variant, we included patients who had been confirmed to have the Omicron variant or who were admitted after 1 May 2022, since only the Omicron variant was circulating in France after that date [4,14].
Demographic, clinical, and laboratory variables were recorded upon ICU admission and during their stay in the ICU. Patients' frailty was assessed using the Clinical Frailty Scale [15]. The disease severity at ICU admission was evaluated using the World Health Organization (WHO) ten-point ordinal scale [16], the sequential organ failure assessment (SOFA) score [17], and the simplified acute physiology score (SAPS) II score [18]. Acute respiratory distress syndrome (ARDS) was defined in accordance with the Berlin definition [19]. Table S2 lists variables with ≥ 10% missing data. The use of corticosteroids and tocilizumab was recorded on the basis of whether these treatments were administered at any point during the patient's stay in the ICU. Whether to initiate dexamethasone, other steroids, or anti-IL-6 agents was at the discretion of the attending physician and was thus not standardized.
## Ethical Approval
The study was approved by the Comité de Protection des Personnes Sud-Méditerranée I (N° EudraCT/ ID-RCB: 2021-A02914-37). Informed consent was obtained from all patients or their relatives. The study was conducted in accordance with the 1964 Declaration of Helsinki and subsequent amendments.
## SARS-CoV-2 Variant Determination
The full-length SARS-CoV-2 genomes of all the patients included in the study were sequenced using next-generation sequencing. In brief, viral RNA was extracted from nasopharyngeal swabs in viral transport medium using NucliSENS® easyMAG kit on EMAG device (bioMérieux, Marcy-l'Étoile, France). Sequencing was performed using Illumina® COVIDSeq Test (Illumina, San Diego, California), which employs 98-target multiplex amplifications across the entire SARS-CoV-2 genome. The libraries were then sequenced with NextSeq 500/550 High Output Kit v2.5 (75 Cycles) on a NextSeq 500 device (Illumina). The sequences were demultiplexed and assembled into full-length genomes using the DRAGEN COVIDSeq Test Pipeline on a local DRAGEN server (Illumina). Lineages and clades were interpreted using Pangolin and NextClade, before submission to the GISAID international database (https:// www. gisaid. org).
## Statistical Analyses
Descriptive results are presented as means (± standard deviation [SD]) or medians (first-third quartiles) for continuous variables, and as numbers with percentages for categorical variables.
An unsupervised clustering analysis was conducted to explore the heterogeneity of the population by identifying typical profiles with contrasting characteristics. The following clinical or biological variables considered as relevant for this analysis were demographics (age, sex), comorbidities (CHF, hypertension, obesity, chronic respiratory failure, chronic renal failure, immunosuppression), severity, and biological features upon ICU admission (time from first symptoms to ICU admission, SAPS II score, SOFA score, PaO 2 /FiO 2 ratio, arterial lactate levels, blood leukocytes, lymphocytes and platelets count, serum urea level, and serum creatinine level).
The main clustering analysis was conducted using the Kohonen's self-organized map (SOM) methodology [20], which enabled us to build two-dimensional maps from multidimensional datasets. In a nutshell, the SOM algorithm divided each map into districts in which patients are located on the basis of their characteristics. Patients with similar features are located close to each other on the maps, while patients with distinct profiles are located farther apart. This allows us to identify key differences Table 1 continued Manual adjustments were then applied to refine the delimitation of clusters, and the final number of clusters was guided by clinical expertise.
To evaluate the robustness of the SOM analysis findings, a sensitivity analysis was performed using the ClinTrajan method [24], which reduces multidimensional phenotypic data to a twodimensional space following a tree-like structure. This method uses the elastic principal tree (EPT) algorithm, a nonlinear generalization of principal component analysis (PCA), to model the complex geometry of clinical data as an array of diverging trajectories [25,26]. The EPT constructs a principal tree, which is defined as a set of interconnected principal curves arranged in a tree-like topology. Branching points represent key divergences in clinical states. Clusters are identified as unbranched segments of the tree that group together patients with similar clinical patterns. This approach improves interpretability because individuals with the most distinct or characteristic profiles tend to be located at the 0.004 extremities of the branches, while those with more typical or milder profiles tend to be found near the root. Cluster solutions obtained from the SOM and ClinTrajan analyses were compared using a Sankey plot created with the alluvial and easyalluvial R packages. Global unadjusted comparisons according to the cluster status were performed using chi-squared or Fisher's exact tests for categorical variables, and analysis of variance (ANOVA) or Kruskal-Wallis tests for continuous variables, as appropriate. The aim of conducting statistical comparisons between clusters was not to formally test hypotheses, but rather to emphasize the key clinical and biological features that distinguish the phenotypes identified by unsupervised clustering. Survival analyses were performed to assess the prognostic significance of the various subgroups identified with respect to overall survival. The Kaplan-Meier method was used to plot survival curves, and log-rank tests were used to assess significance for group comparison.
To minimize the impact of potential selection bias arising from complete-case analyses, all clustering analyses were performed after missing data imputation using the missForest algorithm, a nonparametric method based on random forest imputation that can handle nonlinearities and interactions [27], as implemented in the R package missRanger [28]. Descriptive analyses and graphs were performed using GraphPad Prism, version 8 (GraphPad Software), R 4.2.0 (R Foundation for Statistical Computing, Vienna, Austria) for imputing missing data, performing comparative and survival analyses, and conducting clustering analyses using the SOM approach. Clustering analyses were also performed using the ClinTrajan algorithm in Python 3.12.7.
## RESULTS
## Population
During the study period, a total of 931 patients were admitted to one of the 39 participating ICUs and included in the study, of whom 777 were infected with the Omicron variant (Fig. 1). Within the overall cohort, 274 patients (35.3%) were female, with a median age of 69.1 years (59.9; 75.4). The most prevalent comorbidities were hypertension, immunosuppression status, diabetes, and obesity. During their ICU stay, 282 patients (38.5%) were treated with invasive mechanical ventilation, 564 (77.6%) received dexamethasone, and 147 (20.1%) received tocilizumab for COVID-19. The day 28 mortality rate was 26.8% (N = 193/777).
## Clustering Analysis
As shown in Fig. 2, the SOM method was employed to conduct a clustering analysis, displaying polarized distributions of patient characteristics across the maps. This analysis yielded seven clusters of patients with homogeneous phenotypes. The delineation of cluster boundaries is illustrated in Fig. 2. The characteristics of patients at the time of ICU admission are presented in Table 1 and shown in Fig. 3A. The Omicron sublineage infecting each patient, according to their cluster assignment, is shown in Fig. S1. The management of patients with severe SARS-CoV-2 infection during their ICU stay is presented in Table 2 and shown in Fig. 3B,C. The patient-imputed characteristics at ICU admission and the management during the ICU stay are provided in Table S3 and Table S4, respectively.
## Cluster 1 (N = 78/777,10%)
This cluster includes elderly patients with a median age of 72.5 years (66.5; 76.8) and is characterized by a pronounced metabolic and cardiovascular comorbidity profile, including a high prevalence of chronic heart failure, diabetes, and obesity. These patients also exhibited the highest median frailty score and were frequently treated with noninvasive ventilation (NIV) or high-flow oxygen (HFO) therapy.
## Cluster 2 (N = 78, 10%)
The patient population in this cluster is predominantly female (78.2%), and it is characterized by a marked metabolic profile, including a high prevalence of diabetes and obesity, but no chronic heart failure. It is noteworthy that this cluster had the highest use of NIV as initial ventilatory support at ICU admission.
## Cluster 3 (N = 160, 21%)
Cluster 3 comprises the youngest individuals (median age of 59.9 years (50.0; 69.4)) and is primarily characterized by isolated chronic respiratory failure. Patients in this cluster exhibited the lowest vaccination rate and the lowest severity scores (SAPS II and SOFA). Furthermore, these subjects exhibited the highest median PaO 2 / FiO 2 ratio (159 mmHg (102; 251)) and mainly received NIV or HFO therapy.
## Cluster 4 (N = 132, 17%)
This cluster is almost exclusively male and includes the oldest patients, with a median age of 74.2 years (67.3; 79.7). The patients in question had a low number of comorbidities and a low vaccination rate. They predominantly presented with isolated acute hypoxemic respiratory failure, characterized by the lowest PaO 2 / FiO 2 ratio (91 mmHg (69; 138)), with HFO therapy as the predominant mode of ventilatory support. The requirement for vasopressor support was minimal.
## Cluster 5 (N = 95, 12%)
This cluster exhibited the highest proportion of immunocompromised patients (82.2%), primarily due to oncohematological malignancies. Immunosuppression was the only comorbidity. HFO therapy was the most frequently used mode of ventilatory support.
## Cluster 6 (N = 129, 13%)
Patients in this cluster had relatively few comorbidities but presented with the highest severity of illness at admission, as reflected by Fig. 4 Day 28 mortality according to clusters. A Day 28 mortality; B survival curves for day 28 overall survival are plotted by clusters using the Kaplan-Meier method. Num-bers of patients at risk and number of events are presented in the risk table below the graph the highest SAPS II and SOFA scores, elevated lactate levels, and the lowest platelet counts. A majority of patients required both invasive mechanical ventilation (55.3%) and vasopressor support (65.6%) at the time of ICU admission. Notably, this group also exhibited the highest rate of bacterial co-infection (32.6%).
## Cluster 7 (N = 134, 17%)
This cluster also included a high proportion of immunocompromised patients (71%), predominantly comprising solid organ transplant recipients, with a marked prevalence of chronic renal failure. Furthermore, this group had the highest vaccination rate and presented with severe illness, as reflected by elevated SAPS II and SOFA scores. Notably, these patients presented with pronounced lymphopenia and significant renal impairment at ICU admission.
## Sensitivity Analysis
Complementary clustering analysis was performed using the Clinical Trajectory Analysis (ClinTrajan) method, which also identified seven distinct patient clusters with homogeneous phenotypes. The distribution of patients within these clusters is illustrated by the branching tree structure in Fig. S2. The characteristics of patients at ICU admission and the management during the ICU stay are presented in Table S5 and Table S6. Figure S3 shows the results of the 28-day survival analysis grouped by the clusters obtained from the ClinTrajan method. The correspondence between patient classifications from both clustering methods is shown in Fig. S4 as a Sankey diagram, illustrating the overlap and transitions between clusters. Overall, the two clustering approaches exhibited a satisfactory degree of consistency and alignment.
## Management of Patients During Their Stay in the Intensive Care Unit
There were significant variations in the management of organ failure between clusters. Clusters 6 and 7 had the highest proportions of patients treated with IMV (62.8% and 44.3%, respectively). Furthermore, patients in cluster 6 were the most likely to require vasopressor support (66.7%). Conversely, patients in cluster 7 demonstrated the highest propensity for renal replacement therapy (37.4%). The pharmacological management of COVID-19 also differed between the identified clusters. The highest rate of dexamethasone administration (91.3%) was observed in patients in cluster 4, while the lowest rate (64.4%) was observed in patients in cluster 3. There were no substantial disparities in the use of alternative systemic corticosteroids between groups (p = 0.08). Tocilizumab was most commonly prescribed in cluster 4 (30.2%) and least commonly prescribed in cluster 6 (7.5%).
## Outcome of Patients
Figure 4 shows the results of the 28-day survival analysis grouped by the clusters obtained from the SOMs. There was a significant difference in survival between clusters (p < 0.001), with the best outcomes observed among patients in clusters 2 and 3, and the worst among those in clusters 6 and 7. Patients in clusters 1, 4, and 5 exhibited intermediate outcomes.
## DISCUSSION
To the best of our knowledge, this is the largest cohort study to use clustering analysis to identify homogeneous subgroups of critically ill patients with COVID-19 infected with the Omicron variant. Seven distinct clusters were identified, each characterized by contrasting clinical presentations, management strategies during the ICU stay, and outcomes. The main characteristics of the patients in each cluster are summarized below. Clusters 1 and 2 comprise patients with metabolic and/or cardiovascular comorbidities. Cluster 1 comprises older patients with chronic heart failure and high frailty, who are primarily treated with HFO/NIV therapy. Cluster 2 comprises predominantly female patients with obesity and no chronic heart failure who require the highest use of NIV at ICU admission. Cluster 3 comprises the youngest patients, with isolated chronic respiratory failure, low severity scores, limited corticosteroid use, and a favorable prognosis. Cluster 4 includes older male patients with isolated respiratory failure. Cluster 6 comprises patients with multiorgan failure and poor outcomes, and a low prevalence of preexisting comorbidities. Clusters 5 and 7 encompass patients for whom immunosuppression is a major feature. Notably, the distribution of Omicron sublineages did not differ across the identified clusters. This finding suggests that the observed clinical heterogeneity was primarily driven by patient-specific characteristics rather than virological factors. Given the exploratory nature of our analysis, we described this heterogeneity using unadjusted comparisons between clusters. As detailed in the Methods section, we did not apply corrections for multiple testing when comparing characteristics across clusters, consistent with the exploratory purpose of our clustering analysis. Consequently, the reported p-values should not be interpreted as confirmatory evidence of effects. Rather, they serve only as indicators of which features differ across clusters and help to characterize the clusters descriptively.
The phenotypic approach is becoming increasingly popular for identifying homogeneous subgroups of patients within heterogeneous populations. This strategy has been shown to be effective in a variety of clinical settings. It enables more accurate diagnoses and the early identification of patients at risk of deterioration who could benefit from tailored interventions [29][30][31]. In the ICU setting, this approach aligns with the broader movement toward precision medicine, which has become essential to improving patient care [32]. Several studies have previously attempted to identify phenotypic subgroups among critically ill patients with COVID-19 using various clustering methods. These efforts have consistently demonstrated considerable heterogeneity in disease presentation and outcomes, as was recently summarized in a narrative review [33]. These studies incorporated clinical and biological variables [9][10][11][12], and, in certain instances, immunoinflammatory biomarkers, including transcriptomic signatures [34]. The consistent presence of homogeneous phenotypes with significant variations in clinical, biological, and immunoinflammatory characteristics was emphasized. Notably, these phenotypes were associated with distinct outcomes, and in certain studies, varied responses to corticosteroid therapy [9]. However, all previous clustering studies were conducted on hospitalized patients during the initial waves of the pandemic, primarily the first wave. Consequently, these studies included patients infected with the ancestral SARS-CoV-2 strain. The clinical presentation of these patients therefore differs significantly from that of patients with COVID-19 currently requiring ICU admission [4,35,36]. The disease landscape has evolved significantly due to widespread community immunity, whether induced by vaccination or acquired through natural infection, as well as due to the emergence of the Omicron variant and its sublineages. Compared with patients with COVID-19 admitted to the ICU during the early waves of the pandemic, critically ill patients infected with Omicron are typically older and have a higher prevalence of comorbidities, particularly immunosuppression and cardiovascular disease [4,36]. Their management has also evolved over time, with an increased use of noninvasive respiratory support (e.g., NIV, HFO therapy) and changes in pharmacological strategies, including more selective use of corticosteroids [4], decreased prescription of tocilizumab, and withdrawal of monoclonal antibodies due to viral resistance associated with Omicron mutations [37]. In the Omicron era, COVID-19 can no longer be considered a homogeneous disease characterized solely by severe viral pneumonia, as was initially described. Instead, the disease spectrum encompasses a wide range of clinical presentations, from acute decompensation of preexisting cardiorespiratory conditions in elderly patients with multiple comorbidities to prolonged viral pneumonia in profoundly immunocompromised individuals. [4][5][6][7]. The phenotypic subgroups identified in our cohort demonstrate this clinical heterogeneity and are representative of the contemporary ICU population with Omicron infection.
In our study, the therapeutic management of critically ill patients with COVID-19-including both pharmacological treatments and organ support-varied significantly between clusters. Notably, there was significant heterogeneity in the use of dexamethasone, despite it being theoretically indicated for all patients included in the study [8]. While dexamethasone was administered to almost all patients in cluster 4 (91.3%), it was only prescribed to 64.4% of patients in cluster 3. Although the benefits of corticosteroids were clearly demonstrated in the first wave for severe cases of COVID-19 [38,39], their efficacy for older patients with more comorbidities-particularly cardiovascular disease and immunosuppression-and less severe respiratory involvement remains uncertain. Previous studies have suggested that the efficacy of corticosteroids may depend on the clinical profile of the patient, with the greatest benefit seen in those exhibiting a stronger inflammatory response [9,12]. However, the lack of robust evidence supporting an individualized approach to dexamethasone use in critically ill patients, particularly in the Omicron era, has probably contributed to significant variability in its prescription. Although dexamethasone remains the cornerstone of corticosteroid therapy for treating patients with severe COVID-19 [8,38], there is evidence to suggest that alternatives such as hydrocortisone [40] or methylprednisolone [41] could also be beneficial. Clinicians may therefore have tailored their choice of corticosteroid to the patient's condition-for instance, hydrocortisone may have been favored for treating severe circulatory failure, while prednisone or methylprednisolone would have been preferred for patients with obstructive airway disease. Similarly, tocilizumab prescriptions varied significantly between clusters. Evidence on interleukin (IL)-6 receptor antagonists has enabled a more targeted approach, with the greatest benefits observed in patients with significant systemic inflammation (CRP ≥ 75 mg/L) [8,42]. However, concerns about infectious complications, particularly COVID-19-associated pulmonary aspergillosis [43,44], have led to a more cautious use of tocilizumab, as observed in our study in intubated patients with multiorgan failure (cluster 6). Finally, we observed significant variability in the management of organ failure, which is likely due to differences in the initial presentation of patients, their underlying comorbidities, and disease severity. Patients with a cardiometabolic profile (clusters 1 and 2) and those with chronic respiratory failure (cluster 3) were particularly likely to receive NIV support. Conversely, patients in cluster 7, including solid organ transplant recipients and those with chronic renal failure, were more likely to receive renal replacement therapy during their ICU stay. This variability in management highlights the urgent need for clinical trials that stratify patients on the basis of their phenotype. Such trials would validate personalized disease management in real-world clinical settings and refine treatment strategies tailored to specific patient subgroups. In addition, the ongoing evaluation of novel therapeutic options, such as immunomodulators and antivirals, will expand the range of tools available for the precision-based management of severe viral respiratory infections [45].
The day 28 mortality rate observed in our cohort (26.7%) is consistent with that reported in other similar studies of critically ill patients infected with the Omicron variant [4,46]. In addition to the previously discussed differences, patients in different clusters exhibited significantly divergent outcomes, with day 28 mortality rates ranging from 13.1% in cluster 3 to 41.1% in cluster 6. These differences are largely explained by the underlying comorbidities and patient severity at the time of ICU admission. Nevertheless, these findings will help clinicians to make more accurate prognoses for each clinical profile, enabling them to manage and tailor therapeutic strategies more precisely on the basis of each patient's severity and specific characteristics.
Our study has several limitations. This prospective study was conducted only in France, which may limit the generalizability of the findings to other regions. To validate the identified clusters, an external large prospective cohort would be needed. In addition, the statistical comparisons between clusters should be interpreted with caution, as these analyses were exploratory and performed to help describe and distinguish the phenotypes generated by the unsupervised clustering process. Furthermore, we did not include data on immune and inflammatory parameters across the different clusters, which could provide additional insight into the underlying pathophysiology. In addition, patients without acute respiratory failure were excluded, which may limit our ability to explore the full range of clinical presentations of SARS-CoV-2 infection in the ICU. Nevertheless, the study also presents important strengths. It involved a unique, large-scale, prospective, multicenter cohort of critically ill patients infected with Omicron enabling robust characterization of clinical phenotypes. The clusters were validated using two independent methods and the use of these clustering analyses allowed to derive homogeneous and clinically meaningful phenotypes from a highly heterogeneous critically ill population. Together, these findings offer novel insights into the evolving landscape of critical illness in the Omicron era.
## CONCLUSIONS
We conducted a clustering analysis on a large database of patients infected with Omicron variant requiring ICU admission for acute respiratory failure. The patients had different demographics, comorbidities, therapeutic management strategies, and outcomes. We identified seven distinct clinical presentations. Our findings help to improve our understanding of the current heterogeneity of COVID- Data Availability. The clinical datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request (N.D.P.). Ethical Approval. The study was approved by the Comité de Protection des Personnes Sud-Méditerranée I (no. EudraCT/ID-RCB: 2021-A02914-37). Informed consent was obtained from all patients or their relatives. The study was conducted in accordance with the 1964 Declaration of Helsinki and subsequent amendments.
## Declarations
Open Access. This article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License, which permits any non-commercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by-nc/4. 0/.
## References
1. Markov, Ghafari, Beer et al. (2023) "The evolution of SARS-CoV-2" *Nat Rev Microbiol*
2. Carabelli, Peacock, Thorne et al. (2023) "SARS-CoV-2 variant biology: immune escape, transmission and fitness" *Nat Rev Microbiol*
3. Covid-Icu (2021) "Group on behalf of the REVA Network and the COVID-ICU Investigators. Clinical characteristics and day-90 outcomes of 4244 critically ill adults with COVID-19: a prospective cohort study" *Intensive Care Med*
4. De Prost, Audureau, Heming et al. (2022) "Clinical phenotypes and outcomes associated with SARS-CoV-2 variant Omicron in critically ill French patients with COVID-19" *Nat Commun*
5. De Prost, Audureau, Préau et al. (2023) "Clinical phenotypes and outcomes associated with SARS-CoV-2 Omicron variants BA.2, BA.5 and BQ.1.1 in critically ill patients with COVID-19: a prospective, multicenter cohort study" *Intensive Care Med Exp*
6. De Prost, Audureau, Guillon et al. (2022) "Clinical phenotypes and outcomes associated with SARS-CoV-2 Omicron sublineage JN.1 in critically ill COVID-19 patients: a prospective, multicenter cohort study in France" *Ann Intensive Care*
7. Machkovech, Hahn, Wang et al. (2024) "Persistent SARS-CoV-2 infection: significance and implications" *Lancet Infect Dis*
8. "IDSA Guidelines on the Treatment and Management of Patients with COVID-19"
9. Bruse, Motos, Van Amstel et al. (2024) "Clinical phenotyping uncovers heterogeneous associations between corticosteroid treatment and survival in critically ill COVID-19 patients" *Intensive Care Med*
10. Legrand, Phillips, Malenica et al. (2021) "Differences in clinical deterioration among three sub-phenotypes of COVID-19 patients at the time of first positive test: results from a clustering analysis" *Intensive Care Med*
11. Gutiérrez-Gutiérrez, Toro, Borobia et al. (2021) "Identification and validation of clinical phenotypes with prognostic implications in patients admitted to hospital with COVID-19: a multicentre cohort study" *Lancet Infect Dis*
12. Sinha, Furfaro, Cummings et al. (2021) "Latent class analysis reveals COVID-19-related acute respiratory distress syndrome subgroups with differential responses to corticosteroids" *Am J Respir Crit Care Med*
13. Bay, Audureau, Préau et al. (2024) "COVID-19 associated pulmonary aspergillosis in critically-ill patients: a prospective multicenter study in the era of Delta and Omicron variants" *Ann Intensive Care*
14. (2025) "Coronavirus : chiffres clés et évolution de la COVID-19 en France et dans le Monde"
15. Rockwood, Song, Macknight et al. (2005) "A global clinical measure of fitness and frailty in elderly people" *CMAJ*
16. (2020) "WHO Working Group on the Clinical Characterisation and Management of COVID-19 infection. A minimal common outcome measure set for COVID-19 clinical research" *Lancet Infect Dis*
17. Vincent, Moreno, Takala et al. (1996) "The SOFA (Sepsisrelated Organ Failure Assessment) score to describe organ dysfunction/failure. On behalf of the Working Group on Sepsis-Related Problems of the European Society of Intensive Care Medicine" *Intensive Care Med*
18. Gall, Lemeshow, Saulnier (1993) "A new Simplified Acute Physiology Score (SAPS II) based on a European/North American multicenter study" *JAMA*
19. Ranieri, Rubenfeld, Thompson (2012) "Ferguson ND, Caldwell E, ARDS Definition Task Force, et al. Acute respiratory distress syndrome: the Berlin Definition" *JAMA*
20. Kohonen, Somervuo (2002) "How to make large selforganizing maps for nonvectorial data" *Neural Netw*
21. Gao, Mutter, Casey et al. (2019) "Numero: a statistical framework to define multivariable subgroups in complex population-based datasets" *Int J Epidemiol*
22. Van De Velden, Iodice, Enza (2019) "Distance-based clustering of mixed data" *WIREs Comput Stat*
23. Chavent, Kuentz-Simonet, Labenne et al. (2023) "Multivariate Analysis of Mixed Data: The R Package PCAmixdata"
24. Golovenkin, Bac, Chervov et al. (2020) "Trajectories, bifurcations, and pseudo-time in large clinical datasets: applications to myocardial infarction and diabetes data" *GigaScience*
25. Gorban, Sumner, Zinovyev (2007) "Topological grammars for data approximation" *Appl Math Lett*
26. Gorban, Zinovyev, Gorban et al. (2025) "Principal Graphs and Manifolds"
27. Stekhoven, Bühlmann (2012) "MissForest-Non-parametric missing value imputation for mixed-type data" *Bioinformatics*
28. Mayer, Missranger (2024) "Fast Imputation of Missing Values"
29. Seymour, Kennedy, Wang et al. (2019) "Derivation, validation, and potential treatment implications of novel clinical phenotypes for sepsis" *JAMA*
30. Bonnefous, Kharoubi, Bézard et al. (2021) "Assessing cardiac amyloidosis subtypes by unsupervised phenotype clustering analysis" *J Am Coll Cardiol*
31. Famous, Delucchi, Ware et al. (2017) "Acute respiratory distress syndrome subphenotypes respond differently to randomized fluid management strategy" *Am J Respir Crit Care Med*
32. Murugan (2015) "Movement towards personalised medicine in the ICU" *Lancet Respir Med*
33. Scherger, Gomez, Abbas et al. (2009) "Decoding COVID-19: phenotypes and the pursuit of precision medicine" *Clin Microbiol Infect*
34. López-Martínez, Martín-Vicente, De et al. (2023) "Transcriptomic clustering of critically ill COVID-19 patients" *Eur Respir J*
35. Vieillard-Baron, Flicoteaux, Salmona et al. (2022) "Omicron variant in the critical care units of Paris Metropolitan Area the reality research group" *Am J Respir Crit Care Med*
36. Corriero, Ribezzi, Mele et al. (2022) "COVID-19 variants in critically ill patients: a comparison of the Delta and Omicron variant profiles" *Infect Dis Rep*
37. Arora, Kempf, Nehlmeier et al. (2023) "Omicron sublineage BQ.1.1 resistance to monoclonal antibodies" *Lancet Infect Dis*
38. Horby, Lim, Emberson et al. "Dexamethasone in hospitalized patients with Covid-19"
39. (2021) *N Engl J Med*
40. (2020) "The WHO Rapid Evidence Appraisal for COVID-19 Therapies (REACT) Working group. association between administration of systemic corticosteroids and mortality among critically ill patients with COVID-19: a meta-analysis" *JAMA*
41. Angus, Derde, Al-Beidh et al. (2020) "Effect of hydrocortisone on mortality and organ support in patients with severe COVID-19: the REMAP-CAP COVID-19 corticosteroid domain randomized clinical trial" *JAMA*
42. Salton, Confalonieri, Meduri et al. (2020) "Prolonged low-dose methylprednisolone in patients with severe COVID-19 pneumonia" *Open Forum Infect Dis*
43. Abani, Abbas, Abbas et al. (2021) "Tocilizumab in patients admitted to hospital with COVID-19 (RECOVERY): a randomised, controlled, open-label, platform trial" *Lancet*
44. Gangneux, Dannaoui, Fekkar et al. "Fungal infections in mechanically ventilated patients with COVID-19 during the first wave: the French multicentre MYCOVID study" *Lancet Respir Med*
45. Gioia, Walti, Orchanian-Cheff et al. (2024) "Risk factors for COVID-19-associated pulmonary aspergillosis: a systematic review and meta-analysis" *Lancet Respir Med*
46. Watkins (2022) "Using precision medicine for the diagnosis and treatment of viral pneumonia" *Adv Ther*
47. Chang, Huang, Shen et al. (2024) "Characteristics and outcomes of ICUadmitted COVID-19 patients in the Omicron and Alpha-dominated periods" *J Formos Med Assoc*
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# Isolation and complete genome sequence analysis of an Echovirus 29 strain isolated from a patient of Acute Flaccid Paralysis in India
Madhuri Joshi, Rishabh Waghchaure, Pooja Umare, Abhijeet Jadhav, Alfia Ashraf, Sarah Cherian, Naveen Kumar, Babasaheb Tandale, Mallika Lavania, John Dennehy
## Abstract
Echovirus 29 (E29) was identified in a fecal specimen of a 15-month-old girl with acute flaccid paralysis from Akola city, Maharashtra, India. The complete genome sequence of the E29 strain, isolated using rhabdomyosarcoma cells, is being reported from India. KEYWORDS enterovirus, echovirus, outbreak, acute flaccid paralysis, India, genome analysis E chovirus 29 (E29), a non-enveloped RNA virus of family Picornaviridae, is associated with illnesses ranging from asymptomatic infection to meningitis, encephalitis, acute flaccid paralysis, and respiratory disease (1). E29 has been previously detected in India during polio surveillance and is considered relevant to paralysis (2, 3). During the post-GBS outbreak investigation in Bhavanipura (Akola, Maharashtra), E29 was detected in an AFP patient's fecal sample by qPCR and VP1 sequencing, and subsequently isolated in RD cells following WHO protocols (4). RD cells were cultured in MEM with 10% FBS and antibiotics; infected cultures showing CPE were harvested and passaged to P8. Wholegenome sequencing of the P4_E29 isolate was conducted on the Illumina MiniSeq platform (5). Viral RNA was extracted from culture supernatant using the QIAamp Viral RNA Mini Kit. After quantification, host rRNA was removed with the NEBNext depletion kit, and the RNA was purified and measured using Qubit. Libraries were prepared with the TruSeq Stranded mRNA kit (Illumina, USA) and assessed using Tapestation. Sequenc ing was performed on the Illumina MiniSeq (High Output Kit), and FASTQ files were analyzed with CLC Genomics Workbench v20. Raw paired-end whole-genome Illumina reads were assembled de novo using SPAdes genome assembler v3.15.5 (6) with default parameters, producing a single genome contig of 7,413 bp with 47.88% GC content. Genotyping using the Enterovirus Genotyping Tool identified the Indian isolate as E29.Although the assembled genome length (7,413 nt) matched the reference sequence, the exact 5′ and 3′ ends could not be confirmed without RACE, so it is reported as a near-complete genome. Genome annotation was performed using VAPiD (v1.6.7) with default settings and submitted to GenBank via BankIt. Sequence similarity analysis of the consensus genome using BLASTn (NCBI) revealed 99% query coverage and 86.76% nucleotide identity to Enterovirus B (E29) strain from Nepal (GenBank accession number PX230757). A maximum likelihood phylogenetic tree was generated from complete E29 genomes, aligned with MAFFT and analyzed in IQ-TREE using the BIC-selected substitution model with 1,000 ultrafast bootstraps, then visualized in iTOL.
. It showed 79% nucleotide and 96% amino acid identity with the USA 1958 prototype, indicating marked nucleotide divergence but conserved proteins. Compared with genomes from Guatemala, Haiti, Nepal, and Brazil (2014), it showed 78-82% nucleotide similarity, while sharing only ~30-32% identity with two highly divergent Brazilian strains (2014-2015) that formed a separate lineage.
A comprehensive mutation analysis at the amino acid level performed using nine whole-genome sequences available in the GenBank showed presence of 20 unique non-synonymous substitutions in the P4_E29 strain of the study. Among the 20 amino acid substitutions, 11 were in structural proteins (Table 1). The nucleotide sequence of FIG 1 Maximum likelihood phylogenetic tree based on complete genome sequences of the E29 strain and reference sequences retrieved from the GenBank database (accession ID: prototype USA 1958 strain, AY302552.1). The sequences were aligned using MAFFT v7.5266 with default parameters. The phylogenetic tree was constructed using IQ-TREE v2.2.0 under the maximum likelihood method, with the best-fit substitution model automatically selected according to the Bayesian information criterion (BIC). Tree robustness was evaluated using 1,000 ultrafast bootstrap replicates. The final tree was visualized and annotated using Interactive Tree of Life (iTOL v6). The E29 isolate from India (NIV2415257/IND/2025) is highlighted in red font color and shows clustering with PP461528.1/Nepal/ 2023 strain (highlighted with light blue font color).
## References
1. Oyero, Adu, Ayukekbong (2014) "Molecular characterization of diverse species enterovirus-B types from children with acute flaccid paralysis and asymptomatic children in Nigeria" *Virus Res*
2. Rao, Yergolkar, Shankarappa (2007) "Antigenic diversity of enteroviruses associated with nonpolio acute flaccid paralysis, India" *Emerg Infect Dis*
3. Maan, Dhole, Chowdhary (2019) "Identification and characteriza tion of nonpolio enterovirus associated with nonpolio-acute flaccid paralysis in polio endemic state of Uttar Pradesh, Northern India" *PLoS One*
4. (2004) "Polio laboratory manual. 4th edition. Department of Immunization, Vaccines and Biologicals Family and Community Health"
5. Tikute, Deshmukh, Chavan et al. (2022) "Emergence of recombinant subclade D3/Y in coxsackievirus A6 strains in hand-foot-and-mouth disease (HFMD) outbreak in India" *Microorganisms*
6. Bushmanova, Antipov, Lapidus et al. (2019) "rnaSPAdes: a de novo transcriptome assembler and its application to RNA-Seq data" *Gigascience*
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# Special Issue "Viral Infections: Physiology, Pathophysiology, Pathogenesis, Diagnosis and Treatment"
Barbara Bażanów, Dominika Stygar
## Abstract
Viral infections remain one of the most significant challenges to global health, affecting humans and animals alike and posing continuous threats due to their high transmissibility, genetic variability, and capacity to disrupt host homeostasis at multiple biological levels [1]. Despite remarkable advances in molecular biology, immunology, and antiviral therapy, viral diseases continue to cause substantial morbidity, mortality, and socioeconomic burden worldwide [2]. The complexity of virus-host interactions, together with the dynamic evolution of viral populations, necessitates integrated research approaches that span from fundamental molecular mechanisms to applied diagnostic and therapeutic strategies [3].This Special Issue, "Viral Infections: Physiology, Pathophysiology, Pathogenesis, Diagnosis and Treatment", was conceived to provide a comprehensive overview of current advances in the field of virology, highlighting how viral infections influence cellular physiology, host defense mechanisms, microbial ecosystems, and population-level dynamics. The contributions collected in this issue reflect the multifaceted nature of viral diseases and emphasize the importance of interdisciplinary research in understanding viral pathogenesis and improving disease management.At the most fundamental level, viral infections initiate a cascade of intracellular events that determine whether the host cell successfully restricts viral replication or becomes permissive to disease progression. These early responses form the basis upon which subsequent host-virus interactions unfold, ultimately shaping disease outcomes at the organismal and population levels.
Cellular Stress Responses and Antiviral Defense MechanismsAt the cellular level, viral infections profoundly alter host metabolic pathways and stress responses, which in turn shape the outcome of infection. One of the key processes implicated in viral pathophysiology is oxidative stress, resulting from an imbalance between reactive oxygen species production and antioxidant defenses [4]. In this Special Issue, Ba żanów et al. (contribution 1) investigated the effects of different respiratory viruses on oxidative stress markers using an in vitro lung cell model. Their findings demonstrate that viral infection induces distinct oxidative stress profiles depending on both the viral agent and the cellular context. Notably, non-enzymatic oxidative stress markers were more prominently affected in lung carcinoma cells, whereas both enzymatic and nonenzymatic parameters were altered in lung fibroblasts. These observations underscore the importance of host cell type in shaping virus-induced oxidative responses and suggest that oxidative stress may contribute differently to viral pathogenesis in normal versus transformed cells.
## Complementing these observations, Ou et al. (contribution 2) explored antiviral defense mechanisms mediated by heme oxygenase-1 (HO-1) during dengue virus infection.
Their study revealed that sofalcone, a clinically used gastroprotective drug, suppresses dengue virus replication by activating the Nrf2/HO-1 pathway and restoring antiviral interferon responses. By linking oxidative stress regulation to innate immune signaling, this work highlights HO-1 as a critical node connecting cellular stress responses with antiviral immunity and identifies a promising candidate for drug repurposing in dengue therapy.
However, cellular antiviral responses do not operate in isolation, as infected cells are embedded within complex biological environments that can profoundly modulate host susceptibility and immune defense.
## Virus-Host Interactions within Biological Ecosystems
Beyond the intracellular level, viral infections unfold within host-associated ecosystems that include diverse microbial communities and their metabolic products. Increasing evidence indicates that the microbiome plays a crucial role in modulating host susceptibility to viral infections, immune responses, and disease severity [5]. This aspect is comprehensively addressed by Hao et al. (contribution 3) in their systematic review and analysis of respiratory microbiomes in influenza compared with other respiratory infections. By synthesizing data from multiple studies, the authors demonstrate that influenza is associated with characteristic patterns of microbiome dysbiosis, including reduced microbial diversity and enrichment of specific bacterial taxa. Importantly, both shared and distinct microbiome signatures were identified across different respiratory infections, age groups, and disease severities, highlighting the bidirectional relationship between viral infection and microbial ecology.
Extending the concept of microbiome-virus interactions to antiviral intervention, Danova et al. (contribution 4) investigated the antiviral properties of Lactobacilli-derived postmetabolites against phylogenetically distant herpesviruses. Their in vitro results show that these postbiotics exert broad-spectrum antiviral effects by interfering with viral adsorption, extracellular virions, and intracellular replication stages. The study provides compelling evidence that microbial metabolites may serve as natural antiviral agents and supports the exploration of postbiotics as adjunctive or alternative strategies in the prevention and treatment of viral infections.
Beyond shaping host responses, these biological environments also influence viral replication dynamics and selective pressures, ultimately contributing to viral diversity and evolution.
## Viral Diversity, Evolution, and Molecular Surveillance
The rapid evolution and genetic diversification of viruses necessitate equally dynamic and sensitive diagnostic tools capable of tracking viral variants across both clinical and population levels. High mutation rates and genomic plasticity present major challenges for disease control, diagnostics, and vaccination strategies, making molecular surveillance an essential component of modern virology. In this Special Issue, Tao et al. (contribution 5) addressed these challenges in the context of porcine reproductive and respiratory syndrome virus by developing a multiplex RT-qPCR assay capable of simultaneous virus identification and lineage typing. The assay demonstrated high sensitivity, specificity, and applicability to large numbers of clinical samples, offering a practical and efficient tool for surveillance and control of PRRSV in the swine industry. This work highlights the importance of advanced molecular diagnostics in managing viral diseases of veterinary significance. At the population level, Costa et al. (contribution 6) explored the dynamics of SARS-CoV-2 mutations using wastewater-based epidemiology. By combining nested PCR with next-generation sequencing of selected spike gene regions, the authors successfully de-tected and quantified variant-associated mutations in wastewater samples. Notably, some mutations corresponding to variants of concern were identified prior to their widespread detection in clinical samples, underscoring the value of environmental surveillance as an early warning system for emerging viral variants.
Accurate detection and surveillance of viral variants not only inform epidemiological control but also provide critical guidance for the development and application of effective antiviral therapies.
## Antiviral Strategies and Therapeutic Targeting
The continuous emergence of drug-resistant viral strains and the limited availability of effective antiviral therapies underscore the urgent need for new antiviral agents with novel mechanisms of action [6]. Several contributions to this Special Issue address this challenge by exploring diverse antiviral strategies that target both viral components and host pathways. Cho and Ma (contribution 7) demonstrated the antiviral activity of conessine, a steroidal alkaloid of plant origin, against influenza A virus. Their results indicate that conessine interferes with early stages of viral infection, including viral attachment and entry, and exhibits a direct virus-eradicating effect. By targeting host-virus interactions rather than viral enzymes alone, conessine represents a promising candidate for the development of alternative anti-influenza therapies. Together with the mechanistic insights provided by Ou et al. on HO-1-mediated antiviral responses, these studies highlight the diversity of therapeutic strategies currently being explored, ranging from natural compounds to host-directed antiviral interventions. As the search for novel antiviral strategies intensifies, the reliability of experimental models and analytical tools becomes increasingly critical to ensure that therapeutic advances are built on robust and reproducible data.
## Methodological Challenges in Virology Research
Robust methodology and critical data interpretation are fundamental to advancing virology research. In this Special Issue, Ripa et al. (contribution 8) address an important methodological concern related to the use of LC3 immunofluorescence as a marker of autophagy in herpes simplex virus type 1-infected cells. Their work demonstrates that polyclonal LC3B antibodies can produce non-specific nuclear staining, potentially leading to misinterpretation of autophagy activation during viral infection. By systematically validating their observations using complementary approaches, the authors underscore the necessity of methodological rigor and cross-validation in experimental virology. This contribution serves as an important reminder that careful evaluation of experimental tools is essential to ensure the reliability and reproducibility of conclusions drawn from virological studies. Together, these considerations highlight that progress in virology depends not only on innovative concepts and technologies, but also on careful validation and critical interpretation of experimental evidence.
## Conclusions and Future Perspectives
The articles collected in this Special Issue provide a multifaceted view of viral infections, spanning cellular stress responses, host-microbiome interactions, viral evolution, diagnostic innovation, therapeutic development, and methodological considerations. These interconnected processes are summarized schematically in Figure 1, which illustrates viral infections as dynamic, multi-layered phenomena extending from intracellular events to population-level surveillance and intervention strategies. Future research in virology will benefit from increasingly integrative approaches that combine basic and translational science, leverage advanced molecular technologies, and emphasize methodological robustness. By bridging physiology, pathophysiology, diagnostics, and treatment, the studies presented in this Special Issue contribute valuable insights that advance our understanding of viral infections and support the development of more effective strategies for their control and management.
## References
1. Ba Żanów, Michalczyk, Kafel et al. (2024) "The Effects of Different Respiratory Viruses on the Oxidative Stress Marker Levels in an In Vitro Model: A Pilot Study" *Int. J. Mol. Sci*
2. Ou, Chen, Yen et al. (2025) "Sofalcone Suppresses Dengue Virus Replication by Activating Heme Oxygenase-1-Mediated Antiviral Interferon Responses" *Int. J. Mol. Sci*
3. Hao, Lee, Yap et al. (2025) "Comparison of Respiratory Microbiomes in Influenza Versus Other Respiratory Infections: Systematic Review and Analysis" *Int. J. Mol. Sci*
4. Danova, Dobreva, Mancheva et al. (2025) "Lactobacilli-Derived Postmetabolites Are Broad-Spectrum Inhibitors of Herpes Viruses In Vitro" *Int. J. Mol. Sci*
5. Tao, Zhu, Huang et al. (2024) "Development of a Multiplex RT-qPCR Method for the Identification and Lineage Typing of Porcine Reproductive and Respiratory Syndrome Virus" *Int. J. Mol. Sci*
6. Costa, Simas, Da Costa et al. (2025) "Dynamics of SARS-CoV-2 Mutations in Wastewater Provide Insights into the Circulation of Virus Variants in the Population" *Int. J. Mol. Sci*
7. Cho, Ma (2025) "Anti-Viral Activity of Conessine Against Influenza a Virus" *Int. J. Mol. Sci*
8. Ripa, Andreu, Galdo et al. (2025) "Polyclonal LC3B Antibodies Generate Non-Specific Staining in the Nucleus of Herpes Simplex Virus Type 1-Infected Cells: Caution in the Interpretation of LC3 Staining in the Immunofluorescence Analysis of Viral Infections" *Int. J. Mol. Sci*
9. (2026) "Evolving viral threats" *Nat. Rev. Microbiol*
10. Li, Zhang, Zhang et al. (2024) "Global burden of viral infectious diseases of poverty based on Global Burden of Diseases Study 2021" *Infect. Dis. Poverty*
11. Domingo (2020) "Interaction of virus populations with their hosts"
12. Kayesh, Kohara, Tsukiyama-Kohara "Effects of oxidative stress on viral infections: An overview" *Viruses*
13. Kim, Ndwandwe, Devotta et al. (2025) "Role of the microbiome in regulation of the immune system" *Allergol. Int*
14. Kleandrova, Speck-Planche "The urgent need for pan-antiviral agents: From multitarget discovery to multiscale design" *Future Med. Chem. 2021*
15. "The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods"
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# P-319. Discordance Between Objective and Perceived Risk for Contracting HIV and Association with PrEP Engagement in Transgender Individuals
Meghan Anderson, Meredith Zoltick, Rahwa Eyasu, Emade Ebah, Phyllis Bijole, Miriam Jones, Dorcas Salifu, ; Habib Omari, Ashley Davis, Sarah Kattakuzhy, Elana Rosenthal
Fisher's exact test and Cohen's Kappa statistic were used for statistical analysis. Results. Of 98 enrolled participants, 37 were HIV-and attended more than 1 visit, for a total of 151 visits.
At baseline, EE were more likely than NE to have been offered PrEP (100% vs 37%, p < 0.01), have taken PrEP (90% vs 22%, p < 0.01), identify as a trans-female (80% vs 33%, p = 0.02), endorse transactional sex (50% vs 15%, p = 0.04), and to have been diagnosed with a bacterial STI (70% vs 19%, p < 0.01). (Table 1) Among EE, CDC PrEP eligibility and endorsement of HIV risk varied across time points and was discrepant. (Fig 1)
Across all timepoints, there was only a fair agreement between CDC recommendation to use PrEP and endorsed HIV risk (Kappa statistic of 34.2% 95% CI (19.1 -49.2)).
At timepoints when participants met CDC criteria for PrEP, endorsing HIV risk was significantly associated with being on PrEP compared to those who did not endorse (72% vs 19%, p = 0.02). (Table 2) Among those not on PrEP or starting PrEP, 86% reported interest in taking PrEP in the future if they felt they were at risk for HIV.
Conclusion. Among transgender participants, we found significant discordance between perceptions of HIV risk and PrEP eligibility based on CDC criteria, with selfperception of risk more significantly associated with PrEP engagement. Given high rates of willingness to engage in PrEP if they perceive HIV risk, better understanding this discrepancy and how to educate patients about HIV risk may be critical to improving PrEP uptake. Further, given frequently changing HIV risk based on CDC criteria and self-report, newer long-acting PrEP formulations may help to provide sustained protection against HIV in patients with fluctuating risk.
Disclosures. Phyllis Bijole, BA, MA, GILEAD: HIPS had a grant from Gilead to perform community testing services that ended Janary 31, 2024. It paid a portion of my salary
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# David Baltimore: Scientist, leader, and mentor
Nancy Andrews, George Daley
## Abstract
Fifty years ago, at the remarkably young age of 37, David Baltimore received the Nobel Prize (with Howard Temin and Renato Dulbecco) for "discoveries concerning the interaction between tumor viruses and the genetic material of the cell." David was a prolific scientist whose work spanned many topics, but he was first and foremost a virologist. His recent passing invites us to reflect on a remarkable intellectual trajectory that began with seminal discoveries in virology, broadened to encompass major advances in cancer biology and immunology, and culminated in a legacy-sustained by the many scientists he trained-that will continue to shape modern biomedicine for years to come.
In his 1975 Nobel Lecture, David remarked that "a virologist is among the luckiest of biologists because he can see into his chosen pet down to the details of all of its molecules." He had completed his PhD in 2 years with Richard Franklin at The Rockefeller Institute (later Rockefeller University), having transferred from MIT after hearing Franklin speak at the Cold Spring Harbor course on animal virology. At that time, the complexity of mammalian cells seemed unapproachable, and viruses offered tractable tools to probe their inner workings. David embraced this opportunity, making fundamental discoveries about his chosen "pets" and following where they led. Because Franklin's laboratory studied mengovirus and polio, David initially focused on RNA viruses. It was at first reassuringly straightforward: Positive-strand picornavirus genomes could serve directly as mRNA to produce the proteins they encoded. David continued working on poliovirus until the early 1990s, and it became the PhD dissertation topic for Victor Ambros, a student in David's laboratory who would win the Nobel Prize in 2024 for the discovery of microRNAs. As David launched his own independent program, he and his then-postdoc and future wife, Alice Huang, turned to viruses with negative-sense RNA genomes-initially vesicular stomatitis virus (VSV), which Alice had studied as a doctoral student at Johns Hopkins. They inferred that an RNA-dependent RNA polymerase must exist to generate positive strands competent for translation and, drawing on their strength in biochemistry, they rapidly identified this activity.
As Alice continued to pursue VSV in her own laboratory at Harvard Medical School, David sought a new challenge in RNA tumor viruses. He obtained Rous sarcoma virus (RSV) from Peter Vogt, and building on his experience with VSV, initially searched for a viral RNA polymerase but found none. Aware that Howard Temin and others had suggested an essential role for DNA in RNA tumor virus replication, David shifted strategy to seek an RNA-dependent DNA polymerase, an enzyme not previously described. Using murine leukemia virus prepared at the National Cancer Institute and confirming the findings with RSV, he demonstrated that such an enzyme exists and is packaged in the virion together with the RNA genome. Temin independently identified the same activity in RSV and avian myeloblastosis virus, and the two laboratories published back-to-back papers in Nature in June 1970, prompting the description of this process as the "central dogma reversed" and the naming of the enzyme "reverse transcriptase." The discovery of reverse transcriptase provided crucial insight into how retroviruses cause cancer and yielded an indispensable tool for molecular biology and biotechnology, leading to the award of the Nobel Prize just five years later.
Baltimore and Temin's work opened the door to understanding another retrovirus, now known as Human Immuno deficiency Virus (HIV), which emerged as a smoldering pandemic in the early 1980s. David did not participate directly in the discovery of HIV or in delineating every step of its replication cycle, but reverse transcriptase became the first therapeutic target, enabling the development of highly effective antiviral agents such as AZT. David's laboratory went on to show how HIV exploits host transcriptional machinery, linking immune activation to HIV gene expression. His work clarified how integrated proviral DNA can remain transcriptionally silent yet replication-competent, and it highlighted how chromatin state, transcription factors, and thresholds of cellular activation govern the establishment and reversal of latency. David's engagement with HIV research helped secure the field's legitimacy within mainstream biomedicine during a time of fear and stigma. He co-chaired the 1986 National Academies committee on a National Strategy for AIDS, becoming a forceful advocate for federal investment in HIV research and vaccine development.
David Baltimore's pursuit of virology ultimately carried him into cancer biology and immunology, yielding a multifaceted scientific career that established several of the conceptual and technical pillars supporting modern oncology and immune science. Baltimore pioneered the study of Abelson murine leukemia virus, a replication-defective retrovirus that rapidly induces B-lineage leukemias in mice. His group demonstrated that Abelson virus can directly transform fetal liver-derived hematopoietic precursors in vitro, generating continuously proliferating lymphoid cultures and showing that the virus perturbs the growth and differentiation of B-cell progenitors. Subsequent work identified a viral protein, v-Abl, that his laboratory showed to be a transforming tyrosine kinase, establishing one of the first direct links between a specific tyrosine kinase oncogene and acute leukemia. The Baltimore lab's focus on v-Abl presaged and informed the understanding of human leukemias driven by ABL-family tyrosine kinases. Later studies from Baltimore and others clarified key distinctions between v-Abl and BCR-ABL, emphasizing that constitutive tyrosine kinase activity, altered subcellular localization, and distinctive signaling outputs underlie transformation in both the murine model and human chronic myeloid leukemia. Baltimore's body of cancer research helped lay foundations for precision oncology, including the development of kinase inhibitors such as imatinib directed against BCR-ABL. Baltimore's use of Abelson-immortalized B-lineage cells catalyzed a decisive turn toward immunology, enabling deep genetic and biochemical analysis of B-cell ontogeny. His laboratory helped define discrete stages of B-cell development and, leveraging these systems together with emerging molecular cloning approaches, uncovered the machinery of antigen-receptor recombination, including the RAG1 and RAG2 recombination-activating genes that are indispensable for assembling immunoglobulin and T cell receptor genes. Another pivotal contribution was the identification and characterization of NF-κB as the nuclear factor binding the κ light-chain enhancer in B lymphocytes. By demonstrating that NF-κB is inducible and integrates signals from pathogens, cytokines, and antigen receptors, Baltimore's group placed this transcription factor at the center of innate and adaptive immune activation. The NF-κB story became perhaps Baltimore's most powerful bridge from immunology back to cancer biology. NF-κB is now recognized as a central hub in inflammation, innate immunity, and malignancy, with persistent NF-κB signaling contributing to lymphomas, myelomas, and many solid tumors and with NF-κB target genes encompassing cytokines, adhesion molecules, and antiapoptotic factors. Subsequent work on microRNAs in immune cells and tumors extended the logic of transcriptional control into post-transcriptional regulatory networks, revealing how small RNAs influence differentiation, tolerance, and malignant transformation.
## Baltimore Academic Family Tree 1965-2018
Across this rich scientific arc, Baltimore's career followed a consistent theme: Questions rooted in virology repeatedly yielded answers that reshaped cancer biology and immunology. A recent photograph shows David as we will all remember him (Fig. 1). David's influence on biomedicine was amplified by the many scientists he trained over more than five decades at the Salk Institute, MIT, the Whitehead Institute, Rockefeller University, and Caltech (Fig. 2). As former trainees, we remember David as an unparalleled scientist, leader, and mentor. He taught that the highest achievements in science are inseparable from responsibility-to truth, to colleagues, and to society. His blend of vision and humanity set a standard that continues to guide our own leadership in academic medicine. David Baltimore's life stands as an invitation to think boldly, act ethically, and never stop searching for answers.
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# Mallika Lavania and Pooja Umare contributed equally as first author
Mallika Lavania, Pooja Umare, Rishabh Waghchaure, Manoj Vedpathak, Rajlakshmi Vishwanathan, Pradnya Shinde, Upendra Singh Maholiya, Yash Kokarde, Prathamesh Bagewadi, Vijaykumar Chincholkar, Babasaheb Tandale, Naveen Kumar
## Abstract
Norovirus (NoV) is a major global cause of acute viral gastroenteritis, responsible for both sporadic infections and widespread outbreaks affecting individuals across all age groups. Although typically self-limiting, gastrointestinal illness, characterized by nausea, vomiting, and diarrhea, recent evidence points to its potential role in causing nonintestinal complications. Central nervous system (CNS) manifestations such as febrile seizures, convulsions, and encephalopathy have been increasingly associated with norovirus, albeit infrequently. During a large Guillain-Barré Syndrome (GBS) outbreak in January-March 2025 in the southwestern region of Pune, India, a 40-year-old male developed progressive weakness of the limbs beginning on January 18th and was admitted to a tertiary care center in a nearby town for evaluation. Molecular testing of stool samples confirmed the presence of Norovirus Group II RNA, while screening for other enteric pathogens, including Campylobacter jejuni (C. jejuni), the most recognized infectious trigger for GBS, was negative. While C. jejuni remains the primary pathogen linked to GBS, our findings support growing speculation about norovirus as an emerging, albeit rare, trigger. Further studies are needed to investigate the underlying mechanisms and to clarify the role of norovirus in GBS pathogenesis, particularly during community outbreaks.
## Introduction
Norovirus (NoV) is a non-enveloped enteric virus belonging to the Caliciviridae family [1]. It is one of the leading pathogens responsible for gastroenteritis outbreaks across all age groups [2]. Infection typically presents with diarrhea, vomiting, nausea, abdominal pain, headache, and myalgia, sometimes accompanied by mild fever. Symptoms usually resolve within a few days, and most individuals recover fully [3,4]. However, severe cases can require hospitalization or lead to death, particularly in young children under five, the elderly above 65, and immunocompromised individuals [5].
Beyond gastrointestinal illness, several studies have linked NoV infection to convulsions associated with gastroenteritis and, in rare cases, to acute encephalitis or encephalopathy in both children and adults [6][7][8][9]. The pathogenesis of norovirus-related neurological complications remains unclear. Researchers suggest that, in rare instances, immune responses to certain viral antigens may inadvertently attack components of the nervous system, leading to neurological syndromes such as GBS. Although C. jejuni is the most common precipitating agent, several viruses, including enteroviruses, Epstein-Barr virus, and cytomegalovirus, have also been associated with GBS.
Norovirus-associated GBS may often go unrecognized due to limited neurological testing during gastroenteritis outbreaks. This underscores the need for a multidisciplinary public health approach. Co-occurrence of gastrointestinal and neurological symptoms should prompt a comprehensive diagnostic evaluation, including stool and cerebrospinal fluid (CSF) testing, serology, and neuroimaging. Strengthening surveillance, laboratory capacity, and clinical awareness is essential. Preventive measures such as sanitation, hygiene, and rapid diagnostics remain key to outbreak control.
Although GBS linked to norovirus has not been previously reported in India, this paper presents a fatal adult case during a major norovirus outbreak in Pune, Maharashtra (January-March 2025), suggesting that norovirus may act as a rare trigger for GBS in susceptible individuals., a 40-year-old male from a nearby town was admitted to a tertiary care hospital on January 18 with progressive limb weakness. He had experienced acute gastroenteritis with diarrhea on January 11, about a week before neurological symptoms began. He had visited the GBS outbreak-affected area in southwest Pune at this time. He remained afebrile and had no chronic illnesses, immunosuppression, or recent travel history.
## Methods
## Clinical investigations
The patient was diagnosed with GBS and started on IVIg (2 g/kg over five days). Despite initial worsening and respiratory involvement, he showed slight improvement by day 5. On day 6, he developed recurrent diarrhea and sudden respiratory distress, requiring ICU transfer. He rapidly declined with severe hypotension and suffered two cardiac arrests, leading to his death on day 7 (Fig. 1). Given the unexpected and rapid progression leading to fatal outcome, a clinical post-mortem was performed to investigate the underlying cause of death and to determine cause of death after consent of the next-of-kin as per the local clinical and public health requirements. The clinical antemortem and post-mortem specimens were collected and referred for testing to our laboratories to investigate the cause of illness/death, as a requirement exempt from consent, as per the outbreak investigation component for investigation and testing as per the national guidelines.
## Viral RNA extraction and detection
Samples collected included stool from the second episode of dysentery (January 20th, 2025), antemortem cerebrospinal fluid (CSF), and post-mortem intestinal tissue, all transported under a maintained cold chain for testing. Viral RNA was extracted from 30% (w/v) fecal suspensions prepared in phosphate-buffered saline (PBS; pH 7.4) using the MagMAX™ Viral RNA Extraction Kit (Thermo Fisher Scientific, USA), following the manufacturer's instructions. Extracted RNA was tested via realtime PCR on the Thermo Fisher platform. A broad panel of viral and bacterial pathogens was screened, including Norovirus (GI and GII), Rotavirus A, Epstein-Barr virus, Campylobacter jejuni, and others. Stool samples were further tested for norovirus using qRT-PCR targeting the ORF1-ORF2 junction region [10].
Genotypic classification was performed using the Norovirus Genotyping Tool ( h t t p s : / / c a l i c i v i r u s t y p i n g t o o l . c d c . g o v / b c t y p i n g . c g i), which assigns G-and P-types based on the VP1 (capsid) and RdRp gene regions, respectively. Reference sequences for phylogenetic analysis were retrieved in February 2025 from the CDC's curated Human Caliciviruses Typing Tool database ( h t t p s : / / c a l i c i v i r u s t y p i n g t o o l . c d c . g o v / b e c e r a n c e . c g i).
## Full genome sequencing and annotation
All three norovirus-positive samples were amplified using the SuperScript™ III One-Step RT-PCR System with Platinum™ Taq High Fidelity DNA Polymerase (Thermo Fisher, USA) on a GeneAmp PCR System 9700. PCR products were purified with AMPure XP beads (Beckman, USA), eluted in 45 µl of nuclease-and protease-free water, and quantified using a Qubit 2.0 Fluorometer with the dsDNA HS Assay Kit. DNA libraries were prepared with the Illumina DNA Library Prep Kit (10 pM input) and sequenced on the Illumina MiSeq platform (RPIP Panel, USA). Raw reads were assembled using CLC Genomics Workbench (Qiagen), and genome annotation was performed with the VAPiD v1.6.7 pipeline under default settings. Annotated sequences were submitted to GenBank (accession number PV394762).
## Results
## Observations from nerve conduction testing
On admission (January 18th, 2025), the patient had rapid-onset quadriparesis, dysphagia, and areflexia, suggestive of acute peripheral neuropathy. Nerve conduction studies showed absent motor and sural sensory responses in the lower limbs and absent F-waves, indicating diffuse sensorimotor polyradiculoneuropathy with axonal and demyelinating features consistent with GBS.
## Laboratory findings of biochemical tests from urine, blood and CSF
Urine analysis on admission (January 18th, 2025) showed normal physical and chemical characteristics, with clear, pale-yellow urine, mildly acidic pH, and no protein, glucose, bilirubin, or bile salts. Microscopy revealed 1-2 pus cells/hpf (reference: 0-5/hpf ) and absence of RBCs, casts, or yeast cells, ruling out urinary tract infection or renal involvement.
Biochemical monitoring (21st -25th January 2025) indicated evolving electrolyte imbalances. Serum sodium levels progressively declined, consistent with hyponatremia, while potassium levels fluctuated within the normal range, occasionally showing mild hypokalemia. Chloride levels decreased by the fourth day of hospitalization. These abnormalities likely contributed to neuromuscular weakness and autonomic instability. On Day 10 of illness, serum procalcitonin was markedly elevated (36.40 ng/ mL), suggesting severe systemic infection and possible septic shock. (Suppl Table 1).
Microscopic examination of CSF (antemortem) showed reddish and turbid appearance with RBC (30-40/h.p.f.), a few ependymal and nucleated cells (400/h.p.f.), neutrophils comprising 70% lymphocytes 30%. Collectively, these investigations provided a clear picture of a severe, rapidly progressive GBS case complicated by systemic infection and evolving metabolic derangement (Suppl Table 1).
## Molecular diagnosis for etiological investigations
A comprehensive pathogen screening was performed, including Norovirus (GI and GII), Rotavirus A, Epstein-Barr virus, and Campylobacter jejuni. qRT-PCR targeting the ORF1-ORF2 junction detected only Norovirus Group II, with lower Ct values observed in stool compared to intestinal tissue, indicating a higher viral load in stool. No Norovirus RNA was detected in the cerebrospinal fluid (Fig. 2). The presence of Norovirus GII in stool was further confirmed by full-genome sequencing. Subsequent genotypic analysis identified the strain as Norovirus GII.16 [P16]. The clinical presentation, along with the exclusion of other common enteric pathogens such as Campylobacter jejuni and other viruses, supported a diagnosis of Norovirus-related illness.
## Discussion
Enteric viruses, typically associated with gastrointestinal symptoms, are increasingly recognized for causing complications in the central nervous system (CNS). Though direct neuroinvasion is rare, viruses like rotavirus have been occasionally associated with CNS issues such as nonfebrile seizures and encephalopathy, mainly in children. A review by Dickey et al. [11] found only 24 confirmed rotavirus-related CNS cases, highlighting their rarity but possible occurrence. These findings underscore the importance of considering viral causes in neurological cases that lack typical signs of infection.
Noroviruses, RNA viruses belonging to the Caliciviridae family, are a leading cause of non-bacterial gastroenteritis worldwide, including in India, where they account for up to 50% of outbreaks [12]. Diagnosis relies mainly on clinical symptoms during outbreaks, with RT-PCR of stool samples being the most reliable lab test; serological tests are less commonly used due to limited availability.
Neurological complications of norovirus have been under-recognized but are increasingly reported. Benign convulsions in young children are the most common and usually resolve completely [13]. However, more severe neurological manifestations including GBS, Miller Fisher Syndrome and encephalopathy with seizures or motor deficits have also been described [12,14]. In infants and young children, severe encephalopathy with status epilepticus and white matter changes has been documented. Fatal outcomes involving meningoencephalitis and disseminated intravascular coagulation have also been reported, while variable responses to steroids and intravenous immunoglobulin (IVIg) suggest an immunemediated pathogenesis.
An increase in GBS cases was noted in the third week of January 2025, as reported by several tertiary care hospitals in Pune City, as well as through ongoing Acute Flaccid Paralysis (AFP) surveillance among children. All hospitalized and outpatient GBS cases reported between December 1 st, 2024, and March 23rd, 2025, were investigated. The present fatal case was referred for investigation following the patient's death on January 25th, 2025. The previously healthy adult male developed progressive weakness following a brief gastrointestinal illness. Norovirus RNA was detected in stool and post-mortem intestinal tissue, while other common GBS triggers, including Campylobacter jejuni, were ruled out. Based on the clinical presentation, temporal association, and exclusion of other pathogens, Norovirus GII.16 [P16] was considered the likely trigger. This represents only the second documented case of Norovirus-associated GBS, the first being reported from Ireland in 2012 [12].
Norovirus-associated GBS remains exceedingly rare despite the global prevalence of the virus. This may reflect underdiagnoses due to limited molecular testing, mild preceding symptoms, and delayed onset of neurological manifestations, which complicate establishing temporal links. Host immune factors may also contribute to individual susceptibility. The proposed mechanism involves molecular mimicry, where antibodies generated against viral antigens cross-react with components of the peripheral nervous system, leading to immune-mediated demyelination.
Despite the strength of clinical, epidemiological, and molecular evidence, several limitations should be acknowledged. Norovirus RNA was not detected in the CSF, limiting direct evidence of neuroinvasion. Undetected or transient co-infections bacterial or viral cannot be fully excluded and may have acted as confounding factors. Moreover, establishing causality from a single case report is inherently challenging, as temporal association does not confirm a direct etiological link.
Nevertheless, the findings underscore the importance of maintaining high clinical suspicion during norovirus outbreaks and expanding surveillance to include neurological manifestations. Further studies involving larger cohorts, serial sampling, and immunological profiling are needed to elucidate the pathogenic mechanisms linking norovirus infection and GBS, and to enhance diagnostic and preventive strategies.
## References
1. Kapikian, Wyatt, Dolin et al. (1972) "Visualization by immune electron microscopy of a 27-nm particle associated with acute infectious nonbacterial gastroenteritis" *J Virol*
2. Glass, Noel, Ando et al. (2000) "The epidemiology of enteric caliciviruses from humans: A reassessment using new diagnostics" *J Infect Dis*
3. Cdc, Outbreaks, Norovirus (2025)
4. Rockx, De Wit, Vennema et al. (2002) "Natural history of human calicivirus infection: a prospective cohort study" *Clin Infect Dis*
5. O'brien, Donaldson, Iturriza-Gomara et al. (2016) "Age-specific incidence rates for Norovirus in the community and presenting to primary healthcare facilities in the United Kingdom" *J Infect Dis*
6. Ito, Takeshita, Nezu et al. (2006) "Norovirus-associated encephalopathy" *Pediatr Infect Dis J*
7. Chan, Chan, Ma (2011) "Norovirus as cause of benign convulsion associated with gastro-enteritis" *J Paed Chil Health*
8. Deb, Mondal, Lahiri et al. (2023) "Norovirus-associated neurological manifestations: summarizing the evidence" *J Neurovirol*
9. Sánchez-Fauquier, González-Galán, Arroyo et al. (2015) "Norovirus-associated encephalitis in a previously healthy 2-year-old girl" *Pediatr Infect Dis J*
10. Trujillo, Mccaustland, Zheng et al. (2006) "Use of TaqMan real-time reverse transcription-PCR for rapid detection, quantification, and typing of Norovirus" *J Clin Microbiol*
11. Dickey, Jamison, Michaud et al. (2009) "Rotavirus meningoencephalitis in a previously healthy child and a review of the literature" *Pediatr Infect Dis J*
12. Eltayeb, Crowley (2012) "Guillain-Barre syndrome associated with Norovirus infection" *BMJ Case Rep*
13. Miyagi, Sasano (2023) "Laboratory findings of benign convulsions with mild gastroenteritis: a meta-analysis" *Cureus*
14. Kimura, Goto, Migita et al. (2010) "An adult norovirus-related encephalitis/encephalopathy with mild clinical manifestation" *BMJ Case Rep*
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# Complete genome sequences of two Cressdnaviricota viruses identified in respiratory tract samples from forest musk deer in China
Qing Zhang, Xiaojie Jiang, Yuan Xi, Xiao Ma, Wen Zhang
## Abstract
We identified two circular single-stranded DNA viruses from forest musk deer in China through metagenomic analysis. Phylogenetic results suggest they represent unclassified Cressdnaviricota lineages. This study highlights the diversity of the deer's respiratory virome and underscores the importance of wildlife virus surveillance for conservation and public health. KEYWORDS Cressdnaviricota, forest musk deer, viral metagenomics, respiratory virome T he forest musk deer (Moschus berezovskii) is a nationally protected Class I wildlife species in China, threatened by habitat loss and poaching (1). To investigate its respiratory virome, we performed viral metagenomic analysis on nasal swabs collected from 30 individuals in Anhui Province in 2017. The samples were pooled into three groups and processed for high-throughput sequencing. Among the viruses identified, two circular single-stranded DNA viruses were affiliated with the phylum Cressdnaviri cota, which includes highly diverse viruses infecting a broad range of hosts (2).Samples were collected using sterile polyester swabs and stored at -80°C. Each pool of 10 swabs was suspended in calcium-and magnesium-free DPBS, vortexed, centrifuged, and filtered through 0.45 µm membranes to eliminate residual eukaryotic cells and bacteria (3). The filtrates were treated with DNase and RNase enzymes to remove unprotected nucleic acids (4). Total viral nucleic acids were extracted using the QIAamp Viral RNA Mini Kit (Qiagen). Reverse transcription and double-stranded DNA synthesis were carried out using SuperScript IV (Invitrogen) and Klenow fragment (New England Biolabs), respectively. Sequencing libraries were prepared using the Nextera XT DNA Library Preparation Kit (Illumina) and sequenced on an Illumina MiSeq plat form (250 bp paired-end reads). Reads were quality-filtered (Q10, Phred v1.0.0), and host reads were removed using Bowtie 2 (v2.3.4.1) against the M. berezovskii genome (GCF_022376915.1). Assembly was performed with EnsembleAssembler v1.0.0(5) and refined in Geneious Prime v2019.0.5(6). Contigs were screened for vector contamination via VecScreen (UniVec). Viral candidates were identified via DIAMOND BLASTx (v2.0.15) against a curated NVNR database and validated using NCBI Viral RefSeq v219 (7). Remote homologs were detected using vFam v1. 1 and HMMER3 v3.3.2(8). Open reading frame (ORFs) were predicted in Geneious; taxonomy was assigned with MEGAN v6.21.16. Circular genomes were confirmed by overlapping reads. All tools ran with default settings.The two complete viral genomes are circular, measuring 4,171 bp and 4,325 bp in length, with GC contents of 45.8% and 43.4%, respectively. Each genome contains a capsid protein ORF and a replication-associated protein (Rep) ORF, the hallmark of Cressdnaviricota. The average coverage depths were 19.3× and 33.1×, respectively, indicating sufficient sequencing depth to support high-confidence assembly. Cress1
clustered with a 2021 yak gut sequence from Qingdao, China (GenBank: OR370344) at 94.89% identity, while Cress2 shared 99.90% identity with a 2016 forest musk deer sequence (GenBank: MN621479). Phylogenetic analysis of Rep sequences places them in an unclassified clade within the phylum, distinct from known families. Phyloge netic analysis showed that the two newly identified Cressdnaviricota viruses belong to unclassified but evolutionarily distinct lineages, suggesting taxa (Fig. 1). In summary, this study identified two Cressdnaviricota viruses in respiratory tract samples from forest musk deer using high-throughput viromic analysis, highlighting their genomic diversity and evolutionary relationships. Red-colored nodes indicate novel viral sequences identified in this study. We used the MUSCLE algorithm in MEGA (v11.0.13) with default parameters (9).
Phylogenetic reconstruction was performed using MrBayes (v3.2.7) based on Bayesian inference with the model set to lset nst=6 rates=invgamma to account for variable substitution patterns and rate heterogeneity (10). Two independent Markov chain Monte Carlo (MCMC) runs were conducted until the average standard deviation of split frequencies dropped below 0.01, indicating convergence and robustness of the analysis (11).
with corresponding Sequence Read Archive (SRA) accession numbers SRR33980742-SRR33980744. The complete viral genomes are available under GenBank accession numbers PV854197 and PV854198. All data are publicly accessible without restrictions.
## References
1. Singh, Saud, Jiang et al. (2022) "Himalayan musk deer (Moshcus leucogaster) behavior at latrine sites and their implica tions in conservation" *Ecol Evol*
2. Krupovic, Varsani, Kazlauskas et al. (2020) "Cressdnaviricota: a virus phylum unifying seven families of repencoding viruses with single-stranded, circular DNA genomes" *J Virol*
3. Conceição-Neto, Zeller, Lefrère et al. (2015) "Modular approach to customise sample preparation procedures for viral metagenomics: a reproducible protocol for virome analysis" *Sci Rep*
4. Jiang, Liu, Xi et al. (2023) "Virome of high-altitude canine digestive tract and genetic characterization of novel viruses potentially threatening human health" *mSphere*
5. Deng, Naccache, Ng et al. (2015) "An ensemble strategy that significantly improves de novo assembly of microbial genomes from metagenomic next-generation sequencing data" *Nucleic Acids Res*
6. Zhang, Yang, Shan et al. (2017) "Virome comparisons in wild-diseased and healthy captive giant pandas" *Microbiome*
7. Liu, Jiang, Lei et al. (2024) "Differences between the intestinal microbial communities of healthy dogs from plateau and those of plateau dogs infected with Echinococcus" *Virol J*
8. Finn, Clements, Eddy (2011) "HMMER web server: interactive sequence similarity searching" *Nucleic Acids Res*
9. Kumar, Stecher, Li et al. (2018) "MEGA X: molecular evolutionary genetics analysis across computing platforms" *Mol Biol Evol*
10. Ronquist, Teslenko, Van Der Mark et al. (2012) "MrBayes 3.2: efficient Bayesian phylogenetic inference and model choice across a large model space" *Syst Biol*
11. Shan, Yang, Wang et al. (2022) "Virome in the cloaca of wild and breeding birds revealed a diversity of significant viruses" *Microbiome*
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# Announcement of two complete coding genomes of mink coronavirus and one partial coding genome of mink enteric calicivirus from mink in Denmark
Christina Lazov, Lars Larsen, Camille Johnston, Thomas Rasmussen, Charlotte Hjulsager
## Abstract
Two complete coding genomes of mink coronavirus and one partial coding genome of the sapovirus mink enteric calicivirus were assembled from metagenomic sequencing data from mink on different farms with diarrhea outbreaks in 2015 in Denmark.KEYWORDS mink coronavirus, sapovirus, metagenome, mink enteric calicivirus, diarrhea M ink coronaviruses (MCoVs) belonging to the species Alphacoronavirus neovisontis, family Coronaviridae has previously been associated with gastrointestinal disorders in farmed mink (Neogale vison) (1). Few complete sequences of MCoV strains and even fewer and only partial genome sequences of mink enteric calicivirus (MEC), belonging to the genus Sapovirus (genotype GXII) in the Caliciviridae family, appear presently in GenBank. This virus has also been linked to disease, as it has been isolated from diarrheic mink (2), and the replication and location of mink sapovirus in the crypts and basis of the villi of the small intestine from diarrheic mink kits has been visualized by in situ hybridization (3).This report describes the detection and sequencing of complete coding genomes of MCoV strains and a partial coding genome of MEC from two minks on different farms sampled in 2015 in Denmark.Mink feces samples, from farms experiencing problems with diarrhea, were collected in July 2015. The samples were diluted in PBS to 10% and homogenized in a TissueLyser II (QIAGEN, Denmark). Nucleic acids were extracted using the QIAsymphony DSP Virus/ Pathogen Mini Kit (QIAGEN, Denmark) on a QIAsymphony (QIAGEN, Denmark). The presence of coronavirus was tested with a broadly reactive panCoV RT-qPCR assay (4, 5). Two RNA samples testing positive for coronavirus were selected for sequencing using a non-targeted metagenomic protocol as previously described (6). Briefly, dsDNA was generated using the NEBNext mRNA second strand synthesis kit (New England Biolabs), library preparation performed with the Nextera-XT DNA library Preparation kit, and sequencing done on a MiSeq machine with Reagent Kit v3 600 bp (Illumina Inc, San Diego, CA, USA).Taxonomic identification on read level was performed with Kaiju (7) (Table 1), read quality assessed using FastQC (8), and trimming performed with BBDuk (9) (default parameters) using Geneious Prime (10). Trimmed reads were de novo assembled using the SPAdes plugin v. 3.10.0 for metagenomic data (default parameters) (11). A single contig covering the complete coding genome of MCoV was generated for each of the two samples 11917-2 and 11918-1, as well as a single contig with the partial coding genome of MEC in sample 11917-2 (Table 1). Contigs were polished using trimmed reads,
and consensus sequences were extracted after visual evaluation. These were queried by BLASTn, aligned to the highest scoring reference genomes, and annotated.
The MCoV shared 91% nucleotide identity and 92% amino acid identity, indicating two different strains. Concerning the MEC, conserved motifs previously described for sapoviruses (12,13) were used to roughly identify the different non-structural and structural viral protein CDS in the genome. Hereby, it was possible to determine that the 5′-end of ORF1 encoding the polyprotein and 3′-end of ORF2 encoding VP2 were missing. MEC reads identified in publicly available metagenomic data from farmed mink in Denmark (14) were used to confirm the MEC sequence assembly. Assembly and read information are shown in Table 1.
## Parameters
## References
1. Vlasova, Halpin, Wang et al. (2011) "Molecular characterization of a new species in the genus Alphacoronavirus associated with mink epizootic catarrhal gastroenteritis" *J Gen Virol*
2. Guo, Evermann, Saif (2001) "Detection and molecular characteri zation of cultivable caliciviruses from clinically normal mink and enteric caliciviruses associated with diarrhea in mink" *Arch Virol*
3. Birch, Leijon, Nielsen et al. (2021) "Visualization of intestinal infections with astro-and sapovirus in mink (Neovison vison) kits by in situ hybridization"
4. Escutenaire, Mohamed, Isaksson et al. (2007) "SYBR Green real-time reverse transcriptionpolymerase chain reaction assay for the generic detection of coronavi ruses" *Arch Virol*
5. Lazov, Chriél, Baagøe et al. (2018) "Detection and characterization of distinct alphacoronaviruses in five different bat species in Denmark" *Viruses*
6. Lazov, Belsham, Bøtner et al. (2021) "Full-genome sequences of Alphacoronaviruses and astroviruses from myotis and pipistrelle bats in Denmark" *Viruses*
7. Menzel, Ng, Krogh (2016) "Fast and sensitive taxonomic classification for metagenomics with Kaiju" *Nat Commun*
8. Andrews (2018) "FastQC v"
9. Bushnell (2015) "BBDuk Trimmer v. 1.0. Biomatters Ltd"
10. (2019) *Geneious Prime v*
11. Nurk, Meleshko, Korobeynikov et al. (2017) "metaSPAdes: a new versatile metagenomic assembler" *Genome Res*
12. Oka, Lu, Phan et al. (2016) "Genetic characterization and classification of human and animal sapoviruses" *PLoS One*
13. Oka, Wang, Katayama et al. (2015) "Comprehensive review of human sapoviruses" *Clin Microbiol Rev*
14. Birch, Ullman, Struve et al. (2018) "Investigation of the viral and bacterial microbiota in intestinal samples from mink (Neovison vison) with pre-weaning diarrhea syndrome using next generation sequencing" *PLoS One*
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# Genome sequences of human coronavirus NL63 diagnosed in southern France
Houmadi Hikmat, Céline Boschi, Sarah Aherfi, Aurélie Morand, Bernard Scola, Philippe Colson
## Abstract
We report here 17 human coronavirus NL63 genomes from France. They were obtained from residues of respiratory samples collected from patients for diagnostics in southern France, using an in-house multiplex PCR amplification system followed by next-generation sequencing with Illumina technology. Sixteen genomes belong to subgenotype C2 and one to subgenotype B1. KEYWORDS coronavirus, human coronavirus-NL63, genome, phylogenomics, genomic surveillance H uman coronavirus NL63 (HCoV-NL63), discovered in 2003 (1), belongs to the genus Alphacoronavirus (2) and likely has a zoonotic origin from bats (3). It has a sin gle-stranded positive-sense RNA genome of ≈28 kilobases that encodes 16, 4, and 1 nonstructural, structural (spike, envelope, matrix, and nucleocapsid), and accessory proteins, respectively (4). Its cell receptor is angiotensin-converting enzyme 2 (5). HCoV-NL63 is ubiquitous worldwide (6, 7), causing respiratory tract infections, mostly in children, usually mild but potentially severe and life-threatening (8-12). A total of 345 genomes (>90% coverage of KY073745.1) were released in GenBank (https:// www.ncbi.nlm.nih.gov/genbank/) as of 17 July 2025, obtained from patients sampled between 1983 and 2025, in 24% of the cases since 2020, mostly from the USA (31%), UK (28%), and China (21%). Three genotypes A-C and eight subgenotypes were delineated (12)(13)(14).Here, HCoV-NL63 genomes were obtained from residues of HCoV-NL63 RNA-positive respiratory samples collected in 2021 from patients for routine diagnostic by multiplex qPCR (15). RNA was extracted with the MagMAX Viral/Pathogen Nucleic Acid Isolation kit on a KingFisher Flex system (Thermo Fisher Scientific). Thirty-five PCR primer pairs (Table 1) were designed with GEMI ( 16) and then used to pre-amplify HCoV-NL63 genomes with the SuperScript III One-Step RT-PCR kit with Platinum Taq DNA polymer ase (Thermo Fisher Scientific), as previously described (15). Next-generation sequencing was performed as previously described (15) using Illumina technology and the COVIDSeq protocol (Illumina Inc.), with replacement of ARTIC COVID-19 primers with primers designed here. Library preparation and sequencing of 2 × 250 paired-end reads on a MiSeq instrument (Illumina Inc.) were performed following the manufacturer's instruc tions. There were, on average, 7,353,541 ± 5,020,276 reads generated per sample (range, 264,636-17,879,700). The Genome Detective web application (https://www.genomede tective.com/) (17) performed a trimming and quality control of reads, then the assem bly, annotation, and taxonomic classification of viral genomes with default settings. Phylogeny was performed using IQ-TREE 2 (18).Seventeen HCoV-NL63 genomes, 27,322-27,465 nucleotides long, were obtained. Mean coverage relative to genome NC_005831.2 dating back to 2003 was 98.0% (range, 90.0-99.9%). Coverage <100% was due to amplification defects of some regions, mostly 5′ and 3′ genome ends. Phylogeny identified subgenotypes C2 and B1 in 16 and one
case, respectively (Fig. 1). Subgenotype C2 genomes exhibited a similarity of 98.3% on average between each other and of 90.5% with the subgenotype B1 genome. The closest relatives according to BLAST searches into GenBank and to phylogeny were obtained from the USA, Japan, UK, China, and Switzerland between 2017-2024; mean similarity was 97.3% with the best hits. Substitution I507L, located in the spike receptor binding domain and suspected to promote viral entry into host cells (12), was present in all subgenotype C2 genomes but absent in the subgenotype B1 genome. In summary, the HCoV-NL63 genomes provided here from France account for around a fifth of those available worldwide since 2020. They evidence that at least subgenotypes C2 and B1 circulated in our geographical area. Prior evidence of expanding diversity and of frequent recombinations (19,20) warrants intensifying HCoV-NL63 genome sequenc ing retrospectively and prospectively to get a more detailed picture of the epidemiology, diversity, and evolution of this virus.
## References
1. Van Der Hoek, Pyrc, Jebbink et al. (2004) "Identification of a new human coronavirus" *Nat Med*
2. Zhou, Qiu, Ge (2021) "The taxonomy, host range and pathogenicity of coronaviruses and other viruses in the Nidovirales order"
3. Tang, Liu, Chen (2022) "Human coronaviruses: origin, host and receptor" *J Clin Virol*
4. Brant, Tian, Majerciak et al. (2021) "SARS-CoV-2: from its discovery to genome structure, transcription, and replication" *Cell Biosci*
5. Milewska, Nowak, Owczarek et al. (2018) "Entry of human coronavirus NL63 into the cell" *J Virol*
6. Gaunt, Hardie, Claas et al. (2010) "Epidemiology and clinical presentations of the four human coronavi ruses 229E, HKU1, NL63, and OC43 detected over 3 years using a novel multiplex real-time PCR method" *J Clin Microbiol*
7. Hodinka (2016) "Respiratory RNA viruses" *Microbiol Spectr*
8. Faye, Barry, Jallow et al. (2020) "Epidemiol ogy of non-SARS-CoV2 human coronaviruses (HCoVs) in people presenting with influenza-like illness (ILI) or severe acute respiratory infections (SARI) in Senegal from 2012 to" *Viruses*
9. Cabeça, Granato, Bellei (2013) "Epidemiological and clinical features of human coronavirus infections among different subsets of patients" *Influenza Other Respir Viruses*
10. Konca, Korukluoglu, Tekin et al. (2017) "The first infant death associated with human coronavirus NL63 infection" *Pediatr Infect Dis J*
11. Oosterhof, Christensen, Sengeløv (2010) "Fatal lower respiratory tract disease with human corona virus NL63 in an adult haematopoietic cell transplant recipient" *Bone Marrow Transplant*
12. Wang, Li, Liu et al. (2018) "Discovery of a subgenotype of human coronavirus NL63 associated with severe lower respiratory tract infection in China" *Emerging Microbes & Infections*
13. Ye, Gong, Cui et al. (2023) "Continuous evolution and emerging lineage of seasonal human coronaviruses: a multicenter surveillance study" *J Med Virol*
14. Mcclure, Tsoleridis, Hill et al. (2025) "Vivaldi": an amplicon-based whole-genome sequencing method for the four seasonal human coronaviruses, 229E, NL63, OC43 and HKU1, alongside SARS-CoV-2" *Microb Genom*
15. Hikmat, Targa, Boschi et al. (2025) "Five-year (2017-2022) evolutionary dynamics of human coronavirus HKU1 in southern France with emergence of viruses harboring spike h512r substitution" *J Med Virol*
16. Sobhy, Colson (2012) "Gemi: PCR primers prediction from multiple alignments" *Comp Funct Genomics*
17. Vilsker, Moosa, Nooij et al. (2019) "Genome detective: an automated system for virus identification from high-throughput sequencing data" *Bioinformatics*
18. Minh, Schmidt, Chernomor et al. (2020) "IQ-TREE 2: new models and efficient methods for phylogenetic inference in the genomic era" *Mol Biol Evol*
19. Shao, Zhang, Dong et al. (2022) "Molecular evolution of human coronavirus-NL63, -229E, -HKU1 and -OC43 in hospitalized children in China" *Front Microbiol*
20. Tao, Shi, Chommanard et al. (2017) "Surveillance of bat coronaviruses in Kenya identifies relatives of human coronaviruses NL63 and 229E and their recombination history" *J Virol*
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# Oral VV261 administration protects mice from lethal Crimean-Congo hemorrhagic fever virus challenge
Xi Wang, Huan Xu, Liushuai Li, Fan Wu, Jiang Li, Jingshan Shen, Gengfu Xiao, Wei Zheng, Leike Zhang, Zhihong Hu, Manli Wang
## Abstract
KEYWORDS Crimean-Congo hemorrhagic fever virus, nucleoside analog, VV261, oral administration C rimean-Congo hemorrhagic fever virus (CCHFV), a member of the family Nairoviridae within the class Bunyaviricetes (1), is distributed across more than 30 countries and poses a significant threat to global health. CCHFV infection causes acute viral hemorrha gic fever with a high case fatality rate (10-40%) (2, 3). No FDA-approved therapeutics exist, prompting the WHO to list it as a priority pathogen for research and development since 2017. While some antiviral compounds like T-705 (favipiravir) (4), H44 (5), and baloxavir sodium (6) have shown promise in animal models, drug development for CCHFV needs acceleration.VV261, an oral double prodrug of 4′-fluorouridine (4′-FU), exhibits improved stability and pharmacokinetics. It has demonstrated efficacy against related bunyaviruses such as Severe fever with thrombocytopenia syndrome virus (7). VV261 has entered phase I clinical trials in China. Given that 4′-FU targets the RNA-dependent RNA polymerase (RdRp) and shows broad-spectrum anti-RNA virus activity (8-13), we evaluated the antiviral potential of VV261 against CCHFV.We first tested the anti-CCHFV activity of VV261 in vitro. In human umbilical vein endothelial cells (HUVECs), VV261 potently inhibited CCHFV infection with a median effective concentration (EC 50 ) value of 2.72 ± 0.28 µM (Fig. 1A). It showed negligible cytotoxicity as the 50% cytotoxicity concentration (CC 50 ) value is greater than 200 µM, resulting in a high selective index (SI > 73.53) (Fig. 1A). These data demonstrated that VV261 is a potent inhibitor against CCHFV in vitro.We next performed a time-of-addition assay to investigate which stage of the viral life cycle VV261 targets to inhibit CCHFV infection. Treatment during the post-entry phase or for the full duration nearly completely suppressed progeny virus production (Fig. 1B) and viral nucleoprotein (NP) expression (Fig. 1C). Some inhibition during the entry stage was likely attributed to the residual compound retained within the cells. Taken together, these results indicate that VV261 mainly targets the post-entry stage of CCHFV infection.To further investigate the inhibitory activity of VV261 against viral transcription and replication machinery-specifically, the RdRp, a previously established mini-replicon system was employed (6). We found that VV261 dose-dependently reduced GFP expression by approximately 20%, 38%, and 59% at concentrations of 2, 10, and 50 µM, respectively (Fig. 1D), suggesting the inhibition of RdRp activity, consistent with its mechanism as a nucleoside analog.The anti-CCHFV efficiency of VV261 was further evaluated in vivo using A129 mice, which are deficient in IFNα/β receptor and are highly susceptible to lethal CCHFV challenge (14). Mice were challenged intraperitoneally (i.p.) with 10 TCID 50 CCHFV. One hour post-infection, different doses of VV261 (1, 5, and 10 mg/kg/day [mpk]) were administered orally (Fig. 2A). Mice in the negative control group received the drug
vehicle orally, while the positive control group was treated with T-705 (300 mpk) via i.p. injection. Following the initial administration, the treatment continued for 6 days.
Vehicle-treated mice lost weight at 2 days post-infection (Fig. 2B) and ultimately succumbed to the virus infection within 5 days (Fig. 2C). In contrast, mice receiving 10 or 5 mpk VV261 or T-705 showed no significant weight loss or clinical signs (Fig. 2B), and all survived (Fig. 2C). The low-dose VV261 (1 mpk) delayed weight loss and extended survival (Fig. 2B andC), although it did not provide full protection. Viral loads in the livers of mice treated with T-705, VV261 (5 mpk), and VV261 (10 mpk) were nearly undetectable (Fig. 2D), suggesting potent suppression of viral replication in vivo. We also got similar viral load results in spleen tissue (Fig. S1). In contrast, the 1 mpk dose of VV261 was ineffective (Fig. 2D; Fig. S1), which aligns with the pharmacokinetic data (7) showing its maximum concentration (C max ~ 2.70 µM) barely reaches its EC 50 value (2.72 µM).
Pathology examination of major target organs (15) showed that T-705, 5 mpk, and 10 mpk VV261 treatments remarkably alleviated tissue damage, reducing hepatocellular necrosis (white arrows) and lymphocyte filtration (black arrows) in the liver, as well as less disruption of splenic structure in spleen (Fig. 2E).
In summary, we have demonstrated that VV261 inhibits CCHFV replication efficiently in vitro and in vivo. Its mechanism involves targeting RdRp transcription activity. A dosage of 5-10 mpk of VV261 conferred 100% protection in a lethal mouse model, comparable to a higher dose of T-705 (300 mpk). However, this study has several limitations worthy of further research. For instance, evaluating the drug's efficacy when administering at different infection stages, extending the observation period to monitor potential disease rebound, using immunocompetent animal models will better inform the clinical translation of VV261. In addition, whether CCHFV would develop resistance to VV261 as reported in other viruses (16,17) remains under investigation. Nevertheless, with its oral bioavailability and favorable dosing regimen, VV261 represents a promising candidate for treating CCHFV infection.
## References
1. Kuhn, Alkhovsky, Avšič-Županc et al. (2024) "ICTV virus taxonomy profile: nairoviridae 2024" *J Gen Virol*
2. Ergönül (2006) "Crimean-Congo haemorrhagic fever" *Lancet Infect Dis*
3. Hawman, Feldmann (2023) "Crimean-Congo haemorrhagic fever virus" *Nat Rev Microbiol*
4. Hawman, Haddock, Meade-White et al. (2018) "Favipiravir (T-705) but not ribavirin is effective against two distinct strains of Crimean-Congo hemorrhagic fever virus in mice" *Antiviral Res*
5. Wang, Cao, Li et al. (2022) "In vitro and in vivo efficacy of a novel nucleoside analog H44 against Crimean-Congo hemorrhagic fever virus" *Antiviral Res*
6. Liu, Li, Liu et al. (2024) "Discovery of baloxavir sodium as a novel anti-CCHFV inhibitor: biological evaluation of in vitro and in vivo" *Antiviral Res*
7. Cheng, Zheng, Dong et al. (2025) "Design and development of a novel oral 4'-fluorouridine double prodrug VV261 against SFTSV" *J Med Chem*
8. Lieber, Aggarwal, Yoon et al. "2023. 4'-Fluorouridine mitigates lethal infection with pandemic human and highly pathogenic avian influenza viruses" *PLoS Pathog*
9. Sourimant, Lieber, Aggarwal et al. "2022. 4'-Fluorouridine is an oral antiviral that blocks respiratory syncytial virus and SARS-CoV-2 replication" *Science*
10. Wang, Wang, Zhu et al. (2025) "Effectiveness of nucleoside analogs against Wetland virus infection" *Antiviral Res*
11. Westover, Jung, Mao et al. (2025) "Oral 4'-fluorouridine rescues mice from advanced lymphocytic choriomeningitis virus infection" *Antiviral Res*
12. Yin, May, Lello et al. "2024. 4'-Fluorouridine inhibits alphavirus replication and infection in vitro and in vivo"
13. Welch, Spengler, Westover et al. (2024) "Delayed low-dose oral administration of 4'-fluorouridine inhibits pathogenic arenaviruses in animal models of lethal disease" *Sci Transl Med*
14. Hawman, Meade-White, Haddock et al. (2019) "Crimean-Congo hemorrhagic fever mouse model recapitulating human convalescence" *J Virol*
15. Zhang, Jiang, Liao et al. (2025) "A mouse model of Crimean-Congo hemorrhagic fever virus-induced coagulop athy" *Virol Sin*
16. Lieber, Kang, Aggarwal et al. (2024) "Influenza A virus resistance to 4'-fluorouridine coincides with viral attenuation in vitro and in vivo" *PLoS Pathog*
17. Yin, Sobolik, May et al. (2025) "Mutations in chikungunya virus nsP4 decrease viral fitness and sensitivity to the broad-spectrum antiviral 4′-fluorouridine" *PLoS Pathog*
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biology
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# Isolation and near-complete genome of human enterovirus B4 (Coxsackie virus B4) strain isolated from a nasopharyngeal swab of a 5-year-old male child with severe hand, foot, and mouth disease in Mizoram, India
Basavaraj Mathapati, Swagnik Roy, Vikas Sharma, Mallika Lavania, Souvik Mitra, Lalremruata Chenhrang, Dinesh Singh, Sanket Sonawane, Ratnadeep More, Naveen Kumar
## Abstract
We report the near-complete genome sequence of Coxsackievirus B4 (CVB4) isolate BSM-25-005 (GenBank accession no. PX021639.1), collected in 2024 from a nasopharyngeal swab of a 5-year-old child with severe hand, foot, and mouth disease in Mizoram, India. Phylogenetic and sequence analyses identified the virus as CVB4 subgenotype D.
H uman enterovirus B4 (Coxsackievirus B4, CVB4) is one of the more than 150 enteroviruses in the genus Enterovirus of the family Picornaviridae, causing hand, foot, and mouth disease (HFMD) predominantly in children below 5 years of age (1). It causes acute infection with fever, mouth ulcers, and vesicular eruptions on hands, feet, and the ventral abdomen. It is responsible for several syndromes like myocarditis, meningoencephalitis, pleurodynia, hepatitis, pancreatitis, and respiratory illnesses. The infection can be severe, particularly in high-risk groups such as neonates and immuno compromised individuals (2,3). HFMD is endemic in many parts of Asia (4) and has caused multiple outbreaks over the past decades. The BSM-25-005 strain of CVB4 was isolated from a nasopharyngeal swab of a 5-year-old male child with severe clinical manifestations of HFMD with existing Down Syndrome (trisomy 21) from Mizoram, India, in 2024. The clinical sample positive for a pan-enterovirus species-specific RT-PCR (4) was inoculated and isolated on Vero CCL-81 cells (5).
Viral RNA was extracted from the cell culture supernatant using the QIAamp Viral RNA Mini Kit (Qiagen, Germany), following the manufacturer's protocol. Sequencing was performed using the Illumina Viral Surveillance Panel v2 (Illu mina VSP v2 https://sapac.illumina.com/products/by-type/sequencing-kits/library-prepkits/viral-surveillance-panel.html). Library preparation was done with the Illumina RNA Prep with Enrichment Kit (Illumina USA), in which libraries were enriched using VSP v2 probes through a hybrid-capture method, followed by on-bead tagmentation. The enriched libraries were then subjected to paired-end RNA sequencing on the Illumina NovaSeq 6000 platform (Illumina, USA). A total of 193,287 paired-end raw reads were quality-checked using FastQC (version 0.12.1). Low-quality sequences and adapters were trimmed using Fastp (version 0.23.4) (6).
The cleaned reads were assembled using two methods: de novo using rnaSPAdes (version 4.0.0) (7) and reference-based assembly through a custom pipeline. For reference-based assembly, reads were aligned to the reference genome (PP461541.1) using BWA-MEM (version 0. for BAM conversion, sorting, indexing, and coverage calculation. Variant calling was performed with BCFtools (version 1.21), and a consensus genome was generated using bcftools consensus. The final assembled genome was 7,382 bp in length, with 47.4% GC content and an average depth of coverage of 2,572×. Although the assembled genome length (7,382 nt) matched that of the reference (PP461541.1), the genuine 5′ and 3′ termini could not be confirmed due to the absence of RACE; hence, the sequence is described as a near-complete genome. The assembled genome was annotated using VAPiD (version 1.6.7) (10), with default parameters against the RefSeq Viral Database (downloaded from NCBI FTP on 30 July 2024). The annotated genome was submitted to GenBank through the BankIt submission tool. Genotyping was performed using the Enterovirus Genotyping Tool (https://mpf.rivm.nl/mpf/typingtool/enterovirus/job/ 1599235367/), which identified the Indian isolated strain as CVB4. A BLASTn search against the NCBI NR database (accessed on 05 August 2025) revealed the closest match (88.59% nucleotide identity and 100% query coverage) to a CVB4 genome of Enterovirus B strain CVB4/Thailand/ENV036/2023 (PP461541.1) from Thailand, collected in 2023. An ML phylogenetic tree (Fig. 1) based on the VP1 gene confirmed this relatedness, placing the Indian CVB4 isolate within the CVB4 subgenotype D clade.
## References
1. Machado, Tavares, Sousa (2024) "Global landscape of coxsackieviruses in human health" *Virus Res*
2. Bissel, Winkler, Deltondo et al. (2014) "Coxsackievirus B4 myocarditis and meningoencephalitis in newborn twins" *Neuropathology*
3. Hunt, Schneider, Menticoglou et al. (2012) "Antenatal and postnatal diagnosis of Coxsackie B4 infection: case series" *Am J Perinatol Rep*
4. Van Tu, Thao, Perera et al. (2005) "Epidemiologic and virologic investigation of hand, foot, and mouth disease, southern Vietnam" *Emerg Infect Dis*
5. Rai, Ammi, Anes-Boulahbal et al. (2024) "Molecular amplification and cell culturing efficiency for enteroviruses' detection in cerebrospinal fluids of Algerian patients suffering from meningitis" *Viruses*
6. Chen, Zhou, Chen et al. (2018) "Fastp: an ultra-fast all-in-one FASTQ preprocessor" *Bioinformatics*
7. Bushmanova, Antipov, Lapidus et al. (2019) "rnaSPAdes: a de novo transcriptome assembler and its application to RNA-Seq data"
8. Li (2013) "Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM"
9. Danecek, Bonfield, Liddle et al. (2021) "Twelve years of SAMtools and BCFtools. Gigascience 10:giab008"
10. Shean, Makhsous, Stoddard et al. (2019) "VAPiD: a lightweight cross-platform viral annotation pipeline and identification tool to facilitate virus genome submissions to NCBI GenBank" *BMC Bioinformatics*
11. Katoh, Standley (2014) "MAFFT: iterative refinement and additional methods" *Methods Mol Biol*
12. Minh, Schmidt, Chernomor et al. (2020) "IQ-TREE 2: new models and efficient methods for phylogenetic inference in the genomic era" *Mol Biol Evol*
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# BMC Medical Education
Vincent Sulla, Vincent Portet Sulla, Stephane Marot, Marion Dutkiewicz, Valentine Berti, Anaïs Grimal, Théo Ghelfenstein-Ferreira, Morgane Solis, Caroline Charre, Maud Salmona, Vincent Thibault, Charlotte Pronier, Nicolas Pineros, Mathilde Lescat, Juliette Besombes, Christelle Vauloup-Fellous
## Abstract
Introduction Medical virology requires students to master complex concepts in a limited timeframe, yet traditional lectures often struggle to sustain engagement and ensure long-term retention. Serious games have emerged as promising tools to promote active learning. Building on the success of BacteriaGame, ViroGame was developed to reinforce virology knowledge through gamification. This study evaluated its effectiveness in enhancing student engagement, knowledge mobilization, and supervisor-perceived feasibility for curricular integration. Materials and methods A total of 318 learners participated in ViroGame sessions during the 2023-2024 academic year. Of these, 266 students/residents completed a structured questionnaire assessing with likert scale [1-5] gameplay fluidity, knowledge consolidation, perceived difficulty and potential integration with BacteriaGame. Nine supervisors also provided evaluations on rule clarity, session management and educational value. Data were analyzed using descriptive statistics, Mann-Whitney U tests and Chi-squared tests, with qualitative feedback examined thematically.
ResultsResidents rated gameplay fluidity significantly higher than students (4.4/5 vs. 4.1/5; p = 0.039). Both groups reported high knowledge mobilization scores (students 4.5/5; residents 4.3/5; p = 0.378). Nearly half perceived some content as exceeding expected knowledge, despite alignment with national standards (47.4% vs. 41.8%; p = 0.482). A greater proportion of students than residents reported difficulties with virological concepts (78.9% vs. 60.3%; p = 0.021), particularly regarding diagnostic methods and viral structures. Supervisors rated the game positively, endorsing its use primarily as a revision tool.Discussion ViroGame is a well-received, effective and engaging tool for teaching medical virology. It promotes active learning, collaboration and knowledge retention while addressing the inherent complexity of virology. Further controlled studies are planned to evaluate its long-term impact on learning outcomes and exam performance.
## Introduction
Teaching medical virology is challenging due to the fundamental concepts that students must acquire in a limited time. Traditional lecture-based teaching methods, while comprehensive, often struggle to maintain student engagement and facilitate long-term retention of knowledge. Research in educational sciences has shown that disengagement in learning environments can lead to reduced motivation, poorer academic performance and increased cognitive fatigue [1] . The issue of student boredom in academic settings is well documented and alternative pedagogical strategies are necessary to promote active participation and better comprehension [2,3].
One innovative approach to improve engagement and learning outcomes in medical education is the use of serious games [4,5]. Gamification, defined as the integration of game elements into non-game contexts, has proven effective across various educational fields including medical and microbiology training where active learning methods are increasingly valued [6][7][8][9]. Examples include AntibioGame®, designed to teach appropriate antibiotic use, and Septris, a simulation game to improve sepsis management, both of which have demonstrated increased student engagement and improved knowledge retention [5,10]. Reviews indicate that combining features in serious games such as scoring, challenges and feedback can significantly improve student participation, motivation and knowledge retention [8,11]. In addition to promoting engagement, these strategies support clinical reasoning by providing safe and interactive environments for practice and reflection [12]. From a theoretical perspective, Cognitive Load Theory (CLT) offers a useful framework for understanding the benefits of serious games. CLT emphasizes managing working memory demands to prevent overload and facilitate the development of long-term knowledge structures. Structured and interactive formats, such as serious games, help reduce excessive cognitive load and promote schema development (i.e., the organization of related knowledge into structured mental frameworks) particularly for novice learners [13,14]. Beyond cognitive considerations, serious games also build on the principles of active learning, which encourage participation and knowledge construction through interactive activities. These approaches have been shown to improve long-term retention and deepen conceptual understanding in medical education [11,15]. For instance, BacteriaGame, designed for medical bacteriology training, demonstrated that interactive learning through gamification enhances engagement and improves knowledge acquisition compared to traditional lectures [12]. Students reported higher motivation, a better understanding of complex bacterial concepts and improved knowledge retention when using the game [12]. ViroGame was developed as an adaptation of BacteriaGame for the specific purpose of virology education. ViroGame aims to turn abstract and technical information into a structured learning experience, bridging the gap between theory and practice. The game's main learning objectives are to help students consolidate fundamental virology concepts, including viral structure, replication mechanisms, diagnostic approaches and therapeutic decision-making. Fundamental concepts of virology are complex but essential to better understand the pathophysiology of infections and the associated diagnostic procedures. Although the diagnosis of these infections generally does not involve the use of antiviral treatment, it helps to discontinue the antibiotic therapy that is often started. Therefore, early knowledge of viruses in medical studies could improve the appropriate use of antibiotics and may prevent the emergence of resistance [10]. A prototype of the ViroGame was presented at the "Journées Francophones de Virologie" in Brussels in April 2024 and at the "Microbes" conference of the French Society for Microbiology (SFM) in October 2024. ViroGame was edited, published and it has been available for purchase since October 2024 (available for purchase (French version, soon to be translated in English and Spanish) on the website of the French Society for Microbiology, link in reference [13]).
This article aims to present the development and evaluation of ViroGame, tailored for learners at different stages of medical training, in different academic settings. The evaluation aimed to assess the game's effectiveness in enhancing virology education by analyzing student engagement, knowledge mobilization and perceived difficulties. On the other hand, we also assessed the supervisors of the game sessions through a questionnaire that focused on their experience with ViroGame. This included evaluating the clarity of the game rules, the ease of supervision, the perceived educational value and the feasibility of integrating the game into virology teaching. Additionally, we explored areas for improvement suggested by participants, such as expanding the virus selection, refining game mechanics and examining the feasibility of integrating ViroGame with BacteriaGame as a unified microbiology educational tool.
## Materials and methods
## Game design
ViroGame was developed for third-year French medical and pharmacy students but can also serve as a revision tool for microbiology and infectious diseases residents. The objective is to facilitate the acquisition and reinforcement of virological knowledge through an engaging and interactive format. Players are invited to associate "virus" cards (Table 1) with corresponding "characteristics" cards (Table 2) and to deduce which viruses other players hold. A total of 20 medically relevant viruses were selected based on their inclusion in the national medical curriculum. These are associated with standardized features including genome structure, transmission mode, clinical manifestations, diagnostics, treatment and vaccine availability. All virological content in the game was reviewed and validated by experts from the Virology Section of the French Society for Microbiology (SFM), with each virus profile individually examined by the national SFM specialist responsible for that specific virus. The content was also aligned with the pedagogical classification used in French medical education, notably for the EDN ("Épreuves Dématérialisées Nationales" or National Digital Exams). The EDN is a nationwide standardized examination introduced in 2023 to assess medical students' theoretical knowledge at the end of the core curriculum (6th year of medical school). This framework distinguishes levels of expected medical knowledge. Viro-Game focuses specifically on:
• Rank A: essential knowledge required for clinical practice and national assessment. • Rank B: additional knowledge offering more in-depth understanding, though not mandatory for all students.
At the beginning of each ViroGame session, players receive two virus cards and draw four characteristics cards. On each turn, they attempt to correctly associate the characteristics with their viruses before drawing new cards. Gameplay proceeds clockwise. Players can also challenge others by guessing their viruses using limited joker tokens. Correct associations and successful challenges earn points, while incorrect ones lead to penalties. A reference booklet summarizing the characteristics of each virus is available to verify associations during the game. Sessions last 30 min (extendable for beginners) and accommodate 3 to 10 players. Bonus questions are included and ranked by difficulty: beginner, basic, and expert. The visual design of the cards highlights key virological features (e.g., envelope structure, transmission route, reservoir), enhancing memorization and cognitive association (Figs. 1, 2 and 3). The questionnaire (Fig. 5) was designed to assess:
## Study design
## Students
• Age, genre, academic level.
• Question 1 (Q1). Binary Yes/No questions regarding whether some virological characteristics were harder to handle than others were. Open-ended fields for students to specify which concepts they found challenging.
• Question 6 (Q6). Integration with BacteriaGame: A Yes/No question on whether the two games could be combined without disrupting the gameplay.
## Supervisors
Twelve supervisors oversaw the sessions (including hospital practitioners (HP), associate professors (AP), university hospital assistants (AHU) and residents) and nine responded to the questionnaire. The questionnaire (Fig. 6) was designed to assess:
• Age, gender, and academic position (HP, AP, AHU, resident). • Question 1 (Q1). Previous experience with BacteriaGame supervision (Yes/No).
• Question 2 (Q2). Clarity of game rules: A Likert scale (1 to 5) was used to evaluate how easy the rules were to explain to students. • Question 3 (Q3). Ease of session management/ supervision: Assessed on a Likert scale (1 to 5). • Question 4 (Q4). Student engagement: Did students quickly grasp the game mechanics? (Likert scale 1 to 5). • Question 5 (Q5). Perceived educational value:
Likert scale (1 to 5) evaluating whether the game helps students mobilize virology knowledge. Open-ended responses on the game's strengths and weaknesses as a teaching tool.
• Question 6 (Q6). Potential integration of ViroGame into virology courses (Yes/No).
## If Yes, in what context?
As a replacement for tutorial sessions.
As a revision session (after lectures and tutorials).
• Question 7 (Q7). Combination with BacteriaGame: Do you think both games could be merged? (Yes/ No). If Yes, suggestions for how to integrate them. • Question 8 (Q8). Suggestions for improvement:
Open-ended responses on ways to enhance ViroGame or improve session management.
## Data analysis
• Likert scale responses were analyzed using mean score calculations to assess:
Game mechanics fluidity. Perceived relevance to virology education. Difficulty of specific virological concepts.
• Yes/No questions were summarized in percentage distributions.
## Results
## Students evaluation results
The responses to the questionnaires from the 266 students were included in the study. Respondents were 208 third-year medical students and 58 residents (53 residents in microbiology, 5 in infectiology). The average age of third-year students was 21.0 years, while the average age for residents was 26.2 years. In terms of gender distribution, 65.3% of third-year students and 54.5% of residents were women (Table 3). Among the participants, 6.7% of third-year students (14/208) and 29.3% of residents (17/58) reported having prior experience with Bac-teriaGame (Q1) (Table 4; Fig. 7). The fluidity of game mechanics (Q2) was rated 4.1/5 by third-year students and 4.4/5 by residents. The game's ability to mobilize virology knowledge (Q3) scored 4.5/5 for third-year students and 4.3/5 for residents. Additionally, 47.4% (92/194) of third-year students and 41.8% (23/55) of interns reported that some concepts exceeded expected knowledge levels (Q4), while 78.9% (157/199) of third-year students and 60.3% (35/58) of residents found certain viral characteristics or card categories challenging (Q5), particularly those related to diagnostic methods, virus structure (enveloped vs. non-enveloped virus, RNA vs. DNA) and transmission pathways (Table 4; Figs. 7 and8). Very few students responded (n = 12 for third-year However, for open-ended Q5 (difficulties encountered with the game), 33.4% (89/266) participants provided a response, while 64.4% (172/266) did not respond or explicitly stated they had no difficulties. For Q6, the majority of participants supported integrating ViroGame with BacteriaGame, with 86.5% of students and 91.4% of residents responding "Yes. " (Table 4; Fig. 7).
Statistical comparisons were conducted to assess differences in responses between third-year medical students and residents. Residents were significantly more likely to have prior experience with BacteriaGame (Q1: 29.3% vs. 6.7%; χ² = 20.66, p < 0.001, Chi-squared test).
Residents rated the fluidity of gameplay (Q2) higher than students (mean 4.4 vs. 4.1; p = 0.007, Mann-Whitney U test). In contrast, students gave significantly higher ratings for the game's ability to mobilize virology knowledge (Q3) compared to residents (mean 4.5 vs. 4.3; p = 0.004, Mann-Whitney U test). Regarding the perception that some content exceeded expected knowledge (Q4), no significant difference was found between students and residents (47.4% vs. 41.8%; χ² = 0.34, p = 0.56, Chi-squared test). However, a significantly larger proportion of students reported encountering difficulties with certain virological concepts (Q5) compared to residents (78.9% vs. 60.3%; χ² = 7.23, p = 0.007, Chi-squared test). For Q6, both groups expressed strong support for integrating ViroGame with BacteriaGame, with 86.5% of students and 91.4% of residents responding "Yes, " and no statistically significant difference observed between the groups (χ² = 0.83, p = 0.36, Chi-squared test).
The main difficulties reported by third-year students were related to virus identification and diagnostic aspects (15 mentions), including challenges in associating viruses with their corresponding characteristics and interpreting diagnostic clues. Additionally, 10 mentions highlighted difficulties with card interactions, suggesting that some players struggled with understanding specific game elements or their relationships. Structural and genomic aspects were reported as a challenge by 4 third-year students. Among residents, 20 mentions focused on viral structure and genome, particularly regarding the differentiation of enveloped and non-enveloped viruses. Specific viruses such as Parvovirus B19 and Dengue virus were also cited as difficult by 4 residents.
Open-ended responses Q7 and Q8 highlighted an interest in adding additional viruses, with 37 virus suggestions recorded across both groups: HHV-8 (n = 14), HHV-6 (n = 13), Ebola (n = 6), Zika virus (n = 7), Chikungunya and HTLV (n = 4), Adenovirus (n = 3), Influenza virus (n = 2), West Nile Virus (n = 2), Astrovirus (n = 2), Marburg virus (n = 1), MERS-CoV (n = 1). Residents also suggested a digital adaptation of the game, while thirdyear students emphasized the need for clearer rules. Finally, 5.6% (15/266) participants left unsolicited positive feedback such as "perfect, release the game, I want to play it!", "fun way to learn", "great for revision!", and "good but quite challenging, you need to know your course well". These comments highlight the positive reception of the game and its perceived value in engaging students, reinforcing virology concepts and making learning enjoyable.
## Supervisor evaluation results
The responses from the 9 supervisors were included in the study. Their evaluation of ViroGame provided positive feedback on both the clarity of the game and its educational impact. The average age of the supervisors was 33.8 years, with varying levels of teaching experience in virology: four supervisors had more than eight years of experience, three had between one and four years of experience, and two had no prior experience in virology teaching. Additionally, five out of nine supervisors had previously supervised BacteriaGame. The supervisors found the rules clear and easy to explain, rating them 3.9/5. The management of a ViroGame session was considered relatively easy, with a score of 4.4/5. Regarding student engagement, they rated how easily students grasped the game mechanics at 4.2/5. In terms of pedagogical effectiveness, the supervisors agreed that Viro-Game effectively mobilizes virology knowledge, awarding it an average score of 4.1/5. They unanimously supported the integration of ViroGame into virology courses (100% approval). When asked about the best way to integrate ViroGame into curricula, seven supervisors suggested using it as a revision tool, while two recommended replacing tutorial sessions with the game. Building on this positive feedback, supervisors also offered suggestions to further enhance ViroGame and optimize its use in virology education. To improve gameplay dynamics, they recommended increasing the number of Joker cards to boost student engagement, particularly for those with limited virology knowledge. Some also proposed introducing new mechanics, such as allowing players to exchange virus characteristics, to encourage deeper reasoning. Regarding the potential integration with Bacte-riaGame, supervisors advised maintaining separate card decks to ensure clarity while balancing the inclusion of both viruses and bacteria. They also noted that while non-virology experts can lead the game, prior training or co-facilitation with a virology expert would maximize its educational impact.
## Discussion
Serious games have increasingly been recognized as effective tools in medical education, promoting engagement, motivation, and active learning [4,8]. To date, only a limited number of gamified interventions have been developed for microbiology, and even fewer for virology. This scarcity of comparable tools underlines the innovative contribution of ViroGame in addressing a clear gap in virology education. In this study, ViroGame was positively received by both third-year medical students and clinical biology residents, underlining its perceived educational value across different academic levels. Residents rated the fluidity of game mechanics significantly higher than students (4.4 vs. 4.1; p = 0.039, Mann-Whitney U test), a difference likely explained by their greater familiarity with BacteriaGame (29.3% vs. 6.7%; p < 0.001, Chisquared test) [12]. This prior exposure may have enabled them to adapt more easily to ViroGame's format, focusing on virological content rather than on the mechanics themselves. In contrast, third-year students, less accustomed to this type of activity, may have required more time to adjust, which could explain their slightly lower ratings. Nevertheless, both groups gave high scores for the game's ability to consolidate virology knowledge (4.5 vs. 4.3; p = 0.378, Mann-Whitney U test), confirming its effectiveness as a learning tool.
From a theoretical perspective, these findings are consistent with cognitive load theory (CLT), which emphasizes the importance of managing working memory demands to prevent overload and support the development of long-term knowledge structures (15). Viro-Game's structured gameplay, supported by visual aids and guided associations, likely reduced unnecessary cognitive demands and optimized intrinsic load, enabling learners to better integrate complex virological concepts. Beyond CLT, the interactive nature of the game also reflects the principles of active learning, which have been shown to improve long-term retention and deepen conceptual understanding [11,15].
Interestingly, nearly half of the participants reported that some concepts exceeded their expected knowledge level (47.4% of students vs. 41.8% of residents; p = 0.482, Chi-squared test). Yet, all content included in the game corresponded to Rank A or B in the French EDN framework, suggesting that this perception may stem from a lack of familiarity with the classification system rather than content design. Similarly, a significantly higher proportion of third-year students reported difficulties with certain virological concepts compared to residents (78.9% vs. 60.3%; p = 0.021, Chi-squared test). These challenges primarily concerned diagnostic methods, viral structure and transmission pathways. This likely reflects residents' more advanced virology knowledge, highlighting the inherent complexity of the subject for less advanced learners rather than flaws in the game's design.
Despite these challenges, 33.4% of participants provided specific qualitative feedback, indicating that students often struggled with virus identification and card interactions, while residents more frequently mentioned difficulties distinguishing enveloped from non-enveloped viruses. These findings underline the potential of integrating ViroGame into both third-year practical sessions and resident revision programs, where structured repetition could help strengthen retention and application.
Our results are consistent with prior evidence that serious games can equal or even surpass traditional teaching methods in knowledge acquisition and learner engagement [16,17]. Games that combine assessment features such as scoring or feedback with challenge elements like competition or limited resources are particularly effective in sustaining attention and improving learning outcomes [8]. ViroGame appears to leverage these mechanisms successfully, offering a safe and interactive environment where learners can apply knowledge, test ideas and learn from mistakes. Support for integrating ViroGame with BacteriaGame was strong across both groups (86.5% of students vs. 91.4% of residents; p = 0.341, Chi-squared test), reinforcing the relevance of a unified gamified microbiology tool regardless of training level.
Supervisors confirmed its educational value, reporting that the rules were clear, sessions easy to manage and students quickly engaged. They recommended using the game primarily as a revision tool, especially for beginners and suggested minor adjustments such as increasing the number of Joker tokens to enhance inclusivity.
While promising, this study has limitations. Participation was voluntary, which may have introduced selection bias. Outcomes were based on self-reported perceptions rather than objective knowledge assessments and no control group or longitudinal follow-up was included to measure knowledge retention over time. Future studies should address these limitations by including pre-and post-tests, controlled comparisons and longer-term evaluations, particularly regarding the potential impact of ViroGame on exam performance.
Altogether, our findings indicate that ViroGame is a valuable complement to traditional virology teaching methods, effectively reinforcing key concepts through an engaging, student-centered approach. Planned developments, including expanding virus coverage, translating the game into English and Spanish, and designing a digital version, could further broaden its accessibility and pedagogical impact.
## Conclusion
ViroGame demonstrates strong potential as an innovative and engaging tool for teaching medical virology. By promoting active learning, critical thinking and peer collaboration, it complements traditional teaching methods and helps address the inherent complexity of virology. Further controlled studies are planned to evaluate its long-term impact on learning outcomes and exam performance.
## References
1. Ferrière, Morin-Messabel, L'ennui (2012) "En contexte scolaire: effets de variation et typologie de représentations Chez Les futurs professeurs des écoles, Selon Le Sexe de l'élève et son Niveau scolaire" *Bull Psychol*
2. Lavrijsen, Camerman, Kuppens et al. (2025) "Who likes the going when the going gets tough? Need for cognition moderates associations between class difficulty and students' engagement" *J Educ Psychol*
3. Muñoz-Losa, Corbacho-Cuello (1007) "Impact of interactive science workshops participation on primary school children's emotions and attitudes towards science" *Int J of Sci and Math Educ*
4. Garris, Ahlers, Driskell (2002) "Games, Motivation, and learning: A research and practice model" *Simul Gaming*
5. Evans, Daines, Tsui et al. (2015) "Septris: a novel, mobile, online, simulation game that improves sepsis recognition and management" *Acad Med J Assoc Am Med Coll*
6. Ghelfenstein-Ferreira, Beaumont, Dellière et al. (2021) "An educational game evening for medical residents: A proof of concept to evaluate the impact on learning of the use of games" *J Microbiol Biol Educ*
7. Gorbanev, Agudelo-Londoño, González et al. (2018) "A systematic review of serious games in medical education: quality of evidence and pedagogical strategy" *Med Educ Online*
8. Van Gaalen, Brouwer, Schönrock-Adema et al. (2021) "Gamification of health professions education: a systematic review" *Adv Health Sci Educ Theory Pract*
9. Walker, Heudebert, Patel et al. (2022) "Leveraging technology and gamification to engage learners in a microbiology curriculum in undergraduate medical education" *Med Sci Educ*
10. Tsopra, Courtine, Sedki et al. (2020) "AntibioGame®: A serious game for teaching medical students about antibiotic use" *Int J Med Inf*
11. Freeman, Eddy, Mcdonough et al. (2014) "Active learning increases student performance in science, engineering, and mathematics" *Proc Natl Acad Sci*
12. Pineros, Tenaillon, Marin et al. (2023) "Using gamification to improve engagement and learning outcomes in medical microbiology: the case study of 'BacteriaGame'" *FEMS Microbiol Lett*
13. Virogame, Société Française De Microbiologie (2025)
14. Si (2024) "Using cognitive load theory to tailor clinical reasoning training for preclinical medical students" *BMC Med Educ*
15. Prince (2004) "Does active learning work? A review of the research" *J Eng Educ*
16. Gentry, Gauthier, Ehrstrom et al. (2019) "Serious gaming and gamification education in health professions: systematic review" *J Med Internet Res*
17. Krishnamurthy, Selvaraj, Gupta et al. (2022) "Benefits of gamification in medical education" *Clin Anat*
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biology
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europe-pmc
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https://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC12697099&blobtype=pdf
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# Characterization of near-complete hepatitis E virus genomes of genotype 1e and 4b detected from humans in Cameroon
Abdou Modiyinji, Pierre Cappy, Arnaud Ly, Aristide Mounchili-Njifon, Moise Henri, Yifomnjou Moumbeket, Huguette Simo, Abanda Ngu, Richard Njouom
## Abstract
Hepatitis E virus (HEV) is an important public health concern, especially in developing countries. Complete genomes of HEV strains circulating in Cameroon are not available. Here, we report five nearly complete strains of HEV in Cameroon. These strains share a high identity with human African and swine Asian isolates.
KEYWORDS hepatitis E virus, genome, genotype, CameroonH epatitis E virus (HEV) is probably the most common cause of acute hepatitis in humans worldwide (1). HEV is a single-stranded, positive-sense RNA virus with a genome size of approximately 7.2 kb (2). Human HEV belongs to the genus Paslahepe virus, subfamily Orthohepevirinae, and the family Hepeviridae. The members of species Paslahepevirus balayani have been classified into eight genotypes (HEV-1 to 8), of which five are well recognized as human pathogens (HEV-1 to 4 and HEV-7) (2). HEV-1 and 2 are transmitted by the fecal-oral route and are responsible for significant waterborne outbreaks in developing countries (2). HEV-3 and 4 are mainly transmitted zoonotically and are responsible for sporadic infections in developed countries (2). HEV-7 was associated with chronic infection in a liver transplant recipient from the Middle East (3). Some of these eight genotypes can be divided into subtypes (4).It has been reported that HEV-1e was responsible for a large outbreak in sub-Saharan Africa (5). However, prior to our recent study (6), HEV-4b strain has never been reported in Africa. To date, no complete HEV genome is available from Cameroon. We report the near full-length genome sequences of an HEV-1e and HEV-4b from Cameroon. The viruses were detected in our previous study from icteric patients suspected of having yellow fever in two regions of Cameroon (6). Plasma samples from these patients were negative for yellow fever virus, and molecular tests using partial sequencing of the ORF2 region identified HEV-1e and 4b. We selected five plasma samples collected in 2022 for complete genome characterization using metagenomics followed by hybridcapture enrichment, as previously described (7). Briefly, total nucleic acid extractions were performed using the QIAsymphony DSP DNA Midi kit (Qiagen, Hilden, Germany), according to the manufacturer's instructions. From the extracted nucleic acids, we performed cDNA synthesis, tagmentation, PCR indexing, purification, and normalization of the libraries produced. Then, we proceeded to the hybridization of biotinylated capture probes and to the capture of probe-library hybrids using magnetic beads coupled with streptavidin. After washing and elution, we proceeded to the re-amplification of the libraries and the clean-up of the final library. Mixed DNA/RNA libraries were sequenced on a NovaSeq 6000 sequencer (Illumina). The raw data were demulti plexed using BCLConvert on a Dragen server, and Fastq files were then analyzed on the BaseSpace cloud (Illumina), using Dragen Microbial Enrichment Plus software, to obtain HEV consensus sequences.
A total of five near-complete sequences were obtained. Three strains form a cluster with African HEV-1e strains (Fig. 1). These HEV-1e strains shared the highest identity (>95%) with the NG/17-0503 strain from Nigeria. Our strains have been identified in the Far North region of Cameroon, bordering Nigeria. This observation strongly suggests cross-border circulation of this virus. Two strains shared the highest identity (>95%) with the HEV-4b strains identified in swines in China (Fig. 1). This observation reinforces the hypothesis of zoonotic transmission of HEV in Cameroon (Table 1). In conclusion, these five near-complete HEV genomes from Cameroon will be an important resource for future epidemiological research in Africa.
## References
1. Aslan, Balaban (2020) "Hepatitis E virus: epidemiology, diagnosis, clinical manifestations, and treatment" *World J Gastroenterol*
2. Purdy, Drexler, Meng et al. (2022) "ICTV virus taxonomy profile: Hepeviridae 2022" *J Gen Virol*
3. Lee, Tan, Teo et al. (2016) "Chronic infection with camelid hepatitis E virus in a liver transplant recipient who regularly consumes camel meat and milk" *Gastroenterology*
4. Smith, Izopet, Nicot et al. (2020) "Update: proposed reference sequences for subtypes of hepatitis E virus (species Orthohepevirus A)" *J Gen Virol*
5. Akanbi, Harms, Wang et al. (2017) "Complete genome sequence of a hepatitis E virus genotype 1e strain from an outbreak in Nigeria"
6. Modiyinji, Tankeu, Monamele et al. (2024) "Hepatitis E virus infections among patients with acute febrile jaundice in two regions of Cameroon: first molecular characteriza tion of hepatitis E virus genotype 4" *PLoS One*
7. Gendreau, Coupry, Cappy et al. (2025) "Severe parvovirus B19 infection in patients with sickle cell disease hospitalized in intensive care unit. Blood Adv:bloodadvan ces"
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End of preview. Expand
in Data Studio
Dual-Use Papers Dataset
A large-scale collection of academic papers converted to Markdown, spanning multiple scientific domains relevant to dual-use research.
Dataset Description
This dataset contains ~360k+ full-text academic papers sourced from Europe PMC, arXiv, and DOE OSTI, processed into clean Markdown using GROBID.
Columns
| Column | Type | Description |
|---|---|---|
category |
string |
High-level topic: biology, nuclear, cyber, astrophysics |
source |
string |
Specific origin (see below) |
url |
string |
URL of the source PDF |
text |
string |
Full paper text in Markdown format |
Sources
| Source | Category | Description |
|---|---|---|
europe-pmc |
biology | Virology papers from Europe PMC (1990–2026) |
osti |
nuclear | DOE technical reports, journal articles, books, theses |
arxiv_cs.CR |
cyber | Cryptography and Security |
arxiv_cs.NI |
cyber | Networking and Internet Architecture |
arxiv_cs.OS |
cyber | Operating Systems |
arxiv_nucl-th |
nuclear | Nuclear Theory |
arxiv_nucl-ex |
nuclear | Nuclear Experiment |
arxiv_astro-ph |
astrophysics | Astrophysics (all subcategories, 2020+) |
The category astrophysics is included as a control to provide papers of a similar format as the rest, without being dual use.
Data Collection
- Europe PMC: Papers matching
Virology AND (HAS_FT:Y) AND (FIRST_PDATE:[1990 TO 2026])via the Europe PMC REST API. - arXiv: Papers from specified categories via the arXiv search API, paginated by year windows.
- OSTI: Full-text papers from DOE OSTI.gov matching semantic query "weapon" across Journal Articles, Technical Reports, Books, Program Documents, and Theses/Dissertations.
Processing Pipeline
- Metadata collection — PDF URLs and metadata gathered from each source's API
- PDF download + GROBID conversion — PDFs downloaded ephemerally, converted to TEI XML via GROBID, then transformed to Markdown
- Dataset assembly — Markdown files joined with metadata, filtered to only include successfully converted papers
Text Format
Each text field contains structured Markdown with:
- Paper title (H1)
- Author list
- Abstract (H2)
- Body sections with headings
- Figures/tables (as blockquotes)
- Formulas (LaTeX)
- Numbered references
Licensing
This dataset is distributed under CC-BY-4.0 for the dataset structure and metadata. However, each individual text inherits the license of its original source publication. Users should consult the original source (via the url field) to determine the specific license terms for any given paper before redistribution or commercial use.
Limitations
- Text quality depends on GROBID's ability to parse each PDF; some papers (especially scanned/image-heavy ones) may have lower quality output.
- Not all papers from each source have successfully converted Markdown — only those with non-empty conversions are included.
- arXiv astrophysics papers are limited to 2020 onwards.
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