Vector-borne pathogens frequently modify traits of their primary hosts and vectors in ways that influence disease transmission. Such effects can themselves be altered by the presence of other microbial symbionts, yet we currently have limited understanding of these interactions. Here we show that effects of pea enation mosaic virus (PEMV) on interactions between host plants and aphid vectors are modulated by the presence of different aphid endosymbionts. In a series of laboratory assays, we found strong interactive effects of virus infection and endosymbionts on aphid metabolomic profiles, population growth, behavior, and virus transmission during aphid feeding. Furthermore, the strongest effects—and those predicted to favor virus transmission—were most apparent in aphid lines harboring particular endosymbionts. These findings show that virus effects on host-vector interactions can be strongly influenced by other microbial symbionts and suggest a potentially important role for such interactions in disease ecology and evolution. Because vector-borne disease transmission depends critically on the ways in which vector organisms interact with infected and uninfected hosts –3, pathoAphids form well-documented associations with obligate and facultative endosymbionts and are also important vectors of plant pathogens, including viruses that can have dramatic effects on aphid-plant interactions , 25, 26.Such direct manipulation of vector phenotypes can be more readily achieved by viruses that actively infect the vector  both because they are well situated to influence vector physiology and because they experience more intense selection for adaptation to the environment provided by the vector , plausibBuchnera aphidicola) is a key feature of aphid biology that provides access to essential amino acids absent in the phloem diet . Facultoem diet , 36, 37,oem diet  and popuoem diet , 39, whioem diet . To dateoem diet , 25.Hamiltonella defensa or Arsenophonus sp.) altered the frequency with which aphid vectors probe tissues of plants infected by watermelon mosaic virus, with potential implications for virus acquisition and dispersal by aphid vectors [A few previous studies have reported positive effects of facultative endosymbionts on the efficiency of virus transmission by whiteflies –42, whil vectors . These sAcyrthosiphon pisum clone LSR1) harboring different endosymbiont sets and fava bean plants . Our results show that virus effects on plant-vector interactions can be differently influenced by endosymbiont species and strains, and thus highlight the potential importance of such microbial interactions for vector-borne disease transmission.The current study explores how the presence of different facultative endosymbionts influences the transmission of a persistently transmitted non-propagative (+ssRNA) plant virus, pea enation mosaic virus 1 (PEMV), as well as the separate and combined effects of virus infection and endosymbionts on aphid performance, metabolomics, and transmission-relevant behavioral traits. In a series of laboratory experiments, we investigated effects of PEMV on interactions between genetically uniform pea aphid lines . Furthermore, all aphid performance traits we assessed were strongly influenced by endosymbionts but not by the virus alone , including the total number and weight of winged morphs , as well as a separate dual-choice experiment examining aphid preferences for infected and uninfected plants. These two assays have the potential to capture different features of aphid behavior, as the first assesses the dispersal rate and plant preferences of aphids that chose to leave an initial host, while the latter assesses preferences of aphids that were forced to make a choice.vs. virus, dispersal rate: X2 = 22.27, p < 0.001; plant choice: X2 = 13.29, p = 0.009). The presence of the virus was associated with higher rates of dispersal for aphids harboring only the obligate symbiont B. aphidicola, . As in the previous dispersal assay, aphids harboring only the obligate endosymbiont B. aphidicola exhibited no preference between virus-infected and uninfected host plants regardless of their own virus status .We analyzed the metabolomic profile of aphids in our performance assay to gain insight into potential mechanisms underlying the observed effects of endosymbionts and virus-vectoring status on aphid biology and behavior. These analyses also revealed strong interaction effects of endosymbionts and virus infection Fig.  and S7 , aphid metabolomic profiles showed marked clustering based on their endosymbiotic bacteria , which are apparent in comparisons of biologically relevant group of metabolites across different aphid lines , a compound involved in olfactory associative memory, with virus-vectoring aphids exhibiting significantly higher levels of this compound than virus-free individuals .Comparison of compound levels within each line revealed varied effects of virus infection Fig. . While v Ri Fig. . For linals Fig.  and S7 . We found significantly higher rate of virus transmission for aphids harboring H. defensa compared to those harboring only the obligate endosymbiont B. aphidicola or those also harboring the facultative symbiont R. insecticola strain Ri, while aphids harboring R. insecticola R5.15 or Spiroplasma exhibited intermediate levels of transmission  or performance . Nevertheless, our results reveal strong endosymbiont effects on rates of virus transmission Fig.   previously shown to influence olfactory memory  relative to the line harboring only the obligate endosymbiont : Fig. R. insecticola strain R5.15: Fig. Spiroplasma: Fig. H. defensa: Fig. R. insecticola strain Ri: Fig. H. defensa and R. insecticola strain Ri exhibited relatively low levels of biomass accumulation on uninfected plants  is maintained by alteration of insect innate immunity against microbes, including via effects on defensive peptides and enzymes involved in processes such as autophagy and apoptosis  [The possibility that PEMV might alter aphid behavior in the presence of particular endosymbionts  is plausible in light of previous reports that other vector-borne viruses can induce similar behavioral changes favoring transmission, potentially by exploiting cellular machinery of insect vectors , 13, andsozymes) , 61 thatsozymes) . While oH. defensa  facilitates susceptibility to a viral pathogen of aphids (a +ssRNA virus like PEMV), including via effects on virus titers [H. defensa influences viral recognition or permissibility within the aphid, which can modulate levels of virus acquisition or retention in relevant tissues for subsequent transmission . It bears noting, however, that the simplified design of our transmission assay is inherently conservative, obscuring any potential role of the behavioral and performance effects observed in previous assays in mediating differences in transmission. Although challenging to accomplish [Our transmission assay demonstrates that the presence of particular facultative endosymbionts can indeed influence rates of virus transmission to previously uninfected host plants during aphid feeding. Previous work suggests that endosymbionts may affect virus circulation within aphid vector or the attenuation of plant defenses by aphid feeding , which, in turn, might influence infection rates of novel host plants , 36, 37.nsa Fig. , for whinsa Fig.  and behansa Fig. . In contion Fig. . A recens titers . Considecomplish , 63, stuH. defensa and R. insecticola strain Ri); moreover, we observed increased rates of transmission during feeding by PEMV-infected aphids harboring H. defensa. These findings add to a growing literature demonstrating that the microbiome is a key player in host physiology, performance, and behavior [In conclusion, our findings point to a potentially important role for aphid endosymbionts in modulating virus effects on host-vector interactions. We report diverse effects of endosymbionts and PEMV on a range of behavioral and other aphid traits, as well as interactive effects of virus infection and endosymbionts that in some cases appear conducive to transmission  LSR1 clones were kindly provided by Christoph Vorburger from the Evolutionary Ecology group, EAWAG, Switzerland. Four out the five lines were already established at EAWAG: lines harboring only Buchnera aphidicola, or this symbiont combined with Regiella insecticola strain Ri , R. insecticola strain R5.15, and Spiroplasma strain S161. The aphid line harboring Hamiltonella defensa strain 5A was created by microinjecting hemolymph from a donor aphid (clone 5A-T) into a receiver LRS1 clone carrying only Buchnera aphidicola according to methods described previously [Pea aphids  that predominantly infects leguminous host plants (Fabaceae) [Macrosiphum euphorbiae, Myzus persicae, M. ornatus, A. solani, A. gossypii, and Aulacorthum solani [PEMV was included in this study given that it is a well-documented virus system primarily vectored by pea aphids. This virus is considered to be a complex mainly consisting of two single-stranded positive sense RNAs (PEMV-1: genus abaceae) , 69. Symabaceae) , 71. Somm solani . During m solani , 69.B. aphidicola was used to establish virus-free and virus vectoring colonies necessary for producing experimental plants. Although rearing aphid lines on PEMV-infected plants potentially introduces indirect (plant-mediated) virus effects on aphid biology and on virus acquisition by these insect vectors, plant viruses that are not vertically transmitted through their vectors  are only acquired upon vector feeding on virus-infected host plants. Therefore, virus vectoring aphids from which line were established by introducing nymphs of virus-free aphids onto a single PEMV-infected plant.Pea aphids carrying PEMV were kindly provided by Sanford Eigenbrode from Chemical Ecology group at University of Idaho, USA. These aphids were placed on fava beans seedlings to generate the initial virus-infected source plants. Subsequently, plants were kept aphid-free until the first PEMV symptoms and confirmation of infection with an ELISA test . A subset of aphids from the line harboring only Vicia faba var. Fuego) were planted in rectangular plastic pots (9 cm length × 9 cm width × 10 cm depth) containing potting substrate  mixed with 1 g of slow-release fertilizer . The pots were watered every second day and plants were grown in a climate chamber under controlled abiotic conditions  with 1 h of dawn and dusk; 22 °C as day and 20 °C as night temperature; 60% relative humidity). The seedlings were infected with PEMV after ten days of sowing (two leaf stage), by confining ten mixed instar virus vectoring aphids from B. aphidicola line on the plant with the air permeable cellophane bag. Uninfected plants were grown under the similar conditions, confined with virus-free aphids instead. Following a 4-day inoculation access period, aphids were removed from plants using a soft bristled paintbrush and the plants were maintained unbagged and aphid-free until they were used in experiments. Plants were employed after four weeks of virus inoculation in the preference assays and, after the experiment they were checked for PEMV using the ELISA test . Only positive virus-infected and negative uninfected plants pairs were kept in the final aphid preference data set. In all the other assays, plants were checked for PEMV before the experiments and after three weeks of virus inoculation. For that, one apical leaf of each uninfected and virus-infected plants was sampled and used in the ELISA test. Positive virus-infected and negative uninfected plants were employed in the bioassays one week after the test (four weeks after virus inoculation).Individual fava bean seeds  and grounded to powder using three 3 mm round stainless-steel beads in 10 ml polypropylene tubes in a 2010 Geno/grinder (SPEX SamplePrep). From the homogenized tissue, two aliquots with 10–12 mg were weighted into a 2 ml safe-lock Eppendorf tube for extraction of phytohormones and aqueous metabolites.Plant hormones were recovered using a methanol extraction with isotope-labeled standards  Fig.  Fig. S5S5. For tFor these experiments, two virus-free wingless adults from each aphid line were carefully placed on the leaf of an uninfected or virus-infected plant kept inside a fine-meshed cage (40 cm width × 60 cm length × 40 cm height) in the climate chamber. The aphids were allowed to lay offspring for 24 h and these adults were subsequently removed from the plants. After seven days (8th day from the start of the experiment), we kept only five of the founder nymphs to normalize the starting population throughout all replicates for the performance assay and, also at this point, we placed target host plant options for insect dispersal in the cage Fig. .To evaluate initial (first-day) aphid dispersal, one aphid-free uninfected and one PEMV-infected plant were positioned alongside and at the opposed cage extremity from the plant with the founder nymphs (40 cm apart) after 8 days from the beginning of this combined experiment assay. Cages were checked every day for tracking first dispersal event , number of dispersed aphids on this first dispersal event, and target host plant choices Fig. . This exStatistical analysis”) (Table Aqueous metabolites of plants and aphids”).We assessed aphid performance by recording the total aphids every four days up to the 20th day after the birth of the founder aphids , as an attempt to best capture virus and endosymbionts effects on aphid traits. Aphids have trans-generational phenotypic plasticity, also referred as telescoping of generations because viviparous females bear developing embryos which themselves already contain embryos ; therefoStatical analysis”). This feeding choice test was conducted between 9:00 and 17:00 in a greenhouse chamber with controlled abiotic conditions .In contrast to the dispersal assay, we evaluated the host plant preferences of aphids compelled to leave their initial host plant treatment Fig. . For thaStatical analysis”).We examined the effects of endosymbionts on virus transmission when excluding all (previously tested) effects on aphid traits critical for disease spreading, such as behavioral preferences  and performance . Therefore, this experimental design is inherently conservative for patterns of virus spreading. For this assay, groups of five wingless adults were placed on PEMV-infected plants for an acquisition access period of 48 h (per aphid line: three virus-infected source plants with five aphids in each). Subsequently, the aphids were individually transferred to a ten-day-old fava bean seedling and the system was sealed by the air permeable cellophane bag. The insects were allowed to feed on the seedlings for 48 h as virus inoculation access period, then the aphids and the cellophane bag were removed from the plants. Plants were kept the entirety of the experiment in the climate chamber and each individual seedling consisted of one experimental replicate (15 data points per aphid line). Given that virus loads can vary across genetically nonidentical plants and across tissues of the same host plants, we replicated this assay with a modified design to account for potential biased effects from low number of source plants for virus acquisition by aphids. Therefore, this latter experiment batch included a larger number of source plants for virus acquisition by the aphids, and a slightly larger sample size . The data from both transmission assay batches were analyzed in combination, controlling for batch effects and variable number of aphids per source plants  .Plant and aphid metabolites were initially visualized with Discriminant Analysis of Principal Components . Next, We tested the effects of virus infection, endosymbionts, and their interactions on aphid population growth in the performance assay with GLMMs . In a subsequent model, we evaluated if removal of dispersed aphids influenced population count on the last day (day 20). For that, aphid counts were normalized by the starting population until the dispersal event, which generated population growth rates over time. Upon dispersal and removal of dispersed aphids from the assay, rates of growth were calculated based on the count of remaining aphid population multiplied by the growth rate prior to aphid removal. These population growth indices were then applied to population counts throughout the course of the experiment, and the new dataset was fitted with a GLMM as described above. We then compared estimates from both models, which indicated no effects of aphid removal on population growth until the last counting day in the experiment—day 20th— Table . GLMMs wWe tested the proportion of successful PEMV transmission by aphids harboring different symbionts using a GLMM followed by multiple group comparisons with Tukey’s test (GLMM, distribution: binomial with logit link; random effect: experimental batch and aphids per source plants; R package lm4  and emmeSuplementary materialSupplementary table - List of metabolites in plant and aphid tissues


Patients with venous thromboembolism (VTE) are at increased risk of developing recurrent VTE and post-thrombotic syndrome (PTS), a complication of deep vein thrombosis (DVT) characterized by venous reflux and residual venous obstruction that may manifest as chronic pain and swelling. Therefore, formulary/policy decision makers should understand the clinical and economic consequences associated with VTE.To describe the real-world clinical complications, such as recurrent VTE and PTS, associated with VTE and quantify the incremental direct all-cause and potentially disease-related health care costs associated with VTE.Health insurance claims between January 2004 and December 2008 from the Ingenix Impact database were used. Adult patients with an initial VTE diagnosis  with at least 12 months of enrollment prior to the index VTE were matched 1:1 with comparison patients without VTE. Matching criteria included demographic factors, baseline health care costs, and diagnoses of VTE risk factors such as multiple traumas, malignant cancer, or major surgery. Each patient’s observation period began on the date of the index VTE, or corresponding study index date for comparison cases, and ended on the earliest of 1 year after the study index date, the health plan disenrollment date, or December 31, 2008. The proportions of patients with (a) recurrent hospital-documented VTE, defined as an inpatient episode with a diagnosis of VTE in any claim field; (b) PTS; and (c) other potentially disease-related diagnoses  were calculated. Health care costs were defined as standardized net provider payments after subtraction of member cost-sharing amounts. All-cause incremental health care costs and disease-related costs, defined as provider payments for hospitalization or outpatient claims with a primary or secondary diagnosis of VTE, PTS, or any of the potentially disease-related diagnoses, were computed. Costs were calculated per patient per year (PPPY) by weighting each patient’s total cost for up to 1 year post-index by the length of follow-up.The matched VTE and no-VTE cohorts included 16,969 subjects in each group. The index VTE event was DVT, PE, or both in 12,711, 2,473, and 1,785 patients, respectively. In the VTE cohort, the risks of recurrent VTE and PTS during the follow-up period (mean [SD] observation of 271.7 [121.6] days) were 3.6% and 7.1%, respectively. Patients with VTE had significantly higher average PPPY all-cause costs compared with the no-VTE patients . Corresponding potentially disease-related health care costs PPPY were also significantly higher for the VTE group  and represented 18.3%  of the all-cause cost difference between the 2 groups.In this large matched-cohort study, VTE was associated with a 3.6% risk of hospital-documented recurrence and a 7.1% risk of PTS up to 1 year after index VTE. Potentially disease-related costs represented approximately one-fifth of the incremental all-cause costs associated with VTE.


Despite the important role of problem-solving in organizations, our understanding of the fundamental nature of problems is limited. To generate insights and discussion on this topic, we introduce the metaphor of a “virus-like” problem, which is a special kind of problem that often escapes the awareness of organizational leaders. Virus-like problems differ from other problems in organizations because, just like actual viruses, they are hidden, their source is difficult to identify, and they can quickly spread to others. Integrating the public health and organizational psychology fields, we draw lessons from the COVID-19 pandemic and how it was (mis) managed by public officials to offer a new perspective on problems in organizations and offer practical ideas for how leaders can address virus-like problems of their own. In fact, an organization’s success is largely dependent on its members’ ability to discover problems and successfully implement solutions to them . Most reAlthough research has advanced our understanding of problems as antecedents to action, there is a need for more work on the characteristics of problems. To date, the thrust of attention to this topic focuses on differentiating complex and non-complex problems. Three of the most popular constructs in this domain are ill-structured problems , adaptivTo stimulate research and offer some practical advice for organizational leaders, we depart from this emphasis on complexity by offering a new perspective that contrasts conventional and “virus-like” problems. Conventional problems are common and include the complex and non-complex examples we just discussed . By contrast, we assert that some organizational problems are more insidious and often overlooked: those that resemble the characteristics of viruses. As the COVID-19 pandemic reminds us, if they are not managed properly, viruses can spiral out of control and wreak havoc. With this backdrop, we introduce the novel metaphor of “virus-like” problems and describe how they can be managed by organizational leaders.2.Metaphors are a useful tool to help describe new topics, and they can serve as a creative spark that inspires discussion and ideas . Scholar2.1.Visibility refers to how obvious or well-known the problem is to people who are associated with it . Conventional problems tend to be conspicuous; their existence is self-evident, although there may be some differing opinions about how they should be solved. By contrast, virus-like problems are often hidden or unspoken, even among those who are in close proximity to them. Previous literature on organizational problems does not give much attention to this distinction, although the adaptive problems framework does acknowledge that individuals may sometimes overlook problems  people may silently suffer the consequences . In addiTo better understand how leaders can manage such problems that resemble viruses, we distill some critical lessons from how the SARS-CoV-2 virus was managed (or mismanaged) during the COVID-19 pandemic. Drawing from retrospective analyses of how the pandemic unfolded and play-by-play accounts by key decision-makers , our art2.2.As the SARS-CoV-2 virus began to spread in early 2020, government officials throughout the world were slow to act. This was especially pronounced in the United States whereby no single agency or person took ownership of the problem . To worsThe consequence of this lack of problem ownership was a delay in the ability to confront the pandemic and growing public health issue head-on. For example, the development of COVID-19 testing kits was largely decentralized. Commercial labs and test manufacturers did not coordinate efficiently, and some of the kits being created would only work in certain labs or on instruments that were not widely used . The devFrom a leadership perspective, it is important to recognize the lack of ownership that led to an inability to contain a viral problem. Leaders must be proactive, but this cannot be achieved without recognition of responsibility. For instance, leaders may hear about a virus-like organizational problem (like staff burnout or elevated conflict) and rather than taking time to learn more and help identify the root cause, they may simply delegate its solution to lower-level managers and hope that the issue gets resolved. The reality is that those individuals often have a myopic view of the problem, leading them to seek a localized solution like adding some discussion points to the next department meeting—or delegating the problem even further down the hierarchy by asking supervisors to figure out a solution in their teams. As the problem gets pushed to lower echelons of the organization, it becomes increasingly difficult to address any system-wide issues, such as administrative policies or initiatives set by senior leaders. Further, the longer that no one (particularly high-level leadership) takes ownership of the problem, the more it is likely to multiply in scale and severity.2.3.During the early stages of the COVID-19 pandemic, we did not have a diagnostic test and thus could not effectively track the spread of the virus or learn about its symptoms or other characteristics. Given this lack of robust diagnostic testing, health officials had to rely on syndromic surveillance, which refers to collecting and analyzing different streams of information to monitor the prevalence of disease—such as keeping track of how many people visit emergency rooms for respiratory symptoms or logging increases in medicine purchases. Even when testing became more readily available, appropriate usage of them was another challenge. Altogether, these delays and inconsistencies in testing and tracking the virus led to greater spread and increased risk, much to the frustration of public officials.cannot be said about most leaders in organizations who face virus-like problems in their ranks. Problem detection is often limited to casual observations or hallway conversations . Although it may seem difficult to reliably measure virus-like problems like the ones that appear in Although the public health community is well-versed in the importance of testing for diseases, the same Looking back at the COVID-19 pandemic reminds us that such diagnostic testing is only one form of problem detection. To address virus-like problems, leaders should also borrow from the public health community’s notion of syndromic surveillance testing, since it has a broader scope of detection that can reveal trends and patterns over time. Examples include formalizing exit interviews of employees who are departing the organization or institutionalizing a well-designed annual survey that captures perceptions and feedback among various members of the organization. Such a comprehensive, data-driven approach to testing allows leaders to detect emerging problems early and discover clues about how to proactively put interventions into place before they become a serious problem. This process is becoming easier and more effective with recent advancements in data analytics and artificial intelligence .2.4.A public health best practice is to ensure an intimate relationship between testing and interventions, and this need was made very clear during the pandemic. Before effective COVID-19 tests were developed, officials in many countries enacted sweeping interventions to “slow the spread” by controlling the rapid growth of infections and hospitalizations, including stay-at-home orders and the temporary closure of businesses and schools. After some time, these measures were replaced with others, including masking, physical distancing, and contract tracing. Officials frequently changed their recommendations, which were sometimes contradictory, like when guidance from the Centers for Disease Control and Prevention (CDC) was incompatible with statements issued by the White House . To illuSuch inconsistencies resulted in a lot of confusion in the public , and manThe lesson for leaders in organizations is to clearly explain the purpose of any intervention for a virus-like problem. Taking ownership of a problem and identifying its prevalence do little good if they aren’t followed by appropriate interventions that are implemented in clear and effective ways. Fixes  should be firmly endorsed by leaders, who should plainly articulate how they are tied to organizational goals. It is imperative that leaders also model the desired attitudes or behaviors, particularly given how influential they can be in shaping organizational culture and norms . To trul3.We encourage leaders to meet the needs of their organization as they would a customer, investor, or any other key stakeholder—and this often involves managing virus-like problems. Just like an actual virus, this special kind of problem is hidden to many people, it is difficult to track its source, and it may quickly spread to others. These characteristics make virus-like problems especially pernicious, even compared to complexity-oriented problems that have been identified in past literature . To be clear, complex problems may be very challenging and important, but the stealthy, exponential spread of virus-like problems makes them a unique and time-sensitive threat that should not be ignored.As we have highlighted in this article, organizational leaders should focus on three key tactics to successfully address virus-like problems: they should proactively take ownership of such problems, deploy robust testing procedures to understand them, and implement interventions that are clear and meaningful to people. But what else can be done to tackle this special breed of problems in organizations?First, leaders should establish cultures of accountability and transparency. This involves having candid conversations with their peers and other employees regarding virus-like problems and exchange ideas for how to address them. Genuine discussions of these topics are unlikely to naturally occur given time constraints and power dynamics, so it is the leader’s responsibility to invite open dialogue and honest self-reflection . Our intIn addition, investing time to build and maintain collaborative relationships within one’s organization can play an important role in dealing with virus-like problems. The human resources (HR) department, for example, should be viewed as a strategic partner to help identify virus-like problems and implement effective interventions . Too many leaders view HR as merely serving an administrative or clerical function, but they are well-positioned to enact organization-wide initiatives to understand employee concerns, build trust, and establish the kind of work environment that is equipped to handle virus-like problems before they escalate.As we all witnessed throughout the COVID-19 pandemic, leaders can exacerbate a sense of uncertainty, fear, or distrust within a culture—but they also have the power to make things better. By paying more attention to virus-like problems, leaders can increase their capability to ensure that their organization remains healthy. On the academic front, we hope to inspire future research that unpacks the virus metaphor and ultimately advance a better understanding of the nature of problems in today’s inter-connected and dynamic organizations.The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author.The article idea was proposed by DS and revised by CC. The first draft of the manuscript was written by DS and revised by CC. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Predicting host-virus interactions is fundamentally a network science problem. We develop a method for bipartite network prediction that combines a recommender system (linear filtering) with an imputation algorithm based on low-rank graph embedding. We test this method by applying it to a global database of mammal-virus interactions and thus show that it makes biologically plausible predictions that are robust to data biases. We find that the mammalian virome is under-characterized anywhere in the world. We suggest that future virus discovery efforts could prioritize the Amazon Basin (for its unique coevolutionary assemblages) and sub-Saharan Africa (for its poorly characterized zoonotic reservoirs). Graph embedding of the imputed network improves predictions of human infection from viral genome features, providing a shortlist of priorities for laboratory studies and surveillance. Overall, our study indicates that the global structure of the mammal-virus network contains a large amount of information that is recoverable, and this provides new insights into fundamental biology and disease emergence.  •A novel recommender system using network features predicts mammal-virus interactions•Mapping these predictions in space identifies hotspots of undiscovered viral interactions•The Amazon has undiscovered interactions, and Africa has undiscovered zoonotic hosts•The embedded virome boosts our ability to identify potentially zoonotic viruses Documenting all interactions between viruses and mammals is not feasible; viruses are too small, the world is too big, and viruses and mammals are too diverse. As a consequence, we think we only know about 1% or 2% of the interactions between mammals and viruses. This is a critical gap in our knowledge because it can lead us to missing reservoirs of possible zoonotic viruses. In this article, we develop a process to leverage the information we have about interactions between hosts and viruses to do three things: First, we predict missing interactions in this network and give them a score based on how likely the model guesses they are. Second, we map these predicted interactions in space to provide guidance about where to go and what to look for to collect data that would maximize our knowledge of host-virus interactions. Finally, based on the predicted interactions, we use information about the genome of viruses to identify possible zoonotic viruses. Understanding host-virus interactions is critical to better anticipate where the next pandemic may come from. In this article, we build a new recommender system that is able to infer missing host-mammal interactions. We show that it solves well-known biases in data collection while boosting the predictive ability of a model that identifies potentially zoonotic viruses. Despite growing interest in viral ecology, data remain limited because most of the global virome remains undocumented. Computational methods that can infer undiscovered associations in a partially observed host-virus network can fill in some of these gaps.Here, we propose a novel method for predicting unknown links in partially sampled networks and apply it to the largest database of host-virus associations currently available. The method is based on a combination of linear filtering, which uses high-level network information to generate an initial guess as to the probability of an interaction, and singular value decomposition, which uses the structure of a low-rank approximation  model relies on four hyper-parameters describing the relative importance of network structure and matrix rank used for approximation (SVD). The network structure parameters we use are the in-degree , out-degree , and connectance (proportion of pairs of species that have an interaction); these were picked because they capture a lot of relevant information on network structure and have been established to be enough to start identifying possible missing interactions.We applied four tests of whether model performance was undermined by biases in the partially observed network, a common problem in predicting host-pathogen interactions.First, we tested the effect of passive sampling bias with a regression of host species’ viral diversity against citation counts, a commonly used proxy for scientific research effort. We found that, consistently, citations had a weaker effect predicting viral richness after imputation , suggest,Second, we tested the influence of impact bias, a specific form of active sampling bias driven by relevance to human health. As a simple test of impact bias, we examined the top 10 hosts that shared viruses with humans before and after imputation. In the observed network, domesticated and lab animals dominated this list; although proximity to humans might lead livestock to share many pathogens, this result is generally presumed to be the effect of impact bias. After imputation, many of these species were replaced with a handful of great apes and rodents . The for,Third, we examined whether sampling bias might be creating an undesirable “rich-get-richer” effect, where novel interactions are disproportionately predicted for species that are already oversampled. If training data were unbiased, then this could be a useful property; for example, a model might correctly assign more interactions to some viruses because it “learns” that they have a higher intrinsic host plasticity. However, host-virus networks are heavily shaped by sampling history, creating geographic and taxonomic biases that could produce false inferences.,,To examine the effects of geographic sampling bias, we mapped the number of total known host-virus interactions based on mammal host ranges and compared this with the same map of newly predicted interactions . In prev,,,To examine the effects of taxonomic sampling bias, we estimated the number of “missing” viruses  and microbiologically relevant traits (through identity by descent), which facilitates cross-species transmission, leading to a correlation between evolutionary distance and virome similarity.,Finally, we evaluated the effect of imputation on the spatial distribution of viral biodiversity. Our models predicted that the total number of host-virus interactions generally tracked mammal biodiversity, with a previously unknown hotspot of potential zoonotic disease hosts in sub-Saharan Africa . To furtWe finally explored whether network-wide prediction offered useful insights into zoonotic potential, the ability of a virus to infect humans . Surprisingly, we found that the imputation method did not predict known human-associated viruses any better than random  remain limited by how poorly characterized the host range of most viruses is; the majority of viruses are either undiscovered or known from a single host. As the global virome becomes better sampled, these approaches will be increasingly reliable not just for biological inference but for actionable efforts to prevent zoonotic emergence.timothee.poisot@umontreal.ca).The lead contact for this work is Timothée Poisot . A small portion of records assimilated from NCBI’s GenBank into these other datasets may also record experimental infections, which provides insight into biological compatibility but not necessarily opportunity for infection in nature. Each of the component datasets and the CLOVER dataset are presence only .i and j an initial score given by the dot product of weights and properties of The imputation model uses two steps to chain LF  SVD imputation. Like principal-component analysis (PCA), SVD is an embedding of a starting matrix into latent subspaces; compared with PCA, SVD is a more general solution that also well handles numerical instability because of very small entries,The overall SVD step was conducted as follows: for every interaction To tune the hyper-parameters , we picked a calibration set of 800 positives and 800 assumed negative interactions and imputed them using each possible model  was determined by picking the threshold value maximizing Youden’s J statistic. Each interaction is presented as an evidence score, which is obtained by dividing the values post-imputation (LF-SVD) by the values pre-imputation (LF), minus one. An evidence of 0 means that the imputation did not change the value, and increasingly positive values meant that the change because of imputation was stronger. This interaction evidence was used to rank interactions when required for the analyses.,The LF-SVD method is fairly unique as a feature-agnostic method to predict bipartite ecological networks. Previous studies that have developed predictive models of the mammal-virus network have generally included a mix of host and viral traits as predictors , generaliucnredlist.org) species distribution maps for mammals, downloaded on June 6, 2019. For citation counts, we extracted total virus-related publications for each species  from the PubMed database using the R package rentrez.For phylogenetic analyses, we used a recently published mammalian supertree published by Upham et al.,,,,,Observed host-pathogen association networks compiled from published records are influenced by a passive sampling bias resulting from differential research across host and pathogen species. In comparative analyses of viral richness per host species, the number of publications per host species is often included as a covariate in an attempt to control for variable sampling effort across hosts.Homo sapiens, leaving mostly the former. To test the effect of active sampling bias, we examined the top 10 hosts based on similarity to H. sapiens pre and post imputation. Before imputation, the top 10 list  includes six livestock or companion animals , three primates , and one synanthropic and commonly studied laboratory animal (Mus musculus). After imputation, four of the domesticated or primate species remained . The updated list includes two more primates (Gorilla beringei and Gorilla gorilla) and four more mice or rats . This mostly reflects changes in the network connectivity; all but one of these are in the top 10 species to gain links (with Z. brevicauda replaced by Rattus rattus).In addition to passive sampling bias, host-virus association data are frequently shaped by active or impact bias, where surveillance is targeted based on relevance to human health or economics. This is easily detected in records of virus sharing with humans. In principle, the species with the highest similarity to the human virome should be species that are closely related to humans (primates) or frequently live alongside humans , but domesticated animals and laboratory model systems will also score disproportionately in this metric because of sampling effort. As a new test of model bias, we propose that imputation should reduce the signal of the latter group in viral sharing with The distribution of missing viruses  across space and across the evolutionary tree, are interlinked patterns that are of significant interest to viral ecologists.To assess phylogenetic patterns in the number of missing viruses, we used the previously specified supertree.We identified a weak to moderate overall phylogenetic signal in the number of missing viruses (λ = 0.35), although this estimate was distinct from phylogenetically independent models and Brownian motion models of evolution (both p < 0.01). Phylogenetic factorization, in turn, identified only four small clades with significantly different counts of missing viruses, all of which had fewer missing viruses than the remaining mammal phylogeny . These cTo assess geographic patterns in the number of missing viruses, we evaluated the number of known and missing viruses at the level of each host species and joined these to each host’s IUCN range map. We mapped the total number of hosts with recorded interactions, the total number of known and predicted missing interactions, and the normalized difference between missing interactions and host diversity. Known interactions are recorded disproportionately in Europe and Asia and, to a lesser degree, North America, a pattern that reveals strong sampling bias in viral inventories . This paH. sapiens; i.e., its role in zoonotic disease) in the pre- and post-imputation network. We analyzed these variables as a function of phylogenetic distance using generalized linear models (GLMs), with virus sharing coded as a binomial outcome (logit link) and the count data modeled using a Poisson distribution. GLMs were fit using the stats package in R, and adjusted R-squared values were derived using the rsq package. Model coefficients and significance are given in To test for the signal of evolutionary history in the viral sharing network, we analyzed two outcome variables  for two data structures  In particular, the phylogenetic signal of virus sharing with humans became insignificant (p = 0.52) after imputation, the only insignificant relationship among those we tested. While the count data also recovered a reduction in effect size after imputation, we found that this reduction was much smaller and that the phylogenetic signal of sharing with X), after rows and columns with a marginal sum of 0 have been removed. The We performed a measure of community compositional uniqueness using the local contribution to the beta-diversity approachWithin every location, this value indicates the degree of uniqueness of this location (sampling unit) compared with all other sampling units in the data. LCBD values are typically, but not necessarily, measured after We next tested whether the expanded host-range information available in the imputed network could improve zoonotic risk prediction in cases where information on individual viruses is limited. We expanded a recently developed model that combines summary statistics of viral genome composition and compositional similarity to human genes to predict zoonotic risk.We extracted pseudo-traits from the host-virus network using latent variables by extracting the left latent subspace of a random dot product graph decomposition.Full genomes were available for 612 of the 681 remaining viruses. We used the reference sequence for each virus whenever available or the longest complete genome otherwise. These genomes were used to calculate the relevant genome composition measures described in Mollentze et al.Feature importance was measured using their Shapley values, which measure the contribution of individual features to the final probability predicted for each virus.


Viral diseases are the most common problems threatening human health, livestock, and poultry industries worldwide. Viral infection is a complex and competitive dynamic biological process between a virus and a host/target cell. During viral infection, inflammasomes play important roles in the host and confer defense mechanisms against the virus. Inflammasomes are polymeric protein complexes and are considered important components of the innate immune system. These immune factors recognize the signals of cell damage or pathogenic microbial infection after activation by the canonical pathway or non-canonical pathway and transmit signals to the immune system to initiate the inflammatory responses. However, some viruses inhibit the activation of the inflammasomes in order to replicate and proliferate in the host. In recent years, the role of inflammasome activation and/or inhibition during viral infection has been increasingly recognized. Therefore, in this review, we describe the biological properties of the inflammasome associated with viral infection, discuss the potential mechanisms that activate and/or inhibit NLRP1, NLRP3, and AIM2 inflammasomes by different viruses, and summarize the reciprocal regulatory effects of viral infection on the NLRP3 inflammasome in order to explore the relationship between viral infection and inflammasomes. This review will pave the way for future studies on the activation mechanisms of inflammasomes and provide novel insights for the development of antiviral therapies. The innate immune system is the first-line host defense mechanism against microorganisms. Once the host is infected by a virus, the innate immune system is activated and initiates a series of germline-encoded pattern recognition receptors (PRRs) to recognize microbe-derived pathogen-associated molecular patterns  and endogenous damage-associated molecular patterns  . A wide β and pro-IL-18, release a large amount of pro-inflammatory cytokines, cause acute or chronic inflammation, and thus clear “non-self” substances  to induce pyroptosis  take place, conformational changes occur, self-repression is lost, and oligomerization and recruitment of the adapter protein ASC or pro-caspase-1 happen directly . PTM is 0 dimers . In turnytoplasm . Simultaytoplasm .Among the various types of inflammasomes, the key inflammasomes that recognize the biological information of viruses and give a response are NLRP3, AIM2, and NLRP1 –3.2.1.NLRP1a, NLRP1b, and NLRP1c. It has been proven that 129S1 mNLRP1a and mNLRP1c lack exons 1 and 2, and only mNLRP1b is transcribed in myeloid cells. However, human NLRP1 has only one subtype that works as an epithelial innate immune sensor in human keratinocytes and airway epithelial cells  and a linker segment (95aa-130aa), the FISNA (131aa-218aa), NBD (219aa-372aa), HD1 (373aa-434aa), WHD (435aa-541aa), and HD2 (542aa-649aa) for the NACHT domain; and a transition LRR  and a canonical LRR  for the LRR domain . NLRP3 i2.3.The AIM2 inflammasome differs from NLRP1 and NLRP3 in that the sensor molecule of the AIM2 inflammasome belongs to the ALRs . AIM2 ha3.Viral infections can cause mild or life-threatening diseases, so it is important to understand viral pathogenesis and disease pathogenesis . The maj3.1.The process of viral infection is accompanied by the development of an inflammatory response. Recognition of the different components of the virus leads to the activation of the inflammasome and consequently to the occurrence of a cytokine storm. Some viruses activate a variety of inflammasomes through different pathways and cause inflammation. Among the virus-associated inflammasomes, NLRP1 primarily recognizes dsRNA viruses, NLRP3 primarily recognizes ssRNA (single-stranded RNA) viruses, and AIM2 primarily recognizes DNA viruses. These viruses act as inducers with different effects on individual inflammasomes.3.1.1.Activation of The LRP1 inflammasome is not only initiated with the recognition of viral nucleic acid; viral pathogenic enzymes and structural proteins can also cause the activation of the NLRP1 inflammasome [39]. It was found that NLRP1 forms a complex structure with dipeptidyl peptidase 9 (DPP9), which inhibits the activation of NLRP1. On the other hand, the inhibitor VbP inhibits the formation of this complex and facilitates the activation of NLRP1 . Sand et3.1.1.1.Togaviridae are (+) ssRNA viruses that are transmitted to vertebrate hosts primarily by mosquitoes and are referred to as arthropod-borne viruses or arboviruses (Solenopsis invicta virus (SINV), Ross River virus (RRV), Mayaro virus (MAYV), and Chikungunya virus (CHIKV), was found to be dependent on p38 kinase activity  ssRNA viruses in the family Picornaviridae. The most common species infecting humans are Coxsackievirus B3 (CVB3), Enterovirus D68 (EV68), Poliovirus 1 (PV1), and Human Rhinovirus A (HRV)  . Picornaassembly . These vassembly . When NLassembly . The NLRassembly .3.1.1.3.Herpesviridae family is divided into three sub-families: the alpha herpesvirus, the beta herpesvirus, and the gamma herpesvirus. Kaposi’s sarcoma, associated with the gamma herpesvirus (KSHV), causes an inflammatory and angiogenic endothelial cell neoplasm  production, lysosomal destabilization, and post-translational modification of NLRP3 . A recen3.1.2.1.Paramyxairidae are capable of activating the NLRP3 inflammasome, such as Newcastle disease virus (NDV), human metapneumovirus (HMPV), respiratory syncytial virus (RSV), and Peste Des Petits Ruminants Virus (PPRV)  was first isolated from young children in the Netherlands in 2001, and it is now recognized as a leading cause of respiratory tract infections in infants  ssRNA (single-stranded RNA) virus family s (PPRV) . NDV leand IFN-β . Wang et infants . It is rsubjects . However3.1.2.2.Flaviviridae consists of three genera: flavivirus, pestivirus, and hepacivirus. Viral proteins from the flaviviruses Dengue and Zika and the hepatitis B and C hepaciviruses can activate the NLRP3 inflammasome. This section describes how the Dengue virus and Hepatitis C virus can do so.The (+) ssRNA virus family β in HMEC-1 as well as THP-1 and HepG2 via NLRP3 inflammasome activation. Besides the proteins of DENV discussed above, other proteins, such as NS1 , have been found to play an important role in the inflammasome under DNEV infection (Dengue virus (DENV) is an enveloped virus with positive-sense ssRNA that causes severe clinical symptoms such as fever, myalgia, arthralgia, headache, and even dengue hemorrhagic fever (DHF) if untreated . The virnfection . Howevernfection .Hepatitis C virus (HCV) infection remains a major cause of hepatic inflammation and liver disease. Initial infection with HCV is sufficient to cause chronic inflammatory hepatitis, which may increase the possibility of liver tumorigenesis . The pro3.1.2.3.Picornaviridae is a family of viruses that can cause a wide array of diseases in humans and animals, resulting in large economic losses. It comprises five genera: Enterovirus, Hepatovirus, Rhinovirus, Apthovirus, and Cardiovirus. Enterovirus, Hepatovirus, and Rhinovirus mainly infect humans; Apthovirus (referred to as the foot-and-mouth disease virus) infects cloven-hoofed animals and occasionally humans; and Cardioviruses infect rodents  in domestic cloven-hoofed animals; it is characterized by fever, lameness, and vesicular lesions  2B interacts with NLRP3 to facilitate inflammasome assembly  ssRNA viruses that are characterized by gastrointestinal and respiratory disease in humans, poultry, and bovines  activates the NLRP3 inflammasome differently compared with other viruses. β production. However, the mechanism of HSV-1 on the NLRP3 inflammasome activation still needs to be explored  increased the expression of AIM2 and IL-1β in skin fibroblasts  in young children aged 5 and younger  exert an inhibitory effect on NLRP3 inflammasome activation to facilitate viral replication and assembly, whereas late viral products  motivate NLRP3 inflammasome-mediated pyroptosis, promoting viral spread and pathogenesis . A clear5.in vitro systems, may not fully reflect the human situation.Inflammation is a protective response caused by damage to the organism that effectively removes harmful factors and damaged tissues and facilitates the organism’s recovery. Inflammatory phenomena are prevalent in various pathological changes and are mediated by a series of endogenous and exogenous inflammatory mediators. Inflammatory factors that cause tissue damage and induce an inflammatory response, bind to relevant receptors in the body and subsequently induce the release of pro-inflammatory cytokines. Pro-inflammatory cytokines then activate many types of immune cells and promote the inflammatory response. Infections caused by viruses trigger inflammatory responses to varying degrees. Inflammasomes play an important role in inflammation triggered by viral infections. Once the viral invasion of host cells is recognized by relevant receptors, inflammasome assembly is induced along with the activation of cell death-associated proteins and the prevention of viral parasitism in host cells. Cell death is primarily triggered by the activation of caspase-family-related proteins that cleave the GSDM family, resulting in cell pyroptosis. Upon activation of cell pyroptosis, the cytosolic contents are released, where activated pro-inflammatory factors act on the associated immune cells to activate the inflammatory response and clear the foreign particles. Pyroptosis is also a double-edged sword. Mild pyroptosis facilitates the enhancement of host immunity, while excessive cell pyroptosis can cause serious disease in the organism. However, the limitations of current experiments, such as the use of animal models, cell lines, or Currently, some compounds have been found to have the function of inhibiting the inflammasome (mostly NLRP3), which provides a clear target and idea to inhibit inflammation triggered by viral infection. However, the activation of the inflammasome by viruses is not completely consistent, and some viruses possess both activation and inhibition properties in the inflammasome. Therefore, this review has identified novel inflammasome sensors or regulators, elucidated the molecular mechanisms of inflammasome activation or inhibition by viruses, explored the crosstalk between inflammasome and other immune pathways, and developed novel inflammasome-based antiviral strategies.WS and MJ wrote the original draft. HC, LY, SL, XW, ZW, FM, and FE revised and edited the manuscript. HS and CO supervised the manuscript. All authors read and approved the published version of the manuscript.The project is supported by the National Natural Science Foundation of China (32260876), Guangxi Science and Technology Base and Talent Special (2021 AC19372), and scientific research startup funds of Guangxi University.ZW was employed by QYH Biotech.The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Apis mellifera Linnaeus, Hymenoptera: Apidae) health continues to be parasitism by the mite Varroa destructor, which has been linked to high colony losses worldwide. Besides feeding on developing and adult bees, Varroa is also a prolific vector of honey bee-associated viruses. Because they live in unmanaged conditions, wild honey bee colonies are not treated against Varroa, which has enabled the natural selection of more mite-tolerant bees. To date, few studies have explored the prevalence of viruses in unmanaged colonies. The Welder Wildlife Refuge (WWR) in Texas is a unique site to study the viral landscape of unmanaged honey bees in the United States. The goals of this study were to identify and quantify viruses in wild colonies at the WWR, to examine changes in the prevalence of viruses in these colonies over time, and to compare the presence and titers of viruses between wild colonies at the WWR and those from the nearest managed apiary. We collected bees from colonies at the WWR in 2013, 2016, and 2021, and analyzed selected viruses for their presence and titers via quantitative polymerase chain reaction. In 2021, we also sampled bees from the nearest managed apiary for comparison. We found low average virus titers in all wild colonies sampled, and no difference in virus titers between colonies at the WWR and those from the managed apiary. Our study indicates that virus titers in wild colonies at the WWR are similar to those found in nearby colonies, and that these titers fluctuate over time.A major threat to honey bee ( Varroa destructor, which has been linked to high colony losses worldwide  is arguably the most widely used beneficial insect for the commercial pollination of major agricultural crops, contributing over $200 billion dollars to the global economy annually . But desorldwide .Varroa mites are highly effective vectors of several honey bee-associated viruses, which they can transmit while feeding on the fat body tissue of developing and adult honey bees  complex, which includes Israeli acute paralysis virus (IAPV) and Kashmir bee virus (KBV), as well as the deformed wing virus (DWV) complex, which includes 3 major strains   has usually been associated with both, high mite infestation levels, and high titers of mite-vectored viruses  . ABPV and DWV-C) . Before harmless . However viruses . While t viruses , those selopment . There helopment . Likewisdulthood .Varroa infestation is also at its highest  in the Americas has been attributed to a combination of genetic and ecological factors that have conferred them higher fitness compared to some resident populations of European descent . Some ofThe aim of this study was to identify and quantify selected RNA viruses in unmanaged honey bees at the WWR, and to discern any interesting patterns in virus prevalence and titers in that population over time. In addition, we aimed to compare viral loads between wild colonies at the WWR and those from the nearest managed apiary. We hypothesized that, given the difference in management status, wild colonies at the WWR would have lower viral titers than managed colonies.This study was conducted at the WWR located in San Patricio County, TX . The WWR is positioned in a transitional zone between 3 different ecoregions: the South Texas plains, the Gulf prairies, and the marshes . The veg2 area  by using the primers E2, 5’ GGACTAATATCTATACCACG 3’, and H2, 5’ CCTAAAGATGGAACTGTTC 3’, as done previously , following the manufacturer’s specifications see . Total Deviously .A. mellifera mitochondrial haplotype (mitotype) identification.We performed single PCR reactions in 50-μL volumes using the OneTaq kit from New England Biolabs . Reactions contained the following reagents: 1X Standard Buffer, 200 μM of dNTPs, 0.2 μM of both forward and reverse primers, 1.25 units of OneTaq DNA polymerase, and 5 μL of 10-fold diluted DNA. The PCR thermocycler conditions were 94°C for 30 s followed by 29 cycles of 94°C for 15 s, 48°C for 20 s, 68°C for 45 s, and a final extension period of 68°C for 10 min. Following PCR, the products were separated on a 1% agarose gel stained with ethidium bromide and visualized under UV light. Once the presence of PCR products was confirmed, they were cleaned using Mag-Bind Total Pure NGS . Cleaned PCR products were then sent off for sequencing at Eurofins Genomics . The forward and reverse reads were aligned to each other and were then used for carrying out nucleotide BLAST algorithms in the National Center for Biotechnology Information (NCBI) database for To determine the presence and prevalence of selected honey bee-associated RNA viruses, RNA was extracted from a pool of 10 bees per colony. However, this was done only for wild colonies sampled at the WWR in 2013, 2016, and 2021 for which we had collected at least 10 bees, as we were unable to sample enough workers from some of the colonies. RNA was likewise extracted from a pool of 10 bees per colony for 20 randomly selected colonies at the managed apiary. For each of those colonies, RNA was extracted and homogenized using a mortar and pestle following the Ribozol  protocol. The concentration and purity of the RNA were measured on a Nanodrop2000 Spectrophotometer . Next, 100 ng of RNA was used to generate cDNA. Genomic DNA was then removed, and cDNA was synthesized for each sample using the iScript clear gDNA synthesis kit . The resulting cDNA was then diluted 10-fold and stored at −20°C until it was used as a template for quantitative PCR (qPCR) amplification to identify each virus and quantify its relative abundance.Each RNA sample was first screened for the presence of 8 common honey bee-associated viruses: ABPV, BQCV, Chronic bee paralysis virus (CBPV), DWV-A, IAPV, KBV, Lake Sinai virus (LSV), and Sacbrood virus (SBV), using the primers outlined in To quantify virus titers, 2-step qPCR was performed in 96-well plates on all samples that tested positive for any given virus using a Bio-Rad CFX96 machine . To do this, 1 μL of diluted cDNA was added to 5 μL of SYBR Green master mix . Then, 10 μM of forward and reverse virus-specific primers were added. Each sample was performed in triplicate reactions, as well as having simultaneous water and no-reverse transcriptase reactions as negative controls. Amplification was performed using 39 cycles of 94°C for 2 min, 94°C for 30 s, 60°C for 30 s, and 72°C for 30 s, followed by a melting curve from 55°C to 95°C. The final RNA copy number of each virus per sample was calculated by extrapolation to a standard curve made by serial dilutions (1:10) of a viral fragment used as a reference, which was provided to us by the USDA’s Honey Bee Laboratory in Beltsville, MD.10 viral copy number per sample (± the standard error of the mean (SEM)), as done previously  from wild colonies sampled at the WWR, we conducted analysis of variance (ANOVA) tests to determine if there were differences in virus titers across years, followed by pair-wise Tukey honestly significant difference (HSD) tests. We also conducted Shapiro tests to determine if the quantification data for each identified virus were normally distributed. Because the data were not normally distributed, we performed Wilcoxon rank sum tests to determine if there were differences in virus titers between samples collected at the WWR and those collected from managed colonies in 2021. This comparison was only done for samples collected in 2021 because we did not collect samples from managed colonies in 2013 or 2016. Viral titers are reported as the triplicate average logeviously . Virus ir sample . All desn = 20). We tagged those trees and recorded their GPS coordinates. Those were the only tree cavities that we were able to locate and sample from during our weeklong survey of the refuge.Of the 112 tree cavities surveyed in 2013 and 2016, 28 harbored active colonies in 2013 and 36 harbored active colonies in 2016. Because some of the workers collected from those colonies were used in previous studies , b, the 2 = 6, P-value = 0.199). Some colonies were infected with multiple viruses, which is not uncommon .Of the 8 viruses analyzed, CBPV, IAPV, and KBV were not detected in any of the tested colonies during any of the years sampled. Of the 6, 24, and 20 wild colonies at the WWR that were tested for viruses in 2013, 2016, and 2021, respectively, 4 (66.7%), 12 (50.0%), and 5 (25.5%) tested positive for at least one virus, respectively , 2.86 × 104 ± 3.83 × 104 log10 copy numbers in 2016 (n = 4), and 1.33 × 104 ± 1.12 × 104 log10 copy numbers in 2021 (n = 4), all of which are considered to be medium titers . Furthermore, DWV titers in 2013 were statistically higher than those in 2016  and 2021 . There was no difference in average DWV titers between colonies sampled in 2016 and 2021 .We also found that viral titers in the wild honey bee population varied from year to year, displaying different infection titers depending on the virus and the year. For example, the triplicate sample average (±SEM) titer of DWV in wild bees at the WWR was 8.59 × 104 ± 1.58 × 104 log10 copy numbers in 2013 (n = 3 colonies), 1.93 × 103 ± 2.87 × 103 log10 copy numbers in 2016 (n = 13), and 5.02 × 104 ± 6.88 × 104 log10 copy numbers in 2021 (n = 4), all of which are considered of low to medium titers . BQCV titers of samples collected in 2013 were statistically higher than those collected in 2016 ; they were also higher in samples from 2021 compared to those collected in 2016 . Furthermore, there was no difference in average BQCV titers between samples collected in 2013 and those from 2021 . SBV was only detected at low titers in WWR samples from 2016, with a triplicate sample average (±SEM) titer of 1.25 × 103 ± 1.18 × 103 copy numbers per sample (n = 2). LSV was detected from samples in 2013 with a triplicate sample average (±SEM) titer of 8.31 × 104 ± 5.76 × 103 copy numbers in 2013 (n = 1) and a triplicate sample (±SEM) titer of 5.41 × 104 ± 2.40 × 103 copy numbers per in 2021 (n = 1), both of which are considered medium intensity  titer of BQCV infection was 1.21 × 10m titers ; Fig. 2bW = 48, P = 0.698). Therefore, we pooled the DWV data from both managed locations in our analysis to increase the sample size slightly. DWV was found at relatively medium intensity in samples collected from the pooled samples from managed colonies, with a triplicate sample average (±SEM) titer of 4.84 × 103 ± 2.62 × 103 copy numbers . We found no differences in DWV titers between the wild and managed colonies sampled in 2021 . Furthermore, BQCV was found at relatively medium intensity only in samples from the managed apiary at the WWR, with a triplicate sample average (±SEM) titer of 3.87 × 104 ± 4.24 × 104 copy numbers (n = 2), while no BQCV was detected in any of the colonies tested for viruses from the nearby managed apiary (n = 19 colonies for which there were enough bees to perform the test). There were no differences in BQCV titers between wild and managed colonies sampled at the WWR in 2021 . We found low titers of ABPV in the nearby managed apiary samples, with a triplicate sample average (±SEM) titer of 2.52 × 101 ± 2.50 × 101 copy numbers (n = 3). ABPV was not detected in any of the samples collected at the WWR in 2021. Finally, LSV was found at medium intensity at the off-site managed apiary, with a triplicate sample average (±SEM) titer of 1.97 × 106 ± 6.56 × 105 copy numbers (n = 1).We found no difference in DWV titers in managed colonies when including versus excluding the 2 managed colonies that were sampled at the WWR (Apis mellifera scutellate subspecies) and one (27%) belonged to the C lineage. However, we were able to perform virus detection and quantification from only 6 of the 28 colonies sampled in 2013 (21.4%) because we did not have enough bees from each of the other colonies for analysis. Likewise, we only report haplotypes for 24 of the 36 colonies sampled in 2016 (66.7%), which correspond to those for which we had enough bees to conduct viral identification and quantification. Of the 24 colonies analyzed from 2016, 22 (92%) belonged to the A lineage, one colony (4%) belonged to the O lineage, and one colony (4%) was scored as there being no mitotype match for it in the NCBI database. For bees collected at the WWR in 2021, we only reported the haplotypes of 19 of the 20 colonies sampled because those were the colonies for which we had enough bees to perform viral identification and quantification. Of those 19 colonies analyzed for viruses, 17 (90%) belonged to the A lineage and 2 (10%) belonged to the O lineage , given the difficulty in collecting bees from remote hive entrances in many instances, and the fact that many of the collected bees had been used in a previous study . Therefore, it would be interesting to know if having a larger sample size could have indicated even stronger statistical differences in DWV and BQCV titers over the years. Furthermore, while the titer of DWV infection in wild colonies sampled in 2016 was similar to those sampled in 2021, the titer of BQCV infection was significantly higher in 2021 than in 2016. This result is not surprising, given that viral prevalence and titers are known to fluctuate in managed colonies on a seasonal basis  is an indication that some of these colonies have survived without human intervention year after year, despite them having low to medium intensity of some viruses. However, while other studies have shown that wild colonies are expected to survive at a higher rate than managed colonies , we wereVarroa mite, therefore decreasing the number of mites in the colony, and potentially also reducing the transmission of viruses  than feral colonies . The vast majority of the colonies sampled (90–92%) belonged to the A (African) mitotype, with the A1e version being the most common . Only 1 Varroa mite, colonies of the European linage were displaced in favor of those of primarily African maternal ancestry , which can mask or reverse the evolutionary pressures they are faced with due to human intervention. For example, most managed colonies are treated on an annual basis against high sticides . These csticides . Managed viruses . Managedathogens . In contters low . FurtherWhile our study provides some of the first information about the titers of viral pathogens in a truly wild honey bee population in the southern United States, there are some caveats in our experimental design that need to be considered. First, we only collected foragers from the hives’ entrances because we were not allowed to destructively sample bees from inside the hives. Also, given that some of these tree cavities were either located several meters above ground, were very aggressive when disturbed, or were in a difficult location to collect samples, we were unable to collect many bees per colony. Therefore, for virus quantification, we decided to pool 10 bees per colony to increase the number of total colonies screened, instead of pooling a collective 20–30 individuals, which is recommended by Varroa mite counts, which require sampling at least 300 adult bees from the middle of the brood nest area, where the highest number of mites on adult bees can be found (Second, because we did not collect enough bees from a hive’s entrance, we were unable to perform suitable be found . Also, sbe found .Finally, we were unable to confirm if the same colony was present in a given tree cavity for multiple years, as we could not go inside the hive to monitor the turnover rate of new queens. Even though some colonies in the same tree cavity did maintain the same mitotype from one year to the next, we cannot definitively say that these were the same colonies surviving over multiple years. For instance, we were unable to sample inside the colony to determine if the same queen was present across multiple years, and were also unable to determine if a colony swarmed and re-occupied an available cavity, which would allow a sampled colony to have the same mitotype as that in a previous year, but it would not necessarily be from the same queen. We did return to the WWR in the spring of 2022 to sample bees for another study and found that only 2 of the colonies sampled in 2021 were either too weak to sample, or had died. So, overall, we only saw a 10% “loss” of colonies in 2022 compared to the number of active colonies that we found in 2021. While we are unsure if those colonies were truly lost or if they relocated someplace else, this low rate of loss within a 6-month period suggests resilience in some of these wild colonies. Future work should investigate colony survivorship and tree cavity turnover at the WWR year-round and over time.Despite the aforementioned limitations, it is evident that wild colonies at the WWR only suffered from low to medium infection titers of the tested RNA viruses, potentially indicating that these colonies have higher tolerance to pathogen pressure compared to managed colonies. This is especially important when one considers that the average annual loss of managed colonies in the United States has been upwards of 30% year after year , even thiead117_suppl_Supplementary_Tables_S1Click here for additional data file.iead117_suppl_Supplementary_Tables_S2Click here for additional data file.iead117_suppl_Supplementary_Figures_S1Click here for additional data file.


Sandfly-borne phleboviruses are endemic in countries around the Mediterranean Basin and pose a significant health threat for populations, with symptoms spanning from febrile diseases to central nervous system involvement. We carried out a comprehensive cross-sectional screening via microneutralization (MN) assays for a quantitative assessment of neutralizing antibodies (NAs) to seven phleboviruses representing three distinct serocomplexes, using samples previously screened via immunofluorescence assays (IFAs) in Turkey, an endemic region with various phleboviruses in circulation. We detected NAs to three phleboviruses: Toscana virus (TOSV), sandfly fever Naples virus (SFNV), and sandfly fever Sicilian virus (SFSV), while assays utilizing Adana virus, Punique virus, Massilia virus, and Zerdali virus remained negative. The most frequently observed virus exposure was due to TOSV, with a total prevalence of 22.6%, followed by SFNV (15.3%) and SFSV (12.1%). For each virus, IFA reactivity was significantly associated with NA detection, and further correlated with NA titers. TOSV and SFSV seroreactivities were co-detected, suggesting exposure to multiple pathogenic viruses presumably due to shared sandfly vectors. In 9.6% of the samples, multiple virus exposure was documented. In conclusion, our findings demonstrate widespread exposure to distinct pathogenic phleboviruses, for which diagnostic testing and serological screening efforts should be directed. Psychodidae, Phlebotominae) are capable of transmitting several microbial pathogens including particular bacteria , parasites , and viruses [Phenuiviridae family  [Phlebotomine sandflies  . As vectvirales) . Despitevirales) ,4. Therevirales) ,3.Sicilian phlebovirus species), sandfly fever Naples virus , and their variants [Toscana phlebovirus species), is unique as an agent of central nervous system infections that manifest as aseptic meningitis, encephalitis, and meningoencephalitis following the febrile period, resulting in better chances for virus-specific diagnosis in a clinical setting in regions with virus circulation [Phleboviruses possess a tripartite RNA genome, comprising L, M, and S segments encoding for the virus replicase (RNA polymerase), glycoprotein precursors, and nucleoprotein, respectively . To datevariants ,6,7. Anoculation .Diagnosis of phlebovirus infections and exposure relies on viremia dynamics and host immune response, where viral (antigens or nucleic acids) and immune response elements (IgM and IgG antibodies) can be utilized as targets for detection ,6,7. In Various methods can be used for the detection of phlebovirus antibodies, including the hemagglutination inhibition assay, immunofluorescence assay (IFA), enzyme-linked immunosorbent assay (ELISA), immunoblot (IB) assay, and neutralization-based assays ,9,10. SoTurkey—a Mediterranean country on the Anatolian peninsula and Eastern Thrace—is situated between diverse biogeographical zones and serves as a natural hub for the dispersion of vector-borne viruses among African, Asian, and European continents . SeveralA total of 124 sera were overviewed, collected between January and April 2009 at four major branches of the Turkish Red Crescent Middle Anatolia Regional Blood Center , after informed consent was obtained from volunteer blood donors. Sera were included in the study according to IFA findings and availability. The participants underwent a brief medical examination and were afebrile at the time of the sample collection. Mean participant age was 36.0 , with 3.2% being women. All participants filled out a questionnaire to reveal the presence of risk factors for vector-borne infections. The samples were transported on dry ice and stored in aliquots at −80 °C.Sera were tested using a commercial indirect immunofluorescence assay (IFA) for sandfly fever phleboviruses , according to the manufacturer’s recommendations. The assay allows IgG antibody detection for phleboviruses commonly associated with sandfly fever , as well as TOSV, and utilizes cell-culture-grown isolates as the antigen source, located separately on the chip. The results were interpreted via fluorescence microscopy. Antibody levels were determined at the single serum dilution of 1:100. Positive results were interpreted as weak (+), moderate (++), strong (+++), and very strong (++++) according to visual intensity of fluorescence compared with the serially diluted control sera.50 virus was added into each well except for the controls. The control wells included 50 μL of minimum essential medium (MEM) enriched with 5% fetal bovine serum, 1% penicilin + streptomycin, 1% L-glutamine 200 mM, 1% kanamycin, and 3% fungizone. Following incubation at 37 °C for one hour, a 100 μL suspension of 2 × 105 Vero cells/ mL was added to each well, and the plates were further incubated at 37 °C in the presence of 5% CO2. The microplates were visually interpreted under an inverted microscope after five days  and six days (for SFNV and SFSV) for the emergence of cytopathic effects. Neutralizing antibody titers were expressed at 20, 40, 80, and 160 serum dilutions, and titers ≥ 40 were interpreted as positive for stringency. Each serum was tested for neutralizing antibodies using virus isolates representing three distinct phlebovirus serocomplexes including: (i) TOSV, SFNV, and Zerdali virus (ZERV) (sandfly fever Naples serocomplex); (ii) SFSV (sandfly fever Sicilian serocomplex); and (iii) Adana virus (ADAV) . The microneutralization assay (MN) was performed as described previously (10). Briefly, two-fold serial dilutions from 1:20 to 1:160 were prepared for each sample and a volume of 50 μL was pipetted into 96-well plates. Viruses were titrated in Vero cells (ATCC CCL81) and 50 μL of 100 TCIDhttps://www.european-virus-archive.com).We performed additional MN assays for Punique (PUNV) and Massilia (MASV) viruses (sandfly fever Naples serocomplex) in samples with detectable SFNV neutralizing antibodies. In these samples, two-fold serial dilutions from 1:20 to 1:1280 were tested as explained above. Samples with indeterminate MN results were omitted from the final datasets following repeat testing. All virus isolates were obtained from the European Virus Archive  observed as the most frequently documented NAs in the study cohort . TOSV-spIn comparisons with NA detection and IFA reactivity, TOSV NAs were significantly associated with TOSV IFA positivity, which also correlated with NA titers . SimilarSFNV NAs were detected in 19 samples (15.3%)  and in 9SFSV NAs were present in 15 samples   and in 8In this study, we carried out an MN-based evaluation of sera from blood donors with previous evidence of phlebovirus exposure. For the screening and NA titer determination, we used established and several recently characterized viruses, a total of seven strains in MN assays, making this study the most comprehensive serological evaluation in a sandfly-borne phlebovirus endemic region.We detected NAs to three distinct phleboviruses, namely TOSV, SFNV, and SFSV, in the study cohort. The most frequently observed virus exposure was TOSV, with a total prevalence of 20.9% and a single agent prevalence of 13.7%. Previous reports have already documented widespread TOSV exposure in Turkey, with neutralizing antibody prevalence of 2.9–14.4% in various cohorts, and comparable epidemiological features or risk factors reported for other endemic countries . DespitePhlebotomus perniciosus and Phlebotomus perfiliewi sensu lato are considered efficient TOSV vectors, whereas SFSV is typically vectored by Phlebotomus papatasi [Phlebotomus papatasi, Phlebotomus major complex, and Sergentomyia dentata species [Phlebotomus major complex were also documented as the main vector for SFTV, the regional SFSV variant in Anatolia [We further observed SFNV and SFSV IFA reactivity to correlate with TOSV NA detection in the study. The SFNV findings are not surprising, as TOSV and SFNV are antigenically related, and serological cross-reactions are well documented ,11. Howepapatasi . Howeverpapatasi . Interes species . The sanAnatolia . TherefoAnatolia . These fIn the study, the prevalence of SFNV NAs was observed as 15.3%, with single exposure prevalence of 18%. Similar to the TOSV findings, SFNV NA detection and IFA reactivity were correlated but not to TOSV or other phlebovirus IFAs. Moreover, we could not demonstrate NAs of antigenically related ZERV, MASV, and PUNV in the study group or SFNV-NA-positive samples. Along with SFSV, SFNV is one of the historical agents of sandfly fever and has been endemic in the Mediterranean Basin, the Middle East, Central Asia, and Europe . HoweverFinally, we detected exposure to the other well-established agent of sandfly fever—SFSV— in 12.1% of the samples, representing 8% as the single phlebovirus prevalence in the cohort. Here, SFSV and SFCV IFA reactivity were associated with SFSV NAs detection, and SFSV IFA reactivity significantly increased with NA titers, as observed for SFNV and TOSV. Similarly, an association of NA detection with TOSV IFA reactivity was observed; probable explanations with regards to sandfly vectors are provided above. In parallel with SFNV, historical and recent records of SFSV exposure are available from Turkey ,12. HoweWe could not identify exposure to ADAV, ZERV, PUNV, or MASV in the study. ADAV was isolated from sandflies collected in Mediterranean Anatolia, with documented human exposure and a high prevalence of neutralizing antibodies in domestic animals in the region . It has In conclusion, in an attempt to broadly characterize human exposure to several pathogenic phleboviruses from distinct serocomplexes, we identified TOSV, SFSV, and SFNV NAs as individual or combined markers of previous infections in a selected cohort of blood donors. No evidence of exposure to two local and two global viruses could be demonstrated. Diagnostic testing and serological screening efforts should be focused on these pathogens.


Sampling and testing for SARS‐CoV‐2 is a widely recognized method for identifying patients with COVID‐19. However, there is limited research available on the stability of nucleic acids in viral storage solutions.This paper investigates the components that provide better protection for virus and nucleic acid detection. The study utilized real‐time quantitative fluorescent PCR to detect SARS‐CoV‐2 and evaluate the preservation effect and stability of SARS‐CoV‐2 viral storage solution under various conditions, including different guanidinium salts, brands, and storage conditions.All brands of inactivated virus preservation solutions demonstrated effective preservation and stability. However, 0.5 mol/L guanidine hydrochloride and guanidine isothiocyanate solutions exhibited poor antiseptic effects. Additionally, refrigerated storage showed better preservation compared to room temperature storage.We recommend using inactivated virus collection solution to preserve and transport samples and testing preferably within 6 hours to reduce false negatives of NAT results. We found that the fire‐extinguishing sampling tube  had different effects on the preservation and detection of the virus due to different components, so it was necessary to verify the virus preservation solution. There are two types of virus preservation solutions: noninactivated and inactivated. The noninactivated preservation solution mainly consists of Hank's buffer without lysis solution, which helps enhance the survival and stability of the virus during infection. Additionally, ingredients such as gentamicin, antifungal drugs, cryoprotectants, amino acids, bovine serum albumin, and HEPES are usually added to prevent the viral protein shell from easily decomposing and to maintain the virus's activity across a wide temperature range.Currently, there are numerous brands of virus collection tubes available in China. However, it is important to note that these collection tubes and virus preservation solutions are not regulated as medical devices. Additionally, the quality control requirements for virus preservation solutions may vary among manufacturers. Surprisingly, there is a lack of studies on the effect of preservation solutions on virus preservation or the specific characteristics of virus preservation solutions in NAT research. The storage condition for commercial sample tubes is 1 year at room temperature, but the storage time after sampling is not specified. Additionally, it is important to note that due to distance and operator limitations, the virus sample tubes may be placed for more than 6 h before detection. The preservation stability of virus sampling tubes at room temperature and in cold storage is a significant area of study. This research aims to investigate how the tubes maintain their quality over time. Understanding the stability of these tubes is crucial to ensure their reliability and effectiveness in virus sampling. Additionally, this research allows us to determine the appropriate storage conditions for the tubes, which is essential for preserving the integrity of collected samples. The research findings will offer users a solid research foundation, providing valuable insights into the selection and preservation of viral preservation solution ingredients and brands.22.12.1.1The nucleic acid extraction and purification kit  and the SARS‐CoV‐2 nucleic acid detection kit  were used. Additionally, the SARS‐CoV‐2 pseudovirus  was included in the experiment. Commercial virus preservation solutions were tested in two groups: the inactivated group (IA) and the noninactivated group (NIA). The inactivated group included products from the following companies: Guangzhou Daan Gene (Lot: 2021009), Hangzhou Heshuo Biological Technology (Lot: 20210911), Yocon Biological Technology (Lot: 26210815), Hangzhou Ciping Medical Instrument (Lot: 26210815), Wuhan Easy Diagnosis Biomedicine (Lot: 210202‐05M), Zhejiang Orient Gene Biotech (Lot: S2101051), USTAR Biotechnologies (Lot: 20210820), and Zhejiang Junshan Gene Science (Lot: QY21K01). The noninactivated group included products from Guangzhou Daan Gene (Lot: 2021004), Hangzhou Heshuo Biological Technology (Lot: 20210911), Yocon Biological Technology (Beijing) (Lot: 01210215), Hangzhou Ciping Medical Instrument (Lot: 202108004), and Wuhan Easy Diagnosis Biomedicine (Lot: 210101‐02).2.1.2The storage conditions and validity period of commercial sample tubes containing viral storage solution are 2–40°C storage for 12 months. The State Council of China's regulations on the storage of virus sample tubes2.1.3The automatic fluorescent PCR analyzer used in this study was the Cobas Z480 , while the automatic nucleic acid extraction was performed using the NAE‐96 system from Easy Diagnosis. The experiments were conducted in a biological safety cabinet from Heal Force and a refrigerated centrifuge from Thermo Fisher Scientific, with an oscillator from KylinBell used for mixing. The pipettes used were from Eppendorf, and the tips used were 10, 200, and 1000 μL needle tips with filters from AXYGEN. The LightCycler 480 Multiwell Plate 96 in white  was also utilized.2.22.2.1The self‐prepared inactivated virus preservation solution group (self‐prepared group): Guanidine isothiocyanate . Guanidine hydrochloride . Sterile normal saline water for preparation and dilution of virus preservation solution . the glass tube for configuring sample preservation solution . Prior to the experiment, the glass tube, sterile normal saline water, and experimental consumables underwent autoclaving. Additionally, guanidine isothiocyanate and guanidine hydrochloride were prepared and transformed into guanidinium isothiocyanate(GITC)and Guanidine hydrochloride (GuHCl) solutions with concentrations of 0.5 mol/L, 1.0 mol/L, 2.0 mol/L, 3.0 mol/L, and 4.0 mol/L. These solutions were stored at room temperature (22 ± 2°C) and in a refrigerated environment (6 ± 2°C).2.2.2n = 20) were collected and centrifuged at 1006 g for 10 min. Epithelial cells were obtained and added into self‐made GITC and GuHCl virus storage tubes with 20 equal parts of high‐concentration epithelial cell suspension, each 50 μL, and 140 μL of the SARS‐CoV‐2 pseudovirus. The mixture was then diluted with normal saline to a total volume of 3 mL, mixed evenly, stored at room temperature, and refrigerated for 60 days, respectively. The concentration of simulated SARS‐CoV‐2 positive samples was 8.07 E+2 copies/mL after the initial addition.The negative samples (2.2.3Nucleic acid testing (NAT) was conducted on samples stored for various time intervals ranging from 0 h to 2 months . The testing method employed multiplex PCR‐fluorescence probe technology and targeted the 2019‐nCoV ORF1ab and N genes. A dual‐target gene detection primer probe was designed for the RT‐PCR reaction system. Two target genes, including open reading frame 1ab (ORF1ab) and nucleocapsid protein (N), were simultaneously amplified and tested during the real‐time RT‐PCR assay. Target 1 (ORF1ab): forward primer GGTATGTGGAAAGGTTATGGCTGTA; reverse primer ACGATTGTGCATCAGCTGACTG; and the probe 5′‐VIC‐CCCCAGCGCTTCAGCGTTCTTC‐BHQ1‐3′. Target 2 (N): forward primer GATTACAAACATTGGCCGCAAA; reverse primer TGCCAATGCGCGACATTC; and the probe 5′‐FAM‐ATCAACTCCGCGAACCCATGC‐TAMRA‐3′. The results were evaluated by analyzing the threshold cycle (Ct) value of each channel. Additionally, the kit included the human housekeeping gene RNaseP as an internal standard to monitor the sampling, extraction, sample addition, and amplification processes. Prior to use, all reagents were thawed, mixed thoroughly, and centrifuged at 3000 rpm for a few seconds. RT‐qPCR reaction system referred to 20 μL PCR reaction mixture, 5 μL RNA of samples to be tested. Reaction conditions referred to reverse transcription at 50°C for 15 min, predenaturation at 95°C for 30 s, 95°C for 3 s and 60°C for 40 s (40 cycles).2.2.4R2 ≥ 0.95) was plotted using the above 5 points, forming a standard curve that could be used to calculate the concentration of SARS‐CoV‐2 nucleic acid in the sample. In this study, the ORF1ab gene was selected as the target gene to determine the virus concentration. The logarithm of the virus concentration was used as the final data for analysis and plotting. The calculation formula was as follows:The specific genes detected by Easy Diagnosis Biomedicine amplification reagent were ORF1ab and N gene. S1, S2, S3, S4, and S5 standards were prepared with concentrations of 1.73E+4, 8.65E+3, 4.33E+3, 2.16E+3, and 1.08E+3 copies/mL, respectively. These standards were created by doubling dilution with BDS Biological Technology peak value sample (concentration of 1.73E+4 copies/mL) four times. The diluent used was sterile normal saline water. A standard curve  were conducted. The preservation difference between different brands in the noninactivated virus preservation solution was compared using K Related samples tests in Nonparametric Tests. Additionally, the Wilcoxon test was performed to compare the efficacy and stability of the same concentration of GITC and GuHCl groups for refrigerated and room‐temperature storage of the SARS‐CoV‐2. The Friedman test was performed to compare the stability of SARS‐CoV‐2 preservation among different concentrations of GuHCl solution, different concentrations of guanidine isothiocyanate solution, and different brands of virus preservation solution. The Mann–Whitney 33.1t = 39.07, p < 0.001). According to Figure χ2 = 5.806, p = 0.214).We stored two aliquots of virus preservation solution, of the same volume, from multiple brands of inactivated (IA) and noninactivated (NIA) solutions for different periods of time, as described in Section 3.2p all >0.05, see Table Z = ‐2.295, p = 0.022, see Table Eight types of inactivated virus preservation solution were stored at room temperature 22 ± 2°C) and refrigerated (6 ± 2°C). Nucleic acid extraction and viral nucleic acid detection were performed at various time intervals ranging from 0 h to 2 months. We compared the preservation effect of the same brand of inactivated virus preservation solution at room temperature and in a refrigerated environment. The results showed that there was no significant difference in the preservation effect between different brands of virus preservation solutions at room temperature and in a refrigerated environment. Additionally, we noticed a clear downward trend in the guanidine content of 0.5 mol/L in both GITC and GuHCl groups  recommends using qRT‐PCR as the standard method for confirming SARS‐CoV‐2 detection.In this study, we prepared guanidinium salt virus preservation solution with different concentrations to investigate their preservation stability against SARS‐CoV‐2, finding that the use of GITC preservation solution could reduce the probability of false negative results.Since the samples were unable to meet the required refrigeration conditions of 2–8°C prior to testing, this study aimed to compare the effects of storage under room temperature (20–24°C) and refrigeration (2–8°C) on the preservation solution for the virus. The results showed no significant difference between the virus preservation solution stored at room temperature and refrigeration. This suggests that the buffer solution and other components added to the ready‐made IA have a better ability to resist interference from temperature, pH changes, and temperature fluctuations. The aforementioned results align with a previous study that utilized Innomed VTM001 virus collection tubes to compare the impact of two distinct storage temperatures  on 30 positive samples by RT‐PCR analysis for SARS‐CoV‐2, finding no false negative results within a span of 6 days for all samples.In conclusion, we highly recommended to use inactivated virus collection solution to preserve and transport samples in order to reduce the false negative in NAT results, improve the accuracy of NAT, ensure virus preservation stability, and minimize the risk of virus transmission. Additionally, refrigerated preservation is more suitable for identifying new coronavirus nucleic acid. If it is not possible to perform performance verification of the virus preservation solution before the experiment, NAT should be conducted in the laboratory as soon as possible, preferably within 6 h.Xiuzhi Duan, Letao Huang, and Zhihua Tao contributed to bibliographic research, drafting of the paper, critical revision, and final approval of the submitted version. Xuchu Wang, Yiyi Xie, and Weiwei Liu contributed to critical revision and final approval of the submitted version. Ying Ping and Pan Yu contributed to study conception and design, critical revision, and final approval of the submitted version. All authors read and approved the final manuscript.None.All patients were informed and agree to publish.