Apodemus sylvaticus, A. flavicollis, and M. glareolus) living in temperate woodland over a single year. Our findings indicate that a substantial fraction of the rodent virome is seasonally transient and associated with vertebrate or bacteria hosts. Further analyses of one of the most common virus families, Picornaviridae, show pronounced temporal changes in viral richness and evenness, which were associated with concurrent and up to ~3-month lags in host density, ambient temperature, rainfall and humidity, suggesting complex feedbacks from the host and environmental factors on virus transmission and shedding in seasonal habitats. Overall, this study emphasizes the importance of understanding the seasonal dynamics of wild animal viromes in order to better predict and mitigate zoonotic risks.Viral discovery studies in wild animals often rely on cross-sectional surveys at a single time point. As a result, our understanding of the temporal stability of wild animal viromes remains poorly resolved. While studies of single host-virus systems indicate that host and environmental factors influence seasonal virus transmission dynamics, comparable insights for whole viral communities in multiple hosts are lacking. Leveraging non-invasive faecal samples from a long-term wild rodent study, we characterised viral communities of three common European rodent species ( Our knowledge of the global virosphere has rapidly expanded , mainly While viral discovery studies provide valuable data for understanding the evolutionary history and host range of viruses, they offer limited insights into what factors shape wild animal viromes. In order to understand viral dynamics in wild populations, we need to move from descriptive host-virus associations to a mechanistic understanding of where and when viruses are transmitted and how entire viral communities (viromes) are shaped by the environment and local host communities . For exaCurrent knowledge about what factors shape viral communities in animals comes from a small but growing number of studies. A comparison of viromes from three parasitic wasp species reared in laboratory conditions suggests that host phylogeny influences viral community structure . HoweverAlthough evidence suggests that environmental and host factors influence viral communities in wild animals, these surveys have predominantly been cross-sectional, with any given population sampled at a single time point. As a result, we have a sparse understanding of temporal dynamics in wild animal viromes. Fundamental questions such as how viral diversity varies over time and what proportion of viruses are only detected intermittently or at certain times of year in seasonal environments remain unaddressed. These dynamic environments affect wild animals through seasonally varying birth and death rates and timing of specific behaviours, including mating and other social interactions. As a result, many factors affecting viral transmission vary seasonally , such asConsequently, it is not surprising that zoonotic virus surveillance studies in reservoir populations have consistently observed seasonal variation in virus prevalence in several host species, such as rodents, bats, birds, and racoons . HoweverRodents are a significant zoonotic reservoir globally, and Europe has been identified as a hotspot for rodent reservoir diversity . FurtherApodemus sylvaticus (wood mouse), Apodemus flavicollis (yellow-necked mouse), and Myodes glareolus (bank vole). These three species are ubiquitous across Europe, particularly in woodland habitats. They have fast-paced life histories, with females capable of producing multiple litters in her lifespan, which is typically less than one year. To characterise seasonal variation in viral communities, we generated metaviromic data from pooled faecal samples collected longitudinally from each species over a single year. By combining local microclimate and demographic data from the same period, we explored key factors that predict seasonal variation in the wild rodent virome.To address these questions, we leveraged a long-term capture-mark-recapture study of several sympatric rodent species in Wytham woods, Oxfordshire. Specifically, we characterised the viromes of three common resident species, Apodemus mice  and the bank vole (Myodes glareolus). These are non-group-living, omnivorous woodland rodents with overlapping home ranges that show seasonal variation in reproduction, mortality, diet (Wild rodents were trapped and sampled over a one-year period (January 2017 to January 2018) in Wytham Woods , a 385-ha mixed deciduous woodland near Oxford, UK. Three common rodent species are regularly caught at this site: two species of ty, diet  and socity, diet . One nigA. sylvaticus, 25 A. flavicollis, and 51 M. glareolus). Five sampling intervals were defined, which took into account the breeding cycle of the three rodent species. These were 1) Jan-Feb 2017, 2) Mar-Apr 2017, 3) May-Jul 2017, 4) Aug-Oct 2017, 5) Nov-Jan 2017/18. Faecal samples were pooled by species and sampling interval, using equal aliquots of 40mg faeces per individual per pool. For the last sampling interval, where there were fewer individuals of A. flavicollis and M. glareolus available , greater masses of faeces per individual  were used for pooling to ensure sufficient material for sequencing. Complete sample information for each pooled sample is outlined in We randomly selected 133 individual faecal samples (57 The samples were processed as follows to enrich for RNA within encapsulated viruses: 1) Frozen archived faecal samples were first pooled, then suspended in DNA/RNA Shield Stabilization Buffer (Zymo), vortexed thoroughly, and the supernatant was filtered through a 0.45nm pore filter; 2) RNase treatment (RNase One) to remove non-encapsulated RNA from the sample; 3) RNA extraction using Zymo Quick Viral RNA and RNA Clean and Concentrator 5 kits; 4) DNA digestion following RNA extraction; 5) ribosomal depletion with Illumina Ribo-Zero Plus kit, which allows for ribosomal RNA removal in human, mouse, rat, and bacterial samples, during sequencing library preparation. The Oxford Genomics Centre carried out sequencing library preparation, which included cDNA synthesis, and sequencing on Illumina NovaSeq 6000 platform.de novo assembled into 435,021 contigs by MEGAHIT 1.2.8 with default parameters  were obtained after sequencing. Illumina adaptors were removed, and reads were filtered for quality scores≥30 and read length >45bp) using cutadapt 1.18 . A totalrameters . Viral cters  and the evenness of virus genera (hereafter ‘evenness’), which was measured by calculating the Shannon entropy of virus genera in the community using the Shannon diversity index function in R library vegan. Consequently, viral evenness ranges from 0 and 1, and indicates the degree to which the virus community is dominated by a particular genus (i.e. evenness = 0) or whether different genera are equally abundant (i.e. evenness = 1). To identify unique and shared viruses across all time points, we visualised the distribution of viral contigs (200 bp or longer) with a minimum of 20 reads among host species with Venn diagrams . To deteTo reconstruct the picornavirus phylogeny, we assembled a multiple protein sequence alignment of 93 whole picornavirus genome sequences from the NCBI RefSeq viral database and eight picornavirus genome sequences identified in this study. Maximum-likelihood phylogeny was inferred with IQ-TREE v. 2.1.3  using thWe evaluated drivers of two outcome variables – picornavirus richness and evenness – using Gaussian distributed generalised linear models (GLMs). We modelled picornaviruses in wood mice and bank voles separately and only modelled these virus-host combinations as up to 6 picornaviruses were found, and these hosts were sampled for viruses at each interval throughout the year. Four predictor variables with time series covering the preceding relevant seasons (June 2016 – Dec 2016) and picornavirus characterization period (Jan 2017-Dec 2017) were used to identify significant environmental and population factors affecting picornavirus richness and evenness. Temperature, humidity, and rain data were collected hourly at two microclimate stations within the woodlands. Host population density for each species was measured by the minimum number of known alive per hectare based on bimonthly trapping events across a 2.4ha grid between November 2016 and January 2018. Since predictor and outcome variables were calculated at different frequencies , we used locally estimated scatterplot smoothing (LOESS) and generalised additive models (GAMs ) to modeEnvironmental and host density impacts may have delayed effects on observed picornavirus richness and evenness. Therefore, we first identified the appropriate time lags (if any) for each predictor variable. Significant relationships between picornavirus 1) richness and 2) evenness and the four predictors were identified for each diversity metric and host species using cross-correlation analysis. Cross-correlation analysis (ccf function in R) compares two time series and identifies similarities between the variables. Values range from -1.0 to 1.0; the closer the absolute value is to 1.0, the more linked the two variables are across time. In addition to identifying contemporaneous correlation, cross-correlation can be used to evaluate if there are lagged correlations (i.e. delayed but significant similarities between time series). We evaluated lags from 0 to 14 weeks in two weeks increments and identified significant residual auto-correlation values for each increment. If multiple lags were identified as significant for a given predictor variable, we selected the lag with the highest significant residual auto-correlation value see  to use iWe considered four separate GLMs per host species and diversity metric  to evaluate drivers of picornavirus diversity. However, prior to undertaking a GLM analysis, correlations among the four variables . For each of the five 2-3 month sampling intervals, we randomly selected up to 13 individual samples per species to create species and sampling interval-specific pools for metagenomic sequencing (see A. sylvaticus (wood mouse) and M. glareolus (bank vole) and three pools for A. flavicollis (yellow-necked mouse) which are less abundant at the sampling site.Over a one-year period (January 2017 to January 2018), we characterised viruses in faeces from a total of 133 individual rodents  were taxonomically assigned to known viruses see . Figure Leviviridae (+ssRNA) and Microviridae (ssDNA), which have genome sizes ranging from 4 to 6.5kb , yellow-necked mice = 144/852 (16.9%), bank voles = 223/852 (26.2%), most vertebrate-associated viral contigs were detected in at least two host species . FurtherTo understand how virus detection varies across the year, we quantified the proportion of viral genera detected across all three hosts by the number of times it was detected  and in eThe relative abundance of viruses  is affecEucampyvirinae (family: Myoviridaebacteriophage), unclassified Picobirnaviridae (family: Picobirnaviridae – vertebrate-associated), Mamastrovirus (family: Astroviridae - vertebrate-associated), Cardiovirus (family: Picornavirus - vertebrate-associated), unclassified Dicistroviridae (family: Dicistroviridae – invertebrate-associated).To understand how the viral community composition differs between host species and sampling intervals, we undertook a hierarchical PERMANOVA analysis . OverallPicobirnaviridae (vertebrate-associated) and Leviviridae (bacteriophage), which were detected throughout the year at high read abundance (ranging from 0.66M-2.32M for Picobirnaviridae and 0.29M-1.35M for Leviviridae in pooled samples). Other common vertebrate-associated viruses were members of several non-enveloped ssRNA virus families, such as Picornaviridae, Astroviridae, Hepeviridae, and the dsRNA virus family, Reoviridae. We also detected multiple enveloped RNA and reverse-transcribing viruses  in yellow-necked mice and bank voles.Closer examination of the temporal patterns of the vertebrate-associated and bacteriophage viruses confirmed that considerable virus diversity was detected in the Wytham rodents, corresponding to different virus families, genera, and genome architectures  displaying highly variable patterns of seasonal detection . A solidPicobirnaviridae, Leviviridae, and Microviridae, was not possible due to poor representation of these taxa in reference databases. As a result, it is challenging to ascertain more detailed information about these viruses. For example, which hosts does the bacteriophage infect and how many distinct virus species  are present? We aimed to partly address the latter by considering virus contigs similar in length to complete genomes (see Picobirnavirus genomes (which are bisegmented), 21 putative Levivirus genomes, and nine putative Microvirus genomes , we undertook a more detailed analysis to understand seasonal variation in picornavirus abundance and diversity. We assembled eight virus contigs for the most prevalent picornaviruses (see Mosavirus and Sapelovirus were only observed at a single time point and most abundant in early summer (between May and July), while others  were detected in multiple consecutive periods and reached peak abundance in late summer (between August and October). Importantly, this suggests that even for related viruses, there may be marked variation in the underlying drivers of transmission. Our data also demonstrated that while multiple distinct picornaviruses co-circulate in all three rodent species, for the most part, these viruses are disproportionately associated with a single host species  coincides with a peak in Hunnivirus abundance.Next, we investigated temporal patterns of picornavirus diversity . SimilarWhen restricting the analysis to the eight picornavirus genomes, we observed broadly similar seasonally varying patterns in abundance, richness, and evenness at the species level . Howeverxy = 0.20-0.88; To explore the predictors of picornavirus diversity in Wytham woods, we focused on wood mice and bank voles, which were sampled in each of the five intervals. We evaluated three environmental variables  using data collected from June 2016 to January 2017 from two weather stations located within the woodlands, together with approximately fortnightly estimates of host population density, calculated as the minimum number known alive (MNKA) per hectare from trapping data. Time series data on picornavirus diversity and the four variables was reconciled using interpolation techniques see . SpecifiWe constructed generalised linear models (GLMs) containing each time-lagged variable as predictors for each host species and diversity metric. Sets of GLMs were reduced accordingly to exclude highly correlated variables .We examined the seasonal dynamics of the faecal virome in three wild rodent species widespread in the UK and Europe. Strikingly, we found extensive virus diversity circulating in these rodents throughout the year. Detected viruses were predominantly associated with vertebrate or bacteria hosts and represented a broad range of virus genomic organisation , and virome diversity and community composition varied markedly throughout the year. Although viruses appear to be largely host-specific at the inferred species level, we saw substantial virus sharing among species, particularly among the wood mice and yellow-necked mice, indicating host phylogenetic relatedness is an important determinant of virus ecology. Furthermore, temporal patterns in virus abundance suggest marked variation in the epidemiology of co-circulating viruses, which can differ within and between species. Lastly, seasonal trends in picornavirus diversity suggest that these viral communities are shaped by biological and ecological processes, which likely influence within-host viral dynamics, environmental persistence, and between-host viral transmission.Cardiovirus and Picobirnavirus have previously been reported in four major untargeted viral metagenomic surveys of wild rodents (Kunsagivirus (family Picornaviridae) and Torovirus (family Tobaniviridae). Although information about Kunsagivirus is limited , Torovirus is an enveloped virus commonly found in mammals, including humans, with gastroenteritis (Paramyxovirus detected in bank voles in this study closely matched another Paramyxovirus (genus Jeilongvirus) isolated from bank voles in Slovenia , Leviviridae (bacteriophage) and Microviridae (bacteriophage)) were observed at all sampling intervals at high levels in all three species, indicating that they  persist in the population by establishing a chronic infection or environmental persistence which facilitates frequent reinfection. However, a significant fraction of virus diversity (104/159 genera) was detected only in one or two of five seasonal sampling periods. Hierarchical analysis of virus richness suggests there is substantial turnover in viral diversity in the Wytham rodents, with around half of diversity absent from each sample interval. Although these results suggest that wild rodents may support different virus epidemiological dynamics within a single year, more in-depth investigations will be required to understand the impact of pooling and sampling effort, particularly for viruses with low prevalence or abundance within individuals, which might appear transient despite continuous circulation. Importantly, however, these findings also highlight that crosssectional surveys will miss a large proportion of circulating virus diversity, even when samples are taken during times of the year when virus diversity is maximal, such as the spring and summer months in this population.There was notable variation in observing a specific virus genus in the Wytham rodents across the year. Some viral genera from the most abundant virus families  and lead to a higher number of viral species circulating in the following months. Host density in the previous 2-3 months was associated with lower viral evenness in wood mice – several mechanisms could explain this pattern. For example, a higher population density could facilitate certain viruses to dominate transmission events through smaller home range sizes and reduced frequency of contacts, or the increase in density could affect competition and alter within-host replication dynamics. Future studies that incorporate more samples collected at higher frequencies could be used to test such hypotheses explicitly.The widespread distribution of wood mice and bank voles in the UK makes them highly amenable for long-term field studies and have been previously leveraged to understand natural drivers of virus transmission in wildlife populations , 2007. WHepatovirus, family Picornavidae) is very stable under a broad range of temperature, humidity, and pH conditions and can survive over three months in the environment (Aphthovirus), appear to be less stable in the environment, with longer survival times observed at higher humidity and moderate temperatures . In the future, we plan to develop mechanistic transmission models in these systems using field studies with a higher temporal resolution. Mechanistic models could be adapted to other rodent systems to forecast peaks and troughs in epizootics and test potential interventions in settings where zoonotic viruses are a risk to human populations.We detected long time lags (~3 months) between some environmental variables and picornavirus diversity, particularly for viral evenness. This observation could be because pooled samples were from a time window, where individual samples from 2 to 3 months were aggregated into one ‘timepoint’. Although such temporal pooling is not ideal for time series evaluation, it provides a valid first approximation of important seasonal correlates of viromes and an improvement on previous cross-sectional surveys. We expect many viruses to be transmitted between conspecifics through close contact and between species via the environment. However, the ability of viruses to remain transmissible in the environment is highly variable across taxa. For example, hepatitis A virus (genus ironment . In conteratures . AlthougUnderstanding viral community dynamics is key to predicting and mitigating human risk from known and unknown rodent zoonoses. Improvements in sequencing technology that enable the identification and monitoring of RNA viruses longitudinally in wildlife are crucial to establishing the spatial, temporal, and environmental factors that determine zoonotic risk. Previous work has shown that specific rodent-borne zoonotic viruses exhibit strong seasonal dynamics in the reservoir population . NeverthSupplementary Information


Widespread vaccination programmes led to the global eradication of smallpox, which was certified by the World Health Organisation (WHO), and, since 1978, there has been no case of smallpox anywhere in the world. However, the viable variola virus (VARV), the causative agent of smallpox, is still kept in two maximum security laboratories in Russia and the USA. Despite the eradication of the disease smallpox, clandestine stocks of VARV may exist. In a rapidly changing world, the impact of an intentional VARV release in the human population would nowadays result in a public health emergency of global concern: vaccination programmes were abolished, the percentage of immunosuppressed individuals in the human population is higher, and an increased intercontinental air travel allows for the rapid viral spread of diseases around the world. The WHO has authorised the temporary retention of VARV to enable essential research for public health benefit to take place. This work aims to develop diagnostic tests, antiviral drugs, and safer vaccines. Advances in synthetic biology have made it possible to produce infectious poxvirus particles from chemicals in vitro so that it is now possible to reconstruct VARV. The status of smallpox in the post-eradication era is reviewed. Orthopoxvirus (OPV) genus of the Poxviridae. In the past, the disease was named smallpox because the lesion size was smaller than that of great-pox (syphilis). Edward Jenner described smallpox as “the most dreadful scourge of the human species.” Exact numbers of deaths are not recorded but estimates amount to 400 million people in the 20th century alone [Smallpox is caused by the variola virus (VARV), a member of the ry alone . The oriThe variola virus infection is transmitted through aerosol. This was proven in a hospital building in Meschede, Germany, where a total of 17 smallpox cases were infected by virus particles disseminated by air over a considerable distance . UsuallySmallpox, as a disease, was relatively easy to recognize with its characteristic skin lesions, however, the following exanthematous illnesses have to be considered as differential diagnosis: severe chickenpox rash, monkeypox, generalized vaccinia virus or cowpox virus infections, eczema vaccinatum, disseminated varicella-zoster, herpes simplex virus infections, drug reactions, erythema multiforme, enteroviral infections, rickettsialpox rash, secondary syphilis, scabies, insect bites, impetigo, and molluscum contagiosum . DiseaseInquiry that marked the beginning of the vaccination era [vacca, Latin for cow) became very popular because it achieved the same level of protection as variolation coupled with a considerably lower risk of adverse effects. However, limited supplies of the vaccine did not allow widespread vaccinations. To overcome these shortages, vaccine virus was passed by arm-to-arm transfer in humans during the 19th century. A further step towards vaccination success was the production of CPXV and later of the related vaccinia virus (VACV) in the skin of animals. Vaccine stocks were also mass produced on the chorioallantoic membranes of embryonated eggs. No particular VACV strain was officially recommended, but the WHO advised that either the Lister or the New York City Board of Health strain should be used. A further advance was the development of lyophilisation techniques for vaccines in 1950. The availability of such lyophilised virus abolished the need for refrigeration and massively simplified the transportation possibilities to remote areas. Finally, the bifurcated needle allowed even untrained personnel to conduct vaccinations successfully. Recently, it has been stated that there is a potential role of horsepox virus in the origin of the smallpox vaccine [It was understood early in the history of smallpox that persons who survived an infection never developed the disease again. Additionally, it became clear that many individuals infected by skin lesions suffered a less severe form of smallpox than those infected by the respiratory route. This led to an early immunization procedure that became known as variolation: immunologically naïve persons were infected via cutaneous scratches with pustular fluid/material collected from smallpox patients. Initially developed in India and China, it became common in Europe and its colonies in the 18th century. Despite its success, the number of patients that still developed the classical form of smallpox after variolation with the full clinical picture and high mortality rate was not negligible. In the late 18th century, it was known that milkmaids, who had contracted a zoonotic infection, cowpox, usually survived smallpox . Based otion era . In the  vaccine : the corIn 1959, the twelfth World Health Assembly adopted a resolution to achieve the eradication of smallpox [Only a combination of rapid diagnosis, followed by the subsequent isolation of patients, vaccination, and quarantine of contacts will be successful in case of a smallpox outbreak . Most crThe recent development of portable RT-PCR machines and lyophilized reagents  raise thA variety of VACV strains have been used in different parts of the world . Most ofIn response to the risk of an intentional release of VARV, some countries replenished their vaccine stocks . These s® was licensed in all member states of the European Union and in Iceland, Liechtenstein, Canada, and Norway in 2013. In 2019, the vaccine termed Jynneos® was approved by the US FDA for protection against VARV and MPXV. The indication in adults is for active immunization against smallpox. In the USA, a pre-Emergency Use Authorization would allow the use of Imvamune® in immunocompromised persons. MVA is now part of the US Strategic National Stockpile with an initial order of about 30 million doses. This would allow for the pre-event vaccination of first-line responders—a prerequisite in emergency preparation should an unexpected smallpox outbreak scenario occur. Due to significant adverse events following vaccination with replicating VACVs, research resulted in smallpox vaccines based on highly attenuated VACV strains that (largely) do not replicate in humans after vaccination. Modified vaccinia Ankara (MVA) is a VACV strain derived from chorioallantois vaccinia Ankara with a history of 570 passages in chick embryo fibroblasts . As a reB5R gene [LC16m8 is a VACV strain that was developed by continuous passage of VACV strain Lister in primary kidney epithelial cells at 30 °C. In 1975, it was licensed in Japan ,32. InocB5R gene  that encB5R gene  and agaiB5R gene . LC16m8 Cidofovir (Vistide) is an approved drug for the treatment of HCMV retinitis in HIV-patients and is also effective against poxviruses. Cidofovir is a nucleoside analogue that selectively inhibits the viral DNA polymerase and reduces the replication of VARV in vitro . Only whSIGA Technologies developed TPOXX  for the treatment of smallpox and has already supplied two million treatment courses to the US Strategic National Stockpile. The drug targets the virus F13 phospholipase and acts as an inhibitor of virus egress and blocks the formation of enveloped forms of OPVs, thereby effectively inhibiting virus dissemination both in vitro and in vivo. The US FDA Animal Rule guided TPOXX drug development, as clinical trials are impossible to conduct due to ethical concerns. TPOXX efficacy, against a wide range of OPVs, was demonstrated in several animal models, including VARV in non-human primates ,41. The The availability of at least two antiviral drugs that work by different molecular mechanisms has been recommended by the US Institute of Medicine. Continued research on antiviral compounds is highly desirable because the acquisition of drug resistance cannot be excluded and mutations that mediate the resistances of different OPVs were reported both for cidofovir  and TPOXIn 2006, the genomic sequences of 47 geographically distinct VARV isolates were reported . The seqRecently, VARV-DNA was detected in a mummy in Lithuania dating back to 1643 to 1665. A draft genome could be assembled  with almGene synthesis has been used for the de novo synthesis of infectious poliovirus, and of the 1918 pandemic strain of influenza virus ,52. The De novo synthesis of a viable VARV is only one option for creating VARV or a VARV-like virus. There are other methods available to modify the virulence and host-range pattern of any OPV, including VARV, such as homologous recombination. This method is routinely used in basic and applied research. By this method, drug-resistance markers could be introduced into drug-sensitive viruses. The insertion of genes coding for immunoregulatory active proteins interfering with the host immune system can alter virulence properties. This was demonstrated [nstrated  by inserAccording to Ken Alibek, a former deputy director of the former Soviet Union’s bioweapons program, the former Soviet Union expanded its bioweapons research program during the 1980s and was eventually able to weaponise smallpox . HoweverWith regard to medical countermeasures, it is extremely unlikely that a widespread vaccination programme will be re-introduced. Ultimately, success in controlling any possible future outbreak of smallpox will depend on supplies of medical countermeasures as well as on the following classical methods used in the past: (i) early detection, (ii) the isolation of smallpox patients, (iii) the quarantine of contacts, and (iv) ring vaccination . Today—aIn the past, smallpox was relatively easy to recognise. Today, health care professionals have to be educated about the characteristics of smallpox, which can easily be confused clinically with monkeypox, or even chickenpox caused by varicella-zoster virus, a herpes virus. The disease monkeypox is caused by monkeypox virus (MPXV), a zoonotic OPV endemic in the rainforests of central and western Africa . Since tBeside these known challenges, novel zoonotic poxviruses are being discovered regularly, many of them with zoonotic characteristics like the Alaskapox virus  and the Taken together, the range of risks and biothreats caused by OPVs is particularly broad, and constant vigilance is needed in many different sectors of public life, including the public health service, political decision making, and the surveillance of biomedical research and the biomedical industry, when it comes to synthetic biology.


Macrobrachium rosenbergii, was characterized. The complete genome consisted of 7750 nucleotides and had a similar structure to known hepatitis E virus genomes. Phylogenetic analyses suggested it might be a novel hepe-like virus within the family Hepeviridae. To our knowledge, this is the first hepe-like virus characterized from crustaceans.The family Hepeviridae includes several positive-stranded RNA viruses, which infect a wide range of mammalian species, chicken, and trout. However, few hepatitis E viruses (HEVs) have been characterized from invertebrates. In this study, a hepevirus, tentatively named Crustacea hepe-like virus 1 (CHEV1), from the economically important crustacean, the giant freshwater prawn  Orthohepevirus, including all mammalian and avian HEVs; and Piscihepevirus, including the Cutthroat trout virus [Orthohepevirus, four species have been designated (A–D) that infect mammals and birds [Orthohepevirus A from human, swine, deer, mongoose, rabbit, and camel; Orthohepevirus B from chicken and wild birds; Orthohepevirus C from rat, greater bandicoot, Asian musk shrew, ferret, and mink; and Orthohepevirus D from bats. In addition, different genotypes have also been classified based on the genomic sequence divergence within species. For example, within Orthohepevirus A, eight genotypes have been proposed [The family Hepeviridae belongs to positive-stranded RNA viruses and infects a wide range of mammalian species, chicken, and trout ,2. Due tut virus ,7. Withind birds ,8, incluMacrobrachium rosenbergii) with growth retardation were collected from a farming pond in Jiangsu Province, China. Total RNA and DNA were extracted from each of these M. rosenbergii prawns, and the detection of known shrimp pathogens was performed using RT-PCR or PCR [Enterocytozoon hepatopenaei (EHP), Acute hepatopancreatic necrosis disease-causing Vibrio (AHPND-causing Vibrio), Taura syndrome virus (TSV), Yellow head virus genotype 1 (YHV-1), Infectious myonecrosis virus (IMNV), Decapod iridescent virus 1 (DIV1), Macrobrachium rosenbergii nodavirus (MrNV), and Covert mortality nodavirus (CMNV).In 2018, a total of 18 diseased giant freshwater prawns  and the ribosomal RNA was removed by the Epicentre Ribo-zeroTM rRNA Removal Kit . Sequencing libraries were generated using the rRNA-depleted RNA by NEB Next® UltraTM Directional RNA Library Prep Kit for Illumina® , following the manufacturer’s recommendations. Then, the 150 bp paired-end sequencing of the RNA libraries were conducted using the Illumina Hiseq platform. The raw sequencing reads were adaptor- and quality-trimmed using the Trimmomatic program [E-value threshold set at 1 × 10−5. All potential viral contigs were identified and then merged to form longer viral contigs using Geneious (version 11.1.5).Cephalothoraxes from 15 g  program . The cleTo further confirm the results from next generation sequencing (NGS), RT-PCR and Sanger sequencing of viral crude extracts based on the obtained contigs were performed. Firstly, different primer pairs were designed based on the viral contigs . MeanwhiM. rosenbergii), our molecular tests showed that they were negative for all known shrimp pathogens, including WSSV, IHHNV, EHP, AHPND-causing Vibrio, TSV, YHV-1, IMNV, DIV1, MrNV, and CMNV. However, the NGS of the viral crude extracts from the pooled samples identified one contig (size = 7696 bases) associated with Hepeviridae, with 3608 non-repetitive reads and mean depth of 60.94 ± 18.52. The first hit in the BLASTx output for this contig was AFR11848.1 (506 aa) with a sequence identity of 55%, which was a protein encoded by the opening reading frame (ORF) 2 of a Hepelivirus from sewage from Nepal in 2009 [Halichondria panacea from Scotland in 2014, with only 26% sequence identity in the helicase and RdRp regions from positions 920 to 1771. Because both Hepelivirus and Barns Ness breadcrumb sponge hepe-like virus 2 are currently proposed to be unclassified viruses in the Hepeviridae family, we propose that the novel viral isolate may belong to the Hepeviridae family and was tentatively named as Crustacea hepe-like virus 1 (CHEV1).For all of the diseased giant freshwater prawns ( in 2009 . The secTo further confirm the assembly results, RT-PCR and Sanger sequencing based on this contig were performed using primers designed based on the NGS results . In addiOrthohepevirus and Piscihepevirus genera, as well as several unclassified species from fish, whereas CHEV1 was located in the branch of invertebrate hepeviruses (Hepelivirus (In order to study the phylogenetic position of CHEV1, the RdRp and capsid protein sequences were extracted and phylogenetically analyzed with those of representative HEVs. In both trees, the HEVs clustered into two major branches, which basically represented HEVs infecting vertebrates and invertebrates, respectively. The branch of vertebrate hepeviruses included the two previously classified eviruses . Consisteviruses A. In theelivirus B. Remarkelivirus .M. rosenbergii, which possessed two major ORFs and a hypothetical ORF3 and might represent a novel hepe-like virus in the family Hepeviridae. To our knowledge, it is the first hepe-like virus identified from crustaceans. Although we could not determine the novel virus as the causative agent for the outbreak based on current evidence, our study highlighted the expanding host range and the increasing species diversity of the family Hepeviridae.In summary, we described a novel hepe-like virus from diseased


The scientific community has responded to the coronavirus disease 2019 (COVID-19) pandemic by rapidly undertaking research to find effective strategies to reduce the burden of this disease. Encouragingly, researchers from a diverse array of fields are collectively working towards this goal. Research with infectious severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is undertaken in high-containment laboratories; however, it is often desirable to work with samples at lower-containment levels. To facilitate the transfer of infectious samples from high-containment laboratories, we have tested methods commonly used to inactivate virus and prepare the sample for additional experiments. Incubation at 80°C, a range of detergents, Trizol reagents, and UV energies were successful at inactivating a high titer of SARS-CoV-2. Methanol and paraformaldehyde incubation of infected cells also inactivated the virus. These protocols can provide a framework for in-house inactivation of SARS-CoV-2 in other laboratories, ensuring the safe use of samples in lower-containment levels. SARS-CoV-2 research requires the safe use of samples in low-containment laboratories. Heat, UV, and chemical reagents inactivated high titers of virus in solution and cells. These methods will facilitate use of once-infectious samples in downstream procedures. The novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in December 2019 in Wuhan, China, and spread to the rest of the world in a few months causing a pandemic . ResearcSeveral methods for virus inactivation are available and the choice of which approach to use is often related to their compatibility with downstream applications. Heat inactivation has been used for several viruses , 6 and i2. SARS-CoV-2 isolate SARS-CoV-2/human/Liverpool/REMRQ0001/2020, which was cultured from a nasopharyngeal swab from a patient, was passaged a further 4 times in Vero E6 cells. The fourth passage of virus was cultured in Vero E6 cells with DMEM containing 4% FBS and 0.05 mg/mL gentamycin at 37°C with 5% CO2 and was harvested 48 hours post inoculation. Virus stocks were stored at −80°C.Vero E6 cells  were obtained from Public Health England and maintained in Dulbecco’s minimal essential medium (DMEM) containing 10% fetal bovine serum (FBS) and 0.05 mg/mL gentamycin at 37°C with 5% CO7 plaque forming units (PFU) of virus. Inactivations with Trizol and Trizol LS were performed with 2.0 × 105 PFU. A multiplicity of infection of 0.001 was used to infect cells destined for fixation using 100% ice-cold methanol or 4% paraformaldehyde. Cells were incubated with virus for 20 minutes at 37°C and 5% CO2, prior to the addition of DMEM  and incubated for 48 hours. Heat inactivation was performed by incubating 300 µL of SARS-CoV-2 stock at 80°C for 1 hour. For inactivation with detergents, 0.5% sodium dodecyl sulfate [SDS], 0.5% Triton X-100, 0.5% Tween 20, or 0.5% NP-40 were incubated with virus for 30 minutes at room temperature. Inactivation with Trizol and Trizol LS used a 1:4 ratio of virus:Trizol reagent, which was mixed with the virus load and incubated at room temperature for 5 minutes. For fixation, after 48 hours the medium was removed and cells were removed by scraping. Cell scrapings were mixed 1:1 with the fixative for 30 minutes at room temperature. This was then centrifuged at 2500g for 5 minutes and the supernatant removed. Cell pellets were washed 2 times in phosphate-buffered saline (PBS) prior to resuspension up to 300 µL in PBS. UV inactivation was performed using a CL1000 UVP Crosslinker inside a microbiological safety cabinet. The CL-1000 UVP Crosslinker consisted of 5 × 254 nm shortwave tubes, which is the recommended wavelength for inactivating viruses, in particular SARS-CoV [2–0.8 J/cm2). All inactivation procedures were performed in triplicate, with the exception of NP-40 which was performed in duplicate.Heat, detergent, and UV inactivations were performed with 1.1 × 1050) assay with Vero E6 cells, using the entire volume of the sample. Control virus stocks containing 100, 101, and 102 PFU incubated at room temperature for 1 hour were used to determine the limit of detection of the assay. TCID50 plates were passaged onto fresh cells for 4 days at least twice to ensure no replicative virus remained. Cells were monitored daily for cytopathic effect (CPE).Heat-treated samples were evaluated for viable virus in a 50% tissue culture infectious dose  and centrifuged until ≤300 µL of sample remained. Virus samples treated with Tween 20 and NP-40 were diluted in 50 mL of PBS and concentrated until ≤300 µL of sample remained. Virus samples treated with Trizol reagents were diluted in 40 mL of PBS and concentrated until ≤300 µL of sample remained. Sample was extracted and virus culture medium was added to a final volume of 300 µL. Viable virus was evaluated in a TCIDescribed .0, 101, or 102 PFU using TCID50 assays. This was done by quantifying viral titers directly or by passing the sample through a centrifugal column, which is used to remove the inactivation agent before assaying on cells. Virus prepared without the centrifugal column was detected down to dilutions of 100 PFU of SARS-CoV-2 . Exposure of SARS-CoV-2 to UV light at 0.01 J/cm2 resulted in partial inactivation and this increased with greater UV energy exposure, resulting in complete inactivation at UV energy exposures of more than 0.04 J/cm2 , and down to 10 infectious particles/mL where a centrifugal column is used to concentrate the sample.Virus inactivation can be achieved by several methods. However, specific methods must be chosen to comply with requirements for subsequent downstream experiments. We used a range of techniques that are often used for preserving antigens or immunological proteins to evaluate their ability to inactivate SARS-CoV-2, including a range of common detergents and determining the threshold of inactivation by UV exposure. The assay to assess infectious particles was also shown to be sensitive, in some cases down to a single infectious virus particle (TCIDPhysical inactivation can be performed using heat or exposure to UV. Heat inactivates the virus by denaturing the structure of the proteins, affecting the attachment and replication of the virus in the host cell . In this2. Nevertheless, we recommend that for any changes in cell substrate, virus, or for new operators, the validation process is repeated to ensure that no infectious virus remains after UV inactivation.UV light causes genetic damage by inducing pyrimidine dimers or by producing reactive oxygen species . While oChemical inactivation can be performed using detergents and Trizol reagents, and we successfully demonstrated this with 5 different compounds: 0.5% SDS, 0.5% Triton X-100, 0.5% NP-40, Trizol, and Trizol LS. Conversely, Tween 20 did not inactivate SARS-CoV-2 under the same conditions. Detergents disrupt the lipid coat of enveloped viruses and are often present in lysis buffers of commercial nucleic acid extraction kits. These detergents typically do not affect proteins so they can be used in downstream procedures preserving their native structure. Our findings are consistent with previous studies showing that 0.1% SDS with 0.1% NP-40  could inFixation using 100% methanol or 4% paraformaldehyde is often used for downstream immunohistochemical techniques or methods that require stabilization of the cellular structure. While methanol acts to precipitate large proteins and will permeabilize cells allowing the assessment of intracellular structures , formaldWith the increasing interest in COVID-19, many researchers are now applying their knowledge and expertise to different topics to address this global problem. However, not all researchers have access to containment facilities and essential equipment is not often available at biosafety levels required to work safely with SARS-CoV-2. The inactivation methods described here will contribute to help diverse research groups perform their downstream work on SARS-CoV-2.


The possible association of patient safety events (PSEs) with the costs and utilization remains a concern. In this retrospective analysis, we investigated adult hospitalizations at a medical center between 2010 and 2015 with or without reported PSEs. Administrative and claims data were analyzed to compare the costs and length of stay (LOS) between cases with and without PSEs of the three most common categories during the first 14 days of hospitalization. Two models, including linear regression and propensity score-matched comparison, were performed for each reference day group of hospitalizations. Of 14,181 PSEs from 424,635 hospitalizations, 69.8% were near miss or no-harm events. Costs and LOS were similar between fall cases and controls in all of the 14 reference days. In contrast, for cases of tube and line events and controls, there were consistent differences in costs and LOS in the majority of the reference days . Consistent differences were less frequently seen for medication events and control events . Our study approach of comparing cases with PSEs and those without any PSE showed significant differences in costs and LOS for tube and line events, and medication events. No difference in cost or LOS was found regarding fall events. Further studies exploring adjustments for event risks and harm-oriented analysis are warranted. The context and practice of clinical care have improved in recent years in terms of patient safety through advances in medicine and technology. However, the occurrence of adverse events (AEs), defined as an injury caused during the health care process, rather than by the underlying disease or condition of the patient, remains a significant issue ,2,3,4. TPromoting patient safety usually involves a common strategy to detect patient safety events (PSEs), defined as an unexpected or unintended event that could have led or did lead to harm to the involved patient during the care process. Typical events might include patient falls; medication events such as prescription errors, dispensing errors, and drug administration errors; tube and line events such as inadvertent removal of an indwelling tube or line, etc. With this approach, studies have estimated an AE rate of 2.9–16.6% . TypicalBesides, the association between PSEs and healthcare utilization and costs has also been increasingly studied. A study in the period 2003–2004 reported the AE-related costs of over two billion Australian dollars annually . As therAmong the methods used to analyze PSE-related costs , linear The remainder of the paper is organized as follows. The next section outlines the methodology of this study. We subsequently present and discuss our findings and provide concluding remarks.This retrospective study was conducted at National Taiwan University Hospital to analyze the cost and healthcare utilization associated with PSEs between 1 January 2010, and 31 December 2015. The Institutional Research Ethics Committee of the National Taiwan University Hospital approved this study (#201709046RINA) and waived the need for informed consent from the patients. The hospital is a medical center, with about 2300 beds and approximately 6800 workers, including 460 attending physicians, 650 residents, 2800 nurses, and other workers. The hospital’s Incident Reporting System (IRS) was established in 2002; all of the PSE reports in this study were provided electronically. Staff from the Center for Quality Management of the hospital validated the content, patient harm, and severity of each PSE after receiving the report.Patients admitted to the hospital during the study period were included. Patients with at least one reported PSE were labeled as PSE cases; the remaining hospitalizations without any reported PSE were classified as controls. This labeling was used to retrieve relevant clinical, administrative and claims data upon request from the Integrated Medical Database (NTUH-iMD) of the Department of Research of the National Taiwan University Hospital, who provided de-identified data for research purposes. Patients were excluded if they had at least one of the following conditions: (1) admitted for end-of-life management; (2) admission not paid by the National Health Insurance program.patient safety event (PSE) was defined as an unexpected or unintended event, which could have led to or did lead to harm to the involved patient. These events consisted of the following types: an adverse event (AE) was defined as an injury caused during the health care process rather than by the underlying disease or condition of the patient; a no-harm event was defined as an event which resulted in no harm to the patient, or the effect was so minor that the patient could not even feel it; a near miss event was defined as an event that may have caused an accident, injury or illness, but did not due to an unintentional or timely intervention [A rvention .To analyze the association of PSEs with costs and healthcare utilization, the investigators acquired the following variable: demographic data, admitting department, category of event, the severity of harm to the patient, claimed medical expenses of the hospitalization, and length of stay.The following data were collected for each PSE from the IRS database: age of the patient, department, and ward for admission, category of events resulting in the PSE, severity of harm to the patient. Furthermore, after the approval by the Research Ethics Committee, the study investigators requested the Integrated Medical Database (NTUH-iMD) of the Department of Research of this hospital to providing de-identified clinical and administrative data. The database provided cases with de-identification data for research purposes. Patient-level costs were obtained from the claims for each admission for the reimbursement by the National Health Insurance of the Taiwan, which provided universal healthcare coverage of the patients and set the prices for hospital care. Costs were converted into US dollars based on the exchange rate toward the end of 2015. The inflation rate in Taiwan has been maintained at a steady low level, about 1.09% annually; therefore, the authors decided that the inflation rate would not be included in the adjustment. The primary outcome measures were total financial costs based on the claims and length of stay.selection bias to assigning patients with PSE to the control group because of the non-reporting of events. This situation could also result in information bias, in that the control group could also suffer from unreported PSEs. These types of bias could result in underestimation of the impact of costs and LOS caused by the PSEs.Since the under-reporting of PSEs remains a commonly understood issue across the institution, the study might encounter As the investigators were not able to have a reference cost estimate from a pilot analysis in the same hospital or a similar study from another institution, the study size was not estimated, and the investigators decided that all of the reported PSE in the IRS would be analyzed after proper inclusion and exclusion processes were applied.We first performed a descriptive analysis of the included reported PSEs regarding their category, severity, and time of occurrence concerning the admission date to show the general background scenario. Categorical data are expressed as number (%). Continuous variables were expressed as mean ± SD and 95% confidence interval (95% CI). We then performed inference analysis to compare the direct costs and LOS between the cases and controls according to the model used. We then selected the most common categories of PSE, together contributing to at least 50% of the PSEs. In this study, we used two models to estimate differences in the direct costs and LOS between the cases and controls. In both models, we only selected the cases with a reported PSE within the first 14 days of hospitalization as the cases and then stratified these cases into 14 groups based on the day when the PSE was reported. The control cases were those who did not have any reported PSE and were not discharged until at least the stratified day. For example, for the reported PSEs that occurred on the third day after admission, we only included controls with an LOS of at least three days. In the first model, we performed linear regression analysis to estimate direct costs and LOS, including age, gender, diagnosis, Charlson comorbidity index ,22, and p-value < 0.05 was considered to indicate statistical significance.Continuous variables were expressed as mean ± SD. Statistical analyses were performed using SAS software . All statistical comparisons were two-tailed. A During the 6-year study period, there were 424,635 admissions to the hospital. n = 1695), tube and line events , and medication events . Therefore, we decided to use the first 14 days of hospitalization in these three categories as the analysis cohort.A total of 14,181 PSEs related to hospitalized patients during the study period were reported to the IRS, with an incident rate of 3.3 per 100 admissions. We further analyzed differences in the costs and LOS for the three most common PSEs, including falls, tubes and lines, and medication events. We then selected the three most common categories  of PSE that together contributing to at least 50% of the PSEs. As a result, we chose three of the most commonly reported categories of PSEs for inference analysis based on the rationale that the occurrence of surgical and transfusion events should only develop in those patients who had received or at least been arranged for these two categories of treatment. As our study data lacked a detailed daily list of surgical procedures and the amount of transfusion, adjustment on these factors before comparison by each reference day was not possible. To estimate the differences in direct hospital costs and LOS, we selected PSEs within 14 days of hospitalization and control hospitalizations without PSEs. As can be seen in Our results showed that of the hospitalizations with and without PSEs during the first 14 days of admission, the extents of differences in costs and LOS varied across the three PSE categories. Differences in costs and LOS for fall events were not significant by either of the two comparison models; however, the differences were more consistently significant for tube and line events and were less consistently significant for medication events by the two estimation models. A feature of this paper is the novel approach with the reference day-stratified grouping of cases against controls based on the occurrence or absence of patient safety events. We believe that this methodology has not been reported in the literature. We also employed two analysis methods, including linear regression and propensity score-matched comparison, and the results were similar between the two methods. The comparison between these two methods based on reference day stratification approach has not been reported before, either.The comparisons of costs and LOS in this study were based on the presence or absence of reported PSEs through the institutional IRS; thus, in addition to AEs, the analyses also included events without associated patient harm and those reported for identified undesired processes, materials, equipment and facilities related to patient care. Therefore, this study provides a broader approach to the understanding of undesired contexts of care concerning the burden of healthcare systems. A study showed that AE-related additional financial costs were highest for surgery-related events, with an additional 10.9 hospital days and additional costs of USD 57,727, while an event occurring at medical wards led to an additional 9.8 hospital days and an additional cost of USD 38,656 . AnotherOur claims-data-based approach to estimate costs might have ignored other hidden costs such as administration costs in terms of reporting, data collection, meeting and discussion, improvement activities, and maintenance of the facilities and materials related to health care. Although the cost difference was not significant for fall events, the aggregated cost may still have had a large impact on healthcare. However, standardized methods to assess the burden related to PSEs are currently lacking . AlthougGiven the higher financial burden and longer LOS, our study may not imply causality between the occurrence of PSEs and higher costs. Specific categories of PSE, such as tube and line and medication, would not occur if the patients do not receive the specific materials for the intervention. The complexity of a clinical condition might also be associated with the materials used, and intervention received, contributing to a higher cost and carrying a higher risk of PSE. Therefore, the cost and LOS may have the same risk factors as the cases with PSEs. Moreover, as most of the patients with PSEs did not suffer from any harm, a longer LOS is less likely to be caused by the PSE, but may be related to the disease and the care provided. Another possible explanation is that the occurrence of a PSE, even if it did not result in patient harm, may be linked to an undesired quality relating to the care process and undesired context. This situation may also have affected the effectiveness and efficiency of care, as reflected by increased costs and prolonged LOS. Further studies to comprehensively investigate the financial impact associated with these events are needed. There are several limitations to this study. First, reporting PSE was voluntary; therefore, underreporting of PSEs is highly probable, as has been reported previously . HoweverIn conclusion, we aimed to analyze the difference in cost and length of stay between admission with PSE and without one. Our findings showed mixed results for the differences in costs and length of stay between admission with PSE and without. The differences in costs and LOS were not significant in cases with fall event and those without PSE, while the costs and LOS differences were more significantly different in terms of tube and line events and medication events. We employed reference day-based analysis with propensity score-matched comparison to provide a possible method for the estimation of PSE-associated cost and LOS. This method might provide a deeper understanding of the impact of PSEs on the healthcare burden. Further studies exploring adjustments for event risks and harm-oriented analysis are warranted.


Leucine-rich-repeats (LRRs) belong to an archaic procaryal protein architecture that is widely involved in protein–protein interactions. In eukaryotes, LRR domains developed into key recognition modules in many innate immune receptor classes. Due to the high sequence variability imposed by recognition specificity, precise repeat delineation is often difficult especially in plant NOD-like Receptors (NLRs) notorious for showing far larger irregularities. To address this problem, we introduce here LRRpredictor, a method based on an ensemble of estimators designed to better identify LRR motifs in general but particularly adapted for handling more irregular LRR environments, thus allowing to compensate for the scarcity of structural data on NLR proteins. The extrapolation capacity tested on a set of annotated LRR domains from six immune receptor classes shows the ability of LRRpredictor to recover all previously defined specific motif consensuses and to extend the LRR motif coverage over annotated LRR domains. This analysis confirms the increased variability of LRR motifs in plant and vertebrate NLRs when compared to extracellular receptors, consistent with previous studies. Hence, LRRpredictor is able to provide novel insights into the diversification of LRR domains and a robust support for structure-informed analyses of LRRs in immune receptor functioning. Magnaporthe grisea [The leucine-rich-repeat (LRR) domains are present in all of the tree of life branches. As they are involved in protein–protein interactions, LRR domains are found in receptors having a vast number of functions such as pathogen detection, immune response propagation, hormone perception, enzyme inhibition, or cell adhesion . In bothe grisea  while mue grisea . Additioe grisea ,6, wheree grisea , and lase grisea . Hence, In innate immunity, LRR modules are found in various domain organizations in many receptor classes such as plant receptor-like kinases (RLK), receptor-like proteins (RLP), NOD-like receptors (NLR), or metazoan NLR and Toll-like receptors (TLR). In plant basal immunity, LRR N-terminal domains face the extracellular environment and are found in either receptor-like kinases (RLK) or receptor-like proteins (RLPs) depending on the presence or absence of a C-terminal kinase domain on the cytosolic side of the receptor. By contrast, LRRs constitute the C-terminal domains of intracellular NOD-like receptors (NLR), also known as resistance (R) proteins, and face the cytosolic environment to mediate resistance against specific pathogens. Depending on their N-terminal domain, which is either a coiled-coil (CC) or a toll-like receptor domain (TIR), R proteins fall into two main NLR classes: the CNL and TNL receptors, respectively . Both thFrom a structural point of view LRR domains have a solenoidal ‘horseshoe’ like 3D architecture composed of a variable number of repeats varying each from ≈15 to ≈30 amino acids in length. Repeats are held together through a network of hydrogen bonds which forms a beta sheet located on the ventral side of the ‘horseshoe’. This is generated by a conserved sequence pattern named the LRR motif that in its minimal form is of the type ‘LxxLxL’ where L is generally leucine and to a lesser degree other hydrophobic amino acids . ComprehWhile the above mentioned receptor classes were shown to present LRR irregularities , studiesA proper annotation of each LRR motif in a given LRR domain is instrumental in generating an accurate 3D model ,23 and bBased on our previous work, identifying the individual true motifs in a LRR domain is hindered by the following: (a) in its minimal form, a ‘LxxLxL’ pattern is trivial and frequently occurs randomly in any protein; (b) in many cases several ‘LxxLxL’ patterns do overlap in less than 15 aa range in NLR-LRRs making the precise delineation difficult; (c) the number of 3D experimental structures from which to learn is low; and (d) this small 3D learning set is class and phyla biased—as around half of the structures are of mammalian origin while plant NLRs only have one recently documented structure ,22.Thus, given the above described indeterminacies the precise LRR motif identification becomes the most problematic step in the correct repeat delineation within a LRR domain. This also explains why LRR domains and their individual repeats are poorly annotated in genomes or protein databases in contrast to the better annotated, relatively more conserved NBS domain, which has therefore been used in phylogenetic analyses ,25. HencWe assessed how LRRpredictor behaves within different classes of immune-related receptors that contain LRR domains, such as plant NLRs, RLPs, and RLKs and vertebrate NLRs and TLRs with the aim to provide novel insights into the diversification of LRR domains and their role in the functioning of immune receptors.Various protein domain databases, such as CATH , Pfam 2, and IntUsing this procedure, we generated the 90% identity data set, ID90, consisting of 172 N-ter LRR ‘entry’ repeats (N), 1792 LRR ‘core’ repeats (L), and 154 C-ter LRR ‘exit’ repeats (C) . To avoiJensen–Shannon divergence (JSD) scores e were cothe length of the alignment is over 100 and the identity is over 20%.length of the alignment is between 40 and 100 with an identity over 20% and the overlap with respect to both sequences is more than 60%.LRR repeats with alignments lengths ≥16 aa and ≥50% identical .In order to provide a representative collection of non-LRR examples, we selected a representative example of each CATH  domains’The final dataset built as above and used herein for training and testing classifiers, contains 648 LRR core repeats, 100 N-ter entry, and 67 C-ter exit nonredundant repeats (including the LRR domain flanking regions) and 875 non-LRR domains from CATH.From this set, 1/5th was used to generate the test dataset, while the remaining 4/5 were used to build the training datasets, preserving the class ratio between the sets. The test dataset contains 40,241 amino acid samples of which only 150, i.e., less than 0.4%, are initiating LRR motifs. Similarly, over the training set less than 0.5% of the samples are LRR initiators. The training set was further split into four cross-validation sets that were used for parameter optimization. All these sets are provided in In developing LRRpredictor we tested sequence-based (SeqB) features: solely or combined with structural based (StrB) features. The SeqB features comprise position-specific scoring matrices PSSM over the above discussed 16 amino acids interval summing up to 320 features corresponding to 20 amino acid types over the 16 positions. The StrB features comprise: (a) the three state  secondary structure probabilities, (b) the three class  residue relative solvent accessibility, RSA probabilities and (c) intrinsic disorder probability—summing up to seven extra structural features per residue, resulting in a total of 432 features per 16 aa window. The structural based predictions were performed with RaptorX-Property software ,35,36,37In the pre-processing stage, feature variables were normalized, centered, and rescaled, as standard procedure involves. Data whitening using principal component analysis (PCA) decomposition was not used as it did not provide better performance on the tested classifiers.Several classifiers such as support vector classification (SVC) , multi-lThe best performing classifiers with tuned parameters were further studied in the context of a soft voter (that averages predicted probabilities of the ensemble constituents), and a final predictor, further referred to as LRRpredictor, was chosen based on its out-of-sample performance on test set and overfitting behavior on the training data. LRRpredictor is composed of a set of eight classifiers (C1–C8) that use different strategies and consider all N, L, C motif types as a single class, aggregated within an ensemble based on the soft voting scheme, as shown in Classifiers C1–C4 use solely sequence-based features while C5–C8 use both sequence and structural-based features. Classifiers C1 and C5 use the support vector classification (SVC) algorithm , with a Classifiers C2, C3, C6, C7 use multi-layer perceptron (MLP) ,42. A deClassifiers C3 and C7 approach the imbalance problem through synthetic resampling using the combined over- and under-sampling method SMOTETomek , as implClassifiers C4 and C8 use a ensemble boosting approach—AdaBoost —using trIn order to investigate LRRpredictor behavior on previously annotated LRR domains from various functional protein groups, we generated a collection of randomly selected 500 representatives from Uniprot50 database  which were annotated by Interpro to contain a LRR domain (IPR032675 and Interpro v77.0 protein2ipr database).A total of six groups were generated: four groups of sequences of CNLs, TNLs, RLKs, and RLPs protein classes from flowering plants and two groups of TLRs and NLRs from vertebrates. Given the high conservation of vertebrate TLRs this set gathered only ≈350 sequences .Within the CNL group, there were included only proteins annotated by Interpro to contain a single coiled-coil (CC) domain, a single NBS domain, and a LRR domain in this order, and sequences that contained a different domain organization, such as two annotated NBS domains or a different domain order were not included in the analysis. Similarly, for the TNL group we selected only sequences that contain a TIR-NBS-LRR domain organization. The RLK group was built with sequences displaying a “LRR-TM predicted region-kinase” domain organization, while the RLP group contained sequences with “LRR-TM” organization and did not contain other annotated domains by Interpro. In generating the vertebrate NLR group we included any annotated NACHT or NBS domains followed by a LRR domain annotation without discriminating on the N-terminal domain, as animal NLRs can have upstream of the NACHT/NBS domain a multitude of N-terminal domain types, while vertebrate TLRs group contains sequences with a “LRR-TM-TIR” configuration. Transmembrane predictions were performed using Phobius .In analyzing the length of the LRR domains covered by individual repeat annotations, we used all Interpro annotation codes associated with LRR repeat types. We considered as having the status of ‘annotated as domain‘ LRRs with the IPR032675 label and ‘annotated as repeats’ any amino acids that had attached by at least one predictor part of Interpro collection one of the following tags: leucine-rich repeat (IPR001611), leucine-rich repeat, typical subtype (IPR003591), leucine-rich repeat, cysteine-containing subtype (IPR006553), leucine-rich repeat 2 (IPR013101), leucine-rich repeat 3 (IPR011713), leucine rich repeat 4 (IPR025875), BspA type leucine rich repeat region (IPR026906), CD180 leucine-rich repeat (IPR041281), DUF4458 domain-containing protein, leucine-rich repeat (IPR041403). Annotations referring to the N-ter cap of the LRR domain  were not considered as these are not LRR repeats. d to be the distance between a pair of amino acids i and j, that have the s  BLOSUM score:Intra- and inter-group sequence variability was also analyzed using a subset of 1000 predicted 16 aa extended motifs from each group. In order to avoid a potential bias induced by false ‘entry’ (N) or ‘exit’ (C) repeats, only ‘core’ (L) repeats were used in this analysis. The similarity measure used here is the distance mapping defined by Halperin et al. . This coa and b of equal length l, would be the sum of distances of each pair of amino acids ia and ib across the length of the sequence:The distance between two sequences This definition of distance is expected to reflect amino acids compatibilities, as BLOSUM scores are inferred from amino acid mutation probabilities observed on large datasets. As a BLOSUM matrix we selected an updated version of the original BLOSUM matrix, which was recently recalculated on a large dataset and satisfies the triangle inequality. RBLOSUM59_14.3) ,59._14.3 ,59Starting from the above described distance function, we calculated Silhouette coefficients  between Sequences logos were generated using Weblogo , figuresA collection of 611 PDB structures previously annotated by several protein domain databases, such as CATH , Pfam 2, and Int0) of the minimal motif ‘L0XXL3XL5’, position that marks the beginning of the ventral side of the horseshoe domain . This results in the ID50 nonredundant set consisting of ≈850 LRR repeats, that approximates well the ID90 distribution of lengths c, phyla 0) that is often found solvent exposed, as this position marks the end of the inter-domain linker and the beginning of the LRR domain. By contrast, the ‘exit’ C-ter LRR motifs better resemble the ‘core’ motifs (L) amino acid composition and the conventional LRR motif ‘L0xxL3xL5xx(N/C)8xL10’ 8xL10’ d. Intereequently d. This s to form d. Arabidopsis thaliana [Another important facet that has to be carefully pondered is the high phyla bias of the structural data when compared to the baseline phyla distribution of the UniRef50 database. As can be seen from thaliana ,22. In order to train a machine learning (ML) estimator for detecting LRR motifs we used an overall dataset comprising the filtered LRR ID50 dataset and a collection of 875 non-LRR domains composed of one representative of each CATH topology a.true structural LRR motifs identified in ID50 are quite common in any protein. We will name here such sequence patterns as potential motifs. As expected, potential motifs occur with more or less equal probability in both the LRR and non-LRR domains of the overall dataset. Moreover, even when taking into account only LRR domains the number of potential motifs is larger than the number of true structural LRR motifs  approach, where models were iteratively trained on three of the CV sets and tested on the remaining fourth b. A poolFinally, LRRpredictor was trained on the entire training set  and tested on the test set which had been set aside.The precision of LRRpredictor given by the fraction of true-positives (TP) predicted results over the sum of true-positives (TP) and false-positives (FP) varies between 89% and 97% on the test set and within cross-validation sets a. Similaplants and TNLplants) and extracellular receptors (RLKplants and RLPplants) and two groups from vertebrates—NLRvert and TLRvert as described in As the available structural data is scarce, we further evaluated the extrapolation capabilities of LRRpredictor on a set of LRR domains annotated in Interpro collection. Groups of the most representative protein functional classes containing LRR domains were generated as follows: four groups from flowering plants—resistance proteins  show a sharp peak at 24 amino acids, in agreement with the most frequent repeat length within plant-specific (PS) from KK classification [plants and TNLplants ) have a larger distribution with a lower peak shaping up toward lower value side (20–24 aa) of repeat lengths range.Selected sequences from each group were subjected to LRRpredictor motif detection. The repeat length distribution of the predicted LRR repeats a, is confication ,65. As tAs seen in We further analyzed the percent of the annotated LRR domain span that is covered by LRRpredictor and compared the predicted LRR motifs to LRRfinder  and LRRsThe repeat coverage of analyzed LRR domains predicted by LRRpredictor, LRRfinder and LRRsearch were compared to the extent Interpro repeat annotations using a coverage percentage (CP) defined as the ratio between the sum of predicted/annotated repeat length vs the overall LRR domain length b. any repeat annotation resulting in CP = 0% 8xL10xxxgoxxLxxoLxx’ [This is especially the case for vertebrate NLRs. The consensus follows the ribosomal inhibitor (RI) type - ‘xLxxoLxx’ ,65, with-3xxL0xxL3xL5xxN8xL10xxL13xxxx(F/L)18xxL21xx “T” type motif: Ldefined in Matushima et al. classification  rather t-3xxL0xxL3xL5xxN8xL10xxL13Px(x)LPxx.“S” type motif: Lplant and RLPplant groups show a prolonged pattern that is in perfect agreement with the plant-specific (PS) type from Kobe and Kajava classification [-3xxL0xxL3xL5xxN8xL10(S/T)11GxIPxxLxxLGx. Interestingly, the kinase containing receptors (RLK) have a more prominent consensus  type as defined by Kobe and Kajava classification [On the other hand, the predicted motifs in plant NLRs comprising the CNLfication ,65:-3xxL0xxL3xL5xxC8xxITDxxOxxL(A/G) xx”—where O is any nonpolar residue. “(C/L)0xxL3xL5 motif, with TNL extending it a little bit with C8 position. By contrast, in plant extracellular receptors the consensus expands beyond the 16 amino acids of the ‘extended’ region covering all the four sides of the LRR solenoid. Despite being analogous in composition, the TNLplant group consensus is more pronounced, especially at positions C8 and L11 (While the extended motif is satisfied (16 aa), a difference worth noting is that in both CNL and TNL groups cysteine is rare in position ‘-3’ and outside this region any similarity with CC-type ends. Both plant NLR groups mainly confine their consensus to only the minimal L and L11 .0, L3, and L5 of minimal motif are as expected overwhelmingly hydrophobic, with all three positions occupied by leucine in around 50% of the cases, except CNLs where leucine occurrence seems less stringent , while the extracellular receptors obey in over 60% of the cases the corresponding W11 pattern that is shared simultaneous by all three classes .Sequence variability is of critical importance for LRR domain function and in contrast to their common structural pattern, a wide spread in the sequence space is expected. To assess this, we analyzed the extended motifs, predicted by LRRpredictor, both the intra- and inter- group sequence similarity. This was performed over subsets of randomly selected 1000 examples of ‘core’ (L) motifs from each group. We selected as similarity measure a metric distance function  derived 0XXL3XL5’ there are no significant differences between groups  of this 2D embedding is 0.25 and 0.21 for the extended and minimal motif space, respectively [To have an overall view on the sequence dispersion in each protein class containing LRR domains ectively .From a structural point of view the LRR protein architecture belongs to the larger class of solenoidal architectures which are defined by specific repeated structural patterns. Given the repetitiveness of such structures we asked if LRRpredictor is able to discriminate between LRR motifs and other repetitive sequence patterns. The main candidates considered for possible misclassifications are two classes of beta sheet repeat proteins—which are the closest structural relatives of LRR domains: pectate lyases (PeLs) and trimeric LpxA architectures and two helical repetitive classes: armadillo and ankiryin architecture b. To thiGiven the high number of indeterminacies generated by sequence variability, a proper annotation of LRR motifs and the correct delineation of repeats is critical in identifying potential protein–protein interaction sites of LRR domains. Here, we show that LRRpredictor is able to address this problem and by this, can be of use as a new tool in the analysis of especially plant NLR sequences that display a larger variability and irregularity as compared to other LRR domains ,68. ThisGiven the scarcity of structural learning data consisting of less than 180 LRR structures with lower than 90% identity and only ≈850 motifs at hand in ID50 (<50% identity), in order to maximize LRRpredictor extrapolation abilities, the method was set to rely on aggregating a collection of eight classifiers based on different strategies, two of them designed to perform a massive oversampling of the real data .precision, recall, and F1 scores ranging between 85% and 97% on both test and cross validation sets  perform on average only slightly better compared to those sequence based only (C1–C4), with some interesting improvements on F1 scores b. This oHowever, the increased performance of LRRpredictor comes with an attached computational cost and is not easily scalable for scanning large protein sequences databases such UniprotKB. The main reason for this is that generating case by case sequence profiles and performing predictions for each estimator aggregated in LRRpredictor is more computationally demanding than LRRfinder and LRRsearch workflow.Another matter of concern was related to the phyla bias of the database on which LRRpredictor relies, as ≈50% of ID90 have mammalian origin while the share of mammalian—from total—annotated LRRs in UniRef50 is only 2% c. Moreovplants, TNLplants, RLKplants, RLPplants) and their vertebrate counterparts . The LRR motifs predicted by LRRpredictor show a good coverage of the LRR domains annotated by Interpro and follow the expected repeat length distribution for all these six classes [In this context, in order to investigate the extrapolation capabilities of LRRpredictor we used a set of LRR domains annotated in Interpro collection from the six most representative immune receptor classes: R-proteins and extracellular receptors from flowering plants  of their host targets conferring host specific immunity. Single amino acid changes in the effector can already be detected or are sufficient to evade recognition by a NLR, resulting in a co-evolutionary arms race between pathogen effectors and host immune receptor ,70. In cAnother aspect is that LRR domains in plant NLRs have a dual role. They not only contribute to pathogen recognition, but also negatively regulate the switch function . Hence, The results presented herein indicate that LRRpredictor shows a good performance on the available 3D data and good extrapolation capabilities on plant NLRs (CNL/TNL), which are poorly represented in the training dataset. Predicted LRR repeats using LRRpredictor significantly increase the coverage of Interpro annotated LRR domains from main immune receptors groups. In addition, these predicted repeats are consistent with previously defined motif consensuses from all studied groups and also follow the repeat length range specific to each class. In conclusion, LRRpredictor is a tool worth using in research topics related to understanding immune receptors functions and structure-informed strategies for pathogen control technologies.


Humans are the only natural reservoir for herpes simplex virus 1, which can invade its host after contacting mucosal surfaces or through abraded skin. Primary infection, either ocular or extraocular, manifests clinically only in a percentage of people yet to be determined, considered to be less than 20%. Therefore, since approximately 80% of patients are asymptomatic, the infection can spread without being recognized most of the time.-Following primary infection, the virus establishes in sensory neurons where it remains for the lifetime of the host. Upon reactivation, the virus reappears, usually at the mucocutaneous junction of the lips, causing herpes labialis, commonly known as cold sores or fever blisters. However, following reactivation, the virus rarely involves the ophthalmic branch of the trigeminal nerve and causes ocular disease, including keratitis A 60-year-old woman presented with red eye, a foreign body sensation, and moderate photophobia in the right eye for one week. She was receiving a combination of antibiotics and topical steroids prescribed by a general practitioner.-,Upon examination in the slit lamp, four separate dendritic-shaped epithelial lesions highly suggestive of epithelial herpetic keratitis were found. However, the lesions presented with a multifocal compromise, which is a very uncommon presentation since these corneal epithelial ulcers are usually single lesions of variable size . One facThis case reaffirms the probability that the use of these substances can worsen the clinical picture when the virus is compromising the epithelium. Therefore, since this condition can be confused with infectious conjunctivitis, general practitioners should be cautious and look for symptoms such as photophobia, which would suggest corneal involvement. The use of topical steroids should be avoided, since these drugs can aggravate the ocular affectation caused by the herpes virus, as in this case.Keywords: Keratitis; herpetic eye disease; herpes simplex; cornea; corticosteroids.Palabras clave: queratitis, enfermedad ocular herpética, herpes simplex, córnea, corticosteroides.