Diabrotica balteata LeConte and thereby protect maize plants from damage by these generalist rootworms. EPN formulated in beads were as effective as sprayed EPN at killing D. balteata. They were found to protect maize plants from D. balteata damage, but only if applied in time. The treatment failed when rootworm attack started a week before the EPN beads were applied. Hence, the well-timed application of EPN-containing alginate beads may be an effective way to control root herbivores.To face the environmental problems caused by chemical pesticides, more ecologically friendly alternative pest control strategies are needed. Entomopathogenic nematodes (EPN) have great potential to control soil-dwelling insects that cause critical damage to the roots of cultivated plants. EPN are normally suspended in water and then sprayed on plants or onto the soil, but the inconsistent efficiency of this application method has led to the development of new formulations. Among them is the use of alginate capsules or beads that encapsulate the EPN in favorable conditions for later application. In this study, we evaluated whether alginate beads containing EPN are able to kill larvae of the banded cumber beetle  Xenorhabdus and Photorhabdus, respectively, which are lethal to insects [Environmental and public health concerns linked to the use of chemical insecticides have led to pressures to develop alternative strategies for insect pest control. Biological control using the pests’ natural enemies is commonly recognized as an appropriate alternative . For soi insects . The lif insects . The onl insects . The IJs insects ,7.d-mannuronate and α-d-guluronate in varying proportions and sequential arrangements [Heterorhabditis heliothidis and Steinernema feltiae, in calcium alginate by dropping an EPN-containing alginate solution into a CaCl2 solution, resulting in capsules containing immobilized EPN. Hiltpold et al. (2012) [Heterorhabditis bacteriophora, they obtained promising results when they applied the capsules in the field to protect maize plants from damage by the Western corn rootworm Diabrotica virgifera virgifera [2+ [2+ solution containing EPN into the alginate solution, whereas for the formation of alginate beads the process is reversed. This way, more than 4000 EPN can be successfully incorporated in one bead . Field application of the EPN beads developed by Kim et al. (2019) reduced the damage to maize roots caused by D. virgifera virgifera as effectively as EPN that were applied in water.EPN are used against a wide range of insect pests because of their ease of culture, high lethality against key pests, and nonexistent safety issues ,9. Neverngements . Kaya anngements  were the. (2012)  producedirgifera . Howeverfera [2+ . More refera [2+  adopted Diabrotica balteata LeConte. D. balteata larvae feed on a wide range of plants, with preference for plants of the Cucurbitaceae, Rosaceae, Leguminoseae, and Crucifereae families [Heterorhabditis bacteriophora was found to naturally parasitize D. balteata larvae and to effectively reduce their numbers in pot trials [D. balteata larvae after application of beads with quiescent EPN and compared it to EPN applied in water and (2) we evaluated whether maize plants infested by D. balteata larvae benefit from the protection offered by EPN beads.In the current study, we evaluated the effect of EPN formulated in beads on the larvae of families . They dafamilies ,19. Hetet trials , which iH. bacteriophora strain was obtained from Andermatt Biocontrol  and was maintained using Galleria mellonella (L.) larvae as described by Kaya and Stock (1997) [G. mellonella larvae were obtained from Au pêcheur SARL . The EPN used for the experiments were less than two weeks old.The k (1997) . G. mellZea mays L., variety Delprim) were grown from seed in commercial soil  in individual plastic pots . The plants were grown under natural light conditions (16:8 h L:D) in a greenhouse (24 ± 5 °C) and were watered as needed, they were used for the experiments 8–10 days after planting, when they had four fully expanded true leaves.Maize plants . Beads were sieved after 20 min polymerization time and quickly rinsed with water. The added glycerol induced the nematodes to enter in a state of quiescence, immobilizing the EPN inside the beads.Following the method described by Kim et al. (2014), EPN beads were produced by dropping approximately 85–95 μL of the EPN-alginate-glycerol solution  into a CaH. bacteriophora in killing the target insect , we conducted an experiment comparing four treatments that were applied to the potted maize plants: (1) EPN-containing beads; (2) EPN suspended in water; (3) empty beads; and (4) control (no application). For the bead treatments, two beads containing approximately 5000 IJs or two empty beads were buried in the soil of the pots  when the maize plants were in the four leaves stage. EPN in water solution were applied onto the soil in 10 mL of water (1000 EPN/mL). Two days after the application of the treatments, ten second instar D. balteata larvae were carefully placed on top of the soil. Pots were covered with parafilm to prevent D. balteata larvae from escaping. Four days after the larvae were added, larval mortality in the soil of each pot was assessed by counting the retrieved larvae. Each treatment was replicated 20 times in two separate experimental batches. To analyze the data, expressed as percentages, a General Linear Model fitting with a quasipoisson distribution was used. Statistical differences between treatments were calculated using a Tukey test (package “lsmeans”). Statistics were carried in R version 3.4.1  [To evaluate the efficacy of EPN beads containing quiescent Austria) .H. bacteriophora formulated in EPN beads in protecting plants by killing D. balteata larvae, we conducted an additional experiment with bead treatments. In this case, the potted  maize plants were infested with 6 s instar D. balteata larvae per pot, and an equal number of plants were left uninfested. Two beads each containing 2000 IJs were applied either (1) the same day when the plants were infested with D. balteata larvae (n = 16) or (2) one week after the infestation (n = 12). This time, the beads did not contain glycerol, therefore EPN were not in a quiescent state, which allowed them to escape quicker from the beads and immediately infect the larvae. Pots were covered with parafilm to prevent the larvae from escaping. We measured plant growth, i.e., the difference in plant size  at the beginning and the end of the experiment, as well as plant fresh weight one week after applying the D. balteata larvae. Two by two comparisons were performed using a Wilcoxon Signed-Rank test performed in R version 3.4.1 [To evaluate the efficacy of on 3.4.1 .H. bacteriophora applied in alginate beads (EPN beads) and in aqueous suspension (EPN in water) were equally effective in killing D. balteata larvae. Overall, the differences among the four treatments were highly significant  but the mortality caused by EPN beads (86%) did not significantly differ from mortality caused by free EPN (77%) (p = 0.2). Empty beads had no effect on larval mortality (p = 0.15) ( = 0.15) .H. bacteriophora applied in alginate beads a week after infestation failed to protect the plant from D. balteata damage. In this trial, plant growth and weight were significantly reduced by D. balteata in all treatments except for the plant weight in the empty beads treatment  ( = 0.01) A,B.D. balteata on plant growth and weight in the control and empty bead treatments  but the EPN bead treatment completely neutralized the negative effect of the larvae on plant growth and weight   C,D.Diabrotica spp. have shown inconsistent results, ranging from low to very high levels of efficacy [Diabrotica spp. laboratory tests, but the success of field applications depends largely on environmental conditions, formulation type, and application techniques [2+-alginate capsules to protect them from the abiotic constrains [H. bacteriophora in beads, described by Kim et al. [D. balteata under laboratory conditions. During the production of the beads, quiescence was induced by adding 18% glycerol. After application of the beads into sufficiently moist soil, quiescence was broken and the EPN readily escaped from the alginate beads and infected the target insect. Under laboratory conditions, the control  provided by the beads containing quiescent EPN was just as good as applying equal numbers of EPN in aqueous suspension (Studies that tested EPN to control efficacy . EPN canchniques ,24,25. Snstrains ,16,17,26spension . Thus, tD. balteata-inflicted root damage to an extent that it enhanced plant performance. The timing of the bead application was found to be critical to obtain a protective effect of EPN on maize plants. Indeed, when the D. balteata infestation was initiated one week before bead application, the EPN were not able to prevent a significant reduction in either plant growth or weight (D. balteata larvae because they had tunneled into the root system. This important effect of timing should also be taken into consideration if we wish to use of glycerol to trigger EPN quiescence. Soil moisture and other factors will play a role in the recovery process from quiescence. It is essential that this recovery occurs before the pest larvae start damaging the roots. More studies on the effect of glycerol on EPN release from the beads in different soils will be needed to be able to pinpoint the ideal timing of application to achieve optimal plant protection. Overall, the results of these experiments confirm the potential of EPN formulated in beads to kill rootworms and to protect crops from root damage.We also confirmed that the bead application reduced r weight A,C. Hower weight B,D. The Diabrotica balteata, and were able to protect maize plants from larval damage under laboratory conditions. The timing of alginate beads application was critical to ensure an effective protection of the plants.The alginate beads containing entomopathogenic nematodes were as efficient as entomopathogenic nematodes applied in water to kill larvae of the banded cucumber beetle,


Apis mellifera) colonies in some parts of the world, and regional population declines of some mining bee species (Andrena spp.), are attributed to multiple factors including habitat loss, lack of quality forage, insecticide exposure, and pathogens, including viruses. While research has primarily focused on viruses in honey bees, many of these viruses have a broad host range. It is therefore important to apply a community level approach in studying the epidemiology of bee viruses. We utilized high-throughput sequencing to evaluate viral diversity and viral sharing in sympatric, co-foraging bees in the context of habitat type. Variants of four common viruses  were identified in honey bee and mining bee samples, and the high degree of nucleotide identity in the virus consensus sequences obtained from both taxa indicates virus sharing. We discovered a unique bipartite + ssRNA Tombo-like virus, Andrena-associated bee virus-1 (AnBV-1). AnBV-1 infects mining bees, honey bees, and primary honey bee pupal cells maintained in culture. AnBV-1 prevalence and abundance was greater in mining bees than in honey bees. Statistical modeling that examined the roles of ecological factors, including floral diversity and abundance, indicated that AnBV-1 infection prevalence in honey bees was greater in habitats with low floral diversity and abundance, and that interspecific virus transmission is strongly modulated by the floral community in the habitat. These results suggest that land management strategies that aim to enhance floral diversity and abundance may reduce AnBV-1 spread between co-foraging bees.Bees are important plant pollinators in agricultural and natural ecosystems. High average annual losses of honey bee ( Bees gather plant-produced pollen and nectar as their protein and carbohydrate sources, respectively, and in doing so provide a critical ecosystem service—the pollination of numerous plant species. Approximately 87.5% of flowering plants benefit from animal-mediated pollination, including bees . Bee-polBombus affinis) and the western bumble bee  in the US . N. N139]. overage) . This saactivity .Varroa destructor mites that parasitize honey bees, and infect native and wild bee species and other insects [5 to ≥ 5 × 1012 viral RNA copies per bee  [Varroa destructor mites, which also vector DWV, can kill colonies [Deformed wing virus strains commonly infect honey bees, replicate in and are transmitted by  insects ,140,141. ~50 mg) ,143,144. ~50 mg) ,95,145, colonies ,147,148.Apis mellifera library generated from the Galon3 site (GB3) which was dominated by white mustard , but had low honey bee activity on the sample date , and DWV-C, as well as recombinant viruses ,149,150.ple date . The depSinaivirus group, which includes over 200 NCBI sequence entries ranging in nucleotide identity from ~63% to 100% (Lake Sinai virus-1 (LSV-1) and Lake Sinai virus-2 (LSV-2) were the first two viruses identified in what is now recognized as the  to 100%  [61,111,The majority of the unique LSV contigs in this study were most similar to LSV-2 (HQ888865). The LSV-2 contigs included the longest (up to 5997 nt) and most abundant contig ), which accounted for 37.4% of the LSV-2 contigs that were clearly assigned to a particular NCBI designated LSV species/strain. Contigs most similar to LSV-NE (NC_035113) were also well represented in sequence libraries .Apis mellifera library from the RBSh site, with up to 326,028x coverage in the middle of the genome and over 50,000x coverage at the 5′ and 3′ ends of the genome (The LSV-2 Israel-2018 consensus sequence (MW397637) identified in this study, via alignment and assembly of unique LSV-2 contigs >400 nt, was 77% identical to LSV-2 (HQ888865) . Interese genome . The capApis mellifera sequencing library from the Berko2 (B2) site, which had high floral diversity and low honey bee activity. LSV-NE contig 51 was the longest (5952 nt), well-represented contig  is most similar to LSV-NE sequence (NC_035113) with which it shares 84% nucleotide identity . Unique d counts ).High-throughput sequencing of representative samples obtained from this sample cohort was essential in the development of new qPCR primers designed to detect the viral variants in this study . This reApis mellifera genome nor with the non-viral sequences in the bee holobiome revealed two large, novel contigs that we named AnBV-1 RNA 1 (2005 nt) and RNA 2 (2721 nt)  sequencing , was discovered via high-throughput sequencing of RNA samples from mining bees. Virus discovery efforts focused on sequence data obtained from “virus-augmented” sequence libraries, which were generated from RNA isolated from nuclease treated lysates of pooled mining bee or honey bee samples. The purpose of nuclease treatment prior to RNA isolation was to digest all unprotected, non-encapsidated nucleic acid in bee lysates and thus augment the amount of nucleic acid that was protected by virus capsids in sequencing libraries. Assembly of contigs, using Trinity, from reads that did not align with the 2721 nt)  [100,134quencing . Attemptquencing . CollectAndrena spp. collected at the Ramat Beit Shemesh (RBSh) site, which had low floral diversity and high honey bee activity. AnBV-1 coverage reached up to 1.45 × 106-fold for RNA 1 and up to 5.9 × 105-fold for RNA 2   and quantitative PCR for SBV, BQCV, LSV, DWV, and AmFV (High-throughput sequencing coverage of AnBV-1 was greatest in the library prepared from or RNA 2 . AnBV-1 and AmFV . Rapid AAnBV-1 open reading frames (ORFs) were predicted in SnapGene Viewer and Geneious Prime . PredictAnalysis of the two largest open reading frames of AnBV-1 RNA 2 using BLASTp indicates that ORF2 encodes the putative RNA-dependent RNA polymerase (RdRp) . The putDicistroviridae , Iflaviridae , and Sinaiviridae  families, we constructed a phylogenetic tree of viral RdRp amino acid sequences. This analysis revealed that the AnBV-1 RdRp sequence forms a well-supported monophyletic clade with sequences from tombus-like viruses. In particular, these results indicate that, based on their RdRp sequences, AnBV-1 shares a most recent common ancestor with Castleton Burn virus . Therefore, like other +ssRNA viruses, a negative-strand copy of the viral genome must be produced during viral replication, as it serves as the template for the production of viral transcripts and full-length genome segments. Strand-specific reverse-transcription (RT)-PCR was performed on RNA isolated from AnBV-1-positive samples. Visualization of AnBV-1 RNA 1 and RNA 2 products from negative-strand targeting reactions indicate that AnBV-1 productively infects bees .2 = 44.057, df = 1, and * p-value = 3 × 10−11) (p = 0.0279), whereas the difference in RNA 2 abundance was not significant, p = 0.29  . Comparip = 0.29 . Since qp = 0.29 . TogetheAndrena sample with high AnBV-1 abundance (1.3–2.6 × 108 copies AnBV-1 per µl lysate). Mock infections were carried out with clarified lysate obtained from a separate individual Andrena sample without detectable levels of AnBV-1 (7 +/−2.9 × 106 copies per 24 ng RNA) were greater than at 0 dpi (1.4 × 107 +/−1.5 × 106 copies per 24 ng RNA) . Though a similar trend was observed for AnBV-1 RNA 2, the increase was not statistically significant until 7 dpi (5 × 107 +/−2.5 × 106 copies per 24 ng RNA) as compared to 0 dpi (1.2 × 107 +/−3.4 × 106 copies per 24 ng RNA) , which is likely a reflection of the low sample size  due to the limited availability of honey bee pupal cells for infection in the late fall in Montana, when honey bee queens cease egg laying in preparation for winter. Together the increasing trend in AnBV-1 RNA, which persisted until the conclusion of the experiments at 7 dpi, indicates that AnBV-1 replicates in honey bee pupal cells   the density of AnBV-1-infected mining bees, (ii) the species diversity of flowers utilized by honey bees, and (iii) the abundance of flowers of plant species visited by both mining bees and honey bees. In this study, honey bees, which are generalist pollinators, visited more flower species than the mining bees, which preferred to forage on yellow mustards  . In the  0.0001) B. Indeed 0.0001) . These rp = 0.1496) (p = 0.0038)  B. Howeve 0.0038) C. This s 0.0038) B. The ob 0.0038) . WhereasIn summary, data from this sample cohort underscore the importance of a broad investigation of the bee-associated virome and provide a framework for future investigation of bee virus ecology. The majority of bee-infecting viruses are RNA viruses, which often exist as quasispecies. Therefore, it is important to incorporate sequencing  to identify novel viruses and viral variants, prior to assessing viral prevalence and abundance at the individual bee, colony, and community levels. Virus discovery efforts have primarily focused on honey bees, but it is clear from this and other studies that the diversity of bee-infecting viruses is greater than currently appreciated. Likewise, detection of bee-infecting viruses, which are typically referred to as “honey bee-infecting viruses”, in a broad range of hymenopteran insects, indicates that investigation of interspecific virus transmission should consider a broader potential host range.Apis mellifera and four specialist Andrena spp.) and a novel bipartite +ssRNA virus named Andrena-associated bee virus-1 (AnBV-1) that replicates in mining bees, honey bees, and primary honey bee pupal cell culture. AnBV-1 was most prevalent and abundant in mining bees, though it was also detected in honey bees, albeit at lower frequency. Statistical modeling that examined the roles of ecological factors, including floral diversity and infected mining bee density, indicated that the probability of AnBV-1 infection in honey bees is greater in low-floral-diversity habitats, and suggested that interspecific transmission is strongly modulated by the floral community in the habitat. Future studies that examine the temporal dynamics of bee/insect density, virus prevalence and abundance, and habitat-associated parameters will be important to reveal patterns of virus transmission, clearance, and reinfection of bee taxa within bee communities. Likewise, studies aimed at understanding the impact of AnBV-1 on individual bees, bee colonies, and bee communities are required. It will also be interesting to investigate the impact of multiple viruses  from the individual bee to community levels to determine their impact on bee behavior and pollination activity, and/or survival. The data from this study suggest effects of epidemiological dilution by floral diversity and abundance, and that interspecific virus transmission is modulated by the floral community in the habitat. Therefore, land management strategies that aim to enhance floral diversity and abundance may reduce AnBV-1 (and possibly other viruses) spread between co-foraging bees. Regardless of the impact of the bee/insect community virome, efforts aimed at enhancing and/or maintaining bee forage in managed and wild landscapes will likely benefit all plant and animal communities.For this study, we opted to sample multiple sites, with distinct floral and bee communities, within a single blooming season and focus our analyses on the two most prevalent co-foraging bee taxa (i.e.,


Sixteen grapevine cultivars from Mediterranean Croatia were surveyed for the presence of 10 of the most economically important grapevine viruses. The presence of Grapevine fanleaf virus (GFLV), Arabis mosaic virus (ArMV), Grapevine leafroll associated virus-1, -2, and -3 , Grapevine virus A (GVA) and B (GVB), Grapevine fleck virus (GFkV), Grapevine rupestris stem pitting associated virus (GRSPaV), and Grapevine Pinot gris virus (GPGV) were tested by reverse transcription polymerase chain reaction (RT-PCR) and enzyme-linked immunosorbent assay (ELISA). All 71 analyzed clones were positive for the presence of one or more viruses. The most abundant one, detected in almost 95% of samples was GLRaV-3. In most of cases it was reported in mixed infections with GVA, GRSPaV, and GPGV. Virus genomes of GLRaV-3 infected vines were further characterized molecularly in order to determine their genetic diversity. Different genomic variants of heat shock 70 protein homologue (HSP70h) were identified by single-strand conformation polymorphism (SSCP) and sequenced. Sequence analysis confirmed their clustering into phylogenetic group I and/or phylogenetic group II. This study emphasizes the wide virus heterogenicity in Mediterranean vines and the predominant presence of GLRaV-3 phylogenetic groups I and II, either individually or in combination. In comparison to other cultivated crops, grapevines are amongst those infected with the greatest number of viruses. About 80 virus species have so far been identified in this species. The most damaging and widespread grapevine viruses are those causing four major diseases: leafroll disease complex, rugose wood complex, infectious degeneration and decline, and fleck .Vitis vinifera infected with GLRaV-3 [Grapevine leafroll-associated virus 3 (GLRaV-3), the main causal agent of leafroll disease, is one of the most important virus pathogens worldwide. It is a positive-stranded RNA virus, belonging to the genus Ampelovirus, family Closteroviridae . While s GLRaV-3 .Pseudoccocus spp.; Planococcus spp.) and soft scale insects  which ta vitis; . RepeateSince the first report on GLRaV-3 population structure , geneticOnce infected, symptom expression within each plant depends on virus genotype, environmental conditions and in some minor extant, agrotechnical measures. Some native varieties were found to stand out by their good sanitary status as they were grown in isolation and particularly, not grafted on rootstocks that, although infected, do not show symptoms . SimilarIn this study, 16 most widespread native grapevine varieties from Croatian Adriatic coast were analyzed for the presence and distribution of 10 most important grapevine viruses by both RT-PCR and ELISA. For the most significant pathogen, GLRaV-3, different genomic variants were characterized after their molecular analysis.In each of 71 analyzed plants, the presence of at least one of the ten tested viruses was confirmed by both molecular and serological assays. The most abundant virus was GLRaV-3, which was detected in 94.37% of plants . Only 4 Only two samples of cv Plavac mali were single infected with GLRaV-3, while all other samples had multiple infections. The most common combination (16.42%) comprised GLRaV-3, GVA, GRSPaV, and GPGV .The majority of the samples (26.76%) were infected with four different viruses , while tThirty-three (33) selected GLRaV-3 positive samples were further analyzed in order to identify and characterize different virus variants present within each isolate. The SSCP analysis of 20 clones per sample whenever possible  revealed the existence of more than one genomic variant in tested samples .Different genomic variants were sequenced in a number roughly proportional to its population frequency as suggested by the SSCP results. Not all clones exhibiting different SSCP patterns, showed affiliation to different phylogenic groups . As showAvailable results of viral distribution in Croatian vines mostly rely on serological detection tools ,15,16 soThis research has elucidated GLRaV-3 as the most common virus in native grapevine cultivars from Mediterranean Croatia, highlighting its vast presence in 95% of analyzed vines. GLRaV-3 was mainly detected in mixed infections with GVA, GPGV and RSPaV . PreviouThe damage caused by GLRaV-3 in Croatian vineyards has not been systematically documented, but available data , it has Beside the confirmed presence of GLRaV-3, as in the case of the other Mediterranean countries ,22,23,24The prevalence of nepoviruses which are considered the most dangerous grapevine viruses was much lower than others . The leaAfter GLRaV-3, the second and the third most abundant viruses in the present study were GRSPaV and GVA associated with the rugose wood complex disease. Confirming data from surveys carried out in the Mediterranean basin ,27 as weGPGV, an emerging virus that is not yet included into viticulture certification programs in Europe, appears to be widely distributed in Croatia. RT-PCR detection revealed its high occurrence in Croatian autochthonous vine cultivars . AccordiSignificant differences in virus distribution were found among cultivars. These may be due to their original vine-growing area, exposure to infection, and/or to their specific susceptibility to different viruses. Plavac mali is the main local cultivar of Mediterranean Croatia, representing 9.1% 1697.3 ha) of the total vineyard acreages in the Country, and considered the second rated cultivar . This cu97.3 ha oNot only were the differences in viral distribution between cultivars found, but occasionally also between clones of the same cultivar. Further surveys should be conducted to confirm whether some cultivars such as Plavac mali, Grk, or Prč are generally characterized with the lower susceptibility to certain viruses by taking into consideration their complete area of distribution. There are some findings that native varieties stand out for their good sanitary status  similar No plants were found without viruses, so the deteriorated sanitary status of valuable Croatian cultivars calls for a strategy to ensure virus-free planting material.Due to the highest prevalence, GLRaV-3 was further explored in order to identify and molecularly characterize the different genetic variants present in the tested samples. The obtained sequences clustered into two (groups I and II) out of the ten phylogenetic groups already described . Since tLooking at the distribution of individual viruses in the studied germplasm collection and taking into consideration the mode of their spreading, we can speculate about horizontal virus transmission happened at some point. At the present time, the visual inspection of known viral vectors gave negative results and the collection vineyard is regularly treated against the insect vectors or nematode. Therefore, the correlation of the presented results with the natural incidence of viral infections and their molecular characteristics present in the field cultivars should be explored more cautiously.The samples were collected from a germplasm collection vineyard located at the Institute for Adriatic Crops in Split, Croatia. The collection was established during the long-term identification and conservation program of old native grapevine varieties, encompassing all main autochthonous cultivars from Mediterranean Croatia collected along the coastal area, more than 350 km long. We selected 16 cultivars belonging to the eastern Adriatic autochthonous set of grapevine cultivars: Babica, Babić, Crljenak, Dobričić, Glavinuša, Grk, Kujunđuša, Malvasia dubrovačka, Plavac mali, Plavina, Pošip bijeli, Pošip crni, Prč, Rukatac, Vugava, and Zlatarica vrgorska. Whenever possible, 5 clone of each cultivar, 71 samples in total, were tested for the presence of ten major grapevine viruses both serologically and molecularly. Both reactions were performed on dormant canes collected in the late winter. Fresh tissue was grinded in liquid nitrogen and proceed further for the screening of Grapevine fanleaf virus (GFKV), Arabis mosaic virus (ArMV), Grapevine leafroll associated virus-1, -2, and -3 , Grapevine virus A, (GVA) and B (GVB), Grapevine fleck virus (GFkV), Grapevine rupestris stem pitting associated virus (GRSPaV) and Grapevine Pinot gris virus (GPGV). Selected samples that tested positive for GLRaV-3 were further analyzed based on the nucleotide sequence of HSP70h gene.To gain a preliminary insight into the sanitary status of cultivars in the experiment, all 71 samples were tested by available DAS-ELISA (Double antibody sandwich-ELISA) diagnostic tests  for: ArMAfter grinding in liquid nitrogen, samples were diluted in 2 mL of extraction buffer, prepared according to the manufacturer instructions. The samples were incubated with capture antibody at 4 °C overnight, followed by the incubation with conjugate antibody next day, for 2 h on 37 °C. Two hours after adding substrate p-nitrophenylphosphate, asorbance was recorded at 405 nm using an automatic microplate reader.The total RNA was extracted from 250 mg of cortical scrapings using RNeasy Plant Mini Kit  applying an improved RNA extraction procedure as described by MacKenzie et al. . ReverseThe detection of relevant grapevine viruses associated with leafroll , rugose wood (GVA and GVB), infectious degeneration (GFLV and ArMV), and GFkV, was performed by multiplex PCR as reported by Gambino and Gribaudo , whereasAt least two plants of each of the 16 GLRaV-3-positive cultivars, 33 samples in total, were selected for further molecular characterization. A fragment 545 bp) of the HSP70h gene was amplified using LC1F and LC2R primer pairs . Differe5 bp of tSelected amplicons were sequenced in both directions by Macrogen Europe  using primer pair LC1F/LC2R. Representative sequences of each cultivar were deposited in GenBank under the accession numbers: MW316065-MW316084 Reference sequences from different GLRaV-3 phylogenetic groups were selected from Diaz-Lara et al.  and retrThese are the first results on screening of 10 different viruses in Croatian East Adriatic native grapevines by applying RT-PCR diagnostic tools and on investigating GLRaV-3 genetic variability using SSCP method in Croatia. The study confirmed the dominance of GLRaV-3 and revealed its relatively low genetic variability. The results contribute to the understanding of the global distribution of the virus, the biological significance of coinfections and the potential dangers for the wider region in case of their potential spreading.


To describe the mechanisms of implementation of Zika virus diagnosis, prevention, and management guidelines in Colombia, and to characterize their influence on efforts to defend sexual and reproductive rights.A qualitative study performed between February and April 2018 in three municipalities in Colombia. We conducted 30 semistructured interviews and five focus groups with key informants who played a role during the epidemic. These included decision‐makers, program coordinators, healthcare providers, pregnant women diagnosed with Zika virus, and members of affected communities.We identified barriers to and facilitators for the implementation of the national Zika virus response plan. Barriers included a lack of coordination between vector control efforts and in the realms of sexual and reproductive rights. Facilitators included healthcare providers’ response to the epidemic, the development of technical skills, and the establishment of coordination and referral networks across different institutions.A multidimensional approach that considers healthcare services, gender issues, and the environment is crucial. We highlight the epidemic's effects on women's sexual and reproductive rights, mainly related to inequalities in sexual and reproductive health such as the increased risk of sexually transmitted infections experienced by the poorest and most vulnerable women. The Zika virus epidemic has different effects on women's and men's reproductive health. This calls for a multidimensional approach to the epidemic. Currently, Zika virus exists in 69 countries, and birth defects linked to infection during pregnancy have been described in 29 countries.In Colombia, between 2015 and 2017, vector‐borne diseases like malaria, dengue, chikungunya, and Zika virus affected more women than men.Zika virus has profound consequences for women's SRHIn this context, a plan to address Zika virus should go beyond classic health‐related actions or vector control strategies (such as improving living conditions and water sources) and should incorporate actions that address sexual and reproductive rights (SRR). Addressing the epidemic should include recognition that Zika virus affects women and men differently, that the virus is sexually transmitted, and that early diagnosis of symptomatic pregnant women is key, as is access to contraception and safe abortion.In 2016 the Colombian government prioritized Zika virus in the political agenda and started the implementation of the Zika Virus Fever Response Plan.The present study puts forth a multidimensional approach to Zika virus as a first step to understanding the epidemic's impact on SRH issues. The objective was to describe the mechanisms of implementation of the national guidelines to detect, prevent, and treat Zika virus; then describe their impact on SRR in Colombia. The focus was on three municipalities that were affected differently by the epidemic: Barranquilla, Cúcuta, and San Andrés.2This qualitative study was conducted in Colombia between February 21 and April 10, 2018. The study was approved by the Pan‐American Health Organization Ethical Review Committee (PAHOERC) and the Ethical Committee of Profamilia. All participants signed an informed consent form. We conducted semistructured interviews and focus groups with key informants who belonged to one of the following groups: (1) national government; (2) local/regional government; (3) healthcare providers; (4) community members; and (5) women diagnosed with Zika virus. We used study instruments to identify facilitators and barriers in the implementation of the National Zika Virus Fever Response Plan,We conducted a total of 25 interviews (17 women and eight men) with decision‐makers, those responsible for the development and implementation of the Response Plan, and representatives of local health departments, insurers, and healthcare providers. Respondents also included coordinators of vector‐borne disease programs and other public health surveillance, education, and prevention specialists. We also interviewed five pregnant women with a confirmed Zika virus diagnosis and conducted five focus groups with community leaders and community members.We recruited respondents according to location and logistical capabilities and continued recruiting until we reached thematic saturation. In San Andrés the local health department took the lead in recruitment, while in the other two municipalities Profamilia oversaw recruitment. All interviews were transcribed and then analyzed using ATLAS.ti software 7.5.4 .3We identified facilitating factors that spurred the implementation of actions to address the Zika virus epidemic. We also identified barriers and aspects to improve in the implementation process.3.1The following factors spurred and facilitated the implementation of actions to address the Zika virus epidemic: (1) the healthcare sector's response; (2) development of technical capabilities; and (3) communication among different institutions.3.1.1In all study locations, healthcare providers and community members all agreed that the needs of pregnant women needed to be prioritized because they represent the highest risk group. Some respondents highlighted that healthcare providers emphasized SRR by conducting educational activities with affected communities. These included information about delaying pregnancy, using barrier methods of contraception, and abortion. Educational campaigns were also conducted outside of healthcare centers, and healthcare personnel received training on Zika virus. These activities reduced barriers to care and enhanced the detection of cases among pregnant women as well as their follow‐up care.3.1.2National and local health authorities established agreements and coordinated efforts to conduct trainings on Zika virus and develop programs to care for these individuals. As a result, technical skills and capabilities were enhanced at a national level. The national Ministry of Health also collaborated with the Centers for Disease Control and Prevention (CDC) in the USA on a program to identify pregnant women at risk and detect the consequences of infection  through early diagnosis. Epidemiologic data were used to establish clinical protocols to care for pregnant women, which included early diagnosis and treatment of the disease.Teams of technical experts also improved their skillset by working at the local and regional level. After the first cases were reported, efforts were made to characterize the virus and devise epidemiologic surveillance and clinical practice protocols. However, obstacles were encountered with sending biologic specimens to the National Institute of Health in Bogotá, and long wait times were noted before a confirmatory result was available. This impacted the clinical follow‐up of affected patients.3.1.3Public and private healthcare providers and research institutions coordinated efforts and shared objectives, which helped strengthen the surveillance capacity of the public health system as well as the capacity to diagnose and treat cases at the health system level. This also allowed members of varying institutions to access the trainings described above. At the national and regional level, as well as among healthcare providers, there was consensus that the Ministry of Health and the National Institute of Health had been key in determining the successful implementation of national guidelines.3.2The following paragraphs describe the three barriers that emerged, as a critique to the implementation of the National Zika Virus Response plan: (1) focus on sexual and reproductive health; (2) focus on regional initiatives; and (3) attitudes and knowledge at the community level.3.2.1The profound implications of Zika virus on SRR were not completely recognized and integrated into the Response Plan; for example, recognition that Zika virus is a sexually transmitted infection and that access to health services is key for the affected population. Zika virus outbreak prevention and management efforts focused on vector control rather than on SRH consequences.The focus on SRH was promoted at the national level, but these efforts were not sufficiently integrated with the National Policy on Sexuality, Sexual and Reproductive Rights.3.2.2Training and preparation efforts were primarily oriented toward developing competencies directly related to Zika virus, rather than toward the epidemic's impact on women's SRR. All of the interviewees discussed Zika virus vector control programs, including cleanup of mosquito breeding grounds and management of water and sewage systems. Community education programs encouraged people to wear covering clothing and mosquito repellent to prevent bites. Public information campaigns included information about how to reduce the risk of infection, how to identify the symptoms, and how to access healthcare services. These campaigns targeted people who lived in or transited through areas at high risk of transmission.3.2.3In terms of attitudes, what stood out was the use of knowledge to implement preventative actions against the spread of the vector, as well as the attention to the disease at the individual, family, and community level. However, lack of information and barriers to accessing services persisted, and some infected individuals who did not access healthcare services decided to self‐medicate with “home remedies.”Pregnant women with suspected or confirmed infection expressed the need to reinforce communication and prevention campaigns and to focus on their needs as women. We also found that the modes of transmission of the virus were not well explained to the public, and the recommendation to use barrier methods of contraception was not strong enough. In summary, the response to Zika virus at the community level and in terms of personal behavior change was inadequate.4Our study identified facilitating factors for and barriers to the implementation of the National Response Plan to the Zika virus epidemic in Colombia. The most significant lesson learned was that a multidimensional approach to Zika virus is necessary to address the interaction of the epidemic with gender roles, sexual and reproductive health and rights, and the environment.In addition to providing health care, providers engaged in health education and prevention campaigns. They achieved some success in terms of aligning public health efforts to address Zika virus with efforts to defend sexual and reproductive health and rights and to improve the health system's resilience during the epidemic. However, the National Zika Virus Response Plan was not aligned with the National Policy on Sexual and Reproductive RightsIn the communities, knowledge about SRR in the context of Zika virus was reduced to abortion. However, access was limited because the issue is stigmatized, and women were not well informed about their right to access safe abortion. SRH was not understood as a broad concept, which resulted in the loss of opportunities to provide care and promote contraceptive counseling among women in affected areas.Our findings are aligned with those described by Vilela Borges et al.,At the community level, the absence of effective, gender‐focused messages to prevent Zika virus reinforced stereotypes and inequalities. Healthcare services need a more strategic focus to reach communities early, to broaden their offer of counseling and contraceptive services, and to eliminate barriers to accessing abortion in the context of an epidemic. Health programs that address Zika virus and other epidemics must recognize that affected women and men have different needs and that there can be a significant impact on women's SRH. Furthermore, it is crucial that knowledge and research findings reach affected communities.RM and LF‐M conceptualized the research project. RM directed the fieldwork and data collection. MC‐J analyzed the data. JR‐G edited the original manuscript. All authors contributed to the manuscript and approved the final version.The authors have no conflicts of interest.


RNA viruses are a leading cause of human infectious diseases and the prediction of where new RNA viruses are likely to be discovered is a significant public health concern. Here, we geocoded the first peer-reviewed reports of 223 human RNA viruses. Using a boosted regression tree model, we matched these virus data with 33 explanatory factors related to natural virus distribution and research effort to predict the probability of virus discovery across the globe in 2010–2019. Stratified analyses by virus transmissibility and transmission mode were also performed. The historical discovery of human RNA viruses has been concentrated in eastern North America, Europe, central Africa, eastern Australia, and north-eastern South America. The virus discovery can be predicted by a combination of socio-economic, land use, climate, and biodiversity variables. Remarkably, vector-borne viruses and strictly zoonotic viruses are more associated with climate and biodiversity whereas non-vector-borne viruses and human transmissible viruses are more associated with GDP and urbanization. The areas with the highest predicted probability for 2010–2019 include three new regions including East and Southeast Asia, India, and Central America, which likely reflect both increasing surveillance and diversity of their virome. Our findings can inform priority regions for investment in surveillance systems for new human RNA viruses. There is a lack of evidence on the factors driving the discovery of RNA viruses in general globally. Here, we recorded the initial discovery sites of all 223 human RNA viruses and revealed its global distribution pattern. By using a machine learning method, we found that the virus discovery was driven by a combination of variables describing socio-economic level, land use, climate and biodiversity, with GDP and GDP growth found to be the two leading predictors. We also predicted the probability of virus discovery in 2010–2019 across the globe, and identified three new areas  in addition to the historical high-risk areas. The further stratified analyses  helped pinpoint the explanatory factors for the discovery of specific categories of viruses and confirm the plausibility of the model. The results of our study further understanding of the spatial distribution of human RNA virus discovery, and map the likelihood of further discoveries across the world. By identifying where new viruses are most likely to be discovered in the near future the study helps identify priority areas for surveillance. Since the first identification of a virus in humans—yellow fever virus in 1901—viruses have been recognised as a leading cause of human infectious diseases  to life-Human RNA viruses comprise a total of 214 International Committee on Taxonomy of Viruses (ICTV)-recognised species as of July 2017, classified into 55 genera and 22 families ,9,10, thThe discovery curve of human viruses, for both RNA viruses and DNA viruses, was described for the first time in 2008 . Up to nIn this study, we followed methods and used code derived from Allen, et al . We comphttps://datashare.is.ed.ac.uk/handle/10283/2970), which contains 214 viruses, with discovery dates between from 1901 to 2017. Search terms, databases searched, and inclusion or exclusion criteria for data collection was provided in our previous paper , five climatic variables , and one biodiversity variable . The partial dependence plots shown in S2 Fig showed the relationships between these explanatory factors and virus discovery. For the majority of explanatory factors, the relationship with discovery probability is non-linear, with large effects often seen over a narrow range of values. For example, discovery probability fell sharply if GDP growth was negative, and for very low GDP and low percentage of urbanized land; whereas it rose sharply for high minimum temperature and high mammal richness.Based on the full BRT model involving all 223 viruses, twelve variables had relative contributions greater than the mean (3.03%) , indicating that this modelling method with 33 explanatory factors effectively removed the spatial dependence of the model residuals. Sensitivity analyses  revealed consistent trends with the full model, though with several changes of relative contribution.Our full BRT model reduced the Moran’s I for the raw virus data from a range of 0.04–0.31 to 0.007–0.065 , including four climatic variables , three land use variables , two socio-economic variables , and one biodiversity variable . In contrast, eight variables had relative contributions greater than 3.03% for discovering viruses transmissible in humans , including four explanatory factors involving urbanization , three socio-economic variables , and one climatic variable (minimum precipitation: 5.0%).In the transmissibility-stratified BRT model, ten variables had relative contributions greater than 3.03% for discovering strictly zoonotic viruses , including five climatic variables , two socio-economic variables , one biodiversity variable , and five land use variables . In contrast, seven variables had relative contributions greater than 3.03% for discovering non-vector-borne viruses , including four land use variables , two socio-economic variables , and one climatic variable (minimum precipitation: 3.3%).In the vector-borne-stratified BRT model, thirteen variables had relative contributions greater than 3.03% for discovering vector-borne viruses  to the fitted full BRT model, we obtained a predicted probability of human RNA virus discovery in 2010–2019 . Comparison with Fig 1 indicates that virus discoveries remain relatively likely in eastern North America, Europe, central Africa, eastern Australia and north-eastern South America but, in addition, we predict high probabilities of virus discovery across East and Southeast Asia, India and Central America. All eighteen new virus species since 2010 were discovered in regions of high-risk as predicted by our model , and eleven of them were discovered in very high-risk areas . The predictions of discovery for each category of virus are shown in S7 Fig. Broadly similar patterns as the full prediction model were seen for all four categories: high probabilities of virus discoveries are predicted in East and Southeast Asia, India, and Central America in comparison with the historical distribution . However, there is some variation between virus categories: strictly zoonotic viruses are more likely to be discovered in northern South America, central Africa, and Southeast Asia, while transmissible viruses are more likely to be discovered in North America, East Asia, and India ; and vector-borne viruses are predicted to be more likely to be discovered in northern South America, central Africa, India, and Southeast Asia than non-vector-borne viruses .By applying 2015 values of all 33 explanatory factors , land use variables , climate variables , and biodiversity . The predicted probability map in 2010–2019 identified three new areas across East and Southeast Asia, India, and Central America in addition to the historical high-risk areas.We focused on the discovery of RNA viruses in human(s) in this study, rather than emergence. This is determined by the attribute of the database itself, i.e. the first report of each human RNA virus from the literature review. The discovery location may or may not represent the origin of the virus. For example, HIV-1 is believed to originate from non-human primates in West-central Africa, and is estimated to have transferred to humans in 1920s , but theFig 1 with S6 Fig suggested that more viruses have been discovered in developed regions with/without fast GDP growth including North America, Europe, and Australia. We note that more developed countries are more likely to first capture viruses circulating in multiple regions. Over the last 100 years, North America and Europe have witnessed a decreasing fraction of discovered viruses in more recent decades (1985–2018: 32/86 = 37%) than previously (1901–1984: 78/137 = 57%), but Asia has accounted for a higher fraction  (S1 Video). This can be partly explained by the higher GDP and faster GDP growth in Asia in recent decades. In addition, there have also been historical hotspots in individual countries  associated with active virus discovery initiatives such as those supported by the Rockefeller Foundation  A letter containing a detailed list of your responses to the review comments and a description of the changes you have made in the manuscript. Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.[2] Two versions of the revised manuscript: one with either highlights or tracked changes denoting where the text has been changed; the other a clean version (uploaded as the manuscript file).Important additional instructions are given below your reviewer comments.Please prepare and submit your revised manuscript within 60 days. If you anticipate any delay, please let us know the expected resubmission date by replying to this email. Please note that revised manuscripts received after the 60-day due date may require evaluation and peer review similar to newly submitted manuscripts.Thank you again for your submission. We hope that our editorial process has been constructive so far, and we welcome your feedback at any time. Please don't hesitate to contact us if you have any questions or comments.Sincerely,SteveStephen MorseGuest EditorPLOS PathogensDavid WangSection EditorPLOS PathogensKasturi HaldarEditor-in-ChiefPLOS Pathogensorcid.org/0000-0001-5065-158XMichael MalimEditor-in-ChiefPLOS Pathogensorcid.org/0000-0002-7699-2064***********************Reviewer's Responses to QuestionsPart I - SummaryPlease use this section to discuss strengths/weaknesses of study, novelty/significance, general execution and scholarship.Reviewer #1: This paper uses a number of techniques, most significantly an ensemble of boosted regression trees (BRT)-based approach, to find associations between a set of predictor variables and their outcome variable representing RNA viral discovery.The paper appears to follow methods and workflows strongly derived from Allen et. al. (2017) (currently citation 13), particularly in the generation of its BRT models and associated figures. I would suggest that the Methods section include a statement like, “We followed methods and used code derived from Allen et al (2017)…,” where describing derived methods/code/workflows.https://journals.plos.org/plospathogens/s/materials-and-software-sharing). Sharing model and figure code removes any ambiguity that may be present in the natural language description of a study’s methods as a result of space constraints or the difficulty of describing complex processes in natural language. Code should preferably be shared with an appropriate license on GitHub and assigned a DOI using a service such as Zenodo, but could also be included with the study’s Supplementary Information, as its dataset already is.In addition, the paper should share the code used to generate it’ models, as per the recommendations in PLOS’s Materials and Software Sharing policies  for its main model. That the different datasets would yield finds similar results undergirds the conclusions of the earlier papers. Some of their high-influence variables are similar  and others are divergent (e.g. finding an association and significant impact for climate variables). These differences between similar previous studies are worth publishing.This study also runs different BRT models for biologically distinct subsets of RNA viruses, and the finds notable differences between the drivers of these models. These findings are notable because they describe differences in the global distribution of viral discovery for different types of viruses, and second because they demonstrate that such a method can be used to identify such these sorts of differences between, say, zoonotic-only and human-transmissible diseases.It’s my opinion that these contributions — the influence of climate variables and the stratified analyses — are the biggest points of departure from earlier literature, and this this studies best contributions.There are a few other points that could be improved in the study’s discussion of its findings. These are noted below.Reviewer #2: There are many strengths to this study, including their recognition of the need to deal with the critical issue of variation in research effort.However, throughout the manuscript I found myself challenged by what ‘virus discovery’ really means in the context of this study. I was often confused about whether the authors are truly focused on the factors that influence virus discovery, or if they are actually focused on factors that influence virus emergence. Indeed, the opening two sentences of the abstract reflect this unresolved conflict between predictors of emergence  and virus discovery (“we matched these data….to predict the probability of virus discovery”). While this might seem like semantics, I think it is critical to identify and articulate specifically what the paper is trying to achieve. Discovery and emergence are not the same process, and thus presumably should be approached differently in study design and interpretation. There are many many examples throughout the text where the two processes are confused and, unfortunately, this limited my ability to evaluate the specific contribution of this study.I kept going back to the example of Reston virus, which was discovered in Virginia (U.S), but geocoded to Manilla, Philippines. If the study is about understanding factors underlying virus discovery, it seems odd to ascribe the discovery of Reston virus to Manilla. Reston virus was discovered because infected animals happened to end up in the hands of an ebolavirus expert at a high-containment lab, in a completely different country. I can understand why the authors decided to code Reston virus to the Philippines ; however, I question whether these spatial covariates are relevant to its discovery in the U.S. In this case, I think the spatial data that is linked to the Philippines would tell us more about virus transmission and emergence than discovery, and I worry about spurious associations. The same issue exists for Ebola virus (species Zaire ebolavirus), which was ascribed to DRC when it was actually discovered by Prof Piot in Belgium.Ignoring, for a moment, whether these viruses are appropriately geocoded for their particular question, there is another problem. That is, the approach taken to geocode the viruses is inconsistent. For example, while Reston virus and Ebola virus were coded to reflect their point of origin , other viruses like influenza were coded to reflect where they were indeed first ‘discovered’. Influenza was first characterized in 1933 in Mill Hill, London, and the authors have geocoded it accordingly. The approach taken in coding influenza therefore appears to be different from the approach taken for Reston and Ebola. While the spatial covariates for Reston virus and Ebola virus are capturing one type of information , the covariates for other viruses like Influenza or Hepatitis C are capturing a different type of information .Throughout the paper, I worry that ‘discovery’ and ‘emergence’ have been confused. In the author summary, they state “By identifying where new viruses are most likely to occur in the future, the study helps identify priority areas for surveillance.” The phrase “most likely to occur” suggests they are indeed interested in emergence. Equally, in the discussion they state “This implies that it is the change in habitat—from natural or rural to urban—has a greater influence on virus discovery than human population size or density.” Again, this sentence implies that the study is about emergence, not discovery. However, elsewhere in the text they refer specifically to need to “identify the factors driving the discovery of RNA viruses”. I think it would be important to more clearly articulate exactly what the study is focused on (I think this is virus discovery) and comment on how this links or informs on our understanding of viral emergence (if indeed it does). Currently, I am not sure how to interpret the following statement in their abstract: “…areas with the highest predicted probability for 2015–2024 include new foci in East and Southeast Asia, India, and Central America”. Does this really mean areas where new viruses are likely to be discovered? Or does it mean areas where new viruses are likely to emerge, and then subsequently discovered simply because they are emerging?I also found the definition of ‘discovery’ confusing. It was defined as ‘the first isolation of a virus in a human patient.’ Given that many human viruses were first isolated years after they were actually discovered, it would be good to clarify whether the authors really do mean ‘isolated’. . In reviewing the supplemental material, I see that the authors have used 1989 for hepatitis C. This makes me think they do not actually mean ‘isolated’, but rather ‘discovered’. Hep C was first cloned in 1989, but it was not successfully grown in culture (‘isolated’) until around 2005. Again, this is inconsistent with examples like influenza. Note, I did not review the list of viruses exhaustively, so would recommend that the authors double-check everything. In addition, the term ‘patient’ is included in their definition and is perhaps misleading. Many of the viruses they have included are probably not human pathogens, or at least have not been conclusively linked with human pathology. Reston virus is a good example of this.Reviewer #3: This paper is a timely advance for understanding discovery of human RNA viruses that will be of interest to researchers working on emerging infectious disease. The methods are rigorous and this work uses a new approach to evaluate patterns in virus detections. This work highlights important correlates of newly recognized RNA viruses that can help predict future emergence. The insights are not especially novel but findings are important and confirm findings previously reported using new data and methods. The only major concern is that the very broad presentation of correlates in a model limits the impact and relevance of this work.**********Part II – Major Issues: Key Experiments Required for Acceptanceabsolutely required to validate study conclusions.Please use this section to detail the key new experiments or modifications of existing experiments that should be Generally, there should be no more than 3 such required experiments or major modifications for a "Major Revision" recommendation. If more than 3 experiments are necessary to validate the study conclusions, then you are encouraged to recommend "Reject".Reviewer #1: There are no major new experiments required for publication.Reviewer #2: (No Response)Reviewer #3: The analyses are thorough and evaluate a wide range of potential correlates for virus discovery. But a major limitation of this manuscript is that the inferences are not clear beyond very broad assessment of relative contributions for factors evaluated that are presented in numerous figures in the main text and SI. Concrete and meaningful functional relationships between key predictors and the outcome are not derived or explained in the results and discussion. Exploration of specific values for predictive factors and how these influence quantity of virus discovery would bring meaning to these findings. For example, the authors should explore how urbanization influences virus discovery – what levels of urbanization were more influential and what does this mean for virus discovery based on expected future trends in urbanization? Examples to highlight virus discoveries and the factors related to their first detection that underlie broad patterns in the data would bring clarity and meaning to findings.The many figures in the main paper and SI are largely of the same variety of relative contributions to the model and probability maps – these should be narrowed down to only those that highlight major findings with improved quality. A figure that shows specific values of the one or two key factors and the predicted influence on number of viruses discovered would enhance the presentation of results.Can the authors further explore the rate of detection over time? How did rate of detection or the temporal distribution vary by the 4 classifications of viruses; did the rate of detection vary by geographic region? The influence of the predictors on prediction is the most novel aspect of this work yet it is not explored beyond highlighting broad regions with increased probability. Can the authors add specific inferences based on the analysis in terms of the number of viruses expected to be discovered every year, by virus groups examined? Is it possible to predict this beyond 2024?**********Part III – Minor Issues: Editorial and Data Presentation ModificationsPlease use this section for editorial suggestions as well as relatively minor modifications of existing data that would enhance clarity.Reviewer #1: On line 49, the authors state that they identify “where new viruses are most likely to occur”, but they are actually identifying where they are most likely to be discovered. This might seem like splitting hairs, but since the authors did not treat the effect of reporting bias separately, it’s an important distinction to make.The authors’ discussion of this very phenomenon on lines 74–83 is cogent, and succinct. Distinguishing between virus species range and discovery probability is one of the clearest explanations of this subject I’ve read.However, their methodological treatment of reporting bias is not clearly described here or in the methods section. In the introduction, it is described on lines 84-86: “Here, we take a different approach by identifying explanatory factors of the raw virus discovery data and then considering whether these relate to virus geographic range or discovery effort or both.” This might be made clearer by saying something like “and then interpreting in the results whether these effects might relate to virus geographic range or discovery or both.The Discussion section notes that the paper does not attempt to explicitly correct for reporting bias (line 361). In this mention, they cite Jones et al. (ref. 12) but not the subsequent paper (ref. 13) which was an improvement on the Jones et al. method. However, the study includes variables expected to be associated with discovery effort, e.g. GDP and university count. Earlier in the discussion, it is noted that GDP growth is among the top predictors, and its effect differs across the stratified models. That the study does not attempt to factor out reporting effort means that its authors should be careful to interpret their results only as “viral discovery” and not “viral emergence”.The paper should be given a minor copy edit .The contribution of the paper’s use of k-means clustering to its central research questions is unclear. The paper’s initial discussion of the k-means clustering analysis suggests that this was perhaps used as selection criteria for including points in the model . However, the flow chart in the Supplementary Information gives the impression that the clustering analysis was not part of the BRT modeling workflow, but a separate analysis. If the k-means analysis is part of the modeling workflow, its relation to the BRT models should be made more clear. If it is a separate analysis, the authors should devote space in the Discussion section to interpreting its outcome (the figure in the supplementary information is not self-explanatory) or remove it from the paper entirely.Line 167: For readers unfamiliar with BRTs, perhaps move the sentence about partial dependence plots to a new paragraph, or introduce it in a slightly different way, something like, “Partial dependence plots are a method of visualizing the relationships between a BRT’s predictive variables and its outcome…”.Line 199: The paper talks about making predictions for 2015-2024, but these predictions are in fact the model’s output for 2015 variables. The authors do not go into enough detail about how the variables were matched to decade for us to assess this. If the outcome of the model uses 2015 variables because these are the most recent ones available, the model’s conclusions are still valid, but stating those predictions as “2015-2024” without justification would be misleading.Line 231: The use of Moran’s I to demonstrate the model’s removal of spatial residuals is very clear.Figures 3 and 4 uses the labels “Relative Contribution” and “Relative Influence” inconsistently. It should use only one. Readability would be much improved if the subplots were titled, rather than just being labeled A and B, so the reader can see which group they represent rather than referring to the caption.The maps use a color palette which is not perceptually uniform — a color palette such as Viridis or Google’s Turbo color palette would be better for displaying this kind of continuous data on a map.Line 365: The high-resolution variables were all scaled down to a 1º grid, so the higher resolution of the source variables was not a factor in the models’ output.Reviewer #2: The authors considered a combination of socio-economic, land use, climate, and biodiversity variables as correlates of virus discovery. What is the rationale for how/why they are relevant to virus discovery? And given that land use, climate, and biodiversity variables seem more associated with emergence, can they provide some discussion on why these were selected, beyond saying ‘it’s a way to deal with variation in research effort’.For their future predictions – did they account for projected changes in the factors that were correlated? Forecasted changes in GDP, land use, climate, etc.Line 61 – “Human RNA viruses”. Can they authors define what they mean by a human RNA virus? Is it a virus that was found in a human at least once? And if so, is that sufficient to call it a human virus?Lines 62-64: Species don’t circulate; viruses do. Please correct taxonomy and use virus names (not species names) when talking about viruses as nouns. No italics.Line 66-67: “…some have been identified during active viral discovery programmes”. Please provide citations as examples. Also consider adding a sentence to say that some viruses have been discovered by chance, as incidental findings as part of a disease investigation. .Lines 109-111. I realize that this is likely not a fair thing to ask, but given the impact of the current pandemic, it seems that including SARS-CoV-2 would be good?I would appreciate some justification for the taxonomic boundaries they have used, or at least some discussion about the potential limitations of this approach. While it might seem robust to use ICTV classifications – species demarcations vary widely by virus family. The factors used to demarcate species in one family can be quite different from the factors used to demarcate species in another family. I recognize that there is clearly no good way to address this problem, just as there is no clear answer for how to deal with variation in research effort. However, like the research effort problem, this has the potential to significantly impact their results. For example – is it reasonable to consider all influenza viruses as one example while separating out enteroviruses into so many species?Figure 6. Do these hotspots indicate where good lab capacity exists? I am trying to understand the specific significance of this figure. What does it really mean and how would I use it to guide surveillance?Reviewer #3: Abstract should include mention of investigative effort specifically (the process of virus detection) as a driver of discovery, as highlighted in the paper.Given there is overlap between zoonotic viruses, human transmissible viruses, and vector-borne viruses, this overlap should be clarified in the text, by showing % overlap among categories, ie for human transmissible viruses, what % are zoonotic.Information on how the geocoded location for virus detection was ascertained should be described at least briefly in the main paper, as opposed to only in the SI. When describing patient’s address, was this the location of the hospital or clinic, site of initial human exposure/infection with the virus? Were discoveries of virus in people that likely contacted the virus while travelling included? If so, how would this affect the findings?The methods for data collection should be fully characterized, including search terms, databases searched, and inclusion or exclusion criteria, to enable repeatability. This information does not appear to be readily available in the link provided.Line 224 - The partial dependence plots do not provide detailed descriptions of the relationships. This should be rephrased. For all figures of partial dependence plots, the x axes should be labelled. Many of these variables  are not readily understood unless the SI table is reviewed. Brief descriptions within the main text would ensure improved understanding of the main findings.The relationship between GDP and virus discovery should be better explained, especially with respect to how the GDP of the country of virus detection and GDP change is related to detection. Is the GDP of a country where a virus was first detected generally reflect effort or resources spent toward investigative efforts at that location? Share evidence to support interpretation. Brazil, Nigeria, and Uganda are areas with high virus detection – how does GDP explain these detections and what were the specific factors involved in virus discovery; was this evenly distributed in time or based on unique efforts?Lines 327-331 – does urbanization reflect a change in habitat? If so over what time scale? Or is this variable reflective of the level of urbanization at the time of analysis? Provide supporting evidence or documentation characterizing urbanization as a change.Line 347 – the effect of biodiversity on virus emergence has been debated and this finding is minimally discussed here even though this is one of the effects that could be causally related to virus discovery in humans. Could the authors add more discussion of the relationship between biodiversity and virus discovery – examine this by potentially causal mechanisms and infer what this means for future trends?The conclusion could be more forward looking, rather than a restatement of main findings already discussedMinor editorial suggestionsLine 31: is = areLine 89: Expand literature review methods description.Results: start this section with a description of the major findings, not an immediate reference to a figure in the first word of the first sentence.Line 331: rephrase, missing a verbLine 359: awkward sentence, especially as start of new paragraph – what were other limitations mentioned?Use of the term ‘species’ in figures and legends should be rephrased as ‘virus species’.Figure 1: Legend should provide more detail and clarity on data points . 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In Germany, several triplet therapies for treating relapsed or refractory multiple myeloma (rrMM) patients have recently been approved. While most of them are administered intravenously, ixazomib-based combination is the only orally bioavailable regimen.To conduct a 1-year and 3-year budget impact analysis (BIA) of different novel triplets to treat patients with rrMM in second or subsequent therapy lines accounting for costs covered by German statutory health insurance (SHI).A 3-state partitioned survival model (PSM) was developed to evaluate the budget impact of the following regimens: carfilzomib plus lenalidomide plus dexamethasone (KRd), elotuzumab plus lenalidomide plus dexamethasone (ERd), daratumumab plus lenalidomide plus dexamethasone (DRd), and ixazomib plus lenalidomide plus dexamethasone (IRd). The analysis included direct medical costs such as drug acquisition, comedication and preparation for parenteral solutions, drug administration and other 1-time costs, adverse event management costs and direct non-medical costs, such as transportation costs.Based on current drug market shares in German healthcare market, the estimated costs after 1 year of treatment was €551 million (KRd), €163 million (ERd), €584 million (DRd), and €95 million (IRd). The total budget impact of €1393 million is mainly driven by drug acquisition and subsequent therapy costs.Among the regimens of interest, the oral-based therapy regimens offered cost advantages over intravenous-based therapy regimens. The higher overall costs of intravenous therapy regimens were attributed primarily to higher drug acquisition costs. Multiple myeloma (MM) is an incurable, heterogeneous blood cancer with serious disease-related complications accounting for 1% of all cancer diagnoses worldwide and 13% of all hematologic malignancies . During To perform the budget impact analysis (BIA), a three-state partitioned survival model (PSM) was used, which classifies patients into states of PFS, progressive disease (PD), and death (D). All patients were assumed to have started in the PFS state and either remained progression free (PF), progressed, or died in subsequent cycles. Time to progression was derived from the difference between the areas under the PFS and overall survival (OS) curves. After disease progression, up to three subsequent therapy lines were considered Fig. . To reflThe budget impact analysis was performed from the perspective of the German SHI over a 1-year and 3-year time horizon. A dynamic cohort model to account for the prevalent cohort in the starting year 2019 as well as the incident cohorts in the following years was applied. Additionally, the yearly cohort size was divided by 13  to reflect a constant patient-inflow into the model.The budget impact analysis was conducted using Microsoft Excel 2016 (Microsoft Coporation).The focus of the current analysis was to evaluate the four triplet therapy regimens recently approved for the treatment of rrMM. These were carfilzomib plus lenalidomide plus dexamethasone (KRd), elotuzumab plus lenalidomide plus dexamethasone (ERd), daratumumab plus lenalidomide plus dexamethasone (DRd), which were referred to as intravenous therapy regimens. In addition, ixazomib plus lenalidomide plus dexamethasone (IRd), referred to as oral therapy regimen, was considered. Lenalidomide in these regimens is normally given until progression. Patients progressing under treatment with these triplet regimens are considered to be lenalidomide refractory. Therefore, lenalidomide-free regimens are preferred after lenalidomide-based treatment . Hence, The analysis was limited to the listed therapies above as well as to the order and frequency in which the therapies were prescribed. It was assumed that patients start treatment on one of the four lenalidomide-based therapies in second line, namely, KRd, ERd, DRd or IRd. Once those patients experienced a disease progression, lenalidomide-free therapies  were prescribed in third treatment line, only. Patients with a repeated disease progression were eligible for any of the considered therapies in fourth and fifth treatment line, excepting those previously received. This setting allowed to reflect 312 different treatment combinations across the four therapy lines.The baseline target population of the model was defined as all adult patients with MM who have received at least one prior therapy and who may initiate a second-line therapy and more. The determination of the prevalent target population was based on the estimates from the German Centre for Cancer Registry Data (ZfKD) which regularly reports data on incidence and prevalence of cancer in Germany for the entire population as well as for population stratified by age and gender . As the Clinical efficacy data in terms of PFS and OS curves for each regimen of interest were retrieved from clinical trials –6, 15–17The reconstructed PFS and OS curves, respectively were then fitted to a variety of common parametric distributions, using the maximum likelihood methodology. The distributions that were tested included the exponential, Weibull, Gompertz, log-normal, and log-logistic. The final distributions were chosen based on the following criteria: (1) comparison of Akaike and Bayesian information criteria (AIC/BIC); (2) clinically plausible long-term projections; (3) a comparison of predicted median PFS time and the published figures; (4) visual inspection of the fit to the observed data over the available follow-up time; and (5) residuals against a 45° line . For PFS, a Gompertz distribution was selected for KRd, DVd, and FVd; a Weibull accelerated failure time (AFT) distribution for ERd, DRd, and Pd; and a log-normal distribution for IRd. For OS, a Gompertz distribution was selected for IRd, KRd, ERd, and FVd; a Weibull distribution for DVd; and a Weibull AFT distribution for DRd and Pd.2 and a BW of 76.3 kg. Thus, the estimated drug costs correspond to the number of drug packages/vials used to meet the previously calculated dose intensity. Doses and prices per package were retrieved from a public price list (Lauer-Taxe) [The analysis included both direct medical costs and direct non-medical costs  which are covered by the German SHI. The direct medical costs included the initial and subsequent-line drug acquisition costs, comedication costs, drug administration costs including administration, patient monitoring and laboratory tests, as well as costs for management of adverse events (AE). Drug administration time, dosing, clinical examinations before treatment initiation, and comedications were based on the prescribing information for each agent –29. The er-Taxe) . The druThe incidence of AEs associated with each therapy regimen was obtained from pivotal trials, and the costs of AEs were obtained from the morbidity-oriented risk structure compensation scheme (Morbi-RSA) in Germany . AEs of The considered non-medical costs included transportation costs, which are routinely covered by SHI for oncological intravenous treatments. These costs commonly refer to roundtrips per drug administration and were estimated according to the mode of transport and distance travelled. The average distance travelled for an intravenous rrMM treatment is 37.2 km in Germany . Due to All costs were adjusted for a cycle length of 28 days. Costs were not discounted as recommended by the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) Task Force for BIAs . Data arTo estimate the budget impact of rrMM patients starting second-line treatment with KRd, ERd, DRd or IRd, 6 different scenarios were considered within a 1-year and 3-year time horizon.Using estimates from the TherapyMonitor Report, the current market share of the four considered therapy regimens was 20%, 14%, 5%, and 4% for KRd, DRd, ERd, and IRd, respectively, in Germany during the 1st quarter of 2019 . To alloIn all 3-year scenarios, the prevalent cohort (2019) in second line reflects the market share observed in the TherapyMonitor Report during the 1st quarter 2019. The treatment of the progressed patients after the second line followed a similar logic that was applied in the 1-year analysis Table . The inc2); (b) alternative body weight (70–76.3 kg); (c) replace the treatment duration based on median PFS by separately calculated duration of treatment (DoT) curves; (d) use an alternative parametric distribution such as the second-best fit curve; and (e) assume a 100% mortality rate on progression and thus, only consider second-line treatment costs.The sensitivity of the model was assessed through a combination of one-way and scenario analyses for equivalence scenario and 1-year time horizon. First, deterministic sensitivity analyses were conducted to identify the most influential inputs on the total budget. Second, for scenario analyses, the most plausible alternative values were used. Scenario analyses performed included: (a) alternative body surface area  for treating rrMM patients, the study might ignore further treatment options and may not necessarily reflect real-world treatment patterns. However, the consideration of three additional therapies  in third line is supposed to account for complex and varying treatment patterns among rrMM patients to some extent.The conducted BIA demonstrates that oral-based therapy regimens for rrMM offer cost advantages over intravenous-based therapy regimens. Saving effects were driven primarily by reduction of drug acquisition costs.