The honey bee queen is the central hub of a colony to produce eggs and release pheromones to maintain social cohesion. Among many environmental stresses, viruses are a major concern to compromise the queen’s health and reproductive vigor. Viruses have evolved numerous strategies to infect queens either via vertical transmission from the queens’ parents or horizontally through the worker and drones with which she is in contact during development, while mating, and in the reproductive period in the colony. Over 30 viruses have been discovered from honey bees but only few studies exist on the pathogenicity and direct impact of viruses on the queen’s phenotype. An apparent lack of virus symptoms and practical problems are partly to blame for the lack of studies, and we hope to stimulate new research and methodological approaches. To illustrate the problems, we describe a study on sublethal effects of Israeli Acute Paralysis Virus (IAPV) that led to inconclusive results. We conclude by discussing the most crucial methodological considerations and novel approaches for studying the interactions between honey bee viruses and their interactions with queen health. The high levels of honey bee colony losses call for research to identify all of the factors involved in the honey bee health crisis ,2. AmongAlthough the queen is thought to be protected by several social immune mechanisms in the colony, she is exposed to different environmental stresses during different life stages that possibly have negative effects on her health and quality . InitialAmong the different environmental stressors, viruses are considered to be a major concern across different stages of a queen’s life ,44,45. VUnderstanding transmission routes and directionality of viral spread is a crucial first step in determining the effect and epidemiology of a given pathogen, because transmission routes have direct effects on the prevalence and virulence of viruses ,47,48. VVarroa destructor that vectors many different viruses [Varroa because they do not infest developing queen cells, except in exceedingly rare cases of colonies with very high Varroa mite infestation [Several honey bee viruses, including DWV, BQCV, SBV, KBV, and ABPV, have been detected at the early larval stages in queens ,57,60. T viruses ,67,68, qestation ,70. DeveVarroa mites, which could elevate virus titers and the prevalence of viruses in drones. The viral load of drones directly affects queen health through venereal transmission of many viruses [Varroa destructor [Mating seems to be an important transmission route for viruses to infect queens. Multiple viruses, such as DWV, ABPV, BQCV, and SBV, have been detected from collected seminal fluid of apparently healthy drones, providing evidence that queens can be infected by mating or instrumental insemination ,72. Dron viruses . Among t viruses ,52. DWV structor , and it structor . DWV venstructor  and in fstructor ,76. Highstructor ,77. As astructor ,44,78. Ostructor ,79. In mstructor ,79. Virustructor . Queen pstructor ,82. Sincstructor ,83,84,85Varroa mite) and abiotic  stressors, most virus infections remain covert, without causing any clinical signs or symptoms either at the individual or the colony level [Varroa, but possibly related to other stressors [So far, over 30 viral pathogens have been reported to infect honey bees ,87, someny level ,88. Howeny level ,89. Withtressors . In parttressors ,70,91.Consequently, little is known about how virus infections affect the behavior, physiology, reproduction, and longevity of queens. Numerous viruses  have been detected from heathy looking queens, demonstrating that infection can occur either by single or multiple viruses in a queen ,76,78,92Different studies indicate that highly virulent viruses, such as viruses in the AKI complex, are rarely detected in young unmated queens but occasionally found in older queens with low titers ,60,76,79Several viruses, including DWV, SBV, and ABPV, have been detected from queen larvae or pupae ,60, but Varroa population that causes high DWV viral titer. In these cases, queens can exhibit crippled wings [Specific clinical symptoms in queens have been studied for CBPV and DWV. Even though colonies can collapse with apparently healthy queens due to CBPV infection , CBPV ined wings  and degeed wings . DWV infed wings , which mIn an effort to expand our knowledge on sublethal effects of viral infections in honey bee queens, a study was conducted to test the hypotheses that virus infection reduces queen attractiveness and that offspring of virus-exposed queens better survive inoculation with the same virus due to inter- generational immune priming effects. Even though this study’s outcomes remained inconclusive, we describe it to highlight potential approaches and methodological innovation needed for overall progress in understanding the effects of viruses on their honey bee hosts.The hypothesis of reduced queen attractiveness is based on findings that virus infection can change the cuticular hydrocarbon profile, leading to the recognition and removal of sick individuals ,96. QueeThe second hypothesis of trans-generational survival benefits of a previous virus exposure is motivated by findings of such immune priming in honey bees against bacterial disease . IAPV caThus, experimental queens were generated by standard queen rearing protocols from apparently healthy colonies . After mWorker preference for control versus IAPV-exposed queens varied widely across experimental pairs . Contrar2 = 0.7, Nprimed = 134, Nunprimed = 128, p = 0.404; 2 = 25.2, df = 15, p = 0.047), and offspring survival was more variable for sham-treated than for IAPV-exposed queens.Overall, offspring of IAPV-exposed and sham-treated queens did not significantly differ in surviving an IAPV-inoculation (Kruskal–Wallis test: ΧAt the end of the study, surviving queens were found with low levels of IAPV, regardless of treatment group. Only two queens from the IAPV-treated group were highly infected with IAPV at the end of the experiment .Queens emerge as a central concern for honey bee health and consequently have been receiving increased attention, particularly in relation to viral diseases ,44,79. WHoping to motivate methodological development and help others to plan the needed experiments, we described our case study because it illustrates many of these difficulties. The two most important problems with our above study are that our sham-treated control queens proved to contain IAPV at the end of the experiment and that only two of our IAPV-inoculated queens exhibited the expected high IAPV titers. Multiple explanations exist for both outcomes, even though all queens were grafted from the same, apparently healthy colony, IAPV may have been present below the detection level and increased titers may have been triggered by the experimental stress or other environmental factors. Alternatively, queens may have become infected during the pre-experimental period in their hives, which may have occurred during their natural mating ,77 or a Studies focusing on honey bee queens under normal conditions are necessarily limited in sample size because one queen necessitates the maintenance of an entire colony ,84. In oIn conclusion, the interactions between viruses and their honey bee queen hosts have practical importance for maintaining pollinator health because the queens play an important role in vertical transmission of many health-relevant viruses. More research is needed to document the distribution of viruses and their dynamics across space and time. Continued discovery of novel viruses or virus strains can be anticipated, necessitating continued monitoring efforts. This is particularly true to honey bee queen breeding operations that widely distribute their bees to their customers. Alternative to virus monitoring of queen breeders, local, small-scale queen breeding efforts could mitigate the risk of human-assisted virus spread over long distances. Beyond the practical importance of more virus research in honey bee queens, honey bees present unique opportunities of academic interest to study the relations between host physiology, virus transmission and replication, and virus pathogenicity. Specifically, the interplay between caste, potentially differential virulence evolution, and social immune mechanisms should be of great general interest. However, we believe that many experimental difficulties that complicate investigations into the interactions between viruses and honey bee queens need to be addressed before conclusive studies can improve our current understanding of queen–virus relationships and honey bee virology in general.


Plum bark necrosis and stem pitting-associated virus infecting grapevine in China.Virus disease is one of the main diseases in grapevine, and there has been no report on The leaf samples of grapevine cultivar 'Cabernet Gernischt' were collected from Shandong province, which the leaves suffered from viral-like symptoms with spotting and crinkle.Small RNA-seq combined with reverse transcription PCR (RT-PCR) were performed to detect the potential viruses in these field samples. Phylogenetic tree was constructed using the neighbor joining method in MEGA 5.1This is the first report of PBNSPaV infecting grapevine in China, contributing to a better understanding of the epidemiology and host range distribution of this pathogen. Vitis vinifera) [Grapevine virus diseases affect the yield and quality of grape (inifera) . Howeverinifera) –6. So fainifera) –6. The dCabernet Gernischt), which one of the most common wine grape varieties in China. To detect the potential viruses in these samples, total RNA was extracted by TRIzol reagent and small RNA-seq was performed on the field samples. The FASTQ files of the small RNA HTS have been deposited into GEO and its accession number is PRJNA659879. We acquired 23,725,796 clean reads and 1,868 scaffolds were de novo assembled by velvet . The scaffolds with length more than 50 bp were annotated by BLASTN (identity ≥ 80% and coverage ≥ 80%). Using assembled scaffolds mapped against the viral reference database  from NCBI and identified 42 scaffolds associated with the following seven viral genomes (identity ≥ 80% and coverage ≥ 80%): Grapevine virus A (GVA) (2 scaffolds), Tomato leaf curl mali virus (ToLCMLV) (1 scaffolds), Tomato pseudo-curly top virus (TPCTV) (1 scaffolds), Turnip curly top virus (TCTV) (1 scaffolds), Prune dwarf virus (PDV) (11 scaffolds), Prunus necrotic ringspot virus (PNRSV) (22 scaffolds), and Plum bark necrosisstem pitting-associated virus (PBNSPaV) (4 scaffolds).In September 2017, the leaves with symptoms spotting and crinkle were found in Shandong province from the grapevine   in Dinuba, CA, U.S.A. [As one of the most important grape varieties around the world, 'Cabernet Gernischt' was planted in many wine regions. Shandong is one of the main region of wines, which is the main planting base of the grape. Virus disease is one of the main diseases in grapevine, and may affect the yield and quality of grapevine. PBNSPaV was reported for the first time infecting plum trees  and Plum bark necrosis stem pitting-associated virus (PBNSPaV) were identified. And, this is the first report of PBNSPaV infecting grapevine in China.In September 2017, the leaf samples of grapevine cultivar 'Cabernet Gernischt' were collected from Shandong province, which the leaves suffered from viral-like symptoms with spotting and crinkle. To detect the potential viruses in these samples, small RNA-seq combined with reverse transcription PCR (RT-PCR) were performed on the field samples, and ftp://ftp.ncbi.nlm.nih.gov/refseq/release/viral/viral.1.1.genomic.fna.gz) of the virus through comparing BLASTN homology, if on the level of nucleic acid or protein level high homology (identity ≥ 80% and coverage ≥ 80%) of the transcript is probably from the virus.The total RNA was extracted from infected leaves according to the method of Massart et al.  and mixe


Cholesterol Gallstone Disease (GSD) is a common multifactorial disorder characterized by crystallization and aggregation of biliary cholesterol in the gallbladder. The global prevalence of GSD is ~10–20% in the adult population but rises to 28% in Chile (17% among men and 30% among women). The small intestine may play a role in GSD pathogenesis, but the molecular mechanisms have not been clarified. Our aim was to identify the role of the small intestine in GSD pathogenesis. Duodenal biopsy samples were obtained from patients with GSD and healthy volunteers. GSD status was defined by abdominal ultrasonography. We performed a transcriptome study in a discovery cohort using Illumina HiSeq. 2500, and qPCR, immunohistochemistry and immunofluorescence were used to validate differentially expressed genes among additional case-control cohorts. 548 differentially expressed genes between GSD and control subjects were identified. Enriched biological processes related to cellular response to zinc, and immune and antimicrobial responses were observed in GSD patients. We validated lower transcript levels of metallothionein, NPC1L1 and tight junction genes and higher transcript levels of genes involved in immune and antimicrobial pathways in GSD patients. Interestingly, serum zinc and phytosterol to cholesterol precursor ratios were lower in GSD patients. A significant association was observed between serum zinc and phytosterol levels. Our results support a model where proximal small intestine plays a key role in GSD pathogenesis. Zinc supplementation, modulation of proximal microbiota and/or intestinal barrier may be novel targets for strategies to prevent GSD. World prevalence is 10–20% for the adult population2. However, the Chilean population has the highest prevalence of GSD in the world, it being 17% among men and 30% among women3. Many risk factors for GSD development have been identified such as ethnic background, advanced age, female gender, family history and genetic susceptibility5. The diagnosis of GSD is established by visualization of macroscopic gallstones through an abdominal ultrasound. Surgery (cholecystectomy), which is the only effective form of treatment available, represents a significant health burden in countries with high prevalence6. GSD is associated with common metabolic conditions such as obesity, insulin resistance, diabetes mellitus, hypertriglyceridemia and pregnancy7. In the context of western diet and lifestyle9 and the higher prevalence of GSD and the consequent health burden, it is critical to understand pathogenic mechanisms of this complex disease.Cholesterol Gallstone Disease (GSD) is a common multifactorial disorder characterized by cholesterol crystal formation, precipitation and growth (stones) in the gallbladder10. In particular, lipid absorption (sterols and triglycerides) take place in the proximal small intestine (duodenum and jejunum). Evidence suggests that intestinal cholesterol absorption is a risk factor of GSD disease and inhibiting it with ezetimibe can prevent GSD formation, at least in animal models12. Changes in gut microbiota has been associated with GSD in animal models and humans15. Diet can significantly affect the species composition of gut microbiota and several immune disorders like IBD, allergies and asthma18. Interestingly, the duodenum shares the same embryological origin as the liver and biliary tree and seems to have coordinated regulation of gene expression19. Therefore, the overall impact of the intestine in gallstone formation may at least partly explain the molecular mechanisms involved in GSD pathogenesis.Intestinal mucosa allows the absorption of nutrients, electrolytes and water, while also serving as an effective defense that limits systemic contamination by intraluminal microbes or their productsTo explore the potential role of the upper small intestine in the pathogenesis of GSD, we performed transcriptome analysis of duodenal mucosa from selected cholesterol gallstone subjects and matched controls. We validated our findings using independent case-control cohorts.12. However, the contribution of the small intestine in GSD pathogenesis has not been well-studied. As a first step to determine the role of the small intestine in GSD, we analyzed global gene expression in duodenal mucosa in five selected GSD patients and four paired control subjects using RNA sequencing  counts and lysozyme expression by immunohistochemical and immunofluorescent analysis in formalin-fixed paraffin-embedded tissue samples from duodenal mucosa of GSD and control subjects. The IEL counts and lysozyme expression were significantly higher in the duodenal mucosa of GSD subjects than in that of control subjects  and occluding junction (OCLN) of the first validation cohort using Real-Time PCR. Both genes were downregulated in GSD subjects in contrast to those of control subjects  or distal target organs 10. However, its contribution to GSD pathogenesis have not been well studied12. We found 548 DEGs between GSD and control subjects. Among GSD patients, we observed enriched biological processes related to cellular response to zinc, immune and antimicrobial responses, epithelial tube morphogenesis, apoptosis, DNA replication, and phospholipid metabolic process, and others. In a second step, we validated some of these results in an independent cohort of cases and controls. Consistent with the RNA sequencing analysis, metallothionein genes were down-regulated in GSD subjects in association with lower serum zinc levels than those of control patients. In addition, patients with GSD are characterized by enhanced expression of genes related to antimicrobial and immune processes, intestinal intraepithelial lymphocytes counts and expression of lysozyme proteins, indicating the presence of low-grade inflammation and enhanced antimicrobial responses in the duodenal mucosa of GSD patients. Furthermore, GSD subjects have low levels of tight junction proteins, which indicates that intestinal barrier integrity is altered. The alteration of duodenal mucosal integrity and antimicrobial and immune responses suggests bacterial-mucosal interactions and might be associated with gut microbiota dysbiosis, which is reported in GSD patients15.While biliary cholesterol hypersecretion is the first step in GSD pathogenesis, downstream events  are required for cholesterol stone formation in the gallbladder29. Zinc homeostasis is primarily regulated by intestinal absorption, renal reabsorption, and clearance of endogenous zinc in the intestine  through both pancreatic and liver excretion30. Moderate serum zinc deficiency and low metallothionein expression in duodenal mucosa observed in GSD patients may be due to lower dietary availability, low gastrointestinal absorption or increased zinc loss. However, the mechanism involved in zinc status in GSD subjects has not been studied. Interestingly, we observed that SLC39A11/ZIP11 was the only zinc transporter significantly regulated in GSD using RNA sequencing , which mediates the hepatic uptake and clearance of cholesterol from HDL, has been associated with GSD pathogenesis in murine models of GSD40. In addition, Chinese gallstone patients have an increase expression of SRBI protein in the liver which might contribute to the hypersecretion of HDL-cholesterol derived into the bile35. Clearly, the relationship between zinc levels, low HDL cholesterol and GSD may be complex and need further study.Interestingly, it has been reported that zinc depletion in rats and humans significantly reduces total serum cholesterol, principally because of lower levels of high-density lipoprotein-cholesterol (HDL-C)43. Recent studies have shown that gut bacterial translocation allows some microbiota to reach the biliary tree in some scenarios. Indeed, biliary microbiota can promote gallstones formation in model mice and possibly in humans44. The observed dysfunction of intestinal barrier integrity, in conjunction with higher IEL counts , elevated lysozyme protein and antimicrobial peptide expression in GSD patients strongly suggests that dysbiosis of gut microbiome contributes to GSD pathogenesis. However, the underlying mechanism between GSD pathogenesis and gut microbiome remains unclear and deserves further investigation.The gut microbiome has emerged as a key factor in many gastrointestinal disorders like Crohn’s disease, ulcerative colitis and coeliac disease46. It is also possible that changes in the biliary composition modify proximal gut microbiota, leading to changes in the intestinal barrier and local low-grade inflammation response. A further longitudinal study using healthy individuals carrying D19H polymorphism in ABCG5/8 might clarify whether our findings are cause or consequence of gallstone formation.The limitation of the present study, as with any clinical study of associations, is that we cannot state definitively whether our findings precede and constitute risk factors for gallstone formation or are secondary events. It is possible that chronic exposure of the proximal gut to lithogenic bile  leads to at least some of the observed changes in gene and protein expression profilesIn conclusion, the transcriptome approach allowed us to characterize the global gene expression of the upper small intestine of GSD subjects for the first time. Using two independent cohorts of cases and control, we showed that GDS subjects have lower serum zinc levels, enhanced immune and antimicrobial responses, and disrupted intestinal barrier function. These molecular changes in duodenal mucosa and zinc metabolism may be contributing factors to cholesterol gallstone pathogenesis. However, further exploration and functional characterization of this biological process are needed to identify new strategies to prevent and treat cholesterol gallstone disease.Intestinal cholesterol absorption is a risk factor in GSD.Gut microbiota has been associated with GSD.What is already known about this subject?Patients with GSD display enriched biological processes in cellular response to zinc, and antimicrobial and immune responses.Gallstone disease patients have lower expression levels of key genes involved in zinc metabolism in the proximal gut in association with lower serum zinc levels.Antimicrobial and immune response pathways are enhanced in the proximal gut of gallstone disease patients.Lower expression levels of the tight junction proteins suggest the existence of enhanced mucosal permeability in the proximal gut of gallstone disease patients.What are the new findings?Our transcriptomic and physiological approaches indicate that serum zinc status, microbiota and intestinal mucosal integrity contribute to pathophysiological mechanisms of GSD. Zinc supplementation, and modulation of microbiota and the intestinal barrier may be novel strategies to prevent cholesterol gallstone formation.How might it impact on clinical practice in the foreseeable future?2), with no disease other than being an asymptomatic GSD carrier two months prior to the study. Additional inclusion criteria were at least two months without intake of medications such as non-steroidal anti-inflammatory drugs, aspirin, antibiotics, metformin or lipid lowering drugs, not pregnant and normal blood test results for glycemia, biochemical profile and negative serological test for celiac disease. Examination of duodenal bile after the stimulation of gallbladder contraction with 20% amino acid solution  showed cholesterol crystals under light microscopy, indicative of cholesterol gallstones47. Subjects with gastroesophageal reflux or dyspepsia that were referred for an upper endoscopy and with a normal abdominal ultrasound were invited to participate in the study as controls. Duodenal drainage was not performed with the control group. Biopsy samples were taken from the second segment of the duodenum  in aseptic conditions and were stored in RNA-latter solution for RNA sequencing and real-time reverse transcriptase PCR (RT-PCR).We carried out a whole transcriptome assay using a discovery cohort and two independent cohorts of case and control subjects to validate our findings. The discovery group consisted of 5 cholesterol gallstone patients and 4 matched controls. These individuals were recruited from the Gastroenterology and Endoscopic Unit of the Red Salud UC-Christus, Pontificia Universidad Católica de Chile. Patients that were carriers of gallstones, as determined by abdominal ultrasound, and that met the inclusion criteria  were invited to participate in this study by agreeing to provide blood samples for biochemical tests and to undergo upper endoscopy with duodenal biliary drainage. The inclusion criteria were: women between 18 and 35 years old, non-obese  or fixed in formalin and embedded in paraffin for immunofluorescence and immunohistochemical assays.48. Biobank serum from this cohort were available at the Gastroenterology Department of the Pontificia Universidad Católica de Chile. The selected group consisted of 34 controls and 25 GSD patients that met the same inclusion criteria as the discovery group. The presence or absence of GSD was defined by abdominal ultrasound. Serum samples were used to assess serum zinc and non-cholesterol sterol concentrations.A second validation cohort was a nested case-control study selected from a recent population-based study described elsewhere to identify genetic and metabolic risk factors for GSDAll subjects signed informed consent forms and the protocol was approved by the Institutional Review Board for Human Studies at Pontificia Universidad Católica de Chile and were conducted in accordance with the guidelines of the National Commission on Science and Technology (CONICYT-Chile).49 for alignment with the human genome and Rsubread50 for the read count per transcript. The DESeq. 2 package v1.26 (https://bioconductor.org/packages/DESeq. (2)51 was used to capture differentially expressed genes, with an adjusted false discovery rate P -value of 0.05.To understand the role of the upper small intestine in the molecular mechanism involved in GSD pathogenesis, we employed a transcriptomic approach with RNA sequencing to evaluate changes in the gene expression profile in the upper gut of GSD subjects. Whole transcriptome data were generated from duodenal mucosal biopsy samples of 5 GSD patients and 4 matched control individuals. A PureLink RNA Mini Kit  was used for total RNA preparation. Sequencing was conducted with the TruSeq Stranded mRNA Library Preparation Kit . The prepared libraries were sequenced on an Illumina HiSeq. 2500. The samples were sequenced to a depth of 9 samples per lane, which generated ~18 million 50 bp single-end reads per sample. We used HISAT2http://www.ici.upmc.fr/cluego/)20, a plug-in of Cytoscape52, to identify enriched biological processes from the list of differentially expressed genes (DEG) between control and GSD subjects. A functionally grouped network of enriched biological process terms was generated from the DGE list. The statistical criterion was a two-sided (enrichment/depletion) hyper-geometric distribution test with a p-value significance level of ≤ 0.05. The Kappa-statistic score threshold was set at 0.3 and GO levels was set at 4 to 6. Cluster analysis with clusterMaker2 plugin53 provided a comprehensive view of node connectivity. Community clustering (GLay) determined the modules with functional properties.A functional analysis was conducted with ClueGO v2.3  and reverse-transcribed into cDNA with a High-Capacity cDNA Reverse Transcription Kit . Real-time RT-PCR was performed using System Brillant III Ultra-Fast SyBR Green QPCR Master Mix (Agilent technology) and human primers  spectrometry using an S2 PICOFOX spectrometer . The samples were prepared in simple 1:5 dilutions of serum:ultrapure water. Galio (Ga) was added for internal standardization. After homogenization, the solution was transferred to a quartz glass sample carrier and dried in a desiccator for 10 minutes. All samples were measured at an X-ray excitation of 50 kV/750 µA for 600 seconds.54. The GC/MS were done with an Agilent 6890 N gas chromatograph with a 5973 network mass selective detector (Agilent technologies) equipped with an HP-5ms capillary column (30 m × 0.25 mm × 0.25 µm) (Agilent Technologies).The serum concentration of phytosterols (campesterol and sitosterol) and cholesterol precursors (lathosterol and desmosterol) were measured by gas chromatography/mass spectrometry (GC/MS) as described elsewherehttps://www.microscope.healthcare.nikon.com). TJP1 and OCLN expression were quantified by measuring the average fluorescence intensity in 10 non-overlapping fields per subject using Image Pro .Two-micron sections of duodenal biopsies were deparaffinized and blocked following general procedures and incubated overnight at 4 °C with rabbit anti-ZO-1 (61–7300) and Occludin Antibody (PA5–20755) . The secondary antibody used was goat anti-rabbit Alexa Fluor 594 . Microscopy images of 5 representative non-overlapping fields were obtained with Nikon Eclipse Ni microscope equipped with the software NIS-Element Freeware v2.3 . Immunostaining was performed with an ultraView Universal DAB Detection Kit  using BenchMark ULTRA instruments , according to the manufacturer´s recommendations. Intraepithelial lymphocytes (IELs) were determined by counting at least 300 epithelial cells (ECs). Only lymphocytes above the basal membrane were regarded as IELs. Counts were expressed as the number of IELs per 100 ECs.The population was stratified into two groups: Control and GSD. The results of the quantitative determinations were expressed as the mean ± standard deviation (SD). Differences between the groups were analyzed using a Student’s t-test with a 95% confidence level of significance. A Pearson’s correlation test was applied to determine relationships among the variables. Values of p < 0.05 were considered statistically significant.Supplementary information.Supplementary Table S1.Supplementary Table S2.


Direct payments represent a large share of Swiss farmers’ total household income but compliance with related requirements often entails a high administrative burden. This causes individuals to experience policy implementation as onerous. Based on a framework for administrative burden in citizen-state interactions, we test whether farmers’ individual knowledge, psychological costs and compliance costs help to explain their perception of administrative burden related to direct payments. We refine this framework by testing different specifications of interrelations between psychological costs and perceived administrative burden based on findings from policy feedback theory and education research. Structural Equation Modeling (SEM) is applied to data collected from a representative sample of 808 Swiss farmers by postal questionnaire in 2019. We find that compliance costs and psychological costs contribute significantly to the perceived administrative burden. In contrast, farmers’ knowledge level contributes to this perception not directly but indirectly, with higher knowledge reducing psychological costs. Our results support policy feedback theory, in that a high level of administrative burden increases psychological costs. Furthermore, well-educated and well-informed farmers show a more positive attitude toward agricultural policy and thus perceive administrative tasks as less onerous. Policy-makers should invest in the reduction of administrative requirements to reduce compliance costs. For Swiss farmers, area-based or animal-based direct payments granted by the government for the delivery of public goods such as biodiversity and fresh air, represent an important income contribution. The average annual farm income of a Swiss farmer amounts to CHF 67,190, whereby CHF 73’746 come from direct payments . In addiCross-compliance standards and participation in voluntary agri-environmental schemes require a variety of proofs such as crop rotation plans, nutritional balances or records on grazing and free ranging periods in order to qualify for direct payments. Many farmers perceive their administrative workload associated with the application of direct payments as more onerous than their physical workload on the farm . Indeed,Research in the field of agricultural economics and policy has shown that administrative burden negatively affects farmers’ health , reducesIn recent years, researchers in the field of public administration have developed a framework that conceptualizes the administrative burden of citizen-state interactions , 12. TheThe added value of our study is twofold: First, to the best of our knowledge, the framework of administrative burden on citizen-state interactions has not been used previously in the context of agricultural policies, namely the administrative burden related to direct payments for farmers. Our study therefore aims to close scientific and political knowledge gaps by combining this theory with empirical evidence from the farming sector. Second, we refine the framework by testing different specifications of interrelations between psychological costs and the perceived administrative burden based on findings from policy feedback theory and education research. The results inform agricultural policy-makers as to how the perceived administrative burden related to direct payments can be reduced. define ‘ subsume In this study, the framework developed by  is appliWe subsume factors such as farmers’ education level and information level with regard to the cross-compliance and direct-payment system under the category “knowledge level”. This category reflects “learning costs” as proposed by . With reA high knowledge level decreases the perceived administrative burden (H1).This hypothesis is based on findings from medical research showing that specifically targeted education programs and training can reduce the burden on patients’ caregivers , 15. TheEspecially for small businesses such as Swiss family farms that do not have a person particularly responsible for administrative and legal issues, compliance costs in the form of resources expended on meeting tax obligations are considered as very onerous . TranspoHigh compliance costs increase the perceived administrative burden (H2).Based on farmers’ responses to a questionnaire,  found thHigh psychological costs increase the perceived administrative burden (H3).Furthermore, we empirically test the relationships among the three factors influencing administrative burden. Regarding the relationship between knowledge level and compliance costs, studies in entrepreneurship research indicate that education has a strong positive effect on self-employment success , 18. ExpA high knowledge level decreases compliance costs (H4).Moreover, we expect knowledge level to have a positive effect on psychological costs. Results from entrepreneurial research show that specific entrepreneurship education and information level play an important role in positively influencing the attitude toward starting a business . In our A high knowledge level decreases psychological costs (H5).Finally, it might be assumed that compliance costs influence psychological costs. Empirical evidence in the field of political psychology reveals that compliance costs negatively influence the attitude toward a particular policy, so that “even the good Europeans become unenthusiastic about compliance when costs rise” . This imHigh compliance costs increase psychological costs (H6).A literature review on potential interactions between the factors and administrative burden suggests further relationships between psychological costs and the perceived administrative burden than the one described in Model 1. We capture these findings by conceptualizing two variants of the basic conceptual model (Model 2 and 3).Model 2 reflects the findings of policy feedback theory , 22, 23 A high level of administrative burden increases psychological costs (H3a).Model 3 combines H3 and H3a. We assume that psychological costs and administrative burden positively influence each other. In other words, formulated as Hypothesis 3b:Psychological costs and administrative burden are positively correlated (H3b).Hypothesis H3b is supported by findings from education research indicating that experiences with and positive attitudes toward computers are positively correlated , 25.The three conceptual models displayed in Data from a written questionnaire are used to test the three conceptual models by structural equation modeling. The administrative burden and its three influencing factors are modeled as latent constructs, which are measured based on observed variables.A written survey of 2,000 randomly selected Swiss farmers was conducted from February to April 2019. Farmers’ contact information was provided by the Swiss Federal Office for Agriculture, which maintains a database of all farm households that receive direct payments, comprising about 98% of Swiss farms. Farmers received a written questionnaire via postal mail. The response rate was approximately 40% (N = 808). The database is similar, in terms of region, farm type, farm size, age, and education, to the total farming population . The sur‘Administrative burden’ is measured based on the farmers’ perceived administrative burden today and compared to five years ago. We therefore asked farmers to rate two questions, each on a seven-point Likert scale .‘Compliance costs’ are measured based on three items: (1) farmers’ self-assessments with regard to whether or not the introduction of e-government has increased compliance costs; (2) time spent on completing forms; (3) time required for direct-payment inspections. We therefore asked farmers to rate one question on the introduction of e-government on a seven-point Likert scale. Additionally, we asked farmers to rate one question on time spent providing documents on a four-point ordinal scale, and one question on time spent on direct-payment inspections on a six-point ordinal scale .‘Psychological costs’ are measured based on four statements related to attitude toward, identification with, and loss of freedom caused by the cross-compliance and direct-payment policy. Accordingly, we asked farmers to rate four statements, each on a seven-point Likert scale .Farmers’ ‘knowledge level’ is measured based on their education level and knowledge level regarding the cross-compliance and direct-payment policy. We therefore asked farmers one question to rate their education level on a six-point ordinal scale. Additionally, we asked farmers to rate three statements regarding their knowledge level about agricultural policy, each on a seven-point Likert scale .To model the psychological constructs and test the processes of the three proposed conceptual models presented in In principle, SEM relies on the following two model factors : First, In this context, a distinction is made between endogenous and exogenous latent variables. Endogenous latent variables are considered as dependent variables, which are explained by at least one other (endogenous or exogenous) latent variable. In contrast, exogenous latent variables are strictly considered as explanatory variables . In our Structural equation models are usually illustrated as path-diagrams  λy of y on η and Ʌx is the q×m matrix of coefficients (or loadings) λx of x on ξ. η is a m×1 random vector of endogenous latent variables and ξ is a n×1 random vector of exogenous latent variables. δ and ε are q×1 and p×1 vectors of measurement errors in x and y, respectively. For a detailed description of latent and observed variables, see Where The  structural model that estimates the relationship between latent variables can be formalized as follows:B is the m×m matrix of regression coefficients β related to endogenous latent variables and Γ is the m×n matrix related to the coefficients γ of the exogenous latent variables. ζ depicts a m×m vector of error terms.Where Eqs  to 3) r r3) reprBy way of example, the specification for Model 1 in terms of Eqs  can be wWhere all variables are as previously defined. Eqs  and 5)  5) refer‘Knowledge level’ shows the expected negative effect on ‘administrative burden’. However, for Model 1, the effect is not statistically significant. In contrast, for Model 2 and Model 3, the negative effect is statistically significant. Therefore, H1 cannot be rejected.For all model variants, we find the expected statistically significant positive effect of ‘compliance costs’ on ‘administrative burden’. This implies that farmers who spend more time on providing evidence and on direct-payment inspections taking place at their farm exhibit a higher administrative burden. Consequently, high compliance costs cause administrative burden and H2 cannot be rejected. hypothesAs expected, the effect of the exogenous latent variable ‘knowledge level’ on ‘compliance costs’ is negative. However, for all model variants, the effect is not statistically significant. Thus, H4 has to be rejected. In contrast, H5 cannot be rejected. As hypothesized, the effect of ‘knowledge level’ on ‘psychological costs’ is statistically significantly negative for all model variants. This implies that farmers with a high knowledge level tend to have lower psychological costs. In other words, farmers who are well-educated and well-informed tend to have a significantly more positive attitude toward and a stronger identification with the cross-compliance and direct-payment policy. Furthermore, policy-supporting farmers with a high knowledge level do not feel restricted in their entrepreneurial freedom.Finally, as expected, ‘compliance costs’ increase ‘psychological costs’. More specifically, farmers who spend more time on administrative requirements tend to show a more negative attitude toward the cross-compliance and direct-payment system. Likewise, the perception that the switch to e-government has increased administrative workload causes high psychological costs. For all model variants, the positive effect of ‘compliance costs’ on ‘psychological costs’ is statistically significant. Therefore, H6 cannot be rejected.For all model variants, ‘knowledge level’ negatively influences ‘administrative burden’ through the mediator variable ‘compliance costs’. However, in none of the model variants is this effect statistically significant. Even though psychological costs increase farmers’ administrative burden, the indirect effect of ‘knowledge level’ on ‘administrative burden’ with ‘psychological costs’ as mediator variable is statistically significantly negative. This implies that, first, a high knowledge level reduces psychological costs. Consequently, a positive attitude toward and a strong identification with the cross-compliance and direct-payment policy leads to farmers perceiving administrative tasks as less onerous. The indirect effect of ‘compliance costs’ on ‘administrative burden’ with ‘psychological costs’ as mediator variable is statistically significantly positive. Findings suggest that, first, high compliance costs cause psychological costs to increase; ultimately, high psychological costs lead in turn to administrative burden. The indirect effect of ‘compliance costs’ on ‘psychological costs’ with ‘administrative burden’ as mediator variable is statistically significantly positive (Model 2). This indicates that, first, high compliance costs cause administrative burden. Ultimately, this leads to a negative attitude toward and a lack of identification with the cross-compliance and direct-payment policy.The total effect of ‘knowledge level’ on ‘administrative burden’ (with ‘compliance costs’ as mediator variable) represents a significant negative effect for Model 2 and Model 3. Therefore, farmers with a high knowledge level indicate lower levels of administrative burden. For Model 1, the total effect of ‘knowledge level’ on ‘administrative burden’  is likewise significantly negative. Here too, farmers with a high knowledge level perceive administrative tasks as less onerous. Likewise, for Model 1, the total effect of ‘compliance costs’ on ‘administrative burden’ is statistically significantly positive. Compliance costs in the form of time spent gathering direct-payment evidence and the perceived increase in administrative workload due to the switch to e-government intensify administrative burden. In the case of Model 2, the total effect of ‘compliance costs’ on ‘psychological costs’ is statistically significantly positive. Consequently, high compliance costs cause psychological costs to increase.The administrative burden in citizen-state interactions can be tackled in the realm of agricultural policy! By applying SEM, we are able to refine and test the theoretical framework developed by  in the cDirect payments represent a large share of Swiss farmers’ total household income. Furthermore, voluntary agri-environmental direct-payment schemes in particular require compliance with additional standards, and administrative burden may hinder widespread adoption. Therefore, to increase acceptance of the cross-compliance and direct-payment policy, reducing farmers’ administrative burden is of crucial importance for agricultural policy-makers. The present study stresses the importance of education and information in reducing psychological costs. Well-educated and well-informed farmers exhibit lower psychological costs and perceive administrative tasks as less onerous. In other words, the level of information on the cross-compliance and direct-payment policy is the primary factor that positively influences farmers’ attitudes toward the policy. Since we find a strong positive effect of compliance costs on administrative burden, policy should focus on a successive reduction of administrative requirements. In this context, younger farmers in particular should be better able to cope with new information technologies supporting the efficient handling of administrative requirements, while older farmers should be systematically trained and supported. Thus, political initiatives to reduce farmers’ administrative burden should focus on the one hand on measures that decrease compliance costs. Examples might include reducing the number of application documents to be completed for direct payments, or investing in e-government services. On the other hand, political initiatives focusing on a positive attitude toward agricultural policy could also help to decrease farmers’ perceived administrative burden. Consequently, to tackle farmers’ administrative burden effectively, policy measures and agricultural extension services should aim to increase investments in education and training, especially targeting the handling of administrative requirements.S1 Data(XLS)Click here for additional data file.S1 Fig(TIF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file. 19 Aug 2020PONE-D-20-17818Empirical evidence on factors influencing farmers’ administrative burden: A structural equation modeling approachPLOS ONEDear Dr. Ritzel,Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.Methodological issues raised by Reviewer 1 need to be addressed. In addition, the discussion section needs to explain the limitations of the measurement and structural components of the structural equation models and discuss possible ways to improve model fit in future studies.plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.Please submit your revised manuscript by Oct 03 2020 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at Please include the following items when submitting your revised manuscript:A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). 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Reviewer #1: This study sought to examine the relationship between psychological and compliance costs and perceived administrative burden related to direct payments for Swiss farmers. This is a well written paper in need of just minor revisions/clarifications. These suggested changes are provided below.One minor issue is that direct payments is not exactly defined. While it may be painfully obvious to the authors what this means, it would be helpful to outside readers less familiar with Swiss farming to clarify what is meant by direct payments.The authors state that one of the added benefits of their study is that they are able to refine the framework of administrative burden on Swiss farmers by testing three theoretical models. However, it is not made clear which model the authors determine to be the most appropriate for explaining these interrelationships.The authors also state on page 13 that their specific hypotheses are tested separately (via models 1-3). However, the model fit criteria in Table 1 is exactly the same across all 3 models. Additionally, in the S1 Figure, the title is “Path diagram of the applied structural equation model”, these two things lead me to believe that a single model  was fit, rather than three separate models.If three separate models were in fact examined, while it is important to interpret the results from these various models, it would be helpful if the researchers clarified their conclusion about which model fit best and was the driving force behind their recommendations to policy makers regarding the need to decrease psychological costs and thus the perceived administrative burdens of direct payment for Swiss farmers. Perhaps it would be most appropriate to include the full path diagram for this final model  rather than the current path diagram included in the S1 Figure.X4 is missing from the path diagram in the S1 Figure.One final concern I have is with the specification of the measurement model. I am interested to further understand the authors’ decision to constrain one of the loading for each factor to 1 for model identification rather than estimating each of these factor loadings and imposing other constraints.**********what does this mean?). If published, this will include your full peer review and any attached files.6. PLOS authors have the option to publish the peer review history of their article  digital diagnostic tool,  14 Sep 2020Response to ReviewerReviewer #1: This study sought to examine the relationship between psychological and compliance costs and perceived administrative burden related to direct payments for Swiss farmers. This is a well written paper in need of just minor revisions/clarifications. These suggested changes are provided below.• One minor issue is that direct payments is not exactly defined. While it may be painfully obvious to the authors what this means, it would be helpful to outside readers less familiar with Swiss farming to clarify what is meant by direct payments.Response: Many thanks for this comment! Right at the beginning of the introduction, we now provide a short definition on direct payments. • The authors state that one of the added benefits of their study is that they are able to refine the framework of administrative burden on Swiss farmers by testing three theoretical models. However, it is not made clear which model the authors determine to be the most appropriate for explaining these interrelationships.Response: The AIC, BIC and likelihood-ratio test values do not differ across model variants. Therefore, (unfortunately) comparative fit measures do not allow determining the most appropriate model . • The authors also state on page 13 that their specific hypotheses are tested separately (via models 1-3). However, the model fit criteria in Table 1 is exactly the same across all 3 models. Additionally, in the S1 Figure, the title is “Path diagram of the applied structural equation model”, these two things lead me to believe that a single model  was fit, rather than three separate models.Response: We re-estimated the three different models to get sure that we provide the correct AIC, BIC and likelihood-ratio test values. However, the AIC, BIC and likelihood-ratio test values still do not differ across model variants. To avoid confusion, we now present the three different applied models separately in S1 Figure. • If three separate models were in fact examined, while it is important to interpret the results from these various models, it would be helpful if the researchers clarified their conclusion about which model fit best and was the driving force behind their recommendations to policy makers regarding the need to decrease psychological costs and thus the perceived administrative burdens of direct payment for Swiss farmers. Perhaps it would be most appropriate to include the full path diagram for this final model  rather than the current path diagram included in the S1 Figure.Response: As already mentioned above, unfortunately, comparative fit measures do not differ across model variants. Therefore, we are not able to conclude which model is the most appropriate one & we do not have a “final” model. • X4 is missing from the path diagram in the S1 Figure.Response: Thanks for this hint! We have added x4 to the figure. • One final concern I have is with the specification of the measurement model. I am interested to further understand the authors’ decision to constrain one of the loading for each factor to 1 for model identification rather than estimating each of these factor loadings and imposing other constraints.Response: Stata automatically constraints one estimator (factor loading) of the measurement model to the value of 1 (unstandardized coefficients). Unfortunately, by using gsem, it is not possible to report standardized coefficients as it is possible by using sem. By choosing standardized coefficients in sem, no estimator of the measurement model is constraint to 1. In our opinion, the impossibility of reporting standardized coefficients is one «disadvantage» of the gsem command.AttachmentResponsetoReviewer.docxSubmitted filename: Click here for additional data file. 8 Oct 2020Empirical evidence on factors influencing farmers’ administrative burden: A structural equation modeling approachPONE-D-20-17818R1Dear Dr. Ritzel,We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. 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For more information, please contact Kind regards,Asim Zia, Ph.D.Academic EditorPLOS ONEAdditional Editor Comments :Reviewers' comments: 13 Oct 2020PONE-D-20-17818R1 Empirical evidence on factors influencing farmers’ administrative burden: A structural equation modeling approach Dear Dr. Ritzel:I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department. onepress@plos.org.If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact plosone@plos.org. 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The COVID-19 pandemic is a worldwide public health emergency caused by the  Genome-wide epistasis analysis is a powerful tool to infer gene interactions, which can guide drug and vaccine development and lead to deeper understanding of microbial pathogenesis. We have considered all complete severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomes deposited in the Global Initiative on Sharing All Influenza Data (GISAID) repository until four different cutoff dates, and used direct coupling analysis together with an assumption of quasi-linkage equilibrium to infer epistatic contributions to fitness from polymorphic loci. We find eight interactions, of which three are between pairs where one locus lies in gene ORF3a, both loci holding nonsynonymous mutations. We also find interactions between two loci in gene nsp13, both holding nonsynonymous mutations, and four interactions involving one locus holding a synonymous mutation. Altogether, we infer interactions between loci in viral genes ORF3a and nsp2, nsp12, and nsp6, between ORF8 and nsp4, and between loci in genes nsp2, nsp13, and nsp14. The paper opens the prospect to use prominent epistatically linked pairs as a starting point to search for combinatorial weaknesses of recombinant viral pathogens. The pandemic of the disease COVID-19 has so far led to the confirmed deaths of more than 852,000 people  there ha61022Dataset S3 and in ref. The predicted effective interactions between loci were obtained from pseudo-likelihood maximization (PLM) scores, a standard computational method to perform DCA. Manual inspection shows that about half of the top 50 links and most of the top 200 involve noncoding sites in the 5′ or 3′ region on the “Wuhan-Hu-1”  referencSI Appendix, Table S6, and for the 1 April 2020 and 8 April 2020 datasets in ref. In SI Appendix, Figs. S1–S3 and ref. SI Appendix, Figs. S1–S3 for circos visualizations. To check whether the interactions can be explained by phylogeny (inherited variations), we used two randomization strategies, “profile” and “phylogeny” of the multiple sequence alignments (MSAs). Profile preserves the distribution over alleles at every locus but does so independently at each locus. Profile hence destroys all systematic covariations between loci. Phylogeny additionally preserves the genetic distance between each pair of sequences. Viral genealogies inferred from this information are therefore unchanged under this randomization. PLM scores run on these two types of randomized data (scrambled MSAs) are a background from which the significance of the interactions from the original data can be assessed. Each randomization strategy is repeated 50 times with different realizations of the scrambling; see SI Appendix, Methods of DCA, DCA is based on a global probabilistic model, and therefore ranks interdependency differently than correlations. Epistatic interactions obtained from DCA reflect pair-wise statistical associations, but not correlations. As reviewed in ref. The first-ranked interaction between 1059 and 25563 is between a (C/T), resulting in the T85I nonsynonymous mutation in gene nsp2, and a (G/T), resulting in the Q57H nonsynonymous mutation in gene ORF3a. The nsp2, expressed as part of the ORF1a polyprotein, binds to host proteins prohibitin 1 and prohibitin 2 (PHB1 and PHB2) in SARS-CoV . The varThe association between 8782 and 28144 (rank 5), reported early in SARS-CoV-2 studies , 42. TheThe interaction between 14805 and 26144 (rank 9) leads to nonsynonymous alterations in nsp12  and ORF3a (G251V), respectively. The G251V has been reported by many studies and is defining the GISAID V clade  togetherThe second interaction partner of G251V is the nsp6 L37F variant. The nsp6 has been shown to induce autophagosomes in the host cells in favor for viral replication and propagation SARS-CoV . There iThe interaction between 17747 and 17858 (rank 27) is between two nonsynonymous mutations  and  within the gene nsp13 that codes for a helicase enzyme that unwinds duplex RNA . It is tThe final interaction (rank 21) is a link between a locus carrying a nonsynonymous mutation  in nsp2 position 1059 and a locus carrying a synonymous mutation  in nsp14, position 18877. As the knowledge on nsp2 protein structure is poor, there is no evidence for the effect of this mutation. Also, how the synonymous C/T alterations in nsp14, as well as in the synonymous mutations of the other interactions, affect the virus is unknown, but can be proposed to change RNA secondary structure, RNA modification, or codon usage.In this work, we have considered all whole-genome sequences of SARS-CoV-2 deposited in GISAID up to different cutoff dates. As this coronavirus has extensive recombination, we have assumed that the distribution of genotypes is well described by Kimura’s QLE, and used DCA to infer epistatic contributions to fitness from the sequences. After filtering out all but the strongest effects and variations in noncoding regions with many gaps in the MSA, the remaining predictions are few in number, i.e., 19 predictions in SI Appendix, Tables S6 and S7 and Fig. S6 yielded six retained predictions, involving, however, the same eight viral genes. The question on epistasis vs. effects of inheritance (phylogeny) clearly merits further investigation and testing as more data become available.Covariations between allele distributions at different loci can be due to epistasis and also to inherited effects (phylogeny). We have tested for the second type by randomizing MSA of sequences such that pair-wise distances between sequences are left invariant. We find that the top link 1059–25563 appears three times in 50 phylogeny randomizing samples, although with much lower rank. The other predicted epistatic contributions disappear under phylogenetic randomization, except for pairs in the triple  which appear in from 20 to 35% of 50 randomizations. After eliminating these links as well as links between adjacent loci , we are left with eight predictions listed in Biological fitness is a many-sided concept and can also include aspects of game and cooperation –49. A fiEpistatic interactions are pair-wise statistical associations, but are not simply correlations. The interaction between sites 8782 and 28144, which is the second largest in SI Appendix, Table S4. The Spike protein has been the main target of coronavirus vaccine development to date  and the immune system of elite controllers  , 50, 52. to date , includi to date –56.SI Apppendix, Table S5, following ref. An epistatic interaction means that loss of fitness by a mutation at one locus is enhanced (positive epistasis) or compensated (sign epistasis) by a mutation at another locus. Suppose there are drugs that act on targets around both loci, modulating the fitness of the respective variants. Epistasis then points to the possibility that using both drugs simultaneously may have a more than additive effect. To search whether our analysis offers such a guide to combinatorial drug treatment, we scanned the recent comprehensive compilation of drugs known or predicted to target SARS-CoV-2 . Five ouNevertheless, the number of combinations of potential drug targets, in COVID-19 and many other diseases, is very large. DCA applied to many sampled sequences predicts which genes/loci have mutual dependencies in fitness, and can be used to rank combinations for further, more detailed investigation. We note that one can also start a search for drug treatment from conserved positions, assuming these to be unconditionally necessary for the virus. If so, all potential pairs would, however, be ranked equal based on sample information, and there would be no analogous shortcut to the combinatorial explosion of possibilities. Even if the scan discussed above did not lead to any direct suggestions based on the lists of potential drugs in ref. 37Dataset S1, and is also available on the Github repository  , 59 for pository .The MSA is a big matrix ositions , 21. EacBefore filtering, we transform the MSA in two different ways as follows: 1) The gaps “-” are transformed to “N” while the minors “KFY…” are mapped to “N.” Thus five states remain, where “NACG”’ are represented by “12345”; 2) the minors “KFY…” are mapped to “N.” Then there are six states, with “-NACGT” represented by “012345.”The following criteria are used for prefiltering of the MSA from the 8 August 2020 dataset. If, for one locus, the same nucleotide is found in more than 96.5% of this column, or if the sum of the percentages of A, C, G, and T at this position is less than 20%, then this locus will be deleted. For each sequence, if the percentage of a certain nucleotide is more than 80%, or if the sum of the percentages of A, C, G, and T in this sequence is less than 20%, then this sequence will be deleted. With this filtering criteria, many loci but no sequences are deleted. There are left 51,676 sequences and 689 loci.To mitigate the effects of dependent samplings, it is standard practice to attach to each collected genome sequence eight wb , 16, 60,The reweighting procedure partially addresses a point raised , that seStreptococcus pneumonia  and Its Range of Validity.The phenomenon of QLE was discovered by M. Kimura  while ini and σj –13. The neumonia  and Neisorrhoeae , both ofolyticus . In thatThe basic assumption of modeling the filtered MSA is that it is composed of independent samples that follow the Gibbs–Boltzmann distribution On the other hand, in the context of inference from protein sequences, it has been argued that the one encoded in SI Appendix, Methods of DCA. We employ the PLM method  and would also be retained if selection were made according to some cutoff, or an empirical pipeline . In the SI Appendix, Phylogenetic Randomization of DCA: Principles.To understand the nature of the top 200 PLM scores, we perform two distinct randomization strategies on the MSA, such that its conservation patterns and (or) phylogenetic relations are preserved, while intrinsic coevolutionary couplings (epistatic interactions) are removed . RunningSupplementary FileSupplementary FileSupplementary FileSupplementary File


Togaviridae) is an endemic arbovirus of the Americas with epidemiological similarities with the agents of other more prominent diseases such as dengue (Flaviviridae), Zika (Flaviviridae), and chikungunya (Togaviridae). It is naturally transmitted in a sylvatic/rural cycle by Haemagogus spp., but, potentially, it could be incorporated and transmitted in an urban cycle by Aedes aegypti, a vector widely disseminated in the Americas.Mayaro virus . Magdalena and Maracaibo had the highest flow of land cargo. The results of the simulations indicate that Magdalena, Imerí, and biogeographical Chocó would be the most affected regarding the number of cases of people infected by Mayaro virus over time.The vital rates and the development cycle of Ae. aegypti and passive migration via transport of land cargo plays an important role on how the Mayaro virus would be disseminated in the human population. Once this arbovirus begins an urban cycle, the most-affected departments would be Antioquia, Santander, Norte de Santander, Cesar , and Valle del Cauca, and Chocó , which is why vector control programmes must aim their efforts at these departments and include some type of vector control to the transport of land cargo to avoid a future Mayaro epidemic.The temperature in each of the provinces influences the local population dynamics of  Togaviridae belonging to the genus Alphavirus, which includes another 29 viruses; among them, the causal agents of chikungunya fever and the eastern, Venezuelan, and western equine encephalitis virus [Mayaro is an arbovirus of the family is virus . Mayaro is virus , 4. Sincis virus , 6.Flaviviridae), Zika (Flaviviridae) and chikungunya (Togaviridae) [The disease caused by the Mayaro virus is characterized by high fever and intense joint pain, with duration from 3 to 7 days, causing medical disability . This diviridae) . In addiviridae) –13. Howeviridae) .Haemagogus spp. infected with this virus [Aedes (Stegomyia) aegypti  from South America and North America it has been observed experimentally their susceptibility to being infected and transmitting the genotype D of the Mayaro virus [Ae. aegypti from South America, natural infection with the genotype L of the Mayaro virus has also been observed [The Mayaro virus is endemic in rural areas in South America where it maintains an enzootic sylvan cycle (similar to chikungunya) that involves wild vertebrates (birds and reptiles), including primates and hematophagous mosquitoes . In sylvis virus . Furtherro virus , 17. Addobserved .Aedes aegypti has the human population as preferential source of food, and urban areas as main reproduction areas [Ae. aegypti, as recently observed in chikungunya and Zika arboviruses [on areas , althougon areas . Consequon areas , 21–23. oviruses , 25.Ae. aegypti lies in that it is present in large densities in all continents, with a high degree of anthropophilia, thus, being able to introduce the Mayaro virus into an urban cycle and, thereby, rapidly spread the etiological agent [Ae. aegypti is the primary vector of dengue, chikungunya and Zika [The importance of al agent , 26. In and Zika –29, whicand Zika ; in Coloand Zika , represeAe. aegypti and, given that currently no vaccine is available to prevent Mayaro infection in humans (with good results in mice) [Once the Mayaro virus starts to circulate in the urban area through in mice) , the onlin mice) . Neverthin mice) , which iin mice) . Hence, Ae. aegypti in Colombia, an epidemiological behavior would be expected for Mayaro virus similar to that observed in chikungunya. Recently, it has been observed experimentally that in Ae. aegypti individuals infected initially with chikungunya and then with Mayaro virus, both arboviruses can be transmitted with similar efficiency. Further, if it is initially infected with Mayaro virus and then with chikungunya virus, there is an exclusion due to over-infection to chikungunya; in any of the situations described, Mayaro virus will always be transmitted [Given that the Mayaro virus is genetically related to the chikungunya virus , and chinsmitted .Haemagogus spp., an option to know its epidemiological impact is to formulate a mathematical model that permits simulating its behavior and expansion in the human population, through the Ae. aegypti potential vector, given that it is distributed in peri-urban and urban areas.Since the natural distribution of the Mayaro virus is unknown in sylvatic areas and how it could be potentially transmitted by another vector different from Ae. aegypti, the mathematical models have proven to be useful tools to understand dengue transmission [Ae. aegypti and an SEI/SEIR-type epidemiological model for the population of humans, as well as the passive transport of the mosquito through land cargo, which, has also been used recently to study the dynamics of Rift Valley fever transmission in the human population, with Ae. aegypti as the main vector [Ae. aegypti decreases; however, egg-laying time increases causing a growth in egg number [Ae. aegypti affect the dynamics of Mayaro virus transmission in the Colombian human population, from which the study only quantified transport via land cargo due to the lack of data from other means of transport.For smission , 36 and,smission  and Zikasmission , as wellsmission . Among tsmission , 41 and smission –46. Nonen vector . Among tn vector . For Coln vector , there an vector , 51. Forn vector , while ag number . Moreoveg number , 55. On g number . ConsideAe. aegypti [raster, rgdal [sp [dplyr [st [Ae. aegypti corresponding to different temperatures were obtained from the literature and adjusted by average for each region, taking into account its temperature range. Additionally, in 2018 and for each province, the size of the human population was estimated through the projections of the Departamento Administrativo Nacional de Estadística . The adult stage is divided, in turn, into three classes, based on the SEI epidemiological model: susceptible . Time Figure The variables and parameters used are defined by: Ae. aegypti is the following:The system of differential equations that interprets the dynamics of The human population was divided into four classes: susceptible  to province The entry rate of adult individuals in state The exit rate of adult individuals is state ® environment [Ae. aegypti and the Mayaro virus in each biogeographical province, the behavior of the vector and human populations was simulated in each of the biogeographical provinces, with and without passive transport (migrations), for 300 days. The province of Imerí was taken as center of origin of the infection with Mayaro virus of adult individuals of Ae. aegypti, given that previous reports have been registered in vertebrates for this arbovirus [H: 300, L: 200, P: 200, As: 200 and Ae: 0. Relating to Ai, an infected mosquito was initially placed in the infection focus region and zero in the others.The numerical solution of the model was obtained by using the Runge-Kutta 4th order method implemented in MATLABironment . From thrbovirus . Additiorbovirus . In bothrbovirus , 67. In LHS package [Ai), through a sensitivity test [sensibility package [Ai for those parameters significantly correlated with it, from the data reported, they were simulated taking into account its respective interval scale.A global sensitivity and uncertainty analysis was performed using the partial coefficient correlation index (PRCC). The parameters considered for this analysis were the infection rate from the mosquito to humans (ng (LHS)  method iity test , by mean package  in R. AdAe. aegypti, associated with data for temperature from the biogeographical provinces obtained from the literature, it is possible to observe that the provinces with the best conditions for the development of this mosquito species presenting the ranges of most optimal temperatures for the development and life-cycle of Ae. aegypti, are Imerí, Chocó and Magdalena . Additionally, the carrying capacity (K) did affect the population size of living beings; however here, K did not affect the population size of Ae. aegypti, in these or in the other biogeographical regions, like the size [Ae. aegypti than areas with less rainfall. According to Mohammed & Chadee [Ae. aegypti, with temperatures > 39 °C being lethal for this species [Ae. aegypti adults are lower with respect to the other provinces.Furthermore, changing climate conditions, like global warming, are generating new niches  and optimal for subsequent habitat expansion not only in  species ), but al species , 83. In perature –86, wherperature , in contperature . Consequsmission , in contsmission . Our resthe size  and amouthe size , or climthe size , could bthe size . Accordithe size , areas w& Chadee  temperat species , 96, sup species , having The model presented here has limitations, which originate from the assumptions considered, among which it was assumed that Mayaro disease is caused by the virus genotype D, the most frequent, and it is dispersed from where there are records of disease outbreaks , by Ae. aegypti hatcheries. In this scenario, population control of this vector is essential to prevent a possible Mayaro epidemic; however, currently there are difficulties in controlling Ae. aegypti populations due to the spread of resistance to the insecticides used to control it [Ae. aegypti populations are being evaluated, such as the use and release of mosquitoes infected with Wolbachia pipientis-WMel lineage (mosquitoes with refraction to arboviruses transmitted by Ae. aegypti) [Ae. albopictus, a species that shares ecological niches in Colombia and America with Ae. aegypti and that in other latitudes is the main vector of dengue, chikungunya and Zika is unknown [The HLS/PRCC analysis shows that the contact rate of infected humans and susceptible mosquitoes  . Additio unknown –103.Ae. aegypti plays an important role on how the Mayaro virus would be disseminated in human populations. Once this arbovirus begins an urban cycle from the Imerí or Magdalena provinces, the most-affected departments would be Antioquia, Santander, Norte de Santander, Cesar , and Valle del Cauca and Chocó  as already occurred with dengue, chikungunya and Zika. Therefore, vector control programmes must aim their efforts at these departments and include some type of vector control to the transport of land cargo to avoid a future Mayaro epidemic.Our results indicate that temperature influences local vector proliferation, since vital rates depend on it. That is, in each biogeographical province the dynamics of the vector is different, while the passive migration of