Members of the leucine-rich repeat (LRR) superfamily play critical roles in multiple biological processes. As the LRR unit sequence is highly variable, accurately predicting the number and location of LRR units in proteins is a highly challenging task in the field of bioinformatics. Existing methods still need to be improved, especially when it comes to similarity-based methods. We introduce our DeepLRR method based on a convolutional neural network (CNN) model and LRR features to predict the number and location of LRR units in proteins. We compared DeepLRR with six existing methods using a dataset containing 572 LRR proteins and it outperformed all of them when it comes to overall F1 score. In addition, DeepLRR has integrated identifying plant disease-resistance proteins  and non-canonical domains. With DeepLRR, 223, 191 and 183 LRR-RLK genes in Arabidopsis , rice (Oryza sativa ssp. Japonica) and tomato (Solanum lycopersicum) genomes were re-annotated, respectively. Chromosome mapping and gene cluster analysis revealed that 24.2% (54/223), 29.8% (57/191) and 16.9% (31/183) of LRR-RLK genes formed gene cluster structures in Arabidopsis, rice and tomato, respectively. Finally, we explored the evolutionary relationship and domain composition of LRR-RLK genes in each plant and distributions of known receptor and co-receptor pairs. This provides a new perspective for the identification of potential receptors and co-receptors. N-terminal contains a Toll/interleukin-1 receptor (TIR) domain, TNLs or non-TNLs. Some non-TNLs have a coiled coil domain composed of CNLs [The plant immune system is mainly composed of two mechanisms . The fir of CNLs ,10. Prot of CNLs .Plants use LRR-RLKs residing on the cell surface to sense different external and internal cues and elicit distinct biological responses. The signaling pathway mediated by LRR-RLKs enlists a small group of co-receptors, such as somatic embryogenesis receptor kinases (SERKs), through ligand-induced heterodimerization and transphosphorylation. In Arabidopsis, receptor and co-receptor pairs mainly regulate signal transduction in plant cell differentiation, growth and immunity. Brassinosteroid insensitive 1 (BRI1) interacts with SERK1, SERK3/BAK1 and SERK4/BKK1 to sense plant hormone brassinosteroids (BRs) for regulation of plant growth ,13,14,15LRR is a conserved domain with characteristic hydrophobic leucine residues present in the majority of immune receptors such as NLR, RLK and RLP . Each LRDue to their high level of sequence variability necessitated by the diverse functions of LRR domains, it is a challenging task in bioinformatics research field to predict the exact position and number of LRR units in a protein. Current LRR prediction tools can be divided into two categories: sequence similarity-based and de novo prediction tools. Sequence similarity-based tools usually build the HMM model or PSSM matrix via sequence alignment, such as Pfam , SMART , PrositeRecently, the application of deep learning algorithm in bioinformatics increased exponentially  and its DeepLRR predicts potential LRR domains and units in a protein sequence through concerted local and global analyses. In the local analysis phase, LRR protein sequences are retrieved from the Swiss-Prot database and annotated LRR unit sequences in these proteins are collected to form the positive sample dataset. To construct the negative sample dataset, we predict the highly conserved segment pattern (HSCP) of LRR units on the basis of the constructed positive sample dataset. The negative sample dataset was then artificially generated by eliminating HSCP. Thereafter, we use positive and negative sample datasets to build training, validation and testing datasets to train and test our CNN model. This is then used to evaluate any peptide sequences between 20 to 30 aa in length and to give a LRR unit probability score (LPS) for each peptide sequence. A given protein sequence is divided into 11 groups of short sequences based on a 20 to 30 aa-long sliding window and each short sequence is evaluated based on whether its LPS is a LRR unit. In the global analysis, we combined the CNN model and LRR features to set the dynamically adjustable parameters Lscp, Ldcp and Lncp, respectively. These parameters help us determine whether the multiple LRR units predicted by the CNN model can form a LRR domain. In addition, we have integrated the identification process of NLR, RLK and RLP resistance proteins and their non-canonical domains to improve the applicability of DeepLRR.To ensure the reliability of the LRR positive sample used in this study, 1849 protein sequences containing 18,039 LRR units are downloaded from the Swiss-Prot database.As most LRR units are 20 to 30 aa in length, we filtered LRR units with a length less than 20 aa or a length greater than 30 aa and retain protein sequences with at least three LRR units. To date, we have obtained a total of 1748 protein sequences containing 17,388 LRR units. The last step is to use CD-HIT [Due to the particularity for the negative LRR unit samples, they cannot be downloaded directly from the existing database and so we artificially create the dataset. The first step is to use MEME-5.1.0  to prediIn order to ensure that the performance of the model is stable and reliable, we build training, validation and testing datasets to train and test the performance of our models. The training dataset is used to train the model, and the validation dataset is used to adjust the parameters and avoid overfitting. After a reasonable number of iterative learning cycles, we test the final performance of the model on an independent test dataset. It is important to note that the testing dataset has never been used in the model during training and validation. The ratio of training dataset to testing dataset values is 8:2. Five-fold cross-validation is performed on the training dataset to test the generalization ability of the model, adjust parameters and avoid overfitting . The proThis study does not apply the one-hot encoding commonly used in encoding sequence. Instead, a new sequence encoding method proposed recently is used, and any protein sequence can be encoded as a 20 × 5 binary matrix . For eacThe deep learning model used in this study is the convolutional neural network (CNN). The CNN model is implemented with the Python programming language and the Pytorch library, which consists of multiple layers: one convolutional layer, one max pooling layer, one fully connected layer and one softmax layer . In orde1 = 0.9, β2 = 0.999, ε = 1 × 10−8) is chosen for optimization. The dropout method is used in the fully connected layer and the dropout rate is set to 0.5 to prevent the neural network from overfitting. In addition, we also use the early stop strategy to further prevent the neural network from overfitting. We use 50 epochs with a mini-batch size of 128.During model training, the binary cross-entropy loss function (BCELoss) was minimized for the true label. The negative classes and adaptive momentum (Adam) optimizer  measures the percentages of LRR units correctly classified; Pre indicates the ratio of true LRR units that are classified as LRR units by DeepLRR; the F1 score comprehensively considers precision and sensitivity; MCC represents the balance quality of the positive and negative data.In order to comprehensively assess LRR unit prediction performance, four commonly applied statistical measures are adopted in this work, including sensitivity (Sen), precision (Pre), F1 score and Matthew’s correlation coefficient (MCC). They are defined as follows:In the global analysis, potential LRR units (up to 11) at each position along the protein sequence are further assessed to determine which are able to form a LRR domain. To this end, a LRR score control parameter (Lscp) is implemented for quality control. For example, when the CNN model predicts k LRR units starting from the i-th aa of various lengths, these LRR units are kept if k is greater than or equal to Lscp. Otherwise, they are removed. Next, the protein sequences are divided into consecutive 20-aa segments. In each of these 20-aa regions, the LPS of 20-aa LRR units for each position are compared and only the LRR units at the position where the LPS of each 20-aa LRR unit is the largest are kept. A LRR distance control parameter (Ldcp) is then implemented to divide these 20-aa LRR units kept from the previous step into different groups. For example, two adjacent LRR units are considered to be in different groups if the number of aa between them is greater than Ldcp, otherwise they are considered to be in the same group. In the final step, a LRR number control parameter (Lncp) is implemented to filter unqualified LRR groups. For example, if the number of LRR units in a group is greater than or equal to Lncp, this group of LRR units will be considered a potential LRR motif by DeepLRR. The LRR unit with the largest LPS at each position will be returned without overlapping with adjacent LRR units.In this research, we built an integrated pipeline to characterize NLR, RLK and RLP proteins and their associated non-canonical domains based on their domain architecture. The TIR (PF01582), and NB-ARC (PF00931) domains in NLR and kinase domain  in RLK are identified using Pfam. Transmembrane domains and signal peptides in RLK and RLP are identified using TMHMM  and SignTo re-annotate the LRR-RLK genes of Arabidopsis, rice and tomato, the identification pipeline of DeepLRR was used to browse the proteome of Arabidopsis (TAIR10.1) , rice , random forest (RF) and naïve Bayes (NB) models. Five-fold cross validation was conducted to adjust the parameters for these models. The four models were evaluated using an independent testing dataset based on their precision, sensitivity, F1-score and MCC  performaDeepLRR has three adjustable parameters: Lscp, Ldcp and Lncp. It is essential to understand the impact of these parameters on the performance of DeepLRR. For this, CD-HIT is used to remove the redundancy of 1748 LRR proteins with 80% as the threshold and 1144 non-redundant LRR proteins are divided into two groups. One group was used to evaluate the impact of different combinations for these parameters on DeepLRR performance. The other was used to compare the performance between DeepLRR and six LRR predicting tools currently in use. Based on the overall performance of DeepLRR in the test on the first group of LRR proteins, we found that when parameters Lscp and Ldcp were fixed, the Lncp gradually increased from 2 to 10. The precision in the prediction results of DeepLRR gradually increased and the sensitivity gradually decreased, while the F1-score firstly increased and then decreased. When parameters Lscp and Lncp were fixed, the Ldcp gradually increased from 1 to 20, while the precision in the prediction of DeepLRR firstly increased and then decreased. Sensitivity and F1-score firstly increased and then remained basically unchanged. Finally, when parameters Ldcp and Lncp (Lncp ≤ 4) were fixed, Lscp gradually increased from 1 to 4 and the precision as well as F1-score in the prediction results of DeepLRR gradually increased while the sensitivity was gradually decreased. However, when Lscp gradually increased from 4 to 11, the precision, sensitivity and F1-score in the prediction results of DeepLRR remained basically unchanged. When parameters Ldcp and Lncp (Lncp ≥ 5) were fixed, changing the value of Lscp has almost no effect on the performance of DeepLRR . We perfIt is necessary to compare the proposed method with other existing state-of-the-art tools. We compared the performance of DeepLRR  with the six existing tools using the second group of LRR proteins, which have not been used before. Our results showed that the F1-score with DeepLRR is 0.701, 0.241, 0.528, 0.035, 0.024 and 0.071 higher than that with Pfam, Prosite, SMART, LRRfinder, LRRsearch and LRR predictor, respectively. In addition, DeepLRR is the only tool whose precision and sensitivity are both greater than 0.740 . This rehttp://lifenglab.hzau.edu.cn/DeepLRR/; accessed on 28 November 2021)  . If userUsing the LRR-RLK identification pipeline provided by the DeepLRR website service, 223 LRR-RLK genes were identified from Arabidopsis reference genome ITAG10.1. Comparing the annotation results of LRR-RLK genes from DeepLRR, reference genome ITAG10.1 and a representative paper , we founThe 223, 191 and 183 LRR-RLK genes identified by DeepLRR were mapped to different chromosomes of Arabidopsis, rice and tomato respectively through Mapchart . The LRRGenes in the same gene cluster are marked with rectangles and LRR-RLK genes of Arabidopsis, rice and tomato have a clustering characteristic. Eighteen gene clusters were found in Arabidopsis, including 54 LRR-RLK genes, accounting for approximately 24.2% (54/223) of the total. Arabidopsis contains an average of 3.6 gene clusters per chromosome and each gene cluster contains an average of 3 LRR-RLK genes. Among the 5 chromosomes of Arabidopsis, chromosome 5 contains the largest number of gene clusters, with 6 clusters containing 14 LRR-RLK genes in total. The gene cluster on chromosome 1 contains the largest number of LRR-RLK genes with the 5 gene clusters containing a total of 21 LRR-RLK genes. No gene cluster on chromosome 4 was found B. Two ofThere are also tandem repeats in the LRR-RLK genes of Arabidopsis, rice and tomato. In Arabidopsis, rice and tomato, we found that 21.5% (48/223), 27.7% (53/191) and 14.2% (26/183) of the LRR-RLK genes were tandem duplications . The disIn this work, the LRR-RLK gene with a kinase domain length of 200–300 aa in Arabidopsis, rice and tomato was used for phylogenetic analysis. Phylogenetic analysis found that the number of LRR units for LRR-RLK genes under the same evolutionary branch in Arabidopsis, rice and tomato is mostly distributed the same way . It may In the study, we introduce DeepLRR, an accurate website tool for the detection of LRR units from protein sequences and identification of NLR, RLK, RLP and its non-canonical domains. Compared to existing tools for LRR unit detection, DeepLRR attained a higher F1-score when using the set of 572 LRR proteins and maintained a great balance between precision and sensitivity. Analyzing the domain composition of LRR-RLK genes in Arabidopsis, rice and tomato, we found that LRR units and kinase domains of individual LRR-RLK genes overlap. In the future, we will try to improve the ability of DeepLRR to predict LRR units from protein sequences through a more comprehensive dataset, more structural information and usage of the capsule network . In addiDeepLRR combines deep learning algorithms and LRR features to predict the number and location of LRR units in an unknown protein sequence. By predicting the number of LRR units, it is possible to further study the interaction between receptors and co-receptors in plant immunity. In addition, DeepLRR can help researchers quickly identify disease-resistant proteins and their non-canonical domains in species of interest. This provides a reference for studying integrated decoy domains in plant immunity.


High-throughput nucleic acid sequencing has greatly accelerated the discovery of viruses in the environment. Mosquitoes, because of their public health importance, are among those organisms whose viromes are being intensively characterized. Despite the deluge of sequence information, our understanding of the major drivers influencing the ecology of mosquito viromes remains limited. Using methods to increase the relative proportion of microbial RNA coupled with RNA-seq we characterize RNA viruses and other symbionts of three mosquito species collected along a rural to urban habitat gradient in Thailand. The full factorial study design allows us to explicitly investigate the relative importance of host species and habitat in structuring viral communities. We found that the pattern of virus presence was defined primarily by host species rather than by geographic locations or habitats. Our result suggests that insect-associated viruses display relatively narrow host ranges but are capable of spreading through a mosquito population at the geographical scale of our study. We also detected various single-celled and multicellular microorganisms such as bacteria, alveolates, fungi, and nematodes. Our study emphasizes the importance of including ecological information in viromic studies in order to gain further insights into viral ecology in systems where host specificity is driving both viral ecology and evolution. Next Generation Sequencing (NGS) technology has precipitated the discovery of many viruses, and expanded our knowledge of virus diversity, taxonomy, and evolution5. The RNA viruses of arthropod species, including mosquitoes, have been intensively characterized in the past several years, and stand out in their unparalleled diversity19.Viruses are critically important to human and environmental health, and their diversity is predicted to be vast20. Examples of these viruses are dengue virus of the family Flaviviridae, chikungunya virus of the family Togaviridae, Rift Valley fever virus of the family Phenuiviridae, and Lacrosse virus of the family Peribunyaviridae. In addition to pathogenic viruses, myriad other mosquito-associated RNA viruses belonging to at least 15 other families do not pose direct public health concerns21.Disease vectors such as mosquitoes pose a significant threat to public health. One sixth of the illness and disability suffered worldwide is due to vector-borne diseases, many of which are caused by mosquito-borne RNA viruses26. To maximize throughput while minimizing the sequencing cost, discovery-based studies have benefited from combining a large number of insects into a few pooled samples. Often, individuals from different species, or multiple locations, are pooled together hence information on their host-specificity and geographical location is lost14. The paucity of ecological information in virome studies limits our understanding of the host ranges of these viruses relative to their geographical distribution. As a result, we have little information on the relative importance of ecological drivers determining viral community structure.Most virome studies apply NGS tools to identify new viruses and describe their diversity. This type of study may lead to the discovery of insect-specific viruses that have the ability to interfere with arbovirus transmissionArmigeres subalbatus, Culex fuscocephala, and Mansonia uniformis, collected from three habitat types along a previously characterized rural to urban gradient27. Our full factorial design  allows us to explicitly determine the relative importance of host species and habitat in structuring viral communities. As obligate intracellular parasites, viruses rely on specific molecular interactions with hosts to perform their biological functions. Thus, we hypothesize that the pattern of virus presence in our mosquito samples is defined primarily by host species rather than by the host geographical locations or habitats. Finally, to maximize the sensitivity of NGS, we tested a laboratory method28 to deplete host ribosomal RNA, and increase the relative proportion of microbial genetic material prior to sequencing. This method could help reduce the need to pool samples across locations and species, and maintain ecological information in future virome studies.In this study, we characterized RNA viruses and other microorganisms in pools of individuals of three common but overlooked vector species in Thailand: 27. In summary, the study sites were located along a forest to urban landscape gradient in Nakhon Nayok province of central Thailand. Adult mosquitoes were collected during the rainy season of 2008 using a combination of trap types . In total, over 83,000 adult mosquitoes were collected and transported to the laboratory on dry ice. Species identification based on available morphological keys revealed 109 species of female mosquitoes34.We described study site characteristics and adult mosquito collection in detail in a previous publicationAr. subalbatus, Cx. fuscocephala, and Ma. uniformis, each collected from rural, suburban, and urban habitats  rRNA gene using a set of custom designed mosquito-specific small subunit (SSU) and large subunit (LSU) ribosomal RNA primers attached with T7 promoter sequences . The primer sequences are listed in Supplementary Table rRNA is relatively conserved between these mosquito species , we surmised that the probes constructed from Cx. pipiens would bind to other species of mosquito rRNA.In order to increase the relative proportion of microbial RNA, we used biotin-labeled mosquito-specific ribosomal RNA (rRNA) probes to capture and deplete mosquito rRNA prior to sequencing. The procedures for probe construction and rRNA depletion followed a published protocolTo deplete mosquito rRNA from the samples, the probes and RNA samples were combined and allowed to hybridize at 70 °C for 5 min, before ramping down to 25 °C with increments of 5 °C and one minute. Next, we used streptavidin magnetic beads  to separate out the hybridized rRNA and the un-hybridized probes. The bead wash procedures were repeated three times to improve depletion efficiency. The resulting rRNA-depleted RNA samples, along with their paired non-depleted RNA samples, were used for next steps. We designate rRNA depleted sample “RD” and un-depleted control sample “UD” for the rest of this manuscript.The first strand cDNA synthesis was carried out using SuperScript III Reverse Transcriptase (Life Technologies) and random primers following the manufacturer’s protocol. This was followed by the second strand cDNA synthesis using DNA Polymerase I (New England Biolabs). Sequencing libraries were created using Nextera XT kit (Illumina). Purified libraries were quantified by Qubit 2.0 Fluorometer  and assessed by Agilent 2100 Bioanalyzer  for average fragment sizes. Bioanalyzer traces for representative paired RD and UD samples are shown in Supplementary Figure https://github.com/FelixKrueger/TrimGalore) using default parameters. The remaining reads were assembled into contigs using Trinity v2.8.4 with default parameters36. To identify viruses in the sample, the resulting contigs were compared to the NCBI protein database  using DIAMOND v0.9.2237 with an E-value cutoff of 10−03. The most similar match was identified as having the lowest e-value.Raw sequences were filtered for quality and the Illumina adapters were trimmed using Trim Galore! v0.4.5 (www.ncbi.nlm.nih.gov/orffinder/) and were confirmed to match viral proteins using NCBI blastp suite. To infer phylogenetic relationships between RNA viruses, selected viral amino acid sequences were aligned using MAFFT v7 employing the E-INS-i algorithm39. The best-fit model of amino acid substitutions was determined using ProtTest v3.4.240. Phylogenetic analysis was performed using RAXML blackbox41 implemented in Cipres Science Gateway application42. The resulting phylogenetic trees were visualized in iToL v4.4.143.In order to increase the coverage of viral genomes, viral contigs belonging to the same virus across samples and across the two preparation types were assembled using Sequencher v5.1 . The resulting assembled genomes or partial genomes were checked manually. The open reading frames (ORF) were predicted using ORFfinder  database was used to build a Kraken-specific reference database. In order to confirm the genus of the organism, SortmeRNA was used to filter rRNA reads from the data48. Then, the rRNA reads were used to search for matches using NCBI blast suite against the nucleotide database with an E-value cutoff of 10−3. The most similar match was identified as having the lowest e-value.To classify non-viral reads, Kraken v2.0.7-beta was usedhttps://github.com/lh3/seqtk). This data set was then assembled de novo using Trinity, searched for viral matches with Diamond, and sequence depths for contigs were determined using Bowtie2 v2.2.6 as described previously. A data matrix containing log-transformed read numbers that mapped to each virus were used as input for principal component analysis (PCA) to assess whether their pattern of occurance is determined by host species or habitat. PCA was performed using the “prcomp” function (R v3.5.3), which performs PCA on the given data matrix.To compare virus occurrences across samples, we first normalized the number of reads (i.e. sampling effort) so that they all contained the same number of sequences . All statistical analyses, and data visualization were performed using R v3.5.3 and Rstudio v1.2.all of the following: (1) it was found in the no-template controls (NTCs); (2) it was represented by ≤ 80 reads ; (3) it was found in mosquito samples in a random pattern (i.e. its presence had no species-specific pattern or habitat-specific pattern); and (4) it is not similar to a previously characterized insect-specific virus. In addition, a given viral taxon was likely a cross contamination between samples if it met criteria (1)–(3) mentioned above. We indicated taxa that are likely contaminants or cross-contaminants in Fig. A given virus taxon was defined as an external contamination if it met  (NTCs);  it was rr less);  it was frn); and  it is nop value = 0.003; Supplementary Figure The Illumina MiSeq sequencing generated over 13 million reads. Not including NTCs, the average number of raw reads per sample was 626,988 . After quality filtering, the average number of reads dropped to 616,915 . Our quality filtering step removed on average 1.53% of raw reads , order Mononegavirales (family Rhabdoviridae and Xinmoviridae), family Flaviviridae, family Iflaviridae, an unclassified clade closely related to Luteoviridae and Sobemovirus (referred to as Luteo-Sobemo-related group6), unclassified clades related to family Narnaviridae, family Nodaviridae, and family Picornaviridae . The average percentage of viral reads per contig in the RD samples was significantly higher than UD samples . Successful depletion was apparent only for the small subunit of mosquito ribosomal (SSU) gene but not for the large subunit (LSU). The number of reads mapped to the SSU gene was significantly lower in RD samples than in UD samples , a difference that was not observed for the LSU.We tested a laboratory method to deplete host ribosomal RNA from total RNA. Table Flaviviridae possess positive-sense single-stranded RNA (ssRNA) genomes of approximately 9–13 kbp encoding a single polyprotein49. We found at least two partial viral genomes from the insect-specific clade of the genus Flavivirus in our samples  belonged to a flavivirus isolated from multiple Culex species  collected in Myanmar50. The second putative virus, Mansonia uniformis-associated flavivirus , and Nakiwogo virus isolated from Mansonia sp. in Uganda (45% amino acid identity).Viruses of the family Iflavirus possess linear positive-sense ssRNA genomes of approximately 9–11 kb in length encoding a single polyprotein51. All members infect arthropod hosts with the majority infecting insects51. We found four non-overlapping partial genomic fragments of iflavirus, provisionally called Armigeres subalbatus-associated iflavirus .The viruses of the genus rus Fig. , in the Sobemovirus and Luteoviridae are groups of plant viruses with linear and non-segmented positive sense ssRNA genomes of approximately 4 kb and 6 kb in length, respectively53. A recent study found many new virus genomes in diverse invertebrate species classified to a novel clade that is phylogenetically divergent but related to Sobemovirus and Luteoviridae6. At least three partial virus genomes identified in our samples belonged to this novel clade.Both Ar. subalbatus samples fell within a clade containing Yongsan sobemo-like virus 1, Wenzhou sobemo-like virus 4, and Hubei mosquito virus 2. The second putative virus, provisionally called Culex fuscecephala-associated sobemo-like virus  proteins to construct a phylogeny, the putative viruses identified in this study clustered with 3 different clades. The first putative virus, provisionally called Armigeres subalbatus-associated sobemo-like virus 1 Fig.  found inNarnaviridae possess a single molecule of non-encapsidated positive-sense ssRNA of 2.3–3.6 kb54. Two genera are currently recognized within this family: genus Mitovirus (fungi viruses) and Narnavirus (viruses of yeast)53. Members of the genus Ourmiavirus, closely related to the Narnaviridae, are plant viruses with genomes consisting of three positive-sense ssRNAs55. At least 4 partial virus genomes could be classified within the Narnaviridae and Ourmiavirus in our study. The first virus, provisionally called Armigeres subalbatus-associated ourmia-like virus  identified in a pool of multiple mosquito species collected in China6. Our newly described ourmia-like virus clustered within a clade of ourmia-like invertebrate viruses.Viruses in the family 6. This virus was found in all Cx. fuscocephala samples. The third virus, provisionally called Culex fuscocephala-associated mitovirus 1  and Betanodavirus (infects fish)53. Genus Gammanodavirus infecting prawn and shrimp has also been proposed56. Nodaviruses genomes consist of two molecules of positive sense ssRNA. RNA-1 (3.1 kb) encodes protein A with functions such as polymerase, and RNA-2 (1.4 kb) encodes protein alpha57. Phylogenetic analysis showed that the partial RNA-1 found in our study (~ 2.5 kb) formed a clade with Lunovirus , Caninovirus (from a canid gut virome study), and Sanxia water strider virus 16 (from a pool of multiple water strider species collected in China). The most similar sequence was Sanxia water strider virus 16 (39% amino acid similarity). This virus, provisionally called Armigeres subalbatus-associated nodavirus , likely representing the RNA-2 fragment of the same virus, was also found in all Ar. subalbatus samples.The family  RNA-2 1. kb encodPicornaviridae was discovered in high numbers in the rural Cx. fuscocephala sample . The assembled partial genome of this virus, provisionally called Culex fuscocephala-associated picorna-like virus  genomes distributed among 9 families . The genomes consist of two to six segments encoding structural proteins, and one or more non-structural proteins60. Sequence analysis of the RdRps indicated that the Bunyavirales contigs in our samples could be assembled into at least three distinct clades 12. The second putative virus belongs to the genus Phasivirus in the family Phenuiviridae 13. The third putative virus, provisionally called Culex fuscocephala-associated orthojonvirus  was the Jonchet virus (family Jonviridae) documented from mosquitoes in Côte d’Ivoire61.Order des Fig. . The firdes Fig. , found idae Fig. . This puRhabdoviridae includes viruses with genomes of 10.8–16.1 kb, usually with 5 genes encoding structural proteins62. The family is ecologically diverse with members infecting plants, fish, reptiles, birds, and mammals as well as arthropod hosts or vectors62. Our phylogenetic analysis of the L proteins (RdRp) suggested that there were at least two putative rhabdoviruses in our mosquito samples. The first virus, provisionally called Mansonia uniformis-associated rhabdovirus , which was recently isolated from Ae. albopictus from China63. Another set of rhabdovirus L gene fragments was recovered in all three Ar. subalbatus pools . These fragments showed similarity to known virus sequences (33–50% amino acid similarity) and the closest similarity was to Culex rhabdovirus. Because of their small sizes, the phylogenetic analysis of these L fragments did not result in a strongly supported clade and we cannot conclude whether these fragments belong to the same virus , which consists of multiple insect viruses. The assembled genomic fragments found in our study . This virus, provisionally called Mansonia uniformis-associated anphevirus  while the majority of bacteria found in Cx. fuscocephala were in the class Alphaproteobacteria (48.19%). The composition of the bacteria community in Ma. uniformis samples was more evenly distributed, dividedly between Alphaproteopbacteria (26.12%) and Gammaproteobacteria (19.87%). Other groups of bacteria found in mosquito samples included: phylum Actinobacteria, averaging 3.58% of all bacterial reads across all samples; phylum Firmicutes, 2.39%; class Betaproteobacteria, 1.68%; Bacteroidetes-Chlorobi group, 1.54%; and Delta-Epsilon Proteobacteria subdivision, 1.53%.The majority of non-viral reads, in both the RD and UD samples, were classified to phylum Arthropoda, averaging 79.80% and 81.08%, respectively (Supplementary Table Wolbachia (Alphaproteobacteria), a genus of common obligate intracellular bacteria found in a wide range of invertebrate taxa64, was found in all samples as indicated by 16 s rRNA contigs recovered. We also found rRNA reads of bacteria relating to common house flies and bee gut-associated Apibacter bacteria in all Cx. fuscocephala samples, but not other mosquito species , at 2.56% and 0.88% or all in RD sample and UD sample, respectively.Multiple groups of fungi were detected .Multiple samples collected from rural and urban sites harbored 18 s rRNA reads similar to nematode species in the Mermithidae, and Setariidae families. Of note is a partial 28 s rRNA read (274 bp) found in the rural Cx. fuscocephala sample related to trematode of the order Plagiorchiida such as Collyriclum faba, Paralechithodendrium longiforme, and Lecithodendrium linstowi. C. faba is a bird parasite that has aquatic gastropods as the first intermediate host and mayflies as the second intermediate host67. On the other hand, P. longiforme and L. linstowi are bat parasites with unknown second intermediate hosts69.Several rRNA reads were recovered in the urban Ascogregarina sp., a mosquito-specific apicomplexan parasite, were found in rural and urban Ar. subalbatus samples and were most similar to A. armigerei . The genus Ascogregarina parasitizes mosquitoes and sandflies, and is relatively host specific: A. taiwanensis infects Ae. albopictus, A. culicis infects Ae. aegypti, and A. armigerei infects Ar. subalbatus71.Lastly, 18 s rRNA reads of Ar. subalbatus, Cx. fuscocephala, and Ma. uniformis. We chose the three mosquito species because of their abundance and potential vector status in central Thailand27. Ar. subalbatus transmits Japanese encephalitis virus, Wuchereria bancrofti, and dog heartworm Dirofilaria immitis72. Their larvae have been found in nutrient enriched water including septic tanks and polluted stagnant water73 as well as less enriched bamboo stumps, artificial containers, and tree holes74. Cx. fuscocephala, often associated with flooded rice field and agricultural lands77, is among multiple Culex mosquito species that transmit Japanese encephalitis79. Mansonia uniformis is a vector of Wuchereria malayi. They are associated with vegetated water habitat such as ponds and canals with floating vegetation, rice fields, and swamp forest82.In this study, we used a high throughput RNA sequencing metagenomic approach to characterize the mosquito-associated virome and microbiome. We identified at least 21 putative RNA viruses in three vector species: According to the Principle Component Analysis, we found that the pattern of viruses in our mosquito samples was defined primarily by the host species rather than by geographical location. Viruses are obligate intracellular parasites that can only function when inside host cells, necessitating specific host cell recognition, entry, and manipulation of multiple host cell molecular mechanisms in order to complete their replication cycles. These properties limit viruses’ biological host ranges. It is not surprising that we observe this host species-specific rather than location-specific pattern. This is in contrast to the bacterial community, where grouping patterns did not show association with host or location. Even though the full factorial design in our study allowed us to investigate the relative importance of ecological factors in structuring mosquito virome, the small sample size limits our ability to generalize this finding.84. The results from our study lead to important implications for the surveillance of emerging viral diseases: in order to capture as much viral diversity as possible, monitoring efforts should sample a wide diversity of mosquito species, rather than focus on a narrow set of mosquito species albeit over a larger geographic landscape.On the other hand, the same virus species found in the same mosquito species but from different collection sites showed limited genetic divergence 55. Another putative virus, Culex fuscocephala-associated narna-like virus, was found in Cx. fuscocephala and not the other two mosquito species. This putative virus formed a monophyletic group with a virus found in Cx. pipiens12 and other arthropods6. Although this group of viruses was closely related to mycoviruses, it likely contains a group of novel insect-specific viruses.We found multiple putative viruses associated with certain mosquito species, suggesting that they are likely mosquito-specific. For example, Armigeres subalbatus-associated ourmia-like virus was classified to a novel insect virus clade closely related to plant viruses in the genus Ma. uniformis, suggestive of habitat restriction. Interestingly, the newly proposed genus Anphevirus consists of multiple insect viruses including those isolated from Culex85, Anopheles5, and Aedes mosquitoes84. A recent study showed that Aedes anphevirus may reduce DENV replication in cell culture84. Our phylogenetic analysis indicated that Ma. uniformis-associated anphevirus is separated from other mosquito anpheviruses , and cross-contamination between samples87, occurring either during sample processing , or sequencing (e.g. barcode sequencing errors and ‘index hopping’)87. Despite its importance, not all NGS-based metagenomic studies include controls.We have included no-template controls (NTCs) that were processed alongside mosquito samples from extraction to sequencing. Due to the high sensitivity of NGS and the well-documented risk of contamination in microbiome studies, control samples should always be included to assess contamination89.We detected reads belonging to multiple organisms in the NTCs. The proportion of reads in NTCs classified to each taxon is shown in Supplementary Table 90. Unfortunately, we cannot definitively determine whether the contigs identified in this study originate from viruses or EVEs because the genomes of the three mosquito species are currently unavailable, and because EVEs possess non-specific characteristics that cannot be used for their identification. However, we expect that the RNA extraction protocol adopted in our study likely limited the inclusion of EVEs, and the potentially expressed small EVEs (e.g. EVE-derived piRNAs) in our purified RNA samples.Endogenous viral elements (EVEs) are defined as viral DNA sequences deriving from retroviruses, DNA viruses, or RNA viruses that present within the genomes of non-viral organisms, including mosquitoesWolbachia, we know that elements of the vector virome and microbiome can impact vector fitness and competence: we recovered a virus closely related to Aedes anphevirus previously reported to reduce vector competence to dengue84. Thus, this study may help inform future use of insect-specific virus (ISVs) in the control of vectors and diseases. Because we found that mosquito species determine virome composition more than habitat, urbanization is likely to have the greatest influence on the distribution of microbes by facilitating invasive vector species. This finding is supported by our previous study indicating that under urbanization mosquito communities become less diverse and skewed towards invasive mosquito species27. To test the influence of habitat alone on mosquito-associated virome, future studies should focus on one mosquito species at a time, and sample individually and more intensively across landscape types.Mosquito-borne disease emergence is thought to be associated with socio-economic and ecological factors arising from urbanization and poverty. In this study, we characterized mosquito-associated viromes and microbiomes across a landscape gradient transformed by human activities and urbanization. In addition, the three vector species differ in their biology and ecology. We found that the mosquito-associated virome component was defined primarily by the host species rather than by the geographical locations or habitats, and multiple viruses spread between sample sites and mosquito populations at the scale of this study. Similar to Supplementary Information 1.Supplementary Information 2.Supplementary Information 3.Supplementary Information 4.Supplementary Information 5.Supplementary Information 6.Supplementary Information 7.Supplementary Information 8.Supplementary Information 9.Supplementary Information 10.Supplementary Information 11.


Anticoagulant therapies are used to prevent atrial fibrillation-related strokes, with warfarin and direct oral anticoagulant (DOAC) the most common. In this study, we incorporate direct health care costs, drug costs, travel costs, and lost working and leisure time costs to estimate the total costs of the two therapies.This retrospective study used individual-level patient data from 4000 atrial fibrillation (AF) patients from North Karelia, Finland. Real-world data on healthcare use was obtained from the regional patient information system and data on reimbursed travel costs from the database of the Social Insurance Institution of Finland. The costs of the therapies were estimated between June 2017 and May 2018. Using a Geographical Information System (GIS), we estimated travel time and costs for each journey related to anticoagulant therapies. We ultimately applied therapy and travel costs to a cost model to reflect real-world expenditures.The costs of anticoagulant therapies were calculated from the standpoint of patient and the healthcare service when considering all costs from AF-related healthcare visits, including major complications arising from atrial fibrillation. On average, the annual cost per patient for healthcare in the form of public expenditure was higher when using DOAC therapy than warfarin therapy (average cost = € 927 vs. € 805). Additionally, the average annual cost for patients was also higher with DOAC therapy (average cost = € 406.5 vs. € 296.7). In warfarin therapy, patients had considerable more travel and time costs due the different implementation practices of therapies.The results indicated that DOAC therapy had higher costs over warfarin from the perspectives of the patient and healthcare service in the study area on average. Currently, the cost of the DOAC drug is the largest determinator of total therapy costs from both perspectives. Despite slightly higher costs, the patients on DOAC therapy experienced less AF-related complications during the study period. Atrial fibrillation (AF) is the most common cardiac arrhythmia worldwide . In EuroAF increases mortality, morbidity and healthcare costs, which in turn places a significant burden on healthcare systems . AF is aThe efficacy and safety of DOACs has been compared with warfarin in randomised clinical trials. For the prevention of stroke, the results have shown that DOACs were either non-inferior to warfarin  or were The total cost of anticoagulant therapies is more than the cost of the drug for the patient, especially for those receiving warfarin therapy . The oveResearchers have examined the costs of anticoagulation therapies using several approaches. Many cost-effectiveness studies have been done between warfarin and DOAC therapies, mostly from societal perspective –27. WhilHowever, less attention has been paid to the real-world, data-based costs of anticoagulation therapies, which cover the cost perspective of the patient and healthcare service simultaneously. Our study fills in this gap by utilising electronic health records (EHRs) and travel reimbursement data from North Karelia, in Finland, between June 2017 and May 2018 and implementing a real-world cost calculation of AF-related costs for warfarin and DOAC therapies. We evaluate the costs from the perspective of the patient and healthcare service including the direct and indirect costs of anticoagulation therapies, such as time and travel costs.We performed a retrospective registry study over a 12-month period spanning 1 June 2017 through 31 May 2018. We retrieved information on all patients diagnosed with atrial fibrillation (ICD-10: I48) from the regional patient information system before June 2017. While AF diagnoses were not limited by any starting date, we only included those AF patients who had been on warfarin or DOAC therapy at least two months before the study period.All AF-related costs from the perspective of the patient and healthcare service were added to the cost model based on real-world data sources and realised healthcare contacts (visits and phone consultations). We used EHRs as a source for healthcare contacts, and our data was supplemented using cost information based on healthcare visits, time and travel. For patients, time and travel costs were considered as indirect costs of anticoagulant therapies, while the other costs related to therapies were viewed as direct costs. To complete the AF-related costs of both therapies, the costs of anticoagulants were also included in the analysis. All methods were performed in accordance with the relevant guidelines and regulations.Siun sote). It consists of 14 municipalities, a 2017 population of 166,441 inhabitants and a population density of 8.8 persons per km2 [The study region covers the healthcare district of North Karelia, Finland . Additionally, we considered all major complications resulting from atrial fibrillation: ischemic stroke (ICD-10 code I63), intracerebral haemorrhage (ICD-10 code I60-I62) and gastrointestinal bleeding . The EHRs for ischemic stroke and intracerebral haemorrhage were restricted to contacts that contained stays on an inpatient ward linked with an urgent visit to a healthcare. From the laboratory results, we considered all INR measurements.We linked supplementary real-world travel information and travel reimbursement data to EHRs using the Social Insurance Institution of Finland (Kela) register, since a primary objective of our study is to obtain precise estimates for travel expenses. Kela is a government agency that provides basic economic security and national health insurance in Finland, and it reimburses part of all medical and travel costs when the patient is eligible for reimbursements . We adden = 8448). Our study cohort included only AF patients who were handling the anticoagulant therapy by themselves, i.e. not living in a nursing home  based network analysis, an approach described in . For carThe EHR travel costs were determined using a combination of Kela’s register, information from the EHR and GIS-based network analysis. For each patient, travel costs were calculated as an out-of-pocket expense that they would have to pay for travelling. We included travel costs also for the healthcare services as public expenditures if the journey was reimbursed by Kela. Kela’s data showed travel costs from both perspectives for journeys in which the patient had admitted a travel allowance. However, Kela’s register covered only 5% of EHRs at time of the study period. The rest of the travel costs were calculated via a GIS-based approach.Identifying the mode of travel is an essential component of the time and travel cost estimation. Studies have shown that multiple factors influence the choice of travel mode, such as age, gender, income, distance, physical functioning, weather, car ownership and place of residence , 39. DueWe considered five different modes of travel: car, taxi, public transport, walking and ambulance. The travel mode was conducted individually for each EHR, separately for inbound and outbound journeys. We utilised information on the distance of the journey, the age of the patient, the accessibility to public transport and the emergency classification of the healthcare visit  Table .We used a mixed selection of travel modes because some of the travel modes were known in advance based on Kela’s database. The mode of walking was determined following the travel mode classification scheme Table . Car andA maximum one-day travel limit was set for the patients because the EHRs do not directly report a patient’s daily movements. Daily travel limits were used to exclude those journeys that most likely would not have been made due to the chaining of healthcare visits, which can be implemented at the same healthcare unit. The daily movement to a healthcare facility was limited to no more than two round-trip journeys, although there may have been more journeys based on EHR data. A more detailed classification of trip combinations and daily travel volumes are shown in Table We used GIS to calculate the indirect time and travel costs of healthcare visits shown in the EHRs. We were able to estimate the travel time and distance of healthcare visits shown in each EHR individually. The GIS analyses also supplemented Kela’s data, since only the travel cost of the journey had been recorded in the register.Travel analyses were implemented using ArcGIS Pro 2.5 . EHRs included all essential variables, such as the address of the used healthcare facility and patient’s home address, which are compulsory source information for network analysis with an Origin-Destination (OD) cost matrix . Routes The origin and destination with respect to the OD cost matrix was determined in two ways: (1) if the EHR contained an INR monitoring visit, the origin was the patient’s home address and the destination was the closest INR monitoring laboratory from the patient’s home; (2) if the EHR showed the patient headed to the healthcare unit, then we set the home address as the origin and the address of the healthcare unit as the destination. For INR monitoring, the closest INR laboratory was applied because the accuracy of the EHR records could not be verified in all cases. This also ensured that the cost of INR monitoring is within reason.cc), taxi (cx), walking (cw) and public transport (cb):oc is the operation cost of the car per kilometre, ft is the fixed charge of the taxi, ot is the operation cost of the taxi per kilometre and fb is the public transport fare. We did not add any travel cost for walking. The values of the parameters are presented in Table Travel cost was calculated based on the information in the EHR, if the journey was not included in Kela’s data. The calculation was based on travel distance and fares. We created travel cost equations for each travel mode applying Ford et al.  and Lemic), taxi (Tx), walking (Tw), public transport (Tb) and ambulance (Te):The time cost for the journey was calculated based on the travel time and additional time penalties. The time cost can be calculated with eqs. (5–6) for private car  and Pyykönen et al. (2019), who have explored the cost impacts of medication selection for AF patients. In earlier studies, the costs of anticoagulant therapies have focused on a combination of INR monitoring and anticoagulant medication. We broadened the cost model to cover all AF-related healthcare costs from both perspectives . In addition to previous models, we specified daily travel limits  per visit.where ith patient on day d can be defined as: The direct cost of anticoagulant therapy  per patient . The DOAC group consisted of 829 patients. The mean age of the patients was higher in the warfarin group compared to the DOAC group (76.0 ± 9.2 vs. 72.7 ± 9.8), respectively. The CHA2DS2-VASc score for stroke risk assessment was almost equal in both groups. The most prevalent co-morbidity was hypertension, followed by vascular disease and diabetes in both groups. Females were in the minority in both groups.The total study population consisted of 4000 atrial fibrillation patients after the inclusion and exclusion criteria were applied. The key characteristics of the patient groups are summarised in Table The occurrence of intracerebral haemorrhage and gastrointestinal bleeding during the study period was higher in the warfarin group than the DOAC group Table . Only a The total number of AF-related visits to healthcare facilities varied significantly among the patient groups Table . On averThe number of visits to primary healthcare, specialised healthcare and inpatient wards did not differ between the warfarin and DOAC groups, with only minor differences detected. On average, specialised healthcare visit counts (0.2 ± 0.7 vs. 0.3 ± 0.9) and days on the ward for specialised healthcare (0.1 ± 1.0 vs. 0.2 ± 1.2) were slightly higher for the DOAC group.Travel impedance for both patient groups is reported in Table AF-related real-world costs during the one-year follow-up period for those in the warfarin and DOAC groups is displayed in Table With respect to the real-world cost per patient for healthcare, we included AF-related laboratory monitoring (INR), primary and specialised healthcare visits, phone consultations, stays on inpatient wards, anticoagulation costs, travel costs compensations and INR monitoring provided at the residence of the patient. The largest share of the costs for healthcare was caused by INR monitoring for those receiving warfarin therapy  and drug costs for those receiving DOAC therapy , on average  had to do with indirect time and travel costs and 33.8% (€ 101.1) with direct costs (drugs and fees), on average. Respectively, only 4.3% (€ 17.5) of costs for those receiving DOAC therapy were indirect costs, and direct costs accounted for 95.7% (€ 389.0) of expenses, on average. Due to the sparsely populated area and frequent INR monitoring of those receiving warfarin therapy, the share of indirect costs was relatively high for those receiving warfarin therapy compared with the DOAC therapy group.We gathered a wide array of retrospective, real-world data on atrial fibrillation patients, data which consisted of 4000 patients from North Karelia, Finland. We found marked differences between the annual costs of anticoagulation therapies from the perspectives of the healthcare service and patients. When we included all AF-related, real-world costs of warfarin and DOAC therapies, including the most common complications, the average costs for those receiving warfarin therapy were lower from the perspectives of the healthcare service (€ 927 vs. € 805) and patient (€ 406.5 vs. € 296.7) in the study area, respectively. However, patients receiving the warfarin therapy experienced more AF-related complications  during the study period.The main reason for the higher annual costs for those receiving DOAC therapy currently is the cost of the DOAC drug. However, DOAC drugs eliminate the overall costs for medical consultation, laboratory tests, transportation and the time loss of the patient. On average, the share of drug costs for those receiving DOAC therapy was 88.5% for the patient and 72.1% per patient from the perspective of the healthcare public expenditure in our study settings, meaning that the cost of the DOAC drug primarily determines the cost of the DOAC therapy from both perspectives, but it does not place a burden on healthcare service use. Respectively, INR monitoring accounts for the largest share of warfarin therapy costs (64.5%) per patient for the healthcare service, on average, and the combination of time and travel costs (65.9%) for patients receiving warfarin therapy. In the case of warfarin therapy, INR monitoring induces a burden for healthcare services and travelling causes additional indirect therapy costs for the patient.Although the real-world costs of the DOAC therapy were higher on average, the annual costs can be lower for patients, especially in distant localities. It is important to notice that time and travel costs can significantly contribute to the annual costs of anticoagulation therapies, especially for those receiving warfarin therapy. The annual travel costs for the patient varied from € 0 to € 1888.8 in the study area, depending on the patient’s visits for warfarin therapy. The real-world cost difference between the anticoagulation therapies is not big when the geographical characteristics of the therapies are considered in the cost model. A prior study has shown that the cost-efficiency of the anticoagulation therapy option for the patient depends on travel distance, travel mode, drug costs and the lost working and leisure time of the patient . In urbaThe key strength of this study is the inclusion to several datasets for modeling. Firstly, our data covers all patients having AF diagnosis in the study area. Secondly, our dataset is very rich in terms of information on health care use. Used EHRs and additional data from Kela formulate vast and precise data for the analyses. Thirdly, in addition to direct health care costs, we have been able to consider a wide range of actual other costs based on various register data sources.The study suffers from a few limitations. First, we managed to find information on travel modes from real-world data, but most travel modes had to be set based on the travel mode classification due the lack of travel mode data. This might have a slight effect on travel mode selection for shorter journeys, but the effect on costs is minimal. Second, we considered only the costs of AF-related events when the payment ceiling for healthcare costs was applied. The payment ceiling could limit healthcare costs for the patient more if all-cause healthcare costs would be included in the study. Third, we did not consider the variation in warfarin drug consumption. The annual cost of the warfarin drug was calculated using the average dosage for all patients, thus the annual cost of the warfarin drug might in reality be higher for some patients. In this case, the annual difference between therapies would be narrowed. Fourth, our data was gathered from one healthcare district which represents only a small part of Finland. This might affect to representativeness of results at the level of whole Finland. Fifth, the costs of warfarin therapy are comparable to other studies when the costs are compared after the initiation period of administering the warfarin drug. We did not include patients who had started taking the anticoagulant medication just before or during the study period. The average annual costs for warfarin therapy would be higher due to more frequent monitoring of INR during the initiation phase of taking warfarin.We had to set most travel modes based on the travel mode classification. Despite a lack of real-world travel mode data, our travel mode classification is in line with findings presented in a prior study, which had collected multinational, real-world data on travel modes when patients were attending INR monitoring . The shaWe did not include the lost time spent in hospital as hospitalization is not a typical process in performing the therapy. Hospitalization is related to complications and there are various other factors, which are hard to control and which affects to the length of the stay. In addition, the number of days on wards was very small and similar in both treatment groups.The patients in the DOAC group had a lower mean age (72.7 vs. 76.0) and experienced less gastrointestinal bleeding and intracerebral haemorrhage events. In addition to patients treated with warfarin being at greater risk for these complications , 12, alsThe maximum one-day travel limit was applied when time and travel costs were calculated. If the travel costs for all AF-related trips recorded in the EHRs  would be included in the cost model, time and travel costs would be higher for those receiving warfarin therapy. In that case, the cost difference between anticoagulation therapies would even be narrowed and the annual average costs of anticoagulation therapies would be almost equal from the patients’ perspective. A prior study has reported that patients receiving warfarin therapy have 17.0 INR monitoring visits per year on average . We addeThe costs of drugs, hospitalisations, healthcare visits and travel vary between countries. If the results of this study are compared with other real-world cost studies, these aspects should be taken into account. We also considered reimbursements for drugs and travel and the payment ceiling for the patient based on guidelines from the Social Insurance Institution of Finland. This will have an influence on the observed costs and must be considered when comparing results between countries. In addition to healthcare service cost differences, it is important to note that populations density is quite low in the study area (8.8 per km2). This affects patients’ indirect costs because travel distances can be relatively long for patients in rural areas.The patients in the DOAC group seemed to live closer to the specialised healthcare services (43.9 km ± 40.0 vs. 52.1 km ± 42.1) than patients in the warfarin group. Specialised healthcare services are only served in the provincial centre, Joensuu. This might increase the indirect costs of warfarin therapy, but patients in both groups had only a few visits to specialised healthcare facilities during the study period.The cost model can be applied for different diseases when at least one therapy includes time and travel costs. The use of EHRs is common in health service research, and a few studies have already combined GIS methods to supplement collected real-world data , 43, 44.Due to high drug costs for DOACs, the DOAC therapy had higher total costs than warfarin in the healthcare district but the cost difference between therapies was otherwise narrow in our study setting. However, the causes of the costs are different due to dissimilar implementation practices between the anticoagulation therapies. It is crucial to include additional indirect costs, such as time and travel costs associated with warfarin therapy, due to their distinct effect on the anticoagulation therapy cost. The results suggest that indirect costs should be included in economic evaluations when comparing costs and cost-effectiveness between anticoagulation therapies in society.


Cobalamin is a cofactor present in essential metabolic pathways in animals and one of the water-soluble vitamins. It is a complex compound synthesized solely by prokaryotes. Cobalamin dependence is scattered across the tree of life. In particular, fungi and plants were deemed devoid of cobalamin. We demonstrate that cobalamin is utilized by all non-Dikarya fungi lineages. This observation is supported by the genomic presence of both B12-dependent enzymes and cobalamin modifying enzymes. Fungal cobalamin-dependent enzymes are highly similar to their animal homologs. Phylogenetic analyses support a scenario of vertical inheritance of the cobalamin usage with several losses. Cobalamin usage was probably lost in Mucorinae and at the base of Dikarya which groups most of the model organisms and which hindered B12-dependent metabolism discovery in fungi. Our results indicate that cobalamin dependence was a widely distributed trait at least in Opisthokonta, across diverse microbial eukaryotes and was likely present in the LECA. Cobalamin dependence is scattered across the tree of life. In particular, fungi and plants have been deemed devoid of cobalamin. We demonstrate that cobalamin is utilized by all phyla of non-Dikarya fungal lineages. This observation is supported by the identification of B12-dependent enzymes and cobalamin modifying enzymes in published fungal genomes. We found that cobalamin dependence was a widely distributed trait at least in Opisthokonta, across diverse microbial eukaryotes and likely in the LECA (last eukaryotic common ancestor). Moreover, the genes identified are actively transcribed in many taxa.Cobalamin, also known as vitamin B12, is the most common cobalt-containing compound in nature and one of eight known water-soluble vitamins grouped into B class. Cobalamin is an organometallic complex compound that contains a cobalt atom placed within a corrin ring. Vitamin B12 is derived from uroporphyrinogen III, which is also the first macrocyclic intermediate in a common pathway of heme and chlorophyll biosynthesis . In animPorphyridium purpureum and Amphidinium operculatum, can obtain the cobalamin cofactor from associated bacteria  takes a part in DNA replication and repair  and proper myelin synthesis. Only seven enzymes from the above pathways seem to be uniquely present in B12-dependent organisms. They either modify cobalamin or use it as a cofactor. The former group contains methylmalonyl Co-A mutase-associated GTPase Cob (MeaB), cob(I)yrinic acid a, c-diamide adenosyltransferase (CblAdo transferase), cyanocobalamin reductase (CblC), and cobalamin trafficking protein D (CblD) proteins, whereas the latter includes methionine synthase (MetH), methylmalonyl-CoA epimerase (MM-CoA epimerase), and methylmalonyl-CoA mutase (MM-CoA mutase) . All thed repair . For conPhytophthora spp. uses cobalamin-dependent class II RNR.RNR is an enzyme that catalyzes the formation of deoxyribonucleotides from ribonucleotides. It plays a pivotal role in the synthesis, reparation, and regulation of the total rate of DNA synthesis . RNRs arMethionine synthetase (MetH) comes in two variants: cobalamin-dependent MetH (EC 2.1.1.13) and cobalamin-independent MetE (EC 2.1.1.14). MetH catalyzes the final step in the remethylation of homocysteine which explains increased levels of homocysteine upon vitamin B12 deficiency. In animals, this may lead to blindness, neurological symptoms, and birth defects . MetH reCblAdo transferase, cob(I)yrinic acid a, c-diamide adenosyltransferase, converts cobalamin into adenosylcobalamin (AdoCbl). AdoCbl is a cofactor of multiple enzymes that catalyze unusual rearrangement or elimination reactions. Some of them are restricted to prokaryotes, for example, lysine-5,6-aminomutase, isobutyryl-CoA mutase, and glutamate mutase. Others are present also in Eukaryotes, for example, methylmalonyl-CoA mutase .Methylobacterium sp. showed an inability to convert methylmalonyl-CoA to succinyl-CoA caused by an inactive form of methylmalonyl-CoA mutase -methylmalonyl-CoA to the (R) form and uses a vitamin B12 cofactor . MM-CoA A mutase .Kingdom Fungi comprises several lineages of non-Dikarya which, in the order of divergence, are classified into Chytrydiomycota  and Blas. Seven Mortierellomycotina genomes, which were not included in our data sets, have also MetE copies. This might suggest that the metabolic pathways which first required B12 are still conserved among fungi, but have become independent of cobalamin for some organisms.In order to ensure that all housekeeping functions provided by RNR class II, MM-CoA mutase, and MetH pathways are maintained in all of the studied isolates, even those devoid of B12-dependent enzymes, we searched for cobalamin-independent alternatives. We looked for MetE which can substitute MetH , RNR claSince most of the identified homologs of eight B12-dependent enzymes are annotated as hypothetical unknown proteins without experimental characterization, we performed TBlastN searches on them against the NCBI EST database. This served as intermediate evidence that the predicted B12-related proteins in non-Dikarya fungi originate from active genes. TBlastN search results allow also to expect that genes encoding all identified proteins will be expressed.To trace the evolution of the studied proteins, phylogenetic trees for each of the eight protein families were inferred using Bayesian (BA) and Maximum Likelihood (ML) approaches  online, Syncephalis pseudoplumigaleata  which displayed a very high sequence identity reaching 100% with Afipia alphaproteobacteria which might indicate sample contamination. Notably, misannotated Dikarya sequences, like other Dikarya representatives, could not be found in Eukaryotic clades for the analyzed enzymes.We noticed single bacterial sequences misannotated as fungal due to likely bacterial contamination of the fungal DNA samples. We also noticed single fungal sequences grouping within their bacterial relatives. In most cases these were proteins homologous to our enzyme yet with other function, for example, MeaB is similar to other GTPases (KAA6408927.1). Non-Dikarya fungal sequences rarely grouped with bacterial sequences with the exception of MM-CoA mutase from Exclusively fungal clades can be observed in five protein families . For theFonticula alba (Holomycota), the closest relative of fungi belonging to Nucleariida. This pattern was observed for class II with the following score of aLTR support of the fungal clade: MeaB—1.00, CblD—1.00, MetH—1.00, MM-CoA mutase—1.00, and RNR class II—0.98. In the case of CblAdo transferase, CblC and MM-CoA epimerase fungal sequences group together with either ancient Metazoa representatives (CblAdo transferase) or with microbial eukaryotes from Holomycota, Amoebozoa, and SAR groups (MM-CoA epimerase). Importantly, non-Dikarya fungal sequences are always sister to other Eukaryotic sequences which rules out bacterial contamination. Sequences from model organisms belonging to diverse lineages of microbial eukaryotes, not only Opisthokonta, were represented in these clades, including representatives from Polysphondylium pallidum and Dictyostelium spp. (Amebozoa), Thecamonas trahens (Apusozoa), Chlamydomonas reinhardtii (Chlorophyta), Stentor coeruleus (Alveolata), Emiliania huxleyi (Haptophyta), Thalassiosira pseudonana, and Blastocystis spp. (Heterokonta), Naegleria gruberi and Euglena gracilis (Excavata).For MeaB, CblD, MetH, MM-CoA mutase, and RNR class II, fungal sequences form a sister clade to Our discoveries contradict the current opinion that fungi neither synthesize nor use cobalamin  and do nIn Eukaryotes three main metabolic pathways use cobalamin—RNR class II, MM-CoA mutase, and MetH pathways. Functions provided by these pathways are needed for the independent functioning of a living cell and can be lost in parasites . TranspoWe found traces of all of these pathways among all lineages of early-diverging fungi. The distribution of the genes encoding the above-mentioned enzymes is not uniform across the analyzed organisms. For non-Dikarya fungi, it is common to either have two out of three pathways or to have them incomplete.Only Glomeromycotina and Blastocladiomycota have all three complete B12-dependent pathways. These two taxonomic groups are evolutionary and ecologically distant, they share only a few characteristics among others some of these fungi possess relatively big genomes. The latter may be a highlight of relaxed pressure on genome compactness.The least conserved among fungal lineages is the RNR class II pathway. Organisms missing this class use cobalamin-independent RNR class I, which is the RNR used by animals. It is worth noticing that organisms are not limited to having only one class of RNRs at once . CobalamThe best-conserved pathway in non-Dikarya fungi—MetH—can be substituted with a cobalamin-independent enzyme called MetE . We checBatrachochytrium and Mucor representatives showing no synteny. Moreover, no candidate dehydrogenases were found upstream or downstream of MCS and MCL genes. We assume that the function of MCD can be taken over by other dehydrogenases.MM-CoA mutase pathway is more or less conserved among early-diverging fungal lineages. Interestingly, all Mucorales members lack all three B12-dependent enzymes of that pathway. We checked for alternatives for this metabolism track and it turned up to be more complex than in the other two cases. In Dikarya propionate metabolism is carried out in the methylcitrate cycle (MCC). Three key enzymes for this track are methylcitrate synthase (MCS), methylcitrate dehydrogenase (MCD), and methylisocitrate lyase (MCL) . All of According to our results, we can speculate the best-conserved elements of cobalamin-dependent pathways are key enzymes. For example, in the MetH pathway, the best-conserved element is MetH protein. On the contrary, it is quite common to lose CblC and CblD proteins from the proteome  online. Batrachochytrium sp. and herbivorous mammals symbionts from class Neocallimastigomycetes. For these organisms, no cobalamin-dependent enzyme was found. These organisms may obtain the required resources from the host. However, our observations for plant-associated fungi are different. Mycorrhizal fungi from Glomeromycotina and Endogonales, despite maintaining extensive symbiotic relationships with 80% of plant species . Similarly, parasites of organisms without B12-dependent genes do not have any of these enzymes themselves (Caulochytrium protostelioides—Sordaria sp.). This remark needs further investigation because we do not have enough data to form a reliable conclusion. Our discovery challenges the current view that fungi can neither synthesize nor utilize cobalamin. We proved that non-Dikarya fungal proteomes contain three metabolic pathways utilizing vitamin B12. We speculate these organisms have the possibility to accumulate cobalamin. Yet, these genomic observations need to be tested experimentally. Our discoveries may open the way for the selection of B12 over accumulating strains of food fermenting fungi without the need for genetic material manipulation.During the analysis of obtained results, we tried to understand the evolution of cobalamin-dependent metabolic pathways among kingdom Fungi. To widen the picture we checked studied proteomes for cobalamin-independent alternative metabolic pathways, and we confirmed their occurrence. Based on current knowledge we hypothesized that B12-dependent pathways are replaced by B12-independent alternatives in course of the evolution, and finally disappear in Dikarya lineages. In fact, the ability to utilize cobalamin is either retained or lost independently from the time of phyla divergence. Surprisingly, we observed a correlation between the preservation of this ability and fungal ecology. As we observe, cobalamin-dependent pathways are more common in fungi associated with plants, than in species associated with animals and living as soil saprophytes. Correlation like this is unclear for mycoparasites. In our data set, we have three fungi representing such a lifestyle and they have different enzyme distribution. In this case, we observed a correlation that mycoparasites which have B12-dependent enzymes infect fungi that also possess such enzymes  and Fonticulida. These organisms retain a maximum of only six out of the eight enzymes, but it is worth noting they are not independent, free-living organisms.Dictyostelium discoideum with that vitamin  using PSI-BLAST  , all fungal hits were retained regardless of their sequence similarity. To get only homologs of a protein of our interest, there was a need to discard homologs from related protein families. To do this we visualized protein pairwise similarity using CLANS (Fifty-nine predicted non-Dikarya proteomes were downloaded from NCBI in October 2019  supplem online. ons = 3) . The datng CLANS  and seleIn the next step, sequences were aligned using local iterative mode in Mafft v. 3.7  . The ali2-based parametric branch supports. In the course of BA inference, four Markov chains were run for three runs from random starting trees for 107 generations, and trees were sampled every 2.5×102 generations. The first one-fourth of generations were discarded as burn-in. Then, we used the remaining samples to calculate the tree of maximum clade credibility.All alignments were trimmed with TrimAl (model = gappyout)  to removExpression of representatives of each of the protein sets (three randomly chosen homologs from each family) was confirmed by TBlastN  (defaultCreated phylogenetic trees were visualized and edited by iTOL v4 . Some baGenome Biology and Evolution online.evab043_Supplementary_DataClick here for additional data file.


Plant virus nanoparticles (VNPs) are inexpensive to produce, safe, biodegradable and efficacious as treatments. The applications of r plant virus nanoparticles range from epitope carriers for vaccines to agents in cancer immunotherapy. Both VNPs and virus-like particles (VLPs) are highly immunogenic and are readily phagocytosed by antigen presenting cells (APCs), which in turn elicit antigen processing and display of pathogenic epitopes on their surfaces. Since the VLPs are composed of multiple copies of their respective capsid proteins, they present repetitive multivalent scaffolds which aid in antigen presentation. Therefore, the VLPs prove to be highly suitable platforms for delivery and presentation of antigenic epitopes, resulting in induction of more robust immune response compared to those of their soluble counterparts. Since the tumor microenvironment poses the challenge of self-antigen tolerance, VLPs are preferrable platforms for delivery and display of self-antigens as well as otherwise weakly immunogenic antigens. These properties, in addition to their diminutive size, enable the VLPs to deliver vaccines to the draining lymph nodes in addition to promoting APC interactions. Furthermore, many plant viral VLPs possess inherent adjuvant properties dispensing with the requirement of additional adjuvants to stimulate immune activity. Some of the highly immunogenic VLPs elicit innate immune activity, which in turn instigate adaptive immunity in tumor micro-environments. Plant viral VLPs are nontoxic, inherently stable, and capable of being mass-produced as well as being modified with antigens and drugs, therefore providing an attractive option for eliciting anti-tumor immunity. The following review explores the use of plant viruses as epitope carrying nanoparticles and as a novel tools in cancer immunotherapy. Nanomedicine is an emerging area of multidisciplinary research that has already shown promise of transforming into a disruptive innovative development . AlreadyNanomedicines includes a wide array of nanomaterials with particle size ranging from 1nm to more than 400 nm and are a remarkably diverse group of materials . They maImmunotherapy in cancer treatment simply refers to a strategy with the objective of galvanizing the immune system of the patient to resist the implanting of cancerous cells. There are several approaches to achieve the desired end. One approach involves the use of drugs known as “Immune checkpoint inhibitors, to block immune checkpoints . The chePlant virus-based nanoparticles (VNPs) have been explored as a unique class of nanocarriers for biomedical applications . In addiin vivo. Tobacco mosaic virus can assemble into VLPs without requiring its RNA genome carry a drug payload on the surface or to a limited extent, within the inner channel of the nanoparticle. Potato virus X, cannot self-assemble in the absence of its RNA genome, and thus can only carry a payload on the outer surface. Cowpea mosaic virus can be made to self-assemble into empty virus like particles in the absence of its RNA genome and can thus carry a payload both inside and outside of its protein shell  and Potato virus X (PVX), or icosahedral shaped, such as Cowpea mosaic virus (CPMV). Different shaped viruses respond differently as nanoparticles in shell . In thisin shell . We discin shell .Viruses are composed of outer protein shells which encapsulate the genomic material. The multiple copies of coat proteins that form the virus outer shell of viruses are collectively known as the capsid . PrimariPlant viruses have been used as virus like particles (VLPs) and virus nanoparticles (VNPs) as epitope display systems for vaccine production. VLPs are a subset of the VNPs but lack any nucleic acid genome, thus making them non-infectious. VNPs and VLPs based on plant viruses are both non-pathogenic to humans and biodegradable . VNPs anTobacco mosaic virus (TMV) is the most well-studied plant virus and was initially characterized in the 19th century. TMV can be easily produced and purified in bulk amounts and can be manipulated genetically due to its relatively simple particle structure and genome organization. The rod-shaped virus particle measures 300 nm in length and 18 nm in diameter and contains a 6.7 kb viral RNA genome that is encapsulated by 2,130 identical copies of the capsid protein assembled in a helical arrangement. At neutral pH and in the absence of RNA, the CP assembles itself into an 18 nm double disk, a 20S aggregate or nano-ring containing two layers of 17 CP molecules which can serve as a nanoscale scaffold.Potato virus X (PVX) is a member of the family Alphaflexiviridae, genus Potexvirus, an important plant pathogen of the family Solanaceae, and specifically infects potato, tomato and tobacco  Massumi. It has in vivo or in vitro, is not possible in the absence of genomic RNA. This reflects the unique connection between virus RNA and CP  belongs to the Comovirus genus. CPMV is an icosahedral shaped virus with a diameter of approximately 27 nm. It is composed of RNA-1 and RNA-2 of 6 and 3.5 kb, respectively packed in 60 copies each of Large and Small coat protein . CPMV isVLPs can act as strong vaccine candidates as they simulate the conformations of native viruses, utilizing their intrinsic immunogenicity without compromising their safety. VLPs evoke effectual immune responses as they are readily internalized by antigen presenting cells (APCs) and are ideal platforms for antigen processing and epitope presentation to immune cells. VLPs are composed of multiple copies of their capsid (coat) proteins, which when assembled appear as repetitive, multivalent molecular scaffolds. As a result, the multiple copies of coat protein can facilitate the multivalent presentation of antigens fused to their surface. Therefore, VLP vaccines afford superior immunogenicity compared to antigens in their soluble states. Additionally, plant viral VLPs and VNPs possess inherent adjuvant properties dispensing with the use of additional adjuvants to evoke strong immune responses .TMV VLPs have been utilized as epitope display systems in a variety of settings, with a first example being a polio vaccine by Staphylococcus aureus protein A fragments on its surface, and proved to be easily functionalized with IgG to be used in biosensing plant viruses. VLPs of Papaya mosaic virus (PapMV), of the Potexvirius family, has been engineered for use as a seasonal flu trivalent vaccine  from Staphylococcus aureus, which has been shown to be able to protect rats against endocarditis  effect depending on the type of tumor , computed tomography (CT), positron emission tomography (PET) and optical imaging, which enable the monitoring of molecular and cellular processes in normal and diseased conditions in living subjects.  is a promising technology for the diagnosis of disease due to its high resolution and deep contrast, however, virus-based nanoparticles have been used to increase sensitivity . TMV can barrier . A bimod barrier .Tobacco Mosaic Virus has been used successfully for CD imaging, targeting atherosclerosis and thrombosis by using an NIR dye as well as a targeting peptide conjugated to TMV [96]. These targeted TMV particles were able to identify atherosclerotic lesions in ApoE−/− mice upon intravenous injection, showing that TMV can be used as a platform to detect at-risk lesions.in vivo imaging. N. benthamiana plants. Significantly, fluorescent PVX could successfully be used for in vivo particle tracking in an HT-29 murine model, for in vitro imaging of HT-29 cells, and for tracing viral infection within plants.VNPs based on PVX have been conjugated to fluorescent reporters as well [135]. As mentioned earlier, the small fluorescent iLOV protein was expressed on the surface of PVX through genetic engineering and served as a fluorescent probe which could be of potential use in vivo imaging of tumors, CPMV-based VNPs have been successfully engineered to target specific tissues  and improved permeability with a retention effect that improves tumor penetration . For in  tissues . These t tissues .Clinical treatment for cancer has been routinely addressed by chemotherapy . RegardlPlant virus VLPs have several attractive features that make them appropriate for targeted administration of therapeutic molecules. The anti-cancer drug doxorubicin (DOX), has been successfully delivered using VNPs and VLPs. TMV- and PVX-derived VLPs and VNPs have been successfully used to deliver DOX . In thisTMV can be used as a carrier of peptides with therapeutic or targeting activity against various cancers. Trastuzumab is a cancer cell inhibiting monoclonal antibody that uses the binding sites of human epidermal growth factor receptor 2 (HER2). Trastuzumab-binding peptides (TBP) are immunogenic in nature and capable of initiating production of HER2-inhibiting antibodies to seize the growth of HER2-carrying cancer cells. TMV particles displaying TBP have been created to activate this immunogenicity . SimilarPlant virus VNPs have been used for targeted administration of platinum-based drugs against cancer. This is important as 50% of chemotherapy treatments involve the use of these platinum-derived drugs. TMV has been demonstrated to efficiently deliver Cisplatin  and Phenin vitro Karpas 299 non-Hodgkin’s lymphoma cell line with an IC50 of 250 nM.Another anti-cancer drug, mitoxanthrone (MTO), is a topoisomerase II inhibitor and has been shown to be encapsulated by TMV . VNPs exHelical plant virus nanoparticles (VLPs) have also been used as combination therapies to augment their immune efficacy. The PVX-DOX (doxorubicin)  combinatin vitro in HCC-38 primary ductal carcinoma, BT-549 ductal carcinoma and the MDA-MB-231 breast cancer cell lines -related apoptosis inducing ligand (TRAIL) on the surface of VNPs. Multivalent display of TRAIL enabled increased recruitment and stimulation of death receptors expressed on cancer cell lines (PVX displaying TNF related apoptosis inducing ligand (TRAIL) was used to promote the recruitment and activation of death receptors ll lines . In vivoll lines . SimilarAn efficient and new drug delivery system has been reported for Non-Hodgkin’s B cell lymphomas (NHL) based on PVX binding affinity towards malignant B cells. PVX loaded with monomethyl auristatin (MMAE) and administered to tissues harboring malignant B cells lead to inhibition of NHL growth in a mouse model . Jobsri PVX nanoparticles are increasingly being used for immunotherapy of tumor microenvironments. The monoclonal antibodies of Herceptin or Trastuzumab can be loaded onto PVX nanofilaments, which successfully induced apoptosis in breast cancer cell lines . PVX wasThe tumor microenvironment poses a great challenge to immune clearance by virtue of being immunosuppressive and favoring immune escape of the tumors through the inhibition of anti-tumor T-cells . CPMV VLThe icosahedral shape of CPMV capsid can be loaded with precise drug cargos to target tumor and cancer cells. CPMV VNPs have also been formulated as slow-release aggregates along with polyamidoamine generation 4 dendrimers (CPMV-G4) , where tCPMV VLPs have been attached to TAAs (tumor associated antigens) using chemical conjugation, genetic fusion and enzyme-mediated ligation techniques. For example, the human epidermal growth factor receptor 2 (HER2) epitope, when conjugated to the icosahedral CPMV, was successfully delivered to the lymphatic system with enhanced uptake and activation of APCs that led to an augmented anti-HER2 immune response. The CPMV HER2 candidate vaccine slowed tumor progression and metastasis in mouse models, enhancing survival . ImportaCancer vaccines against carbohydrate antigens associated with tumors (TACAs) could be useful for diminishing tumor progression. Nevertheless, carbohydrates are weakly immunogenic and therefore, plant viruses used as carriers of these molecules could enhance the immune response to TACAs. CPMV-TACA conjugates targeting the Tn antigen   were demThe chemotherapeutic cyclophosphamide, when used in combination with CPMV VNPs, profoundly elicited tumor cell death, releasing extracellular TAAs and stimulating immune cell invasion in addition to augmenting TAA recognition and antigen presentation  in mousein situ vaccine to stimulate an anti-tumor response and overcome local immunosuppression (The CPMV-DOX conjugate was developed using eighty molecules of the chemotherapeutic drug doxorubicin (DOX), covalently bound to carboxylates at the external surface of the CPMV nanoparticle. This drug delivery vehicle was found to be more cytotoxic than free DOX when used in low concentration, however, CPMV-DOX cytotoxicity is time-delayed at higher concentrations . Cancer pression . CPMV ispression . CPMV napression . Furtherpression .in situ tumor vaccine. Further studies by in vivo ( in vivo .The use of plant virus nanoparticles (VNPs) as drug delivery carriers for the treatment of infectious and chronic diseases including cancer are advantageous when compared with naked drugs . The mosIn the future, plant virus nanoparticles will need to be developed for high throughput manufacturing. This will require the dedication of facilities that can produce many grams of plant virus nanoparticles using tens of thousands of plants . Today,