Viroscope.io a web-service to provide on-demand HTS data viral diagnosis to facilitate adoption and implementation by phytosanitary agencies to enable precise viral diagnosis.High-throughput sequencing (HTS) methods are transforming our capacity to detect pathogens and perform disease diagnosis. Although sequencing advances have enabled accessible and point-of-care HTS, data analysis pipelines have yet to provide robust tools for precise and certain diagnosis, particularly in cases of low sequencing coverage. Lack of standardized metrics and harmonized detection thresholds confound the problem further, impeding the adoption and implementation of these solutions in real-world applications. In this work, we tackle these issues and propose biologically-informed viral genome assembly coverage as a method to improve diagnostic certainty. We use the identification of viral replicases, an essential function of viral life cycles, to define genome coverage thresholds in which biological functions can be described. We validate the analysis pipeline, Viroscope, using field samples, synthetic and published datasets, and demonstrate that it provides sensitive and specific viral detection. Furthermore, we developed  Plant viruses are among the most important pathogens for agriculture, causing losses amounting up to more than $30 billion annually . Facile Prunus avium) have been traditionally multiplied using clonal propagation leading to accumulation of a large number of viruses. This is of particular concern since these pathogens may be latent or may cause detectable symptoms in susceptible rootstocks and/or scions  and real-time polymerase chain reaction . ELISA detects structural protein motifs of the virus and can have a broad capability to detect variants, but exhibits limited sensitivity of detection for cases of low virus abundance. On the other hand, PCR-based assays  are considered the gold standard for detection of a virus presence, however it requires tandards .de novo assembly of reads, (7) read counting and/or coverage calculation, (8) identification of present viruses using read and coverage cutoffs  as the responsible for transcription and replication . In otheViroscope.io1 to enable cloud-based HTS data virus diagnosis.Here, we present Viroscope, a diagnostic pipeline that improves virus detection accuracy by using biologically-informed viral genome assembly coverage (VGAC). We introduce the identification of replicases to evaluate how different VGAC threshold levels correlate with functional aspects of virus biology. In addition, we evaluate the performance of Viroscope with field samples of sweet cherry, simulated datasets and external datasets, demonstrating that VGAC is a robust measure for virus detection using total RNA HTS data using Illumina and Nanopore sequencing. Finally, we have implemented the pipeline in the form of a web application called P. avium specimens. In this field, elite varieties ‘Lapins’ and ‘Santina’ were the most common cultivars. Hence, each sample was called L1, L2 and S1 and S2, respectively. In order to evaluate differences in plant virus diagnosis due to season conditions , the same plants were sampled at two different principal growth stages as previously described for Prunus sp.: Stage 3 or shoot development (SD) and Stage 9 or senescence   . Each gr™ plant total RNA kit , according to the manufacturer’s instructions. Total RNA concentration and quality was assessed using an optical microplate reader , through fluorometry  and integrity was verified through agarose gel electrophoresis   and Macrogen Co., Ltd (Korea) . Ribosomal RNA depletion was performed using Ribo-Zero Plant , and library construction was executed using NEBNext Ultra RNA Library Prep kit , and sequenced on a NovaSeq6000, using a 150 bp paired-end cycle.fastp  and seqkit . In the first dataset (Synab dataset), virus abundance was based on experimental data , whereas the second dataset (Synhom dataset) assumed an even distribution of virus read abundance (jackknife process (10 repetitions).Artificial HTS datasets of 20M paired-end reads of 150 bp  were genART  and seqk . Mutation-Simulator (version 2.0.3) (Another dataset of 20M paired-end reads (150 bp) was generated (Mut dataset), where all the viruses (from Pavium panel-I) were randomly mutated at different rates . In this case, a n 2.0.3)  was usedjackknife process to obtain 10 subsets at different depths of sequencing .Two additional datasets were built to further investigate the relationship between virus abundance and depth of sequencing. In this case, datasets of 20M total paired-end reads (150 bp) containing at most 3% of viral reads (600K reads) from the viruses with the shortest  and the longest  genomes from the Pavium panel-I were surveyed (Cva and Lchv1 datatsets). Subsets containing 0.05, 0.1, 0.5, 1, 5, 10% of such 3% of representation of the viral reads defined above were generated. For example, a 20M total read set with 10% of 3% viral reads contains 0.1 × 0.03 × 20M = 60K viral reads, while at 0.5% of 3% viral abundance it contains 3K reads. The remaining reads consisted of background sequences from plant, human, bacteria, and random sequences in the same proportion as listed in Several external datasets containing HTS data were surveyed and tested . A totalKraken2  from 35 to 31 to increase its sensitivity.For read assignment (taxonomic classification of reads according to a panel of virus genomes), two widely used software for metagenome exploration were tested, namely Kraken2  and Centntrifuge , which antrifuge . Minimaplignment  was alsoad hoc viral panels were built in order to set the proper databases required by each software. In the case of both the field samples  and simulated datasets , the analysis was carried out using 11 viruses which affect Prunus sp., some of which were previously reported in Chile: apple chlorotic leaf spot virus (ACLSV), apple mosaic virus (ApMV), cherry green ring mottle virus (CGRMV), cherry necrotic rusty mottle virus (CNRMV), cherry virus A (CVA), little cherry virus 1 (LChV-1), plum bark necrosis stem pitting-associated virus (PBNSPaV), prune dwarf virus (PDV), prunus necrotic ringspot virus (PNRSV) and plum pox virus (PPV)  , and absus (PPV) . Additio , and the genome coverage (percentage of the reference genome that is covered by the assembled contigs) was calculated by SAMtools/BCFtools . Thus, the VGAC is a value that ranges between 0 and 1 .where, SAMtools/BCFtools. These consensus regions were calculated as follows:Consensus regions were determined from mapped contigs onto the reference genome of the virus using $ samtools mpileup -uf reference_genome.fasta -o mpile.vcf aligned_contigs.bam$ bcftools call -c –ploidy 1 -o call_mpile.vcf mpile.vcf$ vcfutils.pl vcf2fq call_mpile.vcf > consensus.fastqRVDB-prot , “RdRp,” “RNA dependent DNA polymerase” (RdDp), “RdDp,” and “polymerase,” which allowed for recovering 13,576 sequences. In addition, the term “reverse transcriptase” (which is a synonym of “RdDp”) was searched, producing 59,525 records. In this case, records belonging to “homo,” “human,” “hepatitis,” and “hiv” were filtered out, remaining 4,114 sequences, which were incorporated into our repository. In the course of evaluating the different panels, some of the virus sequences did not account for the presence of a coding sequence related to replicase or polymerase activities. However, all these cases were manually inspected and found to encode in fact a protein either with a name containing none of the terms searched above, being absent in RVDB-prot or being part of a polyprotein. Finally, all of them were incorporated into our viral replicase protein repository.Each consensus region was compared against a repository of protein sequences related to a virus replication . This repository was built from Diamond (blastx module. The resulting hits were then filtered by similarity (80 or 90%) and length of alignment (80 or 90%). At the 80/80 schema, a replicase was said to be identified in the respective consensus region if it contained 80% similarity and 80% sequence alignment (90/90 represents a more stringent schema).The comparison of the consensus regions was performed using Diamond  with itsIn order to estimate the performance of the different stages of the pipeline on the external datasets, several measures were calculated: sensitivity, specificity, precision, accuracy, and false discovery rate (FDR). These measures were determined as follows:TP (true positives) is the number of cases that were detected that must be detected, FP  is the number of cases that were detected that should have been not detected. In this sense, for negatives, FN  corresponds to the number of cases that were not detected that should have been detected, and TN (true negatives) is the number of cases that were not detected that in fact were not present. Since datasets are reported to have only the viruses to be detected, FP and TN values were estimated from the rest of viruses of the respective panel which should account for misassigned reads by at least one software. For example, in the Viromock datasets there are 15 actual cases to be detected, but the read assignment together yielded 51 additional cases detected, so a maximum of 51 TN cases were assumed to exist in these datasets. The measures listed above were determined for read assignment (the case is assumed to be a TP when existing at least one assigned read), and for detection of replicases (the case is assumed to be a TP either when matching the 80/80 or the 90/90 schema). In the case of VGAC, the measures were calculated for the thresholds 0.1 (the case is assumed to be a TP when VGAC ≥ 0.1), 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, and 1.0. Finally, in order to estimate the similarity between measures for VGAC and the detection of replicases, the euclidean distance d between the measures for VGAC and for the schema 80/80 was determined as:where m could be sensitivity, specificity, precision, accuracy, or FDR, and s runs on the three software .where PDS1) is a plant gene that exhibits constitutive expression and was used in our RT-PCR experiments as an internal control for RNA extraction and reverse transcription. In addition, the 11 purified amplicons were used for positive controls to corroborate amplification of molecular targets of appropriate size  and senescence stage  were confirmed by reverse transcription-polymerase chain reaction (RT-PCR). The two-step RT-PCR for SD samples (Shoot Development) was performed by Laboratorio de Virología, Universidad de Chile , named here as “PCR external.” On the other hand, in the case of samples obtained during the senescence stage , the presence of the viral pathogens was confirmed by a two-step RT-PCR performed in this study. This method was optimized to detect the 11 viruses of the Pavium panel-I, and results were recorded as “PCR internal.” Primers of this panel were exclusively designed for this study based on a local viral sequences database and OligoPerfect™ designer software (ThermoFisher Scientific) . The phyate size .First-strand cDNA synthesis was performed using 70 ng of total RNA. The reverse transcription (RT) mix contained 200 units of recombinant Moloney Murine Leukemia Virus (MMLV) reverse transcriptase (Promega), 20 units of RNAsin (Promega), 1 mM dNTPs, and 1 uM of random hexamers (Promega). The reaction was performed at 20 uL final volume and was incubated at 37°C for 60 min followed by enzyme inactivation at 70°C for 5 min. The PCR mix  contained 1 uL of the cDNA, 1X  GoTaq G2 Flexi Buffer (Promega), 0.15 uM of each primer , 3 mM Mgde novo assembly of assigned reads, (3) reference mapping of assembled contigs, (4) genome coverage calculation of mapped contigs, (5) consensus calling, and (6) replicase identification in consensus sequences. In a second step, Viroscope detects pathogens by considering the VGAC values obtained by three read assignment algorithms and the identification of replicases. The validation of the pipeline was performed with three types of datasets: field samples from a sweet cherry farm, simulated datasets, and publicly available datasets (including different library preparation methods and sequencing technologies). The Viroscope results for field samples were also validated using RT-PCR methods to compare and study diagnostic sensitivity according to the different cutoff levels investigated. The Viroscope algorithm and the experimental design of this study are shown in The Viroscope pipeline consists of two distinct steps for plant virus diagnosis based on HTS data . First, Centrifuge, Kraken2 and Minimap2. The goal of this study was not to evaluate the different software, but to incorporate more than one perspective in the analysis since different algorithms could yield diverse results. Samples from four cherry plants specimens  collected at the shoot development stage were sequenced using Illumina, yielding 17M–22M paired-end reads each , ApMV for SD-L1 (by Centrifuge), LChV-1 for SD-L1 (by Kraken2), LChV-1 for SD-L2 (by Kraken2), ACLSV for SD-S1 (by Centrifuge), ApMV for SD-S1 (by Centrifuge), and LChV-1 for SD-S1 (by Kraken2), although with a relatively low number of reads  and the second one contained a homogeneous distribution of reads from Pavium panel-I, which are composed of 11 viruses (Synhom dataset). In order to mimic real samples, both datasets were built so that they contained background sequences, that is, they were mixed with reads derived from human (2%), plant (75%), bacteria (15%) and random sequences (5%). In both cases, the total quantity of viral reads was limited to 3% of total reads as shown in the distribution listed in synab data in In the simulated data with empirical distribution .Further simulated datasets were generated so as to assess the tolerance of the different software to viral mutation rates. Subsets of 10M paired reads sampled from a set of 20M reads were used. At a 10% mutation rate, all the software were able to assign reads, and even at 20%, there was still a portion of an average ∼2,900 out of 27,000 reads assigned to the viral genomes . Read asIn order to explore beyond the presence of reads and to examine the biological relevance of the assemblies obtained at different sequencing depths and viral abundance, the VGAC was analyzed in terms of the presence of replicases in the assembled contigs. This was performed to understand whether the assembled portion of the virus could encode a relevant biological function to support the use of specific VGAC cutoffs for virus detection.A series of simulated samples containing an increasing amount of viral reads with at most 3% of the total reads were generated . In this case, only the viruses with the shortest and the longest genomes in the Pavium panel-I were surveyed . Simulated data (Cva and Lchv1 datasets) showed that all assessed software reached similar levels of VGAC values. At the lowest depth of sequencing (100K), virus abundance was critical since VGAC turned out to be relevant only from 5% (CVA and LChV-1) of the viral read composition . HoweverRVDB-prot (R2 > 0.9) between the VGAC and the presence of replicases, which is expected as long as a full length virus can be assembled (R2 > 0.9).Further, a set of 17,708 viral proteins related to replication  obtained from VDB-prot  was usedssembled . When loAltogether, according to the simulations and independently of the software used, the identification of replicases began at a VGAC ≈ 0.3 for the lowest depth of sequencing case  or at VGAC ≈ 0.4 for the lowest viral abundance case (0.05%), which represents basically a minimum of 15 viral reads for CVA or 30 reads for LChV-1 required for being able to identify a replicase in some of the simulations . CoincidCentrifuge and Kraken2. These results were the basis for establishing a 90% sequence identity threshold for clustering and to incorporate different isolates in a reference database used in the read assignment process .Additionally, the presence of replicases was evaluated for the simulated mutation datasets, where the identification was expected to be affected according to the mutation rate since changes in the nucleotide sequence may alter the encoded protein. The identification of replicases was severely hampered over a 15% mutation rate , but a VExternal datasets were used in order to challenge the methodology proposed in this study. Ten illumina datasets (V1-V10) comprising a panel of 18 viruses published by Centrifuge, in both schemas) due to lower VGAC values. In the same dataset the cases V3-GRSPaV and V3-GRVFV failed the detection with the reads assigned by Minimap2. In those three cases the VGAC values were less than 0.2, but replicases were detected above this value.According to the performance on Viromock datasets , the pipCentrifuge and Kraken2). The GalLV case can be considered artifactual since the actual replicase is encoded in the first 6,000 bp of the 5′-region of the reference genome , but a shorter replicase was found at the end of the 3′-region . In the case of TVCV, low VGAC values were obtained, however a contig could be assembled around position 4,300 bp of the reference genome  containing the replicase. In the CLCuV case, there was a high number of reads, however they could only assemble a contig that maps in the central region of the reference genome, and the replicase in this virus is at the 3′-region (hence no replicases detected). A similar issue was observed in the case of CYVMV, where no contig could be assembled for the 3′-region of the reference genome where the replicase is located. In the PepLCVB and ToLCBDB cases, despite the relatively high VGAC values, no replicases were found. This was expected since betasatellite viruses have been reported to depend entirely on other viruses for replication, movement, and transmission  or only with Centrifuge (R3). All these cases were manually inspected verifying that either assembly was not possible  or, due to reads being localized in a region of the reference genome that did not contain at least 80% of a replicase , which resulted to be the one encoding the replicase; in the case of N16-ACMV (a 2-segments virus), reads assigned by Minimap2 could assemble both segments of the virus (thus VGAC > 0.9), and reads assigned by Centrifuge and Kraken2 could assemble one segment (VGAC ≈ 0.5), nonetheless the three assemblies encoded the replicase . The Dataset N19 was further investigated to confirm the lack of reads assigned to DBV. Although the dataset was reported to have 156 ONT reads  published by NT reads , it was Minimap2 reaching the highest degree (average = 0.82 in comparison with 0.26–0.36 reached by the other two software). Similar patterns, where Minimap2 outperformed Centrifuge and Kraken2, were obtained for precision (0.81 vs 0.55–0.56) and accuracy (0.85 vs 0.61–0.60). This was in agreement with the FDR values, where the pattern was the opposite, wherein Minimap2 showed the lowest rate (0.19 vs 0.45–0.44). Regarding the metrics for VGAC, sensitivity and accuracy values tended to be lower as long as cutoffs became more stringent, meanwhile specificity, precision, and FDR tended to improve reaching the maximal (1) and the minimal values (0), respectively. In relation to the detection of replicases, both schemas (80/80 and 90/90) appeared to have identical measure values in the Viromock and Nanopore datasets, while the use of either schema appeared to have more impact in the SmallRNA datasets . Altogether, these measures provide additional rationale for the high performance of the Viromock (Illumina datasets) and Nanopore datasets . In that sense, a comparison of the measures obtained under these thresholds with the measures for VGAC at the different cutoffs, was carried out so as to find the cutoff at which similar performance measures are obtained with such a schema, accounting for the minimum VGAC in which replicases could be identified. To identify this VGAC cutoff, the euclidean distance between the measures  was calculated . A minimal of the distances was found at VGAC ≥ 0.2 for Viromock datasets, VGAC ≥ 0.4 for SmallRNA datasets, VGAC ≥ 0.1 for Nanopore datasets, and VGAC ≥ 0.3 taking into account all the datasets .All sweet cherry samples used in this study were tested by external and internal RT-PCR methods  to confirm the presence or absence of the 11 viruses previously evaluated by Illlumina sequencing . AdditioHigh consistency between HTS-based detection and RT-PCR analyses was observed, where 82 out of 88 (93.2%) matches were obtained. The inconsistencies observed between the RT-PCR analyses were detected only for sample S2, which was found positive for the CNRMV in the SD stage by the external PCR and negative in SS for the internal PCR, which was also the case for PDV. Regarding the Viroscope pipeline, CNRMV resulted in a positive case for both samples, that is, independently of the season conditions. On the other hand, the diagnosis by the pipeline agreed with each RT-PCR test in the case of PDV, accounting for the seasonality of the sampling. Contrasting the RT-PCR results obtained in the SD stage and the SS stage, some discrepancies were found : CNRMV in sample SS-S2 and PDV in sample SS-S2. These results suggest that sampling season affects the sensitivity of diagnosis, particularly having increased sensitivity in the shoot development stage.de novo assembly step of the pipeline were screened, finding that ApMV primers did not map to the assembled sequences. On the other hand, the diagnosis for CNRMV in sample SS-S2 was positive according to Viroscope (VGAC = 1 and with identification of replicases), whereas for PDV (sample SS-S1) and PNRSV (sample SS-L2) were negative (VGAC = 0 in both cases). Although the number of reads for PNRSV (sample SS-L2) reached over 4K reads assigned by two out of the three algorithms, they were not able to assemble a contig with a VGAC over 0.1, nor that contained a replicase , since samples SD-L1, SD-S1 and SD-S2 resulted to be positive for ApMV , while neplicase . The speComparing only the seasonal differences in diagnosis obtained through Viroscope, six cases were detected in SD and not in SS: ApMV in L1, L2 and S1; PDV in S1 and S2, and PNRSV in L2. For all these cases VGAC was below the minimum threshold and assigned reads dropped dramatically between both seasons. Furthermore, in the cases of SD-S1 and SD-S2 for PDV and SD-L2 for PNRSV were diagnosed as positive due to VGAC > 0.3, but no replicases were identified. These results again show that the shoot development stage enables increased sensitivity for virus detection using HTS-based diagnosis.Lastly, it is important to highlight the use of different viral panels in the diagnosis process. According to the results described so far, VGAC values were not enough to diagnose LChV-1 as positive in sample SD-S2 when using the Pavium panel-I , whereasViroscope, to enable diagnosis of viruses on-demand from HTS data (accessible at https://www.viroscope.io). Users submit HTS data and then create an analysis instance for the pipeline to work and handle such data. Next, a predefined viral panel is selected to perform virus detection. Once finished, the pipeline outputs graphical results composed of a positivity report and taxonomical profile accounting for the abundance of each virus of the panel in the sample  and detection cutoff parameters are provided for the user to define, although suggestions are made according to the results obtained in this study. Three possible outcomes can be obtained from the pipeline: (i) a categorically positive diagnosis which is defined when Viroscope was able to detect either the presence of a replicase or a VGAC over the upper cutoff defined by the user, (ii) a categorically negative result which implies that read assignment was below the lower threshold of VGAC required for virus detection and with absence of replicase; and (iii) a positive* (positive depicting an asterisk) result which establishes that read assignment resulted in a VGAC sufficient for virus detection but lower than what is required to attribute biological functionality. We surmise that lack of evidence for viral replication in these cases requires confirmation by other traditional methods to ensure diagnostic certainty.The pipeline described in this study was implemented as a web application, e sample . The ideIn the last two decades, HTS methods have become a reliable tool for plant virus diagnostics including managing disease risk, emergence, and the adoption of novel phytosanitary rules . Due to In this work, we developed Viroscope, a HTS data pipeline for virus diagnosis in plants that uses the VGAC and replicase identification to improve virus detection. One of the main challenges of virus diagnosis from HTS data is to define the virus presence when faced with a limiting amount of viral reads. Further, detection of a pathogen does not necessarily imply ongoing infection . We surmThe tests to evaluate metrics showed that VGAC is a more robust measure than read assignment for virus detection. Read assignment can be prone to generate false positives in circumstances where the host has sequences derived from viral origin, such as endogenous viral elements . For insIn turn, replicase identification from the assembled contigs enabled investigating the biological implication of different VGAC thresholds. Defining cutoffs for virus detection is extremely challenging and involves selecting a value that maximizes both sensitivity and specificity. We observed that replicases could be consistently identified only over a VGAC of 0.3, allowing us to define this threshold empirically as a level that enables a biological interpretation of the metric. Additionally, using replicases presence  is already an excellent criteria for virus diagnosis in cases of even lower VGAC values.Validation against external datasets shows that Viroscope has high diagnostic sensitivity and specificity for total RNA sequencing data. In contrast, small RNA data showed poor performance for virus detection with VGAC. Additionally, Nanopore sequencing data was used to evaluate if our pipeline is applicable to long-reads, where a 96% of agreement was obtained in comparison with the original publications . Long-reAnalysis of field samples by sequencing and RT-PCR showed that HTS-based virus diagnosis is more sensitive than RT-PCR and that seasonality of sampling influences viral abundance. Comparisons between RT-PCR panels showed that for both spring and summer samples, virus detection by RT-PCR had a 93.2% correspondence to HTS-based detection using Viroscope, where differences were due to false negative cases and false positive cases of detection by RT-PCR. Additionally, problems with PCR design bias were evident in one case in which primers contained mismatches to a viral variant. Further, the seasonality of sampling affected the capability to detect six viruses in summer through HTS. However, all viruses that could not be detected  were Illarviruses, a genus named after exhibiting lability and described to be thermolabile . Our obsViroscope uses a predefined panel of viral targets to perform diagnosis. This is due to the intended use of Viroscope as a viral diagnosis pipeline for phytosanitary detection rather than for discovery. We found that compiling a database of viral sequences using published reference sequences was insufficient to provide identification, as databases are generally biased toward submissions from certain geographical locations or particular pathogens. One such case occurred in the detection of LChV-1, as it could be identified by both PCR panels but not through Viroscope (using Pavium panel-I). The inclusion of other viral isolates to generate a clustered database (Pavium panel-II) enabled the successful identification through the pipeline. Despite this improvement in the diagnosis (if read assignment is concerned), more misassigned cases could be obtained as a side effect. The pipeline resulted to be sufficiently robust when VGAC and/or the identification of replicases are taken into account as misassigned reads were unable to assemble contigs. This finding is of paramount importance when considering performing the diagnosis in particular geographic contexts since viral isolates could be endemic or affect specific plant varieties, thus the panel construction should consider these distinctive features. Moreover, the viral variants can be dissimilar enough to have an impact on the diagnosis. Noticeably, the isolate included in the Pavium panel-II that allowed the identification of LChV-1 shares a 76% of sequence identity with the respective reference genome present in the Pavium panel-I.in-vitro’ and ‘in-silico’ level during virus diagnosis using HTS pipelines. In agreement with many scientific researchers in the field (HTS is now considered as the gold standard in molecular diagnostics of viral infections since it is a universal technique which is more precise at profiling pathogens . As we hhe field , we endoPRJNA848006 can be found below: https://www.ncbi.nlm.nih.gov/bioproject/PRJNA848006.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number BP designed the study. SV and TN contributed equally to this work and developed the Viroscope pipeline. VM and IF coordinated the sampling and Illumina sequencing. SV, TN, and BP performed bioinformatics analysis. SV, TN, VM, JJ, IF, and BP made theoretical contributions to the experimental design. FG and CN performed the nucleic acids extraction. FG performed internal RT-PCR analysis. SV, TN, VM, and BP interpreted the results, drafted the sections of the manuscript, prepared the figures and tables, and prepared the final version of the manuscript. SV, TN, VM, FG, JJ, CN, IF, and BP critically revised the manuscript. All authors read, reviewed, and approved the manuscript.


Co-infection of plants by pathogens is common in nature, and the interaction of the pathogens can affect the infection outcome. There are diverse ways in which viruses and bacteria are transmitted from infected to healthy plants, but insects are common vectors. The present review aims to highlight key findings of studies evaluating the co-transmission of plant pathogens by insects and identify challenges encountered in these studies. In this review, we evaluated whether similar pathogens might compete during co-transmission; whether the changes in the pathogen titer in the host, in particular associated with the co-infection, could influence its transmission; and finally, we discussed the pros and cons of the different approaches used to study co-transmission. At the end of the review, we highlighted areas of study that need to be addressed. This review shows that despite the recent development of techniques and methods to study the interactions between pathogens and their insect vectors, there are still gaps in the knowledge of pathogen transmission. Additional laboratory and field studies using different pathosystems will help elucidate the role of host co-infection and pathogen co-transmission in the ecology and evolution of infectious diseases. Hemipteran insects are the primary vectors of plant pathogens; they are responsible for transmitting 72% of plant viruses with a known vector . BecauseThe transmission process includes acquisition of the pathogen from an infected source by the arthropod vector and its inoculation into a new host. Virus transmission is classified into four mechanisms depending on where the virus attaches or localizes in the vector, the persistence of the virus in the vector, and whether or not the virus replicates in the vector . Non-perWhile studies generally focus on the transmission of a single pathogen, co-infections of plants by different pathogens are common in nature and occur in diverse plant–vector–pathogen systems ,10. TherIt could be tempting to assume that closely related pathogens have an antagonistic interaction as they compete for similar transmission sites and vector proteins involved in transmission such as receptors. Certainly, closely related viruses that share a similar transmission mechanism and vector species also share similar structural and non-structural proteins. Then, competition during transmission is likely to occur, at least for non-persistently transmitted viruses. This transmission mechanism involves few vector and virus proteins . HoweverClosteroviridae, Geminiviridae, Nanoviridae, Potyviridae, and Rhabdoviridae [Multipartite viruses have a segmented genome and each viral segment is encapsidated in an individual capsid . Therefooviridae . Becauseoviridae . Furtheroviridae . Then, hWhile it could be argued that the transmission of multipartite viruses does not qualify as viral co-transmission of different viruses, the study of their transmission can shed light on several challenges and questions faced during the study of virus co-transmission. Successful host infection relies on the presence of several of the genome segments, and while in nature plant inoculation could be achieved by different insect individuals transmitting some of the segments, a single insect can transmit the virus and cause infection .Nanoviridae) in its insect vector is different from that in the plant host [Bromoviridae) particles affecting their stability were identified [The ability of single insect vectors to transmit multipartite viruses is not surprising because all segments are encapsidated by the same capsid proteins, thus an insect that can transmit one segment should transmit all of them. Indeed, different segments were observed co-localizing in the same aggregates inside the vector gut cells  supportiant host . This fiant host . Differeant host . The lasentified .Closteroviridae) through the transmission by several aphids, with grafting, mutation, and recombination occurring during the life of the plant. These different CTV isolates can be separated by aphid transmission because an individual aphid only transmits a few isolates each time [Toxoptera citricida Kirkaldy (Hemiptera: Aphididae) during aphid acquisition [Potyviridae) strains transmitted by Aceria tosichella Keifer (Acari: Eriophyidae) [Transmission by vectors is considered to be a virus population bottleneck ,38. For ach time ,40,41,42uisition . Similarphyidae) . Both viMyzus persicae Sulzer (Hemiptera: Aphididae) and Aphis gossypii Glover (Hemiptera: Aphididae) transmitted several CMV mutants from leaves inoculated with twelve CMV mutants, but only some of the mutants were transmitted at the same time by individual aphids [A genetic bottleneck was reported for CMV also. Individual l aphids . On averl aphids . HoweverPotyviridae) strains by aphids was also investigated by several laboratories [The co-transmission of different potato virus Y  complex. They are serologically related but not identical, they cross-protect against each other in plants, and they compete for transmission by Sitobion avenae Fabricius (Hemiptera: Aphididae) [Rhopalosiphum padi L. (Hemiptera: Aphididae) was able to transmit PAV but not MAV when it was fed on co-infected plants. Therefore, the proteins allowing PAV transmission in R. padi differ from those in S. avenae.MAV and PAV are two species of the barley yellow dwarf virus  . In sequhididae) . Becausehididae) , the authididae) . HoweverSolemoviridae) RPV and RMV also infect cereals causing barley yellow dwarf. Until recently, the genera Polerovirus and Luteovirus were assigned to the Luteoviridae family but have recently been reclassified as Solemoviridae and Tombusviridae, respectively. Despite having similar particle structures, sharing vectors and the transmission mechanism, no interactions between CYDV-RPV or RMV and BYDV-PAV were identified during their transmission, confirming that the transmission of these viruses relies on different proteins [Schizaphis graminum Rondani (Hemiptera: Aphididae) determined that there was no genetic correlation between the ability to transmit BYDV-PAV and CYDV-RPV and that multiple loci are involved in the transmission of these viruses [The poleroviruses cereal yellow dwarf virus  to pepper plants without competition [B. tabaci [B. tabaci [Begomoviruses are also transmitted in a circulative manner but by whiteflies. Competition may occur depending on the similarity between the viruses. For example, pepper huasteco yellow vein virus (PHYVV) and pepper golden mosaic virus (PGMV) can be co-transmitted by petition . Similar. tabaci , but TYL. tabaci . Specifi. tabaci ,70,71.Geminiviridae [Aphis craccivora Koch (Hemiptera: Aphididae). Because capulaviruses and nanoviruses are structurally different, with no sequence homology between the proteins reported to bind the putative insect receptors, little interaction between these viruses is expected in plants and vectors. Indeed, they infect plants with little or no interference: their titers are not affected by co-infection, and they can co-infect the same cells. ALCV titer was higher than FBNSV in plants and in the insect gut [The recent discovery of whitefly-transmitted poleroviruses ,73 and oiviridae  has opensect gut . Howeversect gut , the nansect gut . It cannIn conclusion, independently of their transmission mechanism, closely related viruses can compete during transmission, but that is not always the case. Virus transmission by insects is a complex phenomenon relying on several transient protein–protein interactions, and therefore the outcome of co-transmission appears to depend on the combination of viruses as well as the insect vector.Circulifer tenellus Baker (Hemiptera: Cicadellidae) and Dalbulus elimatus Ball (Hemiptera: Cicadellidae) can carry and transmit phytoplasma and spiroplasma [Bactericera cockerelli (Šulc) (Hemiptera: Triozidae) can acquire and transmit different ‘Candidatus Liberibacter solanacearum’ haplotypes [Candidatus Liberibacter asiaticus’ was reduced in orange trees previously infected with some CTV strains [Multiple bacterial pathogens or viruses and bacterial pathogens can co-infect plants . For exaroplasma , or the plotypes . Severalplotypes ,83,84. S strains , and theCandidatus Phytoplasma mali’ and its titer was established [Ca. Liberibacter solanacearum’ in the salivary glands of the vector are needed for plant inoculation [The ability of bacteria and persistent propagative viruses to replicate within the vector adds an additional factor that might affect transmission: these pathogens must not only interact with vector proteins and circulate through the vector body avoiding immune responses, but they must also replicate in the vector. Interactions affecting the ability of the pathogens to multiply in the vector because of competition or the induction of immune responses could affect the transmission. Indeed, for many of these pathogens, a minimum titer in the vector seems to correlate with transmission efficiency. For example, a correlation between the ability of psyllids to transmit ‘ablished  and at lculation .Ca. Phytoplasma asteris’ chrysanthemum yellows strain (CYP) by nymphs of the leafhopper Euscelidius variegatus Kirschbaum (Hemiptera: Cicadellidae) determined that both bacteria could be sequentially acquired regardless of the feeding order, and the titer of FDP but not CYP in the vector were affected by the double infection [The study of the co-transmission of grapevine flavescence doree phytoplasma (FDP) and ‘nfection . While Cnfection . Therefonfection . Interacnfection ,90,91; hB. tabaci correlated with these changes: there were no differences in CuLCrV titer in the whiteflies, but the titer of CYSDV was reduced when the virus was acquired from co-infected plants. Because these viruses are not transmitted following the same mechanism and they share little structural similarity, it is expected that they interact with different vector components and therefore, competition between these pathogens during the transmission is unlikely. Whether the change in virus titer in the vector affected its transmission efficiency was not evaluated in the study.Since a pathogen’s titer in a host plant can affect its transmission, if during co-infection the titer of a pathogen changes, its transmission could be affected. Indeed, correlations between changes in virus titers following co-infection and changes in transmission were identified in some pathosystems. For example, plant co-infection with cucurbit leaf crumple virus  and cucurbit yellow stunting disorder virus  resulted in reduced CYSDV titer while CuLCrV titer was not affected . Virus tNicotiana benthamiana plants with tomato chlorosis virus (ToCV) while ToCV titers decreased [Physalis wrightii, the titer of both criniviruses decreased during the co-infection, and ToCV accumulated to higher titers than TICV. These changes in virus titers were reflected in the transmission efficiency by Trialeurodes vaporariorum Westwood (Hemiptera: Aleyrodidae) when the viruses were acquired from co-infected plants. Further, in a different study, the co-infection of tomato plants did not affect the transmission of these two viruses by T. vaporariorum [T. vaporariorum proteins between these viruses. Indeed, competition for T. vaporariorum proteins cannot be excluded because these viruses are assigned to the same family [B. tabaci species can transmit ToCV but not TICV.It was also demonstrated that the changes in virus titer in the host and its effect on transmission could be host plant dependent. For example, tomato infectious chlorosis virus (TICV) titers increased during the co-infection of ecreased . Howeverrariorum . Based oe family . HoweverSeveral other studies demonstrated that a link between viral titer in the plant and the virus transmission efficiency is not always evident. For instance, the ability of aphids to transmit undetectable CTV strains is well established , and theB. tabaci MED [Interesting results were obtained when studying the changes in viral titer of the closely related cucumber chlorotic yellows virus (CCYV) and cucurbit yellow stunting disorder virus (CYSDV) in co-infected plants. In this case, the changes in the viral titer did not correlate with changes in the transmission efficiency by baci MED . Acquisibaci MED ,99,100 lVector acquisition of two pathogens does not ensure that both will be transmitted. The ability of each pathogen to be transmitted will depend on whether they are acquired from an infected host, can attach to their respective vector sites and persist long enough in the vector to be inoculated into a new host, are inoculated into a new host, and establish an infection. Another factor affecting the transmission of circulative pathogens is the efficiency with which they cross transmission barriers within the vector. Therefore, one single experiment evaluating disease development or pathogen titer in the recipient plant is not enough to understand the outcome of the co-transmission. Instead, a variety of approaches are needed as exemplified in some of the papers described above.To evaluate and circumvent the potential competition in plants affecting pathogen acquisition several approaches can be used. One approach is to use co-infected plants as a pathogen source; however, if competition during transmission occurs, obtaining co-infected plants might be challenging. Depending on the pathosystem, this can be achieved by using mechanical inoculation , by usinTransmission is an essential step in a pathogen’s life cycle and is a key element of disease epidemiology. Co-infections can alter the disease phenotype and affect the vector fitness and behavior or the pathogen’s transmission efficiency by vectors. In nature, the co-infection of hosts does not rely exclusively on the co-transmission of pathogens as often several vectors feed on donor and recipient plants. The study of the co-transmission of pathogens can help elucidate the mechanisms involved in these processes. For instance, based on the studies discussed here it appears that virus titer in plants is not always correlated with transmission efficiency or that the interaction between viral proteins and nucleic acids might affect the conformity of the protein in turn affecting its ability to interact with vector proteins or the stability of protein–protein interactions. Pathogen transmission relies on a series of spatially and temporally controlled protein–protein interactions. Identifying key proteins involved in the transmission process and their interactions could lead to the development of tools to block transmission, screen vector populations, identify more transmissible pathogens, and overall improve our ability to manage diseases.NTN to outcompete PVYO, and to be more likely transmitted and infect plants, might be in part associated with the increase in PVYNTN and the decrease in PVYO incidence in potatoes in the US. However, the net result of the competition between pathogens for transmission is only part of the picture that influences the disease epidemiology and spread of a specific pathogen [B. tabaci [A. gossypii [From an epidemiological perspective, the study of co-transmission only explains part of the results from the interaction of two pathogens. For example, it is possible that the ability of PVYpathogen . Even ifpathogen . The stu. tabaci  discussegossypii . TherefoFinally, another aspect that needs further study is the role of endosymbionts in the studied processes. The presence of endosymbionts can alter the vector competence: endosymbionts can produce proteins involved in the pathogen transmission, they can influence the tissue tropism of the pathogen in the vector, they can prime the vector immune response affecting the transmission of the pathogen, they can elicit plant responses affecting the vector behavior and consequently its ability to transmit pathogens, etc. ,108,109.


Criminal justice system costs in the United States have exponentially increased over the last decades, and providing health care to individuals released from incarceration is costly. To better understand how to manage costs to state budgets for those who have been incarcerated, we aimed to assess state-level costs of an enhanced primary care program, Transitions Clinic Network (TCN), for chronically-ill and older individuals recently released from prison.We linked administrative data from Connecticut Department of Correction, Medicaid, and Department of Mental Health and Addiction Services to identify a propensity matched comparison group and estimate costs of a primary care program serving chronically-ill and older individuals released from incarceration between 2013 and 2016. We matched 94 people released from incarceration who received care at a TCN program to 94 people released from incarceration who did not receive care at TCN program on numerous characteristics. People eligible for TCN program participation were released from incarceration within the prior 6 months and had a chronic health condition or were over the age of 50. We estimated 1) costs associated with the TCN program and 2) costs accrued by Medicaid and the criminal justice system. We evaluated associations between program participation and Medicaid and criminal justice system costs over a 12-month period using bivariate analyses with nonparametric bootstrapping method.p < 0.05). We estimate every dollar invested in the TCN program yielded a 12-month return of $2.55 to the state.The 12-month TCN program operating cost was estimated at $54,394 ($146 per participant per month). Average monthly Medicaid costs per participant were not statistically different between the TCN ($1737 ± $3449) and comparison ($1356 ± $2530) groups. Average monthly criminal justice system costs per participant were significantly lower among TCN group ($733 ± $1130) compared with the matched group ($1276 ± $1738, Medicaid investments in an enhanced primary care program for individuals returning from incarceration are cost neutral and positively impact state budgets by reducing criminal justice system costs.The online version contains supplementary material available at 10.1186/s12913-022-07985-5. The cost of the criminal justice system in the United States (US) continues to grow exponentially , 2. The Largely unaccounted for in these estimates are the costs incurred once people are released from incarceration back to the community, especially use of the community healthcare system. People with histories of incarceration have high rates of chronic medical, mental health , and substance use disorders, which tend to be costly health conditions , 8. ManyGiven the outsized healthcare costs, state governments are looking to identify and scale programs that improve patient health and reduce the reliance on the criminal justice system for individuals with mental health and substance use disorders, while minimizing overall costs . FurtherWe present a cost analysis of the Transitions Clinic Network (TCN) program, the largest national network of primary care programs that addresses the health and social needs of people recently released from correctional systems , 20–24. This current work adds to our knowledge in its focus on the costs of TCN. We use data from a past quasi-experimental study of the TCN program in Connecticut to estimate health care and criminal justice costs associated with program participation and calculate a return on investment of the program . DocumenThe TCN is a national consortium of 45 primary care based programs that serve the health needs of individuals returning to the community from incarceration in 14 states and Puerto Rico . Each prConnecticut is a unique state to study costs of healthcare interventions targeting people just released from corrections. It is one of six states with a unified prison-jail system where jails and prisons are under the authority of the state, which provides the opportunity to ascertain costs related to both prison and jail incarceration. Connecticut’s Medicaid program is one of four with a fee-for-service model, unlike most states which have transitioned to a managed care model –30. The We used data from a previous study to assess the impact of the TCN program in New Haven, CT, on the state’s Medicaid and criminal justice system costs . These dTo guide our theoretical framework for how the TCN program may affect state Medicaid and criminal justice costs, we use the Gelberg-Anderson Behavioral Model for Vulnerable Populations to understand potential costs to the state among individuals released from correctional systems Fig.  31]. Wi. Wi31]. We assigned each study participant an index date: admission date for TCN program and date of release from the correctional system for the comparison group. We then calculated costs over a 12-month period following each participant’s index date using data from the Connecticut Department of Correction and Medicaid. We used Medicaid claims data to calculate total 12- month follow-up period cost per participant. Costs were categorized using preset Medicaid categories including dental, pharmaceutical, ED, inpatient, outpatient , primary care , medical , nursing home, and crossover . TCN participants and the comparison group were covered by Medicaid for the duration of the study period, unless participants were re-incarcerated. Medicaid costs per participant for each arm was defined as total paid amount from Medicaid claims in the 12 months following the index date. We did not have access to Connecticut Department of Mental Health and Addiction Services payment records and were unable to calculate the costs to this department or other state services including food stamps or housing in our analyses.To measure costs to Connecticut’s criminal justice system, we obtained facility-specific per diem costs of prison and jail incarceration, costs per day on probation from the Court Support Services Division, and costs per day in a halfway house or on parole from the Department of Correction (FY 2013–2016). The costs per diem while incarcerated ranged from $119 to $512 (based on facility), and the costs per diem on parole and probation were $16 and $12, respectively. Costs per diem incarcerated in jail or prison include health care costs, while costs per day on parole and probation do not, and these health care costs continued to be paid by Medicaid.We calculated the TCN program cost by summing annual salary and fringe benefit support for a community health worker employed at a federally qualified health center, salary and fringe benefit support for supervision of the community health worker, equipment  and transportation costs. These costs are not covered by state Medicaid or the criminal justice system and were only incurred by the TCN program. We estimated the cost of the TCN program per participant by dividing total annual TCN costs by the approximate number of participants one community health worker would manage in each year during the study period (2013–2016). We assumed approximately 31 patients per 1 community health worker per year given that 94 patients were enrolled in the TCN program over a time frame of 3 years (2013–2016) and that this patient load is standard for CHWs .To calculate the potential cost savings of the TCN program, we calculated the total 12-month Medicaid and criminal justice system costs for both the TCN and comparison groups. We then subtracted these total costs of the TCN group from the comparison total costs to represent the savings to the state realized by the TCN program. The return on investment was realized by dividing this savings by the total annual TCN program costs. Because Medicaid is a state-federal partnership with the federal government covering some of Medicaid costs, the return on investment we calculate is likely an underestimate of any return on investment to state. Importantly, Connecticut has the minimum Federal Medical Assistance Percentage at 50%, which is a percentage that varies across states based on the mean per capita resident income. All costs were adjusted to 2015 dollars using the Consumer Price Index.p-values of equal to or less than 0.05 statistically significant. All statistical analyses were performed using SAS version 9.4 . TCN program participants in New Haven provided informed consent for participation in this study and the linkage of their data to administrative databases. The institutional review boards of Yale University School of Medicine, Connecticut Department of Mental Health and Addiction Services, and the US Office of Human Research Protections approved this study. All research was conducted in accordance with the Declaration of Helsinki.We compared participants in each group following propensity score matching on sociodemographics, chronic conditions, and criminal justice and mental health history using t-tests and chi-square tests. To evaluate the associations between the TCN program with costs to the state Medicaid and criminal justice systems, we used t-tests to examine each group’s’ per participant per month differences in costs. To avoid making unrealistic assumptions regarding the distribution of the costs for the underlying population, we performed nonparametric bootstrapping method that applied 1000 replicated random sampling with replacement for the TCN group and the propensity-matched comparison group . For anyThe total 12-month cost of the TCN program in Connecticut was estimated to be $54,027, which was driven largely by salary support and fringe for the community health worker and supervising social worker. The estimated cost per participant per month was $146, assuming a standard patient load of approximately 31 patients per year for the community health worker  per participant for the TCN group, compared to the comparison group of $2633 (± $2788) per participant . The average monthly parole costs (TCN group $58 ± $178 per participant versus comparison $90 ± $264 per participant) and halfway house costs (TCN group $142 ± $470 per participant versus comparison $282 ± $697 per participant) were not statistically different between the two groups  and for the comparison group, $1276 (± $1738)    or pre-post trial designs, and further advances the evidence on how to best meet the needs of individuals belonging to a highly marginalized population. To be sure, other costs to the state were unmeasured, including housing, employment, and food access programs, and reflect future work that can better delineate how health system investments may affect state costs for this population. These data add to the limited evidence examining costs of healthcare interventions for individuals released from incarceration on criminal justice expenditures –27. In cPolicy makers and criminal justice and health care organizations interested in making similar investments utilizing Medicaid dollars to impact criminal justice expenditures should interpret this study in the context of four key points. First, the financial value of a TCN program depends on the baseline costs among the targeted patient pool. Given that individuals leaving incarceration generally have high health needs, there is a limited need to target the interventions to contain a high-risk patient pool. The program is offered to those in need of primary care with a chronic health condition and those over 50 years of age, which is a broad and medically complex group. Even when participation includes a broader population, and not focused on a specific disease category or a predetermined “high cost group,” the community health worker intervention returned $2.55 for every dollar invested.Second, return on investment relies critically on who is making the investment and who is receiving the return. We have presented an economic analysis from the perspective of a state, especially as it is responsible for both Medicaid and the correctional system. It is especially important to note that our findings may not generalize to other states given the unified prison-jail system and Medicaid fee-for-service model in Connecticut, nor does it account for the costs of Medicaid to the federal government. We found that the TCN program was cost neutral for Medicaid , but we would predict that such findings may be different in systems with Medicaid managed care which may incentivize prevention and value-based care.Third, this study suggests that TCN programs are beneficial, even from a narrow financial perspective. That said, the financial return on investment underestimates the true societal return because the benefits of the program related to improvements in health, remaining in the community, employment and labor market outcomes, or even spillover effects to families and communities are not assessed. For example, some interventions such as recommended cancer screening or identifying patients with chronic conditions like hypertension through community outreach may not affect healthcare costs over a 12-month time horizon, but may lead to valuable improvements in health  .Last, returns that accrue to the state from investing in TCN programs are important, especially as states have long desired to control criminal justice system and health care costs and have worked to reduce incarcerated populations before and during the COVID-19 pandemic. Before COVID-19, the Pew Center on the States surveyed 41 states and estimated that if these states reduced their recidivism rates by 10%, 635 million dollars would be saved in 1 year . In the Our findings may not generalize to other states given the Medicaid fee-for-service model in Connecticut. The relatively short 12-month, follow-up period may underestimate the health- related costs associated with medical treatment if its effects of such treatment persist over the longer run, like hepatitis C treatment. Alternatively, the additional benefits accrued beyond the 12-month window might be offset by additional costs if patients require further medical treatment. Next, we chose an index date of TCN enrollment for the TCN group and release from incarceration for the comparison group, which may introduce bias if participants in the TCN group were likely to engage in costlier care prior to enrollment in the TCN program and following their release from incarceration. That said, we found that the mean number of days for TCN participants in this sample to enroll in the program was approximately 48 days following release from incarceration, and that the overwhelming majority of individuals in the comparison group (85%) did not have any ED visits, hospitalizations, or reincarceration experiences during the first 48 days following their release from incarceration—suggesting this concern did not likely influence our findings . Thus, gOur study suggests that investment in enhanced primary care may be advantageous to state budgets as they attempt to manage the rising costs of correctional systems, while providing evidence-based care to individuals released from incarceration , 20, 21.Future studies exploring the long-term cost impacts of primary care-based programs targeted for people released from corrections are critical to health system and criminal justice system reforms. More attention should be given to investigating the mechanisms by which programs impact costs, such as how TCN participation reduced probation costs in this study. Future analyses should consider benefits specific to certain health conditions and treatment options, including but not limited to substance use disorder treatment or preventative treatment such as cancer screening. These studies will provide evidence for how to mitigate the high costs of the criminal justice system and health systems.We find that state investment in TCN programs, an enhanced primary care for individuals recently released from correctional systems, may reduce criminal justice costs, especially through decreased interactions with probation. State policies that fund community health centers to implement such a program for individuals released from incarceration could facilitate cost savings.Additional file 1.


Viruses require the small polyamine spermidine to replicate normally, in part due to its important role in ribosomal translation. We found genes in many viruses of bacteria (bacteriophages) and eukaryotic viruses encoding enzymes that produce spermidine or the structurally similar homospermidine or that release spermidine from an inactive form or render it into an inactive form. Some viruses encode single polyamine metabolic enzymes, while others encode entire or partial metabolic pathways. By biochemically analyzing bacteriophage enzymes that lacked expected activity, we discovered that the enzymes have evolved to recognize different polyamine-related substrates. Giant viruses that are known to encode many translation-related proteins encode the most complete polyamine metabolic pathways, consistent with an important role of polyamines in virus replication. S-adenosylmethionine decarboxylase (AdoMetDC/speD), spermidine synthase, homospermidine synthase, spermidine N-acetyltransferase, and N-acetylspermidine amidohydrolase. We identified homologs of the spermidine-modified translation factor eIF5a encoded by giant viruses of the Imitervirales. Although AdoMetDC/speD is prevalent among marine phages, some homologs have lost AdoMetDC activity and have evolved into pyruvoyl-dependent ADC or ODC. The pelagiphages that encode the pyruvoyl-dependent ADCs infect the abundant ocean bacterium Candidatus Pelagibacter ubique, which we have found encodes a PLP-dependent ODC homolog that has evolved into an ADC, indicating that infected cells would contain both PLP- and pyruvoyl-dependent ADCs. Complete or partial spermidine or homospermidine biosynthetic pathways are found encoded in the giant viruses of the Algavirales and Imitervirales, and in addition, some viruses of the Imitervirales can release spermidine from the inactive N-acetylspermidine. In contrast, diverse phages encode spermidine N-acetyltransferase that can sequester spermidine into its inactive N-acetyl form. Together, the virome-encoded enzymes and pathways for biosynthesis and release or biochemical sequestration of spermidine or its structural analog homospermidine consolidate and expand evidence supporting an important and global role of spermidine in virus biology.Viruses produce more viruses by manipulating the metabolic and replication systems of their host cells. Many have acquired metabolic genes from ancestral hosts and use the encoded enzymes to subvert host metabolism. The polyamine spermidine is required for bacteriophage and eukaryotic virus replication, and herein, we have identified and functionally characterized diverse phage- and virus-encoded polyamine metabolic enzymes and pathways. These include pyridoxal 5′-phosphate (PLP)-dependent ornithine decarboxylase (ODC), pyruvoyl-dependent ODC and arginine decarboxylase (ADC), arginase,  Escherichia coli, 90% of spermidine is noncovalently bound to RNA (E. coli by facilitating the queuosine modification of specific tRNA anticodon wobble bases (E. coli deleted for genes that modify the anticodon wobble position in transfer RNAs (tRNAs), spermidine becomes absolutely essential for growth  encodes an entire functional biosynthetic pathway for production of homospermidine (N-carbamoylputrescine amidohydrolase (NCPAH), and homospermidine synthase (HSS)  –18. In ansferase . A biochge ϕRSL1 . Considege ϕRSL1 .meso-diaminopimelate (DAPDC), L-arginine (ADC), L-carboxyspermidine, L-lysine and L-ornithine (L/ODC), and more specialized substrates such as N-citrylornithine . Although we did not detect likely ODC homologs in phage genomes, we did detect DAPDC , which is the ultimate biosynthetic step for L-lysine and penultimate biosynthetic step for diamine cadaverine in bacteria. To determine the likelihood of the homologs being bona fide ODCs, we constructed a phylogenetic tree  of the virus proteins together with diverse AR-fold decarboxylases of known function . The virus ODCs were limited to a robustly supported clade containing biochemically validated bacterial and eukaryotic ODCs, bifunctional L/ODCs, and ODC proteins that have recently evolved into ADCs. The phage DAPDC homologs were also limited to a strongly supported clade containing validated DAPDCs except for one Podoviridae phage protein that was found in a clade of specialized metabolism enzymes.Ornithine decarboxylase (ODC) is the first and a rate-limiting step in eukaryotic polyamine biosynthesis converting L-ornithine directly to putrescine . The forrnithine . Using PE. coli, protein purification, and kinetic analysis . Genes from Cafeteria roenbergensis virus and bovine gammaherpesvirus 6  produced proteins that were completely insoluble when expressed in E. coli, whereas those from Yellowstone Lake phycodnavirus 2 , Klosneuvirus KNV1, and Tupanvirus soda lake  produced soluble protein that was then purified and assayed for decarboxylase activity with L-ornithine, L-lysine, and L-arginine . Virus ODC homologs are found in at least five discrete clades, and notably, the bovine gammaherpesvirus ODC is found in a clade containing vertebrate ODCs. A group of Imitervirales virus ODCs encoded in Yasminevirus sp. GU-2018, Terrestrivirus 2_66, Tupanvirus soda lake, Tupanvirus deep ocean, Fadolivirus 1, Klosneuvirus KNV1, and phycodnaviruses Dishui lakes 2, 3, and 4 are found together in a robustly supported virus-only clade. A separate clade contains chlorovirus ODC-like ADC  and Yellowstone Lake phycodnavirus ODCs and is closely related to plant and green algal ODCs. The Cafeteria roenbergensis virus ODC homolog is found separately from all other viral ODCs, as is the Organic Lake phycodnavirus ODC. This phylogenetic evidence suggests that ODC has been acquired independently by viruses at least five times and from different hosts.ODC homologs from five viruses were selected for expression in arginine . No actiE. coli or Arabidopsis thaliana ADCs nor any of the bacterial aspartate aminotransferase-fold ADCs , only 28 of those genomes encode a complete AIH, indicating that putrescine biosynthesis is dispensable for replication of some chlorovirus strains. However, all chlorovirus genomes encode an intact HSS, suggesting that some chlorovirus strains rely on host-supplied putrescine to synthesize homospermidine. We did not detect any AIH or NCPAH homologs in viruses outside the chloroviruses, consistent with the other virus ODC homologs acting on L-ornithine rather than L-arginine.We detected neither any phage or virus homologs of the typical AR-fold ADC represented by the old ADCs . It was old ADCs . The chlold ADCs , and agmold ADCs . AlthougImitervirales, one from the Algavirales but none in phage . Four of the Imitervirales genomes encoding a homolog of arginase also encoded ODC: Klosneuvirus KNV1, Terrestrivirus sp. [partial open reading frame (ORF)], Yasminevirus sp. GU-2018, and Fadolivirus 1, which encodes two arginase homologs. No obvious homologs of agmatinase were detected in virus genomes, and only one was detected in a phage genome, an uncultured Caudovirales phage .The availability of L-ornithine can be limiting for putrescine biosynthesis . ArginasE. coli, the KNV1 arginase homolog was assayed with L-arginine or agmatine as a substrate using an assay based on detection of released urea. No activity was detected with agmatine, but activity with L-arginine showed a Km of 1.3 ± 0.16 mM and kcat of 2.8 ± 0.05 s−1, with a kcat/Km of 2.1 × 104 M−1 s−1. In an orthogonal approach to confirm arginase activity, the L-ornithine product of the Klosneuvirus enzyme was detected by Liquid Chromatography Tandem Mass Spectrometry (LC–MS/MS) . A low level of L-ornithine is present in the reaction with the boiled enzyme, which is derived from a low level of L-ornithine in the L-arginine stock, but the active enzyme produced an LC–MS/MS extracted ion chromatogram peak three orders of magnitude greater than the boiled enzyme. Together, these data show that the Klosneuvirus arginase homolog is a highly specific and efficient arginase.To determine whether virus arginase homologs exhibit arginase enzymatic activity, we chose to test the gene from Klosneuvirus KNV1. After expression and purification in S-adenosylmethionine decarboxylase (AdoMetDC/SpeD), a pyruvoyl cofactor-dependent enzyme that undergoes autocatalytic self-processing at a serine residue to form the covalently attached pyruvoyl cofactor  deletion strain of E. coli (BL21ΔspeD). The E. coli AdoMetDC/speD ORF is immediately downstream of the spermidine synthase (SpdSyn/speE) ORF, which is required for transfer of the aminopropyl group from decarboxylated S-adenosylmethionine to putrescine for spermidine biosynthesis. To ensure that full SpdSyn/SpeE activity was maintained in the BL21ΔspeD deletion strain, the E. coli speE ORF was coexpressed with the phage and virus speD ORFs. Restoration of spermidine accumulation confirmed bona fide AdoMetDC function for homologs from Synechococcus phage S-PM2, Thermus phage phiYs40, Sinorhizobium phage phiN3, Croceibacter phage P2559s, and eukaryotic viruses of the Algavirales: Heterosigma akashiwo virus 01 and Yellowstone Lake phycodnavirus 2 . The AdoMetDC-encoding phages infect hosts from diverse bacterial phyla: Cyanobacteria, Deinococcus-Thermus, α-Proteobacteria, and Bacteroidetes. It is notable that the eukaryotic viruses encode prokaryotic AdoMetDCs.A second rate-limiting step in spermidine biosynthesis is performed by cofactor . There a class 2 . We did E. coli BL21ΔspeD strain retains a functional speE gene; however, an AdoMetDC homolog (AHX71538) from pelagiphage HTVC201P did not restore spermidine accumulation to BL21ΔspeD when the positive control speD from Bacillus subtilis did . The host of HTVC201P is found in α-Proteobacteria SAR11 bacteria, in particular Candidatus Pelagibacter ubique, the most abundant ocean bacterial species  had a kcat/Km of 380 M−1 s−1 at 83 °C , and the purified recombinant protein was completely insoluble. However, when expressed at a lower level in E. coli, we detected a threefold higher peak for the product of arginine decarboxylation, agmatine, by Liquid Chromatography-Mass Spectrometry (LC–MS) analysis , consistent with the AdoMetDC homolog BAR31446 being a pyruvoyl-dependent ADC.We were able to determine that the  species . We susp species . No decaat 70 °C . Howeverat 83 °C , which iCa. Pelagibacter HTCC7211 belonging to the Pelagibacterales order of the α-Proteobacteria  were found to exhibit no detectable activity with L-ornithine or L-lysine , suggesting that its host cell does not have sufficient capacity to synthesize putrescine from agmatine. It is likely that the Pelagibacter and Fonsibacter ODC/ADC enzymes evolved separately from the PBCV-1 ODC/ADC, with the PBCV-1 enzyme being derived from an algal/plant origin .The ectively  and 2, iectively . PBCV-1 E. coli BL21ΔspeD . The purified recombinant proteins encoded by a Lake Baikal Myoviridae phage (LBP), a freshwater Myoviridae phage (UCP01), and three uncultured freshwater Caudovirales phages  were kinetically analyzed  and L-ornithine (kcat/Km = 160 ± 4.6 M−1 s−1). All five AdoMetDC homologs exhibited very similar pyruvoyl-dependent ODC decarboxylase activity. These pyruvoyl-dependent ODCs represent a third protein fold able to decarboxylate L-ornithine in addition to the PLP-dependent AR and aspartate aminotransferase folds.AdoMetDC homologs similar to that of the pelagiphage HTVC201P were identified in other uncultured phage genomes, and none of the corresponding ORFs were able to restore spermidine accumulation to analyzed . LBP, UCSI Appendix, Fig. S8). Three strongly supported clades are evident: The first clade contains only bona fide AdoMetDCs from bacteria, archaea, phage, and eukaryotic viruses, the second contains phage AdoMetDC homologs with ADC activity, and the third contains phage AdoMetDC homologs with ODC activity except for one bona fide AdoMetDC from Sinorhizobium phage.Bacterial and archaeal functionally validated class 1b AdoMetDC proteins were aligned with virus- and phage-encoded bona fide AdoMetDCs, and with virus and phage AdoMetDC homologs, and used to generate a phylogenetic tree  gene deletion mutant of E. coli BL21 (ΔspeE) that completely lacks spermidine when grown in chemically defined medium. Expression of each ORF restored spermidine accumulation in BL21ΔspeE, confirming that the ORFs encode functional SpdSyn , and expression of each AdoMetDC homolog restored spermidine accumulation in BL21speD.Spermidine biosynthesis requires the activity not only of AdoMetDC but also of the aminopropyltransferase SpdSyn/SpeE . We iden4,792 bp  that is akashiwo . The nat unknown , 37. To l SpdSyn B. The fuH. akashiwo virus and Dishui Lake phycodnavirus 03 SpdSyn are two, almost identical homologs from the chlorophyte volvocine alga Tetrabaena socialis , which also encodes a homolog of thermospermine synthase , and a eukaryotic-type class 2 AdoMetDC  that is most closely related to AdoMetDCs from other volvocine algae. The volvocine algae include single-celled members like Chlamydomonas reinhardtii, the 4-celled T. socialis, the 8- or 16-celled Gonium pectorale, and the colonial, differentiated multicellular Volvox species. A maximum likelihood tree of bacterial, archaeal, eukaryotic, and virus SpdSyn homologs reveals that the T. socialis SpdSyn paralogs are the closest relatives of the H. akashiwo virus and Dishui Lake phycodnavirus SpdSyns but that they are phylogenetically very distant from the other volvocine algae SpdSyns.BLASTP analysis indicates that the closest homologs of the T. socialis contains two giant endogenous virus elements that are essentially integrated copies of large viruses, and the T. socialis SpdSyn homologs are derived from those integrated virus elements , and the virus AdoMetDCs are also the bacterial-type 1b form. The uncultured Caudovirales phage SpdSyn is phylogenetically unrelated to the two virus SpdSyns and is most closely related to homologs from the γ-Proteobacteria  similar to the corresponding AdoMetDC.The genome of elements . It is tParamecium tetraurelia, homospermidine replaces the essential role of spermidine in growth (N-acetyltransferase (SSAT) recognizes only the aminopropyl side of spermidine and does not N-acetylate homospermidine . A functional HSS is encoded in Ralstonia solanacearum phage ϕRSL1 , whereas in the ϕRSL1 genome, hss and gss are unlinked  and Tupanvirus soda lake HSS  that exhibit only 29% amino acid identity to each other. The ORFs encoding each protein were expressed in either E. coli BL21, which contains putrescine and spermidine, or E. coli BL21ΔspeD, which lacks spermidine. Homospermidine was identified in benzoylated cell extracts by LC–MS detection of tribenzoylated homospermidine. The identity of homospermidine produced by Harvovirus HSS was further confirmed by comparison with a pure sample . Expression of the Harvovirus HSS and the Tupanvirus soda lake HSS  produced homospermidine in both E. coli strains in the presence (BL21) and absence (BL21ΔspeD) of native spermidine. A possible advantage for eukaryotic viruses in producing homospermidine rather than spermidine could be avoidance of host-mediated cytoplasmic N-acetylation that would otherwise eliminate functionality of host- and virus-produced spermidine.The closest homologs of the tic tree A. In theunlinked B. The phe sample C and forN-acetyltransferase (SAT) to N-acetylate spermidine, thereby abrogating spermidine functionality by forming N-acetylspermidine. As SAT is a key regulator of spermidine function, we searched by BLASTP and PSI-BLAST for homologs encoded in phage and virus genomes using sequences of characterized SATs: human SSAT, E. coli SpeG, B. subtilis BltD, and Enterococcus faecalis EfBltD . Conservatively, we identified nine candidate SATs outside of the chloroviruses .When spermidine levels are in excess, bacterial cells employ the enzyme spermidine s EfBltD –49. All speG) knockout strain of E. coli BL21, and benzoylated cell extracts were analyzed by LC–MS to detect dibenzoylated N-acetylspermidine . Peaks for dibenzoylated N-acetylspermidine (m/z = 396.2) and its sodium adduct (m/z = 418.2) confirmed SAT activity of seven phage and one virus proteins. In the E. coli BL21ΔspeG strain lacking SAT activity, a small extracted ion chromatogram peak was present at 3.28 min, whereas the pure N-acetylspermidine peak and the parental strain N-acetylspermidine peak eluted at 3.36/3.33 min. The highest level of N-acetylspermidine accumulation was obtained with the plasmid-borne E. coli speG gene. The other SATs that caused an accumulation of N-acetylspermidine were derived from Erwinia phages vB_EamM_Caitlin and phiEaH2, Bacillus phage SPBc2, and Streptococcus phage phi-m46.1  and from Klebsiella phage ST13-OXA48phi12.4, Lactococcus phage bIL311, Escherichia phage vB_EcoM-Goslar, and the eukaryotic virus Pithovirus LCPAC302 (Pithoviridae) . Expression of the candidate SAT ORF from Salmonella phage SPN3US  did not result in N-acetylspermidine accumulation.To assess SAT activity, each protein was expressed in an SAT  from the α-proteobacterium Mycoplana ramosa . Candidate APAH proteins from Klosneuvirus KNV1 (ARF11327), Tupanvirus soda lake (QKU35181), and Hyperionvirus sp. (AYV83810) were selected for expression in E. coli, purification, and kinetic analysis of substrate specificity.In eukaryotic cells, spermidine is required by RNA and DNA viruses for replication , and thia ramosa  to searcN1-acetylspermidine , whereas the histone acetyltransferase P300/CBP-associated factor (P/CAF) acetylates the aminobutyl side to form N8-acetylspermidine, which has also been identified in Acanthamoeba culbertsoni . Relative efficiency (kcat/Km) of the APAH enzymes with N1-acetylspermidine, N8-acetylspermidine, and N-acetylputrescine was found to be as follows: M. ramosa (100/25/91), Tupanvirus soda lake (100/120/29), Klosneuvirus KNV1 (100/81/10), and Hyperionvirus sp. (100/25/0.2). The three virus APAH proteins preferred N1- and N8-acetylspermidine to N-acetylputrescine, whereas the bacterial enzyme exhibited almost equal preference for N1-acetylspermidine and N-acetylputrescine.In mammalian cells, SSAT acetylates only the aminopropyl side of spermidine to form bertsoni . The pot control . APAH acN1-acetylspermidine or N-acetylputrescine, and the released polyamine was detected by LC–MS after benzoylation . A confounding factor was that the acetylated polyamine stocks contained a background level of free polyamine, but the virus APAH caused the release of 10-fold more spermidine and fivefold more putrescine from the corresponding acetylated polyamines, which is similar to the ratio detected by acetate release.To cross-validate the substrate preferences of the virus APAH enzymes, the Tupanvirus soda lake  protein N-acetylspermidine amidohydrolases, releasing spermidine from N1- and N8-acetylspermidine. The ability of the virus N-acetylspermidine amidohydrolases to act on both N1- and N8-acetylspermidine is in contrast to the vertebrate histone deacetylase HDAC10 enzyme that deacetylates only N8-acetylspermidine . No eIF5a homolog was detected among phage proteins. Although an eIF5a homolog is present in Tupanvirus deep ocean, the Tupanvirus soda lake genome homolog contains a frameshifting mutation and is unlikely to be active. It is notable that every Imitervirales virus that encodes eIF5a also encodes polyamine biosynthetic enzymes unlike the Algavirales genomes.We screened virus proteins by BLASTP for homologs of the eIF5a-modifying enzyme DHS, but none were detected. In contrast, we detected eIF5a homologs encoded in five The presence of more than a single polyamine metabolic enzyme encoded in virus genomes is found primarily in eukaryotic viruses, although one phage genome we identified encodes AdoMetDC and SpdSyn, constituting a putrescine to spermidine biosynthetic module . By far Imitervirales and Algavirales genomes that encode polyamine metabolic enzymes. The Imitervirales encode either a partial or complete pathway from arginine to homospermidine via arginase, ODC, and HSS. They may also encode an APAH enzyme to release spermidine from sequestered N-acetylspermidine and may encode eIF5a. We characterized the L-arginine to putrescine route encoded by Klosneuvirus KNV1 (arginase and ODC), the L-ornithine to homospermidine route in Tupanvirus soda lake (ODC and HSS), and the N-acetylspermidine to spermidine route in both Klosneuvirus KNV1 and Tupanvirus soda lake (APAH). Single metabolic enzymes were characterized from Harvovirus (HSS) and Hyperionvirus (APAH). The most complete representation of polyamine metabolism is encoded in Fadolivirus 1, which encodes homologs of arginase, ODC, HSS, APAH, and eIF5a. In principle, Fadolivirus can reprogram host metabolism to convert L-arginine to deoxyhypusinated eIF5a (using host DHS) via L-ornithine, putrescine, and homospermidine or from N-acetylspermidine via spermidine. The Algavirales encode a complete or partial route from L-ornithine to spermidine  but would not be able to release spermidine from N-acetylspermidine. Presumably, the Imitervirales genomes that do not encode HSS but which do encode the ability to produce putrescine must then rely on host cell AdoMetDC and SpdSyn to produce spermidine rather than homospermidine. It is intriguing that both the Imitervirales and Algavirales genomes encode bacterially derived polyamine metabolic pathways, particularly HSS and class 1b AdoMetDC.Two distinct polyamine metabolic pathways can be distinguished between the N-acetylspermidine to spermidine, and SAT converts spermidine to N-acetylspermidine. Thus, in the virosphere, there are alternative ODC and ADC pathways to putrescine, alternative forms of ODC or ADC, alternative solutions to polyamine biosynthesis (spermidine or homospermidine), an alternative to spermidine biosynthesis, i.e., the release of spermidine from host N-acetylspermidine, and a way to control free spermidine levels through N-acetylation. Together, these different virus-encoded auxiliary metabolic strategies for spermidine control illuminate its prominent role in the virus replication cycle and by inference indicate the central role of spermidine in the core physiological processes of the host cell.Bacterial and eukaryotic viruses often encode auxiliary metabolic enzymes that subvert host metabolism to enable viral replication . The auxWhy would giant viruses be more likely to encode polyamine metabolic enzymes? It is known that they encode more translation-related genes , suggestThe bacteriophage-encoded AdoMetDC homologs that have been neofunctionalized as pyruvoyl-dependent ODCs evolved either as virus-encoded genes or were acquired as neofunctionalized AdoMetDCs from ancestral bacterial hosts. Pyruvoyl-dependent ODCs have not been reported in bacteria or any other organism, and we are currently seeking such enzymes in bacteria and archaea.SI Appendix. These include protein expression and purification, enzyme assays and reactions, mass spectrometry, and phylogenetic analysis.Detailed descriptions of materials and experimental protocols can be found in Appendix 01 (PDF)Click here for additional data file.


In a biomedical diagnosis with a limited sample volume and low concentration, droplet-based microfluidics, also called digital microfluidics, becomes a very attractive approach. Previously, our group developed a magnetic-beads-based digital microfluidic immunoassay with a bead number of around 100, requiring less than 1 μL of sample volume to achieve a pg/mL level limit of detection (LOD). However, the bead number in each measurement was not the same, causing an unstable coefficient of variation (CV) in the calibration curve. Here, we investigated whether a fixed number of beads in this bead-based digital microfluidic immunoassay could provide more stable results. First, the bead screening chips were developed to extract exactly 100, 49, and 25 magnetic beads with diameters of less than 6 μm. Then, four calibration curves were established. One calibration curve was constructed by using varying bead numbers (50–160) in the process. The other three calibration curves used a fixed number of beads, . The results indicated that the CVs for a fixed number of beads were evidently smaller than the CVs for varying bead numbers, especially in the range of 1 pg/mL to 100 pg/mL, where the CVs for 100 beads were less than 10%. Furthermore, the calculated LOD, based on the composite calibration curves, could be reduced by three orders, from 3.0 pg/mL (for the unfixed bead number) to 0.0287 pg/mL (for 100 beads). However, when the bead numbers were too high (more than 500) or too low (25 or fewer), the bead manipulation for aggregation became more difficult in the magnetic-beads-based digital microfluidic immunoassay chip. The immunoassay chip integrates various immunodetection steps in a tiny device. Among the integration techniques, the microfluidics-based immunoassay chip is a popular device because of the small liquid volumes, rapid turnaround time, and high portability offered by microfluidics ,4,5,6,7.In comparison to channel-based microfluidics, droplet-based microfluidics, also known as digital microfluidics (DMF) to indicate the “digitalization” of the liquids into droplets, is outstanding in terms of a small sample volume requirement ,23,24,25To our knowledge, flow cytometry  is curreThe extra time needed in this magnetic-beads-based DMF immunoassay with a fixed number of beads is the time required to prepare the specified bead number, in advance and in batches. The total process time for the current DMF immunoassay chip is identical to that of the previous DMF immunoassay with an unfixed number of beads. The magnetic bead-screening chip allowed the magnetic beads in the beads droplet to fall into a fixed number of wells on the chip. An external magnet was used to move and attract the magnetic beads into falling into the wells. Afterwards, excessive magnetic beads were removed to achieve the required quantitative effect. The measured size of the magnetic beads was between 4.5 μm to 5.5 μm. In order to allow only one magnetic bead to fall into one well, the size of the circular hole of the well structure was designed with a diameter of 6 μm, a depth of 8 μm, and hole-to-hole spacing of 15 μm. The bead screening chip was made of PDMS by micro-molding technology. The micro-mold was fabricated by the photolithography process on a silicon wafer. The negative photoresist SU-8 3005 was used to make an 8 μm thickness pattern of micro-mold. Then, the PDMS casting was performed by mixing a PDMS base with a curing reagent, with a 10:1 ratio. After curing at 80 °C for 2 h, the demolding was implemented to complete the bead-screening chip.For the capture and removal of the magnetic beads, a beads droplet was first placed onto the screening chip by a pipet. Then, the pipet tip was used to drag the beads droplet backward and forward in the wells with a magnet under the chip until the wells were completely filled with the magnetic beads, as shown in The function of the transporting and mixing area was carrying out the reagent’s transportation, mixing, and incubation; the washing area provided for the removal of residual reagents of the magnetic beads after the immunoreaction; the detection area was where the beads were aggregated for fluorescent detection after the immunoreaction. The immunoreaction consisted of five steps in sequence. 2; the electrodes for the beads droplet and the fluorescent reporter droplet were both 2236 × 2236 μm2. In order to have good washing efficiency without bead loss in the droplet cutting and washing steps, the electrode size for the transportation and mixing area was 707 × 707 μm2, with a minimum volume ratio of 5:1 [In a parallel-plate EWOD chip, when the gap between the top plate and the bottom plate is constant, the droplet volume is proportional to the area of the electrode. In the standard ELISA kit, the sample solution and the detection antibody solution are both 80 µL, and the fluorescent reporter solution is 50 µL. By keeping the same volume ratio, 8:5, the electrodes for handling the sample droplet and the detection antibody droplet were both 2828 × 2828 μmo of 5:1 . For bet2O3 deposition, and CYTOP deposition. The ITO layer was patterned as the electrode; the SU-8 and the Al2O3 layer were the dielectric layers; and the CYTOP was the hydrophobic layer. The ITO patterning was performed through lithography and ITO etching. The etching solution was aqua regia (NHO3:HCl:H2O2 = 1:3:6). The 2.8 μm SU-8 was deposited without patterning. The 50 nm Al2O3 deposition was carried out through the in-house 8-inch ALD system developed by the Taiwan Instrument Research Institute with precursors Trimethyl aluminum (TMA) and H2O at process temperature 200 °C. The 700 nm CYTOP deposition was processed through spin-coating and baking. Then, the top plate was fabricated through the CYTOP deposition on the pattern-free ITO glass. Finally, by assembling the top plate and the bottom plate, the magnetic-beads-based digital microfluidic chip was completed. The gap between the two parallel plates was controlled by a spacer with a thickness of 65 μm, except at the detection step where the gap was reduced to 10 μm to avoid the vertical stacking of beads.The magnetic-beads-based digital microfluidic chip was made by assembling the top plate and the bottom plate, as shown in Before immunoreaction proceeded, human IL-1β antigens were diluted in several sample solutions with different concentrations of 0.1 pg/mL, 1 pg/mL, 10 pg/mL, 25 pg/mL, 50 pg/mL, 75 pg/mL, and 100 pg/mL. A human IL-1β detection antibody solution with a concentration of 20 μg/mL was prepared with PBS + PL (Pluronic F-127/PBS = 6 mg/mL). As for the fluorescent reporter, a 2 mg/mL stock solution was prepared by using DI water, then diluting it 100 times with PL + PBS (Pluronic F-127/PBS = 6 mg/mL) to form a fluorescent reporter solution. 6 microspheres) was magnetically applied to remove the supernatant. Beads were washed twice with activation buffer, and resuspended with an activation mix (100 μL) containing N-hydroxysulfosuccinimide sodium salt  and 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide hydrochloride  in an activation buffer. The reaction mixture was incubated for 20 min near 23 °C in darkness in an end-over-end rotator, followed on washing twice with a coupling buffer (500 μL). The activated beads were then incubated with an antibody solution  for at least 2 h near 23 °C in darkness in an end-over-end rotator. The beads were eventually washed twice with a washing buffer (500 μL) and resuspended in PBS . Coated beads were stored in darkness at 2 °C. The antigens used to establish the calibration curves were the recombinant human IL-1β from R&D Systems, USA. The fluorescent reporter was R-PE Streptavidin, from R&D Systems, USA. The absorption and emission wavelengths of the fluorescence fell in the range of 488–494 nm and 515–545 nm, respectively. The Pluronic F-127 and the BSA also came from R&D Systems, USA.The magnetic beads were COOH-modified magnetic beads . The captured antibodies, referring to the human IL-1β antibody, came from R&D Systems, Minneapolis, MI, USA. The conjugation between the captured antibody and the magnetic bead was performed by MagQu Co., Ltd., New Taipei, Taiwan, according to a modified protocol . Briefly2 values were all above 0.99, which meant that all four calibration curves fit the data well. However, the standard deviation of the unquantified magnetic beads was much larger, and the intensity distribution of the different concentrations even overlapped. The standard deviations of the calibration curves developed by the other three fixed numbers of magnetic beads were significantly smaller, and the fluorescence intensity distribution of different concentrations did not overlap. In establishing the calibration curve for human IL-1β, the known concentrations of human IL-1β samples were 0 pg/mL, 0.1 pg/mL, 1 pg/mL, 10 pg/mL, 25 pg/mL, 50 pg/mL mL, 75 pg/mL, and 100 pg/mL. There were four calibration curves, including the curve for an unfixed number of magnetic beads (50–160) and the curves for fixed numbers of magnetic beads . In all four calibration curves, the experiments were repeated three times at each concentration for standard deviation and mean value calculations. Using the composite calibration curves, the calculated LODs for variable beads, 100 beads, 49 beads, and 25 beads were 3.0 pg/mL, 0.0287 pg/mL, 0.0255 pg/mL, and 0.0508 pg/mL, respectively. The method to calculate the LOD value was the typical blank determination method, in which the concentration was calculated by the calibration curve with the fluorescent intensity of the mean value plus three standard deviations at blank. The LOD was reduced by about three orders from 3.0 pg/mL (for varying numbers of beads) to 0.0287 pg/mL (for 100 beads).While performing fluorescence measurement, the magnetic beads were first gathered to enhance the fluorescence intensity . In ordeWhen the number of magnetic beads was more than 500, as shown in In this study, bead-screening chips were successfully developed to quantify the required magnetic beads. By fixing 100, 49, or 25 magnetic beads, the fluorescence intensity of the human IL-1β samples were measured with concentrations at 0 pg/mL, 0.1 pg/mL, 1 pg/mL, 10 pg/mL, 25 pg/mL, 50 pg/mL, 75 pg/mL, and 100 pg/mL to establish four composite calibration curves. Compared to the unfixed number of beads (50–160), with the sample size of three at each concentration in all four calibration curves, there was a significant decrease in the CVs for the fixed number of magnetic beads. Among the three different numbers of fixed magnetic beads, the CV values for 100 beads were best. In the range of 1.0 pg/mL to 100 pg/mL, the CVs for 100 beads were all less than 10%. When the number of beads was not fixed, the fluorescence intensity distribution at different concentrations may have overlapped, while there was no overlapping area with a fixed number of beads. Furthermore, the calculated LOD based on the composite calibration curves could be reduced by three orders, from 3.0 pg/mL (for the unfixed number of beads) to 0.0287 pg/mL (for 100 beads).When there were 25 magnetic beads, the magnetic beads sometimes could not be aggregated properly due to insufficient magnetic force. When the number was more than 500, the magnetic beads easily became stuck and hard to move for complete aggregation. These results showed that using a fixed number of magnetic beads can evidently reduce the CV values and detect the concentration of the analyte to 0.1 pg/mL, which effectively improves the previous magnetic-beads-based digital microfluidic immunoassay. The use of smaller magnetic beads might reduce the chance of the beads becoming stuck in the gap, but the dispersed small magnetic beads will be harder to manipulate and aggregate by the external magnet. The optimal magnetic bead size for our DMF immunoassay needs to be further investigated in the future.