IMGT® is based on a standardized nomenclature of the immunoglobulin (IG) and T cell receptor (TR) genes and alleles from fish to humans and on the IMGT unique numbering for the variable (V) and constant (C) domains of the immunoglobulin superfamily (IgSF) of vertebrates and invertebrates, and for the groove (G) domain of the major histocompatibility (MH) and MH superfamily (MhSF) proteins. IMGT® comprises 7 databases, 17 tools and more than 25,000 pages of web resources for sequences, genes and structures, based on the IMGT Scientific chart rules generated from the IMGT-ONTOLOGY axioms and concepts. IMGT® reference directories are used for the analysis of the NGS high-throughput expressed IG and TR repertoires  and for bridging sequences, two-dimensional (2D) and three-dimensional (3D) structures. This manuscript focuses on the IMGT®Homo sapiens IG and TR loci, gene order, copy number variation (CNV) and haplotypes new concepts, as a paradigm for jawed vertebrates genome assemblies. IMGT The definition of the IMGT-LOCUS-UNIT and its qualifiers are given in Macaca mulatta (rhesus monkey) IGH (IMGT000064), IGL (IMGT000062) and IGK (IMGT000063) have recently been created and annotated for the Mmul_10 assembly (GCF_003339765.1). Three IMGT/LIGM-DB locus reference sequences for the FT IMGT-LOCUS-UNIT 1..1300711FT /IMGT_locus_3prime_borne=“RSPH14”FT /IMGT_locus_3prime_gene=“IGLC7”FT /IMGT_locus_5prime_borne=“TOP3B”FT /IMGT_locus_5prime_gene=“IGLV(IV)-127”FT /IMGT_locus_ID=”Macmul_IGL_2”FT /IMGT_locus_chromosome=”10”FT /IMGT_locus_length=“1300711 bp”FT /IMGT_locus_name=“Macaca mulatta IGL”FT /IMGT_locus_orientation=“reverse (REV)”FT /IMGT_locus_positions=“CM014345.1FT (29621424-30922134 complement)”As an example, the IMGT000062 qualifiers for the IMGT-LOCUS-UNIT (1..1300711) of the Macmul_IGL_2 (IMGT_locus_ID) are the following: ® http://www.imgt.org (accessed on 20 February 2022), IMGT Web resources > IMGT Repertoire (IG and TR) > 1. Locus and genes > 3. Locus descriptions > Locus bornes: IGH, IGK, IGL, TRA, TRB, TRG, TRD). If IMGT bornes are not yet identified or are too distant to be included in the locus sequence, a minimal 10 kb sequence is added upstream of the first IG or TR gene in 5′ and/or downstream from the last IG or TR gene in 3′. A preliminary overview of the locus IG and TR 5′ and 3′ bornes is shown in The IMGT Locus 5′ borne (IMGT_locus_5prime_borne) and the IMGT Locus 3′ borne (IMGT_locus_3prime_borne)  are defiThe section “LOCALIZATION IN GENOME ASSEMBLIES”  integratHomo sapiens), NCBI gene ID (if available), NCBI Mmul_10 Primary Assembly Chromosome (NC) accession number and IMGT label positions in the NC sequence. The results page for the query Species: Macaca mulatta|AG07107 (Rhesus monkey) Locus: IGH B provideThe list of genes known to belong to the locus but not localized (NL) in the assembly is also provided in this section as this may correspond to polymorphism by copy number variation, insertion/deletion, or gaps in the assembly.Homo sapiens IGH locus is located on chromosome 14, at the telomeric extremity of the long arm, at band 14q32.33 [Homo sapiens IGH locus spans 1250 kb [The IGH Locus Gene orderHomo sapiens IGH locus from its 5′ end to its 3′ end (® http://www.imgt.org (accessed on 20 February2022 ), IMGT Repertoire (IG and TR) > 1. Locus and Genes > 3. Locus descriptions > Locus gene order > IGH. The number ‘0’ indicates that the relative position is unknown. The three most recently identified genes  are numbered as insertions with a dot  in the gene order to preserve comparisons with the reference ruler adopted in the description of the Homo sapiens IGH polymorphisms [The relative positions (locus gene order) of the IGHV, IGHD, IGHJ and IGHC genes are shown in the s 3′ end . Gene orThe gene IGHV(II)-20-1 (gene order 129) is an exception as being only represented by a V-RS. Its V-REGION is replaced by the Alu Homo-sapiens_IGH_Alu-20-1 preceded by an undetermined region and the Alu Homo-sapiens_IGH_Alu-20-3 (AC245166 accession number in IMGT/LIGM-DB).2.Homo sapiens IGH locusIMGT copy number variation (CNV) nomenclature and definition illustrated with the ® http://www.imgt.org (accessed on 20 February 2022), IMGT Web resources > IMGT Repertoire (IG and TR) > Locus gene order > Human (Homo sapiens) IGH). Seven CNV are displayed in Homo sapiens), the locus  and the CNV number . A CNV is delimited by a 5prime gene and a 3prime gene. Copy number variations (CNV) ,5,141 arHomo sapiens IGH CNV1′ is ‘IGHV(17-20)7’ , a dash and the order of the end gene (the gene which precedes the CNV-3prime), then the total number of genes involved in the CNV  if present) followed, between parentheses, by the number of IG or TR per functionality (and the number of RPI if present). For instance, the definition of ‘’ . The let3.Homo sapiens IGH locusIMGT CNV haplotypes illustrated with the ® http://www.imgt.org (accessed on 20 February 2022), IMGT Web resources > IMGT Repertoire (IG and TR) > 1. Locus and genes > 2. Locus representation > IGH: Human). The horizontal main line is conventionally referred to as ‘haplotype A’. IMGT CNV haplotypes are described based on the variability of the number of genes present for a given CNV. The description of the CNV is achieved by comparison with the IGH locus ,5 (IMGT®Homo sapiens IGH CNV3 IGHV(87-112)26, for which six haplotypes A to F [A well-characterized CNV example is the s A to F ,141 corrIGHV87-1126 Polymorphism by insertion/deletion between IGHV4-34 and IGHV4-28 (haplotypes A to F) on chromosome 14 (14q32.33) (http://www.imgt.org/IMGTrepertoire/index.php?section=LocusGenes&repertoire=locus&species=human&group=IGH/haplotypes#locus) (accessed on 20 February 2022).The graphical representation of the CNV3 is available at Human (Homo sapiens IGH locus on chromosome 14 (14q32.33) is characterized by a remarkable IGH CNV, the CNV7 IGHC(203-211)9 with seven haplotypes A to G , IMGT Repertoire (IG and TR) > 1. Locus and Genes > 3. Locus descriptions > Locus gene order > IGK) [IGK Gene order  is accorr > IGK) .Homo sapiens IGK CNV1 IGKV(1-36)36 corresponds to two haplotypes , IMGT Repertoire (IG and TR) > 1. Locus and Genes > 3. Locus descriptions > Locus gene order > IGL.IGL gene order is according to the IMGT Locus gene order   is located upstream of PRSS58  opposite orientation relative to the locus, identified 41 kb upstream of TRBV1 (P). EPHB6 (EPH receptor B6)  has been identified 41 kb downstream of TRBV30 (F), the most 3′ gene in the locus.® http://www.imgt.org (accessed on 20 February 2020), IMGT Repertoire (IG and TR) > 1. Locus and Genes > 3. Locus descriptions > Locus gene order > TRB. TRB gene order is according to the IMGT Locus gene order (IMGTHomo sapiens TRB CNV1 TRBV(11-14)4 (Homo sapiens TRB CNV2 T4-T8(70-74)5(nr) involves trypsinogene-like genes localized between TRBV29-1 and TRBD1  . A seconnd TRBD1 . Two hapHomo sapiens TRA locus is located on chromosome 14, on the long arm, at band 14q11.2 [Homo sapiens TRA spans 1000 kb [The  14q11.2 . The ori 1000 kb  , the most 5′ gene in the locus. DAD1 (defender against cell death) (3’ borne) has been identified 13 kb downstream of TRAC (F), the most 3’ gene in the locus . ® http://www.imgt.org (accessed on 20 February 2020), IMGT Repertoire (IG and TR) > 1. Locus and Genes > 3. Locus descriptions > Locus gene order > TRA/TRD. TRA/TRD gene order is according to the IMGT Locus gene order , IMGT Repertoire (IG and TR) > 1. Locus and Genes > 3. Locus descriptions > Locus gene order > TRG. TRG gene order is according to the IMGT Locus gene order  (5′ borne) has been identified 16 kb upstream of TRGV1 (ORF), the most 5′ gene in the locus. STARD3NL  (3′ borne) has been identified 9,4 kb downstream of TRGC2 (F), the most 3′ gene in the locus.® http://www.imgt.org (accessed on 20 February 2022), IMGT Repertoire (IG and TR) > 1. Locus and Genes > 3. Locus descriptions > Locus gene order > TRG. The total number of TRG genes per haploid genome is 19 or 22 of which 11 to 13 are functional  (accessed on 20 February 2022) are published on the IMGT site after completion of the biocuration of new IG and TR loci, genes and/or alleles. IMGTIMGT/LIGM-DB: the creation of the IMGT Locus reference , if relevant, and updates of annotation of previous references sequences.IMGT/GENE-DB: number of genes and alleles per group , entered in the database, following the annotation of the IMGT Locus reference (with a link to IMGT/GENE-DB update), and FASTA file of the sequences per group.Web resources: creation or update of up to 18 web pages in IMGT Repertoire (IG and TR) . These wThe lists of the data available for validation by IUIS NOM IMGT-NC comprise:The IUIS NOM validation consists in the control of the conformity of the data to the IUIS NOM IMGT-NC requirements for nomenclature assignment and to the IMGT Scientific chart rules based on CLASSIFICATION  NUMEROTATION (IMGT unique numbering), DESCRIPTION (labels) and that of their presentation in the IMGT Repertoire (IG and TR) ,2,3,114.Canis lupus familiaris) and Rhesus monkey (Macaca mulatta) have been fully annotated. Species for which most of the IG and TR loci are annotated include cat (Felis catus), bovine (Bos taurus), sheep (Ovis aries) and goat (Capra hircus). Standardized IMGT biocuration led to the comparative study of the T cell receptor beta locus of veterinary species [Homo sapiens TRB locus and to a comparative analysis of Bos taurus and Ovis aries TRA/TRD loci [The seven IG and TR loci of the dog , the global reference in immunogenetics and immunoinformatics created by Marie-Paule Lefranc , provides a unique scientific and computing frame for bridging loci, genes, alleles, sequences and structures of the IG or antibodies, TR and MH of the adaptive immune responses in humans and in other jawed vertebrates [IMGTtebrates ,181,182.tebrates ,189,190,tebrates ,192, sustebrates ,197,198,tebrates ,205,206,tebrates ,212,213,tebrates ,50,196. tebrates ,140.


The evolution of influenza viruses is fundamentally shaped by within-host processes. However, the within-host evolutionary dynamics of influenza viruses remain incompletely understood, in part because most studies have focused on infections in healthy adults based on single timepoint data. Here, we analyzed the within-host evolution of 82 longitudinally sampled individuals, mostly young children, infected with A/H1N1pdm09 or A/H3N2 viruses between 2007 and 2009. For A/H1N1pdm09 infections during the 2009 pandemic, nonsynonymous minority variants were more prevalent than synonymous ones. For A/H3N2 viruses in young children, early infection was dominated by purifying selection. As these infections progressed, nonsynonymous variants typically increased in frequency even when within-host virus titers decreased. Unlike the short-lived infections of adults where de novo within-host variants are rare, longer infections in young children allow for the maintenance of virus diversity via mutation-selection balance creating potentially important opportunities for within-host virus evolution. Influenza A viruses (IAV) are some of the most prevalent human respiratory pathogens, infecting hundreds of millions of people worldwide each year. Because of the high error rates of the viral RNA polymerase complex, de novo mutants are generated as the viruses replicate within infected hosts . HoweverFor seasonal IAVs at the global population level, antibody-mediated immune selection pressure from natural infection or vaccination positively selects for novel antigenic variants that facilitate immune escape, resulting in antigenic drift . HoweverSeparate from seasonal IAVs, zoonotic IAVs constantly pose new pandemic threats. Prior to becoming human-adapted seasonal strains, IAVs are introduced into the human population from an animal reservoir through the acquisition of host-adaptive mutations, sometimes via reassortment, resulting in global pandemics such as the 2009 swine influenza pandemic . In the At the individual level, the within-host evolutionary dynamics of the pandemic A/H1N1pdm09 virus, particularly in the early stages of the 2009 pandemic, have been relatively underexplored. To date, the only within-host genetic diversity analysis of A/H1N1pdm09 viruses during the initial phase of the pandemic was based on mostly single-timepoint samples collected within ~7 days post-symptom onset . DespiteHere, we deep-sequenced 275 longitudinal clinical specimens sampled from 82 individuals residing in Southeast Asia between 2007 and 2009 that were either infected with seasonal A/H3N2 or pandemic A/H1N1pdm09 viruses. By analyzing minority variants found across the whole IAV genome, we characterized the evolutionary dynamics of within-host virus populations in these samples collected up to 2 weeks post-symptom onset.The A/H3N2 virus samples were collected from 51 unlinked individuals as part of an oseltamivir dosage trial . 48 of tWe used the number of minority intra-host single-nucleotide variants  iSNVs were calculated by normalizing the summation of observed iSNV frequencies with the number of available sites and time since symptom onset . The overall within-host evolutionary rates of A/H3N2 viruses observed here are in the same order of magnitude  and neuraminidase (NA) gene segments .As observed in a previous study using different data , prematuThe premature stop codon mutations were mostly found at low frequencies for both influenza subtypes <10%; . The excDespite the dominance of purifying selection in seasonal A/H3N2 intra-host viral populations, we detected several nonsynonymous variants of interest. Amino acid variants emerging in the HA and NA proteins were discussed in a previous work . D101N/GNP-G384R emerged in 16 unlinked patients infected by A/H3N2 virus. Even though G384R did not become the majority variant in any of these individuals , the variant emerged around days 4–5 post-symptom onset and mostly persisted within each individual for the rest of sampled timepoints. G384R is a stabilizing mutation in the A/Brisbane/10/2007 A/H3N2 virus NP background  that is Using a maximum-likelihood (ML) approach to reconstruct and estimate the frequencies of the most parsimonious haplotypes of each gene segment, we computed linkage disequilibrium and found evidence of potential epistatic co-variants to NP-G384R in the A/H3N2 virus populations of multiple individuals . When ann humans .For the pandemic A/H1N1pdm09 viruses, most of the nonsynonymous variants were found singularly in individual patients . PutativWe also found oseltamivir resistance mutation H275Y  in the NTo investigate the evolutionary pressures that likely underpin the observed within-host dynamics of A/H3N2 viruses in young children , we perfOur simulations used a simple within-host evolution model represented by a binary genome that distinguishes between synonymous and nonsynonymous loci. Given that the estimated transmission bottleneck sizes for seasonal A/H3N2 viruses  are narrulations .Multiple next-generation sequencing studies have found little evidence of positive selection in seasonal influenza virus populations of acutely infected individuals . Recent Viral shedding duration is often longer in young children infected with seasonal influenza virus compared to otherwise healthy adults . ChildreSimilar to the aforementioned within-host studies, the A/H3N2 virus population within these children was characterized by low genetic diversity and dominated by purifying selection early in the infection. Due to a lack of antibody response against the antigenic regions of HA , it is uThrough simulations of a within-host evolution model, we investigated the hypothesis that in the absence of any positive selection, the accumulation of nonsynonymous iSNVs was a result of their neutral or only weakly deleterious effects and the expanding within-host virion population size during later timepoints in longer infections of naïve young children such that mutation-selection balance was reached. In contrast, this balance was not detected in otherwise healthy older children or adults with short-lived influenza virus infections lasting no more than a week where de novo nonsynonymous iSNVs are rarely found . The maiInterestingly, we also observed G384R in the minority virus population of 15 other unlinked individuals. Besides improving NP stability, G384R restores recognition by HLA-B*2705-positive NP-specific CTLs . The NP In contrast, we found a substantially higher fraction of nonsynonymous variants in the within-host virus populations of individuals infected with A/H1N1pdm09 virus during the pandemic. Owing to the low number of A/H1N1pdm09 virus samples and different next-generation sequencing platforms used to sequence samples of the two virus subtypes and consequently differences in base calling error rates and depth of coverage , we wereinimized . Importainimized .Given the tight estimated transmission bottleneck size (Appendix A4), the relatively large number of iSNVs identified at the start of symptom onset and simulations of within-host evolution  and 6D, For both A/H3N2 and A/H1N1pdm09 virus samples, nonsense iSNVs resulting in premature stop codons were found to accumulate within host, even though only at low proportions. The accumulation of premature stop codon mutations further suggests that while purifying selection dominates within-host influenza virus populations, it may not be acting strongly enough to completely purge these lethal nonsense mutations . AdditioOur study has several limitations. The number of iSNVs identified can potentially be biased by variations in sequencing coverage . As suchtatistic  may be atatistic . ComputiTo ensure accurate measurements of virus diversity in intra-host populations, we would also need to be certain that the estimated variant frequencies precisely reflect the distributions of variants that comprise the sampled virus populations. The inferred variant frequencies can be significantly distorted if virus load is low . As suchWe also performed additional checks to ensure that our results were not driven by potential PCR and/or technical artifacts. First, we excluded all iSNVs found under the 75th percentile of frequency range of A/H3N2 variants that were found in only one of the overlapping amplicons. We then recomputed the daily within-host evolutionary rates with the remaining iSNVs  and founFinally, most study participants received oseltamivir during the course of their infections . AlthougTo conclude, we presented how intra-host populations of seasonal and pandemic influenza viruses are subjected to contrasting evolutionary selection pressures. In particular, we showed that the evolutionary dynamics and ensuing genetic variation of these within-host virus populations changes during the course of infection, highlighting the importance for sequential sampling, particularly for longer-than-average infections such as those in the young children studied here.The A/H3N2 virus samples were collected from 52 patients between August 2007 and September 2009 as part of an oseltamivir dosage trial conducted by the South East Asia Infectious Disease Clinical Research Network (SEAICRN), which is detailed in a previous work . BrieflyLibrary preparation and viral sequencing protocols performed on these A/H3N2 virus samples are elaborated in detail in The A/H1N1pdm09 virus samples were obtained as part of a household-based influenza virus cohort study that was also performed by SEAICRN. The study was conducted between July and December 2009, involving a total of 270 households in Ha Nam province, Vietnam . SimilarGCCGGAGCTCTGCAGATATCAGCRAAAGCAGG-3′), common-uni12G (5′-GCCGGAGCTCTGCAGATATCAGCGAAAGCAGG-3′), and common-uni13 (5′-CAGGAAACAGCTATGACAGTAGAAACAAGG-3′). The first RT-PCR mixture contained the primers common-uni12R and common-uni13. The second RT-PCR mixture contained the primers common-uni12G and common-uni13, which greatly improved the amplification of the PB2, PB1, and PA segments. Reactions were performed using the One-Step RT-PCR kit High Fidelity (Invitrogen) in a volume of 50 μl containing 5.0 μl eluted RNA with final concentrations of 1xSuperScript III One-Step RT-PCR buffer, 0.2 μM of each primer, and 1.0 μl SuperScript III RT/Platinum Taq High Fidelity Enzyme Mix (Invitrogen). Thermal cycling conditions were as follows: reverse transcription at 42°C for 15 min, 55°C for 15 min, and 60°C for 5 min; initial denaturation/enzyme activation of 94°C for 2 min; 5 cycles of 94°C for 30 s, 45°C for 30 s, slow ramp (0.5 °C/s) to 68°C, and 68°C for 3 min; 30 cycles of 94°C for 30 s, 57°C for 30 s, and 68°C for 3 min; and a final extension of 68°C for 5 min. After the PCR, equal volumes of the two reaction mixtures were combined to produce a well-distributed mixture of all eight influenza virus segments. All RT-PCRs were performed in duplicate. Samples were diluted to a DNA concentration of 50 ng/µl followed by ligation of 454 sequencing adaptors and molecular identifier (MID) tags using the SPRIworks Fragment Library System II for Roche GS FLX+ DNA Sequencer (Beckman Coulter), excluding fragments smaller than 350 base pairs, according to the manufacturer's protocol to allow for multiplex sequencing per region. The quantity of properly ligated fragments was determined based on the incorporation efficiency of the fluorescent primers using FLUOstar OPTIMA (BMG Labtech). Emulsion PCR, bead recovery and enrichment were performed manually according to the manufacturer's protocol (Roche), and samples were sequenced in Roche FLX+ 454. Sequencing was performed at the Sanger Institute, Hinxton, Cambridge, England, as part of the FP7 program EMPERIE. Standard flowgram format (sff) files containing the filter passed reads were demultiplexed based on the molecular identifier (MID) sequences using QUASR package version 7.0  with an on-column DNase treatment according to the manufacturer’s protocol. Total RNA was eluted in a volume of 50 μl. Universal influenza virus full-genome amplification was performed using a universal 8-segment PCR method as described previously . In shorsion 7.0 .Q-score) below 20 were discarded.Trimmomatic v0.39;  was usedMinority variants of each nucleotide site with a frequency of at least 2% were called if the nucleotide position was covered at least 50× (H1N1pdm09) or 100× (H3N2) and the probability that the variant was called as a result of base calling errors  and the D8 sample for subject 1878 (73 iSNVs) were substantially greater than the numbers in all other samples. The putative majority and minority segment-concatenated sequences of these samples did not cluster as a monophyletic clade among themselves phylogenetically , suggestThe empirical within-host evolutionary rate  for mapping in the case of the seasonal A/H3N2 virus samples. Additionally, the single-stranded A/H1N1pdm09 viral reads generated from 454 sequencing can be as long as ~500 base pairs. Consequently, there was a non-trivial number of reads where co-mutations were observed in multiple polymorphic sites. Since iSNV frequencies are generally low, haplotypes with co-mutating sites would inevitably be relegated to the end of the list order if ranked by their expected joint probabilities. As such, the list of full potential haplotypes was ordered in descending order based on the score of each full haplotype set The resulting For example, given a segment where iSNVs were found in three sites, the following reads were mapped: , , , , , and . We can immediately see that the top hierarchal combination of polymorphic sites  are , , and   was computed for each set of full haplotypes considered, and the most parsimonious set of Using the estimated frequencies of the most parsimonious reconstructed haplotypes, conventional Lewontin’s metrics of linkage disequilibrium were computed to detect for potential epistatic pairs of nonsynonymous variants:https://foldxsuite.crg.eu/) was used to estimate structural stability effects of likely linked nonsynonymous minority variants found in the NP of within-host A/H3N2 virus populations. At the time of writing of this paper, there was no A/H3N2-NP structure available. Although the eventual NP structure (PDB: 3ZDP) adopted for stability analyses was originally derived from H1N1 virus (A/WSN/33) (FoldX (/WSN/33) , it was /WSN/33) . FoldX fWe implemented forward-time Monte Carlo simulations with varying population size using a simplified within-host evolution model to test if our hypotheses could explain the different evolutionary dynamics observed between A/H3N2 and A/H1N1 viral populations. We assumed that a single virion leads to a productive influenza virus infection within an individual and computed changes in the virus population size μ≫1All ML phylogenetic trees were reconstructed with IQTREE (v. 1.6.10)  using thhttps://github.com/AMC-LAEB/Within_Host_H3vH1 (copy archived at swh:1:rev:44e44ddbfab4d157a3c5efd559972f51dec6454a), All raw sequence data have been deposited at NCBI sequence read archive under BioProject Accession number PRJNA722099. All custom Python code and Jupyter notebooks to reproduce the analyses in this paper are available online:  public reviews designed to be posted alongside the preprint for the benefit of readers; ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.Our editorial process produces two outputs: i) Acceptance summary:This paper provides important observations and insights on the within-host evolution of seasonal influenza, particularly H3N2 in children. As in adults, H3N2 appears to evolve largely by purifying selection during the initial stages of infection when transmission is likely to occur, and nonsynonymous mutations accumulate later. The authors propose a model of mutation-selection balance that might explain diversity in young children with longer infections.Decision letter after peer review:eLife. Your article has been reviewed by 3 peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by Miles Davenport as the Senior Editor. The reviewers have opted to remain anonymous.Thank you for submitting your article "Within-host evolutionary dynamics of seasonal and pandemic human influenza A viruses in young children" for consideration by The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.Essential revisions:This study adds to our knowledge of the evolutionary dynamics of influenza within hosts. The longitudinal data – from likely primary H3N2 infections in children and infections with emerging H1N1pdm09 – are interesting, and the evolutionary analysis has laudably broad scope. That said, the reviewers agreed that limitations in both the data and analysis cast doubt on the conclusions. More analysis could strengthen the study. After consultation, our major concerns are that:1. Not enough attention is given to potential errors affecting the sequences. Reviewers discussed both systematic sequencing and random PCR errors. Ideally plasmid controls would have been used, but under the circumstances, we suggest the authors instead perform sensitivity analysis and other checks recommended by Reviewers 2 and 3.2. The temporal trends are not analyzed in a statistically careful way. Reviewers 1 and 3 raised concerns about the trends reported in Figure 2.  This affects the general conclusions about H1N1pdm09 v. H3N2.3. The simulations assume an odd distribution of fitness effects, which might skew the conclusions about the evolutionary regimes of the two subtypes. (It is not unclear why they should have contrasting evolutionary dynamics.) More thorough sensitivity analysis could help here.The essential revisions would address these concerns. The reviewers were also in agreement on their more specific comments, which I hope you can use to strengthen the work.Reviewer #1 (Recommendations for the authors):each other within a time point or gene or between time points/genes? I initially thought the latter given the claims in the text about temporal dynamics. I believe more tests are needed for these claims, not only for H1N1pdm09 (ll. 180-182). This will influence the comparison with simulation output. The Discussion might then also need updating (ll. 474-477).My primary suggestion is effectively to clarify the statistics for the temporal trends. For instance, in Figure 2, do the p-values test for NS and S being significantly distinct from Reviewer #2 (Recommendations for the authors):Figure 1 figure supplement 2 appears to show variants present at reported frequencies that where not in all overlapping amplicons. These could be PCR artifacts or potentially real variants that were missed in one of the amplicons. Are the evolutionary rate dynamics driven by variants in this frequency range? Is there enough signal to filter out similar variants and validate the robustness of the findings?Line 205: It is not clear how the frequency of synonymous mutations, by themselves indicates negative selection in the antigenic sites. What is the importance of the higher frequency of synonymous mutations found in antigenic sites?Line 663: How were overlapping reading frames accounted for in the evolutionary rate calculation?The qualitative similarity between the simulated and observed rates is nice addition to the manuscript. Is it possible to use the frequencies of mutations in longitudinal pairs to further support the hypothesis of mutational-selection balance?Reviewer #3 (Recommendations for the authors):1. The authors identify and analyze several recurrent mutations in the H3N2 M2 and NP genes, which were found in anywhere from 16 to 27 unlinked individuals. They argue that the recurrent NP mutation is a stabilizing mutation and is epistatically linked to several co-variants that may have destabilizing effects . I am concerned that these mutations may result from technical artifacts and may not represent genuine within-host variants, particularly since amino-acid variants that are known to be associated with oseltamivir resistance were only identified in two patients despite the administration of oseltamivir in many patients. In data from McCrone et al., 2018, for example, several sites appear to harbor low-frequency variants in unrelated individuals, much like the authors describe here, but those variants are also present in the plasmid controls, suggesting that they represent common, site-specific polymerase errors rather than recurrent mutations.To try to determine whether these variants are technical errors, the authors would ideally sequence plasmid controls using the same protocols that were used to sequence the original samples. Since this is likely infeasible, the authors should also check their data to see if variation is present at M2-77, NP-G384R, and other apparent sites of recurrent mutation at frequencies below the variant-calling threshold. If variation is present at these sites in most samples at a frequency much higher than in neighboring sites, then this variation may reflect technical errors in sequencing. The authors called variants after mapping reads to a reference genome, but they might also try remapping to the sample consensus sequence to reduce the risk that mapping artifacts are causing these recurrent variant calls.2. The authors write that non-synonymous mutations accumulate later in patient infections, but it's not clear to me how strongly the data in Figure 2A supports that argument, particularly given the limited number of samples collected at any given day following symptom onset. Does nonsynonymous diversity tend to increase over time in successive timepoints collected from the same individual? Does noise in variant frequencies account for the apparent fluctuations in synonymous diversity over time, and if so, how does noise affect the interpretation of nonsynonymous diversity?3. In the simulations of A/H1N1pdm09 in Figure 6A and 6B, the authors simulate a distribution of fitness effects in which 1% of nonsynonymous mutations are neutral and the remaining mutations are weakly (s=0.01) or strongly (s=0.1) beneficial (lines 785-6). This distribution of fitness effects seems unrealistic to me – even for an emerging virus that may be adapting to a new host, most nonsynonymous mutations will still be deleterious because they affect basic protein functions. It's not clear to me that the large increase in nonsynonymous diversity that the authors observe from these simulations would be observed if deleterious mutations were adequately accounted for; the distribution would probably look much more similar to Figures 6D and 6E. Essential revisions:This study adds to our knowledge of the evolutionary dynamics of influenza within hosts. The longitudinal data---from likely primary H3N2 infections in children and infections with emerging H1N1pdm09---are interesting, and the evolutionary analysis has laudably broad scope. That said, the reviewers agreed that limitations in both the data and analysis cast doubt on the conclusions. More analysis could strengthen the study. After consultation, our major concerns are that:1. Not enough attention is given to potential errors affecting the sequences. Reviewers discussed both systematic sequencing and random PCR errors. Ideally plasmid controls would have been used, but under the circumstances, we suggest the authors instead perform sensitivity analysis and other checks recommended by Reviewers 2 and 3.2. The temporal trends are not analyzed in a statistically careful way. Reviewers 1 and 3 raised concerns about the trends reported in Figure 2.  This affects the general conclusions about H1N1pdm09 v. H3N2.3. The simulations assume an odd distribution of fitness effects, which might skew the conclusions about the evolutionary regimes of the two subtypes. (It is not unclear why they should have contrasting evolutionary dynamics.) More thorough sensitivity analysis could help here.The essential revisions would address these concerns. The reviewers were also in agreement on their more specific comments, which I hope you can use to strengthen the work.We thank the editors for considering our manuscript for review. We have addressed all of the reviewers’ concerns and comments in detail below. The revised manuscript is submitted with tracked changes relative to the original submission.Reviewer #1 (Recommendations for the authors):My primary suggestion is effectively to clarify the statistics for the temporal trends. For instance, in Figure 2, do the p-values test for NS and S being significantly distinct from each other within a time point or gene or between time points/genes? I initially thought the latter given the claims in the text about temporal dynamics. I believe more tests are needed for these claims, not only for H1N1pdm09 (ll. 180-182). This will influence the comparison with simulation output. The Discussion might then also need updating (ll. 474-477).We thank the reviewer for their careful consideration of our manuscript.We agree that there is a lack in statistical power in the A/H1N1pdm09 virus dataset to claim meaningful differences in temporal trends to A/H3N2 within-host dynamics. The only reasonable conclusion that can be made here is that there was a greater accumulation in nonsynonymous iSNVs relative to synonymous ones in A/H1N1pdm09 within-host virus populations. As per the reviewer’s suggestion, we have now removed the boxplots for the A/H1N1pdm09 virus panel in Figure 2B, replacing it with a scatter plot. We have also updated the manuscript to reflect our inability to characterise the within-host temporal trends for A/H1N1pdm09 viruses using this dataset:Line 210: “We observed higher nonsynonymous evolutionary rates relative to synonymous ones initially after symptom onset but were unable to determine if they were significantly different due to the low number of samples (i.e. median = 2 samples per day post-symptom onset). In turn, we also could not meaningfully characterise the temporal trends of within-host evolution for the pandemic virus with this dataset. Nonetheless, consolidating over all samples across all time points, there was significantly higher rates of accumulation of nonsynonymous variants in the polymerase basic 2 (PB2), polymerase acidic (PA), HA and matrix (M) gene segments . All gene segments also yielded NS/S ratios > 1 (Table S1).”Line 565: “Owing to the low number of A/H1N1pdm09 virus samples and different next-generation sequencing platforms used to sequence samples of the two virus subtypes and consequently differences in base calling error rates and depth of coverage , we were unable to directly compare the observed levels of within-host genetic diversity and evolutionary dynamics between the two influenza subtypes here.”Linderman et al.,  and Huang et al.,  found that individuals born prior to the early 1980s possessed antibodies that recognized HA-166K (H3 numbering) residing in the Sa antigenic site of A/H1N1pdm09 viruses. They attributed this to previous exposures to seasonal A/H1N1 viruses with the HA-166K Sa epitope. This adaptive immune response likely led to the fixation of HA-K166Q in A/H1N1pdm09 viruses, which abrogated antibody recognition of this epitope. However, this epitope was shielded by glycans in seasonal A/H1N1 viruses in 1986 due to the acquisition of a glycosylation site in HA-129. As such individuals born after the late 1980s did not possess the same antibodies and are therefore unlikely to exert the same adaptive immune pressure as their older counterparts.Out of the 32 A/H1N1pdm09-infected individuals analysed in our study, only six of them were born before 1986. The median birth year of all individuals was 1999 . Hence, the same adaptive immune pressure on HA-166K was not present in these younger individuals during the first wave of the A/H1N1pdm09 pandemic then. We also did not detect the HA-166Q variant in any of the six older individuals born prior to 1986.Besides HA-166K, Li et al.,  also found that individuals born between 1983 and 1996 have narrowly focused antibodies against the HA-133K epitope as a result of previous exposures to seasonal A/H1N1 viruses. HA-133K has, however, remained conserved in the global A/H1N1pdm09 virus population to date. We also did not find any variants above the calling threshold in any of the individuals investigated.The HA protein is the primary target of human adaptive immune response, which in turn drives its antigenic evolution . In terms of cellular immunity, HA encodes few CTL epitopes . Most CTL epitopes are found in the nucleoprotein (NP), which we have considered here in our discussion observing recurrent NP-G384R variants independently found in multiple individuals.As mentioned earlier, the A/H1N1pdm09 virus dataset lack statistical power. As such, we are unable to characterise temporal trends for the pandemic virus and have no longer discuss this in the updated manuscript (see response to reviewer #3 as well).However, the reviewer was right to point out one of our key conclusions that mutation-selection balance is only observed in naïve young children with longer A/H3N2 virus infections and would be less likely to hold for the typically shorter-lived infections of older children and adults. We have now put more emphasis on this conclusion in the abstract and discussion:Line 42: “For A/H3N2 viruses in young children, early infection was dominated by purifying selection. As these infections progressed, nonsynonymous variants typically increased in frequency even when within-host virus titres decreased. Unlike the short-lived infections of adults where de novo within-host variants are rare, longer infections in young children allow for the maintenance of virus diversity via mutation-selection balance creating potentially important opportunities for within-host virus evolution.”4,8–11,44.”Line 530: “Through simulations of a within-host evolution model, we investigated the hypothesis that in the absence of any positive selection, the accumulation of nonsynonymous iSNVs was a result of their neutral or only weakly deleterious effects and the expanding within-host virion population size during later timepoints in longer infections of naïve young children such that mutation-selection balance was reached. In contrast, this balance was not detected in otherwise healthy older children or adults with short-lived influenza virus infections lasting no more than a week where de novo nonsynonymous iSNVs are rarely found Reviewer #2 (Recommendations for the authors):Figure 1 figure supplement 2 appears to show variants present at reported frequencies that where not in all overlapping amplicons. These could be PCR artifacts or potentially real variants that were missed in one of the amplicons. Are the evolutionary rate dynamics driven by variants in this frequency range? Is there enough signal to filter out similar variants and validate the robustness of the findings?th percentile of frequency range of variants detected in only one of the overlapping. We then recomputed the day-by-day evolutionary rates  and found similar relative rate dynamics as those presented in the main results . We added the following text to manuscript:As per the suggestion by the reviewer, we first excluded all iSNVs found under the 75th percentile of frequency range of A/H3N2 variants that were found in only one of the overlapping amplicons. We then recomputed the daily within-host evolutionary rates with the remaining iSNVs  and found that the relative temporal trends in synonymous and nonsynonymous rates remain similar to those in Figure 2A. We also checked that the distributions of frequencies for iSNVs found in recurrent mutation sites (i.e. NP-384 and M2-77) that are below variant calling threshold are comparable to those found in their neighbouring sites . Furthermore, we remapped the sample reads to their respective consensus sequences to minimize mapping of technical artefacts. We were still able to detect the recurring NP-G384R and M2-R77* amino acid mutations in multiple individuals and timepoints at similar frequencies when mapped to the reference genome . As such, these recurrent mutations are unlikely to have been resulted from erroneous variant calls of artefacts.”Line 632: “We also performed additional checks to ensure that our results were not driven by potential PCR and/or technical artefacts. First, we excluded all iSNVs found under the 75See response to reviewer #3 for further validation on the robustness of our variant calls.Line 205: It is not clear how the frequency of synonymous mutations, by themselves indicates negative selection in the antigenic sites. What is the importance of the higher frequency of synonymous mutations found in antigenic sites?Under neutral selection, the frequency of synonymous and nonsynonymous mutations should be similar to one another. However, if there was negative (purifying) selection to purge deleterious amino acid changes (nonsynonymous mutations) such as those that affect protein functions, this should result in an imbalance in mutation frequencies where there are relatively higher frequencies of synonymous mutations compared to nonsynonymous ones. As many antigenic sites of HA are close to the receptor-binding region, many nonsynonymous mutations in these sites could have negatives effects on host cell binding and thus be purged by negative selection.Line 663: How were overlapping reading frames accounted for in the evolutionary rate calculation?We considered a variant to be a nonsynonymous mutation if any of the overlapping reading frames engender nonsynonymous changes. This is now explicitly stated in the Methods (Line 783):“If a variant was found in overlapping reading frames and a nonsynonymous change was observed in any of those frames, it would be accounted for as a nonsynonymous mutation.”The qualitative similarity between the simulated and observed rates is nice addition to the manuscript. Is it possible to use the frequencies of mutations in longitudinal pairs to further support the hypothesis of mutational-selection balance?Given that we are unable to quantify the range and level of uncertainty in variant frequencies of our dataset, it is difficult to use mutation frequencies to support the mutation-selection balance hypothesis. However, we observed that some nonsynonymous variants, including lethal stop-codon mutations such as M2-R77*, persisted in low frequencies within the same individual across multiple timepoints . Furthermore, in response to reviewer #3, we performed linear regression on the computed evolutionary rates for each individual with samples collected at multiple timepoints to ascertain that the patterns observed were not because of aggregating the data from multiple individuals. See detailed response to reviewer #3 below.Reviewer #3 (Recommendations for the authors):1. The authors identify and analyze several recurrent mutations in the H3N2 M2 and NP genes, which were found in anywhere from 16 to 27 unlinked individuals. They argue that the recurrent NP mutation is a stabilizing mutation and is epistatically linked to several co-variants that may have destabilizing effects . I am concerned that these mutations may result from technical artifacts and may not represent genuine within-host variants, particularly since amino-acid variants that are known to be associated with oseltamivir resistance were only identified in two patients despite the administration of oseltamivir in many patients. In data from McCrone et al., 2018, for example, several sites appear to harbor low-frequency variants in unrelated individuals, much like the authors describe here, but those variants are also present in the plasmid controls, suggesting that they represent common, site-specific polymerase errors rather than recurrent mutations.To try to determine whether these variants are technical errors, the authors would ideally sequence plasmid controls using the same protocols that were used to sequence the original samples. Since this is likely infeasible, the authors should also check their data to see if variation is present at M2-77, NP-G384R, and other apparent sites of recurrent mutation at frequencies below the variant-calling threshold. If variation is present at these sites in most samples at a frequency much higher than in neighboring sites, then this variation may reflect technical errors in sequencing. The authors called variants after mapping reads to a reference genome, but they might also try remapping to the sample consensus sequence to reduce the risk that mapping artifacts are causing these recurrent variant calls.Following the reviewer’s suggestion, we did not find that there was any significant difference in frequencies of iSNVs below the variant-calling threshold between those found in the recurrent sites and neighbouring sites that are ±10 nucleotide positions away . We also remapped the sample reads to their respective consensus sequence and still detected these recurrent calls across multiple individuals and timepoints at similar frequencies when mapped to a reference genome .Line 638: “We also checked that the distributions of frequencies for iSNVs found in recurrent mutation sites (i.e. NP-384 and M2-77) that are below variant calling threshold are comparable to those found in their neighbouring sites . Furthermore, we remapped the sample reads to their respective consensus sequences to minimize mapping of technical artefacts. We were still able to detect the recurring NP-G384R and M2-R77* amino acid mutations in multiple individuals and timepoints at similar frequencies when mapped to the reference genome . As such, these recurrent mutations are unlikely to have been resulted from erroneous variant calls of artefacts.”2. The authors write that non-synonymous mutations accumulate later in patient infections, but it's not clear to me how strongly the data in Figure 2A supports that argument, particularly given the limited number of samples collected at any given day following symptom onset. Does nonsynonymous diversity tend to increase over time in successive timepoints collected from the same individual? Does noise in variant frequencies account for the apparent fluctuations in synonymous diversity over time, and if so, how does noise affect the interpretation of nonsynonymous diversity?To minimize the effect noise, we performed linear regression on the computed evolutionary rates in Figure 2A for each A/H3N2 infected individual with at least three sampled timepoints (n=39). Among these, 25 of them (64%) had a positive correlation in nonsynonymous iSNV accumulation rates against time . For individuals where samples were only collected at two timepoints , 6 of them had higher accumulation rates of nonsynonymous variants in the successive sample.In contrast, synonymous evolutionary rates were negatively correlated against time for 27 (69%) of the 39 A/H3N2 infected individuals with at least three sampling timepoints. If noise was the primary determinant for fluctuations in diversity over time, we should observe similar effects on the trends of nonsynonymous and synonymous variants over time. However, the contrasting patient-specific temporal trends between the two type of variants supports our conclusion that nonsynonymous mutations accumulated later in time when within-host virus population is large enough in size such that mutation-selection balance is reached.Line 175: “To ensure that this temporal trend was not due to aggregated effects across multiple individuals, we performed linear regression on the computed evolutionary rates for each A/H3N2 infected individual with at least three sampling timepoints (n=39). Nonsynonymous evolutionary rates were positively correlated against time for 25/39 individuals . In contrast, synonymous evolutionary rates were negatively correlated against time for 27 (69%) individuals.”3. In the simulations of A/H1N1pdm09 in Figure 6A and 6B, the authors simulate a distribution of fitness effects in which 1% of nonsynonymous mutations are neutral and the remaining mutations are weakly (s=0.01) or strongly (s=0.1) beneficial (lines 785-6). This distribution of fitness effects seems unrealistic to me – even for an emerging virus that may be adapting to a new host, most nonsynonymous mutations will still be deleterious because they affect basic protein functions. It's not clear to me that the large increase in nonsynonymous diversity that the authors observe from these simulations would be observed if deleterious mutations were adequately accounted for; the distribution would probably look much more similar to Figures 6D and 6E.We agree that the distribution of fitness effects that was previously used to simulate A/H1N1pdm09 virus dynamics needed to be reworked. However, as in our response to reviewer 1 on the lack of statistical power in the A/H1N1pdm09 virus dataset. Since we cannot reliably characterise the temporal trends of nonsynonymous and synonymous iSNVs in A/H1N1pdm09 infected individuals, we have now removed simulation results for A/H1N1pdm09 viruses.


Mosquitoes transmit hundreds of arboviruses. The ability to estimate the titers of viruses transmitted from infectious mosquitoes to a host is critical. In this study, we evaluated forced salivation techniques to estimate and compare titers of Zika virus and chikungunya virus transmitted by the mosquitoes. We demonstrated that performing forced salivation on mosquitoes after blood feeding might be an efficient way to estimate the virus transmitted during blood feeding. Additionally, by comparing titers of bloodmeals and saliva post-feeding, we showed that mosquitoes re-ingest much of their saliva during artificial blood feeding. The results from this study add new information to understanding and quantifying the transmission of arboviruses.Aedes aegypti. Infectious virus and viral genomes of both viruses were detected more often from individual mosquitoes using immersion oil for the FS media compared to fetal bovine serum (FBS) plus glycerol, but the FS media had no influence on virus quantification from positive samples. FS virus titers were equivalent when comparing individuals or groups of mosquitoes that never received a blood meal compared to those that were blood fed immediately prior, showing that blood feeding does not influence FS. This suggested that performing FS on mosquitoes after blood feeding might be an efficient way to estimate virus transmitted during blood feeding. However, detecting virus from the blood remaining in an artificial feeder post-blood feeding was mostly unsuccessful relative to quantifying virus from FS of the post-blood fed mosquitoes. In contrast, immunocompromised mice always became infected after being fed on by Zika-infected mosquitoes, even when no infectious virus was detected in their saliva by FS post-blood feed. Due to this discrepancy, we tested the ingested bloodmeals of individual mosquitoes that fed on artificial blood feeders for virus, and compared these to virus in their saliva harvested from FS and to virus in their bodies. These experiments revealed ~50–100 times higher virus titers in the dissected bloodmeals compared to those detected in the same mosquitoes’ saliva, demonstrating how mosquitoes re-ingest much of their saliva during artificial blood feeding, and highlighting a large increase in virus transmission during Aedes aegypti blood feeding. Both FS and the dissected bloodmeals of artificially blood-fed mosquitoes showed that the quantity of viral RNA expectorated by mosquitoes was 2–5 logs more than the quantity of infectious virus. The results from this study add critical information to understanding and quantifying the transmission of Aedes aegypti arboviruses.Arbovirus transmission studies are dependent on the ability to estimate the titer of virus transmitted from infectious mosquitoes to a host. There are several methods for estimating virus titer in mosquito saliva, including (1) using forced salivation (FS) whereby the infectious mosquito’s proboscis is forced into a capillary tube containing media to collect and test their saliva for virus, and (2) by quantifying virus expectorated into host tissues or into the blood contained in an artificial feeder immediately after blood feeding. We studied FS and bloodmeals to estimate and compare titers of Zika virus and chikungunya virus transmitted by the mosquito vector  Estimating the titer of arboviruses transmitted by mosquitoes during blood feeding on a host is critical to understand arbovirus transmission, especially by accurately simulating these natural infections in laboratory studies. There are several documented methods for collecting saliva and/or determining the efficiency and titer of virus transmitted from mosquito saliva to a host: 1) forced salivation (FS) of infectious mosquitoes into media contained in a capillary tube and testing the captured saliva for virus, 2) detecting virus in host tissues immediately after infectious mosquitoes blood feed or by later examining host infection or seroconversion rates, and 3) detecting virus in the remaining blood from artificial feeders fed upon by infectious mosquitoes [ forced s detectin detectinAedes albopictus infected with Venezuelan equine encephalitis virus (VEEV) showed that there was no difference between virus titers when saliva was collected in either immersion oil or FBS using the FS technique [Forced salivation is often used as it can determine virus expectorated from a single mosquito and is not dependent on the mosquito blood feeding. Within this method, different media have been used within the capillary tube, the most common being either microscope immersion oil ,4 or fetAedes aegypti used mouse intracerebral 50% lethal doses to show that the amount of virus transmitted varied from being undetectable to 1.0 × 105 [3 PFU as measured by FS collections with immersion oil [Culex tarsalis after blood feeding determined that virus transmitted was approximately 600-fold higher than virus transmitted during the FS technique [Culex pipiens quinquefasciatus by blood feeding and forced salivation [In addition to forced salivation, vertebrate hosts or artificial feeders can be used to estimate the amount of virus being transmitted from an infectious mosquito bite. However, mosquito feeding behaviors are inconsistent, especially in high-containment (BSL-3) laboratory settings because of rapid air exchange and personal protective equipment that limit body heat and odor cues, so there is no way to ensure any one mosquito, or recalcitrant species or strains, will take a blood meal . A study.0 × 105 . An addision oil . Compariechnique , but anolivation . Taken tAedes aegypti mosquitoes infected with Zika virus (ZIKV) and chikungunya virus (CHIKV). Our efforts examined variations on the FS technique and compared it to virus transmission during blood feeding on animals and artificial feeders, and also to re-ingested virus recovered from bloodmeals dissected out of the mosquitoes , originally isolated from a human traveler to Puerto Rico in 2015 with three rounds of amplification on Vero cells, was obtained from Dr. Aaron Brault . CHIIKV strain LR2006_OPY1 (GenBank: KT449801.1) was obtained from the University Texas Medical Branch and isolated from outbreak in Reunion Island in 2006 with three rounds of amplification on Vero cells.African Green Monkey kidney cells  were maintained in Dulbecco’s modified Eagle medium (DMEM) supplemented with 10% fetal bovine serum , 2 mM L-glutamine (Gibco Thermo Fisher), 1.5 g/L sodium bicarbonate (Gibco Thermo Fisher), 100 U/mL penicillin (Gibco Thermo Fisher) and incubated at 37 °C in 5% COAedes aegypti Poza Rica strain mosquitoes were fed an infectious artificial blood meal containing either CHIKV or ZIKV and held for 10–14 days before all subsequent experiments to ensure dissemination of virus to the salivary glands. Infectious bloodmeals were prepared with 1 mL fresh virus contained in the cell-culture supernatant of infected Vero cells and 1 mL of defibrinated calf blood. Back-titering of the bloodmeals ranged between 1 × 106–5 × 106 PFU/mL. Mosquitoes were sorted post blood feeding and were placed in cartons  with an organdy cover and given water and a sugar source.Aedes aegypti Poza Rica strain mosquitoes were fed an infectious blood meal and held for 14–17 days. Mosquitoes were sorted post blood feeding and 10–20 blood-fed mosquitoes were place in cartons with an organdy cover and given water and a sugar source. To allow the mosquitos to feed on the mice, each mouse was anesthetized using 100 mg/kg ketamine/10 mg/kg xylazine  and placed on the organdy cover of one carton for ~20 min.A129 mice  8–12 weeks old were obtained from breeding colony maintained at Colorado State University. Use of mice was approved by the Colorado State University Institutional Animal Care and Use Committee (protocol 15-6677 AA). All procedures were done in accordance with the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. To infect mice by mosquito bite, Mosquitoes were immediately cold-anesthetized post-blood feeding and their saliva was collected by the FS method described previously , brieflyBloodmeal dissections were done immediately after individual mosquitoes underwent FS. Mosquitoes were dissected on the sides of glass wells partially filled with 200 µL of DMEM supplemented with 10% fetal bovine serum, 2 mM L-glutamine, 1.5 g/L sodium bicarbonate, 100 U/mL penicillin. The midguts were dissected out and spilt open so that the blood meal contents could spill out into the media, and the torn midgut swished into media to extract the whole blood meal. Mock blood meal dissections were performed on non-blood fed mosquitoes exactly the same way but there was no blood meal that could spill out into the media. The media  was then collected and placed into a tube for later testing, and the body plus torn midgut were placed into another tube and frozen at −80 °C to be homogenized in media for later testing.g. Bloodmeals were collected in 150 µL of DMEM supplemented with 10% fetal bovine serum, 2 mM L-glutamine, 1.5 g/L sodium bicarbonate, 100 U/mL penicillin. RNA was extracted from all samples using the Mag-Bind Viral DNA/RNA 96 kit (Omega Bio-Tek) on the KingFisher Flex Magnetic Particle Processor (Thermo Fisher Scientific). RNA was eluted in 30 µL nuclease-free water. Progmeg GoTaq Probe 1-Step RT-qPCR System Kits were used on RNA extracted from saliva and bodies to quantify CHIKV and ZIKV RNA according to manufacturers’ instructions. Standard cycling condition were followed, one cycle at 45 °C for 15 min, one cycle at 95 °C for 2 min and 40 cycles of 95 °C for 15 s and 60 °C for 1 min. Primers used for CHIKV were Forward (5′-CTTTGAAGTTTCCTTTCGGTGG-3′) and Reverse (5′-ACFFAAFFRAAACTGGTATGG-3′) and Probe-FAM (5′-TCTGCAGCGTCTTTATCCACGGG-3′). Primers used were ZIKV 1086 (5′-CCGCTGCCCAACACAAG-3′) and ZIKV 1162c (5′-CCACTAACGTTCTTTTGCAGACAT-3′). The probe used was ZIKV 1107-FAM (5′-AGCCTACCTTGACAAGCAGTCAGACACTCAA-3′) [2 value of 0.971 to 0.997, a slope of −3629 to −3269, and y-intercept = 37.966 to 47.270.Tubes containing mosquito bodies were homogenized and both saliva and bodies where centrifuged for 5 min at 14,000× t-test or a one-way analysis of variance (ANOVA) with a Tukey’s multiple-comparison in GraphPad Prism. Correlation was determined by Spearman’s rank-order correlation in the GraphPad Prism , La Jolla, CA, USA).Results in figures were expressed as mean values  with individual values showing the variance. The statistical details are noted in the figures and/or in the corresponding figure legends. Statistical significance was primarily determined using either Fisher’s exact test, unpaired Student’s Aedes aegypti were subjected to FS using two different media, there was a significant difference in the proportion of positive saliva samples from mosquitoes that salivated into oil compared to FBS + glycerol regardless of virus (p ≤ 0.05). The same pattern was seen with detection of infectious CHIKV, with 38% (38/100) of positive saliva samples collected in oil compared to 14% (14/100) from FBS + glycerol (p ≤ 0.05). When testing for viral RNA in the same samples, the same pattern was observed, with oil resulting in more positive samples than the FBS + glycerol media for both viruses. However, viral RNA was detected in more samples overall than infectious virus for both viruses. Despite the increased virus prevalence in saliva samples collected using oil, positive samples from both FS media did not significantly differ in the quantity of infectious virus or viral RNA for either virus Comparisons of virus titers transmitted by ZIKV- and CHIKV-infected  fed see . There w fed see . Overall2–6.8 × 104 genome copies) via FS  from Ae. aegypti. Considering that quantification of viral RNAs from FS of both groups of blood fed mosquitoes was roughly equal, the combined data suggested that some factor was inhibiting our ability to detect arbovirus transmission by Ae. aegypti from the blood in the artificial feeders. We hypothesized that this inhibitory factor was mosquito re-ingestion of their saliva, and thus the virus they expectorated, during blood feeding on artificial feeders.Overall, 53 immunocompromised mice were fed upon by groups of ZIKV-infected mosquitoes  and all became infected with ZIKV. ) via FS . In cont) via FS . These dp ≤ 0.05). A similar difference was seen with CHIKV-infected mosquitoes (none = 2.067 PFU/5.195 genome copies (gc), artificial feeder = 3.443 PFU/6.832 gc, artificial feeder + mixing = 3.739 PFU/6.659 gc) (p ≤ 0.05).To test our hypothesis of re-ingested virus during blood feeding, we dissected and tested the bloodmeals of ZIKV- and CHIKV-infected mosquitoes after they fed on artificial feeders and then underwent forced salivation. As the process of dissecting out the blood meal results in some contamination of virus into the dissecting media from the hemolymph and midgut tissue of these infected mosquitoes, mock blood meal dissections were performed as treatment controls on groups of mosquitoes that did not get a second bloodmeal. Approximately 100-fold more infectious ZIKV or ZIKV RNA, as well as infectious CHIKV or CHIKV RNA, was detected in the bloodmeals from individual mosquitoes relative to the treatment controls . These rAedes aegypti mosquitoes after being infected with either ZIKV or CHIKV. Different medias have been previously evaluated for FS, including immersion oil and a mixture of 1:1 FBS + glycerol, as well as blood and other medias [Ae. aegypti infected with CHIKV or ZIKV. We found no evidence that FBS + glycerol aided in viral stabilization and preservation as was previously suggested [Aedes aegypti expectorated a mean of 6.8 nL using oil-based FS and observed no correlation of CHIKV titers with the saliva volume expectorated. Another potential benefit of oil-based FS is that the mosquitoes are unable to re-ingest their own saliva. It has been previously shown that anopheline mosquitoes will re-ingest many of the Plasmodium sporozoites they deposit in the host when blood feeding [We used different experimental procedures and methods to estimate the titer of virus transmitted by r medias ,17,18,19uggested ,9 and nouggested . In the  feeding . It follFS has been used as the standard method to determine transmission of mosquito-borne arboviruses ,2,4,5, bTo compare our post-blood feeding FS data with the quantity of virus transmitted during the blood feed, we analyzed outcomes of ZIKV-infected mosquitoes that blood fed on immunocompromised mice, and virus transmitted to artificial feeders after ZIKV- or CHIKV-infected mosquitoes fed on them. Mice became infected after mosquito feeding even when no infectious virus was detected in the mosquito saliva post feeding. Our limit of detection is 2 PFU and 10 genomes copies; however, we never detected anything under 10 PFU. Based on these results, we can assume these mice can become infected with less than 10 PFU transmitted by mosquitoes blood feeding on them. When examining the blood remaining in the artificial feeders, however, infectious virus was never recovered, and viral RNA was only recovered in four out of nine ZIKV groups and one out of nine CHIKV groups. This observation could be explained in at least two non-exclusive hypotheses. It may be that live virus was quickly inactivated and viral nucleic acid sequences were destroyed by proteases and nucleases in the artificial blood meal, making their detection difficult by plaque assay and qRT-PCR, respectively. However, this seems unlikely given that we rarely record a drop in virus titer of the original blood meal used to first infect the mosquitoes when it is ‘back-titered’ after sitting in the artificial feeder for ~30 min during the blood feed. Another possibility is that virus expelled with the saliva into the artificial blood meal may be immediately re-ingested through the suction force needed to bring blood into the food canal.Ae. aegypti transmitting either ZIKV or CHIKV and between quantities of infectious virus or viral RNA detected; between 50–100 times more virus is secreted during blood feeding than is detected in FS performed immediately after blood feeding, suggesting a large increase in virus transmission during blood feeding. One limitation of these data are that they are estimates of virus transmission by Ae. aegypti derived from blood feeding on artificial feeders, which may not accurately reflect what occurs during blood feeding on live hosts, including transmission during probing but not blood feeding [2–2.1 × 1010 when the mouse ear tissue was immediately removed and homogenized after being fed on by ZIKV-infected Ae. aegypti mosquitoes [To address the latter hypothesis, we dissected out bloodmeals from the infected mosquito midguts after they were given different blood feeding treatments and then were processed with FS. For one of the blood feeding treatments, blood was pipetted up and down in the blood feeder during the time of feeding to determine if blood mixing might counteract the re-ingestion of a mosquito’s own expectorated virus during blood feeding. Compared to the control treatment , significantly more virus and viral RNA was recovered from the bloodmeals of the two blood feeding treatment groups, and the mixing of the blood in the artificial feeder did not influence this. This indicated that mosquitoes re-ingest much of their expectorated virus while feeding on the artificial feeder and that poor detection of virus in the remaining blood from artificial feeding is likely due to re-ingestion. As each mosquito dissected of its bloodmeal was also processed via FS, we could determine the difference of virus titers between the bloodmeal and saliva to estimate the quantity of virus transmitted during blood feeding. The estimate was consistent between  feeding . However feeding . Each ofsquitoes . In our Ae. aegypti mosquito by performing FS on it immediately post-blooding, quantifying infectious virus or viral RNA and then multiplying the titer determined by ~50–100. Quantifying infectious virus ensures measurement of true infectious units, but it is clearly of low sensitivity and so simultaneously quantifying viral RNA will give the best estimates of transmission dose. More experiments will be necessary to determine if the increase in virus transmission during blood feeding relative to FS we observed is because of (a) more saliva being released by Ae. aegypti or (b) more virus being released from the salivary glands, or both. Similarly, results from this study should be replicated with other arbovirus vectors to determine if they are consistent across mosquito species. Overall, the methods developed here can be used as a better way to estimate the titer of arboviruses transmitted by blood feeding Ae. aegypti and may be valuable for similar estimations with other mosquitoes.Studies using different combination of viruses and mosquito species have evaluated the use of vertebrate hosts or artificial feeders to estimate the amount of virus being transmitted from an infectious mosquito and found varying results ,12,13,14


Healthcare costs related to ESRD are well-described, but broader societal costs of ESRD are less known. This study aimed to estimate patient and family costs, including informal care costs and out-of-pocket costs, and costs due to productivity loss related to ESRD, for patients receiving dialysis and living with a kidney transplant, using a bottom-up approach.A total of 655 patients were asked to complete a digital questionnaire consisting of two standardised instruments (iMCQ and iPCQ) from November 2016 through January 2017. We applied a retrospective bottom-up cost estimation by combining data from the questionnaire with unit prices from the Dutch costing manual.Our study sample consisted of 230 patients, of which 165 were kidney transplant recipients and 65 received dialysis. The total annual non-healthcare related costs were estimated at €8284  for transplant recipients and €23,488  for dialysis patients. Costs due to productivity loss contributed most to the total non-healthcare costs , followed by informal care costs (26% resp. 29%) and out-of-pocket costs, such as medication and travel expenses (8% resp. 6%).By exposing patient, family and productivity costs, our study revealed that dialysis and transplantation are not only costly within the healthcare system, but also incur high non-healthcare costs . It is important to reveal these types of non-healthcare costs in order to understand the full burden of ESRD for society and the potential impact of new therapies. The disease burden of end-stage renal disease (ESRD) is one of the highest worldwide . In the The economic impact of ESRD stretches further than the healthcare budget alone. For example, ESRD has a major impact on the patients’ productivity, both related to an impaired health status and the time-consuming and intensive character of dialysis. Approximately 70% of the ESRD patients are unemployed . Furtherall relevant costs, thus not limited to the healthcare system, in economic evaluations ),n = 264), whereof 5% was excluded from further analysis, either because the respondent was still pre-dialysis (n = 32) or because of empty cases (n = 2).We sent 677 RRT patients digital questionnaires for the purpose of our study Fig. . We apprThe distribution of dialysis patients and transplant recipients in the approached group was highly skewed, with relatively more transplant recipients. Although this distribution of treatment modalities complies with the real-world situation in the Netherlands, the total number of dialysis patients was too small to enable subgroup analysis for all dialysis modalities , haemodialysis in-centre (CHD) or haemodialysis at home (HHD)). Therefore, we divided our study population in the following main RRT modalities: 1) patients who receive dialysis, and 2) kidney transplant recipients.https://www.imta.nl/questionnaires/.We sent a digital questionnaire consisting of two standardised instruments to the patients. The iMTA Medical Consumption Questionnaire (iMCQ) is an instrument to collect formal and informal care service use and out-of-pocket expenses . For theWe applied a bottom-up cost estimation by multiplying the data on the number of hours of informal care, travel expenses and productivity costs at the individual patient level with unit prices from the Dutch costing manual . Table 1Although patient and family costs are directly associated with the disease or treatment, they occur outside the formal healthcare system. The main driver of these costs is usually informal caregiving, whereby relatives take over certain tasks of the patient who is no longer able to perform the tasks himself due to the disease. In this study, informal care concerns household or practical support , assistance in personal care or treatment (e.g. medication), or transport. According to the Dutch costing manual the costs of informal care were calculated based on the substitution value method, which values time spent by informal caregivers according to professionals’ wage rates for household work .Beside the costs of informal care, we also took into account the out-of-pocket expenses by the patient or family. First, we included patients’ spending on medication or formal home care that was not covered by health insurance. Second, travel expenses associated with medical treatment or appointments with the General Practitioner (GP) or medical specialist was measured. Depending on the type of transport, the usage costs were calculated based on the average distance from home to the GP or hospital combined with the average fuel cost per kilometre or taxi costs  and the number of visits per year. The parking costs were considered for appointments in the hospital only. Usually a special regulation (no parking costs) applies to patients undergoing dialysis treatment in the hospital.Productivity losses comprise the costs associated with absenteeism, presenteeism and reduced participation in unpaid work activities. Absenteeism for paid work was measured using the friction-cost method . The friUnpaid work includes voluntary work and also household activities, such as cooking, cleaning and gardening and is valuated at professionals’ wage rates for housekeeping .We calculated the mean and standard deviation (SD) of the units or the costs per variable over all respondents. In addition, frequency distributions were used to present the number of patients per modality and the number of patients who used a certain type of care . For the limited number of  missing values , the mean of the observed user values per modality was imputed. The Mann-Whitney test was used to assess differences between modality for continuous variables and the Pearson Chi-Square test for the categorical variables. All prices were reported in Euros and converted to the year 2016 according to the Dutch Consumer Price Index (2014 to 2016: 1.009).Table P < 0.001) compared to transplant recipients. The difference is in part related to the timing of the questionnaire versus transplantation: 90% of the transplant recipient respondents received their graft more than 1 year prior to filling out the questionnaire. Because our sample consisted most stable transplant recipients, we found a lower percentage of patients that used informal care among the transplant recipients  compared to the transplant recipients (€3120). Within the dialysis group, 52% of the patients reported having problems with unpaid work as household activities or voluntary work. This percentage was 41% in the group of transplant recipients. Averaged over all patients, the number of missed hours of unpaid work was about 3.5 times higher in dialysis patients than in transplant recipients.Table This is one of first studies that investigated the non-healthcare costs of ESRD by estimating informal care costs, out-of-pocket costs as well as costs due to productivity losses using a bottom-up approach in 2016. We estimated the mean total annual non-healthcare related costs at €8284 for transplant recipients and €23,488 for dialysis patients. Costs due to productivity loss contributed most to the total non-healthcare costs 66% for transplant recipients and 65% for dialysis patients), followed by costs related to informal care and out-of-pocket costs, such as medication and travel expenses. In all cost categories, costs among dialysis patients exceeded those of kidney transplant recipients.Our study was performed supplementary to the top-down cost estimation of healthcare costs from Mohnen, Los , which wOther studies estimating non-healthcare related ESRD costs using a bottom-up approach are few , 22–24 aThe production losses were valued using the average wage by gender, in accordance with the Dutch costing manual. This approach is the most common applied method to increase the generalizability of the study. Given the relatively old age of our study population, this may be an underestimate, as the average Dutch worker is younger than 58. Another reason that we might have underestimated productivity costs, is that we valued production losses by applying the friction cost method . This meOur study has several limitations. First, the number of respondents in the survey was relatively small and highly skewed with relatively more transplant recipients. Comparing our study sample with Dutch Renal Registry data showed that the distribution over treatment modalities roughly complies with the real-world situation in the Netherlands. However, patients receiving dialysis in our sample were substantially younger compared to patients in the Dutch Renal Registry data . This coSecond, due to the small number of dialysis respondents, it was not possible to conduct subgroup analysis for different dialysis modalities . It is likely that depending on dialysis modality, patients show differing patterns of costs outside the healthcare system.Third, we did not include the health status of patients in our study. This is important as it influences the non-healthcare costs through the need for informal care and through an increased likelihood of productivity losses with impaired health state. Further research should be employed to address different dialysis modalities and health status in bottom-up cost estimation of non-health care related ESRD costs.By exposing costs outside the healthcare system, our study reveals that dialysis and transplantation are not only costly within the healthcare system, but also incur high non-healthcare costs suffered by patients and family. It is important to reveal these types of non-healthcare costs in order to understand the full burden of ESRD for society and the potential impact of new therapies.