Nasal microbial diversity is associated with survival in piglets infected by a highly virulent PRRSV-1 strain

Nasal microbial diversity is associated with survival in piglets infected by a highly virulent PRRSV-1 strain | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Nasal microbial diversity is associated with survival in piglets infected by a highly virulent PRRSV-1 strain Pau Obregon-Gutierrez, Martí Cortey, Gerard E. Martín-Valls, Hepzibar Clilverd, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4762608/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 17 Jan, 2025 Read the published version in Animal Microbiome → Version 1 posted 9 You are reading this latest preprint version Abstract Background Porcine Reproductive and Respiratory Syndrome Virus (PRRSV) is a major threat to swine industry worldwide, especially virulent variants arising during the last years, such as Spanish PRRSV-1 Rosalia strain. The role of the nasal microbiota in respiratory viral infections is still to be unveiled, but may be promisingly related with the health status of the animals and thus, their susceptibility. The goal of this project was to study the nasal microbiota composition of piglets during a highly virulent PRRSV-1 outbreak comparing animals that died due to the infection with animals that survived it. The microbiota composition was inferred by V3-V4 regions of the 16S rRNA gene sequencing and bioinformatics analysis. To deepen the analysis, we added samples taken from piglets before the outbreak as well as from the sows giving birth to piglets under study. Results Piglets that survived the PRRSV-1 outbreak reported a more diverse and different nasal microbiota at three weeks of age compared to piglets dying, which was highly related with the litter of origin and the sow of the piglets. In addition, the high abundance of classical swine nasal colonizers belonging to genera such as Bergeyella , Glaesserella , Neisseria and Moraxella (among others), was related with good outcome. On the other hand, a dysbiotic community dominated by Escherichia and a different clade of Moraxella was found in piglets with bad outcome. Moreover, samples taken before the outbreak showed similar dynamics prior to virulent PRRSV-1 arrival, suggesting that microbiota-related susceptibility was already occurring in the animals and that the increase in mortality seen was related to the new highly virulent strain. Conclusion Our study suggests that the susceptibility to an infection such as PRRSV could be related to the nasal microbiota composition at the moment of infection and may serve as starting point to explore animal resilience. Since the dysbiosis detected as an initial response to infection may be not specific for this virus, further investigations should explore this phenomenon in the context of other viral infections. Pig Swine Virus PRRSV Nasal microbiota Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction For the last three decades, porcine reproductive and respiratory syndrome virus (PRRSV) has established as one of the major pathogens of swine worldwide. In sows, PRRSV infection is characterized by outbreaks of reproductive disease, which hallmark feature is abortion of fresh or mummified foetuses after day 90 of gestation. Stillbirths, weak-born piglets and an increase in pre-weaning mortality are other common consequences of PRRSV infection ( 1 ). In piglets, the infection manifests as a respiratory disease that is usually complicated with other viral or bacterial respiratory agents. Furthermore, the association of secondary bacterial infections leads to increased use of antimicrobials and mortality in piglets ( 2 ). PRRSV belongs to the genus Betaarterivirus within the family Arteriviridae , Order Nidovirales . Two PRRSV species are recognised, PRRSV-1 and PRRSV2 ( 3 ). One of the key features of both PRRSV species is the high genetic and antigenic diversity and the existence of strains of diverse virulence, among which the so-called highly pathogenic PRRSV that emerged in China in 2006 has been one of the most devastating ones ( 4 ). Once, it was thought that highly virulent PRRSV strains only existed for PRRSV2, while PRRSV-1 was considered of lower virulence. However, the description of strains such Lena and Rosalia ( 5 , 6 ) made clear that highly virulent strains also exist within PRRSV-1. Besides causing severe abortion storms and mortality of sows, highly virulent strains can cause high mortalities in the farrowing units and nurseries of the affected farms. This increased mortality is probably the result of a combination of factors, including the high viral loads and the consequent inflammatory response, the induction of apoptosis in the thymus of the infected piglets and the frequency of secondary infections ( 7 ). The role of the microbiota in health and disease has been extensively studied and nowadays is one of the major research areas in biomedical sciences. However, in the veterinary field the available information is still limited. In previous studies, it was shown that PRRSV infection impacts the composition of the gut microbiome and that this impact depended on the virulence of the PRRSV strain ( 8 – 10 ). Also, other studies ( 9 , 11 , 12 ) have shown that the diversity and composition of the gut microbiome may influence the outcome of PRRSV or porcine circovirus 2 infections, highlighting the complex interplay of the microbiota and these viral infections. However, little is known about the role and the interaction between the respiratory microbiota and respiratory viral infections in pigs. Among the few available studies, Gierse et al. ( 13 ) showed that infection with a swine influenza virus H1N1 resulted in lower richness of the nasal microbial community, while other studies ( 14 ) showed little impact of a H3N2 influenza A virus infection on the richness of the nasal microbiota, but associated the increase of certain species with the PRRSV and/or IAV challenge. Similarly, Hau et al. ( 15 ) have recently shown that the nasal microbiota seems to be resilient to influenza A virus infection. In the present study, we examined the nasal microbiota composition of piglets naturally infected in the first three weeks of life by a highly virulent PRRSV-1 strain of a newly emerged clade in Spain, comparing the microbial communities of the animals that ended up dying due to the infection with the microbiota of those that survived it. Material and methods Farm, pigs and sampling The farm where the study was conducted was a 1,400-sow operation, working with weekly farrowing batches (approximately 62 sows/batch). Piglets were weaned at 4 weeks of age when they were moved to nursery pens. At 9 weeks of age, they were moved to a fattening unit where they remained until reaching market weight. Although viral circulation occurred in growers, the farm was considered stable for PRRSV since weaned pigs routinely tested negative for PRRSV by RT-qPCR (LSI Vetmax PRRSV EUNA 2.0, Thermofisher). In late December 2021, a highly virulent PRRSV-1 strain belonging to a new clade recently reported ( 16 ) entered the farm. The strain had a 97.7% nucleotide identity with strain R1, the first isolate described for this clade. The introduction of the new PRRSV strain caused a reproductive outbreak and most piglets already tested positive for the virus by RT-qPCR before weaning. The farm was also known to be infected by an influenza A virus (IAV, H1N2; 1C.1.2.1 clade). Sampling of piglets was conducted during a follow-up study initially aimed at determining the dynamics of IAV in the farm. For that purpose, a cohort of 40 piglets belonging to ten randomly chosen litters (4 piglets randomly selected per sow) was followed from birth until the end of the nursery period. Blood samples were collected at 1, 3, 4, 5, 6, 7, 8 and 12 weeks of age to assess the evolution of pigs. Nasal swabs were also taken at 3 weeks of age in piglets and simultaneously from their sows and immediately transported to the lab at 4°C in transport medium for viral isolation (Virocult®). Animals were followed until 12 weeks of age and mortality was recorded. Since the mortality in the followed cohort surpassed 50%, we deemed interesting to determine the possible relationship between the outcome of PRRSV infection and the nasal microbiota composition. In the study, we included 31 animals from ten different litters after discarding animals not infected by PRRSV at 3 weeks of age. All animals included in this study developed typical clinical signs of PRRSV infection. Of those 31 animals, 16 survived until the end of the follow-up period (group S) while 15 died before that timepoint (group D). All animals included in the study were infected by PRRSV at the moment of collecting the nasal swabs; of these, 13 were also positive for IAV (8 from D and 5 from S groups). Samples taken from eight of the sows giving birth to these litters were also included (n = 8). Moreover, we disposed of some nasal swabs from 3-week-old piglets and their sows together with the clinical outcome information (survived or dead by 12 weeks of age), taken before the introduction of the highly virulent PRRSV strain. Thus, 8 samples from piglets (4 litters) and their respective sows (n = 4) were also included in the study for comparative purposes (S 0 , n = 4; and D 0 , n = 4). All of them were negative for PRRSV at the moment of sampling. Determination of PRRSV and IAV infections For the detection of PRRSV a commercial RT-qPCR kit was used (LSI Vetmax PRRSV EUNA 2.0, Thermofisher). The kit includes an internal positive control to ensure the adequacy of the RNA extraction process. IAV was detected in nasal swabs using an RT-qPCR, whose primers and probe have been described elsewhere ( 17 ). DNA extraction, 16S rRNA gene library preparation and sequencing Genomic DNA was extracted from nasal swabs with the NucleoSpin Blood kit (Machinery Nagel, GmbH & Co, Düren; Germany), following manufacturer’s instructions. The concentration of the eluted DNA was measured in a BioDrop DUO device (BioDrop Ltdre). Genomic libraries of the variable regions V3-V4 from 16S rRNA gene were prepared by PCR using Illumina recommended primers (fwd 5'TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCTACGGGNGGCWGCAG3’, rev 5'GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGACTACHVGGGTATCTAATCC3’) and sequencing was done with Illumina MiSeq pair-end (2X300 bp, MS-102–2003 MiSeq Re-agent Kit v2, 500 cycle) at the Servei de Genòmica, Universitat Autònoma de Barcelona (Spain). The length of the amplicons (460 bp) was verified on a Bioanalyzer DNA 1000 chip (Agilent). After sorting reads into samples (demultiplexing), samples were further processed through bioinformatics analyses. Microbiota bioinformatics analysis Raw reads obtained in the sequencing were processed and analyzed using quantitative insights into microbial ecology (QIIME2) software, version 2023.9 ( 18 ). Then, primers were removed with q2 cutadapt ( 19 ). Quality filtering, low-quality 3’ positions removal, paired-end merging, elimination of chimeras, and classification of reads into Amplicon Sequence Variants (ASVs) was performed with DADA2 ( 20 ) integrated as a QIIME2 plugin. Two additional filtering steps were performed to curate the pool of ASVs. Firstly, to remove any non-prokaryotic sequence, ASVs not matching Greengenes database ( 21 ) vs. 13.8. (available at http://greengenes.microbio.me/greengenes_release/gg_13_8_otus/ ) aligning at 65% identity and 50% query coverage were filtered out using VSEARCH ( 22 ) within q2 quality control ( 23 ). Secondly, non-bacterial sequences classified as Archaea , Chloroplast or Mitochondria using the same Greengenes 13.8 taxonomy were also discarded. Since the reads included in the analysis were obtained from two different runs, all data was merged for the downstream analysis. Q2 greengenes2 ( 24 ) was used to infer the taxonomy of the ASVs using the non-v4-16s function, where ASVs were mapped to unique 16S rRNA sequences in Greengenes2 (available at https://ftp.microbio.me/greengenes_release/current/ ) , referred as features. The phylogenetic tree was obtained from the same repository. Diversity analyses were performed using q2 diversity at a common depth of 1 8,090 , corresponding to the lowest sample depth. Alpha diversity was estimated with the Chao1 index for richness estimation ( 25 ), and the Shannon index ( 26 ), which combines richness and evenness measures. The significance of the differences between the study groups were tested with pairwise t-tests (999 random permutations), included in alpha-group-significance ( 27 ). Jaccard ( 28 ) and Bray-Curtis ( 29 ) dissimilarity indexes were used to calculate the qualitative and quantitative beta diversity (diversity between samples), respectively. Distance matrices were computed and principal coordinate analysis (PCoA) and PCoA biplots ( 30 , 31 ) were performed using core-metrics and visualized using Emperor ( 32 ). The effect size of the variables under study was measured using the Adonis function from vegan package (R software) ( 33 ), by nesting the variables. To diminish the run effect, it was always included as the first variable in the nested formulas. The significance of the comparisons was calculated with PERMANOVA pairwise tests (999 random permutations) using beta-group-significance ( 34 ). To detect differentially abundant taxa among study groups, three different methods were followed. First, the Songbird ( 35 ) differential ranking method was used as a QIIME2 plugin to rank the features with the covariates in this study. Second, two different and complementary methods were used to compare and validate the associations: dsf-dr ( 36 ) and ANCOM with bias correction ( 37 ), both also used as QIIME2 plugins. The functional prediction from the inferred metagenome was done with PICRUSt2 ( 38 ), using EPA-NG ( 39 ) for phylogenetic placement of reads, KEGG orthologs database ( 40 ) to infer gene families and functional pathways and modules with MinPath ( 41 ). To do so, all singletons (features present only in one sample and/or one time) were removed. To visualize and compute statistics on PICRUSt2 output, Statistical Analysis of Microbial Profiles (STAMP) ( 42 ) was used. Welch’s test ( 43 ) with Bonferroni ( 44 ) correction were used to find differentially abundant modules between the study groups. KEGG modules were also inferred with the reconstruction function of the KEGG-mapper online tool (available in https://www.genome.jp/kegg/mapper/reconstruct.html ) and compared qualitatively between the groups under study (presence/absence and degree of completeness of the modules). In all statistical tests, significance was set at P < 0.05. Microbiome analysis output data was further processed and used to create figures and tables in RStudio version 2022.07.0 ( 45 ) with the packages qiime2r ( 46 ), reshape2 ( 47 ), ggplot2 ( 48 ) and tidyverse ( 49 ). Results The nasal microbiota composition differed according to the outcome of the piglets After raw read processing, 4,180 different ASVs were obtained with a mean frequency of 157,350 counts per sample. ASVs were mapped to Greengenes2 database and a total number of 1,447 features (greengenes2 IDs) was obtained, with a mean frequency of 123,725 counts per sample. When the nasal microbiota composition was compared between the animals that survived (group S; n = 16) or died (group D; n = 15) after the highly virulent PRRSV-1 outbreak, a higher bacterial richness was observed in group S than in group D (Chao1 index P = 0.001, Fig. 1 A), while no differences were observed according to the Shannon index ( P = 0.66). The composition differed also between groups S and D in the beta diversity analyses, which grouped into separate clusters in both qualitative and quantitative analyses (Jaccard and Bray-Curtis PERMANOVA P = 0.001; Fig. 1 B), with an estimated effect size of 8.3% and 23% for Jaccard and Bray-Curtis matrices, respectively. In this study, the piglets came from three types of litters: three litters where all piglets died, three where all piglets survived and four mixed litters (with piglets either dying or surviving). The percentage of explanation of the variable “litter of origin” was similar to the previously quantified outcome effect (R 2 = 7.6% for Jaccard and R 2 = 17.8% Bray-Curtis, respectively, P = 0.001, supplementary Fig. 1 ), indicating that this variable is a major driver of the difference between clinical outcomes. To assess the bias caused by the selection of litters, the litter was used as a nested variable in the analysis. Interestingly, after accounting for such bias, the outcome of the PRRSV infection was still significantly associated with the microbial composition (R 2 of 5.4% and 10.2% in Jaccard and Bray-Curtis analyses, respectively, P < 0.006). This combination of outcome and litter effects would help to explain outlier samples, some of them clustering with animals from the same litter rather than with other piglets that had a similar outcome (such as samples #108 and #93, see supplementary Fig. 1 ), and vice-versa, i.e. animals clustering by outcome instead of litter (#87, #101 and #105). To unveil the microbial composition, the features found in the microbiota were studied at different taxonomic levels. The nasal microbiota of the piglets from group S was dominated by Weeksellaceae (22.59% ± 10.64, Neisseriaceae (19.75% ± 10.31), Moraxellaceae (19.55% ± 16.20) and Pasteurellaceae (18.12% ± 11.54), in similar proportions ( supplementary Fig. 2A ), while the nasal microbiota from group D was dominated by Enterobacteriaceae (28.57 ± 24.75%) and Moraxellaceae (28.06% ± 18.00). At genus level ( supplementary Fig. 2B ), the nasal microbiota of the piglets from group S was dominated by taxa commonly found in the respiratory tract of pigs, such as Bergeyella (22.57% ± 10.65), Neisseria (originally classified as Eikenella , but confirmed as Neisseria in other databases, i.e. NCBI and Greengenes 13_8) (19.73% ± 10.33), Glaesserella (15.20% ± 11.7) and Moraxella A (12.99% ± 16.88). In the case of piglets from group D, the most relatively abundant genera were Escherichia (28.17% ± 24.47) and Moraxella C (22.92% ± 18.65), followed by Bergeyella (11.66% ± 4.85). The complete list of microbiota composition at all taxonomic levels can be found in supplementary Table 1 . To study in more detail the taxonomic differences between groups S and D, we performed a differential ranking model (Songbird). Eighty-seven features with more than 0.01% mean relative abundance in at least one group (considered non-artifacts) were identified by the model as associated with either group S or group D ( supplementary Table 2 ). Among all the significant differential features, forty-eight presented more than 1 log 10 -fold change between groups, (Fig. 2 A), where features classified as Mesomycoplasma hyorhinis (present only in 4 animals from group S and totally absent in group D), Caryophanon latum and an unclassified Acinetobacter species showed the highest association with group S, while Suipraeoptans intestinalis , Escherichia_710834 and Prevotella heparinolytica were the most associated with group D. Regarding the most abundant differential taxa (Fig. 2 B), several features from common nasal colonizers were found in higher relative abundances in group S compared to D, such as Bergeyella , Glaesserella parasuis , Moraxella A and Neisseria (despite some of these presented a negative fold change towards group S). Conversely, in group D, two features classified as Escherichia were associated with group D (25.03% and 3.65% mean relative abundance, respectively) together with Moraxella C and Rothia , all of them more abundant in this group. When the same songbird model was computed at family and genus level, most of the taxa associated to either S or D groups were the families and genera corresponding to the features indicated above (see supplementary Table 2 ). Moreover, a biplot analysis showed that Moraxella C and Escherichia were the most informative taxa for group D in both the quantitative and qualitative beta diversity analysis. On the other hand, another member of the Moraxella genus (Moraxella A), together with Neisseria, Glaesserella and Pasteurella , contributed the most to the clustering of group S ( supplementary Fig. 3A and B ). Similar results were obtained using ANCOM-BC and dsf-dr, which also reflected most of the same features, genera and families as differentially abundant ( supplementary Table 3 ), reinforcing the results found by the Songbird model. To study in more detail the different communities found in the two groups, a functional inference of the predicted metagenomic composition was performed. The principal component analysis of the predicted functions from the KEGG public database showed clustering according to the outcome of the infection (PERMANOVA using Bray-Curtis distance matrix P = 0.001, Fig. 3 A), suggesting a different abundance of the functional capabilities for the nasal microbiota of piglets from each group. Moreover, group D exhibited a higher level of dispersion in the main PCA space compared to group S (PERMDISP using Bray-Curtis distance matrix P = 0.047), showing that the microbiota was quantitatively less similar within group D. Then, the predicted microbial capabilities of communities S and D were qualitatively assessed by comparing the presence and absence of KEGG genes and modules. A similar number of KO genes was predicted for groups S and D (6,319 and 6,273, respectively), which made up an almost equal ensemble of modules ( supplementary Table 4 ). In agreement, no statistically significant differences were detected between the two groups (PERMANOVA and PERMDISP using Jaccard distances p > 0.05). Only a few amino acid and glycan metabolism modules, as well as methicillin resistance were missing or showed to be incomplete in the microbiome of D group, while two multidrug resistance efflux pumps and shiga toxin were missing or showed an incomplete module in group S. Quantitative analyses were performed by submitting these inferred modules to differential analysis, where thirteen of these were found as significantly different between the two study groups; seven of them enriched in group D and six in group S (Fig. 3 B). Microbiota-related susceptibility existed in piglets before PRRSV-1 outbreak For comparative purposes, samples from same aged piglets that died or survived before the outbreak caused by the new PRRSV strain were also analysed (S 0 , n = 4; and D 0 , n = 4), as well as samples from sows (n = 8 and n = 4, after and before the outbreak, respectively). In the beta diversity analysis (Fig. 4 ), these samples clustered with the corresponding outcome group, i.e. S 0 with S, and D 0 with D. (PERMANOVA P = 0.001, with an estimated effect size of 7.44% and 20.08% o for Jaccard and Bray-Curtis indexes, respectively). When the analysis was performed including only S 0 and D 0 , despite the low number of samples prevented to find significant differences in alpha diversity, these groups showed to form two separated clusters in the beta diversity analysis (Jaccard PERMANOVA P = 0.022, and a tendency using Bray-Curtis P = 0.082). When the different tests were run to find differentially abundant taxa (the Songbird differential ranking model was not run due to the low number of samples), only ANCOM-BC reported a few low-abundant taxa ( supplementary table 5 ). Nevertheless, the two groups reported a similar composition to S and D groups, respectively ( supplementary Fig. 4 ). On the other hand, the nasal microbiota of sows giving birth to healthy or mixed litters reported a higher richness than sows giving birth to piglets that died (chao1 index P < 0.05, supplementary Fig. 5A ), suggesting that the differences observed in richness could be traced back to the sows. No significant differences in beta diversity were detected between any of these groups of sows. However, when the communities of the two timepoints were compared (before and after the entrance of the virulent PRRSV strain), the microbiota significantly varied (Jaccard and Bray-Curtis PERMANOVA P < 0.006, Adonis R 2 effect size of 11.7% and 16.4% respectively, supplementary Fig. 5B ). Among the differences found in the sows between these two timepoints ( supplementary table 6 ) , several taxa found were previously identified discriminating S or D groups, such as Rothia , Neisseria , Moraxella C or Bergeyella , more relatively abundant in sows before the outbreak; or Fusobacterium and Bacteroides , which were increased in sows during the outbreak. Discussion In this study, the composition of the nasal microbiota in a cohort of piglets infected by a highly virulent PRRSV-1 strain was analyzed. Despite being equally infected by the same PRRSV-1 strain, only a group of piglets had a fatal outcome in the end of the follow-up period. Our results indicated that survival was related to a higher species richness in the nasal microbial community as well as with the dominance of certain bacterial taxa. Several studies have reported that healthy animals have higher bacterial diversity compared to diseased pigs ( 9 , 11 , 50 – 52 ) and, in general, it is considered that lower bacterial diversity is a marker for impaired health ( 53 , 54 ). In the present case, lower richness was associated with a fatal outcome despite all the animals were infected by PRRSV. Previous studies targeting the swine fecal microbiota role in PRRSV and PCV2 infections outcome ( 11 , 12 ) yielded similar results. Besides, changes in the microbiota composition were also related with the outcome of the disease, especially regarding its’ most abundant members, as shown by a major significance level in the quantitative analysis compared to the qualitative one. Similarly, changes in the fecal microbiota populations were also related with PRRSV and PCV2 outcome ( 11 , 12 ). In our study, surviving piglets reported a high abundance of genera frequently associated with a healthy swine respiratory tract such as Bergeyella , Neisseria ( Eikenella ), Glaesserella and Moraxella ( Moraxella _A) ( 50 , 53 , 55 – 58 ). In agreement, most of these taxa were found to contribute the most in the clustering of group S samples in the PCoA biplot analysis. On the contrary, Escherichia was significantly more abundant in piglets that died. Previous studies reported that taxa commonly found in the fecal microbiota or from the environment can be present in the pig nasal microbiota ( 53 , 58 , 59 ), and become predominant when the professional colonizers are not present ( 60 , 61 ). Our results are compatible with a dysbiosis in the nasal microbiota of the piglets that died, which resulted in an increased relative abundance of fecal bacteria, especially Escherichia . However, also a different clade of Moraxella ( Moraxella _C) and Rothia , common colonizers of healthy piglets, were among the most abundant taxa associated with the fatal outcome. The presence of these common colonizers of the pig respiratory tract ( 53 , 58 ) may indicate that these genera are more resilient to the disturbance of the nasal microbial community and proportionally increase when other taxa decreased. A previous study focusing on the nasal microbiota changes after challenging the animals with PRRSV and IAV ( 14 ) associated Weeksellaceae and Neisseriaceae with PRRSV infected groups while Enterobacteriaceae with uninfected. Despite these results may seem opposite to ours’, Chrun et al. focused on immediate microbiota changes after challenge in PRRSV-free animals, while in this study two groups with different outcome in a virulent PRRSV-1 endemic farm were analyzed. Most of the taxa with the highest fold change between the two study groups were found in low relative abundance ( Mesomycoplasma hyorhinis , Caryophanon latum and Acinetobacter associated with group S, and Suipraeoptans intestinalis , Prevotella heparinolytica with group D). Despite most of these taxa have a relationship with the swine nasal and/or gastrointestinal microbiota ( 52 , 58 , 59 , 62 ), the biological significance of these changes in the present study is still unknown and deserve further investigation. Also, features from other taxa frequently found in the swine respiratory tract, such as Lactobacillus , Streptococcus or Prevotella ( 53 , 58 , 59 ) were associated with both disease outcomes, suggesting that different strains from the same genera could play different roles and be involved in disease outcome. The functional analysis also reflected differences between the examined groups of pigs, especially quantitatively, in agreement with the beta diversity analysis. Despite few differences were found in the qualitative comparison of the inferred functions, group D exhibited less glycan and amino acid metabolic modules (more incomplete or absent), suggesting some loss of functions in the microbiota of this group. Moreover, quantitative PCA analysis of functional modules revealed a bigger dispersion in group D pointing towards a less-uniform functionality in a more unstable (or unspecific) microbial community. In agreement with our findings, group dissimilarity can be related to dysbiotic microbiotas in unhealthy animals ( 63 ). In this study, samples collected before the introduction of the new PRRSV strain were also analyzed. Despite the reduced number of samples examined, animals that died or survived before the outbreak had a similar microbiota composition compared with animals sampled after the outbreak. This finding suggests that this highly virulent PRRSV strain was not the cause of the changed microbiota composition between the different outcome groups. In fact, it is more likely that the microbiota would be a predisposing factor for survivability or susceptibility while the virulence of the new PRRSV strain would be responsible for the increased mortality. The microbiota composition as a factor of susceptibility has been highly studied because the huge impact it can have on host immunity and pathogen exclusion ( 64 ). In pigs, this microbiota-related susceptibility has already been demonstrated in the case of bacterial and viral pathogens ( 11 , 12 , 50 , 52 , 65 – 67 ). The findings in this study reported a different microbiota in animals with good outcome which could be protective for PRRSV but also in secondary infections that may arise in diseased animals. Nevertheless, the examination of the underlying mechanisms explaining the predictive value of the nasal microbiota composition is beyond the scope of the present study but merits further investigation. Finally, it is worth noting that belonging to a given litter had a strong influence on the composition of the nasal microbiota and thus, in the outcome of the animal. This is in agreement with studies that highlighted the role of the sows in the piglets’ microbiota development ( 60 , 68 , 69 ). In fact, there were two samples from piglets clustering with their siblings rather than with piglets with the same clinical outcome (one animal clustering with the survivors, died, and vice versa). This can be simply explained by the individual (diverse) resilience of the animals or because some animals may have died from causes other than the PRRSV infection. However, there was still an outcome effect acting independently of the litter, as shown by animals from mixed litters whose samples clustered by outcome and detected in the litter-nested beta diversity analysis. Interestingly, the differences observed in the alpha diversity of the piglets were a reflection of the differences observed in their dams, since sows giving birth to surviving and mixed litters reported a higher microbial richness than those to a litter with fatal outcome. The idea that sows with a rich microbiota transfer “more” than sows with a less rich microbiota is in agreement with previous studies discussing the sow-to-piglet microbiota transmission ( 60 , 69 , 70 ). Moreover, the sows’ microbiota showed substantial changes before and after the PRRSV outbreak. However, these variations could be more likely related to analyzing a low number of samples from different sows or changes in environmental and/or farming conditions. Further studies may aid confirming whether these changes observed in sows are caused by this new virus variant and more importantly, whether these variations can be related to an increase in mortality between the two timepoints. Conclusion In conclusion, this study suggests that the nasal microbiota could be closely related to virulent PRRSV-1 survival. Specifically, we show that high species richness and the presence of common nasal colonizing bacteria may be the key to a good outcome, while dysbiosis seems to be related to a bad outcome. At last, the analysis of samples taken before the outbreak indicates that such differences between piglets were already present, and the increased PRRSV-1 virulence triggered a fatal outcome in susceptible animals. Future studies may help explaining the connection between such microbiota characteristics and PRRSV susceptibility/resistance as well as investigating whether this microbiota-related susceptibility also exists in other viral respiratory infections. Moreover, this investigation serves as starting point towards exploring the potential of the nasal microbiota in the prevention of viral respiratory infections. Declarations Ethics approval and consent to participate The present project was carried out after approval by the Ethics in Human and Animal Experimentation Committee (nº 5691) and followed all national and EU regulations. The farm consented the participate in the study after being informed of the procedures to be carried out. Availability of data and material The raw data used in this study are available at SRA database (NCBI) under BioProject ID PRJNA1132954. Processed data is available as supplementary tables. Competing interests The authors declare no competing interest Funding This project was funded by project PCI2020-120688 from the Spanish “Agencia Estatal de investigaciónn” and by project ICRAD ID86-PIGIE of the European Union. POG is supported by FPU (FPU19/02126) fellowship from Spanish Government. Authors' contributions POG performed the analysis. FCF and VA supervised the analysis. FCF, VA, MC and EM contributed to the discussion and interpretation of the results. GEMV, MC and EM designed the study. GEMV and HC performed the sampling of the animals and performed the viral detection. POG drafted the manuscript with contributions from all authors. All authors approve the final submitted version. Acknowledgements Our grateful thanks to Dr. Cristina Laza for helping us in selecting the farms, for veterinary supervision of the farm and the sampling. References Zimmerman J, Dee SA, Holtkamp DJ, Murtaugh MP, Stadejek T, Stevenson GW, Torremorell M, Yang H, Zang J. 2019. Porcine Reproductive and Respiratory Syndrome Viruses (Porcine Arteriviruses). In: Diseases of swine 11th Edition, Zimmerman JJ, Karricker LA, Ramirez A, Schwartz KJ, Stevenson GW, Zhang J. (eds), John Wiley & Sons, Inc. Publishers, Hoboken (NJ), USA, 685-708. Trevisi P, Amatucci L, Ruggeri R, Romanelli C, Sandri G, Luise D, Canali M, Bosi P. Pattern of Antibiotic Consumption in Two Italian Production Chains Differing by the Endemic Status for Porcine Reproductive and Respiratory Syndrome. Front Vet Sci. 2022 Mar 28;9:840716. doi: 10.3389/fvets.2022.840716. PMID: 35419448; PMCID: PMC8996257. Lefkowitz, E. J., Dempsey, D. M., Hendrickson, R. C., Orton, R. J., Siddell, S. G., & Smith, D. B. (2018). Virus taxonomy: the database of the International Committee on Taxonomy of Viruses (ICTV). Nucleic acids research, 46(D1), D708–D717. https://doi.org/10.1093/nar/gkx932. Tian K, Yu X, Zhao T, Feng Y, Cao Z, Wang C, Hu Y, Chen X, Hu D, Tian X, Liu D, Zhang S, Deng X, Ding Y, Yang L, Zhang Y, Xiao H, Qiao M, Wang B, Hou L, Wang X, Yang X, Kang L, Sun M, Jin P, Wang S, Kitamura Y, Yan J, Gao GF. Emergence of fatal PRRSV variants: unparalleled outbreaks of atypical PRRS in China and molecular dissection of the unique hallmark. PLoS One. 2007 Jun 13;2(6):e526. doi: 10.1371/journal.pone.0000526. Karniychuk UU, Geldhof M, Vanhee M, Van Doorsselaere J, Saveleva TA, Nauwynck HJ. Pathogenesis and antigenic characterization of a new East European subtype 3 porcine reproductive and respiratory syndrome virus isolate. BMC Vet Res. 2010 Jun 4;6:30. doi: 10.1186/1746-6148-6-30. Martín-Valls GE, Cortey M, Allepuz A, Illas F, Tello M, Mateu E. Introduction of a PRRSV-1 strain of increased virulence in a pig production structure in Spain: virus evolution and impact on production. Porcine Health Manag. 2023 Jan 3;9(1):1. doi: 10.1186/s40813-022-00298-3. PMID: 36597152; PMCID: PMC9811746. Ruedas-Torres I, Rodríguez-Gómez IM, Sánchez-Carvajal JM, Larenas-Muñoz F, Pallarés FJ, Carrasco L, Gómez-Laguna J. The jigsaw of PRRSV virulence. Vet Microbiol. 2021 Sep;260:109168. doi: 10.1016/j.vetmic.2021.109168. Epub 2021 Jun 30. PMID: 34246042. Argüello H, Rodríguez-Gómez IM, Sánchez-Carvajal JM, Pallares FJ, Díaz I, Cabrera-Rubio R, Crispie F, Cotter PD, Mateu E, Martín-Valls G, Carrasco L, Gómez-Laguna J. Porcine reproductive and respiratory syndrome virus impacts on gut microbiome in a strain virulence-dependent fashion. Microb Biotechnol. 2022 Mar;15(3):1007-1016. doi: 10.1111/1751-7915.13757. Epub 2021 Mar 3. PMID: 33656781; PMCID: PMC8913879. Constance LA, Thissen JB, Jaing CJ, McLoughlin KS, Rowland RRR, Serão NVL, Cino-Ozuna AG, Niederwerder MC. Gut microbiome associations with outcome following co-infection with porcine reproductive and respiratory syndrome virus (PRRSV) and porcine circovirus type 2 (PCV2) in pigs immunized with a PRRS modified live virus vaccine. Vet Microbiol. 2021 Mar;254:109018. doi: 10.1016/j.vetmic.2021.109018. Epub 2021 Feb 16. PMID: 33639341. Gaire TN, Odland C, Zhang B, Ray T, Doster E, Nerem J, Dee S, Davies P, Noyes N. The impacts of viral infection and subsequent antimicrobials on the microbiome-resistome of growing pigs. Microbiome. 2022 Aug 4;10(1):118. doi: 10.1186/s40168-022-01312-0. PMID: 35922873; PMCID: PMC9351240. Ober RA, Thissen JB, Jaing CJ, Cino-Ozuna AG, Rowland RRR, Niederwerder MC. Increased microbiome diversity at the time of infection is associated with improved growth rates of pigs after co-infection with porcine reproductive and respiratory syndrome virus (PRRSV) and porcine circovirus type 2 (PCV2). Vet Microbiol. 2017 Sep;208:203-211. doi: 10.1016/j.vetmic.2017.06.023. Epub 2017 Aug 18. PMID: 28888639. Niederwerder MC, Jaing CJ, Thissen JB, Cino-Ozuna AG, McLoughlin KS, Rowland RR. Microbiome associations in pigs with the best and worst clinical outcomes following co-infection with porcine reproductive and respiratory syndrome virus (PRRSV) and porcine circovirus type 2 (PCV2). Vet Microbiol. 2016 May 30;188:1-11. doi: 10.1016/j.vetmic.2016.03.008. Epub 2016 Mar 11. PMID: 27139023. Gierse, L. C., Meene, A., Schultz, D., Schwaiger, T., Schröder, C., Mücke, P., Zühlke, D., Hinzke, T., Wang, H., Methling, K., Kreikemeyer, B., Bernhardt, J., Becher, D., Mettenleiter, T. C., Lalk, M., Urich, T., & Riedel, K. (2021). Influenza A H1N1 Induced Disturbance of the Respiratory and Fecal Microbiome of German Landrace Pigs - a Multi-Omics Characterization. Microbiology spectrum, 9(2), e0018221. https://doi.org/10.1128/Spectrum.00182-21. Chrun T, Leng J, La Ragione RM, Graham SP, Tchilian E. Changes in the Nasal Microbiota of Pigs Following Single or Co-Infection with Porcine Reproductive and Respiratory Syndrome and Swine Influenza A Viruses. Pathogens. 2021 Sep 22;10(10):1225. doi: 10.3390/pathogens10101225. Hau SJ, Nielsen DW, Mou KT, Alt DP, Kellner S, Brockmeier SL. Resilience of swine nasal microbiota to influenza A virus challenge in a longitudinal study. Vet Res. 2023 May 2;54(1):38. doi: 10.1186/s13567-023-01167-9. PMID: 37131235; PMCID: PMC10152739. Martín-Valls, G. E., Cortey, M., Allepuz, A., Illas, F., Tello, M., & Mateu, E. (2022). Description of a New Clade within Subtype 1 of Betaarterivirus suid 1 Causing Severe Outbreaks in Spain. Microbiology resource announcements, 11(7), e0030422. https://doi.org/10.1128/mra.00304-22. Busquets N, Segalés J, Córdoba L, Mussá T, Crisci E, Martín-Valls GE, Simon-Grifé M, Pérez-Simó M, Pérez-Maíllo M, Núñez JI, Abad FX, Fraile L, Pina S, Majó N, Bensaid A, Domingo M, Montoya M. Experimental infection with H1N1 European swine influenza virus protects pigs from an infection with the 2009 pandemic H1N1 human influenza virus. Vet Res. 2010 Sep-Oct;41(5):74. doi: 10.1051/vetres/2010046. Epub 2010 Jul 29. PMID: 20663475; PMCID: PMC2939699. Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA, Alexander H, Alm EJ, Arumugam M, Asnicar F, Bai Y, Bisanz JE, Bittinger K, Brejnrod A, Brislawn CJ, Brown CT, Callahan BJ, Caraballo-Rodríguez AM, Chase J, Cope EK, Da Silva R, Diener C, Dorrestein PC, Douglas GM, Durall DM, Duvallet C, Edwardson CF, Ernst M, Estaki M, Fouquier J, et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol. 2019;37:852–857. doi: 10.1038/s41587-019-0209-940. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet. journal, 17(1):pp–10, 2011. doi:10.14806/ej.17.1.200. Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581–583. doi: 10.1038/nmeth.3869. McDonald D, Price MN, Goodrich J, Nawrocki EP, DeSantis TZ, Probst A, Andersen GL, Knight R, Hugenholtz P. An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea. ISME J. 2012;6:610–618. doi: 10.1038/ismej.2011.139. Rognes T, Flouri T, Nichols B, Quince C, Mahé F. VSEARCH: a versatile open source tool for metagenomics. PeerJ. 2016;4:e2584. doi: 10.7717/peerj.2584. Camacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J, Bealer K, Madden TL. BLAST+: architecture and applications. BMC Bioinform. 2009;10:421. doi: 10.1186/1471-2105-10-421. McDonald D, Jiang Y, Balaban M, Cantrell K, Zhu Q, Gonzalez A, et al. Greengenes2 unifies microbial data in a single reference tree. Nat Biotechnol [Internet]. 2023 Jul 27; Available from: https://doi.org/10.1038/s41587-023-01845-1 Chao A. Nonparametric estimation of the number of classes in a population. Scand J Stat. 1984;11:4. Weaver WSCE. The mathematical theory of communication. Bell Syst Tech. 1949;27:379–423. Kruskal WH, Wallis WA. Use of ranks in one-criterion variance analysis. J Am Stat Assoc. 1952;47:583–621. doi: 10.1080/01621459.1952.10483441. Jaccard P (1908) Nouvelles recherches sur la distribution florale. Bull Société Vaud Des Sci Nat 44:223–270. Sørensen TJ. A method of establishing groups of equal amplitude in plant sociology based on similarity of species content and its application to analyses of the vegetation on danish commons. Kongelige Danske Videnskabernes Selskab. 1948;5:1–34. Halko N, Martinsson P-G, Shkolnisky Y, Tygert M. An algorithm for the principal component analysis of large data sets. SIAM J Sci Comput. 2011;33:2580–2594. doi: 10.1137/100804139. Legendre LLP. Numerical Ecology. 3. Elsevier; 2012. Vázquez-Baeza Y, Pirrung M, Gonzalez A, Knight R. EMPeror: a tool for visualizing high-throughput microbial community data. GigaSci. 2013;2:16. doi: 10.1186/2047-217X-2-16. Oksanen J, Simpson GL, Blanchet FG,Kindt R, Legendre P, Minchin PR, O’Hara RB, Solymos P, Stevens MHH, Szoecs E, Wagner H, Barbour M, Bedward M, Bolker B, Borcard D, Carvalho G, Chirico M, De Caceres M, Durand S, Evangelista HBA, FitzJohn R, Friendly M, Furneaux B, Hannigan G, Hill MP, Lahti L, McGlinn D, Ouellette MH, Cunha ER, Smith T, Stier A, Ter Braak CJF, Weedon J (2016) Vegan: Community Ecology Package. Retrieved from https://cran.r-project.org/package=vegan. Anderson MJ. A new method for non-parametric multivariate analysis of variance. Austral Ecol. 2001;26:32–46. doi: 10.1111/j.1442-9993.2001.01070.pp.x. Morton JT, Marotz C, Washburne A, Silverman J, Zaramela LS, Edlund A, et al. Establishing microbial composition measurement standards with reference frames. Nat Commun. 2019 Jun 20;10(1):2719. Jiang L, Amir A, Morton JT, Heller R, Arias-Castro E, Knight R. Discrete false-discovery rate improves identification of differentially abundant microbes. mSystems. 2017;2:e00092–e117. doi: 10.1128/mSystems.00092-17. Lin H, Peddada SD. Analysis of compositions of microbiomes with bias correction. Nat Commun. 2020;11:3514. doi: 10.1038/s41467-020-17041-7. [. Douglas GM, et al. PICRUSt2 for prediction of metagenome functions. Nat. Biotechnol. 2020;38:685–688. Pierre Barbera, Alexey M Kozlov, Lucas Czech, Benoit Morel, Diego Darriba, Tomáš Flouri, Alexandros Stamatakis, EPA-ng: Massively Parallel Evolutionary Placement of Genetic Sequences, Systematic Biology, Volume 68, Issue 2, March 2019, Pages 365–369, https://doi.org/10.1093/sysbio/syy054. Kanehisa M, Goto S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28:27–30. Ye Y, Doak TG. A parsimony approach to biological pathway reconstruction/inference for genomes and metagenomes. PLOS Comput. Biol. 2009;5:e1000465. Parks DH, Tyson GW, Hugenholtz P, Beiko RG. STAMP: statistical analysis of taxonomic and functional profiles. Bioinformatics. 2014;30:3123–3124. Welch, B. L. (1938). The significance of the difference between two means when the population variances are unequal. Biometrika, 29, 350-362. Adbi, H. (2007) Encyclopedia of Measurement and Statistics. Thousand Oaks, CA: Sage. RStudio Team (2020). RStudio: integrated development for R. RStudio, PBC, Boston. URL http://www.rstudio.com/. Bisanz J. Tutorial: integrating QIIME2 and R for data visualization and analysis using qiime2R (v0.99.6). 2021. Wickham H (2007). “Reshaping Data with the reshape Package.” Journal of Statistical Software, 21(12), 1–20. Wickham H. ggplot2: elegant graphics for data analysis. New York: Springer-Verlag; 2009. Wickham H, Averick M, Bryan J, Chang W, McGowan LD, François R, et al. Welcome to the tidyverse. J Open Source Softw. 2019;4:1686. Correa-Fiz F, Fraile L, Aragon V. Piglet nasal microbiota at weaning may influence the development of Glässer’s disease during the rearing period. BMC Genomics. 2016 May 26;17:404. doi: 10.1186/s12864-016-2700-8. PMID: 27230662; PMCID: PMC4881051. Correa-Fiz, F., Gonçalves Dos Santos, J. M., Illas, F., & Aragon, V. (2019). Antimicrobial removal on piglets promotes health and higher bacterial diversity in the nasal microbiota. Scientific reports, 9(1), 6545. https://doi.org/10.1038/s41598-019-43022-y. Blanco-Fuertes M, Correa-Fiz F, Fraile L, Sibila M, Aragon V. Altered Nasal Microbiota Composition Associated with Development of Polyserositis by Mycoplasma hyorhinis. Pathogens. 2021 May 14;10(5):603. doi: 10.3390/pathogens10050603. PMID: 34069250; PMCID: PMC8156107. Pirolo, M., Espinosa-Gongora, C., Bogaert, D., & Guardabassi, L. (2021). The porcine respiratory microbiome: recent insights and future challenges. Animal microbiome, 3(1), 9. https://doi.org/10.1186/s42523-020-00070-4. Carding, S., Verbeke, K., Vipond, D. T., Corfe, B. M., & Owen, L. J. (2015). Dysbiosis of the gut microbiota in disease. Microbial ecology in health and disease, 26, 26191. https://doi.org/10.3402/mehd.v26.26191. De Arriba L.M., Lopez-Serrano S., Galofre-Mila N., Aragon V. Characterisation of Bergeyella Spp. Isolated from the Nasal Cavities of Piglets. Vet. J. 2018;234:1–6. doi: 10.1016/j.tvjl.2018.01.004. Pena Cortes, L. C., LeVeque, R. M., Funk, J. A., Marsh, T. L., & Mulks, M. H. (2018). Development of the Tonsil Microbiome in Pigs and Effects of Stress on the Microbiome. Frontiers in veterinary science, 5, 220. https://doi.org/10.3389/fvets.2018.00220. Pena Cortes, L. C., LeVeque, R. M., Funk, J., Marsh, T. L., & Mulks, M. H. (2018). Development of the tonsillar microbiome in pigs from newborn through weaning. BMC microbiology, 18(1), 35. https://doi.org/10.1186/s12866-018-1176-x. Pirolo, M., Espinosa-Gongora, C., Alberdi, A., Eisenhofer, R., Soverini, M., Eriksen, E. Ø., Pedersen, K. S., & Guardabassi, L. (2023). Bacterial topography of the upper and lower respiratory tract in pigs. Animal microbiome, 5(1), 5. https://doi.org/10.1186/s42523-023-00226-y. Obregon-Gutierrez P, Bonillo-Lopez L, Correa-Fiz F, Sibila M, Segalés J, Kochanowski K, et al. Gut-associated microbes are present and active in the pig nasal cavity. Sci Rep. 2024 Apr 11;14(1):8470. Obregon-Gutierrez, P., Aragon, V., & Correa-Fiz, F. (2021). Sow Contact Is a Major Driver in the Development of the Nasal Microbiota of Piglets. Pathogens (Basel, Switzerland), 10(6), 697. https://doi.org/10.3390/pathogens10060697. Blanco-Fuertes, M., Sibila, M., Franzo, G., Obregon-Gutierrez, P., Illas, F., Correa-Fiz, F., & Aragón, V. (2023). Ceftiofur treatment of sows results in long-term alterations in the nasal microbiota of the offspring that can be ameliorated by inoculation of nasal colonizers. Animal microbiome, 5(1), 53. https://doi.org/10.1186/s42523-023-00275-3. Wylensek, D., Hitch, T. C. A., Riedel, T., Afrizal, A., Kumar, N., Wortmann, E., Liu, T., Devendran, S., Lesker, T. R., Hernández, S. B., Heine, V., Buhl, E. M., M D’Agostino, P., Cumbo, F., Fischöder, T., Wyschkon, M., Looft, T., Parreira, V. R., Abt, B., Doden, H. L., … Clavel, T. (2020). A collection of bacterial isolates from the pig intestine reveals functional and taxonomic diversity. Nature communications, 11(1), 6389. https://doi.org/10.1038/s41467-020-19929-w. Yeoman, C. J., & White, B. A. (2014). Gastrointestinal tract microbiota and probiotics in production animals. Annual review of animal biosciences, 2, 469–486. https://doi.org/10.1146/annurev-animal-022513-114149. Pickard, J. M., Zeng, M. Y., Caruso, R., & Núñez, G. (2017). Gut microbiota: Role in pathogen colonization, immune responses, and inflammatory disease. Immunological reviews, 279(1), 70–89. https://doi.org/10.1111/imr.12567. Espinosa-Gongora, C., Larsen, N., Schønning, K., Fredholm, M., & Guardabassi, L. (2016). Differential Analysis of the Nasal Microbiome of Pig Carriers or Non-Carriers of Staphylococcus aureus. PloS one, 11(8), e0160331. https://doi.org/10.1371/journal.pone.0160331. Wang, Q., Cai, R., Huang, A., Wang, X., Qu, W., Shi, L., Li, C., & Yan, H. (2018). Comparison of Oropharyngeal Microbiota in Healthy Piglets and Piglets With Respiratory Disease. Frontiers in microbiology, 9, 3218. https://doi.org/10.3389/fmicb.2018.03218. Li, Z., Wang, X., Di, D., Pan, R., Gao, Y., Xiao, C., Li, B., Wei, J., Liu, K., Qiu, Y., & Ma, Z. (2021). Comparative analysis of the pulmonary microbiome in healthy and diseased pigs. Molecular genetics and genomics : MGG, 296(1), 21–31. https://doi.org/10.1007/s00438-020-01722-5. Nowland, T. L., Plush, K. J., Barton, M., & Kirkwood, R. N. (2019). Development and Function of the Intestinal Microbiome and Potential Implications for Pig Production. Animals : an open access journal from MDPI, 9(3), 76. https://doi.org/10.3390/ani9030076. Niederwerder M.C. Role of the Microbiome in Swine Respiratory Disease. Vet. Microbiol. 2017;209:97–106. doi: 10.1016/j.vetmic.2017.02.017. Bonillo-Lopez L, Obregon-Gutierrez P, Huerta E, Correa-Fiz F, Sibila M, Aragon V. Intensive antibiotic treatment of sows with parenteral crystalline ceftiofur and tulathromycin alters the composition of the nasal microbiota of their offspring. Vet Res. 2023 Nov 24;54(1):112. doi: 10.1186/s13567-023-01237-y. Additional Declarations No competing interests reported. Supplementary Files figuresupp1.pdf Supplementary figure 1. Principal Coordinate Analysis of S (triangles) and D (spheres) groups using Bray-Curtis dissimilarity index. Different colours correspond to the litter of origin. Outlier samples to outcome or litter effect are shown specifically. figuresupp2A.pdf Supplementary figure 2. Most relatively abundant families (A) and genera (B) (global mean >1%) in S and D groups. figuresupp2B.pdf Supplementary figure 2. Most relatively abundant families (A) and genera (B) (global mean >1%) in S and D groups. figuresupp3.pdf Supplementary figure 3. Biplot Analysis of S (green) and D (red) groups with the four genera contributing the most to sample ordination shown in arrows (arrow size is indicative of the effect size) using Jaccard (A) and Bray-Curtis (B) dissimilarity indexes. figuresupp4A.pdf Supplementary figure 4. Most relatively abundant genera (A) and features (B) (global mean >1%) in S 0 and D 0 groups. figuresupp4B.pdf Supplementary figure 4. Most relatively abundant genera (A) and features (B) (global mean >1%) in S 0 and D 0 groups. figuresupp5.pdf Supplementary figure 5. Diversity analysis of the sows giving birth to the litters under study. A) Alpha diversity measured with Chao1 index of sows giving birth to dying (red), surviving (green) and mixed (blue) litters. Shape corresponds to the moment of sampling: prior (squares) or after (spheres) the arrival of the virulent PRRSV-1 strain. B) Principal Coordinate Analysis of the sows using Bray-Curtis dissimilarity index. Different colours represent the moment of sampling: prior (purple) or after (orange) the arrival of the virulent PRRSV-1 strain. Ellipses of confidence (95%) are calculated assuming a multivariate t-distribution of distances within the samples of each group. supptable1.xlsx Supplementary table 1. Microbiota composition (relative abundance) at all taxonomic levels of the samples of piglets under study. Colour of cells corresponds to groups. D = red, D 0 = purple, S = green, S 0 = light green. Taxonomic classification and amplicon sequences are provide for the feature level. supptable2.xlsx Supplementary table 2. Differentially abundant taxa at feature, genus and family level found between S and D groups using Songbird differential ranking method. Taxonomy, fold change, mean abundances in each group, and individual abundances in each sample are provided. Colour code corresponds to fold change S (green) or D (red) association. supptable3.xlsx Supplementary table 3. Differentially abundant taxa at feature, genus and family level found between S and D groups using ANCOM-BC and DSF-DR. Taxonomy, significance, mean abundances in each group, and individual abundances in each sample are provided. The abundances of the differential features in D 0 and S 0 groups are shown as well. supptable4.xlsx Supplementary table 4. KEGG modules found in S and D groups. The level of completeness for each metabolic module is indicated: complete, 1 module missing, 2 modules missing, incomplete and not present. Modules more complete in D group are highlighted in red. Modules more complete in S group are highlighted in green. supptable5.xlsx Supplementary table 5. Differentially abundant taxa at feature, genus and family level found between S 0 and D 0 groups using ANCOM-BC. Taxonomy, significance, mean abundances in each group, and individual abundances in each sample are provided. supptable6.xlsx Supplementary table 6. Differentially abundant taxa at feature, genus and family level found between sow groups using ANCOM-BC. Taxonomy, significance, mean abundances in each group, and individual abundances in each sample are provided. Colour code corresponds to timepoint: samples taken before in purple, and in orange, samples taken after PRRSV-1 outbreak. Cite Share Download PDF Status: Published Journal Publication published 17 Jan, 2025 Read the published version in Animal Microbiome → Version 1 posted Editorial decision: Revision requested 03 Dec, 2024 Reviews received at journal 03 Dec, 2024 Reviewers agreed at journal 21 Nov, 2024 Reviews received at journal 15 Nov, 2024 Reviewers agreed at journal 11 Nov, 2024 Reviewers invited by journal 29 Sep, 2024 Editor assigned by journal 18 Jul, 2024 Submission checks completed at journal 18 Jul, 2024 First submitted to journal 18 Jul, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4762608","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":334488012,"identity":"0bb1aaa3-407b-482f-b06b-557297423908","order_by":0,"name":"Pau Obregon-Gutierrez","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIiWNgGAWjYLACxgYI9SCBVC3MBgkMBkh8IrSwSTAQo8Wc/YwBw8cd9+Tk3XuPVTzc80eegf3w8wf4tFj25BgwzjxTbGx45lzajYRnBoYNPGmGeG0xOJBjwMzblpC4cUaO2Y2EAwaMDRIMBLScfwPWUg/SUgDUYt8gwf4Rv5YbEFsS5CVyzBiAWhIbJHjw22I541nBwZlnEgw38Jwxlkg4YJzcxpNTOAOfFnP+5I0PPu5IkJdv7zH8+OOAnG0/+/ENH/A6DIgPgBkHoCJs+JTDtICBPF73j4JRMApGwYgGANE9S30CeVyCAAAAAElFTkSuQmCC","orcid":"","institution":"Unitat mixta d’Investigació IRTA-UAB en Sanitat Animal, Centre de Recerca en Sanitat Animal (CReSA), Campus de la Universitat Autònoma de Barcelona (UAB)","correspondingAuthor":true,"prefix":"","firstName":"Pau","middleName":"","lastName":"Obregon-Gutierrez","suffix":""},{"id":334488013,"identity":"13a0d3f2-4c51-4ac9-ac07-9c056d518774","order_by":1,"name":"Martí Cortey","email":"","orcid":"","institution":"Universitat Autònoma de Barcelona","correspondingAuthor":false,"prefix":"","firstName":"Martí","middleName":"","lastName":"Cortey","suffix":""},{"id":334488015,"identity":"0e8540e1-0f29-4b36-a44e-58c53c2fce8a","order_by":2,"name":"Gerard E. Martín-Valls","email":"","orcid":"","institution":"Universitat Autònoma de Barcelona","correspondingAuthor":false,"prefix":"","firstName":"Gerard","middleName":"E.","lastName":"Martín-Valls","suffix":""},{"id":334488017,"identity":"1f8368f2-b91e-4e15-bc06-60e7e6a1b5a3","order_by":3,"name":"Hepzibar Clilverd","email":"","orcid":"","institution":"Universitat Autònoma de Barcelona","correspondingAuthor":false,"prefix":"","firstName":"Hepzibar","middleName":"","lastName":"Clilverd","suffix":""},{"id":334488020,"identity":"65d5001e-ffc2-4136-b963-c9ff1f548c6a","order_by":4,"name":"Florencia Correa-Fiz","email":"","orcid":"","institution":"Unitat mixta d’Investigació IRTA-UAB en Sanitat Animal, Centre de Recerca en Sanitat Animal (CReSA), Campus de la Universitat Autònoma de Barcelona (UAB)","correspondingAuthor":false,"prefix":"","firstName":"Florencia","middleName":"","lastName":"Correa-Fiz","suffix":""},{"id":334488021,"identity":"f4caf353-982d-48cf-9087-03c5fe4564a7","order_by":5,"name":"Virginia Aragón","email":"","orcid":"","institution":"Unitat mixta d’Investigació IRTA-UAB en Sanitat Animal, Centre de Recerca en Sanitat Animal (CReSA), Campus de la Universitat Autònoma de Barcelona (UAB)","correspondingAuthor":false,"prefix":"","firstName":"Virginia","middleName":"","lastName":"Aragón","suffix":""},{"id":334488022,"identity":"6a9ad354-82b4-4712-b586-bd99544f32bb","order_by":6,"name":"Enric Mateu","email":"","orcid":"","institution":"Universitat Autònoma de Barcelona","correspondingAuthor":false,"prefix":"","firstName":"Enric","middleName":"","lastName":"Mateu","suffix":""}],"badges":[],"createdAt":"2024-07-18 12:23:46","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4762608/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4762608/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s42523-024-00371-y","type":"published","date":"2025-01-17T15:57:11+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":62360385,"identity":"bd079d0a-f55e-4a79-8fdf-6968e44c978a","added_by":"auto","created_at":"2024-08-13 10:05:09","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":51134,"visible":true,"origin":"","legend":"\u003cp\u003e\u0026nbsp;A) Alpha diversity measured with chao1 index of S (green) and D (red) groups. B) Principal Component Analysis of S ( green) and D (red) groups using Bray-Curtis dissimilarity index. Ellipses of confidence (95%) are calculated assuming a multivariate t-distribution of distances within the samples of each group.\u003c/p\u003e","description":"","filename":"Binder11.png","url":"https://assets-eu.researchsquare.com/files/rs-4762608/v1/67b689c64f14101cb89f1cae.png"},{"id":62359916,"identity":"92d11174-7b72-45dd-afa5-016fe0b645c5","added_by":"auto","created_at":"2024-08-13 09:57:09","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":154189,"visible":true,"origin":"","legend":"\u003cp\u003eSongbird model differentially abundant taxa associated to group S (green) or D (red). A) Features with fold change \u0026gt; 1. B) Abundance of the 10 features identified by the model that are most relatively abundant (globally). Each dot represents a sample.\u003c/p\u003e","description":"","filename":"Binder12.png","url":"https://assets-eu.researchsquare.com/files/rs-4762608/v1/13e20b14a6f354b077442a38.png"},{"id":62359912,"identity":"2d48494c-07c5-47de-85dd-2113c9325484","added_by":"auto","created_at":"2024-08-13 09:57:09","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":66060,"visible":true,"origin":"","legend":"\u003cp\u003ePICRUSt2 functional prediction of group S (green) or D (red). A) Principal Component Analysis of the functional KEGG orthologs database modules predicted from the inferred metagenome composition. B) Differentially abundant modules found between the two study groups.\u003c/p\u003e","description":"","filename":"Binder13.png","url":"https://assets-eu.researchsquare.com/files/rs-4762608/v1/fdb6929fb00cfb8cecd6de19.png"},{"id":62359915,"identity":"5d7dc57d-b8d7-4e9e-99cf-29bf1b2497ae","added_by":"auto","created_at":"2024-08-13 09:57:09","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":43664,"visible":true,"origin":"","legend":"\u003cp\u003eA) Alpha diversity measured with Chao1 index of S\u003csub\u003e0\u003c/sub\u003e (green) and D\u003csub\u003e0\u003c/sub\u003e (red) groups, in squares, and S (green) and D (red) groups, in spheres. B) Principal Coordinate Analysis of S and S\u003csub\u003e0\u003c/sub\u003e (green) with D and D\u003csub\u003e0\u003c/sub\u003e (red) groups using Bray-Curtis dissimilarity index. Shape corresponds to the moment of sampling (before or after the virulent PRRSV-1 outbreak in squares or spheres, respectively).\u003c/p\u003e","description":"","filename":"Binder14.png","url":"https://assets-eu.researchsquare.com/files/rs-4762608/v1/6bc79e4d254a75fbfb5c05c6.png"},{"id":74284504,"identity":"8fb701ef-8404-4fd9-942e-6544f5f7c010","added_by":"auto","created_at":"2025-01-20 16:08:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1112728,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4762608/v1/d2c525fb-ea67-4a39-bd5d-596a98bfe4b2.pdf"},{"id":62361254,"identity":"f4761f01-e61b-46f2-a931-bb922c9b68c0","added_by":"auto","created_at":"2024-08-13 10:13:09","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":33420,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary figure 1. Principal Coordinate Analysis of S (triangles) and D (spheres) groups using Bray-Curtis dissimilarity index. Different colours correspond to the litter of origin. Outlier samples to outcome or litter effect are shown specifically.\u003c/p\u003e","description":"","filename":"figuresupp1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4762608/v1/2448459b433c84586922fb5d.pdf"},{"id":62359925,"identity":"41704da5-8814-4782-aeba-7ee7e3ebe8d4","added_by":"auto","created_at":"2024-08-13 09:57:10","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":54842,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary figure 2. Most relatively abundant families (A) and genera (B) (global mean \u0026gt;1%) in S and D groups.\u003c/p\u003e","description":"","filename":"figuresupp2A.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4762608/v1/d84cbb7b7f61729d8a558ca0.pdf"},{"id":62360387,"identity":"38792521-19b3-434d-aa69-9ab0534f2e7e","added_by":"auto","created_at":"2024-08-13 10:05:09","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":58151,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary figure 2. Most relatively abundant families (A) and genera (B) (global mean \u0026gt;1%) in S and D groups.\u003c/p\u003e","description":"","filename":"figuresupp2B.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4762608/v1/af5bb42d73c12ca7ebd0c467.pdf"},{"id":62359927,"identity":"eb094675-1a53-4a77-897f-5cd509102b02","added_by":"auto","created_at":"2024-08-13 09:57:10","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":124312,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary figure 3. Biplot Analysis of S (green) and D (red) groups with the four genera contributing the most to sample ordination shown in arrows (arrow size is indicative of the effect size) using Jaccard (A) and Bray-Curtis (B) dissimilarity indexes.\u003c/p\u003e","description":"","filename":"figuresupp3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4762608/v1/b504b67e64b37831cd6ee9c4.pdf"},{"id":62360388,"identity":"6a20b7ab-fad7-490f-8a95-2cab7db34f4f","added_by":"auto","created_at":"2024-08-13 10:05:09","extension":"pdf","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":32748,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary figure 4. Most relatively abundant genera (A) and features (B) (global mean \u0026gt;1%) in S\u003csub\u003e0\u003c/sub\u003e and D\u003csub\u003e0\u003c/sub\u003e groups.\u003c/p\u003e","description":"","filename":"figuresupp4A.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4762608/v1/a0d89fd529a9d20c32f77e76.pdf"},{"id":62359918,"identity":"9f5f6f9a-a482-4b3d-87ae-1850a218b740","added_by":"auto","created_at":"2024-08-13 09:57:09","extension":"pdf","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":48289,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary figure 4. Most relatively abundant genera (A) and features (B) (global mean \u0026gt;1%) in S\u003csub\u003e0\u003c/sub\u003e and D\u003csub\u003e0\u003c/sub\u003e groups.\u003c/p\u003e","description":"","filename":"figuresupp4B.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4762608/v1/d6dd0160cb35886fbdef0dfb.pdf"},{"id":62359921,"identity":"6427638d-dd88-4074-a50f-52ad62beb528","added_by":"auto","created_at":"2024-08-13 09:57:09","extension":"pdf","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":24183,"visible":true,"origin":"","legend":"\u003cp\u003e\u0026nbsp;Supplementary figure 5. Diversity analysis of the sows giving birth to the litters under study. A) Alpha diversity measured with Chao1 index of sows giving birth to dying (red), surviving (green) and mixed (blue) litters. Shape corresponds to the moment of sampling: prior (squares) or after (spheres) the arrival of the virulent PRRSV-1 strain. B) Principal Coordinate Analysis of the sows using Bray-Curtis dissimilarity index. Different colours represent the moment of sampling: prior (purple) or after (orange) the arrival of the virulent PRRSV-1 strain. Ellipses of confidence (95%) are calculated assuming a multivariate t-distribution of distances within the samples of each group.\u003c/p\u003e","description":"","filename":"figuresupp5.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4762608/v1/de9969c0a16389ea59ca48a1.pdf"},{"id":62361256,"identity":"4ada51b6-2ba3-4fbb-bc56-7a488c9513cd","added_by":"auto","created_at":"2024-08-13 10:13:09","extension":"xlsx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":527423,"visible":true,"origin":"","legend":"\u003cp\u003e\u0026nbsp;Supplementary table 1. Microbiota composition (relative abundance) at all taxonomic levels of the samples of piglets under study. Colour of cells corresponds to groups. D = red, D\u003csub\u003e0\u003c/sub\u003e = purple, S = green, S\u003csub\u003e0\u003c/sub\u003e = light green. Taxonomic classification and amplicon sequences are provide for the feature level.\u003c/p\u003e","description":"","filename":"supptable1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4762608/v1/5c2dfc0b0e397bb38109c307.xlsx"},{"id":62359928,"identity":"2a427b37-d489-41ed-8ce1-6490f7c843aa","added_by":"auto","created_at":"2024-08-13 09:57:10","extension":"xlsx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":109051,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary table 2. Differentially abundant taxa at feature, genus and family level found between S and D groups using Songbird differential ranking method. Taxonomy, fold change, mean abundances in each group, and individual abundances in each sample are provided. Colour code corresponds to fold change S (green) or D (red) association.\u003c/p\u003e","description":"","filename":"supptable2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4762608/v1/e51d7977d3e979b685d468e8.xlsx"},{"id":62361255,"identity":"c4bd5c32-255a-47f9-a18a-7b80b870ed55","added_by":"auto","created_at":"2024-08-13 10:13:09","extension":"xlsx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":52251,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary table 3. Differentially abundant taxa at feature, genus and family level found between S and D groups using ANCOM-BC and DSF-DR. Taxonomy, significance, mean abundances in each group, and individual abundances in each sample are provided. The abundances of the differential features in D\u003csub\u003e0\u003c/sub\u003e and S\u003csub\u003e0\u003c/sub\u003e groups are shown as well.\u003c/p\u003e","description":"","filename":"supptable3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4762608/v1/d5bd776329ba1f626c893639.xlsx"},{"id":62360392,"identity":"683a76dc-399a-4d54-b6bf-d96861b6b98d","added_by":"auto","created_at":"2024-08-13 10:05:09","extension":"xlsx","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":22201,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary table 4. KEGG modules found in S and D groups. The level of completeness for each metabolic module is indicated: complete, 1 module missing, 2 modules missing, incomplete and not present. Modules more complete in D group are highlighted in red. Modules more complete in S group are highlighted in green.\u003c/p\u003e","description":"","filename":"supptable4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4762608/v1/5214212226fa93290cce1a94.xlsx"},{"id":62360390,"identity":"90dba28a-57c2-41f7-b113-827d737aad51","added_by":"auto","created_at":"2024-08-13 10:05:09","extension":"xlsx","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":11542,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary table 5. Differentially abundant taxa at feature, genus and family level found between S\u003csub\u003e0\u003c/sub\u003e and D\u003csub\u003e0\u003c/sub\u003e groups using ANCOM-BC. Taxonomy, significance, mean abundances in each group, and individual abundances in each sample are provided.\u003c/p\u003e","description":"","filename":"supptable5.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4762608/v1/e478a3e474ca4c99e2c5ccbf.xlsx"},{"id":62359926,"identity":"254187b8-1746-4302-8bc1-f767735d07bd","added_by":"auto","created_at":"2024-08-13 09:57:10","extension":"xlsx","order_by":13,"title":"","display":"","copyAsset":false,"role":"supplement","size":19859,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary table 6. Differentially abundant taxa at feature, genus and family level found between sow groups using ANCOM-BC. Taxonomy, significance, mean abundances in each group, and individual abundances in each sample are provided. Colour code corresponds to timepoint: samples taken before in purple, and in orange, samples taken after PRRSV-1 outbreak.\u003c/p\u003e","description":"","filename":"supptable6.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4762608/v1/a62d63834b56b2a1f5167a74.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Nasal microbial diversity is associated with survival in piglets infected by a highly virulent PRRSV-1 strain","fulltext":[{"header":"Introduction","content":"\u003cp\u003eFor the last three decades, porcine reproductive and respiratory syndrome virus (PRRSV) has established as one of the major pathogens of swine worldwide. In sows, PRRSV infection is characterized by outbreaks of reproductive disease, which hallmark feature is abortion of fresh or mummified foetuses after day 90 of gestation. Stillbirths, weak-born piglets and an increase in pre-weaning mortality are other common consequences of PRRSV infection (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). In piglets, the infection manifests as a respiratory disease that is usually complicated with other viral or bacterial respiratory agents. Furthermore, the association of secondary bacterial infections leads to increased use of antimicrobials and mortality in piglets (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePRRSV belongs to the genus \u003cem\u003eBetaarterivirus\u003c/em\u003e within the family \u003cem\u003eArteriviridae\u003c/em\u003e, Order \u003cem\u003eNidovirales\u003c/em\u003e. Two PRRSV species are recognised, PRRSV-1 and PRRSV2 (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). One of the key features of both PRRSV species is the high genetic and antigenic diversity and the existence of strains of diverse virulence, among which the so-called highly pathogenic PRRSV that emerged in China in 2006 has been one of the most devastating ones (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Once, it was thought that highly virulent PRRSV strains only existed for PRRSV2, while PRRSV-1 was considered of lower virulence. However, the description of strains such Lena and Rosalia (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) made clear that highly virulent strains also exist within PRRSV-1. Besides causing severe abortion storms and mortality of sows, highly virulent strains can cause high mortalities in the farrowing units and nurseries of the affected farms. This increased mortality is probably the result of a combination of factors, including the high viral loads and the consequent inflammatory response, the induction of apoptosis in the thymus of the infected piglets and the frequency of secondary infections (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe role of the microbiota in health and disease has been extensively studied and nowadays is one of the major research areas in biomedical sciences. However, in the veterinary field the available information is still limited. In previous studies, it was shown that PRRSV infection impacts the composition of the gut microbiome and that this impact depended on the virulence of the PRRSV strain (\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Also, other studies (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) have shown that the diversity and composition of the gut microbiome may influence the outcome of PRRSV or porcine circovirus 2 infections, highlighting the complex interplay of the microbiota and these viral infections. However, little is known about the role and the interaction between the respiratory microbiota and respiratory viral infections in pigs. Among the few available studies, Gierse \u003cem\u003eet al.\u003c/em\u003e (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e) showed that infection with a swine influenza virus H1N1 resulted in lower richness of the nasal microbial community, while other studies (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) showed little impact of a H3N2 influenza A virus infection on the richness of the nasal microbiota, but associated the increase of certain species with the PRRSV and/or IAV challenge. Similarly, Hau \u003cem\u003eet al.\u003c/em\u003e (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e) have recently shown that the nasal microbiota seems to be resilient to influenza A virus infection.\u003c/p\u003e \u003cp\u003eIn the present study, we examined the nasal microbiota composition of piglets naturally infected in the first three weeks of life by a highly virulent PRRSV-1 strain of a newly emerged clade in Spain, comparing the microbial communities of the animals that ended up dying due to the infection with the microbiota of those that survived it.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eFarm, pigs and sampling\u003c/h2\u003e \u003cp\u003eThe farm where the study was conducted was a 1,400-sow operation, working with weekly farrowing batches (approximately 62 sows/batch). Piglets were weaned at 4 weeks of age when they were moved to nursery pens. At 9 weeks of age, they were moved to a fattening unit where they remained until reaching market weight.\u003c/p\u003e \u003cp\u003eAlthough viral circulation occurred in growers, the farm was considered stable for PRRSV since weaned pigs routinely tested negative for PRRSV by RT-qPCR (LSI Vetmax PRRSV EUNA 2.0, Thermofisher). In late December 2021, a highly virulent PRRSV-1 strain belonging to a new clade recently reported (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e) entered the farm. The strain had a 97.7% nucleotide identity with strain R1, the first isolate described for this clade. The introduction of the new PRRSV strain caused a reproductive outbreak and most piglets already tested positive for the virus by RT-qPCR before weaning. The farm was also known to be infected by an influenza A virus (IAV, H1N2; 1C.1.2.1 clade).\u003c/p\u003e \u003cp\u003eSampling of piglets was conducted during a follow-up study initially aimed at determining the dynamics of IAV in the farm. For that purpose, a cohort of 40 piglets belonging to ten randomly chosen litters (4 piglets randomly selected per sow) was followed from birth until the end of the nursery period. Blood samples were collected at 1, 3, 4, 5, 6, 7, 8 and 12 weeks of age to assess the evolution of pigs. Nasal swabs were also taken at 3 weeks of age in piglets and simultaneously from their sows and immediately transported to the lab at 4\u0026deg;C in transport medium for viral isolation (Virocult\u0026reg;). Animals were followed until 12 weeks of age and mortality was recorded.\u003c/p\u003e \u003cp\u003eSince the mortality in the followed cohort surpassed 50%, we deemed interesting to determine the possible relationship between the outcome of PRRSV infection and the nasal microbiota composition. In the study, we included 31 animals from ten different litters after discarding animals not infected by PRRSV at 3 weeks of age. All animals included in this study developed typical clinical signs of PRRSV infection. Of those 31 animals, 16 survived until the end of the follow-up period (group S) while 15 died before that timepoint (group D). All animals included in the study were infected by PRRSV at the moment of collecting the nasal swabs; of these, 13 were also positive for IAV (8 from D and 5 from S groups). Samples taken from eight of the sows giving birth to these litters were also included (n\u0026thinsp;=\u0026thinsp;8). Moreover, we disposed of some nasal swabs from 3-week-old piglets and their sows together with the clinical outcome information (survived or dead by 12 weeks of age), taken before the introduction of the highly virulent PRRSV strain. Thus, 8 samples from piglets (4 litters) and their respective sows (n\u0026thinsp;=\u0026thinsp;4) were also included in the study for comparative purposes (S\u003csub\u003e0\u003c/sub\u003e, n\u0026thinsp;=\u0026thinsp;4; and D\u003csub\u003e0\u003c/sub\u003e, n\u0026thinsp;=\u0026thinsp;4). All of them were negative for PRRSV at the moment of sampling.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eDetermination of PRRSV and IAV infections\u003c/h2\u003e \u003cp\u003eFor the detection of PRRSV a commercial RT-qPCR kit was used (LSI Vetmax PRRSV EUNA 2.0, Thermofisher). The kit includes an internal positive control to ensure the adequacy of the RNA extraction process. IAV was detected in nasal swabs using an RT-qPCR, whose primers and probe have been described elsewhere (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eDNA extraction, 16S rRNA gene library preparation and sequencing\u003c/h2\u003e \u003cp\u003eGenomic DNA was extracted from nasal swabs with the NucleoSpin Blood kit (Machinery Nagel, GmbH \u0026amp; Co, D\u0026uuml;ren; Germany), following manufacturer\u0026rsquo;s instructions. The concentration of the eluted DNA was measured in a BioDrop DUO device (BioDrop Ltdre). Genomic libraries of the variable regions V3-V4 from 16S rRNA gene were prepared by PCR using Illumina recommended primers (fwd 5'TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCTACGGGNGGCWGCAG3\u0026rsquo;, rev 5'GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGACTACHVGGGTATCTAATCC3\u0026rsquo;) and sequencing was done with Illumina MiSeq pair-end (2X300 bp, MS-102\u0026ndash;2003 MiSeq Re-agent Kit v2, 500 cycle) at the Servei de Gen\u0026ograve;mica, Universitat Aut\u0026ograve;noma de Barcelona (Spain). The length of the amplicons (460 bp) was verified on a Bioanalyzer DNA 1000 chip (Agilent). After sorting reads into samples (demultiplexing), samples were further processed through bioinformatics analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eMicrobiota bioinformatics analysis\u003c/h2\u003e \u003cp\u003eRaw reads obtained in the sequencing were processed and analyzed using quantitative insights into microbial ecology (QIIME2) software, version 2023.9 (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Then, primers were removed with \u003cem\u003eq2 cutadapt\u003c/em\u003e (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Quality filtering, low-quality 3\u0026rsquo; positions removal, paired-end merging, elimination of chimeras, and classification of reads into Amplicon Sequence Variants (ASVs) was performed with DADA2 (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) integrated as a QIIME2 plugin. Two additional filtering steps were performed to curate the pool of ASVs. Firstly, to remove any non-prokaryotic sequence, ASVs not matching Greengenes database (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e) vs. 13.8. (available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://greengenes.microbio.me/greengenes_release/gg_13_8_otus/\u003c/span\u003e\u003cspan address=\"http://greengenes.microbio.me/greengenes_release/gg_13_8_otus/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e aligning at 65% identity and 50% query coverage were filtered out using VSEARCH (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e) within \u003cem\u003eq2 quality control\u003c/em\u003e (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Secondly, non-bacterial sequences classified as \u003cem\u003eArchaea\u003c/em\u003e, \u003cem\u003eChloroplast\u003c/em\u003e or \u003cem\u003eMitochondria\u003c/em\u003e using the same Greengenes 13.8 taxonomy were also discarded.\u003c/p\u003e \u003cp\u003eSince the reads included in the analysis were obtained from two different runs, all data was merged for the downstream analysis. \u003cem\u003eQ2 greengenes2\u003c/em\u003e (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e) was used to infer the taxonomy of the ASVs using the \u003cem\u003enon-v4-16s\u003c/em\u003e function, where ASVs were mapped to unique 16S rRNA sequences in Greengenes2 (available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ftp.microbio.me/greengenes_release/current/\u003c/span\u003e\u003cspan address=\"https://ftp.microbio.me/greengenes_release/current/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e, referred as features. The phylogenetic tree was obtained from the same repository.\u003c/p\u003e \u003cp\u003eDiversity analyses were performed using \u003cem\u003eq2 diversity\u003c/em\u003e at a common depth of 1\u003cem\u003e8,090\u003c/em\u003e, corresponding to the lowest sample depth. Alpha diversity was estimated with the Chao1 index for richness estimation (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e), and the Shannon index (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e), which combines richness and evenness measures. The significance of the differences between the study groups were tested with pairwise t-tests (999 random permutations), included in \u003cem\u003ealpha-group-significance\u003c/em\u003e (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Jaccard (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e) and Bray-Curtis (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e) dissimilarity indexes were used to calculate the qualitative and quantitative beta diversity (diversity between samples), respectively. Distance matrices were computed and principal coordinate analysis (PCoA) and PCoA biplots (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e) were performed using \u003cem\u003ecore-metrics\u003c/em\u003e and visualized using Emperor (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). The effect size of the variables under study was measured using the Adonis function from vegan package (R software) (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e), by nesting the variables. To diminish the run effect, it was always included as the first variable in the nested formulas. The significance of the comparisons was calculated with PERMANOVA pairwise tests (999 random permutations) using \u003cem\u003ebeta-group-significance\u003c/em\u003e (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e To detect differentially abundant taxa among study groups, three different methods were followed. First, the Songbird (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e) differential ranking method was used as a QIIME2 plugin to rank the features with the covariates in this study. Second, two different and complementary methods were used to compare and validate the associations: dsf-dr (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e) and ANCOM with bias correction (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e), both also used as QIIME2 plugins.\u003c/p\u003e \u003cp\u003eThe functional prediction from the inferred metagenome was done with PICRUSt2 (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e), using EPA-NG (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e) for phylogenetic placement of reads, KEGG orthologs database (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e) to infer gene families and functional pathways and modules with MinPath (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). To do so, all singletons (features present only in one sample and/or one time) were removed. To visualize and compute statistics on PICRUSt2 output, Statistical Analysis of Microbial Profiles (STAMP) (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e) was used. Welch\u0026rsquo;s test (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e) with Bonferroni (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e) correction were used to find differentially abundant modules between the study groups. KEGG modules were also inferred with the reconstruction function of the KEGG-mapper online tool (available in \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.genome.jp/kegg/mapper/reconstruct.html\u003c/span\u003e\u003cspan address=\"https://www.genome.jp/kegg/mapper/reconstruct.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e and compared qualitatively between the groups under study (presence/absence and degree of completeness of the modules). In all statistical tests, significance was set at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003eMicrobiome analysis output data was further processed and used to create figures and tables in RStudio version 2022.07.0 (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e) with the packages qiime2r (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e), reshape2 (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e), ggplot2 (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e) and tidyverse (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eThe nasal microbiota composition differed according to the outcome of the piglets\u003c/h2\u003e \u003cp\u003eAfter raw read processing, 4,180 different ASVs were obtained with a mean frequency of 157,350 counts per sample. ASVs were mapped to Greengenes2 database and a total number of 1,447 features (greengenes2 IDs) was obtained, with a mean frequency of 123,725 counts per sample.\u003c/p\u003e \u003cp\u003eWhen the nasal microbiota composition was compared between the animals that survived (group S; n\u0026thinsp;=\u0026thinsp;16) or died (group D; n\u0026thinsp;=\u0026thinsp;15) after the highly virulent PRRSV-1 outbreak, a higher bacterial richness was observed in group S than in group D (Chao1 index \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA), while no differences were observed according to the Shannon index (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.66). The composition differed also between groups S and D in the beta diversity analyses, which grouped into separate clusters in both qualitative and quantitative analyses (Jaccard and Bray-Curtis PERMANOVA \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB), with an estimated effect size of 8.3% and 23% for Jaccard and Bray-Curtis matrices, respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn this study, the piglets came from three types of litters: three litters where all piglets died, three where all piglets survived and four mixed litters (with piglets either dying or surviving). The percentage of explanation of the variable \u0026ldquo;litter of origin\u0026rdquo; was similar to the previously quantified outcome effect (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;7.6% for Jaccard and R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;17.8% Bray-Curtis, respectively, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001, \u003cb\u003esupplementary Fig.\u0026nbsp;1\u003c/b\u003e), indicating that this variable is a major driver of the difference between clinical outcomes. To assess the bias caused by the selection of litters, the litter was used as a nested variable in the analysis. Interestingly, after accounting for such bias, the outcome of the PRRSV infection was still significantly associated with the microbial composition (R\u003csup\u003e2\u003c/sup\u003e of 5.4% and 10.2% in Jaccard and Bray-Curtis analyses, respectively, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.006). This combination of outcome and litter effects would help to explain outlier samples, some of them clustering with animals from the same litter rather than with other piglets that had a similar outcome (such as samples #108 and #93, see \u003cb\u003esupplementary Fig.\u0026nbsp;1\u003c/b\u003e), and vice-versa, i.e. animals clustering by outcome instead of litter (#87, #101 and #105).\u003c/p\u003e \u003cp\u003eTo unveil the microbial composition, the features found in the microbiota were studied at different taxonomic levels. The nasal microbiota of the piglets from group S was dominated by \u003cem\u003eWeeksellaceae\u003c/em\u003e (22.59% \u0026plusmn; 10.64, \u003cem\u003eNeisseriaceae\u003c/em\u003e (19.75% \u0026plusmn; 10.31), \u003cem\u003eMoraxellaceae\u003c/em\u003e (19.55% \u0026plusmn; 16.20) and \u003cem\u003ePasteurellaceae\u003c/em\u003e (18.12% \u0026plusmn; 11.54), in similar proportions (\u003cb\u003esupplementary Fig.\u0026nbsp;2A\u003c/b\u003e), while the nasal microbiota from group D was dominated by \u003cem\u003eEnterobacteriaceae\u003c/em\u003e (28.57\u0026thinsp;\u0026plusmn;\u0026thinsp;24.75%) and \u003cem\u003eMoraxellaceae\u003c/em\u003e (28.06% \u0026plusmn; 18.00). At genus level (\u003cb\u003esupplementary Fig.\u0026nbsp;2B\u003c/b\u003e), the nasal microbiota of the piglets from group S was dominated by taxa commonly found in the respiratory tract of pigs, such as \u003cem\u003eBergeyella\u003c/em\u003e (22.57% \u0026plusmn; 10.65), \u003cem\u003eNeisseria\u003c/em\u003e (originally classified as \u003cem\u003eEikenella\u003c/em\u003e, but confirmed as \u003cem\u003eNeisseria\u003c/em\u003e in other databases, i.e. NCBI and Greengenes 13_8) (19.73% \u0026plusmn; 10.33), \u003cem\u003eGlaesserella\u003c/em\u003e (15.20% \u0026plusmn; 11.7) and \u003cem\u003eMoraxella A\u003c/em\u003e (12.99% \u0026plusmn; 16.88). In the case of piglets from group D, the most relatively abundant genera were \u003cem\u003eEscherichia\u003c/em\u003e (28.17% \u0026plusmn; 24.47) and \u003cem\u003eMoraxella\u003c/em\u003e C (22.92% \u0026plusmn; 18.65), followed by \u003cem\u003eBergeyella\u003c/em\u003e (11.66% \u0026plusmn; 4.85). The complete list of microbiota composition at all taxonomic levels can be found in \u003cb\u003esupplementary Table\u0026nbsp;1\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eTo study in more detail the taxonomic differences between groups S and D, we performed a differential ranking model (Songbird). Eighty-seven features with more than 0.01% mean relative abundance in at least one group (considered non-artifacts) were identified by the model as associated with either group S or group D (\u003cb\u003esupplementary Table\u0026nbsp;2\u003c/b\u003e). Among all the significant differential features, forty-eight presented more than 1 log\u003csub\u003e10\u003c/sub\u003e-fold change between groups, (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA), where features classified as \u003cem\u003eMesomycoplasma hyorhinis\u003c/em\u003e (present only in 4 animals from group S and totally absent in group D), \u003cem\u003eCaryophanon latum\u003c/em\u003e and an unclassified \u003cem\u003eAcinetobacter\u003c/em\u003e species showed the highest association with group S, while \u003cem\u003eSuipraeoptans intestinalis\u003c/em\u003e, \u003cem\u003eEscherichia_710834\u003c/em\u003e and \u003cem\u003ePrevotella heparinolytica\u003c/em\u003e were the most associated with group D. Regarding the most abundant differential taxa (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB), several features from common nasal colonizers were found in higher relative abundances in group S compared to D, such as \u003cem\u003eBergeyella\u003c/em\u003e, \u003cem\u003eGlaesserella parasuis\u003c/em\u003e, \u003cem\u003eMoraxella A\u003c/em\u003e and \u003cem\u003eNeisseria\u003c/em\u003e (despite some of these presented a negative fold change towards group S). Conversely, in group D, two features classified as \u003cem\u003eEscherichia\u003c/em\u003e were associated with group D (25.03% and 3.65% mean relative abundance, respectively) together with \u003cem\u003eMoraxella\u003c/em\u003e C and \u003cem\u003eRothia\u003c/em\u003e, all of them more abundant in this group. When the same songbird model was computed at family and genus level, most of the taxa associated to either S or D groups were the families and genera corresponding to the features indicated above (see \u003cb\u003esupplementary Table\u0026nbsp;2\u003c/b\u003e). Moreover, a biplot analysis showed that \u003cem\u003eMoraxella\u003c/em\u003e C and \u003cem\u003eEscherichia\u003c/em\u003e were the most informative taxa for group D in both the quantitative and qualitative beta diversity analysis. On the other hand, another member of the \u003cem\u003eMoraxella\u003c/em\u003e genus \u003cem\u003e(Moraxella\u003c/em\u003e A), together with \u003cem\u003eNeisseria, Glaesserella\u003c/em\u003e and \u003cem\u003ePasteurella\u003c/em\u003e, contributed the most to the clustering of group S (\u003cb\u003esupplementary Fig.\u0026nbsp;3A and B\u003c/b\u003e). Similar results were obtained using ANCOM-BC and dsf-dr, which also reflected most of the same features, genera and families as differentially abundant (\u003cb\u003esupplementary Table\u0026nbsp;3\u003c/b\u003e), reinforcing the results found by the Songbird model.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo study in more detail the different communities found in the two groups, a functional inference of the predicted metagenomic composition was performed. The principal component analysis of the predicted functions from the KEGG public database showed clustering according to the outcome of the infection (PERMANOVA using Bray-Curtis distance matrix \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA), suggesting a different abundance of the functional capabilities for the nasal microbiota of piglets from each group. Moreover, group D exhibited a higher level of dispersion in the main PCA space compared to group S (PERMDISP using Bray-Curtis distance matrix \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.047), showing that the microbiota was quantitatively less similar within group D. Then, the predicted microbial capabilities of communities S and D were qualitatively assessed by comparing the presence and absence of KEGG genes and modules. A similar number of KO genes was predicted for groups S and D (6,319 and 6,273, respectively), which made up an almost equal ensemble of modules (\u003cb\u003esupplementary Table\u0026nbsp;4\u003c/b\u003e). In agreement, no statistically significant differences were detected between the two groups (PERMANOVA and PERMDISP using Jaccard distances p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Only a few amino acid and glycan metabolism modules, as well as methicillin resistance were missing or showed to be incomplete in the microbiome of D group, while two multidrug resistance efflux pumps and shiga toxin were missing or showed an incomplete module in group S. Quantitative analyses were performed by submitting these inferred modules to differential analysis, where thirteen of these were found as significantly different between the two study groups; seven of them enriched in group D and six in group S (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMicrobiota-related susceptibility existed in piglets before PRRSV-1 outbreak\u003c/h3\u003e\n\u003cp\u003eFor comparative purposes, samples from same aged piglets that died or survived before the outbreak caused by the new PRRSV strain were also analysed (S\u003csub\u003e0\u003c/sub\u003e, n\u0026thinsp;=\u0026thinsp;4; and D\u003csub\u003e0\u003c/sub\u003e, n\u0026thinsp;=\u0026thinsp;4), as well as samples from sows (n\u0026thinsp;=\u0026thinsp;8 and n\u0026thinsp;=\u0026thinsp;4, after and before the outbreak, respectively). In the beta diversity analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), these samples clustered with the corresponding outcome group, i.e. S\u003csub\u003e0\u003c/sub\u003e with S, and D\u003csub\u003e0\u003c/sub\u003e with D. (PERMANOVA \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001, with an estimated effect size of 7.44% and 20.08% o for Jaccard and Bray-Curtis indexes, respectively). When the analysis was performed including only S\u003csub\u003e0\u003c/sub\u003e and D\u003csub\u003e0\u003c/sub\u003e, despite the low number of samples prevented to find significant differences in alpha diversity, these groups showed to form two separated clusters in the beta diversity analysis (Jaccard PERMANOVA \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.022, and a tendency using Bray-Curtis \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.082). When the different tests were run to find differentially abundant taxa (the Songbird differential ranking model was not run due to the low number of samples), only ANCOM-BC reported a few low-abundant taxa (\u003cb\u003esupplementary table \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u003c/b\u003e). Nevertheless, the two groups reported a similar composition to S and D groups, respectively (\u003cb\u003esupplementary Fig.\u0026nbsp;4\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOn the other hand, the nasal microbiota of sows giving birth to healthy or mixed litters reported a higher richness than sows giving birth to piglets that died (chao1 index \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u003cb\u003esupplementary Fig.\u0026nbsp;5A\u003c/b\u003e), suggesting that the differences observed in richness could be traced back to the sows. No significant differences in beta diversity were detected between any of these groups of sows. However, when the communities of the two timepoints were compared (before and after the entrance of the virulent PRRSV strain), the microbiota significantly varied (Jaccard and Bray-Curtis PERMANOVA \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.006, Adonis R\u003csup\u003e2\u003c/sup\u003e effect size of 11.7% and 16.4% respectively, \u003cb\u003esupplementary Fig.\u0026nbsp;5B\u003c/b\u003e). Among the differences found in the sows between these two timepoints (\u003cb\u003esupplementary table \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003e6\u003c/span\u003e)\u003c/b\u003e, several taxa found were previously identified discriminating S or D groups, such as \u003cem\u003eRothia\u003c/em\u003e, \u003cem\u003eNeisseria\u003c/em\u003e, \u003cem\u003eMoraxella C\u003c/em\u003e or \u003cem\u003eBergeyella\u003c/em\u003e, more relatively abundant in sows before the outbreak; or \u003cem\u003eFusobacterium\u003c/em\u003e and \u003cem\u003eBacteroides\u003c/em\u003e, which were increased in sows during the outbreak.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, the composition of the nasal microbiota in a cohort of piglets infected by a highly virulent PRRSV-1 strain was analyzed. Despite being equally infected by the same PRRSV-1 strain, only a group of piglets had a fatal outcome in the end of the follow-up period. Our results indicated that survival was related to a higher species richness in the nasal microbial community as well as with the dominance of certain bacterial taxa.\u003c/p\u003e \u003cp\u003eSeveral studies have reported that healthy animals have higher bacterial diversity compared to diseased pigs (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan additionalcitationids=\"CR51\" citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e) and, in general, it is considered that lower bacterial diversity is a marker for impaired health (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e). In the present case, lower richness was associated with a fatal outcome despite all the animals were infected by PRRSV. Previous studies targeting the swine fecal microbiota role in PRRSV and PCV2 infections outcome (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) yielded similar results.\u003c/p\u003e \u003cp\u003eBesides, changes in the microbiota composition were also related with the outcome of the disease, especially regarding its\u0026rsquo; most abundant members, as shown by a major significance level in the quantitative analysis compared to the qualitative one. Similarly, changes in the fecal microbiota populations were also related with PRRSV and PCV2 outcome (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). In our study, surviving piglets reported a high abundance of genera frequently associated with a healthy swine respiratory tract such as \u003cem\u003eBergeyella\u003c/em\u003e, \u003cem\u003eNeisseria\u003c/em\u003e (\u003cem\u003eEikenella\u003c/em\u003e), \u003cem\u003eGlaesserella\u003c/em\u003e and \u003cem\u003eMoraxella\u003c/em\u003e (\u003cem\u003eMoraxella\u003c/em\u003e_A) (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan additionalcitationids=\"CR56 CR57\" citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e). In agreement, most of these taxa were found to contribute the most in the clustering of group S samples in the PCoA biplot analysis. On the contrary, \u003cem\u003eEscherichia\u003c/em\u003e was significantly more abundant in piglets that died. Previous studies reported that taxa commonly found in the fecal microbiota or from the environment can be present in the pig nasal microbiota (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e), and become predominant when the professional colonizers are not present (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e). Our results are compatible with a dysbiosis in the nasal microbiota of the piglets that died, which resulted in an increased relative abundance of fecal bacteria, especially \u003cem\u003eEscherichia\u003c/em\u003e. However, also a different clade of Moraxella (\u003cem\u003eMoraxella\u003c/em\u003e_C) and \u003cem\u003eRothia\u003c/em\u003e, common colonizers of healthy piglets, were among the most abundant taxa associated with the fatal outcome. The presence of these common colonizers of the pig respiratory tract (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e) may indicate that these genera are more resilient to the disturbance of the nasal microbial community and proportionally increase when other taxa decreased. A previous study focusing on the nasal microbiota changes after challenging the animals with PRRSV and IAV (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) associated \u003cem\u003eWeeksellaceae\u003c/em\u003e and \u003cem\u003eNeisseriaceae\u003c/em\u003e with PRRSV infected groups while \u003cem\u003eEnterobacteriaceae\u003c/em\u003e with uninfected. Despite these results may seem opposite to ours\u0026rsquo;, Chrun \u003cem\u003eet al.\u003c/em\u003e focused on immediate microbiota changes after challenge in PRRSV-free animals, while in this study two groups with different outcome in a virulent PRRSV-1 endemic farm were analyzed. Most of the taxa with the highest fold change between the two study groups were found in low relative abundance (\u003cem\u003eMesomycoplasma hyorhinis\u003c/em\u003e, \u003cem\u003eCaryophanon latum\u003c/em\u003e and \u003cem\u003eAcinetobacter\u003c/em\u003e associated with group S, and \u003cem\u003eSuipraeoptans intestinalis\u003c/em\u003e, \u003cem\u003ePrevotella heparinolytica\u003c/em\u003e with group D). Despite most of these taxa have a relationship with the swine nasal and/or gastrointestinal microbiota (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e), the biological significance of these changes in the present study is still unknown and deserve further investigation. Also, features from other taxa frequently found in the swine respiratory tract, such as \u003cem\u003eLactobacillus\u003c/em\u003e, \u003cem\u003eStreptococcus\u003c/em\u003e or \u003cem\u003ePrevotella\u003c/em\u003e (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e) were associated with both disease outcomes, suggesting that different strains from the same genera could play different roles and be involved in disease outcome. The functional analysis also reflected differences between the examined groups of pigs, especially quantitatively, in agreement with the beta diversity analysis. Despite few differences were found in the qualitative comparison of the inferred functions, group D exhibited less glycan and amino acid metabolic modules (more incomplete or absent), suggesting some loss of functions in the microbiota of this group. Moreover, quantitative PCA analysis of functional modules revealed a bigger dispersion in group D pointing towards a less-uniform functionality in a more unstable (or unspecific) microbial community. In agreement with our findings, group dissimilarity can be related to dysbiotic microbiotas in unhealthy animals (\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this study, samples collected before the introduction of the new PRRSV strain were also analyzed. Despite the reduced number of samples examined, animals that died or survived before the outbreak had a similar microbiota composition compared with animals sampled after the outbreak. This finding suggests that this highly virulent PRRSV strain was not the cause of the changed microbiota composition between the different outcome groups. In fact, it is more likely that the microbiota would be a predisposing factor for survivability or susceptibility while the virulence of the new PRRSV strain would be responsible for the increased mortality. The microbiota composition as a factor of susceptibility has been highly studied because the huge impact it can have on host immunity and pathogen exclusion (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e). In pigs, this microbiota-related susceptibility has already been demonstrated in the case of bacterial and viral pathogens (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan additionalcitationids=\"CR66\" citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e). The findings in this study reported a different microbiota in animals with good outcome which could be protective for PRRSV but also in secondary infections that may arise in diseased animals. Nevertheless, the examination of the underlying mechanisms explaining the predictive value of the nasal microbiota composition is beyond the scope of the present study but merits further investigation.\u003c/p\u003e \u003cp\u003eFinally, it is worth noting that belonging to a given litter had a strong influence on the composition of the nasal microbiota and thus, in the outcome of the animal. This is in agreement with studies that highlighted the role of the sows in the piglets\u0026rsquo; microbiota development (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e). In fact, there were two samples from piglets clustering with their siblings rather than with piglets with the same clinical outcome (one animal clustering with the survivors, died, and vice versa). This can be simply explained by the individual (diverse) resilience of the animals or because some animals may have died from causes other than the PRRSV infection. However, there was still an outcome effect acting independently of the litter, as shown by animals from mixed litters whose samples clustered by outcome and detected in the litter-nested beta diversity analysis. Interestingly, the differences observed in the alpha diversity of the piglets were a reflection of the differences observed in their dams, since sows giving birth to surviving and mixed litters reported a higher microbial richness than those to a litter with fatal outcome. The idea that sows with a rich microbiota transfer \u0026ldquo;more\u0026rdquo; than sows with a less rich microbiota is in agreement with previous studies discussing the sow-to-piglet microbiota transmission (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e). Moreover, the sows\u0026rsquo; microbiota showed substantial changes before and after the PRRSV outbreak. However, these variations could be more likely related to analyzing a low number of samples from different sows or changes in environmental and/or farming conditions. Further studies may aid confirming whether these changes observed in sows are caused by this new virus variant and more importantly, whether these variations can be related to an increase in mortality between the two timepoints.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, this study suggests that the nasal microbiota could be closely related to virulent PRRSV-1 survival. Specifically, we show that high species richness and the presence of common nasal colonizing bacteria may be the key to a good outcome, while dysbiosis seems to be related to a bad outcome. At last, the analysis of samples taken before the outbreak indicates that such differences between piglets were already present, and the increased PRRSV-1 virulence triggered a fatal outcome in susceptible animals. Future studies may help explaining the connection between such microbiota characteristics and PRRSV susceptibility/resistance as well as investigating whether this microbiota-related susceptibility also exists in other viral respiratory infections. Moreover, this investigation serves as starting point towards exploring the potential of the nasal microbiota in the prevention of viral respiratory infections.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe present project was carried out after approval by the Ethics in Human and Animal Experimentation Committee (nº 5691) and followed all national and EU regulations. The farm consented the participate in the study after being informed of the procedures to be carried out.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAvailability of data and material\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe raw data used in this study are available at SRA database (NCBI) under BioProject ID\u0026nbsp;PRJNA1132954. Processed data is available as supplementary tables.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interest\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis project was funded by project PCI2020-120688 from the Spanish “Agencia Estatal de investigaciónn” and by project ICRAD ID86-PIGIE of the European Union. POG is supported by FPU (FPU19/02126) fellowship from Spanish Government.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthors' contributions\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003ePOG performed the analysis. FCF and VA supervised the analysis. FCF, VA, MC and EM contributed to the discussion and interpretation of the results. GEMV, MC and EM designed the study. GEMV and HC performed the sampling of the animals and performed the viral detection. POG drafted the manuscript with contributions from all authors. \u0026nbsp;\u003cbr\u003e\u0026nbsp;All authors approve the final submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAcknowledgements\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eOur grateful thanks to Dr. Cristina Laza for helping us in selecting the farms, for veterinary supervision of the farm and the sampling.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eZimmerman J, Dee SA, Holtkamp DJ, Murtaugh MP, Stadejek T, Stevenson GW, Torremorell M, Yang H, Zang J. 2019. Porcine Reproductive and Respiratory Syndrome Viruses (Porcine Arteriviruses). In: Diseases of swine 11th Edition, Zimmerman JJ, Karricker LA, Ramirez A, Schwartz KJ, Stevenson GW, Zhang J. (eds), John Wiley \u0026amp; Sons, Inc. Publishers, Hoboken (NJ), USA, 685-708.\u003c/li\u003e\n \u003cli\u003eTrevisi P, Amatucci L, Ruggeri R, Romanelli C, Sandri G, Luise D, Canali M, Bosi P. Pattern of Antibiotic Consumption in Two Italian Production Chains Differing by the Endemic Status for Porcine Reproductive and Respiratory Syndrome. Front Vet Sci. 2022 Mar 28;9:840716. doi: 10.3389/fvets.2022.840716. PMID: 35419448; PMCID: PMC8996257.\u003c/li\u003e\n \u003cli\u003eLefkowitz, E. J., Dempsey, D. M., Hendrickson, R. C., Orton, R. J., Siddell, S. G., \u0026amp; Smith, D. B. (2018). Virus taxonomy: the database of the International Committee on Taxonomy of Viruses (ICTV). Nucleic acids research, 46(D1), D708\u0026ndash;D717. https://doi.org/10.1093/nar/gkx932.\u003c/li\u003e\n \u003cli\u003eTian K, Yu X, Zhao T, Feng Y, Cao Z, Wang C, Hu Y, Chen X, Hu D, Tian X, Liu D, Zhang S, Deng X, Ding Y, Yang L, Zhang Y, Xiao H, Qiao M, Wang B, Hou L, Wang X, Yang X, Kang L, Sun M, Jin P, Wang S, Kitamura Y, Yan J, Gao GF. Emergence of fatal PRRSV variants: unparalleled outbreaks of atypical PRRS in China and molecular dissection of the unique hallmark. PLoS One. 2007 Jun 13;2(6):e526. doi: 10.1371/journal.pone.0000526.\u003c/li\u003e\n \u003cli\u003eKarniychuk UU, Geldhof M, Vanhee M, Van Doorsselaere J, Saveleva TA, Nauwynck HJ. Pathogenesis and antigenic characterization of a new East European subtype 3 porcine reproductive and respiratory syndrome virus isolate. BMC Vet Res. 2010 Jun 4;6:30. doi: 10.1186/1746-6148-6-30.\u003c/li\u003e\n \u003cli\u003eMart\u0026iacute;n-Valls GE, Cortey M, Allepuz A, Illas F, Tello M, Mateu E. Introduction of a PRRSV-1 strain of increased virulence in a pig production structure in Spain: virus evolution and impact on production. Porcine Health Manag. 2023 Jan 3;9(1):1. doi: 10.1186/s40813-022-00298-3. PMID: 36597152; PMCID: PMC9811746.\u003c/li\u003e\n \u003cli\u003eRuedas-Torres I, Rodr\u0026iacute;guez-G\u0026oacute;mez IM, S\u0026aacute;nchez-Carvajal JM, Larenas-Mu\u0026ntilde;oz F, Pallar\u0026eacute;s FJ, Carrasco L, G\u0026oacute;mez-Laguna J. The jigsaw of PRRSV virulence. Vet Microbiol. 2021 Sep;260:109168. doi: 10.1016/j.vetmic.2021.109168. Epub 2021 Jun 30. PMID: 34246042.\u003c/li\u003e\n \u003cli\u003eArg\u0026uuml;ello H, Rodr\u0026iacute;guez-G\u0026oacute;mez IM, S\u0026aacute;nchez-Carvajal JM, Pallares FJ, D\u0026iacute;az I, Cabrera-Rubio R, Crispie F, Cotter PD, Mateu E, Mart\u0026iacute;n-Valls G, Carrasco L, G\u0026oacute;mez-Laguna J. Porcine reproductive and respiratory syndrome virus impacts on gut microbiome in a strain virulence-dependent fashion. Microb Biotechnol. 2022 Mar;15(3):1007-1016. doi: 10.1111/1751-7915.13757. Epub 2021 Mar 3. PMID: 33656781; PMCID: PMC8913879.\u003c/li\u003e\n \u003cli\u003eConstance LA, Thissen JB, Jaing CJ, McLoughlin KS, Rowland RRR, Ser\u0026atilde;o NVL, Cino-Ozuna AG, Niederwerder MC. Gut microbiome associations with outcome following co-infection with porcine reproductive and respiratory syndrome virus (PRRSV) and porcine circovirus type 2 (PCV2) in pigs immunized with a PRRS modified live virus vaccine. Vet Microbiol. 2021 Mar;254:109018. doi: 10.1016/j.vetmic.2021.109018. Epub 2021 Feb 16. PMID: 33639341.\u003c/li\u003e\n \u003cli\u003eGaire TN, Odland C, Zhang B, Ray T, Doster E, Nerem J, Dee S, Davies P, Noyes N. The impacts of viral infection and subsequent antimicrobials on the microbiome-resistome of growing pigs. Microbiome. 2022 Aug 4;10(1):118. doi: 10.1186/s40168-022-01312-0. PMID: 35922873; PMCID: PMC9351240.\u003c/li\u003e\n \u003cli\u003eOber RA, Thissen JB, Jaing CJ, Cino-Ozuna AG, Rowland RRR, Niederwerder MC. Increased microbiome diversity at the time of infection is associated with improved growth rates of pigs after co-infection with porcine reproductive and respiratory syndrome virus (PRRSV) and porcine circovirus type 2 (PCV2). Vet Microbiol. 2017 Sep;208:203-211. doi: 10.1016/j.vetmic.2017.06.023. Epub 2017 Aug 18. PMID: 28888639.\u003c/li\u003e\n \u003cli\u003eNiederwerder MC, Jaing CJ, Thissen JB, Cino-Ozuna AG, McLoughlin KS, Rowland RR. Microbiome associations in pigs with the best and worst clinical outcomes following co-infection with porcine reproductive and respiratory syndrome virus (PRRSV) and porcine circovirus type 2 (PCV2). Vet Microbiol. 2016 May 30;188:1-11. doi: 10.1016/j.vetmic.2016.03.008. Epub 2016 Mar 11. PMID: 27139023.\u003c/li\u003e\n \u003cli\u003eGierse, L. C., Meene, A., Schultz, D., Schwaiger, T., Schr\u0026ouml;der, C., M\u0026uuml;cke, P., Z\u0026uuml;hlke, D., Hinzke, T., Wang, H., Methling, K., Kreikemeyer, B., Bernhardt, J., Becher, D., Mettenleiter, T. C., Lalk, M., Urich, T., \u0026amp; Riedel, K. (2021). Influenza A H1N1 Induced Disturbance of the Respiratory and Fecal Microbiome of German Landrace Pigs - a Multi-Omics Characterization. Microbiology spectrum, 9(2), e0018221. https://doi.org/10.1128/Spectrum.00182-21.\u003c/li\u003e\n \u003cli\u003eChrun T, Leng J, La Ragione RM, Graham SP, Tchilian E. Changes in the Nasal Microbiota of Pigs Following Single or Co-Infection with Porcine Reproductive and Respiratory Syndrome and Swine Influenza A Viruses. Pathogens. 2021 Sep 22;10(10):1225. doi: 10.3390/pathogens10101225.\u003c/li\u003e\n \u003cli\u003eHau SJ, Nielsen DW, Mou KT, Alt DP, Kellner S, Brockmeier SL. Resilience of swine nasal microbiota to influenza A virus challenge in a longitudinal study. Vet Res. 2023 May 2;54(1):38. doi: 10.1186/s13567-023-01167-9. PMID: 37131235; PMCID: PMC10152739.\u003c/li\u003e\n \u003cli\u003eMart\u0026iacute;n-Valls, G. E., Cortey, M., Allepuz, A., Illas, F., Tello, M., \u0026amp; Mateu, E. (2022). Description of a New Clade within Subtype 1 of Betaarterivirus suid 1 Causing Severe Outbreaks in Spain. Microbiology resource announcements, 11(7), e0030422. https://doi.org/10.1128/mra.00304-22.\u003c/li\u003e\n \u003cli\u003eBusquets N, Segal\u0026eacute;s J, C\u0026oacute;rdoba L, Muss\u0026aacute; T, Crisci E, Mart\u0026iacute;n-Valls GE, Simon-Grif\u0026eacute; M, P\u0026eacute;rez-Sim\u0026oacute; M, P\u0026eacute;rez-Ma\u0026iacute;llo M, N\u0026uacute;\u0026ntilde;ez JI, Abad FX, Fraile L, Pina S, Maj\u0026oacute; N, Bensaid A, Domingo M, Montoya M. Experimental infection with H1N1 European swine influenza virus protects pigs from an infection with the 2009 pandemic H1N1 human influenza virus. Vet Res. 2010 Sep-Oct;41(5):74. doi: 10.1051/vetres/2010046. Epub 2010 Jul 29. PMID: 20663475; PMCID: PMC2939699.\u003c/li\u003e\n \u003cli\u003eBolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA, Alexander H, Alm EJ, Arumugam M, Asnicar F, Bai Y, Bisanz JE, Bittinger K, Brejnrod A, Brislawn CJ, Brown CT, Callahan BJ, Caraballo-Rodr\u0026iacute;guez AM, Chase J, Cope EK, Da Silva R, Diener C, Dorrestein PC, Douglas GM, Durall DM, Duvallet C, Edwardson CF, Ernst M, Estaki M, Fouquier J, et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol. 2019;37:852\u0026ndash;857. doi: 10.1038/s41587-019-0209-940.\u003c/li\u003e\n \u003cli\u003eCutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet. journal, 17(1):pp\u0026ndash;10, 2011. doi:10.14806/ej.17.1.200.\u003c/li\u003e\n \u003cli\u003eCallahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581\u0026ndash;583. doi: 10.1038/nmeth.3869.\u003c/li\u003e\n \u003cli\u003eMcDonald D, Price MN, Goodrich J, Nawrocki EP, DeSantis TZ, Probst A, Andersen GL, Knight R, Hugenholtz P. An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea. ISME J. 2012;6:610\u0026ndash;618. doi: 10.1038/ismej.2011.139.\u003c/li\u003e\n \u003cli\u003eRognes T, Flouri T, Nichols B, Quince C, Mah\u0026eacute; F. VSEARCH: a versatile open source tool for metagenomics. PeerJ. 2016;4:e2584. doi: 10.7717/peerj.2584.\u003c/li\u003e\n \u003cli\u003eCamacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J, Bealer K, Madden TL. BLAST+: architecture and applications. BMC Bioinform. 2009;10:421. doi: 10.1186/1471-2105-10-421.\u003c/li\u003e\n \u003cli\u003eMcDonald D, Jiang Y, Balaban M, Cantrell K, Zhu Q, Gonzalez A, et al. Greengenes2 unifies microbial data in a single reference tree. Nat Biotechnol [Internet]. 2023 Jul 27; Available from: https://doi.org/10.1038/s41587-023-01845-1\u003c/li\u003e\n \u003cli\u003eChao A. Nonparametric estimation of the number of classes in a population. Scand J Stat. 1984;11:4.\u003c/li\u003e\n \u003cli\u003eWeaver WSCE. The mathematical theory of communication. Bell Syst Tech. 1949;27:379\u0026ndash;423.\u003c/li\u003e\n \u003cli\u003eKruskal WH, Wallis WA. Use of ranks in one-criterion variance analysis. J Am Stat Assoc. 1952;47:583\u0026ndash;621. doi: 10.1080/01621459.1952.10483441.\u003c/li\u003e\n \u003cli\u003eJaccard P (1908) Nouvelles recherches sur la distribution florale. Bull Soci\u0026eacute;t\u0026eacute; Vaud Des Sci Nat 44:223\u0026ndash;270.\u003c/li\u003e\n \u003cli\u003eS\u0026oslash;rensen TJ. A method of establishing groups of equal amplitude in plant sociology based on similarity of species content and its application to analyses of the vegetation on danish commons. Kongelige Danske Videnskabernes Selskab. 1948;5:1\u0026ndash;34.\u003c/li\u003e\n \u003cli\u003eHalko N, Martinsson P-G, Shkolnisky Y, Tygert M. An algorithm for the principal component analysis of large data sets. SIAM J Sci Comput. 2011;33:2580\u0026ndash;2594. doi: 10.1137/100804139.\u003c/li\u003e\n \u003cli\u003eLegendre LLP. Numerical Ecology. 3. Elsevier; 2012.\u003c/li\u003e\n \u003cli\u003eV\u0026aacute;zquez-Baeza Y, Pirrung M, Gonzalez A, Knight R. EMPeror: a tool for visualizing high-throughput microbial community data. GigaSci. 2013;2:16. doi: 10.1186/2047-217X-2-16.\u003c/li\u003e\n \u003cli\u003eOksanen J, Simpson GL, Blanchet FG,Kindt R, Legendre P, Minchin PR, O\u0026rsquo;Hara RB, Solymos P, Stevens MHH, Szoecs E, Wagner H, Barbour M, Bedward M, Bolker B, Borcard D, Carvalho G, Chirico M, De Caceres M, Durand S, Evangelista HBA, FitzJohn R, Friendly M, Furneaux B, Hannigan G, Hill MP, Lahti L, McGlinn D, Ouellette MH, Cunha ER, Smith T, Stier A, Ter Braak CJF, Weedon J (2016) Vegan: Community Ecology Package. Retrieved from https://cran.r-project.org/package=vegan.\u003c/li\u003e\n \u003cli\u003eAnderson MJ. A new method for non-parametric multivariate analysis of variance. Austral Ecol. 2001;26:32\u0026ndash;46. doi: 10.1111/j.1442-9993.2001.01070.pp.x.\u003c/li\u003e\n \u003cli\u003eMorton JT, Marotz C, Washburne A, Silverman J, Zaramela LS, Edlund A, et al. Establishing microbial composition measurement standards with reference frames. Nat Commun. 2019 Jun 20;10(1):2719.\u003c/li\u003e\n \u003cli\u003eJiang L, Amir A, Morton JT, Heller R, Arias-Castro E, Knight R. Discrete false-discovery rate improves identification of differentially abundant microbes. mSystems. 2017;2:e00092\u0026ndash;e117. doi: 10.1128/mSystems.00092-17.\u003c/li\u003e\n \u003cli\u003eLin H, Peddada SD. Analysis of compositions of microbiomes with bias correction. Nat Commun. 2020;11:3514. doi: 10.1038/s41467-020-17041-7. [.\u003c/li\u003e\n \u003cli\u003eDouglas GM, et al. PICRUSt2 for prediction of metagenome functions. Nat. Biotechnol. 2020;38:685\u0026ndash;688.\u003c/li\u003e\n \u003cli\u003ePierre Barbera, Alexey M Kozlov, Lucas Czech, Benoit Morel, Diego Darriba, Tom\u0026aacute;\u0026scaron; Flouri, Alexandros Stamatakis, EPA-ng: Massively Parallel Evolutionary Placement of Genetic Sequences, Systematic Biology, Volume 68, Issue 2, March 2019, Pages 365\u0026ndash;369, https://doi.org/10.1093/sysbio/syy054.\u003c/li\u003e\n \u003cli\u003eKanehisa M, Goto S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28:27\u0026ndash;30.\u003c/li\u003e\n \u003cli\u003eYe Y, Doak TG. A parsimony approach to biological pathway reconstruction/inference for genomes and metagenomes. PLOS Comput. Biol. 2009;5:e1000465.\u003c/li\u003e\n \u003cli\u003eParks DH, Tyson GW, Hugenholtz P, Beiko RG. STAMP: statistical analysis of taxonomic and functional profiles. Bioinformatics. 2014;30:3123\u0026ndash;3124.\u003c/li\u003e\n \u003cli\u003eWelch, B. L. (1938). The significance of the difference between two means when the population variances are unequal. Biometrika, 29, 350-362.\u003c/li\u003e\n \u003cli\u003eAdbi, H. (2007) Encyclopedia of Measurement and Statistics. Thousand Oaks, CA: Sage.\u003c/li\u003e\n \u003cli\u003eRStudio Team (2020). RStudio: integrated development for R. RStudio, PBC, Boston. URL http://www.rstudio.com/.\u003c/li\u003e\n \u003cli\u003eBisanz J. Tutorial: integrating QIIME2 and R for data visualization and analysis using qiime2R (v0.99.6). 2021.\u003c/li\u003e\n \u003cli\u003eWickham H (2007). \u0026ldquo;Reshaping Data with the reshape Package.\u0026rdquo; Journal of Statistical Software, 21(12), 1\u0026ndash;20.\u003c/li\u003e\n \u003cli\u003eWickham H. ggplot2: elegant graphics for data analysis. New York: Springer-Verlag; 2009.\u003c/li\u003e\n \u003cli\u003eWickham H, Averick M, Bryan J, Chang W, McGowan LD, Fran\u0026ccedil;ois R, et al. Welcome to the tidyverse. J Open Source Softw. 2019;4:1686.\u003c/li\u003e\n \u003cli\u003eCorrea-Fiz F, Fraile L, Aragon V. Piglet nasal microbiota at weaning may influence the development of Gl\u0026auml;sser\u0026rsquo;s disease during the rearing period. BMC Genomics. 2016 May 26;17:404. doi: 10.1186/s12864-016-2700-8. PMID: 27230662; PMCID: PMC4881051.\u003c/li\u003e\n \u003cli\u003eCorrea-Fiz, F., Gon\u0026ccedil;alves Dos Santos, J. M., Illas, F., \u0026amp; Aragon, V. (2019). Antimicrobial removal on piglets promotes health and higher bacterial diversity in the nasal microbiota. Scientific reports, 9(1), 6545. https://doi.org/10.1038/s41598-019-43022-y.\u003c/li\u003e\n \u003cli\u003eBlanco-Fuertes M, Correa-Fiz F, Fraile L, Sibila M, Aragon V. Altered Nasal Microbiota Composition Associated with Development of Polyserositis by Mycoplasma hyorhinis. Pathogens. 2021 May 14;10(5):603. doi: 10.3390/pathogens10050603. PMID: 34069250; PMCID: PMC8156107.\u003c/li\u003e\n \u003cli\u003ePirolo, M., Espinosa-Gongora, C., Bogaert, D., \u0026amp; Guardabassi, L. (2021). The porcine respiratory microbiome: recent insights and future challenges. Animal microbiome, 3(1), 9. https://doi.org/10.1186/s42523-020-00070-4.\u003c/li\u003e\n \u003cli\u003eCarding, S., Verbeke, K., Vipond, D. T., Corfe, B. M., \u0026amp; Owen, L. J. (2015). Dysbiosis of the gut microbiota in disease. Microbial ecology in health and disease, 26, 26191. https://doi.org/10.3402/mehd.v26.26191.\u003c/li\u003e\n \u003cli\u003eDe Arriba L.M., Lopez-Serrano S., Galofre-Mila N., Aragon V. Characterisation of Bergeyella Spp. Isolated from the Nasal Cavities of Piglets. Vet. J. 2018;234:1\u0026ndash;6. doi: 10.1016/j.tvjl.2018.01.004.\u003c/li\u003e\n \u003cli\u003ePena Cortes, L. C., LeVeque, R. M., Funk, J. A., Marsh, T. L., \u0026amp; Mulks, M. H. (2018). Development of the Tonsil Microbiome in Pigs and Effects of Stress on the Microbiome. Frontiers in veterinary science, 5, 220. https://doi.org/10.3389/fvets.2018.00220.\u003c/li\u003e\n \u003cli\u003ePena Cortes, L. C., LeVeque, R. M., Funk, J., Marsh, T. L., \u0026amp; Mulks, M. H. (2018). Development of the tonsillar microbiome in pigs from newborn through weaning. BMC microbiology, 18(1), 35. https://doi.org/10.1186/s12866-018-1176-x.\u003c/li\u003e\n \u003cli\u003ePirolo, M., Espinosa-Gongora, C., Alberdi, A., Eisenhofer, R., Soverini, M., Eriksen, E. \u0026Oslash;., Pedersen, K. S., \u0026amp; Guardabassi, L. (2023). Bacterial topography of the upper and lower respiratory tract in pigs. Animal microbiome, 5(1), 5. https://doi.org/10.1186/s42523-023-00226-y.\u003c/li\u003e\n \u003cli\u003eObregon-Gutierrez P, Bonillo-Lopez L, Correa-Fiz F, Sibila M, Segal\u0026eacute;s J, Kochanowski K, et al. Gut-associated microbes are present and active in the pig nasal cavity. Sci Rep. 2024 Apr 11;14(1):8470.\u003c/li\u003e\n \u003cli\u003eObregon-Gutierrez, P., Aragon, V., \u0026amp; Correa-Fiz, F. (2021). Sow Contact Is a Major Driver in the Development of the Nasal Microbiota of Piglets. Pathogens (Basel, Switzerland), 10(6), 697. https://doi.org/10.3390/pathogens10060697.\u003c/li\u003e\n \u003cli\u003eBlanco-Fuertes, M., Sibila, M., Franzo, G., Obregon-Gutierrez, P., Illas, F., Correa-Fiz, F., \u0026amp; Arag\u0026oacute;n, V. (2023). Ceftiofur treatment of sows results in long-term alterations in the nasal microbiota of the offspring that can be ameliorated by inoculation of nasal colonizers. Animal microbiome, 5(1), 53. https://doi.org/10.1186/s42523-023-00275-3.\u003c/li\u003e\n \u003cli\u003eWylensek, D., Hitch, T. C. A., Riedel, T., Afrizal, A., Kumar, N., Wortmann, E., Liu, T., Devendran, S., Lesker, T. R., Hern\u0026aacute;ndez, S. B., Heine, V., Buhl, E. M., M D\u0026rsquo;Agostino, P., Cumbo, F., Fisch\u0026ouml;der, T., Wyschkon, M., Looft, T., Parreira, V. R., Abt, B., Doden, H. L., \u0026hellip; Clavel, T. (2020). A collection of bacterial isolates from the pig intestine reveals functional and taxonomic diversity. Nature communications, 11(1), 6389. https://doi.org/10.1038/s41467-020-19929-w.\u003c/li\u003e\n \u003cli\u003eYeoman, C. J., \u0026amp; White, B. A. (2014). Gastrointestinal tract microbiota and probiotics in production animals. Annual review of animal biosciences, 2, 469\u0026ndash;486. https://doi.org/10.1146/annurev-animal-022513-114149.\u003c/li\u003e\n \u003cli\u003ePickard, J. M., Zeng, M. Y., Caruso, R., \u0026amp; N\u0026uacute;\u0026ntilde;ez, G. (2017). Gut microbiota: Role in pathogen colonization, immune responses, and inflammatory disease. Immunological reviews, 279(1), 70\u0026ndash;89. https://doi.org/10.1111/imr.12567.\u003c/li\u003e\n \u003cli\u003eEspinosa-Gongora, C., Larsen, N., Sch\u0026oslash;nning, K., Fredholm, M., \u0026amp; Guardabassi, L. (2016). Differential Analysis of the Nasal Microbiome of Pig Carriers or Non-Carriers of Staphylococcus aureus. PloS one, 11(8), e0160331. https://doi.org/10.1371/journal.pone.0160331.\u003c/li\u003e\n \u003cli\u003eWang, Q., Cai, R., Huang, A., Wang, X., Qu, W., Shi, L., Li, C., \u0026amp; Yan, H. (2018). Comparison of Oropharyngeal Microbiota in Healthy Piglets and Piglets With Respiratory Disease. Frontiers in microbiology, 9, 3218. https://doi.org/10.3389/fmicb.2018.03218.\u003c/li\u003e\n \u003cli\u003eLi, Z., Wang, X., Di, D., Pan, R., Gao, Y., Xiao, C., Li, B., Wei, J., Liu, K., Qiu, Y., \u0026amp; Ma, Z. (2021). Comparative analysis of the pulmonary microbiome in healthy and diseased pigs. Molecular genetics and genomics : MGG, 296(1), 21\u0026ndash;31. https://doi.org/10.1007/s00438-020-01722-5.\u003c/li\u003e\n \u003cli\u003eNowland, T. L., Plush, K. J., Barton, M., \u0026amp; Kirkwood, R. N. (2019). Development and Function of the Intestinal Microbiome and Potential Implications for Pig Production. Animals : an open access journal from MDPI, 9(3), 76. https://doi.org/10.3390/ani9030076.\u003c/li\u003e\n \u003cli\u003eNiederwerder M.C. Role of the Microbiome in Swine Respiratory Disease. Vet. Microbiol. 2017;209:97\u0026ndash;106. doi: 10.1016/j.vetmic.2017.02.017.\u003c/li\u003e\n \u003cli\u003eBonillo-Lopez L, Obregon-Gutierrez P, Huerta E, Correa-Fiz F, Sibila M, Aragon V. Intensive antibiotic treatment of sows with parenteral crystalline ceftiofur and tulathromycin alters the composition of the nasal microbiota of their offspring. Vet Res. 2023 Nov 24;54(1):112. doi: 10.1186/s13567-023-01237-y.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"animal-microbiome","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"amic","sideBox":"Learn more about [Animal Microbiome](http://animalmicrobiome.biomedcentral.com)","snPcode":"42523","submissionUrl":"https://submission.nature.com/new-submission/42523/3","title":"Animal Microbiome","twitterHandle":"@bmc","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Pig, Swine, Virus, PRRSV, Nasal microbiota ","lastPublishedDoi":"10.21203/rs.3.rs-4762608/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4762608/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePorcine Reproductive and Respiratory Syndrome Virus (PRRSV) is a major threat to swine industry worldwide, especially virulent variants arising during the last years, such as Spanish PRRSV-1 Rosalia strain. The role of the nasal microbiota in respiratory viral infections is still to be unveiled, but may be promisingly related with the health status of the animals and thus, their susceptibility. The goal of this project was to study the nasal microbiota composition of piglets during a highly virulent PRRSV-1 outbreak comparing animals that died due to the infection with animals that survived it. The microbiota composition was inferred by V3-V4 regions of the 16S rRNA gene sequencing and bioinformatics analysis. To deepen the analysis, we added samples taken from piglets before the outbreak as well as from the sows giving birth to piglets under study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePiglets that survived the PRRSV-1 outbreak reported a more diverse and different nasal microbiota at three weeks of age compared to piglets dying, which was highly related with the litter of origin and the sow of the piglets. In addition, the high abundance of classical swine nasal colonizers belonging to genera such as \u003cem\u003eBergeyella\u003c/em\u003e, \u003cem\u003eGlaesserella\u003c/em\u003e, \u003cem\u003eNeisseria\u003c/em\u003e and \u003cem\u003eMoraxella\u003c/em\u003e (among others), was related with good outcome. On the other hand, a dysbiotic community dominated by \u003cem\u003eEscherichia\u003c/em\u003eand a different clade of \u003cem\u003eMoraxella\u003c/em\u003e was found in piglets with bad outcome. Moreover, samples taken before the outbreak showed similar dynamics prior to virulent PRRSV-1 arrival, suggesting that microbiota-related susceptibility was already occurring in the animals and that the increase in mortality seen was related to the new highly virulent strain.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur study suggests that the susceptibility to an infection such as PRRSV could be related to the nasal microbiota composition at the moment of infection and may serve as starting point to explore animal resilience. Since the dysbiosis detected as an initial response to infection may be not specific for this virus, further investigations should explore this phenomenon in the context of other viral infections.\u003c/p\u003e","manuscriptTitle":"Nasal microbial diversity is associated with survival in piglets infected by a highly virulent PRRSV-1 strain","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-13 09:57:04","doi":"10.21203/rs.3.rs-4762608/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-12-04T03:30:27+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-12-03T08:53:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"315158870158138184416110986000099371849","date":"2024-11-21T13:06:23+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-15T17:08:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"180553912429356165820743904213304295648","date":"2024-11-11T14:55:28+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-09-30T02:14:54+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-18T17:44:47+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-18T16:42:12+00:00","index":"","fulltext":""},{"type":"submitted","content":"Animal Microbiome","date":"2024-07-18T12:22:20+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"animal-microbiome","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"amic","sideBox":"Learn more about [Animal Microbiome](http://animalmicrobiome.biomedcentral.com)","snPcode":"42523","submissionUrl":"https://submission.nature.com/new-submission/42523/3","title":"Animal Microbiome","twitterHandle":"@bmc","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bf260a1b-2b71-4747-9601-cf1fd5d4ad5d","owner":[],"postedDate":"August 13th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-01-20T16:00:18+00:00","versionOfRecord":{"articleIdentity":"rs-4762608","link":"https://doi.org/10.1186/s42523-024-00371-y","journal":{"identity":"animal-microbiome","isVorOnly":false,"title":"Animal Microbiome"},"publishedOn":"2025-01-17 15:57:11","publishedOnDateReadable":"January 17th, 2025"},"versionCreatedAt":"2024-08-13 09:57:04","video":"","vorDoi":"10.1186/s42523-024-00371-y","vorDoiUrl":"https://doi.org/10.1186/s42523-024-00371-y","workflowStages":[]},"version":"v1","identity":"rs-4762608","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4762608","identity":"rs-4762608","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}