Blanco‑Fuertes et al. Animal Microbiome (2023) 5:53 Animal Microbiome https://doi.org/10.1186/s42523-023-00275-3 RESEARCH Open Access 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 Miguel Blanco‑Fuertes1,2,3,4, Marina Sibila1,2,3, Giovanni Franzo5, Pau Obregon‑Gutierrez1,2,3, Francesc Illas6, Florencia Correa‑Fiz1,2,3*† and Virginia Aragón1,2,3*† Abstract Background The nasal microbiota of the piglet is a reservoir for opportunistic pathogens that can cause polyse‑ rositis, such as Glaesserella parasuis, Mycoplasma hyorhinis or Streptococcus suis. Antibiotic treatment is a strategy to control these diseases, but it has a detrimental effect on the microbiota. We followed the piglets of 60 sows from birth to 8 weeks of age, to study the effect of ceftiofur on the nasal microbiota and the colonization by patho‑ gens when the treatment was administered to sows or their litters. We also aimed to revert the effect of the antibiotic on the nasal microbiota by the inoculation at birth of nasal colonizers selected from healthy piglets. Nasal swabs were collected at birth, and at 7, 15, 21 and 49 days of age, and were used for pathogen detection by PCR and bacterial culture, 16S rRNA amplicon sequencing and whole shotgun metagenomics. Weights, clinical signs and production parameters were also recorded during the study. Results The composition of the nasal microbiota of piglets changed over time, with a clear increment of Clostridiales at the end of nursery. The administration of ceftiofur induced an unexpected temporary increase in alpha diversity at day 7 mainly due to colonization by environmental taxa. Ceftiofur had a longer impact on the nasal microbiota of piglets when administered to their sows before farrowing than directly to them. This effect was partially reverted by the inoculation of nasal colonizers to newborn piglets and was accompanied by a reduction in the number of animals showing clinical signs (mainly lameness). Both interventions altered the colonization pattern of different strains of the above pathogens. In addition, the prevalence of resistance genes increased over time in all the groups but was significantly higher at weaning when the antibiotic was administered to the sows. Also, ceftiofur treatment induced the selection of more beta-lactams resistance genes when it was administered directly to the piglets. Conclusions This study shed light on the effect of the ceftiofur treatment on the piglet nasal microbiota over time and demonstrated for the first time the possibility of modifying the piglets’ nasal microbiota by inoculating natural colonizers of the upper respiratory tract. † Florencia Correa-Fiz and Virginia Aragon equally contributed. *Correspondence: Florencia Correa‑Fiz flor.correa@irta.cat Virginia Aragón virginia.aragon@irta.cat Full list of author information is available at the end of the article © The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. Blanco‑Fuertes et al. Animal Microbiome (2023) 5:53 Page 2 of 17 Background intended to provide benefits to the host (probiotics), and The relationship between the host and the bacterial com- therefore pathogen exclusion. Interventions with probi- munities in the swine microbiota from different tissues otics may reduce or delay the colonization of the niche has been an issue exponentially assessed during the last by pathogens. Probiotics are frequently used in humans decade [1–3]. As in other studies and animal species, but less commonly in pigs, where all of them target the bacterial communities are niche-specific [4]. The major- gut microbiota [22, 23]. However, the use of probiotics in ity of the studies on the pig microbiome focus on the gut, the respiratory tract has only been investigated in a few but in recent years more studies are investigating other studies where they proved their variable action against niches such as the skin, oropharyngeal and nasal cavities respiratory-associated infections [24–27]. [2, 5, 6]. Here, we present the results obtained in a longitudinal Recent microbiota studies focused on the specific fac- study in a swine farm with respiratory problems, where tors that contribute to microbiota shifts, such as breed, ceftiofur was applied to pregnant sows or piglets. The feed system, environment or antimicrobial treatment findings of this study revealed a more prolonged effect on among others [1, 7, 8]. Usually, these factors have a the piglets’ nasal microbiota when ceftiofur was admin- deeper impact when the microbiota is unstable or imma- istered to the sows than directly to the newborn piglets. ture and, therefore, more susceptible to external changes We also demonstrated for the first time the ability to [9–11]. Colonization of the respiratory tract starts at modify the nasal microbiota of the piglets by inoculating birth when most of the early colonizers are transferred nasal colonizers at birth. from the dam and/or from environment [8]. Several stud- ies have shown the importance of the microbiota popu- Methods lation structure in the development of posterior gut and Study design respiratory diseases [3, 4, 12]. For that reason, cross-sec- Animal experimentation was performed following proper tional studies are key to expand the knowledge about the veterinary practices, under European (Directive 2010/63/ dynamics of the host-microbiome interactions. EU) and Spanish (Real Decreto 53/2013) regulation and In swine industry, the prevention of diseases during with the approval of the Ethics Commission in Animal the postweaning period has a fundamental impact on Experimentation of the Generalitat de Catalunya (Proto- the subsequent production phases [3, 13]. Weaning is col number 9211). a crucial moment in piglet’s life due to the implications A field study was conducted in a 2-site pig commercial in the maturation of the immune system and the intes- farm with recurrent respiratory problems (site 1: gesta- tine [14, 15]. In addition, immune protection acquired tion and lactation; site 2: nursery) located 15 km apart. from the mothers starts to decline during this stage and, The breeding herd (300 sows) was managed as a 1 week together with the stress and changes associated with batch farrowing system and the duration of lactation was weaning (separation from the sows, mixing of litters and 3 weeks. After weaning, piglets were transferred to the social challenge, nutritional changes…), some patholo- nursery facility, where they stayed until nine weeks of age. gies caused by bacteria present in the normal microbiota Four days before farrowing (D-4), 23 sows were intra- line ceftiofur, a third-generation cephalosporin (Naxcel®; (pathobionts) can be triggered [16, 17]. For instance, muscularly treated (treated sows) with 10 mL of crystal- polyserositis is frequently observed in nursery pigs when members of the nasal microbiota, such as Glaesserella 100 mg/mL), while 31 sows remained untreated (non- parasuis, Streptococcus suis, or Mycoplasma hyorhinis, treated sows). With this product, maximum concentra- spread systemically [16, 18]. tion of ceftiofur in plasma is reached within the following Due to the lack of effective vaccines, the main strat- 22 h of intramuscular administration, and 75% of the egy to control these diseases in piglets continues to be drug is excreted within 10 days after administration. the use of antimicrobials [16, 19]. The use of antibiotics Ceftiofur is not expected to be found in milk. Sows were in sows is thought to reduce the transfer of pathogens to distributed in five different rooms in the lactation facili- their offspring and control the pathogen load globally in ties (one group per room). Piglets were divided accord- the farm [20]. However, antimicrobials will also affect the ing to the treatment received by the sows, where 284 composition of the beneficial microbiota [21]. Moreover, piglets born to treated sows and 407 born to non-treated the indiscriminate use of antibiotics to control bacterial sows were included in the study. To assess the effect of diseases (mostly as metaphylaxis) has been questioned the antibiotic on the nasal microbiota of piglets when the due to the emergence of antimicrobial resistant bac- treatment was applied either directly to them or to their teria. One of the alternative strategies to promote the sows, 3 groups were established (Table 1). At birth (D0), health of piglets, and therefore reduce antibiotic usage 129 piglets born to treated sows remained nontreated in swine production, could be the use of microorganisms (TS group), while 115 piglets born to non-treated sows Blanco‑Fuertes et al. Animal Microbiome (2023) 5:53 Page 3 of 17 Table 1 Number of sows and piglets per study group according to the sow and/or piglet treatment Sow treatment Piglet treatment (at birth) Group* (at 1 week pre-farrowing) Non-treated (NTS) Non-Treated (n = 118 piglets from 10 sows) Control Treated (n = 115 piglets from 9 sows) TP Inoculated (n = 175 piglets from 12) IP Treated (TS) Non-Treated (n = 129 piglets from 11 sows) TS Inoculated (n = 154 piglets from 12 sows) TS-IP * For clarity, no acronym is explicitly mentioned when no treatment was applied were intramuscularly treated with 1 mg of crystalline for bacterial culture, PCR or microbiota analysis, as ceftiofur (Treated Piglet group = TP). As control, 118 described in Table 2. piglets born to non-treated sows remained non-treated Nasal swabs were resuspended in 500 µL of PBS and (control group). kept refrigerated until arrival at research facilities where On the other hand, to study the effect of early coloniza- they were vortexed and stored at − 20 °C. A total of 200 tion with selected colonizers on the piglets’ nasal micro- µL of the suspensions was processed using the Nucle- biota, piglets were inoculated at D0 with a cocktail of 5 ospin Blood kit (Macherey–Nagel, Düren, Germany) selected bacterial strains at ­104–105 CFU/mL (dose of 200 according to the manufacturer’s instructions. Total µL) using a nasal spray (Table 1). These strains belonged extracted DNA was quantified using BioDrop DUO (Bio- to five different species: Vagococcus lutrae, Streptococ- Drop Ltd., Cambridge, UK) and stored at − 20 °C for fur- cus pluranimalium, Moraxella pluranimalium, Rothia ther processing. nasimurium and Glaesserella parasuis. The inoculated pigs were non-treated and originated from two different PCR detection of Glaesserella parasuis, Streptococcus suis, groups: 154 piglets were born to ceftiofur-treated sows and Mycoplasma hyorhinis (Treated sow − Inoculated piglet = TS − IP) and 175 were Swabs from 5 animals per litter from each group were born to non-treated sows (IP group). used for pathogen detection by PCR. These five piglets The number of born and weaned piglets per litter and were initially chosen randomly, and the same animals group was registered. All piglets (n = 691) were followed were tested by PCR at the different timepoints. If one of during the first three weeks of age (lactation facilities) the piglets was not available at any timepoint, a littermate and after weaning a subset of them (n = 490) was followed was chosen. until 8 weeks of age (nursery facilities), collecting data on Detection of the pathogens present in the nasal cav- the general conditions and health status of the animals ity was done individually using specific PCRs on the 2 µl (Table 2). During the nursery period, all the animals that from the total extracted DNA. For G. parasuis, a specific (1 mL / 10 kg of Gentamox®; 150 mg of amoxicillin and showed any clinical signs were treated with antibiotics vtaA leader sequence PCR [30] was used for the detection and differentiation of virulent or non-virulent strains. 40 mg of gentamicin per mL) and were removed from Specific PCRs for S. suis detection were performed, as the study. Body weight was recorded at birth (D0) and at well as the specific PCRs for serotype 2 and serotype 9 weaning (D21) and average daily weight gain (ADWG) as described before [31, 32]. In the case of M. hyorhinis, was calculated through this period and compared among qPCRs were performed as previously described [33]. groups using a mixed effect linear model using lme4 R Samples with a Ct value < 37 were considered positive. package [28]. The number of animals showing lameness PCR or qPCR results were expressed as percentage of or other clinical signs compatible with systemic infection positive samples per group and were compared through and the associated mortality among groups were com- contingency tables with Chi-square test. pared using Fisher test with Bonferroni correction [29]. The number of born and weaned piglets per group was analyzed by a Chi-squared test. Genotyping of Glaesserella parasuis and Streptococcus suis isolates Swabs from 1 animal per litter from each group were Sample collection and processing used for bacterial culture. As above described for Nasal swabs were taken from piglets belonging to each PCRs, the same animals were used at the different group at different timepoints (D0, D7, D15, D21, D49) timepoints. Blanco‑Fuertes et al. Animal Microbiome (2023) 5:53 Page 4 of 17 Table 2 Study design and actions performed to followed piglets born to treated or non-treated sows Production phase Day (D) Action performed Analysis Non-treated sow Treated-sow Control (N = 10) TP (N = 9) IP (N = 12) TS (N = 11) TS-IP (N = 12) Lactation D0 Piglet treatment 0 115 0 0 0 Piglet inoculation 0 0 175 0 154 Weight 118 115 175 129 154 Nasal sampling M. hyorhinis, G. parasuis 0 10 0 10 0 and S. suis ­PCRsa Microbiota 16S sequenc‑ 0 5 0 5 0 ing D7 Nasal sampling G. parasuis and S. suis 10 9 12 11 12 ­cultureb M. hyorhinis, G. parasuis 50 45 60 55 60 and S. suis ­PCRsc Microbiota 16S sequenc‑ 5 5 5 5 5 ing D15 Nasal sampling G. parasuis and S. suis 10 9 12 11 12 ­cultureb M. hyorhinis, G. parasuis 50 45 60 55 60 and S. suis ­PCRsc Nursery D21 Weight 101 85 118 89 97 Nasal sampling G. parasuis and S. suis 10 9 12 11 12 ­cultureb M. hyorhinis, G. parasuis 50 45 60 55 60 and S. suis ­PCRsc Microbiota 16S sequenc‑ 5 5 5 5 5 ing WGS metagenomics 5 5 5 5 5 D49 Nasal sampling G. parasuis and S. suis 10 9 12 11 12 cultureb M. hyorhinis, G. parasuis 25 20 25 25 25 and S. suis ­PCRsc Microbiota 16S sequenc‑ 5 5 5 5 5 ing WGS metagenomics 5 5 5 5 5 a 5 animals were randomly selected among the 10 samples for PCR testing b Corresponded to one piglet per litter c Corresponded to five piglet per litter Control, non-treated piglets born to non-treated sows; TP, piglets treated with ceftiofur born to non-treated sows; IP, inoculated piglets born to non-treated sows; TS, piglets born to treated sows; TS-IP, inoculated piglets born to treated sows Swabs were plated on chocolate agar and up to 4 Amplification was carried out with an initial denatura- colonies morphologically compatible with G. parasuis tion of 94 °C for 2 min followed by 30 cycles of 30 s at and 4 colonies compatible with S. suis were selected for 94 °C, 1 min at 50 °C and 2.5 min at 72 °C, and finally an identification and characterization. extension of 20 min at 72 °C. In order to discriminate different strains, G. parasuis and S. suis isolates were genotyped by enterobacte- 16S amplicon sequencing and in silico analysis rial repetitive intergenic PCR (ERIC-PCR) with prim- Five animals per group were selected for longitudinal ers ERIC-1F (ATG​TAA​G CT​CCT​G GG​GAT​TCAC) analysis (D7, D15, D21 and D49) of the nasal micro- and ERIC-2R (AAG​TAA​GTG​ACT​G GGG​TGA​G CG) biota by 16S sequencing. At farrowing (D0), 5 piglets [34]. The PCR reaction mixture consisted of 3 mM of from non-treated and 5 piglets from treated sows were 0.75 U of GoTaq® polymerase (Promega, Madison ­MgCl2, 1.2 μM of each primer, 0.23 mM of dNTPs, selected. The region targeted for the Illumina 16S ampli- Wisconsin, USA) and 100 nanograms of DNA sample. con sequencing was the V3-V4. This region was Blanco‑Fuertes et al. Animal Microbiome (2023) 5:53 Page 5 of 17 amplified using primers 341F (5′- 516 CCT​AYG​GGRB- WGS metagenomic sequencing and in silico analysis GCASCAG-3′) and 806R (5′-GGA​CTA​CNNGGG​TAT​ Extracted DNAs from the same swabs used for 16S CTAAT-3′) [35]. Read length was 2 × 250 bp and Illu- sequencing at weaning (D21) and at the end of nurs- mina MiSeq technology was used [35]. The bioinfor- ery (D49) were used for whole genome shotgun (WGS) matic downstream analysis was done using Quantitative metagenomics. insights into microbial ecology (Qiime2) software toolkit WGS metagenomic sequencing of the samples was [36]. Denoising and quality-control step was done using done using Illumina Novaseq 6000 (2 × 150 bp) technol- q2-dada2 plugin [37]. The following step was to remove ogy. The throughput required per sample was at least 15 all the reads with no match with at least an 80% of identity Gigabases to acquire enough sequencing depth. Genomic against Greengenes (v13.8) rRNA database [38] and 50% data were analyzed under the biobakery3 [49] metagen- of length. Diversity analyses were done using core-metrics omic profiling workflow. Raw reads were QC processed plugin with a rarefied sample depth of 12,124. One sam- and trimmed using Kneaddata pipeline [49], which ple was removed from the analysis due to not reach the uses Trimmomatic [50] and Bowtie2 [51]. In addition, query sample depth threshold. Alpha diversity was done trimmed reads were aligned to the Sus scrofa reference using the Shannon diversity index [39], and Chao index genome v11.1 [52], to remove any read matching the [40]. Beta diversity distance matrices were calculated host. Taxonomic profiling of each sample was assessed based on the weighted Unifrac index [41]. A permuta- using Metaphlan4 [49, 53] software, on cleaned read tion-based analysis of variance (PERMANOVA) [42] was level. Functional profiling of all the samples was done done to test if the centroid of two or more groups were through HUMAnN [49] which quantifies gene families, significantly different. The percentage of the variance EC enzyme modules, and pathways, using the UniRef explained by the study groups was calculated through the [54] and MetaCyc databases [55]. Differences among Adonis function from the Vegan package [43] in R. Taxo- groups were estimated through a multivariate asso- nomic assignment of each amplicon sequence variant ciation analysis with linear models using MaAsLin2 R was done using the Qiime2 classifier (q2-feature-classifier package [56], embedded in the Biobakery3 toolkit. Abun- plugin) trained with the V3-V4 region from 16S gene and dances were passed through a basic filter requiring each the Greengenes database (v13.8) [38]. Analysis of com- pathway and taxa to have at least 0.01% abundance in at position of microbiomes with bias correction (ANCOM- least 3% of all samples. Assembly of the clean reads was BC) [44] function was done at each timepoint among all done using MetaSpades [57]. Taxonomic profiling of the the groups to perform differentially abundant analysis metagenome-assembled genomes (MAGs) was done at both amplicon sequence variant (ASV) level and col- using Kraken2 [58]. lapsed at different taxonomic levels. All data was pro- Analysis of the antimicrobial resistance genes (AMR) cessed for tables, plots and figures using Rstudio [45] and was done using Abricate software [59] over the MAGs qiime2R [46], ggplot2 [47] and tidyverse [48] packages. with the NCBI AMRFinderPlus database [60]. Table 3 Number of liveborn and weaned piglets per litter, body weight and average daily weight gain per each treatment group Groups Farrow Weaning Farrowing to Weaning Liveborn piglets Body weight (Kg) Weaned piglets Body weight (Kg) ADWG# (gr) Total Mean per litter ± SD Mean ± SD Total Mean per litter ± SD Mean ± SD n* Mean ± SD Control 118 13.10 ± 2.76 1.71 ± 0.35 108 10.80 ± 1.46 5.59 ± 1.08 101 0.18 ± 0.05 TS 129 13.08 ± 2.70 1.51 ± 0.35 122 11.08 ± 1.38 5.10 ± 0.95 89 0.17 ± 0.04 TP 115 12.58 ± 2.68 1.38 ± 0.34 97 10.77 ± 1.25 5.60 ± 1.34 85 0.19 ± 0.06 IP 175 13.39 ± 2.64 1.53 ± 0.36 149 12.41 ± 1.30 5.60 ± 1.35 118 0.17 ± 0.05 TS-IP 154 13.25 ± 2.71 1.58 ± 0.30 143 11.91 ± 1.67 5.51 ± 0.97 97 0.18 ± 0.04 * The number of the animals weighed at weaning was lower than the number of weaned animals due to the fact that the rooms in the nursery facilities were smaller and not all the weaned animals included in the study could be allocated in them # Only animals present at both timepoints were included Control, non-treated piglets born to non-treated sows; TS, piglets born to treated sows; TP, piglets treated with ceftiofur born to non-treated sows; IP, inoculated piglets born to non-treated sows ; TS-IP, inoculated piglets born to treated sows Blanco‑Fuertes et al. Animal Microbiome (2023) 5:53 Page 6 of 17 Table 4 Proportion (and percentages) of animals showing consequently, three animals from the control group were lameness and mortality rate compatible with respiratory or treated (gentamicin-amoxicillin). Throughout the nurs- systemic disease at nurseries ery period, the main clinical signs observed were lame- Group Lameness Mortality rate ness and diarrhea, and piglets showing lameness were (Affected/total) treated with gentamicin-amoxicillin (Table 4). To avoid Dead/total Dead/affected any bias due to the effect of this antibiotic treatment on Control 48/101 (41%) 8/ 101 (7.90%) 8/ 48 (16.6%) the microbiota, the gentamicin-amoxicillin treated ani- TS 36/100 (28%) 9/100 (9%) 9/36 (25%) mals were excluded from the study. During nursery, no TP NA NA NA significant differences were found in mortality rates IP 47/118(28%) 11/118 (9.32%) 11/47 (23.4%) among the groups. Notably, the TS-IP group showed TS-IP 24/96 (15%)* 1/96 (1.04%) 1/24 (4.16%) a lower prevalence of lameness than the control group NA Not available (P < 0.05, Chi-squared test; Table 4). * Significantly different when compared to control group (P < 0.05) Control, non-treated piglets born to non-treated sows; TS, piglets born to Sow treatment induced long term changes in the nasal treated sows; TP, piglets treated with ceftiofur born to non-treated sows; IP, microbiota of the offspring inoculated piglets born to non-treated sows ; TS-IP, inoculated piglets born to treated sows Globally, a total of 14,655,905 sequences were obtained by 16S amplicon (V3-V4) sequencing, with 12,084 dif- ferent ASVs. Diversity analysis was calculated at a Results 12,124 read depth per sample, which required the Production parameters and health status elimination of one sample from the dataset due to low The mean number of liveborn piglets per litter was not sequencing throughput (6,832 reads in a sample from significantly different among the groups (Table 3). No the IP group at D7). stillborns or mummies were recorded during farrowing. The effect induced by ceftiofur treatment in piglets’ From the individual weights obtained at farrowing and nasal microbiota was evaluated when administered to at weaning (D21), the ADWG of the piglets was calcu- sows before farrowing. Alpha diversity was estimated lated and computed in a linear mixed effect model where longitudinally (Chao and Shannon indexes), from birth the individual variation was computed as a random to fattening at four time points. Initially (D0), alpha effect. No statistically significant differences between the diversity was not different in piglets born to treated mean body weight between groups at D0 (birth) and at or non-treated sows, but piglets born to treated sows D21 (weaning) were detected (Table 3). showed a higher inter-individual variability (Fig. 1). During the lactation period (first three weeks of The treatment on sows induced a temporal increase in age), sporadic episodes of diarrhea were observed and, the alpha diversity of the nasal microbiota of piglets at Fig. 1 Alpha diversity (Shannon index) of nasal microbiota from piglet at different ages (D = days of age) and groups. Pregnant sows were treated with ceftiofur or remained untreated. A group of piglets born to treated sows remained nontreated (TS group), while piglets born to non-treated sows were treated with ceftiofur (TP group). A group of piglets were inoculated with selected colonizers of the upper respiratory tract either born to ceftiofur-treated sows (TS-IP) and to non-treated sows (IP group). As control, piglets born to non-treated sows remained non-treated Blanco‑Fuertes et al. Animal Microbiome (2023) 5:53 Page 7 of 17 Fig. 2 Beta diversity on weighted Unifrac distance matrices. PCoA was performed at D7 (A), D21 (B) and D49 (C). Orange dots represent samples from piglets born to ceftiofur treated sows (TS group) and green dots represent non-treated piglets born to non-treated sows (Control) D7 (Shannon diversity index P = 0.028) compared with Ceftiofur treatment directly administered to piglets in the control group (Fig. 1). This rise in alpha diversity early life (TP) produced similar trends in alpha diver- was not maintained through time and no significant sity than the treatment applied to their sows (TS). A differences were observed between TS and the con- significant increase in alpha diversity (P = 0.009, Shan- trol group at D21 or D49. When the beta diversity was non diversity index) was detected at D7 in TP in com- analyzed, significant differences were detected at all parison to the control group (Fig. 1). As observed in TP sampling times, by either quantitative (Bray Curtis, group, this increment was not maintained through time, weighted Unifrac) or qualitative (Jaccard, unweighted and it was not observed at weaning (D21) or at the end Unifrac) indexes. A stronger divergence was observed of the study (D49). When beta diversity of TP and con- at D7 (Fig. 2), since the percentage of explanation trol groups was estimated, we detected differences at D7 measured on weighted Unifrac between groups was and D21 using quantitative (Fig. 3) and qualitative met- higher ­(R2 = 35%) at this timepoint compared to D21 rics. At D49, the difference between these two groups ­(R2 = 28%) and D49 ­(R2 = 22%). was only detected using Jaccard distance (qualitative Fig. 3 Beta diversity on weighted Unifrac distance matrices. PCoA was performed at D7 (A), D21 (B) and D49 (C). Blue dots represent samples from ceftiofur treated piglets born to non-treated sows (TP group) and green dots represent non-treated piglets born to non-treated sows (Control) Blanco‑Fuertes et al. Animal Microbiome (2023) 5:53 Page 8 of 17 measurement). Importantly, the effect was more evi- (qualitative metrics) but did not have the same outcome dent at D7 (Adonis function, R ­ 2 = 0.45, P = 0.012) than when taking into account also the relative abundance of at the other timepoints (Adonis function, R ­ 2 = 0.24, and these taxa (quantitative metrics), indicated that these 2 ­R = 0.15, at D21 and D49 respectively), underlying the qualitative differences were produced by bacterial taxa different microbial composition in TP compared to the with low relative abundance. control soon after the antibiotic treatment. To understand the microbial changes leading to these Lastly, both treated groups (TP and TS) were compared differences, we performed an analysis of the composi- to determine if the changes led by the antibiotic treat- tional microbiome with bias correction (ANCOM-BC) at ment were similar when administered to the piglets or each timepoint based on either amplicon sequence vari- to their sows. No significant differences were observed ants or at different taxonomic levels. At D7, we found 488 in alpha diversity at any sampling point. When beta ASV in TS and 205 ASVs in TP that were differentially diversity was analyzed, Jaccard, unweighted Unifrac and abundant when compared to the control group (ASVs; Bray Curtis metrics detected significative differences at Additional file 2: Table S1), which was in agreement with D7 (P = 0.01, P = 0.026 and P = 0.03, respectively), but the higher alpha diversity observed in this group. A sig- this difference was not observed using weighted Uni- nificant increase in the relative abundance of ASVs from frac (Additional file 1: Fig. S1). Quantitative differences Bacteroidales (3.972% in TS, 3.534% in TP versus 0.357% (weighted Unifrac and Bray Curtis) were not maintained in control) and Clostridiales (18.487% in TS, 7.733% in at D21, while they were still detected with the qualitative TP vs. 2.452% in control) was observed at D7 in both analysis (Jaccard and unweighted Unifrac). Similar results antibiotic-treated groups. On the other hand, typical that those found at D7 were obtained at D49 (unweighted members of the nasal microbiota were decreased in the Unifrac P = 0.006; Jaccard P = 0.008; Bray Curtis treated groups with respect to the non-treated control P = 0.012). The disparities observed in the beta diversity group, including Glaesserella (at D21), Moraxella (at D7 estimation, which detected significant differences when and D21) and Rothia (at D21) (Table 5). the analysis was made based on taxon presence-absence Table 5 Relative abundance of genera belonging to the core microbiota from control group at weaning from each group at D7 and D21 Taxa D7 D21 Control TS TP Control TS TP p__Proteobacteria;o__Pasteurellales;f__Pasteurellaceae;g__Glaesserella 0.0183 0.0601 0.0017 0.2754 0.1094 0.1490 p__Bacteroidetes;o__Flavobacteriales;f__[Weeksellaceae];g__Bergeyella 0.0000 0.0250 0.0000 0.1063 0.2005 0.0679 p__Proteobacteria;o__Pseudomonadales;f__Moraxellaceae;g__Enhydrobacter 0.1252 0.0013 0.0010 0.0876 0.1265 0.1616 p__Firmicutes;o__Lactobacillales;f__Streptococcaceae;g__Streptococcus 0.0243 0.0249 0.0145 0.0320 0.0746 0.0368 p__Proteobacteria;o__Caulobacterales;f__Caulobacteraceae;g__Caulobacter 0.1005 0.0773 0.1593 0.0761 0.0801 0.1341 p__Proteobacteria;o__Pseudomonadales;f__Moraxellaceae;g__Moraxella 0.3518 0.0500 0.0476 0.0673 0.0298 0.0190 p__Proteobacteria;o__Rhizobiales;f__Rhizobiaceae;g__Rhizobium 0.0495 0.0307 0.0705 0.0402 0.0718 0.0785 p__Firmicutes;o__Lactobacillales;f__Lactobacillaceae;g__Lactobacillus 0.0174 0.0530 0.1236 0.0022 0.0095 0.0052 p__Actinobacteria;o__Actinomycetales;f__Micrococcaceae;g__Rothia 0.0069 0.0232 0.0100 0.0173 0.0026 0.0058 p__Bacteroidetes;o__Bacteroidales;f__Bacteroidaceae;g__Bacteroides 0.0008 0.0087 0.0214 0.0025 0.0018 0.0044 p__Proteobacteria;o__Rhizobiales;__;__ 0.0495 0.0307 0.0705 0.0003 0.0009 0.0017 p__Bacteroidetes;o__Bacteroidales;f__Prevotellaceae;g__Prevotella 0.0008 0.0090 0.0064 0.0012 0.0015 0.0010 p__Firmicutes;o__Clostridiales;f__Ruminococcaceae;g__ 0.0008 0.0046 0.0010 0.0020 0.0019 0.0009 p__Proteobacteria;o__Pseudomonadales;f__Moraxellaceae;g__Acinetobacter 0.0151 0.0996 0.0816 0.0018 0.0048 0.0151 p__Proteobacteria;o__Rhizobiales;f__Bradyrhizobiaceae;g__Bradyrhizobium 0.0064 0.0029 0.0078 0.0034 0.0035 0.0113 p__Proteobacteria;o__Enterobacteriales;f__Enterobacteriaceae;g__Escherichia 0.0018 0.0104 0.0229 0.0055 0.0058 0.0071 p__Firmicutes;o__Clostridiales;f__Clostridiaceae;g__Clostridium 0.0025 0.0247 0.0047 0.0009 0.0014 0.0007 p__Proteobacteria;o__Burkholderiales;f__Comamonadaceae;g__Variovorax 0.0060 0.0047 0.0083 0.0029 0.0072 0.0017 p__Firmicutes;o__Clostridiales;f__;g__ 0.0025 0.0247 0.0047 0.0020 0.0019 0.0009 p__Firmicutes;o__Turicibacterales;f__Turicibacteraceae;g__Turicibacter 0.0009 0.0049 0.0016 0.0013 0.0002 0.0015 Control, non-treated piglets born to non-treated sows; TS, piglets born to treated sows; TP, piglets treated with ceftiofur born to non-treated sows Blanco‑Fuertes et al. Animal Microbiome (2023) 5:53 Page 9 of 17 At D21, the number of differential ASVs was higher three groups (Additional file 4: Table S3). At genus level, when comparing the control group with TS (92 ASVs) some differentially abundant genera showed to be group- than with TP (68 ASVs). Among the ASVs affected by specific, such as Mycoplasma, whose abundance was the antibiotic treatment, we found an ASV from Myco- higher in the control group (1.28% vs. 0.15% in TP and plasma genus that was decreased in TS and absent in 0.33% in TS). In agreement, higher prevalence of M. hyor- TP. This relative decrease in Mycoplasma in TP group hinis was detected in the control group by PCR (95% vs. was assigned, at least partially, to M. hyorhinis by PCR 74% in TP and 66% in TS). Sixteen genera were differen- (Additional file 3: Table S2). Differentially abundant ASVs tially abundant comparing the three groups (TS, TP and among the three groups (TS, TP and control) belonged control), including some nasal-associated commensals: to nasal-associated taxa, such as Streptococcus (more Actinobacillus (higher in TP), Bordetella (higher in TS), relatively abundant in TP), Moraxella and Glaesserella Lactobacillus (higher in TP), Staphylococcus (higher in (both more relatively abundant in the control group). TS) and a genus from the Pasteurellaceae family (higher Interestingly, the same evidence was not observed at in TS) (Additional file 5: Table S4). Ceftiofur treatment the genus level, suggesting that the antibiotic treatment did not affect the prevalence of S. suis as detected by in sows or piglets selected specific strains in each case. PCR, with a prevalence of over 90% at D7 and 100% at Within Glaesserella genus, G. parasuis is the only known weaning and onwards in the three groups. member of the swine microbiota, for which a virulence- specific PCRs is available. We observed different coloni- Inoculation of early colonizers of the upper respiratory zation dynamics by virulent or non-virulent strains in the tract modifies the nasal microbiota of the piglets three groups, especially at early time points (Additional Inoculation of non-treated piglets with selected coloniz- file 3: Table S2). The differences in ASVs were also sup- ers (IP group) had an impact on their nasal microbiota. ported by the isolation of colonies with a different fin- At D7, the mean alpha diversity measured by Shannon gerprinting profile in ERIC-PCR in the different groups. diversity index was higher in the inoculated IP piglets At genus level, not significant differences were detected, (Fig. 1) compared to the control group, although not sig- suggesting that the distribution of the different ASVs nificant at any timepoint (Kruskal–Wallis, P = 0.075, 0.14 compensate each other yielding similar relative abun- and 0.25 for D7, D21 and D49, respectively). The micro- dances in the groups. At D49, a global increase in the biota composition estimated through beta diversity based number and abundance of ASV was evident in all groups on weighted Unifrac distance matrix was calculated for (as indicated in alpha diversity) and corresponded mostly each timepoint (Fig. 4). Control and inoculated groups to ASVs from Bacteroidales (9.40% in TS, 13.01% in TP showed statistically different beta diversity at each time- and 12.56% in control) and Clostridiales (23.79% in TS, point (PERMANOVA, P = 0.02, P = 0.01 and P = 0.043 21.54% in TP and 40.63% in control). At the end of nurs- for D7, D21 and D49, respectively). The percentage of ery 593 ASVs were differentially abundant among the explanation attributed to the inoculation of the piglets Fig. 4 Beta diversity on weighted Unifrac distance matrices. PCoA was performed at D7 (A), D21 (B) and D49 (C). Purple dots represent samples from inoculated piglets with selected colonizers born to non-treated sows (IP) and green dots represent non-treated piglets born to non-treated sows (Control) Blanco‑Fuertes et al. Animal Microbiome (2023) 5:53 Page 10 of 17 Fig. 5 Heatmap representing the relative abundance (percentage) of the ASVs of the selected colonizers inoculated at birth in the inoculated (IP and TS-IP) and the control groups at D7, D21 and D49 (estimated through the Adonis function on the weighted although significative differences were only detected with Unifrac distance matrix) was 53% at D7, 44% at D21 and the Streptococcus ASV. These ASVs were not significantly 36% at D49. Differential abundance analysis performed different at the end of nursery where only Glaesserella with ANCOM-BC methodology showed a total of 379 and Streptococcus were detected at this latter timepoint. differential ASV, including the specific ASVs from the The inoculated Glaesserella strain was also recovered by inoculated Moraxella pluranimalium, Rothia nasimu- culture at all the timepoints. rium, Streptococcus pluranimalium and Glaesserella par- The colonization of the piglets by the selected colo- asuis strains which showed higher abundance in the IP nizers seemed not to be favored by treating the sows group compared with the control group at D7 (Fig. 5), with ceftiofur. When alpha diversity (measured through indicating that four out of five strains inoculated were Shannon index) of inoculated piglets from treated or able to colonize the nasal cavity of the piglets. The ASVs non-treated sows (IP vs TS-IP) was compared, no signifi- corresponding to the inoculated Glaesserella, Streptococ- cant differences were detected at any of the time points cus and Moraxella were also present at weaning (D21), analyzed (Kruskal Wallis, P > 0.05; Fig. 1). Differences Fig. 6 Beta diversity on weighted Unifrac distance matrices. PCoA was performed at D7 (A), D21 (B) and D49 (C). Purple dots represent samples from inoculated piglets with selected colonizers born to non-treated sows (IP) and dark orange dots represent inoculated piglets born to treated sows (TS-IP) Blanco‑Fuertes et al. Animal Microbiome (2023) 5:53 Page 11 of 17 in microbiota composition (beta diversity) were not presented different composition than control at D49. The detected with any of the metrics/distances analyzed IP group also showed a different microbiota than control (Jaccard, Bray Curtis and Unifrac; Fig. 6) at any of the at D49 (P = 0.008). Interestingly, the inoculated coloniz- sampling timepoints (IP vs. TS-IP; Bray Curtis PER- ers were found in higher relative abundance in IP than in MANOVA, P = 0.396). The nasal microbiota composi- TS-IP. tion of the two inoculated groups was not different at D7, although it was different for both groups when com- WGS metagenomics confirmed the differences observed pared to the control (PERMANOVA, TS-IP vs. control, in beta diversity between weaning and the end of nursery P = 0.008 and IP vs. control, P = 0.013, at D7). ANCOM- in all groups BC analysis showed a total of 379 ASVs at D7, includ- A total of 143 species and 99 genera were identified in ing the ASV of the inoculated Rothia (0.0006% in IP the nasal microbiota samples from D21 and D49. In the and 0.0002% in TS-IP, while absent in control group). control group, we identified a total of 10 genera and Similar findings were observed at D21 (PERMANOVA, 14 species at weaning (D21), and the composition of IP vs. TS-IP, P = 0.142; IP vs. control P = 0.011; TS-IP the nasal microbiota was clearly modified through the vs. control, P = 0.007) in beta diversity analysis, but at incorporation of other members at D49 (Fig. 7A Top 50 this timepoint none of the colonizers were differentially species z-score control group D21 y D49). The diver- abundant. At the end of nursery (D49), inoculated groups gence between D21 and D49 was also observed in pig- were not significantly different (IP vs. TS-IP; Bray Cur- lets from the rest of the groups in a clustering analysis tis PERMANOVA, P = 0.062). At this timepoint, only by average abundance (Z-score) at different taxonomic Glaesserella and Streptococcus were detected in the sam- levels, including species (Additional file 6: Fig. S2). At ples, but no significant differences were found in the dif- D21, the top 10 most abundant genera in all groups ferential abundance analysis (ANCOM-BC). Inoculation were Glaesserella, Moraxella, Bergeyella, Mycoplasma, of the selected nasal colonizers reduced the divergence of Streptococcus, Pasteurella, Neisseria, Mannheimia, the microbiota composition produced by the treatment Lactobacillus and Acinetobacter, which belong to taxa of the sows. The differences detected at D7 and D21 in commonly associated to nasal microbiota. At the end beta diversity were absent at the end of the study, indicat- of the nursery (D49), a plethora of species from taxa ing TS-IP piglets had a similar composition to the con- classically associated to gut microbiota and absent at trol (P = 0.163, with all beta diversity metrics used, while weaning was detected. In inoculated piglets, shotgun piglets born to treated sows but not inoculated (TS) metagenomic data for the inoculated Glaesserella, Fig. 7 Heatmap representing the top 50 species by average abundance clustered with Z-score normalization in non-treated piglets born to non-treated sows (control) at D21 and D49 (A). Top 50 pathways by average abundance by Spearman correlation coefficient in control group at D21 and D49 (B) Blanco‑Fuertes et al. Animal Microbiome (2023) 5:53 Page 12 of 17 Moraxella and Streptococcus agreed with the 16S a delay in their natural appearance: superpathway of sequencing data. Rothia was not detected at weaning, branched chain aminoacid, L-isoleucine biosynthesis I but on the other hand, it was detected at D49 by shot- (from threonine) and L-isoleucine biosynthesis III. In gun sequencing, contrary to the 16S data. addition, some differences were specifically observed in Several species within Archaea domain were affected each inoculated group. The IP group showed two differ- by the antibiotic treatment. While they were not found entially abundant pathways: one in higher abundance at in any group at weaning, at the end of the nursery they D49 (chitin derivatives degradation), and a second with were found more relatively abundant in the control and lower abundance also at D49 (beta-(1,4)-mannan degra- the IP group than the piglets born to treated sows (TS dation). In TS-IP group, we observed five pathways that and TS-IP), although in less than one percent in all were more abundant at D21: incomplete reductive TCA groups. Archaea were absent in the TP group at this cycle, NAD salvage pathway II (PNC IV cycle), 4-deoxy- latter timepoint as well. L-threo-hex-4-enopyranuronate degradation, superpath- A total of 314 pathways and 1580 enzymes were found way of arginine and polyamine biosynthesis, and purine considering all samples. Most of the pathways were pre- nucleobases degradation II (anaerobic), where the latter sent in all groups (Additional file 7: Fig. S3), except TS three pathways appeared in lower relative abundance at group which showed several specific pathways at D21, D49 in the same group TS-IP (Additional file 8: Fig. S4). although represented in very low relative abundance. The The divergence observed in the taxonomic profiles differences found at taxa level between weaning and the among the groups was not so evident at the functional end of nursery were also found in the functional profiling level, suggesting that different microbial communities but were not so remarkable (Fig. 7B and Additional file 6: may be responsible for similar functions. Fig. S2). In the control group, 12 significantly different path- A higher number of beta‑lactamase genes were detected ways were detected (through multivariable association in antibiotic‑treated piglets analysis with linear model) when compared longitudi- The genes involved in antibiotic resistances (resistome) nally (between D21 and D49 timepoints).These pathways were predicted using the AMRFinderPlus database by were not among the top-50 most abundant pathways, and comparing the MAGs present in each group at D21 included several pathways in relative higher abundance and D49. For each group and timepoint, the total num- at D21 (peptidoglycan maturation, heme b biosynthe- ber of genes associated to AMR found in each group is sis II, and two lipid IVA -precursor of lipid A- biosyn- listed in Additional file 9: Table S6. Globally, the number thesis pathways) and others with higher abundance at of unique resistance genes increased significantly over D49 (anaerobic degradation of acetylene, degradation of time in all groups except TS. This TS group showed the stachyose, degradation of fucose and rhamnose, incom- highest number of unique resistance genes at D21 (31 plete reductive TCA cycle, de novo biosynthesis of NAD genes from 17 families), a number significantly higher from aspartate, L-arginine biosynthesis and two path- than the control (Fisher test with Bonferroni correction, ways of biosynthesis of pyrimidine). P = 0.026), which presented the lowest number at D21 Focusing on differences due to the antibiotic treatment (14 genes from 9 families). The number of unique AMR at each time point, we detected few differential pathways, genes after the inoculation of colonizers was not differ- which were in higher abundance in the TS group (L-glu- ent from the control group at D21 or D49. The counts of tamate and L-glutamine biosynthesis at D21 but also at genes from the predicted resistome belonging to different D49, superpathway of anaerobic sucrose degradation at antibiotic families is represented in Fig. 8. D21, glycolysis-TCA-Glyox-bypass: superpathway of gly- Since ceftiofur was the antibiotic administered in this colysis at D49, and L-glutamine biosynthesis II at D21) study, we focused on the genes related to this resistance. when compared to TP and control groups. In general, we detected 7 genes for class-A beta-lacta- In inoculated groups, we observed several pathways mases: cfxA and cfxA6 (broad spectrum), blaTEM-122 differentially increased at D21. Interestingly, these path- (inhibitor-resistant broad spectrum), blaROB-1 (cephalo- ways increased in all groups at D49, indicating that these sporin hydrolyzing enzyme), blaACI-1 and blaCTX-M-32 functions appeared earlier due to intervention, such as (extended spectrum) and blaBRO-1 (Fig. 9). The number stachyose degradation, NAD de novo biosynthesis from of these genes increased with age in all groups. No major aspartate, dTDP-beta-L-rhamnose biosynthesis (Addi- differences were detected among the distribution of these tional file 8: Fig. S4). On the other hand, three pathways genes between the groups, except for blaCTX-M-32 and were found at lower abundance in both inoculated groups blaTEM-122, which were found only in the TP group at at D21 when compared to the control group, showing D21 and D49, respectively. Blanco‑Fuertes et al. Animal Microbiome (2023) 5:53 Page 13 of 17 Fig. 8 Number of genes associated with the different antibiotic classes found in the MAGs in the different groups and timepoints (D21 and D49). Pregnant sows were treated with ceftiofur or remained untreated. A group of piglets born to treated sows remained nontreated (TS group), while piglets born to non-treated sows were treated with ceftiofur (TP group). A group of piglets was inoculated with selected colonizers of the upper respiratory tract either born to ceftiofur-treated sows (TS-IP) and to non-treated sows (IP group). As control, piglets born to non-treated sows remained non-treated Fig. 9 Beta-lactam genes presence (in red) individually in piglets by group and timepoint (D21 and D49). Pregnant sows were treated with ceftiofur or remained untreated. A group of piglets born to treated sows remained nontreated (TS group), while piglets born to non-treated sows were treated with ceftiofur (TP group). A group of piglets were inoculated with selected colonizers of the upper respiratory tract either born to ceftiofur-treated sows (TS-IP) and to non-treated sows (IP group). As control, piglets born to non-treated sows remained non-treated Discussion from healthy piglets. The administration of ceftiofur This study was motivated by two main questions, firstly had a longer impact on the nasal microbiota composi- to assess the impact of the ceftiofur treatment either tion of piglets when administered to their sows before in sows or piglets over time on their nasal microbiota farrowing than directly applied to the piglets at birth. and secondly, to explore the possibility of modify it The effect of ceftiofur on nasal microbiota composi- by inoculation at birth of natural colonizers selected tion was partially reverted by the inoculation of nasal Blanco‑Fuertes et al. Animal Microbiome (2023) 5:53 Page 14 of 17 colonizers to newborn piglets and was accompanied ecological niches [21, 66–68]. However, this was a transi- by an improvement in piglet health. In addition, the tory increment in the diversity derived from the presence prevalence of antibiotic resistance genes increased of a plethora of bacterial taxa that are not consistently over time in all groups, being higher at weaning in the described in the respiratory microbiota of suids, whose group of piglets born to treated sows. The selection of presence can be considered as dysbiotic. These intrusive more beta-lactam resistance genes (blaCTX-M32 and bacteria belonged mainly to taxa that could be traced blaTEM-122) was also observed when ceftiofur was back to the environment and were probably occupying administered directly to the piglets. Of note, our find- the space available on the nasal mucosa after the dras- ings may be farm specific since one of the major fac- tic reduction of the nasal-associated bacteria, caused by tors involved in shaping the microbiota of the animals the antibiotic treatment. Contrary to the digestive tract, is the environment [6, 61, 62]. However, the strength which has several barriers to avoid contamination with of this study relies in the number of animal and the foreign bacteria, such as the acidic pH of the stomach, longitudinal approach to study the nasal microbiota, the nasal cavity is directly open to the exterior and this together with the two sequencing techniques used at a may facilitate temporal colonization by environmental high sequencing depth. In agreement with Pirolo et al. bacteria when the diversity of the resident microbiota is [63], we found less diversity in the samples sequenced reduced. by WGS metagenomics than by amplicon sequencing, In the long term, our data showed that the effect of the although the specific results are not completely compa- antibiotic treatment lasted longer when administered to rable due to the different study design (age of the ani- the sows compared to the direct administration to the mals, sampling technique, bioinformatic analysis, etc.). piglets. This finding is in agreement with other stud- The nasal microbiota composition of the followed-up ies where the sow contact with the newborn piglets was piglets switched from a clear predominance of Firmi- reported as one of the major drivers in the nasal microbi- cutes, Proteobacteria and Bacteroidetes at weaning to a ota composition [8]. The treatment of the sows probably microbiota composition with an important contribu- reduced the bacterial load in their microbiota, conse- tion of Clostridiales and other taxa classically found in quently reducing the possibility of bacterial transmission gastrointestinal tract [62]. This can be explained by the to the piglets. Management of the sow seems to have an change from milk to solid feed [62] and the natural root- important impact on the piglet’s nasal microbiota at least ing behavior of the piglets that put the nostrils in contact during the first weeks of age when the microbiota is still with fecal material [64]. In the postweaning barn, the not stable [62], as previously observed when vaccination higher animal density worsens the air quality (increased performed on sows modified the nasal microbiota of pig- levels of ammonia and dust), probably contributing to an lets [69]. Interestingly, inoculation of nasal colonizers inflammation state in the nose allowing the presence of reverted the effect of the ceftiofur administration in sows, anaerobic taxa. In fact, the increased presence of anaero- reducing the fluctuations in diversity and modulating the bic bacteria in the human sinus correlates with inflam- microbiota towards a more stable scenario. Inoculation mation and chronic diseases related to failure in immune delayed the appearance of virulent strains of G. parasuis system priming by nasopharyngeal microbiota [65]. and we detected different dynamics of the prevalence These changes underline the importance to conduct lon- of G. parasuis, S. suis and M. hyorhinis strains, but it is gitudinal studies to understand the evolution of the nasal difficult to correlate these changes to the clinical status. microbiota over time. Although the differences observed Nevertheless, the group of piglets born to treated sows in composition seemed to be reduced at functional that were inoculated with colonizers showed better clini- level, the specific pathways that were modulated by the cal status after weaning. It is tempting to hypothetise that treatment and/or the inoculation of colonizers deserve the sow treatment resulted in a reduction of the trans- further studies to understand the implications in micro- mission of pathogens from the sows to the piglets, which biota functionality. It would be also important to deter- may have been then replaced by the inoculated natural mine which member of the microbiota is implicated in members of the microbiota. In humans, the impact of health improvement in order to be able to design rational antibiotic administration before delivery has been also interventions. associated with alteration in the infant’s microbiota and As expected, ceftiofur treatment affected the micro- health [70, 71]. Our findings suggest that the inoculation biota composition. However, early after administration, of potential beneficial colonizers may open a strategy to the antibiotic induced an unforeseen increase in the promote newborn health in case the mother needs to be diversity of the piglets’ nasal microbiota. This finding treated. In fact, some studies have tested nasal probiot- confronts many studies that have shown that antibiotic ics against respiratory infections in broilers and humans administration reduces bacterial diversity in different with positive results [24, 26]. However, the strains used in Blanco‑Fuertes et al. Animal Microbiome (2023) 5:53 Page 15 of 17 this study may not be the optimal colonizers and micro- Additional file 2. Table S1. Statistical results from differential abundance biota intervention may be improved by choosing other analysis at ASV level of the TP, TS and control groups at D7 and D21 strains with better colonization capacities. (ANCOM-BC). Each comparison performed is indicated in the column header and includes a column with the W value and a second column In the present study, ceftiofur treatment by itself, with the significance. either of the sows or the piglets, did not improve the Additional file 3. Table S2. Results of the detection by PCRs of the query health status or the productivity of the piglets, support- pathogens (G. parasuis, M. hyorhinis and S. suis) in the different study ing that metaphylactic treatments can be avoided with- groups at D7, D15, D21, and D49. out deleterious effects on production. In addition, we Additional file 4. Table S3. Statistical results from differential abundance have observed that, with age, piglets showed an increas- analysis at ASV level of the TP, TS and control groups at D49 (ANCOM- BC). Each comparison performed is indicated in the column header ing number of antibiotic resistances, in agreement with and includes a column with the W value and a second column with the previous reports [72]. In the current scenario of anti- significance. microbial reduction in farms, the manipulation of the Additional file 5. Table S4. Statistical results from differential abundance microbiota to maintain animal health appears as a prom- analysis at genus level of the TP, TS and control groups at D49 (ANCOM- BC). Each comparison performed is indicated in the column header ising strategy. Here, we have demonstrated that modifi- and includes a column with the W value and a second column with the cation of the piglets’ nasal microbiota by the inoculation significance. of natural colonizers is possible. However, it would be Additional file 6. Figure S2. Heatmap representing the top 25 species essential to select natural colonizers free of antimicro- by average abundance with Bray Curtis coefficient (A and C) and top 25 bial resistances, especially those in mobile elements that pathways by average abundance by Spearman correlation coefficient (B and D). Pregnant sows were treated with ceftiofur or remained untreated. could be easily transferable. Interestingly, the inoculated A group of piglets born to treated sows remained nontreated (TS group), bacteria seemed to colonize better in piglets born to non- while piglets born to non-treated sows were treated with ceftiofur (TP treated sows, probably due to some beneficial interac- group). A group of piglets were inoculated with selected colonizers of the upper respiratory tract either born to ceftiofur-treated sows (TS-IP) and to tions with other members of the nasal microbiota. non-treated sows (IP group). As control, piglets born to non-treated sows remained non-treated. Groups TS, TP and control groups are compared in panels A and B. Groups IP, TS-IP and control are compared in panels C and D. Conclusions Additional file 7. Figure S3. Venn diagrams representing the pathways This study shed light on the influence of the antibiotic presence and absence at weaning (A) and the end of nursery (B) in the treatment on the piglets’ nasal microbiota over time. We different study groups. Pregnant sows were treated with ceftiofur or have demonstrated that ceftiofur treatment has a longer remained untreated. A group of piglets born to treated sows remained nontreated (TS group), while piglets born to non-treated sows were effect on the piglet’s nasal microbiota when it is adminis- treated with ceftiofur (TP group). A group of piglets were inoculated with tered to the sow than directly to the piglet. Moreover, the selected colonizers of the upper respiratory tract either born to ceftiofur- effects of the sow antibiotic treatment on piglet’s nasal treated sows (TS-IP) and to non-treated sows (IP group). As control, piglets born to non-treated sows remained non-treated. microbiota were partially reverted by inoculating a pool Additional file 8. Figure S4. Relative abundance of the significant of nasal colonizers. This might represent a strategy to pathways found in inoculated piglets. Pregnant sows were treated with improve pig’s health by using a non-invasive alternative ceftiofur or remained untreated. Ppiglets were inoculated with selected to antibiotics. colonizers of the upper respiratory tract either born to ceftiofur-treated sows (TS-IP) or to non-treated sows (IP group). As control, piglets born to non-treated sows remained non-treated and non-inoculated. Sampling time points were 21 days of age (D21, weaning) and 49 days of age (D49, Abbreviations end of nursery). ASV(s) Amplicon sequence variant(s) ADWG Average daily weight gain Additional file 9. Table S5. Total number of AMR genes, unique genes TS Treated sow and gene families at D21 and D49 in the different study groups. Pregnant NTS Non-treated sow sows were treated with ceftiofur or remained untreated. A group of piglets TP Treated piglet born to treated sows remained nontreated (TS group), while piglets born IP Inoculated piglet to non-treated sows were treated with ceftiofur (TP group). A group of WGS Whole genome shotgun piglets were inoculated with selected colonizers of the upper respira‑ MAG Metagenome assembled genome tory tract either born to ceftiofur-treated sows (TS-IP) and to non-treated AMR Antimicrobial resistance sows (IP group). As control, piglets born to non-treated sows remained ANCOM-BC Analysis of compositions of microbiomes with bias correction non-treated. Supplementary Information Acknowledgements The online version contains supplementary material available at https://​doi.​ The authors are grateful to the personnel from the farm facilities and all the org/​10.​1186/​s42523-​023-​00275-3. personnel from IRTA that helped in the farm study, especially Diego Pérez, Raúl Cuadrado, and Rosa López. The authors are also grateful to Nuria Galofré- Milà and Eva Huerta for their technical support. The authors are also grateful Additional file 1. Figure S1. Beta diversity on weighted Unifrac distance to the Centres de recerca de Catalunya (CERCA) Programme. matrices. PCoA was performed at D7 (A), D21 (B) and D49 (C). Blue dots represent samples from treated piglets born to non-treated sows (TP) and orange dots represent non-treated piglets born to treated sows (TS). Blanco‑Fuertes et al. Animal Microbiome (2023) 5:53 Page 16 of 17 Author contributions pigs on swine farms in Ontario, Canada during 2017 and 2018. Porc MBF performed the experiments, analyzed data and drafted the manuscript Health Manag. 2022;8:17. and figures. POG performed the first steps of the bioinformatics analyses. 8. Obregon-Gutierrez P, Aragon V, Correa-Fiz F. Sow contact is a major FI, MS and VA participated in the in-vivo study and the experimental design. driver in the development of the nasal microbiota of piglets. Pathogens. GF was involved in the planning and supervision of statistical analyses. FCF 2021;10:697. supervised the bioinformatics analyses. MS, FCF and VA were in charge of the 9. Gibson MK, Crofts TS, Dantas G. Antibiotics and the developing infant gut experimental design, discussion, supervision and correction of the manu‑ microbiota and resistome. Curr Opin Microbiol. 2015;27:51–6. script. All authors read and approved the final manuscript. 10. Aranda-Díaz A, Ng KM, Thomsen T, Real-Ramírez I, Dahan D, Dittmar S, et al. Establishment and characterization of stable, diverse, fecal-derived Funding in vitro microbial communities that model the intestinal microbiota. Cell This work was funded by the Ministry of Science and Innovation Host Microbe. 2022;30:260-272.e5. (MICINN) from the Spanish Government (Grants AGL2016-77361-R and 11. Rosier BT, Marsh PD, Mira A. Resilience of the oral microbiota in health: PID2019-106233RB-I00/AEI/10.13039/501100011033). M.B-F. is a recipient of a mechanisms that prevent dysbiosis. J Dent Res. 2018;97:371–80. FPI fellowship from the same institution (BES-2017-082224). 12. Wang B, Yao M, Lv L, Ling Z, Li L. The human microbiota in health and disease. Engineering. 2017;3:71–82. Data availability 13. Gao J, Yin J, Xu K, Li T, Yin Y. What is the impact of diet on nutritional diar‑ The sequencing data produced for this study are available in the Sequence rhea associated with gut microbiota in weaning piglets: a system review. Read Archive (SRA) under accession number; Bioproject PRJNA954430. BioMed Res Int. 2019;2019:e6916189. 14 Zheng L, Duarte ME, Sevarolli Loftus A, Kim SW. Intestinal health of pigs upon weaning: challenges and nutritional intervention. Front Vet Sci. Declarations 2021;8:628258. 15 Tang X, Xiong K, Fang R, Li M. Weaning stress and intestinal health of Ethics approval and consent to participate piglets: a review. Front Immunol. 2022;13:1042778. Animal experimentation was performed following proper veterinary practices, 16. Costa-Hurtado M, Barba-Vidal E, Maldonado J, Aragon V. Update on under European (Directive 2010/63/EU) and Spanish (Real Decreto 53/2013) Glässer’s disease: how to control the disease under restrictive use of regulation and with the approval of the Ethics Commission in Animal Experi‑ antimicrobials. Vet Microbiol. 2020;242:108595. mentation of the Generalitat de Catalunya (Protocol number 9211). 17. Ober RA, Thissen JB, Jaing CJ, Cino-Ozuna AG, Rowland RRR, Nieder‑ werder MC. Increased microbiome diversity at the time of infection is Competing interests associated with improved growth rates of pigs after co-infection with The authors declare no competing interests. porcine reproductive and respiratory syndrome virus (PRRSV) and por‑ cine circovirus type 2 (PCV2). Vet Microbiol. 2017;208:203–11. Author details 18. Salogni C, Lazzaro M, Giovannini S, Vitale N, Boniotti MB, Pozzi P, et al. 1 Centre de Recerca en Sanitat Animal (CReSA), Unitat Mixta d’Investigació Causes of swine polyserositis in a high-density breeding area in Italy. J Vet IRTA-UAB en Sanitat Animal, Campus de la Universitat Autònoma de Barcelona Diagn Investig Off Publ Am Assoc Vet Lab Diagn Inc. 2020;32:594–7. (UAB), 08193 Bellaterra, Barcelona, Spain. 2 IRTA, Programa de Sanitat Animal, 19. Hattab J, Marruchella G, Trachtman AR, Gabrielli L, Bernabò N, Mosca F, Centre de Recerca en Sanitat Animal (CReSA), Campus de la Universitat et al. Effect of vaccination against Glässer’s disease in a farm suffering Autònoma de Barcelona (UAB), 08193 Bellaterra, Barcelona, Spain. 3 WOAH from polyserositis in weaned pigs. Vet Sci. 2022;9:691. Collaborating Centre for the Research and Control of Emerging and Re- 20. Alexander TJ, Thornton K, Boon G, Lysons RJ, Gush AF. Medicated early Emerging Swine Diseases in Europe (IRTA-CReSA), 08193 Bellaterra, Barcelona, weaning to obtain pigs free from pathogens endemic in the herd of Spain. 4 Present Address: Ciber in Epidemiology and Public Health, Instituto de origin. Vet Rec. 1980;106:114–9. Salud Carlos III, Madrid, Spain. 5 Department of Animal Medicine, Production 21. Correa-Fiz F, Gonçalves dos Santos JM, Illas F, Aragon V. Antimicrobial and Health (MAPS), University of Padua, 35020 Legnaro, PD, Italy. 6 Selección removal on piglets promotes health and higher bacterial diversity in the Batallé, Avinguda dels Segadors, 17421 Riudarenes, Spain. nasal microbiota. Sci Rep. 2019;9:6545. 22. Shin D, Chang SY, Bogere P, Won K, Choi J-Y, Choi Y-J, et al. Beneficial roles Received: 19 April 2023 Accepted: 10 October 2023 of probiotics on the modulation of gut microbiota and immune response in pigs. PLoS One. 2019;14:e0220843. 23. Hansen LHB, Cieplak T, Nielsen B, Zhang Y, Lauridsen C, Canibe N. Screen‑ ing of probiotic candidates in a simulated piglet small intestine in vitro model. FEMS Microbiol Lett. 2021;368:fnab045. References 24. Al-Romaih S, Harati O, Mfuna LE, Filali-Mouhim A, Pelletier A, Renteria 1. Bergamaschi M, Tiezzi F, Howard J, Huang YJ, Gray KA, Schillebeeckx C, Flores A, et al. Response to intranasal Lactococcus lactis W136 probiotic et al. Gut microbiome composition differences among breeds impact supplementation in refractory CRS is associated with modulation of feed efficiency in swine. Microbiome. 2020;8:110. non-type 2 inflammation and epithelial regeneration. Front Allergy. 2. Pirolo M, Espinosa-Gongora C, Bogaert D, Guardabassi L. The porcine 2023;4:1046684. respiratory microbiome: recent insights and future challenges. Anim 25. Niittynen L, Pitkäranta A, Korpela R. Probiotics and otitis media in chil‑ Microbiome. 2021;3:9. dren. Int J Pediatr Otorhinolaryngol. 2012;76:465–70. 3. Gresse R, Chaucheyras-Durand F, Fleury MA, Van de Wiele T, Forano E, 26. Luan SJ, Sun YB, Wang Y, Sa RN, Zhang HF. Bacillus amyloliquefaciens Blanquet-Diot S. Gut microbiota dysbiosis in postweaning piglets: under‑ spray improves the growth performance, immune status, and respiratory standing the keys to health. Trends Microbiol. 2017;25:851–73. mucosal barrier in broiler chickens. Poult Sci. 2019;98:1403–9. 4. Mach N, Baranowski E, Nouvel LX, Citti C. The airway pathobiome in 27. Mårtensson A, Nordström FU, Cervin-Hoberg C, Lindstedt M, Sakellariou complex respiratory diseases: a perspective in domestic animals. Front C, Cervin A, et al. Nasal administration of a probiotic assemblage in Cell Infect Microbiol. 2021;11:583600. allergic rhinitis: a randomised placebo-controlled crossover trial. Clin Exp 5. Strube ML, Hansen JE, Rasmussen S, Pedersen K. A detailed investigation Allergy J Br Soc Allergy Clin Immunol. 2022;52:774–83. of the porcine skin and nose microbiome using universal and Staphylo- 28. Bates D, Mächler M, Bolker B, Walker S. Fitting linear mixed-effects models coccus specific primers. Sci Rep. 2018;8:12751. using lme4. J Stat Softw. 2015;67:1–48. 6. Fredriksen S, Guan X, Boekhorst J, Molist F, van Baarlen P, Wells JM. Envi‑ 29. Rey D, Neuhäuser M. Wilcoxon-signed-rank test. In: Lovric M, editor. ronmental and maternal factors shaping tonsillar microbiota develop‑ International encyclopedia of statistical science. Berlin: Springer; 2011. p. ment in piglets. BMC Microbiol. 2022;22:224. 1658–9. 7. Bosman AL, Deckert AE, Carson CA, Poljak Z, Reid-Smith RJ, McEwen SA. Antimicrobial use in lactating sows, piglets, nursery, and grower-finisher Blanco‑Fuertes et al. Animal Microbiome (2023) 5:53 Page 17 of 17 30. Galofré-Milà N, Correa-Fiz F, Lacouture S, Gottschalk M, Strutzberg-Minder 55. Caspi R, Altman T, Billington R, Dreher K, Foerster H, Fulcher CA, et al. K, Bensaid A, et al. A robust PCR for the differentiation of potential viru‑ The MetaCyc database of metabolic pathways and enzymes and the lent strains of Haemophilus parasuis. BMC Vet Res. 2017;13:124. BioCyc collection of pathway/genome databases. Nucleic Acids Res. 31. Marois C, Bougeard S, Gottschalk M, Kobisch M. Multiplex PCR assay for 2014;42:D459–71. detection of Streptococcus suis species and serotypes 2 and 1/2 in tonsils 56. Mallick H, Rahnavard A, McIver LJ, Ma S, Zhang Y, Nguyen LH, et al. Mul‑ of live and dead pigs. J Clin Microbiol. 2004;42:3169–75. tivariable association discovery in population-scale meta-omics studies. 32. Okura M, Lachance C, Osaki M, Sekizaki T, Maruyama F, Nozawa T, et al. PLoS Comput Biol. 2021;17:e1009442. Development of a two-step multiplex PCR assay for typing of capsular 57. Nurk S, Meleshko D, Korobeynikov A, Pevzner PA. metaSPAdes: a new polysaccharide synthesis gene clusters of Streptococcus suis. J Clin Micro‑ versatile metagenomic assembler. Genome Res. 2017;27:824–34. biol. 2014;52:1714–9. 58. Wood DE, Lu J, Langmead B. Improved metagenomic analysis with 33. Clavijo MJ, Oliveira S, Zimmerman J, Rendahl A, Rovira A. Field evalua‑ Kraken 2. Genome Biol. 2019;20:257. tion of a quantitative polymerase chain reaction assay for Mycoplasma 59. Seemann T. ABRicate. 2022. https://​github.​com/​tseem​ann/​abric​ate hyorhinis. J Vet Diagn Investig Off Publ Am Assoc Vet Lab Diagn Inc. 60. Feldgarden M, Brover V, Haft DH, Prasad AB, Slotta DJ, Tolstoy I, et al. 2014;26:755–60. Validating the AMRFinder tool and resistance gene database by using 34. Versalovic J, Koeuth T, Lupski JR. Distribution of repetitive DNA sequences antimicrobial resistance genotype-phenotype correlations in a collection in eubacteria and application to fingerprinting of bacterial genomes. of isolates. Antimicrob Agents Chemother. 2019;63:e00483-e519. Nucleic Acids Res. 1991;19:6823–31. 61. Megahed A, Zeineldin M, Evans K, Maradiaga N, Blair B, Aldridge B, et al. 35. Klindworth A, Pruesse E, Schweer T, Peplies J, Quast C, Horn M, et al. Impacts of environmental complexity on respiratory and gut micro‑ Evaluation of general 16S ribosomal RNA gene PCR primers for classical biome community structure and diversity in growing pigs. Sci Rep. and next-generation sequencing-based diversity studies. Nucleic Acids 2019;9:13773. Res. 2013;41:e1. 62. Slifierz MJ, Friendship RM, Weese JS. Longitudinal study of the early- 36. Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA, life fecal and nasal microbiotas of the domestic pig. BMC Microbiol. et al. Reproducible, interactive, scalable and extensible microbiome data 2015;15:184. science using QIIME 2. Nat Biotechnol. 2019;37:852–7. 63. Pirolo M, Espinosa-Gongora C, Alberdi A, Eisenhofer R, Soverini M, Eriksen 37. Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. EØ, et al. Bacterial topography of the upper and lower respiratory tract in DADA2: high-resolution sample inference from Illumina amplicon data. pigs. Anim Microbiome. 2023;5:5. Nat Methods. 2016;13:581–3. 64. Mkwanazi MV, Ncobela CN, Kanengoni AT, Chimonyo M. Effects of 38. McDonald D, Price MN, Goodrich J, Nawrocki EP, DeSantis TZ, Probst environmental enrichment on behaviour, physiology and performance of A, et al. An improved Greengenes taxonomy with explicit ranks for pigs—a review. Asian-Australas J Anim Sci. 2019;32:1–13. ecological and evolutionary analyses of bacteria and archaea. ISME J. 65. Kumpitsch C, Koskinen K, Schöpf V, Moissl-Eichinger C. The microbiome 2012;6:610–8. of the upper respiratory tract in health and disease. BMC Biol. 2019;17:87. 39. Shannon CE. Mathematical theory of communication. Bell Syst Tech J. 66. Ramirez J, Guarner F, Bustos Fernandez L, Maruy A, Sdepanian VL, Cohen 1948;27:379–423. H. Antibiotics as major disruptors of gut microbiota. Front Cell Infect 40. Eren MI, Chao A, Hwang W-H, Colwell RK. Estimating the richness of a Microbiol. 2020;10:572912. population when the maximum number of classes is fixed: a nonpara‑ 67. Raymann K, Bobay L-M, Moran NA. Antibiotics reduce genetic diver‑ metric solution to an archaeological problem. PLoS One. 2012;7:e34179. sity of core species in the honeybee gut microbiome. Mol Ecol. 41. Lozupone C, Knight R. UniFrac: a new phylogenetic method for compar‑ 2018;27:2057–66. ing microbial communities. Appl Environ Microbiol. 2005;71:8228–35. 68. Huang C, Feng S, Huo F, Liu H. Effects of four antibiotics on the diversity 42. Tang Z-Z, Chen G, Alekseyenko AV. PERMANOVA-S: association test for of the intestinal microbiota. Microbiol Spectr. 2022;10:e01904-e1921. microbial community composition that accommodates confounders and 69. Blanco-Fuertes M, Correa-Fiz F, Lopez-Serrano S, Sibila M, Aragon V. Sow multiple distances. Bioinforma Oxf Engl. 2016;32:2618–25. vaccination against virulent Glaesserella parasuis shapes the nasal micro‑ 43. lab M. Vegan: community ecology package. McGlinn lab. 2019. /publica‑ biota of their offspring. Sci Rep. 2022;12:3357. https://​doi.​org/​10.​1038/​ tion/2019-01-01_oksanen_vegan_2019/. Accessed 11 Mar 2021. s41598-​022-​07382-2 44. Lin H, Peddada SD. Analysis of compositions of microbiomes with bias 70. Prescott S, Dreisbach C, Baumgartel K, Koerner R, Gyamfi A, Canellas M, correction. Nat Commun. 2020;11:3514. et al. Impact of intrapartum antibiotic prophylaxis on offspring micro‑ 45. RStudio Team (2020). RStudio: integrated development for R. RStudio, biota. Front Pediatr. 2021;9:754013. PBC, Boston. URL http://​www.​rstud​io.​com/. 71. Barnett DJM, Endika MF, Klostermann CE, Gu F, Thijs C, Nauta A, et al. 46. Bisanz J. Tutorial: integrating QIIME2 and R for data visualization and Human milk oligosaccharides, antimicrobial drugs, and the gut micro‑ analysis using qiime2R (v0.99.6). 2021. biota of term neonates: observations from the KOALA birth cohort study. 47. Wickham H. ggplot2: elegant graphics for data analysis. New York: Gut Microbes. 2023;15:2164152. Springer-Verlag; 2009. 72. Gaire TN, Scott HM, Noyes NR, Ericsson AC, Tokach MD, Menegat MB, et al. 48. Wickham H, Averick M, Bryan J, Chang W, McGowan LD, François R, et al. Age influences the temporal dynamics of microbiome and antimicrobial Welcome to the tidyverse. J Open Source Softw. 2019;4:1686. resistance genes among fecal bacteria in a cohort of production pigs. 49. Beghini F, McIver LJ, Blanco-Míguez A, Dubois L, Asnicar F, Maharjan Anim Microbiome. 2023;5:2. S, et al. Integrating taxonomic, functional, and strain-level profiling of diverse microbial communities with bioBakery 3. eLife. 2021;10:e65088. 50. Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illu‑ Publisher’s Note mina sequence data. Bioinformatics. 2014;30:2114–20. Springer Nature remains neutral with regard to jurisdictional claims in pub‑ 51. Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat lished maps and institutional affiliations. Methods. 2012;9:357–9. 52. Warr A, Affara N, Aken B, Beiki H, Bickhart DM, Billis K, et al. An improved pig reference genome sequence to enable pig genetics and genomics research. GigaScience. 2020;9:giaa051. 53. Blanco-Míguez A, Beghini F, Cumbo F, McIver LJ, Thompson KN, Zolfo M, et al. Extending and improving metagenomic taxonomic profiling with uncharacterized species using MetaPhlAn 4. Nat Biotechnol. 2023. https://​doi.​org/​10.​1038/​s41587-​023-​01688-w. 54. Suzek BE, Huang H, McGarvey P, Mazumder R, Wu CH. UniRef: compre‑ hensive and non-redundant UniProt reference clusters. Bioinformatics. 2007;23:1282–8.