Dysbiosis of the cervical lymph node microbiome associated with lymphadenitis in guinea pigs (Cavia porcellus)

Dysbiosis of the cervical lymph node microbiome associated with lymphadenitis in guinea pigs (Cavia porcellus) | 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 Article Dysbiosis of the cervical lymph node microbiome associated with lymphadenitis in guinea pigs (Cavia porcellus) Nayeli Alison Vilca Barrientos, Jakson Jacob Socrates Chuquimia del Solar, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9383588/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 10 You are reading this latest preprint version Abstract Cervical lymphadenitis is a significant infectious disease in guinea pig (Cavia porcellus) production, although the microbiota associated with affected lymph nodes remains poorly characterized. This study compared the microbiota of cervical lymph nodes from healthy guinea pigs and those with lymphadenitis using 16S rRNA gene sequencing, bioinformatics analysis, and molecular validation by PCR. The results showed that healthy lymph nodes harbor diverse bacterial communities, while infected lymph nodes exhibit a marked reduction in microbial diversity and a dominance of Streptococcus equi subsp. zooepidemicus. Furthermore, a subset of samples revealed an alternative etiology characterized by the dominance of the genus Caviibacter, suggesting etiological heterogeneity of the disease in this study. Taken together, these findings suggest that cervical lymphadenitis in guinea pigs is associated with dysbiosis of the lymph node microbiome and support the use of metagenomic approaches for etiological characterization. Health sciences/Diseases Biological sciences/Microbiology 16S rRNA metabarcoding PCR Streptococcus equi Caviibacter Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Cervical lymphadenitis is an infectious disease primarily characterized by chronic abscess formation in lymph nodes, particularly in the cervical region. The etiological agent has been identified as Streptococcus equi subsp. zooepidemicus , a facultative pathogen and commensal that may reside in the tonsils, lower respiratory tract, skin, and urogenital system of certain animals [1]. Abscesses in affected lymph nodes may lead to sinusitis and otitis and may extend to the respiratory tract, contributing to bronchitis and interstitial pneumonia. This disease is highly contagious and can present high mortality, largely associated with uncontrolled animal movement between regions [2]. Guinea pigs may be affected by infectious and parasitic diseases, often associated with abrupt changes in temperature, humidity, air drafts, poor sanitary conditions in housing, and drastic dietary modifications [3]. Salmonellosis (also referred to as “peste”) is one example; it is caused by Salmonella spp., which can be present in the feces of multiple animal species (including guinea pigs, poultry, pigs, ruminants, and horses) and may cause high mortality in guinea pigs of any age [4,5]. Pneumonia has also been linked to sudden variations in temperature and humidity and exposure to air currents; Streptococcus pneumoniae has been reported as a causative agent, inducing pulmonary inflammation and significant production losses [6]. Several studies in Peru have investigated the etiology of cervical abscesses/lymphadenitis in guinea pigs. In Huamanga (Ayacucho), microbiological isolation from subcutaneous abscesses in growing guinea pigs indicated that most cases were attributed to Streptococcus spp.(100 %), Staphylococcus spp.(90 %), Salmonella spp. (20 %)and Corynebacterium spp.(20 %) [7]. In a breeder production center in Huayllapampa (San Jerónimo, Cusco, Peru), microbiological analyses of 43 samples identified Streptococcus spp. in 69.77% of cases, Staphylococcus spp. in 20.93%, and Corynebacterium spp. in 9.30% [8]. Likewise, studies conducted in family–commercial production systems in Cusco have identified Streptococcus spp. as the predominant bacteria in cervical abscesses of guinea pigs, although other genera such as Staphylococcus and Klebsiella have also been reported [9]. The guinea pig ( Cavia porcellus ) is a rodent native to the Andean region and is widely distributed in countries such as Peru, Ecuador, Colombia, and Bolivia. Owing to its adaptability to diverse ecosystems, it can be found from sea level [10] to elevations above 3,550 m.a.s.l. [11], under both cold and warm climatic conditions. As an autochthonous animal resource, the guinea pig has high nutritional value and requires relatively low investment for production, contributing to food security in rural communities with limited resources [11]. In Peru, guinea pig production is predominantly carried out under family-based systems. Consequently, animal health is essential to achieve efficient production outcomes. Given the low profitability of agricultural activities, production systems tend to intensify, which may increase animal health problems, particularly when stocking density rises or when dietary changes occur frequently [12]. However, knowledge regarding the pathology and epidemiology of guinea pig diseases remains limited, highlighting the importance of investigating disease events to expand understanding of health conditions affecting this species. In this context, molecular approaches have been applied to the investigation of lymphadenitis in guinea pigs in Arequipa, including the sequencing of the 16S rRNA gene from bacterial isolates obtained from cervical abscesses. These analyses generated nucleotide sequences with less than 100% similarity to GenBank entries, and the isolates exhibited 95–99% similarity to existing sequences, among which three were found to be closely related to Streptococcus equi [13]. The use of molecular methodologies for the identification of Streptococcus equi has proven to be a reliable tool for the taxonomic confirmation of this pathogen in clinical samples from animals with conditions such as cervical lymphadenitis. In particular, studies such as those by Båverud et al. [14] indicate that the molecular characterization of Streptococcus equi can be supported by the analysis of conserved regions of the 16S rRNA gene, which allow its differentiation from other phylogenetically related streptococci when combined with specific genetic markers. This molecular approach, complemented by amplification techniques and sequence comparison against reference databases, improves diagnostic accuracy compared to conventional bacteriological methods, especially in infections where phenotypic identification can be challenging. Recently, an emerging bacterium associated with cases of cervical lymphadenitis in guinea pigs has been described. Previously reported as Streptobacillus moniliformis , it has now been reclassified as Caviibacter abscessus . Clinical studies have shown that this microorganism may be the only agent isolated in cervical abscesses, exhibiting fastidious growth characteristics that hinder its detection using conventional bacteriological methods [15]. Molecular identification through 16S rRNA gene analysis could also suggest the presence of Caviibacter abscessus as part of the microbiota associated with the infections in this study, given its potential relevance in the emerging etiology of lymphadenitis in guinea pigs. Methodology Study area and facilities The study was conducted in the district of Luya, province of Chachapoyas, in the southwestern Amazonas region of Peru. The area lies within the Andean highlands at elevations ranging from 1,713 to 3,240 m a.s.l., with average temperatures of 16–19 °C and relative humidity of 70–80% [16]. The experiment was conducted at the Experimental Station and the Laboratory of Molecular Physiology of the Institute of Livestock and Biotechnology Research (IGBI), Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas (UNTRM-A). Ethical statement This study was approved by the Institutional Research Ethics Committee (CIEI) of UNTRM-A (CIEI No. 00106). All procedures complied with Peruvian Law No. 30407 on Animal Protection and Welfare [17]. Euthanasia procedures followed the Mexican Official Standard NOM-033-ZOO-1995 for humane slaughter of domestic and wild animals [18]. Study design and animals A total of 40 guinea pigs from Luya (Amazonas, Peru) were included. Twenty clinically healthy animals without signs of lymphadenitis comprised the control group (10 of the Peru breed and 10 of the Inti breed of guinea pigs). The infected group consisted of 20 animals with clinical signs compatible with cervical lymphadenitis (10 of the Peru breed and 10 of the Inti breed of guinea pigs). These results should be interpreted within the limitations of the non-probabilistic sampling design. Farms and husbandry conditions Infected animals were sampled from three farms operating under family–commercial production systems, whereas healthy controls were obtained from an experimental station under a fully commercial system. In all holdings, animals were raised under pen-based systems adapted to local climatic conditions. Management practices differed between systems, with family-based management in smallholder farms and more standardized practices in the commercial system. Animals were fed forage-based diets and had continuous access to water. Sampling strategy and case definition Sampling was non-probabilistic (convenience sampling) and included two study groups (infected and healthy). Animals were classified by health status by a veterinarian experienced in rodent management and infectious disease diagnosis, following established clinical and husbandry criteria for guinea pigs [19]. Clinical assessment consisted of general examination based on observation and careful palpation to identify signs compatible with cervical lymphadenitis. Common findings among infected animals included encapsulated cervical abscesses and anorexia. Given the variability in clinical presentation, the presence of encapsulated cervical lymph node abscesses was used as the diagnostic clinical criterion, consistent with the literature [2,20,21]. Recorded variables For each animal, breed, sex, body weight, number of lymph nodes identified, and lymph node diameter were recorded (Supplementary Table 1 and 2). For infected animals, the volume of euthanasia agent administered was additionally recorded, following standard laboratory animal anaesthesia and euthanasia guidelines [22]. Lymph node collection protocol Infected guinea pigs were euthanized using a two-step protocol. First, ketamine hydrochloride (Ketagal, Laboratorios Galmedic, Paraguay) and xylazine hydrochloride (Xilagal, Laboratorios Galmedic, Paraguay) were administered, followed by a euthanasia solution (T61, MSD Animal Health/Intervet International GmbH, Germany) containing embutramide (200 mg), mebezonium iodide (50 mg), and tetracaine hydrochloride (5 mg). This pharmacological approach was selected to minimize cervical manipulation and reduce the risk of capsular rupture of abscessed lymph nodes, thereby preserving sample integrity. Anesthetic doses were calculated individually using VetHelp AM [24], considering the commercial formulation. Dose details for each anesthetic and T61 are provided in Supplementary Table 1. Dosages were based on recommendations from Flecknell [22], which suggest ketamine at 20–40 mg/kg and xylazine at 2 mg/kg, administered intramuscularly. Additionally, T61 was administered at a dose of 0.5–2 mL per animal, depending on body weight, via the intrapulmonary route [25]. In contrast, healthy guinea pigs were euthanized by cervical dislocation, as the absence of lymph node abscesses eliminated the risk of compromising sample integrity. This method is considered acceptable under established laboratory animal euthanasia guidelines when performed by trained personnel [22,19]. Dissection was conducted under strictly sterile conditions. A ventral aseptic incision was performed using sterile scissors and forceps, extending from the oral region to the thorax. After identification of cervical lymph nodes, tissues were excised; infected nodes were handled with additional care to maintain abscess capsule integrity and preserve purulent contents. Sample handling and storage were performed following Vargas-Rocha [26], with slight modifications. Samples were maintained at 8–10 °C until molecular processing at the Laboratory of Molecular Physiology. Bacterial DNA extraction from lymph nodes For initial preparation, samples were brought to room temperature. For infected lymph nodes, a cut was made to obtain 50 mg of purulent material. For healthy lymph nodes, adjacent tissues (muscle, fat, or membranes) were removed, the node was minced into small fragments, and 50 mg of tissue was weighed. Genomic bacterial DNA was extracted using the MAGBIO™ HighPrep Blood and Tissue DNA Kit (Magbio Genomics, USA). As part of quality control, extracted bacterial genomic DNA was quantified using both the NanoDrop One Microvolume UV–Vis spectrophotometer (Thermo Scientific, USA) and the Qubit 3 Fluorometer (Invitrogen, Thermo Fisher Scientific, USA). Spectrophotometric assessment was used to evaluate DNA purity, considering acceptable A260/280 ratios within the range of 1.8–2.0 or values close to this interval. In parallel, fluorometric quantification was performed to accurately determine DNA concentration. 16S rRNA gene sequencing (V3–V4) Sequencing of the V3–V4 hypervariable regions of the 16S rRNA gene was outsourced to BGI Tech Solutions (Hong Kong) Co., Limited. Sequencing was performed on the DNBseq platform using a paired-end 300 bp (PE300) configuration with primers 338F (5′-ACTCCTACGGGAGGCAGCAG-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′). A total of 36 genomic DNA samples, previously extracted and quantified, were submitted. Amplicon libraries targeting the V3–V4 region were prepared using the standard “ 16S Amplicon Sequencing” protocol, generating approximately 50,000 clean tags per sample and 43 Gb of clean data in total. Bioinformatics analysis Bioinformatic processing was performed using the nf-core/ampliseq pipeline v2.15.0 [27] from the nf-core collection [28]. Sequence quality was assessed with FastQC [29] and summarized using MultiQC [30]. Reads were processed independently with DADA2 [31] to remove PhiX contamination, truncate reads at the first occurrence of a quality score ≤2, trim reads before the median quality dropped below 25, and retain at least 75% of reads. DADA2 was also used for read demultiplexing, paired-end merging, removal of PCR-generated chimeras, and inference of amplicon sequence variants (ASVs). The resulting feature table included the total number of ASVs, their abundance per sample, and the total number of reads retained after filtering. The resulting feature table included the total number of ASVs, their abundance per sample, and the total number of reads retained after filtering. Taxonomic classification of ASVs was performed in DADA2 using the SILVA v138.2 reference database with a minimum bootstrap confidence threshold of 50 [32]. The resulting ASV table, along with taxonomic assignments, was then imported into QIIME 2 for downstream analyses [33]. ASVs assigned to mitochondria or chloroplasts were removed prior to analysis. Community composition was visualized using bar plots, and sequencing depth was assessed through alpha rarefaction curves in QIIME 2 [33]. All statistical and bioinformatic analyses were performed in R v4.4.1 using Bioconductor packages. Diversity, abundance, and rarefaction analyses were conducted using phyloseq [34]. Additional custom scripts were implemented using microeco [35] and MicrobiomeMarker, while MicrobiotaProcess [36] was used to generate LDA plots, Venn diagrams, and cladograms. Alpha diversity was estimated using the Shannon index, Simpson's index, Pielou’s evenness, and the number of observed operational taxonomic units (OTUs). Group comparisons were performed using the non-parametric Kruskal–Wallis test with Bonferroni correction for multiple comparisons (p < 0.05). Dimensionality reduction was performed using principal component analysis (PCA) to explore variation in the relative abundance of major taxa. Beta diversity was assessed using principal coordinates analysis (PCoA) based on dissimilarity matrices calculated using Bray–Curtis, Jaccard, weighted UniFrac, and unweighted UniFrac distances. Statistical significance of between-group differences in community composition was assessed using PERMANOVA with 999 permutations, as implemented by the nf-core ampliseq workflow within the QIIME2 framework. Functional potential was predicted from ASVs using PICRUSt2 [37], estimating the relative abundance of genes and metabolic pathways associated with dietary transition. Functional outputs were grouped using KEGG Orthology (KO) [38], Enzyme Commission (EC) [39], and MetaCyc [40] annotations and visualized using heatmaps and functional enrichment plots. Data availability The DNA sequences generated and analyzed during the current study are available in the NCBI SRA repository under BioProject PRJNA1419848 (https://www.ncbi.nlm.nih.gov/search/all/?term=PRJNA1419848). Other data from the study are available from the corresponding author upon reasonable request. Confirmation of bacterial DNA ( 16S rRNA PCR) To confirm the presence of bacterial genomic DNA, conventional PCR amplification of the 16S rRNA gene was performed using universal primers 27F and 1492R to generate an amplicon of approximately 1,500 bp [41]. Amplification conditions were adapted from Frank et al. [42] and Hou et al. [43]. The modified protocol included an initial denaturation at 94 °C for 5 min, followed by 40 cycles of denaturation at 94 °C for 30 s, annealing at 50 °C for 1 min, and extension at 70 °C for 1.5 min. A final extension at 70 °C for 2 min was performed, followed by a hold at 4 °C. Detection of Streptococcus spp., Streptococcus equi subsp. zooepidemicus , and Streptococcus equi subsp. equi by sequential PCR Identification of Streptococcus equi subsp. zooepidemicus was performed using conventional PCR targeting three genetic markers sequentially. This stepwise approach was used to maximize specificity for subspecies discrimination in complex samples. First, the sodA gene was used as a screening assay to confirm the presence of Streptococcus spp. in the sample; detection of an amplicon of approximately 235 bp was required to proceed, following Alber et al. [44]. Subspecies-level differentiation was then conducted by amplifying comB and seeI markers. The comB marker was used for specific identification of S. equi subsp. zooepidemicus , with an expected amplicon of approximately 450 bp [45], whereas seeI marker was used complementarily to rule out S. equi subsp. equi , which yields an amplicon of approximately 520 bp [44]. Tabla 1. Primers for amplification of the sod A gene for Streptococcus spp . ; the com B gene for Streptococcus equi subsp. zooepidemicus and the see I gene for Streptococcus equi subsp. equi Target gene Oligonucleotide primer Secuencing (5’-3’) Size of PCR product (bp) Reference Sod A Sod A equi/zooep-F Sod A equi/zooep-R CAG CAT TCC TGC TGA CAT TCG TCA GG CTG ACC AGC CTT ATT CAC AAC CAG CC 235 Alber et al., 2004 Com B Com B-F ComB-R GCT GGT TCA GGA GTG CAA CA TTC GTG GAA AAT CGT GCT GT 450 Morris et al., 2023 See I See I-F See I-R GAA GGT CCG CCA TTT TCA GGT AGT TTG GCA TAC TCT CTC TGT CAC CAT GTC CTG 520 Alber et al., 2004 Each PCR was performed separately in a final volume of 25 µL, following the amplification parameters of each primer with slight modifications. Reactions contained 12.5 µL GoTaq Green Master Mix (Promega, Madison, WI, USA), 0.5 µL forward primer, 0.5 µL reverse primer, 10.5 µL nuclease-free water, and 1.0 µL template DNA. Amplifications were run on a SimpliAmp Thermal Cycler (Applied Biosystems, Carlsbad, CA, USA) under marker-specific conditions. For sodA and seeI , cycling conditions followed Alber et al. [44]: initial denaturation at 94 °C for 3 min, followed by 30 cycles of 94 °C for 30 s, 59 °C for 30 s, and 72 °C for 40 s, with a final extension at 72 °C for 5 min and a hold at 4 °C. For comB , cycling conditions followed Morris et al. [45]: initial denaturation at 95 °C for 2 min, followed by 32 cycles of 95 °C for 30 s, 55 °C for 30 s, and 72 °C for 15 s, with a final extension at 72 °C for 5 min and a hold at 4 °C. PCR products and a 100 bp DNA ladder (Promega, Madison, WI, USA) were resolved on 2% agarose gels using an E1201-BLT electrophoresis chamber (ACCURIS Instruments, Edison, NJ, USA) at 100 V for 45–50 min, and bands were visualized using a SmartDoc Imaging Enclosure (ACCURIS Instruments, Edison, NJ, USA). Results Taxonomic and functional characterization of the lymph node microbiota Alpha diversity, calculated at the individual sample level, revealed a significant reduction in microbial diversity and evenness in infected animals. Comparisons of alpha diversity distributions between groups showed that, regardless of breed, microbial richness (Figure 3a) was significantly higher in healthy animals (p = 0.0003). This pattern was consistent across Pielou’s evenness (p < 0.0001), Shannon (p < 0.0001), and Simpson (p < 0.0001) indices, indicating that infected lymph nodes harbored less diverse and more uneven microbial communities. When stratified by condition (Figure 3b), alpha diversity comparisons across groups indicated that samples from the Healthy_Inti and Healthy_Peru groups exhibited higher phylogenetic diversity (Faith’s PD) and richness than those from infected groups. No significant statistical differences were detected when comparing animals of the same health status between breeds. Regarding beta diversity, PCA revealed a segmentation of bacterial communities primarily based on health status. At the family level (Figures 3c and 3d), samples were distributed along principal component 1 (PC1), with healthy individuals clustering toward positive values and infected ones toward negative values. Families such as Pseudomonadaceae, Moraxellaceae, Comamonadaceae, and Sphingomonadaceae were associated with the positive sectors, while Streptococcaceae and Leptotrichiaceae projected toward the negative sectors. In the healthy group, additional dispersion was observed along the PC2 and PC3 axes, linked to the breed of the specimens. At the genus level (Figures 3e and 3f), the PC1 projection showed a separation where healthy samples were associated with Pseudomonas , Sphingomonas , Pantoea , Bradyrhizobium , and Acinetobacter . Conversely, infected samples converged with the vector of the genus Streptococcus . Other genera, such as Caviibacter , Kinneretia , Pantoea , Bradyrhizobium , and Acinetobacter , contributed to the dispersion observed specifically in the secondary components (PC2 and PC3), primarily within the healthy animal groups (Figure 3f). Taxonomic characterization showed differences in bacterial composition between healthy and infected cervical lymph nodes. At the phylum level, healthy groups (Healthy_Inti and Healthy_Peru) presented a diverse community dominated by Proteobacteria, followed by smaller proportions of Actinobacteriota, Bacteroidota, and Fusobacteriota. Conversely, infected groups exhibited a substantial reduction in diversity, accompanied by the predominance of the phylum Firmicutes (recently renamed as Bacillota) (Figures 4a and 4d). This dominance pattern remained consistent across lymphadenitis-positive samples, regardless of the host breed. At the family and genus levels, the microbial structure in healthy individuals was characterized by high heterogeneity. In the Healthy_Inti group, genera such as Pseudomonas , Sphingomonas , Cutibacterium , Acinetobacter , and Pantoea were prominent. Similarly, in Healthy_Peru, Pseudomonas remained one of the predominant genera, alongside a wide variety of other taxa (Figures 4b, 4c, and 4f). In contrast, most infected individuals were characterized by a strong predominance of the family Streptococcaceae and the genus Streptococcus, with limited taxonomic representation beyond these groups (Figures 4e and 4f). Three atypical cases were identified within the infected groups (two in Infected_Inti and one in Infected_Peru) where a drastic shift in taxonomic dominance was observed. In these samples, the family Leptotrichiaceae and the genus Caviibacter emerged as the predominant taxa, almost entirely displacing Streptococcus (Figures 4e and 4f). Finally, absolute abundance analysis (Figure 4g) allowed the evaluation of sequencing depth per sample, revealing that six libraries exhibited markedly lower coverage compared to the rest. As these samples did not reach the predefined minimum depth threshold required for robust comparative analyses, they were excluded from subsequent visualizations and downstream analyses. This decision was made prior to alpha and beta diversity calculations in order to avoid biases associated with unequal sampling effort. Differential biomarker analysis identified the microbial clades that define each health status with high statistical precision. In infected animals, a dominant and continuous taxonomic signal was detected within the phylum Bacillota, specifically concentrated in the class Bacilli, the order Lactobacillales, the family Streptococcaceae, the genus Streptococcus , and the species Streptococcus equi (Figure 5a). This dominance was validated through Linear Discriminant Analysis (LDA), where these taxa exhibited both high relative abundance and elevated LDA scores, confirming them as the primary discriminants of the infection state (Figure 5b). Conversely, healthy guinea pigs exhibited a characteristic enrichment of the phylum Pseudomonadota (formerly Proteobacteria). This signal was organized into a well-defined clade encompassing the class Gammaproteobacteria, the order Pseudomonadales, and the family Pseudomonadaceae (Figure 5a). The LDA analysis ratified that these taxa, along with their high abundance levels, constitute the characteristic microbiological profile of homeostasis in the cervical lymph nodes of healthy animals (Figure 5b). Community structure comparison using ASVs revealed a drastic reduction in taxonomic exclusivity associated with the disease. While healthy guinea pigs presented 499 exclusive ASVs (68.7%), infected individuals showed only 173 exclusive ASVs (23.8%), reflecting a reduction in taxonomic richness during the infectious process (Figure 5c). Despite these differences, a shared core of 54 ASVs (7.4%) was identified across both health states, including resident genera that persist regardless of the clinical presentation. When broken down by specific condition (Figure 5d), breed was observed to influence the number of unique sequences in the healthy state, with the Healthy_Inti group showing the highest number of exclusive ASVs (280). Nevertheless, a minimum "core" of 9 ASVs common to all four evaluated conditions was identified. This core is composed of genera consistently detected across all evaluated conditions, suggesting that these taxa may represent resilient members of the lymph node microbiota, including Streptococcus, Caviibacter, Sphingomonas, Kinneretia, Novosphingobium, Escherichia–Shigella, the Lachnospiraceae NK4A136 group, and Clostridium . Functional profiles were inferred from 16S rRNA gene data using PICRUSt2 and therefore represent predicted, rather than directly measured, metabolic potential. Predicted functional analysis suggested that the cervical lymph node microbiota may exhibit distinct predicted metabolic profiles that are altered during the infectious process. In both breeds, healthy individuals displayed highly homogeneous and consistent predicted functional profiles. In the Inti breed, healthy animals showed a stable enrichment pattern in predicted functional genes, enzymes, and MetaCyc pathways (positive Z-scores), with the sole exception of individual SI1, which exhibited higher levels of predicted proteins involved in polar amino acid transport systems (Figures 6a, 6b, and 6c). Similarly, healthy individuals of the Peru breed showed a uniform enrichment of predicted metabolic pathways and a coherent inferred functional structure, albeit with slight variation in enzymatic patterns (Figures 6d, 6e, and 6f). Conversely, the infected group showed greater functional variability and the presence of recurring atypical profiles. In the Inti breed, while most infected animals maintained moderate activity levels, samples II1, II9 (and occasionally II3) exhibited a generalized reduction in predicted functional potential, evidenced by negative Z-score values (blue tones) across nearly all evaluated levels (Figures 6a, 6b, and 6c). This phenomenon suggests a drastic reduction in the predicted metabolic capacity of these specific individuals. An analogous pattern was observed in the Peru breed, where infected animals were divided into two well-defined groups. The first group maintained a high relative abundance of pathways and functions, while a second subgroup, comprising samples IP6 and IP8, exhibited a marked functional decrease. Notably, in the case of IP8, despite showing global depletion, it retained a specific overexpression point in the bacterial ABC transporter pathway (ATP-binding cassette, subfamily B), distinguishing it from other atypical cases (Figure 6d). Taken together, these results indicate that while Streptococcus infection tends to homogenize the taxonomy, the functional impact on the host can present significant heterogeneity, with cases of microbial metabolic collapse in specific individuals of both breeds. After characterizing the microbiota and predicted functional profiles, a targeted molecular validation was performed to confirm the identity of the predominant pathogen detected in the bioinformatic analyses. PCR-based detection of Streptococcus spp. and Streptococcus equi subspecies To validate the findings from the microbiota analysis and confirm the identity of the causative agent, molecular characterization was performed using PCR. Samples were labeled according to health status and breed (IP, II: infected; SP, SI: healthy), allowing direct comparison between PCR amplification patterns and microbiome-based classifications. First, the presence of bacterial genetic material and sample viability were verified through the amplification of the 16S rRNA gene. Consistent bands of ~1500 bp were observed in all evaluated individuals, both in the lymphadenitis group (IP and II) (Supplementary 2) and the healthy animals (SP and SI) (Supplementary 3). These results confirm that all samples contained sufficient bacterial DNA for analysis, regardless of their health status or breed. Regarding pathogen detection, screening for the genus Streptococcus spp. using the sodA gene (~235 bp) was positive in the vast majority of infected animals from both Inti and Peru breeds (Figures 7a and 7b). However, three exceptional cases were identified within the infected group (IP8, II1, and II3) that showed no amplification for this marker. In contrast, no amplification signal was detected in any of the healthy animals, confirming the absence of the genus under homeostatic conditions. A detailed summary of sample classification and PCR results is provided in Supplementary Table S4. Specific identification of S. equi subsp. zooepidemicus using the comB marker (~450 bp) showed complete concordance with the sodA results, appearing only in infected individuals, with the exception of the aforementioned cases (Figures 7c and 7d). Furthermore, the absence of amplification of the seeI gene (~520 bp) allowed the exclusion of S. equi subsp. equi in all evaluated samples (Figures 7e and 7f). Notably, although individuals IP8, II1, and II3 did not show amplification of Streptococcus spp. or the zooepidemicus subspecies, successful amplification of the 16S rRNA internal control confirmed the presence of bacterial DNA in these samples (Supplementary Figure 2). This finding is consistent with the bioinformatic analysis, which indicated that these samples were dominated by the genus Caviibacter. Together, these results support the presence of an alternative etiology in a subset of lymphadenitis cases, where other taxa may displace the predominant pathogen. Discussion In this study, we investigated the microbiome associated with cervical lymphadenitis in guinea pigs using a combined metagenomic and molecular approach. Our findings indicate that cervical lymphadenitis in guinea pigs is associated with microbiome dysbiosis, consistent with pathogen-induced patterns reported in other hosts. Infected lymph nodes showed a significant reduction in microbial diversity and evenness, reflecting a loss of taxonomic richness and community structure. Similar reductions have been observed in abscesses and severe infections in other species, where proliferating pathogens displace resident microbial communities [46,47]. For example, bovine liver abscesses harbor low-diversity communities, often composed of fewer than 10 taxa and dominated by one or two bacterial genera [46]. Similarly, in human pneumonia and critical illness, dysbiosis is characterized by reduced microbial diversity and increased pathogen burden [48, 49]. The dominance of Streptococcus equi subsp. zooepidemicus in infected guinea pigs exemplifies this pattern, where a single opportunistic pathogen monopolizes the niche at the expense of a diverse commensal community. Similar pathogen-driven dominance has been reported in critically ill patients, where endogenous pathogens overgrow and overwhelm the microbiota, leading to “gut domination” by specific strains [50]. In our study, this is reflected by the expansion of Streptococcus from negligible levels in healthy nodes to a predominant genus in abscessed nodes, replacing a previously heterogeneous community rich in Proteobacteria. These findings are consistent with the known etiology of cervical lymphadenitis and illustrate how a facultative pathogen can shift a polymicrobial ecosystem toward near monoculture. Beta diversity analyses indicate shifts in microbial community composition during infection, primarily driven by health status. Similar patterns have been reported in other contexts, where infections lead to distinct segregation of microbial profiles, overriding host or environmental factors [47]. In our study, variation between healthy and infected microbiotas was mainly explained by disease status, with breed-related differences masked in infected animals. This is consistent with evidence that acute infections exert strong ecological pressure on the microbiome, shifting it from a commensal-dominated state to a pathogen-enriched profile regardless of host background [47]. The taxonomic shift observed, from Pseudomonadota (Proteobacteria) in healthy lymph nodes to dominant Streptococcaceae (Bacillota/Firmicutes) in diseased nodes, is consistent with dysbiosis patterns reported in other host-associated microbiomes. Health is typically associated with diverse communities, whereas disease favors expansion of Gram-positive opportunists such as streptococci. This pattern is consistent with culture-based studies identifying S. zooepidemicus as the main etiological agent in guinea pigs and with microbiome studies of abscesses showing dominance by a limited number of taxa [46]. Variability among infected animals ranged from moderate dysbiosis to near-complete Streptococcus dominance. Similar variability has been reported in cattle liver abscesses, where most cases are near-monocultures but some retain higher diversity [46]. This heterogeneity may reflect differences in abscess stage, host immune response, or co-infections, indicating that dysbiosis is not a uniform outcome even under the same etiological agent. This study provides one of the first culture-independent characterizations of the lymph node microbiota in a veterinary species. Healthy guinea pig cervical lymph nodes harbored diverse bacterial communities, dominated by environmental Proteobacteria, indicating that these tissues are not sterile and may be continuously exposed to microbes via the bloodstream or draining mucosal sites. This finding is consistent with evidence from other mammals. In pigs, mesenteric lymph nodes contain a subset of the gut microbiome with lower diversity than the gut itself [51], while in humans, lymph nodes exhibit disease-associated shifts in microbial composition and reduced diversity [52]. Together, these observations suggest that lymph nodes can support resident or transient microbial communities, but that this equilibrium is sensitive to disruption. In this context, infection appears to drive a marked shift from a diverse, Proteobacteria-rich community to a pathogen-dominated state. Our results extend the concept of a lymph node microbiome to guinea pigs and show how it is altered during lymphadenitis, where microbial diversity is reduced and community structure becomes skewed toward a dominant taxon. Functional predictions based on PICRUSt2 indicated that infected nodes had a reduced repertoire of metabolic pathways compared to healthy nodes. This reduction likely reflects taxonomic dominance, as communities largely composed of Streptococcus contain fewer functional genes than more diverse consortia. Similar reductions in predicted functional potential have been reported in severe dysbiosis. In critical illness, the loss of commensal taxa such as Bifidobacterium and Faecalibacterium is associated with decreased production of beneficial metabolites, including short-chain fatty acids [53,54]. In our study, healthy microbiomes showed enrichment of basic metabolic functions, whereas some infected samples exhibited near absence of these pathways, suggesting reduced metabolic versatility. Notably, this functional decline was not uniform across infected individuals, reflecting heterogeneity in the dysbiotic state. Some infected nodes retained non-streptococcal bacteria and a broader metabolic potential, whereas others were almost exclusively dominated by S. zooepidemicus and showed reduced functional profiles. Similar heterogeneity has been reported in human dysbiosis, where microbiome responses range from moderate disruption to near-complete pathogen dominance [53]. These inter-individual differences suggest that pathogen-induced dysbiosis varies in severity and may be influenced by factors such as abscess stage or host immune status. Loss of microbial diversity and function in severely affected cases may have clinical consequences, as reduced metabolic capacity could impair nutrient cycling or immune modulation, potentially exacerbating tissue damage or delaying recovery. This is consistent with evidence that microbiome resilience and functional redundancy protect against pathogen overgrowth, whereas their loss is associated with poorer outcomes [35,55,56]. Our results place cervical lymphadenitis in guinea pigs within the broader framework of pathogen-driven microbiome dysbiosis. The disease is characterized by reduced microbial diversity and evenness, dominance of S. zooepidemicus , and loss of predicted functional potential, consistent with findings in abscesses, critical illness, and mucosal infections across species [46,47,50]. Our findings show that the lymph node microbiome, typically reflecting environmental and host-associated microbiota [51], undergoes compositional changes during infection. This supports the concept that microbiome simplification and functional decline are key features of abscess pathology. In this context, cervical lymphadenitis may serve as a model to study pathogen-driven microbiome disruption. Future studies should investigate the mechanisms underlying this process and evaluate whether microbiome-targeted interventions could mitigate disease progression. Recognizing these parallels may contribute to improved management strategies that consider both the pathogen and the microbiome. High-throughput sequencing revealed that pathogen dominance was not restricted to Gram-positive cocci, identifying the presence of Caviibacter in three infected samples. This Gram-negative bacillus, described as a novel genus within the family Leptotrichiaceae [57], has been reported as an etiological agent capable of independently inducing abscess formation in guinea pigs [15]. C. abscessus is considered part of the normal nasopharyngeal and oral microbiota, suggesting that its translocation to cervical lymph nodes may occur through microtraumas in the oral mucosa [57]. The detection of this fastidious organism, rarely identified through conventional culture due to its strict growth requirements, highlights the value of metagenomic approaches in capturing the etiological complexity of lymphadenitis. These findings indicate that dysbiosis may involve not only well-known pathogens such as Streptococcus equi subsp. zooepidemicus , but also emerging or previously underrecognized taxa. In this context, molecular methods provide increased sensitivity compared to conventional microbiology by enabling detection independent of bacterial viability [58,59], supporting their utility for improving pathogen identification in complex infections. Conclusion This study demonstrates that cervical lymphadenitis in guinea pigs is associated with marked dysbiosis of the lymph node microbiota, characterized by a significant reduction in microbial diversity and the almost exclusive dominance of pathogenic taxa. The integration of 16S rRNA sequencing analysis, bioinformatics, and molecular validation using PCR confirmed that Streptococcus equi subsp. zooepidemicus is the main etiological agent in most of the cases evaluated, demonstrating its ability to displace the commensal microbiota and establish low-diversity bacterial communities in the infected lymph nodes. Furthermore, the presence of alternative etiologies was identified, mainly associated with the genus Caviibacter , suggesting that cervical lymphadenitis in guinea pigs has a more heterogeneous microbiological nature than previously recognized. These etiological variations were accompanied by predicted functional changes in microbial metabolism, indicating that the infection not only alters the taxonomic composition but also the metabolic potential of the bacterial communities present in the affected tissues. Taken together, the results demonstrate that the combined use of high-throughput sequencing tools and specific molecular markers significantly improves the etiological identification of cervical lymphadenitis in guinea pigs, providing key information for epidemiological diagnosis, the design of health control strategies, and the ecological understanding of host-microbiota interactions in Andean production systems. The taxonomic composition of healthy lymph nodes is characterized by high heterogeneity, with the genera Pseudomonas , Sphingomonas , Acinetobacter , Pantoea , and Bradyrhizobium being particularly prominent. In contrast, the microbiota of infected lymph nodes undergoes extreme dysbiosis, in which the genus Streptococcus monopolizes the ecological niche. Nevertheless, the identification of cases dominated by Caviibacter confirms the existence of alternative infectious profiles, suggesting that lymphadenitis should be approached as a condition that may be driven by different members of the opportunistic microbiota. Declarations Acknowledgements The authors gratefully acknowledge the Laboratory of Molecular Physiology at the Instituto de Investigación en Ganadería y Biotecnología (IGBI), Universidad Nacional Toribio Rodríguez de Mendoza (UNTRM), for providing technical support, laboratory facilities, and assistance during the experimental procedures conducted in this study. This research was funded by the National Program for Scientific Research and Advanced Studies (PROCIENCIA) of the National Council of Science, Technology, and Technological Innovation (CONCYTEC), Peru, under a competitive research grant and the Vice-Rectorate for Research at the Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas, Peru Author contributions All authors made substantial contributions to the research article: N.V.B.: Sample collection, laboratory procedures, experiments, writing–original draft, figure preparation; J.C.D.S.: Conceptualization, study design, sample collection, laboratory procedures, experiments, writing–original draft; J.M.: Writing–original draft; P.F.C.: Bioinformatic and statistical analyses; W.B.: Data interpretation, writing–review & editing; R.C.P.: Data interpretation, writing–review & editing; D.D.S.V.: Data interpretation, writing–review & editing; J.L.M.Q.: Supervision; H.F.T.: Supervision; R.M.L.L.: Conceptualization, study design, supervision. All authors reviewed and approved the final manuscript. Data availability statement The authors declare that all data and materials used in this study comply with field standards and are available upon request. The DNA sequences generated and analyzed during the current study are available in the NCBI SRA repository under BioProject PRJNA1419848 (https://www.ncbi.nlm.nih.gov/search/all/?term=PRJNA1419848). Other data from the study are available from the corresponding author upon reasonable request. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Funding This project was funded by the National Council of Science, Technology, and Technological Innovation (CONCYTEC) and the National Program for Scientific Research and Advanced Studies (PROCIENCIA), under the call for proposals “E041–2024–03 Basic Research Projects” (Contract No. PE501088227–2024-PROCIENCIA), and by the Vice-Rectorate for Research at the Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas, Peru. Ethical approval The experimental protocol was approved by the Institutional Committee on Research Ethics of the Universidad Nacional Toribio Rodríguez de Mendoza (UNTRM) under protocol number CIEI-No. 106. All experiments were conducted in accordance with the approved institutional guidelines and national regulations. References Morales, S. Patógenos bacterianos y parasitarios más frecuentes en cuyes de crianza familiar-comercial en tres distritos de la Provincia de Bolognesi, Departamento de Ancash en época de seca. 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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-9383588","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":627988069,"identity":"d1c49b73-5d4b-497d-8baa-465faa1b7ea3","order_by":0,"name":"Nayeli Alison Vilca Barrientos","email":"","orcid":"","institution":"National University Toribio Rodríguez de Mendoza","correspondingAuthor":false,"prefix":"","firstName":"Nayeli","middleName":"Alison Vilca","lastName":"Barrientos","suffix":""},{"id":627988072,"identity":"51e7173b-66e8-40f4-bdfa-03b1a5f3dd07","order_by":1,"name":"Jakson Jacob Socrates Chuquimia del Solar","email":"","orcid":"","institution":"National University Toribio Rodríguez de Mendoza","correspondingAuthor":false,"prefix":"","firstName":"Jakson","middleName":"Jacob Socrates Chuquimia del","lastName":"Solar","suffix":""},{"id":627988080,"identity":"ef6dc8e6-fc8a-48d5-91d3-dfcaf76ff41a","order_by":2,"name":"Jhorsan David Mauri Pablo","email":"","orcid":"","institution":"National University Toribio Rodríguez de Mendoza","correspondingAuthor":false,"prefix":"","firstName":"Jhorsan","middleName":"David Mauri","lastName":"Pablo","suffix":""},{"id":627988081,"identity":"c54e2139-e612-4ada-93cb-75f116629372","order_by":3,"name":"Paul Antonio Fernandez Castro","email":"","orcid":"","institution":"National University Toribio Rodríguez de Mendoza","correspondingAuthor":false,"prefix":"","firstName":"Paul","middleName":"Antonio Fernandez","lastName":"Castro","suffix":""},{"id":627988082,"identity":"11e5e7fc-8ca9-48f4-a732-f0182bb46f12","order_by":4,"name":"Richard Costa Polveiro","email":"","orcid":"","institution":"Federal University of Uberlândia","correspondingAuthor":false,"prefix":"","firstName":"Richard","middleName":"Costa","lastName":"Polveiro","suffix":""},{"id":627988085,"identity":"92dec7f3-df0f-494c-b280-0eed616575e9","order_by":5,"name":"Dielson da Silva Vieira","email":"","orcid":"","institution":"Purdue University West Lafayette","correspondingAuthor":false,"prefix":"","firstName":"Dielson","middleName":"da Silva","lastName":"Vieira","suffix":""},{"id":627988087,"identity":"89d05980-0392-42b3-b1de-4db4140efb92","order_by":6,"name":"William Bardales Escalante","email":"","orcid":"","institution":"National University Toribio Rodríguez de Mendoza","correspondingAuthor":false,"prefix":"","firstName":"William","middleName":"Bardales","lastName":"Escalante","suffix":""},{"id":627988091,"identity":"f16d4232-4111-409d-822c-1602f834c044","order_by":7,"name":"Jorge Luis Maicelo Quintana","email":"","orcid":"","institution":"National University Toribio Rodríguez de Mendoza","correspondingAuthor":false,"prefix":"","firstName":"Jorge","middleName":"Luis Maicelo","lastName":"Quintana","suffix":""},{"id":627988098,"identity":"4368e65f-d14e-41ee-a076-49d6fb9d538a","order_by":8,"name":"Hugo Frias Torres","email":"","orcid":"","institution":"National University Toribio Rodríguez de Mendoza","correspondingAuthor":false,"prefix":"","firstName":"Hugo","middleName":"Frias","lastName":"Torres","suffix":""},{"id":627988100,"identity":"8a792bbc-15e9-4393-9d2b-7115f4ee12a1","order_by":9,"name":"Rainer Marco López Lapa","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCElEQVRIiWNgGAWjYFACxgYQKQNmf4AJ8hChBayGcQYDgwQRWpCMZeYhRot5++G2Bx/3MPDwSzcf/mzbdqfOfNoBxgdv2xjy+HFokTmT2G444xkDj+ScYwnGuW3PJGRuJzAbzm1jKJZswK5FgiGxTZrnAAOPwY0cg+TctsMSEtIJbNK8bQyJGw7g0ML/sE36D1hL/ofDlhAt7L9BWvbj0iIBtIUBYgtjMyPUFmawLTj8IiHxsE2y54AEyC/GjD3nDkvOkE5slpxzTqJYAqfD0p9J/DhgIwcMsccffpQd5peQTj744U2ZTR4/Du/DQwEaISAAjlyJBLwaYLpQABFaRsEoGAWjYIQAALoFUvy938ZmAAAAAElFTkSuQmCC","orcid":"","institution":"National University Toribio Rodríguez de Mendoza","correspondingAuthor":true,"prefix":"","firstName":"Rainer","middleName":"Marco López","lastName":"Lapa","suffix":""}],"badges":[],"createdAt":"2026-04-11 00:53:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9383588/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9383588/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108177758,"identity":"5e5f987f-2616-4f16-9dc6-57b834f02571","added_by":"auto","created_at":"2026-04-30 08:16:39","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":48161,"visible":true,"origin":"","legend":"\u003cp\u003eAnatomical location of lymph nodes in the hamster, used as a reference for the guinea pig. Reproduced from “Anatomy, Physiology, and Behavior,” by K. A. Murray [23]. Reproduced with permission.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-9383588/v1/22c457ff6efa1344e85dcb64.png"},{"id":108182805,"identity":"672f5567-270c-4ecb-80a6-8f06eee164f3","added_by":"auto","created_at":"2026-04-30 08:59:33","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":8003426,"visible":true,"origin":"","legend":"\u003cp\u003eMethodological workflow for microbiome analysis and pathogen detection. The diagram illustrates the study phases, with the analysis divided into two complementary bacterial identification strategies: (\u003cstrong\u003eA\u003c/strong\u003e) Sequencing and bioinformatic analysis, including sequencing (V3–V4 region) and data processing through QIIME2 and DADA2 pipelines; and (\u003cstrong\u003eB\u003c/strong\u003e) Conventional PCR and electrophoresis, employing specific genetic markers (\u003cem\u003esodA\u003c/em\u003e, \u003cem\u003ecomB\u003c/em\u003e, and \u003cem\u003eseeI\u003c/em\u003e) for the molecular validation of the findings. Created in BioRender. Vilca, N. (2026) \u003ca href=\"https://biorender.com/pibrh46\"\u003ehttps://BioRender.com/pibrh46\u003c/a\u003e\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-9383588/v1/1e8e223d7d5ae4eebf5e1dbc.png"},{"id":108177751,"identity":"a1858fb9-e6c5-4274-89db-766e09784652","added_by":"auto","created_at":"2026-04-30 08:16:39","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":11587953,"visible":true,"origin":"","legend":"\u003cp\u003eMicrobial diversity analysis in cervical lymph nodes of guinea pigs from healthy and diseased groups. (\u003cstrong\u003ea\u003c/strong\u003e) Alpha diversity indices (Observed Features, Pielou, Shannon, and Simpson) compared by health status (Healthy vs. Infected). (\u003cstrong\u003eb\u003c/strong\u003e) Alpha diversity comparison across the four experimental categories derived from the combination of breed (Inti and Peru) and health status. In a and b, violin plots illustrate data distribution, and p-values (Kruskal–Wallis test) indicate statistical significance. (\u003cstrong\u003ec\u003c/strong\u003e–\u003cstrong\u003ef\u003c/strong\u003e) Principal Component Analysis (PCA) of beta diversity at the family (\u003cstrong\u003ec\u003c/strong\u003e, \u003cstrong\u003ed\u003c/strong\u003e) and genus (\u003cstrong\u003ee\u003c/strong\u003e, \u003cstrong\u003ef\u003c/strong\u003e) levels. Panels \u003cstrong\u003ec\u003c/strong\u003eand \u003cstrong\u003ee\u003c/strong\u003e show clustering by health status, while \u003cstrong\u003ed\u003c/strong\u003e and \u003cstrong\u003ef\u003c/strong\u003edetail the distribution by breed and health status (condition). Ellipses represent sample clustering for each category, and vectors indicate the taxa with the greatest contribution to the observed variation; statistical differences in beta diversity were assessed using PERMANOVA with 999 permutations.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-9383588/v1/8d4709dafb67f7b146749048.png"},{"id":108182349,"identity":"5b109ac0-f9d8-4933-9e20-8224c80a85f5","added_by":"auto","created_at":"2026-04-30 08:59:20","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1836249,"visible":true,"origin":"","legend":"\u003cp\u003eTaxonomic relative and absolute abundance profiles of the microbiota in healthy and infected cervical lymph nodes. (\u003cstrong\u003ea\u003c/strong\u003e–\u003cstrong\u003ec\u003c/strong\u003e) Average relative abundance (%) of the ten most abundant taxa at the (\u003cstrong\u003ea\u003c/strong\u003e) Phylum, (\u003cstrong\u003eb\u003c/strong\u003e) Family, and (\u003cstrong\u003ec\u003c/strong\u003e) Genus levels, compared across groups by breed and health status (condition). (\u003cstrong\u003ed\u003c/strong\u003e–\u003cstrong\u003ef\u003c/strong\u003e) Relative abundance distribution by individual sample at the (\u003cstrong\u003ed\u003c/strong\u003e) Phylum, (\u003cstrong\u003ee\u003c/strong\u003e) Family, and (\u003cstrong\u003ef\u003c/strong\u003e) Genus levels, grouped by condition. (\u003cstrong\u003eg\u003c/strong\u003e) Absolute abundance (read counts) at the family level by individual sample; the y-axis indicates the total number of assigned reads. In all panels, taxonomic units with abundances below the main threshold are grouped into the “Others” category.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-9383588/v1/75ee04ecf08475e1d9bc7576.png"},{"id":108182374,"identity":"1700edd1-5d80-4de2-9863-6a9c76597080","added_by":"auto","created_at":"2026-04-30 08:59:20","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":18824426,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential and shared bacterial composition plots. (\u003cstrong\u003ea\u003c/strong\u003e) Circular cladogram of differentially enriched microbial biomarkers. (\u003cstrong\u003eb\u003c/strong\u003e) Relative abundance and Effect Size (LDA) of the primary biomarkers; values on the left represent the average relative abundance of each taxon, while values on the right show \u003cem\u003eLog\u003c/em\u003e₁₀ (LDA) scores, indicating the degree of discriminant contribution. (\u003cstrong\u003ec\u003c/strong\u003e) Venn diagram of shared and exclusive ASVs between healthy and infected guinea pigs; the attached table indicates the taxonomic assignment (family and genus) of the ASVs within the intersection. (\u003cstrong\u003ed\u003c/strong\u003e) Venn diagram of shared ASVs among the four experimental conditions; the table shows the taxonomic assignment of the ASVs common to all evaluated groups (n = 9). Note: In all panels, blue represents the healthy group and red represents the infected group.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-9383588/v1/93186e3b8009255cdd68647c.png"},{"id":108183083,"identity":"d2ca572b-d113-440a-ac49-e49bbbfffb8b","added_by":"auto","created_at":"2026-04-30 08:59:47","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":17973713,"visible":true,"origin":"","legend":"\u003cp\u003ePredicted functional profiles of the microbiota in guinea pig cervical lymph nodes. Heatmaps showing the relative enrichment of (\u003cstrong\u003ea\u003c/strong\u003e, \u003cstrong\u003ed\u003c/strong\u003e) predicted functional genes (KEGG Orthologs – KO), (\u003cstrong\u003eb\u003c/strong\u003e, \u003cstrong\u003ee\u003c/strong\u003e) predicted enzymes (EC), and (\u003cstrong\u003ec\u003c/strong\u003e, \u003cstrong\u003ef\u003c/strong\u003e) metabolic pathways (MetaCyc) for the Inti (upper panels) and Perú (lower panels) breeds. Samples are grouped by condition (Healthy and Infected) in rows. Functional abundance values are normalized using Z-scores, where red indicates relative enrichment and blue indicates relative depletion of each predicted functional gene, enzyme, or metabolic pathway.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-9383588/v1/861ad4ca14f1b72c3c7995dd.png"},{"id":108182897,"identity":"c5bec082-f8ee-4d0b-a65e-6df6c2cbf9b4","added_by":"auto","created_at":"2026-04-30 08:59:40","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":53066619,"visible":true,"origin":"","legend":"\u003cp\u003eMolecular identification of \u003cem\u003eStreptococcus equi\u003c/em\u003e subsp. \u003cem\u003ezooepidemicus\u003c/em\u003e through conventional PCR markers. Agarose gel electrophoresis results for three genetic markers in cervical lymph node samples. (\u003cstrong\u003ea\u003c/strong\u003e–\u003cstrong\u003eb\u003c/strong\u003e) Amplification of the \u003cem\u003esodA\u003c/em\u003e marker (~235 bp) for the detection of the genus \u003cem\u003eStreptococcus\u003c/em\u003e spp. in infected (\u003cstrong\u003ea\u003c/strong\u003e) and healthy (\u003cstrong\u003eb\u003c/strong\u003e) animals. (\u003cstrong\u003ec\u003c/strong\u003e–\u003cstrong\u003ed\u003c/strong\u003e) Amplification of the\u003cem\u003ecomB\u003c/em\u003e marker (~450 pb) for the specific identification of \u003cem\u003eS\u003c/em\u003e. \u003cem\u003eequi\u003c/em\u003esubsp. \u003cem\u003ezooepidemicus\u003c/em\u003e in infected (\u003cstrong\u003ec\u003c/strong\u003e) and healthy (\u003cstrong\u003ed\u003c/strong\u003e) animals. (\u003cstrong\u003ee\u003c/strong\u003e–\u003cstrong\u003ef\u003c/strong\u003e) Amplification of the \u003cem\u003eseeI \u003c/em\u003emarker (~520 pb) for the exclusion of \u003cem\u003eS\u003c/em\u003e. \u003cem\u003eequi \u003c/em\u003esubsp. \u003cem\u003eequi\u003c/em\u003e in infected (\u003cstrong\u003ee\u003c/strong\u003e) and healthy (\u003cstrong\u003ef\u003c/strong\u003e) animals. In all panels, upper lanes correspond to the Perú breed and lower lanes to the Inti breed. L: Molecular weight marker (100 bp DNA Ladder); C: Negative control. Codes above the lanes (IP, II, SP, SI) indicate the individuals evaluated by group and health status.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-9383588/v1/d8b957bfe2de8e8961fa7323.png"},{"id":108183258,"identity":"bbed4a7b-8033-4015-a3a2-eed425cb95fd","added_by":"auto","created_at":"2026-04-30 09:00:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":8503913,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterialOfficialLINFAArticle.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9383588/v1/88aa9101f1d8a8cac57f8828.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Dysbiosis of the cervical lymph node microbiome associated with lymphadenitis in guinea pigs (Cavia porcellus)","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCervical lymphadenitis is an infectious disease primarily characterized by chronic abscess formation in lymph nodes, particularly in the cervical region. The etiological agent has been identified as \u003cem\u003eStreptococcus equi\u003c/em\u003e subsp. \u003cem\u003ezooepidemicus\u003c/em\u003e, a facultative pathogen and commensal that may reside in the tonsils, lower respiratory tract, skin, and urogenital system of certain animals [1]. Abscesses in affected lymph nodes may lead to sinusitis and otitis and may extend to the respiratory tract, contributing to bronchitis and interstitial pneumonia. This disease is highly contagious and can present high mortality, largely associated with uncontrolled animal movement between regions [2].\u003c/p\u003e\n\u003cp\u003eGuinea pigs may be affected by infectious and parasitic diseases, often associated with abrupt changes in temperature, humidity, air drafts, poor sanitary conditions in housing, and drastic dietary modifications [3]. Salmonellosis (also referred to as “peste”) is one example; it is caused by \u003cem\u003eSalmonella\u003c/em\u003e spp., which can be present in the feces of multiple animal species (including guinea pigs, poultry, pigs, ruminants, and horses) and may cause high mortality in guinea pigs of any age [4,5]. Pneumonia has also been linked to sudden variations in temperature and humidity and exposure to air currents; \u003cem\u003eStreptococcus pneumoniae\u003c/em\u003e has been reported as a causative agent, inducing pulmonary inflammation and significant production losses [6].\u003c/p\u003e\n\u003cp\u003eSeveral studies in Peru have investigated the etiology of cervical abscesses/lymphadenitis in guinea pigs. In Huamanga (Ayacucho), microbiological isolation from subcutaneous abscesses in growing guinea pigs indicated that most cases were attributed to \u003cem\u003eStreptococcus\u0026nbsp;\u003c/em\u003espp.(100 %),\u003cem\u003e\u0026nbsp;Staphylococcus\u0026nbsp;\u003c/em\u003espp.(90 %), \u003cem\u003eSalmonella\u0026nbsp;\u003c/em\u003espp. (20 %)and\u003cem\u003e\u0026nbsp;Corynebacterium\u0026nbsp;\u003c/em\u003espp.(20 %) [7]. In a breeder production center in Huayllapampa (San Jerónimo, Cusco, Peru), microbiological analyses of 43 samples identified \u003cem\u003eStreptococcus\u003c/em\u003e spp. in 69.77% of cases, \u003cem\u003eStaphylococcus\u003c/em\u003e spp. in 20.93%, and \u003cem\u003eCorynebacterium\u003c/em\u003e spp. in 9.30% [8]. Likewise, studies conducted in family–commercial production systems in Cusco have identified \u003cem\u003eStreptococcus\u003c/em\u003e spp. as the predominant bacteria in cervical abscesses of guinea pigs, although other genera such as \u003cem\u003eStaphylococcus\u003c/em\u003e and \u003cem\u003eKlebsiella\u003c/em\u003e have also been reported [9].\u003c/p\u003e\n\u003cp\u003eThe guinea pig (\u003cem\u003eCavia porcellus\u003c/em\u003e) is a rodent native to the Andean region and is widely distributed in countries such as Peru, Ecuador, Colombia, and Bolivia. Owing to its adaptability to diverse ecosystems, it can be found from sea level [10] to elevations above 3,550 m.a.s.l. [11], under both cold and warm climatic conditions. As an autochthonous animal resource, the guinea pig has high nutritional value and requires relatively low investment for production, contributing to food security in rural communities with limited resources [11].\u003c/p\u003e\n\u003cp\u003eIn Peru, guinea pig production is predominantly carried out under family-based systems. Consequently, animal health is essential to achieve efficient production outcomes. Given the low profitability of agricultural activities, production systems tend to intensify, which may increase animal health problems, particularly when stocking density rises or when dietary changes occur frequently [12]. However, knowledge regarding the pathology and epidemiology of guinea pig diseases remains limited, highlighting the importance of investigating disease events to expand understanding of health conditions affecting this species. In this context, molecular approaches have been applied to the investigation of lymphadenitis in guinea pigs in Arequipa, including the sequencing of the 16S rRNA gene from bacterial isolates obtained from cervical abscesses. These analyses generated nucleotide sequences with less than 100% similarity to GenBank entries, and the isolates exhibited 95–99% similarity to existing sequences, among which three were found to be closely related to \u003cem\u003eStreptococcus equi\u003c/em\u003e [13].\u003c/p\u003e\n\u003cp\u003eThe use of molecular methodologies for the identification of \u003cem\u003eStreptococcus equi\u003c/em\u003e has proven to be a reliable tool for the taxonomic confirmation of this pathogen in clinical samples from animals with conditions such as cervical lymphadenitis. In particular, studies such as those by Båverud et al. [14] indicate that the molecular characterization of \u003cem\u003eStreptococcus equi\u003c/em\u003e can be supported by the analysis of conserved regions of the 16S rRNA gene, which allow its differentiation from other phylogenetically related streptococci when combined with specific genetic markers. This molecular approach, complemented by amplification techniques and sequence comparison against reference databases, improves diagnostic accuracy compared to conventional bacteriological methods, especially in infections where phenotypic identification can be challenging.\u003c/p\u003e\n\u003cp\u003eRecently, an emerging bacterium associated with cases of cervical lymphadenitis in guinea pigs has been described. Previously reported as \u003cem\u003eStreptobacillus moniliformis\u003c/em\u003e, it has now been reclassified as \u003cem\u003eCaviibacter abscessus\u003c/em\u003e. Clinical studies have shown that this microorganism may be the only agent isolated in cervical abscesses, exhibiting fastidious growth characteristics that hinder its detection using conventional bacteriological methods [15]. Molecular identification through 16S rRNA gene analysis could also suggest the presence of \u003cem\u003eCaviibacter abscessus\u003c/em\u003e as part of the microbiota associated with the infections in this study, given its potential relevance in the emerging etiology of lymphadenitis in guinea pigs.\u003c/p\u003e"},{"header":"Methodology","content":"\u003ch3\u003e\u003cstrong\u003eStudy area and facilities\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eThe study was conducted in the district of Luya, province of Chachapoyas, in the southwestern Amazonas region of Peru. The area lies within the Andean highlands at elevations ranging from 1,713 to 3,240 m a.s.l., with average temperatures of 16\u0026ndash;19 \u0026deg;C and relative humidity of 70\u0026ndash;80% [16]. The experiment was conducted at the Experimental Station and the Laboratory of Molecular Physiology of the Institute of Livestock and Biotechnology Research (IGBI), Universidad Nacional Toribio Rodr\u0026iacute;guez de Mendoza de Amazonas (UNTRM-A).\u003c/p\u003e\n\u003ch4\u003e\u003cstrong\u003eEthical statement\u003c/strong\u003e\u003c/h4\u003e\n\u003cp\u003eThis study was approved by the Institutional Research Ethics Committee (CIEI) of UNTRM-A (CIEI No. 00106). All procedures complied with Peruvian Law No. 30407 on Animal Protection and Welfare [17]. Euthanasia procedures followed the Mexican Official Standard NOM-033-ZOO-1995 for humane slaughter of domestic and wild animals [18].\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eStudy design and animals\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eA total of 40 guinea pigs from Luya (Amazonas, Peru) were included. Twenty clinically healthy animals without signs of lymphadenitis comprised the control group (10 of the Peru breed and 10 of the Inti breed of guinea pigs). The infected group consisted of 20 animals with clinical signs compatible with cervical lymphadenitis (10 of the Peru breed and 10 of the Inti breed of guinea pigs). These results should be interpreted within the limitations of the non-probabilistic sampling design.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eFarms and husbandry conditions\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eInfected animals were sampled from three farms operating under family\u0026ndash;commercial production systems, whereas healthy controls were obtained from an experimental station under a fully commercial system. In all holdings, animals were raised under pen-based systems adapted to local climatic conditions. Management practices differed between systems, with family-based management in smallholder farms and more standardized practices in the commercial system. Animals were fed forage-based diets and had continuous access to water.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eSampling strategy and case definition\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eSampling was non-probabilistic (convenience sampling) and included two study groups (infected and healthy). Animals were classified by health status by a veterinarian experienced in rodent management and infectious disease diagnosis, following established clinical and husbandry criteria for guinea pigs [19]. Clinical assessment consisted of general examination based on observation and careful palpation to identify signs compatible with cervical lymphadenitis. Common findings among infected animals included encapsulated cervical abscesses and anorexia. Given the variability in clinical presentation, the presence of encapsulated cervical lymph node abscesses was used as the diagnostic clinical criterion, consistent with the literature [2,20,21].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRecorded variables\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor each animal, breed, sex, body weight, number of lymph nodes identified, and lymph node diameter were recorded (Supplementary Table 1 and 2). For infected animals, the volume of euthanasia agent administered was additionally recorded, following standard laboratory animal anaesthesia and euthanasia guidelines [22]. \u0026nbsp;\u003c/p\u003e\n\u003ch4\u003e\u003cstrong\u003eLymph node collection protocol\u003c/strong\u003e\u003c/h4\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eInfected guinea pigs were euthanized using a two-step protocol. First, ketamine hydrochloride (Ketagal, Laboratorios Galmedic, Paraguay) and xylazine hydrochloride (Xilagal, Laboratorios Galmedic, Paraguay) were administered, followed by a euthanasia solution (T61, MSD Animal Health/Intervet International GmbH, Germany) containing embutramide (200 mg), mebezonium iodide (50 mg), and tetracaine hydrochloride (5 mg). This pharmacological approach was selected to minimize cervical manipulation and reduce the risk of capsular rupture of abscessed lymph nodes, thereby preserving sample integrity.\u003c/p\u003e\n\u003cp\u003eAnesthetic doses were calculated individually using VetHelp AM [24], considering the commercial formulation. Dose details for each anesthetic and T61 are provided in Supplementary Table 1. Dosages were based on recommendations from Flecknell [22], which suggest ketamine at 20\u0026ndash;40 mg/kg and xylazine at 2 mg/kg, administered intramuscularly. Additionally, T61 was administered at a dose of 0.5\u0026ndash;2 mL per animal, depending on body weight, via the intrapulmonary route [25].\u003c/p\u003e\n\u003cp\u003eIn contrast, healthy guinea pigs were euthanized by cervical dislocation, as the absence of lymph node abscesses eliminated the risk of compromising sample integrity. This method is considered acceptable under established laboratory animal euthanasia guidelines when performed by trained personnel [22,19].\u003c/p\u003e\n\u003cp\u003eDissection was conducted under strictly sterile conditions. A ventral aseptic incision was performed using sterile scissors and forceps, extending from the oral region to the thorax. After identification of cervical lymph nodes, tissues were excised; infected nodes were handled with additional care to maintain abscess capsule integrity and preserve purulent contents. Sample handling and storage were performed following Vargas-Rocha [26], with slight modifications. Samples were maintained at 8\u0026ndash;10 \u0026deg;C until molecular processing at the Laboratory of Molecular Physiology.\u003c/p\u003e\n\u003ch4\u003e\u003cstrong\u003eBacterial DNA extraction from lymph nodes\u003c/strong\u003e\u003c/h4\u003e\n\u003cp\u003eFor initial preparation, samples were brought to room temperature. For infected lymph nodes, a cut was made to obtain 50 mg of purulent material. For healthy lymph nodes, adjacent tissues (muscle, fat, or membranes) were removed, the node was minced into small fragments, and 50 mg of tissue was weighed. Genomic bacterial DNA was extracted using the MAGBIO\u0026trade; HighPrep Blood and Tissue DNA Kit (Magbio Genomics, USA).\u003c/p\u003e\n\u003cp\u003eAs part of quality control, extracted bacterial genomic DNA was quantified using both the NanoDrop One Microvolume UV\u0026ndash;Vis spectrophotometer (Thermo Scientific, USA) and the Qubit 3 Fluorometer (Invitrogen, Thermo Fisher Scientific, USA). Spectrophotometric assessment was used to evaluate DNA purity, considering acceptable A260/280 ratios within the range of 1.8\u0026ndash;2.0 or values close to this interval. In parallel, fluorometric quantification was performed to accurately determine DNA concentration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e16S\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;rRNA gene sequencing (V3\u0026ndash;V4)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSequencing of the V3\u0026ndash;V4 hypervariable regions of the \u003cem\u003e16S\u003c/em\u003e rRNA gene was outsourced to BGI Tech Solutions (Hong Kong) Co., Limited. Sequencing was performed on the DNBseq platform using a paired-end 300 bp (PE300) configuration with primers 338F (5\u0026prime;-ACTCCTACGGGAGGCAGCAG-3\u0026prime;) and 806R (5\u0026prime;-GGACTACHVGGGTWTCTAAT-3\u0026prime;). A total of 36 genomic DNA samples, previously extracted and quantified, were submitted. Amplicon libraries targeting the V3\u0026ndash;V4 region were prepared using the standard \u0026ldquo;\u003cem\u003e16S\u003c/em\u003e Amplicon Sequencing\u0026rdquo; protocol, generating approximately 50,000 clean tags per sample and 43 Gb of clean data in total.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBioinformatics analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBioinformatic processing was performed using the nf-core/ampliseq pipeline v2.15.0 [27] from the nf-core collection [28]. Sequence quality was assessed with FastQC [29] and summarized using MultiQC [30]. Reads were processed independently with DADA2 [31] to remove PhiX contamination, truncate reads at the first occurrence of a quality score \u0026le;2, trim reads before the median quality dropped below 25, and retain at least 75% of reads. DADA2 was also used for read demultiplexing, paired-end merging, removal of PCR-generated chimeras, and inference of amplicon sequence variants (ASVs). The resulting feature table included the total number of ASVs, their abundance per sample, and the total number of reads retained after filtering.\u003c/p\u003e\n\u003cp\u003eThe resulting feature table included the total number of ASVs, their abundance per sample, and the total number of reads retained after filtering. Taxonomic classification of ASVs was performed in DADA2 using the SILVA v138.2 reference database with a minimum bootstrap confidence threshold of 50 [32]. The resulting ASV table, along with taxonomic assignments, was then imported into QIIME 2 for downstream analyses [33]. ASVs assigned to mitochondria or chloroplasts were removed prior to analysis. Community composition was visualized using bar plots, and sequencing depth was assessed through alpha rarefaction curves in QIIME 2 [33].\u003c/p\u003e\n\u003cp\u003eAll statistical and bioinformatic analyses were performed in R v4.4.1 using Bioconductor packages. Diversity, abundance, and rarefaction analyses were conducted using phyloseq [34]. Additional custom scripts were implemented using microeco [35] and MicrobiomeMarker, while MicrobiotaProcess [36] was used to generate LDA plots, Venn diagrams, and cladograms. Alpha diversity was estimated using the Shannon index, Simpson\u0026apos;s index, Pielou\u0026rsquo;s evenness, and the number of observed operational taxonomic units (OTUs). Group comparisons were performed using the non-parametric Kruskal\u0026ndash;Wallis test with Bonferroni correction for multiple comparisons (p \u0026lt; 0.05). Dimensionality reduction was performed using principal component analysis (PCA) to explore variation in the relative abundance of major taxa. Beta diversity was assessed using principal coordinates analysis (PCoA) based on dissimilarity matrices calculated using Bray\u0026ndash;Curtis, Jaccard, weighted UniFrac, and unweighted UniFrac distances. Statistical significance of between-group differences in community composition was assessed using PERMANOVA with 999 permutations, as implemented by the nf-core ampliseq workflow within the QIIME2 framework.\u003c/p\u003e\n\u003cp\u003eFunctional potential was predicted from ASVs using PICRUSt2 [37], estimating the relative abundance of genes and metabolic pathways associated with dietary transition. Functional outputs were grouped using KEGG Orthology (KO) [38], Enzyme Commission (EC) [39], and MetaCyc [40] annotations and visualized using heatmaps and functional enrichment plots.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe DNA sequences generated and analyzed during the current study are available in the NCBI SRA repository under BioProject PRJNA1419848 (https://www.ncbi.nlm.nih.gov/search/all/?term=PRJNA1419848). Other data from the study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConfirmation of bacterial DNA (\u003cem\u003e16S\u003c/em\u003e rRNA PCR)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo confirm the presence of bacterial genomic DNA, conventional PCR amplification of the \u003cem\u003e16S\u003c/em\u003e rRNA gene was performed using universal primers 27F and 1492R to generate an amplicon of approximately 1,500 bp [41]. Amplification conditions were adapted from Frank et al. [42] and Hou et al. [43]. The modified protocol included an initial denaturation at 94 \u0026deg;C for 5 min, followed by 40 cycles of denaturation at 94 \u0026deg;C for 30 s, annealing at 50 \u0026deg;C for 1 min, and extension at 70 \u0026deg;C for 1.5 min. A final extension at 70 \u0026deg;C for 2 min was performed, followed by a hold at 4 \u0026deg;C.\u003c/p\u003e\n\u003ch4\u003e\u003cstrong\u003eDetection of \u003cem\u003eStreptococcus\u003c/em\u003e spp., \u003cem\u003eStreptococcus equi\u003c/em\u003e subsp. \u003cem\u003ezooepidemicus\u003c/em\u003e, and \u003cem\u003eStreptococcus equi\u0026nbsp;\u003c/em\u003esubsp.\u003cem\u003e\u0026nbsp;equi\u003c/em\u003e by sequential PCR\u003c/strong\u003e\u003c/h4\u003e\n\u003cp\u003eIdentification of \u003cem\u003eStreptococcus equi\u003c/em\u003e subsp.\u003cem\u003e\u0026nbsp;zooepidemicus\u003c/em\u003e was performed using conventional PCR targeting three genetic markers sequentially. This stepwise approach was used to maximize specificity for subspecies discrimination in complex samples. First, the \u003cem\u003esodA\u003c/em\u003e gene was used as a screening assay to confirm the presence of \u003cem\u003eStreptococcus\u0026nbsp;\u003c/em\u003espp. in the sample; detection of an amplicon of approximately 235 bp was required to proceed, following Alber et al. [44]. Subspecies-level differentiation was then conducted by amplifying \u003cem\u003ecomB\u003c/em\u003e and \u003cem\u003eseeI\u003c/em\u003e markers. The \u003cem\u003ecomB\u003c/em\u003e marker was used for specific identification of \u003cem\u003eS. equi\u0026nbsp;\u003c/em\u003esubsp. \u003cem\u003ezooepidemicus\u003c/em\u003e, with an expected amplicon of approximately 450 bp [45], whereas \u003cem\u003eseeI\u003c/em\u003e marker was used complementarily to rule out\u003cem\u003e\u0026nbsp;S. equi\u0026nbsp;\u003c/em\u003esubsp.\u003cem\u003e\u0026nbsp;equi\u003c/em\u003e, which yields an amplicon of approximately 520 bp [44].\u003c/p\u003e\n\u003cp\u003eTabla 1. Primers for amplification of the \u003cem\u003esod\u003c/em\u003eA gene for \u003cem\u003eStreptococcus\u0026nbsp;\u003c/em\u003espp\u003cem\u003e.\u003c/em\u003e; the\u003cem\u003e\u0026nbsp;com\u003c/em\u003eB gene for \u003cem\u003eStreptococcus equi\u003c/em\u003e subsp. \u003cem\u003ezooepidemicus\u003c/em\u003e and the \u003cem\u003esee\u003c/em\u003eI gene for\u003cem\u003e\u0026nbsp;Streptococcus equi\u0026nbsp;\u003c/em\u003esubsp. \u003cem\u003eequi\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTarget gene\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOligonucleotide primer\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSecuencing (5\u0026rsquo;-3\u0026rsquo;)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSize of PCR product (bp)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReference\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cem\u003eSod\u003c/em\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cem\u003eSod\u003c/em\u003eA equi/zooep-F\u003cbr\u003e\u003cem\u003eSod\u003c/em\u003eA equi/zooep-R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003eCAG CAT TCC TGC TGA CAT TCG TCA GG\u003cbr\u003e\u0026nbsp; CTG ACC AGC CTT ATT CAC AAC CAG CC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e235\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eAlber et al., 2004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cem\u003eCom\u003c/em\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cem\u003eCom\u003c/em\u003eB-F\u003cbr\u003e\u0026nbsp; ComB-R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003eGCT GGT TCA GGA GTG CAA CA\u003cbr\u003e\u0026nbsp; TTC GTG GAA AAT CGT GCT GT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e450\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eMorris et al., 2023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cem\u003eSee\u003c/em\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cem\u003eSee\u003c/em\u003eI-F\u003cbr\u003e\u003cem\u003eSee\u003c/em\u003eI-R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 200px;\"\u003e\n \u003cp\u003eGAA GGT CCG CCA TTT TCA GGT AGT TTG\u003cbr\u003e\u0026nbsp; GCA TAC TCT CTC TGT CAC CAT GTC CTG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e520\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eAlber et al., 2004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEach PCR was performed separately in a final volume of 25 \u0026micro;L, following the amplification parameters of each primer with slight modifications. Reactions contained 12.5 \u0026micro;L GoTaq Green Master Mix (Promega, Madison, WI, USA), 0.5 \u0026micro;L forward primer, 0.5 \u0026micro;L reverse primer, 10.5 \u0026micro;L nuclease-free water, and 1.0 \u0026micro;L template DNA. Amplifications were run on a SimpliAmp Thermal Cycler (Applied Biosystems, Carlsbad, CA, USA) under marker-specific conditions. For \u003cem\u003esodA\u003c/em\u003e and \u003cem\u003eseeI\u003c/em\u003e, cycling conditions followed Alber et al. [44]: initial denaturation at 94 \u0026deg;C for 3 min, followed by 30 cycles of 94 \u0026deg;C for 30 s, 59 \u0026deg;C for 30 s, and 72 \u0026deg;C for 40 s, with a final extension at 72 \u0026deg;C for 5 min and a hold at 4 \u0026deg;C. For \u003cem\u003ecomB\u003c/em\u003e, cycling conditions followed Morris et al. [45]: initial denaturation at 95 \u0026deg;C for 2 min, followed by 32 cycles of 95 \u0026deg;C for 30 s, 55 \u0026deg;C for 30 s, and 72 \u0026deg;C for 15 s, with a final extension at 72 \u0026deg;C for 5 min and a hold at 4 \u0026deg;C. PCR products and a 100 bp DNA ladder (Promega, Madison, WI, USA) were resolved on 2% agarose gels using an E1201-BLT electrophoresis chamber (ACCURIS Instruments, Edison, NJ, USA) at 100 V for 45\u0026ndash;50 min, and bands were visualized using a SmartDoc Imaging Enclosure (ACCURIS Instruments, Edison, NJ, USA).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eTaxonomic and functional characterization of the lymph node microbiota\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAlpha diversity, calculated at the individual sample level, revealed a significant reduction in microbial diversity and evenness in infected animals. Comparisons of alpha diversity distributions between groups showed that, regardless of breed, microbial richness (Figure 3a) was significantly higher in healthy animals (p = 0.0003). This pattern was consistent across Pielou\u0026rsquo;s evenness (p \u0026lt; 0.0001), Shannon (p \u0026lt; 0.0001), and Simpson (p \u0026lt; 0.0001) indices, indicating that infected lymph nodes harbored less diverse and more uneven microbial communities. When stratified by condition (Figure 3b), alpha diversity comparisons across groups indicated that samples from the Healthy_Inti and Healthy_Peru groups exhibited higher phylogenetic diversity (Faith\u0026rsquo;s PD) and richness than those from infected groups. No significant statistical differences were detected when comparing animals of the same health status between breeds.\u003c/p\u003e\n\u003cp\u003eRegarding beta diversity, PCA revealed a segmentation of bacterial communities primarily based on health status. At the family level (Figures 3c and 3d), samples were distributed along principal component 1 (PC1), with healthy individuals clustering toward positive values and infected ones toward negative values. Families such as Pseudomonadaceae, Moraxellaceae, Comamonadaceae, and Sphingomonadaceae were associated with the positive sectors, while Streptococcaceae and Leptotrichiaceae projected toward the negative sectors. In the healthy group, additional dispersion was observed along the PC2 and PC3 axes, linked to the breed of the specimens.\u003c/p\u003e\n\u003cp\u003eAt the genus level (Figures 3e and 3f), the PC1 projection showed a separation where healthy samples were associated with \u003cem\u003ePseudomonas\u003c/em\u003e, \u003cem\u003eSphingomonas\u003c/em\u003e, \u003cem\u003ePantoea\u003c/em\u003e, \u003cem\u003eBradyrhizobium\u003c/em\u003e, and \u003cem\u003eAcinetobacter\u003c/em\u003e. Conversely, infected samples converged with the vector of the genus \u003cem\u003eStreptococcus\u003c/em\u003e. Other genera, such as \u003cem\u003eCaviibacter\u003c/em\u003e,\u003cem\u003e\u0026nbsp;Kinneretia\u003c/em\u003e, \u003cem\u003ePantoea\u003c/em\u003e, \u003cem\u003eBradyrhizobium\u003c/em\u003e, and \u003cem\u003eAcinetobacter\u003c/em\u003e, contributed to the dispersion observed specifically in the secondary components (PC2 and PC3), primarily within the healthy animal groups (Figure 3f).\u003c/p\u003e\n\u003cp\u003eTaxonomic characterization showed differences in bacterial composition between healthy and infected cervical lymph nodes. At the phylum level, healthy groups (Healthy_Inti and Healthy_Peru) presented a diverse community dominated by Proteobacteria, followed by smaller proportions of Actinobacteriota, Bacteroidota, and Fusobacteriota. Conversely, infected groups exhibited a substantial reduction in diversity, accompanied by the predominance of the phylum Firmicutes (recently renamed as Bacillota) (Figures 4a and 4d). This dominance pattern remained consistent across lymphadenitis-positive samples, regardless of the host breed.\u003c/p\u003e\n\u003cp\u003eAt the family and genus levels, the microbial structure in healthy individuals was characterized by high heterogeneity. In the Healthy_Inti group, genera such as\u003cem\u003e\u0026nbsp;Pseudomonas\u003c/em\u003e, \u003cem\u003eSphingomonas\u003c/em\u003e, \u003cem\u003eCutibacterium\u003c/em\u003e, \u003cem\u003eAcinetobacter\u003c/em\u003e, and \u003cem\u003ePantoea\u003c/em\u003e were prominent. Similarly, in Healthy_Peru, Pseudomonas remained one of the predominant genera, alongside a wide variety of other taxa (Figures 4b, 4c, and 4f). In contrast, most infected individuals were characterized by a strong predominance of the family Streptococcaceae and the genus Streptococcus, with limited taxonomic representation beyond these groups (Figures 4e and 4f).\u003c/p\u003e\n\u003cp\u003eThree atypical cases were identified within the infected groups (two in Infected_Inti and one in Infected_Peru) where a drastic shift in taxonomic dominance was observed. In these samples, the family Leptotrichiaceae and the genus\u003cem\u003e\u0026nbsp;Caviibacter\u0026nbsp;\u003c/em\u003eemerged as the predominant taxa, almost entirely displacing \u003cem\u003eStreptococcus\u003c/em\u003e (Figures 4e and 4f). Finally, absolute abundance analysis (Figure 4g) allowed the evaluation of sequencing depth per sample, revealing that six libraries exhibited markedly lower coverage compared to the rest. As these samples did not reach the predefined minimum depth threshold required for robust comparative analyses, they were excluded from subsequent visualizations and downstream analyses. This decision was made prior to alpha and beta diversity calculations in order to avoid biases associated with unequal sampling effort.\u003c/p\u003e\n\u003cp\u003eDifferential biomarker analysis identified the microbial clades that define each health status with high statistical precision. In infected animals, a dominant and continuous taxonomic signal was detected within the phylum Bacillota, specifically concentrated in the class Bacilli, the order Lactobacillales, the family Streptococcaceae, the genus \u003cem\u003eStreptococcus\u003c/em\u003e, and the species Streptococcus equi (Figure 5a). This dominance was validated through Linear Discriminant Analysis (LDA), where these taxa exhibited both high relative abundance and elevated LDA scores, confirming them as the primary discriminants of the infection state (Figure 5b).\u003c/p\u003e\n\u003cp\u003eConversely, healthy guinea pigs exhibited a characteristic enrichment of the phylum Pseudomonadota (formerly Proteobacteria). This signal was organized into a well-defined clade encompassing the class Gammaproteobacteria, the order Pseudomonadales, and the family Pseudomonadaceae (Figure 5a). The LDA analysis ratified that these taxa, along with their high abundance levels, constitute the characteristic microbiological profile of homeostasis in the cervical lymph nodes of healthy animals (Figure 5b).\u003c/p\u003e\n\u003cp\u003eCommunity structure comparison using ASVs revealed a drastic reduction in taxonomic exclusivity associated with the disease. While healthy guinea pigs presented 499 exclusive ASVs (68.7%), infected individuals showed only 173 exclusive ASVs (23.8%), reflecting a reduction in taxonomic richness during the infectious process (Figure 5c). Despite these differences, a shared core of 54 ASVs (7.4%) was identified across both health states, including resident genera that persist regardless of the clinical presentation.\u003c/p\u003e\n\u003cp\u003eWhen broken down by specific condition (Figure 5d), breed was observed to influence the number of unique sequences in the healthy state, with the Healthy_Inti group showing the highest number of exclusive ASVs (280). Nevertheless, a minimum \u0026quot;core\u0026quot; of 9 ASVs common to all four evaluated conditions was identified. This core is composed of genera consistently detected across all evaluated conditions, suggesting that these taxa may represent resilient members of the lymph node microbiota, including \u003cem\u003eStreptococcus, Caviibacter, Sphingomonas, Kinneretia, Novosphingobium, Escherichia\u0026ndash;Shigella, the Lachnospiraceae NK4A136 group,\u003c/em\u003e and \u003cem\u003eClostridium\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eFunctional profiles were inferred from 16S rRNA gene data using PICRUSt2 and therefore represent predicted, rather than directly measured, metabolic potential. Predicted functional analysis suggested that the cervical lymph node microbiota may exhibit distinct predicted metabolic profiles that are altered during the infectious process. In both breeds, healthy individuals displayed highly homogeneous and consistent predicted functional profiles. In the Inti breed, healthy animals showed a stable enrichment pattern in predicted functional genes, enzymes, and MetaCyc pathways (positive Z-scores), with the sole exception of individual SI1, which exhibited higher levels of predicted proteins involved in polar amino acid transport systems (Figures 6a, 6b, and 6c). Similarly, healthy individuals of the Peru breed showed a uniform enrichment of predicted metabolic pathways and a coherent inferred functional structure, albeit with slight variation in enzymatic patterns (Figures 6d, 6e, and 6f).\u003c/p\u003e\n\u003cp\u003eConversely, the infected group showed greater functional variability and the presence of recurring atypical profiles. In the Inti breed, while most infected animals maintained moderate activity levels, samples II1, II9 (and occasionally II3) exhibited a generalized reduction in predicted functional potential, evidenced by negative Z-score values (blue tones) across nearly all evaluated levels (Figures 6a, 6b, and 6c). This phenomenon suggests a drastic reduction in the predicted metabolic capacity of these specific individuals.\u003c/p\u003e\n\u003cp\u003eAn analogous pattern was observed in the Peru breed, where infected animals were divided into two well-defined groups. The first group maintained a high relative abundance of pathways and functions, while a second subgroup, comprising samples IP6 and IP8, exhibited a marked functional decrease. Notably, in the case of IP8, despite showing global depletion, it retained a specific overexpression point in the bacterial ABC transporter pathway (ATP-binding cassette, subfamily B), distinguishing it from other atypical cases (Figure 6d). Taken together, these results indicate that while \u003cem\u003eStreptococcus\u003c/em\u003e infection tends to homogenize the taxonomy, the functional impact on the host can present significant heterogeneity, with cases of microbial metabolic collapse in specific individuals of both breeds.\u003c/p\u003e\n\u003cp\u003eAfter characterizing the microbiota and predicted functional profiles, a targeted molecular validation was performed to confirm the identity of the predominant pathogen detected in the bioinformatic analyses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePCR-based detection of Streptococcus spp. and Streptococcus equi subspecies\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo validate the findings from the microbiota analysis and confirm the identity of the causative agent, molecular characterization was performed using PCR. Samples were labeled according to health status and breed (IP, II: infected; SP, SI: healthy), allowing direct comparison between PCR amplification patterns and microbiome-based classifications. First, the presence of bacterial genetic material and sample viability were verified through the amplification of the 16S rRNA gene. Consistent bands of ~1500 bp were observed in all evaluated individuals, both in the lymphadenitis group (IP and II) (Supplementary 2) and the healthy animals (SP and SI) (Supplementary 3). These results confirm that all samples contained sufficient bacterial DNA for analysis, regardless of their health status or breed.\u003c/p\u003e\n\u003cp\u003eRegarding pathogen detection, screening for the genus Streptococcus spp. using the sodA gene (~235 bp) was positive in the vast majority of infected animals from both Inti and Peru breeds (Figures 7a and 7b). However, three exceptional cases were identified within the infected group (IP8, II1, and II3) that showed no amplification for this marker. In contrast, no amplification signal was detected in any of the healthy animals, confirming the absence of the genus under homeostatic conditions. A detailed summary of sample classification and PCR results is provided in Supplementary Table S4. Specific identification of S. equi subsp. zooepidemicus using the comB marker (~450 bp) showed complete concordance with the sodA results, appearing only in infected individuals, with the exception of the aforementioned cases (Figures 7c and 7d). Furthermore, the absence of amplification of the seeI gene (~520 bp) allowed the exclusion of S. equi subsp. equi in all evaluated samples (Figures 7e and 7f).\u003c/p\u003e\n\u003cp\u003eNotably, although individuals IP8, II1, and II3 did not show amplification of Streptococcus spp. or the zooepidemicus subspecies, successful amplification of the 16S rRNA internal control confirmed the presence of bacterial DNA in these samples (Supplementary Figure 2). This finding is consistent with the bioinformatic analysis, which indicated that these samples were dominated by the genus Caviibacter. Together, these results support the presence of an alternative etiology in a subset of lymphadenitis cases, where other taxa may displace the predominant pathogen.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we investigated the microbiome associated with cervical lymphadenitis in guinea pigs using a combined metagenomic and molecular approach. Our findings indicate that cervical lymphadenitis in guinea pigs is associated with microbiome dysbiosis, consistent with pathogen-induced patterns reported in other hosts. Infected lymph nodes showed a significant reduction in microbial diversity and evenness, reflecting a loss of taxonomic richness and community structure. Similar reductions have been observed in abscesses and severe infections in other species, where proliferating pathogens displace resident microbial communities [46,47]. For example, bovine liver abscesses harbor low-diversity communities, often composed of fewer than 10 taxa and dominated by one or two bacterial genera [46]. Similarly, in human pneumonia and critical illness, dysbiosis is characterized by reduced microbial diversity and increased pathogen burden [48, 49]. The dominance of\u003cem\u003e\u0026nbsp;Streptococcus equi\u0026nbsp;\u003c/em\u003esubsp. \u003cem\u003ezooepidemicus\u003c/em\u003e in infected guinea pigs exemplifies this pattern, where a single opportunistic pathogen monopolizes the niche at the expense of a diverse commensal community. Similar pathogen-driven dominance has been reported in critically ill patients, where endogenous pathogens overgrow and overwhelm the microbiota, leading to “gut domination” by specific strains [50]. In our study, this is reflected by the expansion of \u003cem\u003eStreptococcus\u003c/em\u003e from negligible levels in healthy nodes to a predominant genus in abscessed nodes, replacing a previously heterogeneous community rich in Proteobacteria. These findings are consistent with the known etiology of cervical lymphadenitis and illustrate how a facultative pathogen can shift a polymicrobial ecosystem toward near monoculture.\u003c/p\u003e\n\u003cp\u003eBeta diversity analyses indicate shifts in microbial community composition during infection, primarily driven by health status. Similar patterns have been reported in other contexts, where infections lead to distinct segregation of microbial profiles, overriding host or environmental factors [47]. In our study, variation between healthy and infected microbiotas was mainly explained by disease status, with breed-related differences masked in infected animals. This is consistent with evidence that acute infections exert strong ecological pressure on the microbiome, shifting it from a commensal-dominated state to a pathogen-enriched profile regardless of host background [47].\u003c/p\u003e\n\u003cp\u003eThe taxonomic shift observed, from Pseudomonadota (Proteobacteria) in healthy lymph nodes to dominant Streptococcaceae (Bacillota/Firmicutes) in diseased nodes, is consistent with dysbiosis patterns reported in other host-associated microbiomes. Health is typically associated with diverse communities, whereas disease favors expansion of Gram-positive opportunists such as streptococci. This pattern is consistent with culture-based studies identifying \u003cem\u003eS. zooepidemicus\u003c/em\u003e as the main etiological agent in guinea pigs and with microbiome studies of abscesses showing dominance by a limited number of taxa [46].\u003c/p\u003e\n\u003cp\u003eVariability among infected animals ranged from moderate dysbiosis to near-complete Streptococcus dominance. Similar variability has been reported in cattle liver abscesses, where most cases are near-monocultures but some retain higher diversity [46]. This heterogeneity may reflect differences in abscess stage, host immune response, or co-infections, indicating that dysbiosis is not a uniform outcome even under the same etiological agent. This study provides one of the first culture-independent characterizations of the lymph node microbiota in a veterinary species. Healthy guinea pig cervical lymph nodes harbored diverse bacterial communities, dominated by environmental Proteobacteria, indicating that these tissues are not sterile and may be continuously exposed to microbes via the bloodstream or draining mucosal sites.\u003c/p\u003e\n\u003cp\u003eThis finding is consistent with evidence from other mammals. In pigs, mesenteric lymph nodes contain a subset of the gut microbiome with lower diversity than the gut itself [51], while in humans, lymph nodes exhibit disease-associated shifts in microbial composition and reduced diversity [52]. Together, these observations suggest that lymph nodes can support resident or transient microbial communities, but that this equilibrium is sensitive to disruption. In this context, infection appears to drive a marked shift from a diverse, Proteobacteria-rich community to a pathogen-dominated state. Our results extend the concept of a lymph node microbiome to guinea pigs and show how it is altered during lymphadenitis, where microbial diversity is reduced and community structure becomes skewed toward a dominant taxon.\u003c/p\u003e\n\u003cp\u003eFunctional predictions based on PICRUSt2 indicated that infected nodes had a reduced repertoire of metabolic pathways compared to healthy nodes. This reduction likely reflects taxonomic dominance, as communities largely composed of \u003cem\u003eStreptococcus\u003c/em\u003e contain fewer functional genes than more diverse consortia. Similar reductions in predicted functional potential have been reported in severe dysbiosis. In critical illness, the loss of commensal taxa such as \u003cem\u003eBifidobacterium\u003c/em\u003e and \u003cem\u003eFaecalibacterium\u003c/em\u003e is associated with decreased production of beneficial metabolites, including short-chain fatty acids [53,54]. In our study, healthy microbiomes showed enrichment of basic metabolic functions, whereas some infected samples exhibited near absence of these pathways, suggesting reduced metabolic versatility. Notably, this functional decline was not uniform across infected individuals, reflecting heterogeneity in the dysbiotic state.\u003c/p\u003e\n\u003cp\u003eSome infected nodes retained non-streptococcal bacteria and a broader metabolic potential, whereas others were almost exclusively dominated by \u003cem\u003eS. zooepidemicus\u003c/em\u003e and showed reduced functional profiles. Similar heterogeneity has been reported in human dysbiosis, where microbiome responses range from moderate disruption to near-complete pathogen dominance [53]. These inter-individual differences suggest that pathogen-induced dysbiosis varies in severity and may be influenced by factors such as abscess stage or host immune status. Loss of microbial diversity and function in severely affected cases may have clinical consequences, as reduced metabolic capacity could impair nutrient cycling or immune modulation, potentially exacerbating tissue damage or delaying recovery. This is consistent with evidence that microbiome resilience and functional redundancy protect against pathogen overgrowth, whereas their loss is associated with poorer outcomes [35,55,56].\u003c/p\u003e\n\u003cp\u003eOur results place cervical lymphadenitis in guinea pigs within the broader framework of pathogen-driven microbiome dysbiosis. The disease is characterized by reduced microbial diversity and evenness, dominance of \u003cem\u003eS. zooepidemicus\u003c/em\u003e, and loss of predicted functional potential, consistent with findings in abscesses, critical illness, and mucosal infections across species [46,47,50]. Our findings show that the lymph node microbiome, typically reflecting environmental and host-associated microbiota [51], undergoes compositional changes during infection. This supports the concept that microbiome simplification and functional decline are key features of abscess pathology. In this context, cervical lymphadenitis may serve as a model to study pathogen-driven microbiome disruption. Future studies should investigate the mechanisms underlying this process and evaluate whether microbiome-targeted interventions could mitigate disease progression. Recognizing these parallels may contribute to improved management strategies that consider both the pathogen and the microbiome.\u003c/p\u003e\n\u003cp\u003eHigh-throughput sequencing revealed that pathogen dominance was not restricted to Gram-positive cocci, identifying the presence of \u003cem\u003eCaviibacter\u0026nbsp;\u003c/em\u003ein three infected samples. This Gram-negative bacillus, described as a novel genus within the family Leptotrichiaceae [57], has been reported as an etiological agent capable of independently inducing abscess formation in guinea pigs [15]. \u003cem\u003eC. abscessus\u003c/em\u003e is considered part of the normal nasopharyngeal and oral microbiota, suggesting that its translocation to cervical lymph nodes may occur through microtraumas in the oral mucosa [57]. The detection of this fastidious organism, rarely identified through conventional culture due to its strict growth requirements, highlights the value of metagenomic approaches in capturing the etiological complexity of lymphadenitis. These findings indicate that dysbiosis may involve not only well-known pathogens such as \u003cem\u003eStreptococcus equi\u003c/em\u003e subsp. \u003cem\u003ezooepidemicus\u003c/em\u003e, but also emerging or previously underrecognized taxa. In this context, molecular methods provide increased sensitivity compared to conventional microbiology by enabling detection independent of bacterial viability [58,59], supporting their utility for improving pathogen identification in complex infections.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study demonstrates that cervical lymphadenitis in guinea pigs is associated with marked dysbiosis of the lymph node microbiota, characterized by a significant reduction in microbial diversity and the almost exclusive dominance of pathogenic taxa. The integration of 16S rRNA sequencing analysis, bioinformatics, and molecular validation using PCR confirmed that\u003cem\u003e\u0026nbsp;Streptococcus equi\u0026nbsp;\u003c/em\u003esubsp. \u003cem\u003ezooepidemicus\u0026nbsp;\u003c/em\u003eis the main etiological agent in most of the cases evaluated, demonstrating its ability to displace the commensal microbiota and establish low-diversity bacterial communities in the infected lymph nodes. Furthermore, the presence of alternative etiologies was identified, mainly associated with the genus \u003cem\u003eCaviibacter\u003c/em\u003e, suggesting that cervical lymphadenitis in guinea pigs has a more heterogeneous microbiological nature than previously recognized. These etiological variations were accompanied by predicted functional changes in microbial metabolism, indicating that the infection not only alters the taxonomic composition but also the metabolic potential of the bacterial communities present in the affected tissues.\u003c/p\u003e\n\u003cp\u003eTaken together, the results demonstrate that the combined use of high-throughput sequencing tools and specific molecular markers significantly improves the etiological identification of cervical lymphadenitis in guinea pigs, providing key information for epidemiological diagnosis, the design of health control strategies, and the ecological understanding of host-microbiota interactions in Andean production systems. The taxonomic composition of healthy lymph nodes is characterized by high heterogeneity, with the genera \u003cem\u003ePseudomonas\u003c/em\u003e, \u003cem\u003eSphingomonas\u003c/em\u003e, \u003cem\u003eAcinetobacter\u003c/em\u003e, \u003cem\u003ePantoea\u003c/em\u003e, and \u003cem\u003eBradyrhizobium\u003c/em\u003e being particularly prominent. In contrast, the microbiota of infected lymph nodes undergoes extreme dysbiosis, in which the genus \u003cem\u003eStreptococcus\u003c/em\u003e monopolizes the ecological niche. Nevertheless, the identification of cases dominated by \u003cem\u003eCaviibacter\u003c/em\u003e confirms the existence of alternative infectious profiles, suggesting that lymphadenitis should be approached as a condition that may be driven by different members of the opportunistic microbiota.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors gratefully acknowledge the Laboratory of Molecular Physiology at the Instituto de Investigación en Ganadería y Biotecnología (IGBI), Universidad Nacional Toribio Rodríguez de Mendoza (UNTRM), for providing technical support, laboratory facilities, and assistance during the experimental procedures conducted in this study. This research was funded by the National Program for Scientific Research and Advanced Studies (PROCIENCIA) of the National Council of Science, Technology, and Technological Innovation (CONCYTEC), Peru, under a competitive research grant and the Vice-Rectorate for Research at the Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas, Peru\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors made substantial contributions to the research article: N.V.B.: Sample collection, laboratory procedures, experiments, writing–original draft, figure preparation; J.C.D.S.: Conceptualization, study design, sample collection, laboratory procedures, experiments, writing–original draft; J.M.: Writing–original draft; P.F.C.: Bioinformatic and statistical analyses; W.B.: Data interpretation, writing–review \u0026amp; editing; R.C.P.: Data interpretation, writing–review \u0026amp; editing; D.D.S.V.: Data interpretation, writing–review \u0026amp; editing; J.L.M.Q.: Supervision; H.F.T.: Supervision; R.M.L.L.: Conceptualization, study design, supervision. All authors reviewed and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that all data and materials used in this study comply with field standards and are available upon request. The DNA sequences generated and analyzed during the current study are available in the NCBI SRA repository under BioProject PRJNA1419848 (https://www.ncbi.nlm.nih.gov/search/all/?term=PRJNA1419848). Other data from the study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Competing Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis project was funded by the National Council of Science, Technology, and Technological Innovation (CONCYTEC) and the National Program for Scientific Research and Advanced Studies (PROCIENCIA), under the call for proposals “E041–2024–03 Basic Research Projects” (Contract No. PE501088227–2024-PROCIENCIA), and by the Vice-Rectorate for Research at the Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas, Peru.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe experimental protocol was approved by the Institutional Committee on Research Ethics of the Universidad Nacional Toribio Rodríguez de Mendoza (UNTRM) under protocol number CIEI-No. 106. All experiments were conducted in accordance with the approved institutional guidelines and national regulations.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMorales, S. Pat\u0026oacute;genos bacterianos y parasitarios m\u0026aacute;s frecuentes en cuyes de crianza familiar-comercial en tres distritos de la Provincia de Bolognesi, Departamento de Ancash en \u0026eacute;poca de seca. Tesis de maestr\u0026iacute;a, Universidad Nacional Mayor de San Marcos. https://hdl.handle.net/20.500.12672/6875 (2017).\u003c/li\u003e\n\u003cli\u003eShomer, N. H., Holcombe, H., \u0026amp; Harkness, J. E. Biology and diseases of guinea pigs. In \u003cem\u003eLaboratory Animal Medicine\u003c/em\u003e 247\u0026ndash;283. https://doi.org/10.1016/B978-0-12-409527-4.00006-7 (Academic Press, 2015).\u003c/li\u003e\n\u003cli\u003eAstocaza, A. Enfermedades y tratamientos en cuyes y conejos. Tesis de bachiller, Univ. Nac. 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Evaluation of a multiplex-PCR method for rapid detection of \u003cem\u003eSalmonella\u003c/em\u003e Typhimurium and Enteritidis in naturally infected guinea pigs (\u003cem\u003eCavia porcellus\u003c/em\u003e). \u003cem\u003eRevista de Investigaciones Veterinarias del Per\u0026uacute;, \u003c/em\u003e\u003cstrong\u003e28,\u003c/strong\u003e 713-722. https://doi.org/10.15381/rivep.v28i3.13361 Vivas (2017).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":false,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"16S rRNA metabarcoding, PCR, Streptococcus equi, Caviibacter","lastPublishedDoi":"10.21203/rs.3.rs-9383588/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9383588/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Cervical lymphadenitis is a significant infectious disease in guinea pig (Cavia porcellus) production, although the microbiota associated with affected lymph nodes remains poorly characterized. This study compared the microbiota of cervical lymph nodes from healthy guinea pigs and those with lymphadenitis using 16S rRNA gene sequencing, bioinformatics analysis, and molecular validation by PCR. The results showed that healthy lymph nodes harbor diverse bacterial communities, while infected lymph nodes exhibit a marked reduction in microbial diversity and a dominance of Streptococcus equi subsp. zooepidemicus. Furthermore, a subset of samples revealed an alternative etiology characterized by the dominance of the genus Caviibacter, suggesting etiological heterogeneity of the disease in this study. Taken together, these findings suggest that cervical lymphadenitis in guinea pigs is associated with dysbiosis of the lymph node microbiome and support the use of metagenomic approaches for etiological characterization.","manuscriptTitle":"Dysbiosis of the cervical lymph node microbiome associated with lymphadenitis in guinea pigs (Cavia porcellus)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-30 08:16:32","doi":"10.21203/rs.3.rs-9383588/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-04T09:56:03+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-02T05:48:37+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-01T17:24:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"171365023856648229819180510033643731571","date":"2026-04-22T08:46:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"102190193734292525521507887511836250733","date":"2026-04-22T08:33:05+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-22T08:16:19+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-15T02:57:47+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-11T14:10:37+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-11T14:09:50+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-04-11T00:38:43+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"826ea155-b038-4eaf-8f15-2577c19d4126","owner":[],"postedDate":"April 30th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-04T09:56:03+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-02T05:48:37+00:00","index":49,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-01T17:24:44+00:00","index":48,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[{"id":66842636,"name":"Health sciences/Diseases"},{"id":66842637,"name":"Biological sciences/Microbiology"}],"tags":[],"updatedAt":"2026-05-04T10:08:29+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-30 08:16:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9383588","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9383588","identity":"rs-9383588","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}