Distinct cerebrospinal fluid microbiome dysbiosis associated with disease severity in tick-borne encephalitis

Distinct cerebrospinal fluid microbiome dysbiosis associated with disease severity in tick-borne encephalitis | Research Square 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 Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Distinct cerebrospinal fluid microbiome dysbiosis associated with disease severity in tick-borne encephalitis Jarinya Chaopreecha, Satoshi Suyema, Andrea Fořtová, Martina Pychova, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8958950/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Tick-borne encephalitis is the most prevalent viral encephalitis in Europe, causing inflammation of the central nervous system (CNS) and often leading to persistent neurological complications. While alterations in microbial composition have been linked with neuroinflammation and disease progression, data on microbiota profiles in patients with tick-borne encephalitis virus (TBEV) infection are lacking. Methods Cerebrospinal fluid (CSF) samples were obtained, following informed consent, from patients with TBEV–associated meningitis (n = 25) or encephalitis (n = 25) and from uninfected controls (n = 13). Full-length 16S rRNA gene sequencing was performed to characterise bacterial community composition. Genus-level relative abundance profiles were analysed, and differential abundance testing was conducted using non-parametric statistics with false discovery rate correction. Enrichment analysis, LEfSe-like statistical approaches, and supervised multivariate modelling were applied to identify taxa associated with severity of the infection. Results Distinct alterations in microbial diversity and community composition were observed across TBE with neurological involvement and controls. Alpha-diversity analyses demonstrated increased diversity and richness in meningitis samples across Shannon, Simpson, Chao1, and observed species metrics, whereas encephalitis samples exhibited reduced richness compared with controls. Beta-diversity analysis revealed clear separation between controls and infected groups, with partial overlap between meningitis and encephalitis, indicating syndrome-associated shifts in community structure. Taxonomic composition analysis further identified differentially enriched taxa across groups, with a subset of Enterobacteriaceae-associated genera significantly enriched in encephalitis, consistent with a distinct microbial signature associated with increased disease severity. Conclusions These findings indicate that TBEV-associated meningitis and encephalitis are characterised by distinct CSF microbiome profiles rather than a uniform infection-related shift. The increased diversity and richness observed in meningitis, together with reduced richness and enrichment of specific genera in encephalitis, suggest a microbiome signature associated with more severe CNS involvement. Together, these results support the presence of disease severity–specific microbial dysbiosis in TBEV infection and highlight the possible relevance of CSF microbiota in the pathogenesis of neuroinflammatory disease. Tick-borne viral inflammations/tick-borne orthoflavivirus microbial diversity Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Tick-borne encephalitis virus (TBEV) is a neurotropic orthoflavivirus and the etiological agent of tick-borne encephalitis (TBE) [ 1 , 2 ], a potentially life-threatening infection of the central nervous system (CNS). TBEV infection exhibits marked clinical heterogeneity, ranging from a mild, self-limiting febrile illness to severe neurological manifestations, including meningitis and encephalitis [ 3 ]. Among symptomatic individuals, approximately 30% progress to CNS involvement during a second, neurological phase of disease[ 4 , 5 ] Of these patients, around 10% develop long-term neurological sequelae, with disease severity and overall burden being greatest among adults over 50 years of age [ 6 , 7 ]. Despite the substantial clinical impact of TBE, the host and viral determinants underlying this variability in disease outcome remain poorly understood, and no specific antiviral therapy for TBEV infection is currently available. The primary route of TBEV transmission remains via the bite of an infected tick [ 8 ], with initial viral replication occurring at the site of inoculation and in local lymphoid tissues [ 9 ]. However, an increasing number of food-borne TBEV outbreaks have been reported [ 10 – 13 ], highlighting alimentary transmission as an alternative route of infection and a public health concern in endemic areas. Such outbreaks have been documented in Hungary[ 10 , 14 ] and with 17% of all recorded human TBE cases linked with alimentary transmission in Slovakia [ 11 ], and more recently in France [ 15 ]. These outbreaks have been associated with the consumption of unpasteurised milk or dairy products derived from TBEV-infected animals, predominantly goats, sheep, and cows [ 15 ]. Supporting this route of transmission, TBEV has been shown to infect human intestinal epithelial colorectal adenocarcinoma (Caco-2) cells [ 16 ], leading to apoptosis and compromised epithelial barrier integrity. These findings suggest that the gastrointestinal (GI) tract may represent an important and underexplored site of viral interaction with the host. A recent study has demonstrated that the gastric environment influences viral stability and viability [ 17 ]. However, it remains unclear how virus-induced inflammation affects the composition of the commensal microbiota and whether such changes promote microbial translocation to other body sites, including the CNS. In parallel, accumulating evidence indicates that neuroinflammatory conditions are associated with significant alterations in gut microbiota composition [ 18 , 19 ]. In healthy individuals, the gut microbiota is typically dominated by the phyla Bacteroidetes and Firmicutes [ 20 , 21 ], while potentially pathogenic taxa, including members of Proteobacteria [ 22 , 23 ], are present at relatively low abundance. Increasing attention has been directed toward the bidirectional communication between the gut microbiota and the central nervous system [ 24 ], including potential interactions with the cerebrospinal fluid (CSF). Nevertheless, the mechanisms governing this gut–brain–CSF axis remain incompletely understood. Patients suffering from neuroinflammatory or neurodegenerative diseases frequently exhibit gastrointestinal dysfunction accompanied by gut microbial dysbiosis [ 24 – 26 ], often characterised by enrichment of oral-associated taxa [ 27 – 29 ], which has been linked to increased disease severity. Comparable microbiome perturbations have also been described in several viral infections, including COVID-19 [ 30 ], HIV [31,32], and avian influenza [33], suggesting a broader relationship between systemic viral infection, immune dysregulation, and intestinal microbial imbalance. In line with these observations, our recent study of hospitalised TBEV-infected patients during the neurological phase of disease demonstrated that gastrointestinal symptoms, particularly nausea and vomiting, were present in both meningitis and encephalitis groups [ 3 ]. Notably, these symptoms were more prevalent among patients with meningitis (52%) compared with those with encephalitis (32%). Despite these clinical associations, the role of the commensal microbiota in TBEV infection and disease progression has not yet been investigated. To address this knowledge gap, we leveraged our well-characterised cohorts of patients with confirmed TBEV infection and performed 16S rRNA sequencing of CSF samples obtained from individuals diagnosed with meningitis (n = 25) and encephalitis (n = 25), alongside control subjects without TBE (n = 13). Using the universally conserved 16S rRNA gene, this comparative approach aimed to identify microbiota-associated differences between TBE clinical phenotypes and provide new insights into microbial signatures involved in TBEV-associated neuroinflammation. Materials and methods Clinical information and sample collection. Cerebrospinal fluid (CSF) samples were collected, after obtaining written informed consent, from individuals previously hospitalized with confirmed tick-borne encephalitis virus (TBEV) infection or with non-TBEV neurological disease at the University Hospital in Brno, Czech Republic. The study protocol was approved by the Ethics Committee of the University Hospital in Brno (approval date: 27 June 2018). Clinical data were obtained from medical records at the treating hospital. Disease severity was classified into two categories: mild, defined as flu-like symptoms with meningeal irritation (meningitis), including fever, fatigue, nausea, headache, back pain, arthralgia/myalgia, and neck or back stiffness; and severe, defined as encephalitis, typically presenting with ataxia, titubation, altered mental status, memory impairment, quantitative disturbances of consciousness, and/or cranial nerve pareses, followed by prolonged neurological sequelae as previously described [ 3 ]. CSF samples were obtained by lumbar puncture and stored at − 80°C until further analysis. Serological and molecular analysis were performed in line with “The Code of Ethics of the World Medical Association (Declaration of Helsinki)” and according to good clinical practice guidelines. All cohorts developed TBE following a TBEV-Hypr infected tick bite. Control participants were individuals initially presenting with suspected TBE, but for whom TBEV infection was excluded following comprehensive laboratory testing. DNA extraction from CSF DNA was extracted from 100 µL of CSF using the ZymoBIOMICS DNA/RNA Miniprep Kit (Zymo Research, R2002) according to the manufacturer’s instructions. DNA was eluted and quantified using Qubit™ dsDNA Assay Kits (Thermo Fisher Scientific, Q32851). Extracted DNA was stored at − 80°C until further analysis. 16S rRNA sequencing and data analysis. Nanopore 16S Barcoding Kit 24 V14 (Oxford Nanopore Technologies, SQK-16S114.24) was used to amplify full length 16S rRNA sequences of extracted DNA from CSF samples. 16S rRNA barcoding PCR was performed on 10 ng of input DNA. DNA libraries were prepared with equimolar ratios of each barcoded samples and sequenced on a Flow Cell R10.4.1 (Oxford Nanopore Technologies, FLO-MIN114) with a MinION Mk1C sequencing device (Oxford Nanopore Technologies). Base-calling of 16S rRNA sequencing was run by the Dorado (Oxford Nanopore Technologies, v.0.6.1). Sequencing reads that passed the filtering conditions (mean Q score > 8) were analysed by EPI2ME (Oxford Nanopore Technologies) for bacterial classification. Given the ultra-low microbial biomass of CSF, a conservative, multi-step decontamination strategy was applied in line with best-practice recommendations for low-biomass microbiome studies [34, 35]. Negative (buffer) controls processed alongside CSF samples were used to identify taxa enriched in controls, which were classified as high-confidence contaminants and removed prior to downstream analyses, with additional prevalence- and biology-informed filtering applied to taxa shared between CSF and controls and removal of non-bacterial 16S artifacts, consistent with published CSF microbiome and contamination profiling studies [36]. Of note, only culture-independent (non-viable) bacteria were assessed; all analyses were based on microbial genetic material present in the CSF rather than live isolates. In total, 1,842 16S rRNA gene sequences were in silico extracted from 327 publicly available bacterial genomes (1–9 copies per genome). Downstream bioinformatic processing and statistical analyses were performed and implemented in Python 3 (version 3.9.13), utilizing the pandas, NumPy, SciPy, seaborn, matplotlib, and scikit-learn libraries. Alpha diversity was assessed at the species level using the Shannon diversity index and Simpson diversity index. In addition, species richness was estimated using the Chao1 index. Group-wise differences in Shannon, Simpson, and Chao1 indices were evaluated using the Kruskal–Wallis test. Beta diversity was evaluated using Bray–Curtis dissimilarity calculated from species-level relative abundance data. A custom “safe” Bray–Curtis function was implemented to avoid division-by-zero errors. Principal coordinate analysis (PCoA) was performed on the resulting distance matrix, and the first two principal coordinates were visualized. Group-specific confidence ellipses were added to illustrate within-group dispersion. Taxonomic composition was visualized using stacked bar plots at the phylum, genus, and species levels, displaying the 12 most abundant taxa per group. Heatmaps were generated for the top 25 taxa at the class/family level based on mean relative abundance across clinical groups. For machine learning analysis, a random forest classifier was trained on species-level relative abundance data to evaluate the ability of microbial profiles to discriminate between clinical groups (encephalitis and meningitis). Five-fold stratified cross-validation was used to assess classification performance, and feature importance scores were extracted to identify the most informative taxa. Statistical analyses All statistical analyses were performed using Python 3. Alpha diversity indices (Shannon, Simpson, Chao1, and observed species) were calculated based on operational taxonomic unit (OTU). Differences in alpha diversity among encephalitis, meningitis, and control groups were assessed using the non-parametric Kruskal–Wallis test with Benjamini–Hochberg false discovery rate (FDR) correction. Beta diversity was evaluated using Bray–Curtis dissimilarity and weighted UniFrac distances and visualized by principal coordinates analysis (PCoA). Taxonomic composition at the phylum, genus, and species levels was analyzed based on relative abundance. Differentially abundant taxa across groups were identified using the Kruskal–Wallis test with Benjamini–Hochberg FDR correction. Random Forest analysis was applied to identify key discriminatory taxa between encephalitis and meningitis groups. Associations between selected taxa and clinical groups were assessed using Kruskal–Wallis tests or Spearman correlation, as appropriate, with Benjamini–Hochberg FDR correction. Log2 fold change and prevalence analyses were performed for oral-associated taxa to compare meningitis and encephalitis groups. All statistical tests were two-sided, and FDR-adjusted p-values < 0.05 were considered statistically significant unless otherwise stated. Data Availability The 16S rRNA sequencing data analysis workflow is available at https://timkahlke.github.io/LongRead_tutorials/ . The Kraken2 software is available at https://ccb.jhu.edu/software/kraken2/ , and Pavian is available at https://github.com/fbreitwieser/pavian . Python packages can be accessed at https://www.python.org/downloads/ . All other data supporting the findings of this study are included within the article. Results CSF microbial diversity differs between control, meningitis, and encephalitis groups Alpha diversity analysis demonstrated significant differences in microbial diversity and richness among control, meningitis, and encephalitis groups (Fig. 1 ). Shannon diversity differed significantly across groups (Kruskal–Wallis, p = 0.0165), with the highest median diversity observed in the meningitis group, followed by encephalitis and controls (Fig. 1 A). Similarly, Simpson diversity showed significant group-wise differences ( p = 0.0044), indicating increased community evenness in meningitis compared with controls (Fig. 1 B). Species richness, assessed using the Chao1 index, was significantly higher in meningitis compared with both encephalitis and controls ( p = 0.017; Fig. 1 C). In contrast, encephalitis samples exhibited reduced richness relative to controls. This pattern was further supported by observed species counts, which differed significantly among groups ( p = 0.000523), with meningitis showing the highest number of detected OTUs and encephalitis the lowest (Fig. 1 D). Together, these data indicate increased microbial diversity and richness in meningitis and reduced richness in encephalitis. Taxonomic composition of CSF microbiota varies according to clinical phenotype Taxonomic profiling at the phylum, genus, and species levels revealed compositional differences among encephalitis, meningitis, and control groups (Fig. 2 ). At the phylum level, relative abundance profiles varied between clinical groups (Fig. 2 A). At the genus level, examples such as Cedecea, Pseudomonas, and Streptococcus contributed to group-specific patterns (Fig. 2 B). Species-level analysis highlighted differences in taxa such as Cedecea lapagei , Cutibacterium acnes , and Salmonella enterica across groups (Fig. 2 C). Venn diagram analysis demonstrated both shared and unique taxa among groups, with meningitis samples exhibiting the highest number of unique species, followed by encephalitis and controls (Fig. 2 D). A total of 507 species were shared across all three groups. Machine learning identifies discriminatory taxa and distinct CSF microbial community structures across clinical groups Beta diversity analysis using PCoA based on Bray–Curtis dissimilarity revealed groupwise differences among controls and CSF samples from neurological TBEV cases, with partial overlap between meningitis and encephalitis (Fig. 3 A). Weighted UniFrac PCoA further illustrated pairwise separations in ordinations comparing encephalitis vs meningitis (Fig. 3 B), encephalitis vs controls (Fig. 3 C), and meningitis vs controls (Fig. 3 D), with 95% confidence ellipses displayed. These analyses indicate that CSF microbial community structure varies with clinical presentation. Existence of discriminatory taxa was examined using machine learning and differential abundance. A random forest model trained on inferred species-level relative abundances identified OTUs, and associated taxa, that best discriminate encephalitis from meningitis (Fig. 4 A). OTUs linked to representative species observed in the broader analysis included Citrobacter koseri , Salmonella enterica , and Trabulsiella guamensis (Figs. 2 C, 4 B), while genus-level patterns involved taxa such as Cedecea and Cronobacter (Fig. 4 C). Heatmaps of differentially abundant taxa across control, meningitis, and encephalitis groups (Fig. 4 B) and of top genera (Fig. 4 C) highlight group-specific enrichment patterns. Representative genera contributing to these differences include examples such as Cedecea and Cronobacter (see also Fig. 5 panels for selected genera). Comparative analysis reveals Enterobacteriaceae enrichment in encephalitis and increased oral-associated taxa in meningitis Comparative genus-level analysis revealed significant differences in relative abundance across control, meningitis, and encephalitis groups (Fig. 5 A–J). Several members of the family Enterobacteriaceae, including Cedecea , Cronobacter , Escherichia , Salmonella , and Trabulsiella , showed significant group-wise differences (Kruskal–Wallis with Benjamini–Hochberg FDR correction, FDR-adjusted p < 0.05). In many cases, encephalitis samples displayed higher median relative abundance for these genera. Additional genera with significant variation included Kosakonia , Pragia , and Pseudocitrobacter . PCoA biplot visualization highlighted the contribution of taxa such as Trabulsiella guamensis , Citrobacter koseri , Cronobacter dublinensis , and Pseudescherichia vulneris to separation between clinical groups (Fig. 5 L). Oral-associated taxa showed distinct patterns between meningitis and encephalitis (Fig. 6 ). Log2 fold-change analysis indicated that taxa including Campylobacter ureolyticus , Prevotella histicola , Leptotrichia trevisanii , and Porphyromonas gingivalis were more abundant in meningitis, whereas Campylobacter massiliensis and Campylobacter portucalensis were enriched in encephalitis. Additionally, prevalence analysis showed that oral-associated taxa were detected more frequently in meningitis samples than in encephalitis, with several taxa present in the range between 4 and ≥ 8.5% of meningitis samples (Fig. 6 B). Discussion In this study, we profile the microbial community of cerebrospinal fluid in patients with TBEV infection. We show that meningitis and encephalitis are associated with distinct microbial diversity patterns, community structures, and taxonomic signatures compare to controls. Our analysis revealed increased microbial diversity and richness in the meningitis group, reduced richness in encephalitis, and distinct separation of microbial community structures between clinical phenotypes. Encephalitis was characterized by Enterobacteriaceae enrichment, correlating with severe symptoms and ICU admission, whereas meningitis showed a higher prevalence of oral-associated taxa. Collectively, these findings suggest that TBEV-associated neuroinflammation is accompanied by phenotype-specific microbial dysbiosis, as opposed to a uniform infection-driven shift. CSF microbiome alterations in TBEV reflect disease severity and clinical phenotype. Alpha diversity analyses revealed contrasting patterns. Meningitis samples showed significantly greater diversity and richness, while encephalitis samples exhibited marked reductions compared with controls. These opposing trends are consistent with distinct pathological mechanisms shaping host–microbiota interactions across TBE clinical presentations. Similar patterns of diversity in milder or early disease followed by loss of diversity in severe states has been reported in viral and neuroinflammatory conditions. In influenza infection, reduced respiratory α-diversity has been observed in H1N1 patients [37,38], and SARS-CoV-2 [30,39] infection shows a similar decline in α-diversity in severe COVID-19 cases. Similarly, decreased gut bacterial diversity has been reported in autoimmune encephalitis (e.g., anti-NMDAR encephalitis) [40,41], supporting a broader link between microbiome dysbiosis and disease severity. Beta diversity analyses further demonstrated clear separation of microbial community structure between controls, meningitis, and encephalitis, indicating that TBEV infection was associated with distinct CSF community profiles depending on disease severity. This stratification was reinforced by machine learning and heatmap analyses identifying specific taxa driving group separation in the model. These observations suggest that there may be disease-associated microbiotas. Enrichment of Enterobacteriaceae in encephalitis suggests inflammation-associated pathobiont expansion. A central finding of this study is the inferred enrichment of multiple Enterobacteriaceae genera, including Cedecea , Cronobacter , Escherichia , Salmonella , and Trabulsiella in encephalitis samples. This is consistent with known clinical infections in which members of the Enterobacteriaceae can cause CNS disease, including neonatal E. coli and Cronobacter meningitis and rare human infections with Cedecea and Trabulsiella [42–44]. The Enterobacteriaceae are facultative anaerobes, with certain pathobionts potentially gaining a selective advantage under host inflammatory conditions involving barrier disruption, availability of host-derived electron acceptors, oxidative stress, and immune activation[45,46]. The overrepresentation of certain species is linked to gut dysbiosis in inflammatory bowel disease and sepsis [47,48], where ‘blooms’ of Enterobacteriaceae accompany reduced diversity and barrier dysfunction. Expansion of Enterobacteriaceae during viral infections has been documented across multiple studies. This may be because members of this family thrive when host immune responses are compromised by viral pathogens. In SARS-CoV-2 infection, severe COVID-19 is characterized by a marked depletion in beneficial commensal bacteria (e.g., Faecalibacterium prausnitzii , Bifidobacterium ) and a concurrent enrichment of opportunistic Enterobacteriaceae, which correlates with heightened systemic inflammation and worsened disease severity [49, 50]. During influenza infection, gut microbial homeostasis is disrupted [51], often leading to an expansion of Proteobacteria, notably Enterobacteriaceae, and a resulting increase in susceptibility to secondary bacterial infections caused by bacteria such as Salmonella or Streptococcus [52]. In HIV infection [53], progressive disease is accompanied by microbial translocation across the gut barrier, enrichment of Enterobacteriaceae, and chronic immune activation. All of which are implicated in the pathogenesis of HIV-associated neuroinflammation and neurocognitive impairment via the microbiota–gut–brain axis. Enrichment of Enterobacteriaceae in encephalitis, including in our own findings on TBEV, reflects a convergence of systemic inflammation, barrier dysfunction, and neuroinflammatory processes. Although data within the Orthoflaviviridae family remain limited, emerging evidence supports similar patterns of barrier perturbation across flaviviral infections. Zika virus infection in adult immunocompetent mice induces significant perturbations in gut microbiota composition, marked by reductions in Firmicutes and Actinobacteria alongside increases in Spirochaetes, and is associated with intestinal epithelial damage and heightened leukocyte recruitment, reflecting increased intestinal permeability [54]. West Nile virus and Japanese encephalitis virus infections are each characterized by severe neuroinflammation and disruption of the blood–brain barrier[55, 56], facilitating central nervous system bacterial invasion. Our work on TBEV, together with related observations across encephalitic cohorts, highlights the consistent enrichment of Enterobacteriaceae in the central nervous system, underscoring their potential biological significance. Although the detection of Enterobacteriaceae in cerebrospinal fluid—whether as live bacteria, microbial fragments, or DNA signatures—remains to be fully characterised, their recurrent presence across cases suggests a meaningful role in neuroinflammatory processes. Enterobacteriaceae express lipopolysaccharide (LPS), which can potently activate Toll-like receptor 4 (TLR4) signaling, drive cytokine production, and amplify neuroinflammation, particularly via the TLR4–NF-κB pathway [56, 57]. In contrast, TBEV-associated meningitis shows higher prevalence of oral-associated taxa such as Prevotella , Porphyromonas , and Fusobacterium , supporting an oral–gut–brain axis and mucosal barrier dysfunction. These taxa, implicated in neurodegenerative diseases and systemic viral infections [58–60], may enter the CNS during acute meningeal inflammation, whereas the restrictive microbial profile in encephalitis group likely reflects a hostile parenchymal environment favouring survival of specific pathobionts. CSF microbiome signatures in TBEV parallel those observed in neurodegenerative and neuroinflammatory diseases. The microbial patterns observed in this study show striking parallels with those reported in neurodegenerative and neuroinflammatory disorders. Reduced microbial diversity, enrichment of Proteobacteria, and increased abundance of opportunistic pathogens have been described in Alzheimer’s disease, Parkinson’s disease, multiple sclerosis, and amyotrophic lateral sclerosis [61–63]. In these conditions, microbial dysbiosis is thought to contribute to chronic neuroinflammation through microglial activation, cytokine production, and disruption of the blood–brain barrier (Quan, et al. 2025). Notably, Escherichia , Salmonella , Enterobacter , and Cronobacter species have all been linked to neuroinflammatory processes, potentially through direct invasion, endotoxin-mediated effects, or molecular mimicry [64]. The enrichment of these taxa in TBEV-associated encephalitis suggests that common inflammatory pathways may be engaged across infectious and non-infectious neurological diseases [57]. This convergence supports the hypothesis that microbial dysbiosis is not merely a bystander effect but may have a role in modulating disease severity. Potential implications for TBEV pathogenesis and disease progression Our findings raise the possibility that microbiome composition may play a role in susceptibility to neurological disease following TBEV infection. The enrichment of Enterobacteriaceae in encephalitis could reflect a pre-existing dysbiotic state that predisposes individuals to neuroinflammation, or it may emerge secondary to viral-induced barrier disruption and immune activation. Similarly, the increased presence of oral-associated taxa in meningitis may indicate early mucosal involvement and microbial translocation during the initial stages of neurological disease [65]. Food-borne transmission of TBEV via unpasteurised dairy products has been confirmed in multiple outbreaks across Europe, and TBEV can infect and compromise human intestinal epithelial cells, reducing barrier integrity. These observations, together with our microbiome data, support a model in which the gastrointestinal tract serves as a site of virus–host–microbiome interaction during TBEV infection [10,66], where gut and oral barrier disruption may facilitate microbial translocation or immune activation, during CNS involvement. Given that facultative anaerobes, such as Enterobacteriaceae, thrive in the inflamed gut by utilizing alternative electron acceptors in oxygen-depleted conditions, targeting anaerobic respiration pathways, such as via iNOS inhibition to limit inflammatory nitrate production [67,68], may suppress these blooms. Prospective evaluation of respiration-pathway modulation during TBEV infection could therefore potentially attenuate systemic and CNS inflammation and could be a feasible microbiome-directed disease management strategy. Limitations and future directions This study has several limitations. First, 16S rRNA sequencing provides taxonomic profiles but cannot assess microbial viability, metabolic activity, or functional roles. Also detected DNA may represent non-viable organisms or fragments rather than active bacteria. Second, cerebrospinal fluid is a low-biomass sample, traditionally considered “sterile” under physiological conditions, but is prone to contamination during sample collection or subsequent routine laboratory processing. Therefore, while comparisons can be made, technical controls and longitudinal sampling would be needed for stronger infrence. Third, 16S rRNA has limited power for resolving species and strains and excludes viruses, fungi, and archaea, limiting broader microbiome characterization. Fourth, the cross-sectional design precludes causal inference and it reminas unclear if microbiome changes precede disease or result from TBEV-induced inflammation. Finally, incomplete clinical metadata (e.g., such as diet or comorbidities) introduces potential confounders. Conclusions Despite these constraints, consistent patterns across analytical approaches and strong biological plausibility support with high statistical confidence the relevance of CSF microbiome signatures in TBEV-associated second-phase neurological disease. This study demonstrates that TBEV-associated meningitis and encephalitis exhibit distinct CSF microbiome profiles: meningitis is characterised by increased diversity and the presence of oral-associated taxa, whereas severe encephalitis shows marked enrichment of Enterobacteriaceae. These patterns parallel observations in other viral and neurodegenerative conditions, suggesting that microbiome-driven mechanisms may contribute to neuroinflammation and disease progression. Future work should incorporate longitudinal sampling, rigorous contamination controls, and multi-omics approaches (metagenomics, metabolomics, immune profiling) to define functional roles and mechanistic pathways. In addition, exploring gut–oral–CSF microbial interactions and incorporating clinical factors will be essential. Together, these insights underscore the intricate interplay between TBEV infection and host microbial ecology in the CNS and indicate that microbiome-targeted interventions may offer novel strategies to modulate neuroinflammation and improve outcomes. Declarations Acknowledgement We are grateful to Distinguish Professor Diane E. Griffin (Johns Hopkins Bloomberg School of Public Health, USA) for kind mentorship to N.G. N.G. and the study is supported by a Wellcome Trust CDA Fellowship in Infection and Immunology (226484/Z/22/Z). The funder had no role in the conceptualization, design, collection of data/analysis, nor the decision to publish the manuscript. A.F., L.K., M.P. and D.R. were supported by the Czech Ministry of Health (grant No. NU22-05–00659) and the project National Institute of Virology and Bacteriology (Programme EXCELES, ID Project No. LX22N-PO5103)—Funded by the European Union—Next Generation EU. Disclosure and competing interest statement The authors declare no competing interest Author Contributions Statement J.C., S.S., methodology, conducted the study or collected data. A.F., M.P., L. K., D.R. sample collected. J.C., G.E.B., S.K.S., N.G. analysed or interpreted the data. N.G. conceived, supervised or designed the study, and drafted the manuscript. G.E.B., D.R., S.K.S., N.G. critically revised the article. All authors provided final approval of the version to be published. Ethics declarations Ethical approval and consent to participate Samples of CSF were obtained upon consent from individuals hospitalized with suspected TBEV infection in Brno under protocol approved by the ethical committee of the University Hospital Brno, Czech Republic (date of approval: 27 June 2018). Storage and shipping were also be covered by the above ethical approvals. Clinical data were obtained at the treating hospitals. Disease severity was evaluated according to the following scale: mild, flu-like symptoms with meningeal irritation defined as meningitis, characterized by fever, fatigue, nausea, headache, back pain, arthralgia/myalgia and neck or back stiffness; moderate, previous symptoms together with tremor, vertigo, somnolence, and photophobia defined as meningoencephalitis; severe, prolonged neurological consequences including ataxia, titubation, altered mental status, memory loss, quantitative disturbance of consciousness, and palsy revealed as encephalitis, encephalomyelitis, or encephalomyeloradiculitis (Bogovic and Strle, 2015; Ruzek et al., 2019). Serological and molecular analysis were performed in line with “The Code of Ethics of the World Medical Association (Declaration of Helsinki)” and according to good clinical practice guidelines. In accordance with local legislation, no formal approval by a research ethics committee was required, as either anonymous or clinical samples were used for research purposes. Patients (or their parents) signed informed consent prior to sample collection. Samples were investigated anonymously. Clinical trial number: not applicable References Gelpi, E., Preusser, M., Garzuly, F., Holzmann, H., Heinz, F.X. and Budka, H. 2005 Visualization of Central European tick-borne encephalitis infection in fatal human cases. J Neuropathol Exp Neurol , 64 (6), 506-512. Gunther, G., Haglund, M., Lindquist, L., Forsgren, M. and Skoldenberg, B. 1997 Tick-bone encephalitis in Sweden in relation to aseptic meningo-encephalitis of other etiology: a prospective study of clinical course and outcome. J Neurol , 244 (4), 230-238. 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