Microbiota-induced intestinal barrier disruption drives BAFF-mediated B-cell dysregulation and autoimmunity in long COVID

Microbiota-induced intestinal barrier disruption drives BAFF-mediated B-cell dysregulation and autoimmunity in long COVID | 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 Biological Sciences - Article Microbiota-induced intestinal barrier disruption drives BAFF-mediated B-cell dysregulation and autoimmunity in long COVID Kim Doyon-Laliberté, Matheus Aranguren, Fandi Gao, Laurence Leclerc, and 18 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8876163/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Long COVID (post-coronavirus disease 2019 (COVID-19) condition) is an infection-associated chronic condition that can follow severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and persist for months to years1-3. Symptoms can be severe and significantly impact functional status and quality of life2,4,5, but the mechanisms linking barrier dysfunction to systemic immune dysregulation remain unclear. Markers consistent with impaired intestinal mucosal barrier integrity and microbial translocation have been linked to long COVID6. Here we show that non-hospitalized individuals with long COVID have intestinal barrier dysfunction associated with increased B-cell activating factor (BAFF), perturbation of the B cell compartment and autoimmunity that peak at 12 months after infection and begin to resolve by 24 months. Transfer of faecal microbiota from individuals with severe long COVID into germ-free mice is sufficient to reproduce intestinal barrier dysfunction, systemic immune dysregulation with autoimmunity, and neuroinflammation. Treating recipient gnotobiotic mice with a BAFF-neutralizing monoclonal antibody, an approach supported by BAFF biology and clinical efficacy in autoantibody-mediated disease7,8, markedly improves these abnormalities. Together, these findings implicate microbiota-linked intestinal barrier disruption as a driver of autoimmunity and end-organ complications in long COVID and identify BAFF as a therapeutic target. Biological sciences/Immunology/Autoimmunity Biological sciences/Immunology/Mucosal immunology Health sciences/Diseases/Infectious diseases/Viral infection Biological sciences/Microbiology/Microbial communities/Microbiome Biological sciences/Immunology/Adaptive immunity/Humoral immunity/Antibodies Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Since the start of the coronavirus disease 2019 (COVID-19) pandemic, a substantial proportion of individuals infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have developed post-COVID-19 condition or long COVID (LC), defined as symptoms typically beginning within 3 months of acute infection and lasting for ≥2 months, not explained by alternative diagnoses 1,9 . LC can be disabling, last for years 10 , and occurs across ages 1,9,11 . Although the risk of LC increases with the severity of the acute infection, many LC cases follow an initially mild infection 12 . LC is heterogeneous across organ systems, but convergent analyses now suggest phenotypic and biological subtypes rather than a single syndrome 13 . A common, often disabling phenotype, which is more prevalent among women, features post-exertional malaise, profound fatigue, cognitive dysfunction (“brain fog”), and symptoms of cardiac and/or neurological dysautonomia 14-16 . Recent large-scale immune phenotyping highlights mechanistic clusters that plausibly reflect distinct pathobiology 17,18 . Multiple, potentially interacting pathways are under investigation, including viral antigen persistence, immune dysregulation and autoimmunity, coagulation abnormalities, and gut-brain-axis dysfunction 19 . Consistent with this, several cohorts report increased autoreactivity and higher rates of new or persistent autoimmune diagnoses in patients with LC compared to controls 20,21 . Emerging data indicate that SARS-CoV-2 proteins can persist at tissue interfaces leading to neuroinflammation 22 , while independent studies report sustained alterations in the intestinal microbiome 23,24 and biomarkers of impaired epithelial barriers and microbial translocation 6,25,26 . Consistent with a neuroimmune axis, several cohorts report elevations of astroglial injury markers (e.g., glial fibrillary acidic protein [GFAP]) and blood-brain barrier disruption signatures in LC subsets 27,28 . Together, these observations support a model in which barrier disruption enables persistent antigen exposure and chronic innate and adaptive immune activation. Whether intestinal barrier injury contributes to B-cell dysregulation and autoimmunity in LC remains unclear. B-cell-activating factor (BAFF) provides essential survival and differentiation cues to B cell populations; excess BAFF can erode tolerance and favour polyreactivity and autoreactivity, a pattern observed across autoimmune diseases 29 . Marginal zone (MZ) B cells are particularly BAFF-dependent, respond predominantly to T-independent antigens and harbour a B-cell receptor repertoire biased towards polyreactivity, a feature linked with autoreactivity 30 . In our prior work, we identified a MZ “precursor-like” population (MZp) with high B-cell regulatory capacity 31 that becomes impaired under BAFF excess, acquiring an exhaustion-associated CD11c⁺T-bet⁺ phenotype, polyclonal immunoglobulin (Ig) responses and autoimmune potential 32-35 . We therefore hypothesized that intestinal barrier dysfunction could sustain BAFF signalling and drive a T-bet-skewed, regulatory-impaired MZp state that promotes autoreactivity in LC. Here, we longitudinally profiled adults with LC in the Institut de Recherches Cliniques de Montréal (IRCM) post-COVID-19 (IPCO) cohort at 3–6, 12, and 24 months after infection. We measured circulating indicators of intestinal barrier dysfunction and microbial translocation, and related them to symptom severity, BAFF levels, and B-cell subset dynamics, including MZp B cells, and autoantibody reactivity. Markers of intestinal barrier injury were elevated in LC compared with pandemic controls and were associated with higher BAFF levels, MZp perturbation, and features of autoreactivity. To test causality, we transferred faecal microbiota from individuals with severe LC into germ-free mice, which reproduced barrier disruption and BAFF-linked immune abnormalities that were attenuated by BAFF blockade. Together, these findings implicate a microbiota-intestinal barrier-BAFF axis in LC-associated B-cell dysregulation and autoimmunity and identify BAFF as a candidate therapeutic target. Results Viral antigen persistence, intestinal barrier dysfunction and sustained BAFF/APRIL pathway modulation in LC We established the IPCO longitudinal sampling framework incorporating pandemic controls and individuals with LC evaluated at 3-6, 12 and 24 months after SARS-CoV-2 infection (Fig.1a). Participant characteristics are summarized in Extended Data Table 1. We quantified circulating SARS-CoV-2 antigens across this time course. Plasma spike protein was detectable in a subset of participants with LC, increased between 3-6 to 12 months in paired analyses, and declined by 24 months (Fig.1b, left). In contrast, plasma nucleocapsid protein was highest at 3-6 months and decreased thereafter (Fig.1b, right), indicating distinct antigen kinetics over the course of LC. To assess intestinal barrier integrity, we measured serum zonulin. Zonulin concentrations were higher in LC than in pandemic controls across time points, with the greatest separation at 24 months (Fig.1c, left). Zonulin correlated positively with markers of microbial translocation, including ꞵ-D-glucan and lipopolysaccharide-binding protein (LBP), most prominently at 12 months (Extended Data Fig.1a). Zonulin also correlated positively with circulating BAFF at 12 months (Fig.1c, right), linking barrier disruption with modulation of the BAFF axis. Given reported connections between intestinal barrier dysfunction, chronic viral antigen exposure and dysregulation of the BAFF/a proliferation-inducing ligand (APRIL) pathway 32,36-42 , we longitudinally profiled circulating BAFF/APRIL pathway components. Soluble B-cell maturation antigen (BCMA) and soluble transmembrane activator and CAML interactor (TACI) were strongly correlated at 3-6 and 12 months, but this coupling was attenuated by 24 months (Fig.1d, left). In parallel, BAFF and APRIL displayed an inverse relationship that was most evident at 3-6 and 12 months (Fig.1d, right). Correlation matrices and network visualizations further highlighted coordinated relationships between zonulin, markers of microbial translocation (LBP and ꞵ-D-glucan) and BAFF/APRIL pathway measures, with denser connectivity at 3-6 and 12 months and partial loosening by 24 months (Fig.1e). To identify cellular sources of BAFF and connect BAFF upregulation with viral antigen persistence, we quantified membrane BAFF (mBAFF) expression and intracellular spike protein in circulating monocyte subsets. mBAFF staining was increased in LC relative to pandemic controls across classical, intermediate and non-classical monocytes, with particularly prominent differences at 12 months (Fig.1f,g). Intracellular spike was rare in classical monocytes and, when detected, occurred at low frequency. In contrast, spike-positive cells were detectable within intermediate and non-classical monocytes at 12 months and declined by 24 months in paired analyses (Extended Data Fig.1b,c). Circulating spike and BAFF levels were positively correlated (Extended Data Fig.1d, left). Across intermediate and non-classical subsets, frequencies of spike-positive cells tracked with mBAFF expression (Extended Data Fig.1d, right). Finally, mBAFF expression was also increased on circulating T cells and B cells in LC compared to pandemic controls across time points (Fig.1h). Collectively, these data support a model in which prolonged viral antigen exposure and intestinal barrier disruption are associated with sustained remodelling of BAFF/APRIL pathway signalling in LC. B cell compartment dysregulation in people with LC Based on our observed longitudinally remodelling of BAFF/APRIL pathway measures, we performed high-dimensional immunophenotyping of circulating B cells in pandemic controls and individuals with LC at 3-6, 12 and 24 months after infection (gating strategy, Extended Data Fig.2). Across time points, the overall frequency of circulating CD19 + B cells was broad similar between groups (Fig.2a). However, LC was associated with time-dependent shifts in B cell subset composition. Naïve and transitional immature B cells were increased at 3-6 and 12 months in LC and converged toward control levels by 24 months, whereas total memory and resting switched memory B cells were increased at 24 months relative to earlier time points in LC (Fig.2a) In contrast to MZ B cells, which showed little separation between groups, MZp B cells were increased in LC and remained elevated through 24 months (Fig.2a). We next related this sustained MZp B cell expansion to BAFF/APRIL pathway activation. Correlation analyses across time points revealed coordinated relationships between MZp frequencies and BAFF/APRIL pathway measures, including soluble TACI and BCMA and monocyte subset mBAFF expression, most prominently at 3-6 and 12 months, with attenuation by 24 months (Fig.2b). These data support a model in which early BAFF-axis activation is linked to expansion of the MZp compartment, which persists despite partial relaxation of the broader correlation patterns over time. To probe the functional state of MZp cells, we assessed markers previously associated with inflammatory versus regulatory programmes, focusing on T-bet and NR4A3, respectively 33 . In individuals with LC, Tbet + MZp B cells were increased at earlier time points in a BAFF- and intestinal barrier-context-dependent manner. Stratifying LC participants by circulating BAFF levels (low/intermediate/high) revealed higher T-bet + MZp frequencies in those with higher BAFF at 3-6 and 12 months, with no clear separation by 24 months (Fig.2c). A similar pattern was observed upon stratification by zonulin status, with elevated T-bet + MZp frequencies most evident at earlier time points (Fig.2d). Conversely, NR4A3 expression within MZp cells was reduced in LC, particularly among individuals with higher BAFF and/or zonulin at 3-6 and 12 months, and differences were less pronounced by 24 months (Fig.2e,f). Together, these data define a sustained expansion of the MZp compartment in LC coupled to an early inflammatory skew, characterized by increased T-bet and reduced NR4A3 in the setting of elevated BAFF and markers of barrier disruption, that partially normalizes over time while MZp abundance remains increased. Excess BAFF and intestinal barrier dysfunction associate with altered immunoglobulin profiles and circulating autoantibodies To connect B-cell compartment dysregulation with humoral outputs, we quantified serum immunoglobulins in pandemic controls and individuals with LC, focusing on 3-6 and 12 months after infection, when BAFF and zonulin perturbations were most prominent. Total immunoglobulin levels were higher in LC at 12 months, and paired analyses showed an increase between 3-6 and 12 months that was most evident for IgA (Fig.3a). In contrast, IgG1-IgG3 varied modestly across time and across BAFF or zonulin strata (Extended Data Fig.3). IgM increased from 3-6 to 12 months in paired analyses (Fig.3b) and was most clearly linked to barrier dysfunction as IgM differed across zonulin strata at both 3-6 and 12 months, with higher concentrations in the high zonulin group (Fig.3d). By comparison, associations between IgM and BAFF strata were most apparent at 3–6 months and less evident at 12 months (Fig. 3c). We additionally observed a modest increase in IgG4 between 3–6 and 12 months (Fig. 3e), with higher IgG4 among individuals with higher BAFF at 12 months (Fig. 3f). To relate these systemic Ig profiles to the antibody-secreting compartment, we quantified circulating Ig + B cell blasts over time. Across 3-6 and 12 months, stratifying LC participants by BAFF, APRIL and zonulin status revealed generally modest shifts in the isotype composition of Ig + blasts, with the clearest separation for IgM + blasts across BAFF strata at 3-6 months (Extended Data Fig.4). Over the longitudinal time course, frequencies of IgG + and IgM + B-cell blasts increased at 24 months, whereas IgA+ blasts showed little change (Fig.3h). Together, these data indicate sustained remodelling of the antibody-secreting compartment in a subset of individuals with LC. To assess autoreactivity, we measured IgG autoantibodies to a panel of nuclear antigens at 3–6, 12 and 24 months. LC was associated with increased seropositivity for several antigens including DNA topoisomerase I, Mi-2 and SmD, with the strongest enrichment at 3–6 and 12 months post-infection (Fig. 3i). Stratifying LC participants by zonulin status further showed that seropositivity across multiple autoantibodies was enriched among individuals with higher zonulin (Fig. 3j), consistent with an association between barrier dysfunction with increased systemic autoreactivity in LC. LC faecal microbiota transfer reproduces barrier disruption and BAFF-associated B cell dysregulation in germ-free mice To test whether the LC-associated intestinal microbiota contribute causally to intestinal barrier dysfunction and immune perturbations observed in the IPCO cohort, we performed faecal microbiota transplantation (FMT) into germ-free mice using stool from human donors selected on the basis of neurological symptom burden together with serum zonulin and plasma BAFF levels (Extended Data Table 6). “Severe” donors were defined by the presence of neurological symptoms (listed in Extended Data Table 2) and elevated zonulin/BAFF, whereas “mild” donors lacked neurological symptoms and had low zonulin/BAFF levels (Fig.4a; Extended Data Table 6). All donor stool samples except 1 had undetectable SARS-CoV-2 spike, reducing the likelihood that transferable faecal viral antigen accounts for the observed phenotypes (Extended Data Table 6). Following engraftment, mice colonized with severe LC microbiota showed a trend toward shorter colon length relative to mice receiving mild LC microbiota (Fig.4b). In contrast, serum LBP was increased in mice colonized with severe LC microbiota (Fig.4c, left), consistent with increased microbial translocation. Immunostaining for the tight-junction protein zonula occludens-1 (ZO-1) further revealed more discontinuous and punctate epithelial junctional staining in the colons of mice colonized with severe LC microbiota relative to mild LC microbiota recipients (Fig.4d), indicative of impaired barrier integrity. Barrier disruption was accompanied by heightened BAFF activity in vivo. Severe LC-microbiota recipients exhibited increased serum BAFF (Fig.4c, right) together with higher frequencies of BAFF-expressing CD45 + immune cells in both the colonic LP and spleen (Fig.4e). In the spleen, immunofluorescence revealed fewer discrete peanut agglutinin (PNA) + foci, diminished CD169 signal intensity on marginal zone metallophilic macrophages, altered CD169 + marginal zone architecture, and a more diffuse IgM bright staining pattern extending across marginal zone, follicular and extra-follicular areas in severe LC microbiota recipients (Fig.4f), suggesting disruption of splenic organization and reduced germinal-centre-like structures. Consistent with a shift toward IgM-biased B-cell responses, flow cytometry showed increased frequencies of CD19 + CD1d + IgM bright B cells and increased frequencies of IgM + cells within the blast compartment in mice colonized with severe LC-microbiota (Fig.4g). In Peyer’s patches, regions of IgA + B220 + double-positive cells were more prominent in mice colonized with mild LC microbiota and reduced in severe LC-microbiota recipients (Fig.4h), suggesting an alteration in IgA mucosal B-cell features in the severe LC-microbiota condition. Together, these findings indicate that microbiota from severe LC donors is sufficient to induce gut barrier disruption and microbial translocation alongside systemic BAFF upregulation and remodelling of B-cell organization and isotype output in gnotobiotic mice. BAFF antagonism ameliorates LC-like immune and intestinal barrier features in gnotobiotic mice Given the convergence of BAFF-axis perturbation, intestinal barrier disruption and B-cell dysregulation in the human cohort and FMT model, we asked whether BAFF blockade could modify the LC-like phenotype. Germ-free mice colonized with faecal microbiota from a severe LC donor received a single intraperitoneal dose of anti-mouse BAFF monoclonal antibody (Sandy-2) 43 or isotype control and were analysed 15 days later (Fig.5a). Anti-BAFF treatment markedly reduced circulating BAFF concentrations and decreased mBAFF expression on CD45 + cells (Fig.5c). Consistent with the known dependence of peripheral B cells on BAFF, anti-BAFF treatment reduced the frequency of B cells and decreased the frequency of total blasts (Fig.5d). Within the CD19 + CD1d + IgM bright compartment, anti-BAFF increased CD73 expression (Fig.5d), a marker previously linked to immunoregulatory programmes 44 . Anti-BAFF also impacted the immunoglobulin isotype composition of the blast compartment, significantly increasing proportions of IgA + and IgM + blasts, whereas IgG + blasts did not change significantly (Fig.5e). In parallel, serum anti-nuclear antibody (ANA) levels were reduced in anti-BAFF-treated mice (Fig.5b), consistent with attenuation of systemic autoreactivity. At the tissue level, anti-BAFF treatment was associated with improved epithelial junctional organization, with more continuous ZO-1 staining in colonic epithelium compared with isotype-treated controls (Fig.5f), and colon length showed a non-significant trend toward increase in anti-BAFF-treated mice (Extended Data Fig.5b). Given the severe LC-donor microbiota was selected from individuals with neurological symptom burden, we also evaluated neuroinflammatory and blood-brain barrier associated readouts across specific brain regions (Extended Data Fig.5a). Anti-BAFF treatment reduced glial fibrillary acidic protein (GFAP) staining in the hippocampus and decreased ionized calcium-binding adapter molecule 1 (IBA1) signal in the pons/medulla (Fig.5g). In representative sections, astrocytes appeared less hypertrophic/bushy and microglia less amoeboid in anti-BAFF-treated mice than in isotype controls, consistent with attenuated neuroinflammatory features. Anti-BAFF treatment was also associated with more linear ZO-1 staining along the cortical vasculature (Extended Data Fig.5c), suggestive of improved junctional organization and possibly blood-brain barrier integrity. BAFF blockade is associated with microbiota restructuring To determine whether BAFF antagonism was accompanied by changes in the engrafted microbial community, we profiled faecal microbiota composition in gnotobiotic mice following treatment. Alpha diversity metrics were largely unchanged, with a trend toward higher Shannon diversity (Fig.5h), whereas beta diversity analyses showed separation between anti-BAFF-treated and isotype-treated mice (Fig.5i). Differential-abundance analyses identified multiple genera contributing to this shift (Fig. 5j–l). Thus, while BAFF blockade does not appear to broadly increase community richness, it is associated with a compositional restructuring of the microbiota in this gnotobiotic setting, along with reduced BAFF-linked immune remodelling and improvements in barrier-associated features and neuroinflammatory signatures. Discussion In this prospective cohort focused on non-hospitalized adults with LC, we identified a signature of intestinal barrier dysfunction linked to BAFF/APRIL pathway remodelling, BAFF-biased B-cell dysregulation and systemic autoreactivity that is most pronounced within the first year after SARS-CoV-2 infection and only partially relaxes by 24 months. Using gnotobiotic mouse models, we showed that faecal microbiota from individuals with severe LC was sufficient to induce barrier disruption and microbial translocation alongside elevated BAFF and B-cell compartment remodelling, including systemic autoreactivity. Several of these features were attenuated by BAFF blockade. Together, these findings support a model in which dysbiosis-associated barrier disruption sustains a BAFF-dependent, marginal zone-skewed B-cell response linked to autoreactivity and multi-organ consequences in LC while identifying BAFF signalling as a potential therapeutic axis. Other independent cohorts have reported persistent intestinal dysbiosis 24 , microbial translocation and epithelial barrier injury in LC 6 , as well as increased rates of autoimmune disease 20,45 and sustained autoantibody responses 21,46 . In our cohort, LC participants exhibited higher serum zonulin levels and markers of microbial translocation than pandemic controls, with differences persisting at 24 months. This intestinal barrier phenotype was tightly associated with modulation of the BAFF/APRIL system as demonstrated by increased soluble BAFF, elevated mBAFF on monocytes and T cells, higher levels of soluble TACI and BCMA, and an inverse relationship between BAFF and APRIL. These patterns mirror BAFF/APRIL dysregulation described in systemic autoimmune diseases 47,48 and chronic viral infections 32,33,35,49 , and are notable here in a cohort of individuals with LC who experienced mild acute SARS-CoV-2 infection. Within this context, we observed sustained alterations of the B-cell compartment centred on MZp B cells. Although overall CD19 + B-cell frequencies and classical MZ B-cell frequencies were largely preserved, MZp cells were expanded in LC for at least 24 months and were enriched among individuals with higher BAFF and higher zonulin. This expansion was accompanied at earlier time points by increased T-bet expression and reduced NR4A3 in MZp cells, features previously associated with chronic activation, diminished regulatory programmes and extra-follicular, lower-affinity responses 33,35,50-52 . The association between MZp frequency, BAFF, zonulin and soluble TACI, together with an IgM-skewed humoral profile and nuclear autoreactivity, is consistent with a BAFF-driven shift of this BAFF-sensitive population toward a more inflammatory state 31,34,35 . While our data are correlative in humans, they align with work implicating BAFF and T-bet + age-associated B-cell-like populations in loss of tolerance and autoimmunity, and suggest that MZp dysfunction may be a feature of post-infection chronic inflammatory conditions 34 . The serological data further support this model. LC participants showed rising immunoglobulin levels over the first year after infection, with IgM most clearly associated with zonulin status. We also detected IgG autoreactivity to selected nuclear antigens, including Mi-2, SmD and topoisomerase I, which were enriched in individuals with LC, particularly those with high zonulin, whereas pandemic controls were consistently negative. Although the breadth of our autoantibody panel was limited and we did not assess antigen specificity at the single-cell level, these findings position a subset of individuals with LC within the spectrum of BAFF- and barrier compromise-associated autoimmune risk states. Our gnotobiotic experiments provide orthogonal support for a contribution of the LC-associated intestinal microbiota in shaping intestinal barrier and immune phenotypes. Germ-free mice colonized with microbiota from donors with severe LC, selected based on neurological symptom burden together with higher BAFF and zonulin, developed features consistent with barrier compromise and microbial translocation, including disrupted colonic ZO-1 organization and elevated LBP, alongside increased systemic BAFF and a higher frequency of BAFF-expressing CD45 + cells in gut and spleen. These changes were accompanied by remodelling of the B-cell compartments, with expansion of CD19 + CD1d + IgM bright B cells, an increased fraction of IgM + cells within the blast compartment and reduced IgA + B220 + regions in Peyer’s patches, together with alterations in splenic organization suggestive of IgM bright extra-follicular foci, altered MZ and follicular areas with reduced germinal-centre-like structures. In contrast, mice colonized with mild LC microbiota showed comparatively preserved barrier integrity and lymphoid architecture. Notably, most donor stool samples had undetectable SARS-CoV-2 spike and BAFF, reducing the likelihood that passive transfer of faecal viral antigen or human BAFF accounts for the observed phenotypes and instead implicating the microbial community and/or its products as upstream drivers (Extended Data Table 6). Although these experiments do not establish that identical mechanisms operate in humans, they show that a severe LC-associated microbiota can be sufficient to induce an LC-like barrier-BAFF-B-cell axis in vivo. BAFF neutralization in severe-LC-microbiota-colonized mice supports a functional role for BAFF signalling in linking intestinal barrier perturbation to downstream immune and neuroinflammatory features. Beyond the expected suppression of BAFF availability and BAFF-dependent B-cell compartments, anti-BAFF treatment was accompanied by reduced ANA titres and improved intestinal epithelial junctional organization, as well as attenuation of astrocytic and microglial activation together with more linear vascular ZO-1 staining in the cerebral cortex. Collectively, these observations are consistent with BAFF acting at the intersection of a barrier-immune-neuroinflammation axis in this model, although the intervening cellular pathways and tissue specificity remain to be defined. Because mucosal immunity can reciprocally shape microbial community structure, we asked whether BAFF blockade altered the engrafted microbiota. BAFF neutralization was associated with restructuring of the microbial community: alpha diversity metrics changed modestly, whereas beta diversity separated anti-BAFF-treated mice from isotype controls with differential abundance of multiple taxa. This finding raises the possibility of bidirectional coupling, in which BAFF-driven immune and barrier changes feed back to shape microbial ecology, rather than BAFF acting solely downstream of dysbiosis. Together with the established roles of BAFF in sustained autoreactive B-cell responses and extra-follicular responses, and with the clinical efficacy of BAFF inhibition in systemic lupus erythematosus and related autoimmune diseases 7,8,35,47,48,53-58 , these data provide preclinical proof-of-concept that targeting the BAFF pathway can ameliorate LC-like features induced by human LC microbiota, while motivating future work to uncover direct versus indirect effects on the microbiota. Our findings should be interpreted in the context of certain limitations. This is a single-centre cohort with a defined sample size, enriched for women (as is the case for most LC cohorts), and restricted to non-hospitalized individuals who provided stool samples. Associations between BAFF, zonulin, MZp features and autoreactivity in humans are correlative and could be influenced by comorbidities or concomitant medications, although we controlled for SARS-CoV-2 reinfection and vaccination timing. The autoantibody panel was targeted and does not capture the full breadth of potential autoreactivities, nor did we resolve specificity at the level of individual B-cell clones. Donors selected for gnotobiotic experiments were female representing defined severe or mild LC phenotypes. Therefore, microbiota-driven effects may not generalize to all LC endotypes. Finally, BAFF-blockade was evaluated using a short-term, single-dose regimen in a defined gnotobiotic setting. As such, response duration, safety and the optimal therapeutic window would need to be defined before translation into clinical trials. Despite these caveats, the convergence of longitudinal human profiling, networked biomarker relationships, microbiota-transfer experiments and pharmacologic BAFF inhibition provides a coherent framework for LC pathogenesis centered on a gut-BAFF-B-cell axis. In this framework, dysbiosis and intestinal barrier disruption promote microbial translocation and prolong inflammatory cues, including persistent viral antigen exposure, thereby sustaining BAFF production by myeloid and stromal compartments. Elevated BAFF would be expected to favour expansion and reprogramming of MZp and related innate-like B-cell subsets, biasing humoral outputs toward IgM while still permitting class-switching, given that MZ populations can undergo isotype switching 30 , and promoting extra-follicular and autoreactive responses 52 with potential consequences for mucosal, vascular and neurological compartments. This model integrates several leading hypotheses for LC pathogenesis (e.g., intestinal barrier failure, viral antigen persistence, autoimmunity and neuroimmune dysregulation) into a pathway that can be therapeutically targeted. Our data highlight several translational opportunities. First, combining profiling of BAFF/APRIL outputs with zonulin and markers of microbial translocation, together with MZp features and autoantibody profiles, could help define a BAFF-biased, barrier-injury endotype of LC and support biomarker-guided stratification in future studies. Second, longitudinal interventional studies are needed to determine how antiviral therapy, microbiota-directed strategies and BAFF/APRIL pathway modulation interact over time, and whether particular treatment sequences or combinations are required for durable benefit. Third, carefully designed trials of BAFF pathway inhibitors, potentially combined with approaches to restore intestinal barrier integrity or reshape the microbiota, should be pursued in well-characterized LC populations, while monitoring safety, infection risk and effects on vaccine responsiveness. In conclusion, our study connects intestinal barrier dysfunction with BAFF/APRIL pathway remodelling, marginal-zone-skewed B-cell perturbations and systemic autoreactivity in LC and shows that an LC-like phenotype induced by severe LC microbiota is modifiable by BAFF blockade in vivo. These findings provide mechanistic support for a gut-immune axis in LC and identify BAFF signalling as a target for precision therapeutics in this emerging post-infectious chronic disease. Methods Study design and population The Institut de Recherches Cliniques de Montréal (IRCM) post-coronavirus disease 2019 (COVID-19) research clinic integrates clinical care with a prospective observational cohort and biobank (IPCO; protocol #2021-1092; ClinicalTrials.gov NCT04736732 ) 59 . Between 12 February 2021 and 25 July 2022, Quebec residents aged 18 to 100 years were enrolled after written informed consent. The study was approved by the IRCM Research Ethics Board. The cohort comprised individuals meeting the WHO clinical case definition 60 of post-COVID-19 condition (long COVID; LC), and pandemic control participants who had never tested positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and did not report symptoms consistent with acute COVID-19. LC participants had PCR-confirmed SARS-CoV-2 infection and were enrolled at least 3 months after infection. Study visits with biobanking were scheduled at 3-6, 12 and 24 months after infection, although participants could enrol at any time after infection and contributed samples at the nearest scheduled time point(s). For the present analyses, we included participants who provided stool samples and did not experience severe acute COVID-19 (defined as requiring supplemental oxygen, presenting to the emergency department or being hospitalized). This subset included 103 LC participants (36 males, 67 females) and 17 pandemic controls (9 males, 8 females) (Fig. 1a). Clinical and demographic characteristics are summarized in Extended Data Table 1. Clinical procedures Demographic data were collected using a self-administered questionnaire. Clinical information was recorded by a healthcare professional using a standardized case-report form. At each visit, participants underwent a structured assessment of 51 symptoms associated with LC along with documentation on vaccination status (Extended Data Tables 1,2). Blood samples were collected at IRCM. Clinical laboratory testing was performed at the Centre Hospitalier de l'Université de Montréal (CHUM) Department of Laboratory Medicine according to standard operating procedures. Sample collection and processing Whole blood and stool samples were collected at 3-6, 12 and 24 months after infection. Blood was drawn into ethylenediaminetetraacetic acid (EDTA) and acid citrate dextrose (ACD) tubes for plasma and peripheral blood mononuclear cell (PBMC) isolation, and into serum separator tubes (SST) for serum, and processed within 6 h of collection. Whole blood was centrifuged at 400g for 10 min at room temperature to separate plasma and serum, which were aliquoted and stored at -80°C. PBMCs were isolated using SepMate tubes (STEMCELL Technologies) according to the manufacturer’s instructions and cryopreserved in freezing medium (10% dimethyl sulfoxide (DMSO) in heat inactivated foetal bovine serum (FBSi)) in liquid nitrogen. Sample availability at each time point are shown in Extended Data Table 3. Stool was self-collected into sterile, dry tubes, stored immediately at −20°C by participants, and transferred to −80°C upon receipt at the clinic 61 . Soluble biomarkers in blood Humans Plasma B-cell activating factor (BAFF), a proliferation inducing ligand (APRIL), lipopolysaccharide-binding protein (LBP), and ꞵ-D-glucan were quantified by enzyme-linked immunosorbent assay (ELISA) using commercial kits (Human BAFF/BLyS, R&D systems; human APRIL, Thermo Fisher Scientific; human LBP, Abcam; ꞵ-D-glucan, Glucatell, Associates of Cape Cod) according to the manufacturers’ instructions. Serum zonulin was quantified by ELISA using a commercial kit (human Zonulin, Elabscience) according to the manufacturer’s instructions. Serum soluble transmembrane activator and calcium-modulating cyclophilin ligand interactor (TACI) and B-cell maturation antigen (BCMA) were measured using Meso Scale Discovery (MSD) assays (R-PLEX human TACI; U-PLEX human BCMA) according to the manufacturer’s instructions. Plasma SARS-CoV-2 spike and nucleocapsid (N) proteins were quantified using S-PLEX SARS-CoV-2 Spike and S-PLEX SARS-CoV-2 Nucleocapsid assays (MSD). MSD data were analysed using Discovery Workbench software (MSD). Mice Serum BAFF and LBP were measured using mouse-specific ELISA kits (BAFF/BlyS, R&D Systems; LBP, Abcam) according to the manufacturers’ instructions. Immunoglobulin isotypes and autoantibody measurements Humans Serum immunoglobulin isotypes were quantified with the MILLIPLEX MAP Human Immunoglobulin Isotyping Magnetic Bead Panel (MilliporeSigma), with data acquisition on a Luminex MAGPIX system and analysis using the manufacturer’s recommended settings. Serum IgG autoantibodies were measured using an electrochemiluminescence-based multiplex assay (MSD U-PLEX). Briefly, pooled biotinylated antigens (double stranded DNA (dsDNA), DNA topoisomerase I, histidyl-tRNA-synthetase, Mi-2, SmD, U1-snRNP, U1-snRNP A, Ro/SS-A, La/SS-B and Ro/SS-A52; Surmodics IVD) were prepared according to the MSD Biotinylation QuickGuide and coupled to U-PLEX linkers (final coating concentration, 66 nM per antigen). A fixed positivity threshold of 1,000 electrochemiluminescence (ECL) counts was used, selected a priori as threefold the blank (background) signal to prioritize specificity. Data were analysed in Discovery Workbench (MSD). Mice Serum anti-nuclear IgG antibodies (ANA) were quantified by ELISA using a commercial kit (US Biological Life Sciences) according to the manufacturer’s instructions. Multicolour flow cytometry Humans Cryopreserved PBMCs were thawed, washed with Iscove's Modified Dulbecco's Medium (IMDM; Gibco) and phosphate-buffered saline (PBS), and stained for viability using LIVE/DEAD Fixable Aqua (Invitrogen). Non-specific binding was blocked in FACS buffer (PBS, 2% heat-inactivated foetal bovine serum (FBSi; Gibco) and 0.1% sodium azide) supplemented with 20% FBSi, mouse IgG (50µg; Sigma-Aldrich) and Human BD Fc Block (7µL per 10 6 cells; BD Biosciences). Surface staining was performed using fluorochrome-conjugated mouse anti-human monoclonal antibodies against CD19, IgM, CD10, CD83, CD27, IgG, CD14, CD20, CD21, CD1c, CD38, CD16, CD11c, BAFF, CD3, CD66b and IgA. For intracellular antigen detection, cells were fixed and permeabilized using Cytofix/Cytoperm(BD Biosciences) and stained with Alexa Fluor 700 anti-SARS-CoV-2 Spike S1 (R&D Systems). For intranuclear staining, cells were processed with the FoxP3/Transcription Factor Staining Buffer Set (Thermo Fisher Scientific) and stained with antibodies against NR4A3 and T-bet (BioLegend). Fluorescence-minus-one (FMO) controls were used to define gates, and anti-mouse Igκ compensation beads (Thermo Fisher Scientific) were used for compensation. Antibody details are provided in Extended Data Table 4a. Mice Single-cell suspensions from spleen and lamina propria (LP) were prepared fresh (see below). Cells were blocked in FACS buffer supplemented with 20% FBSi, anti-Mouse CD16/CD32 (2µL per 10 6 cells; eBioscience), and normal goat and rat serum (2µL each per well). Surface staining was performed using fluorochrome-conjugated antibodies against CD45, CD19, CD138, CD11b, Ly6C, Ly6G, CD3, IgM, CD1d, CD73, IgA, IgG, CD11c, MHC class II, F4/80 and BAFF. For intranuclear staining, cells were processed using the FoxP3/Transcription Factor Staining Buffer Set (Thermo Fisher Scientific) and stained with antibodies against FoxP3, T-bet and RORℽt. Antibody details are provided in Extended Data Table 4b. For all experiments, cells were fixed in 1.25% paraformaldehyde at 4°C before acquisition on a BD LSRFortessa. Data were analysed using FlowJo v10.8.1 and GraphPad Prism v10.3.1. Mice (housing and ethics) Female C57BL/6 mice (5-7 weeks old) were obtained from the University of Calgary Germ-Free and Gnotobiotic Platform and housed in the IRCM Germ-Free and Gnotobiotic Facility in sterile cages within flexible-film isolators (Class Biologically Clean) at 22±1°C on a 12-h light/dark cycle with autoclaved chow and water provided ad libitum .Mice were routinely monitored for microbial contamination. All procedures were performed under sterile conditions in a biosafety cabinet, in accordance with Canadian Council of Animal Care guidelines, and were approved by the IRCM Animal Care Committee. Faecal microbiota transplantation into germ-free mice Human stool aliquots were resuspended anaerobically in brain-heart infusion (BHI) broth containing 30% glycerol (200mg stool per 1mL), passed through a 70-µm cell strainer and kept on ice until administration. After a 2-week acclimation period, germ-free mice received 200µL of faecal suspension by oral gavage on three occasions over 1 week (every other day), followed by a 3-week engraftment period. Three independent experiments were performed using stool from donors with mild versus severe LC (all mice were germ-free and female; n=5 per group). Experimental group sizes and donor group assignment for each experiment are summarized in Extended Data Table 5. Stool donor characteristics are shown in Extended Data Table 6. Anti-BAFF treatment in the gnotobiotic long COVID model Gnotobiotic C57BL/6 mice colonized with stool from a donor with severe LC received a single intraperitoneal dose of an anti-mouse BAFF monoclonal antibody (Sandy-2) or mouse IgG1 isotype control (AdipoGen Life Sciences; n=5 per group). Tissues were harvested 18 days after treatment, as described previously 43 . This experiment was performed twice; experimental group sizes and treatments are summarized in Extended Data Table 5. Mouse tissue harvest, processing and immunofluorescence Mice were sedated with ketamine and euthanized by terminal cardiac puncture. Blood was allowed to clot, and serum was isolated and stored at -80°C. Colon, Peyer's patches, spleen and brain were collected into ice-cold Hank’s Balanced Salt Solution (HBSS; Multicell). Colons were measured and divided: one half was fixed in 10% neutral-buffered formalin and the other was cut into 2-cm segments for colon epithelial cell (CEC) isolation and LP cell preparation. CECs were isolated by washing in Ca 2+ /Mg 2+ -free HBSS supplemented with 25mM HEPES, followed by incubation in pre-warmed HBSS containing 15mM HEPES, 10% FBSi, 5mM EDTA, and 1mM dithiothreitol (DTT) for 15min at 37°C with intermittent shaking. Cell suspensions were passed through 100-μm strainers. LP cells were obtained by enzymatic digestion in 5mL IMDM supplemented with 1% penicillin-streptomycin-glutamine (Gibco), Liberase and DNase I (Roche) for 1h at 37°C with agitation every 15min, followed by sequential filtration (100- and 40-μm strainers), washing in PBS and downstream flow-cytometry staining. Spleens were divided, with one portion processed for flow cytometry and the other reserved for imaging. Spleens and Peyer’s patches were embedded in optimal cutting temperature (OCT) compound (Scigen) and stored at -80℃. Serial 10µm cryosections were fixed in cold acetone (-20℃), air dried and stored at -80℃ until immunolabelling, as described previously 62 . Formalin-fixed colons were paraffin-embedded and sectioned at 10µm (IRCM Histology Core). Brains were fixed in 4% paraformaldehyde, cryoprotected in 30% sucrose, embedded in OCT, sectioned at 14µm and stored at -80℃ (IRIC Histology core). Immunolabelling was performed at the CRCHUM Molecular Pathology Core using a Discovery Ultra automated stainer (Roche). For paraffin sections, antigen retrieval was performed with Cell Conditioning 1 (Tris-EDTA, pH 7.8) for 60min at 95°C. Slides were blocked in PBS containing 1% bovine serum albumin (BSA) (30min, room temperature), incubated with primary and secondary antibodies (2h each, room temperature), counterstained with DAPI (1:3,000; 10min) and washed in PBS. Autofluorescence was reduced with 0.1% Sudan Black B in 70% ethanol (15min). Slides were mounted with Fluoromount (Sigma) and scanned on an Aperio VERSA 200 (20×/0.8 NA; 0.275mm per pixel; Leica Biosystems). Images were reviewed in Aperio ImageScope (Leica Biosystems). Reagents used for immunolabelling included biotinylated peanut agglutinin (PNA), biotinylated anti-IgA and fluorophore-conjugated secondary antibodies/streptavidin, together with primary antibodies against IgM, B220 (CD45R), zonula occludens-1 (ZO-1), glial fibrillary acidic protein (GFAP) and ionized calcium-binding adaptor molecule 1 (IBA1). Full reagent details (including supplier, catalogue number, clone (where applicable) and dilution) are provided in Extended Data Table 4c. Mouse faecal microbiome analysis Mouse faecal pellets were collected into sterile, dry tubes on dry ice and stored at -80°C. DNA was extracted using the Dneasy 96 PowerSoil Pro QIAcube HT Kit on a QIAcube 96 system (QIAGEN). The V4 region of the bacterial 16S rRNA genes was amplified and sequenced on an Illumina MiSeq platform (McGill Centre for Microbiome Research). Read quality metrics (including Phred scores and duplicate rate) were assessed with FastQC 63 . Adapters and primer sequences were trimmed with Trimmomatic 64 (paired-end; LEADING=3, TRAILING=3, SLIDINGWINDOW=4:15). Paired reads were merged with PANDAseq 65 (minimum overlap, 30 nt; minimum length, 50 nt; sequences containing ambiguous bases were removed). Non-specific reads and chimeras were filtered as described previously 66 . Sequences were clustered with CD-HIT-EST 67 and taxonomically classified using Kraken2 68 against an in-house RefSeq-based database (updated November 2023). Downstream analyses were performed in R (v4.2) using phyloseq 69 and tidyverse 70 . Alpha diversity was quantified using the Shannon index; two-group comparisons were performed using t-tests and comparisons across >2 groups were performed using Kruskal-Wallis tests. Differential abundance was assessed using linear discriminant effect size (LEfSe) implemented with edgeR 71 and microbiomeMarker 72 (LDA ≥3.5; Kruskal-Wallis P ≤0.05). Beta diversity was evaluated by principal coordinate analysis (PCoA) using Bray-Curtis distances; overall differences between groups were tested by PERMANOVA (adonis2, vegan v2.6-6.1) with 1,000 permutations. Pairwise PERMANOVA comparisons used an adapted pairwise.adonis procedure 73 with Bonferroni adjustment. Unless otherwise stated, P values were two-sided and P ≤0.05 were considered statistically significant. Statistical analysis For clustering of plasma BAFF and zonulin concentrations, outliers were removed by Z-score filtering 74 . The maximum number of clusters for each analyte was estimated using the silhouette method, and k-means clustering was performed with 25 random starts and a maximum of 50 iterations 75,76 . For each analyte, clusters were subsequently consolidated and labelled as low, intermediate (when present) or high based on the observed ranges. Group comparisons were performed using one-way ANOVA with Tukey’s post hoc test for normally distributed data or Kruskal-Wallis tests with Dunn’s post hoc test for non-normally distributed data. For two-group comparisons, paired t-tests were used for normally distributed paired data and Wilcoxon matched-pairs signed-rank tests were used otherwise. Normality was assessed with the Shapiro-Wilk test. Correlations were assessed using Pearson’s correlation for normally distributed data and Spearman’s rank correlation otherwise. Unless otherwise stated, statistical tests were two-sided. Analyses were performed in GraphPad Prism v10.3.1, and P ≤0.05 were considered statistically significant. Declarations Acknowledgements E.L.F is supported by a Tier 2 Canada Research Chair in Role of the Microbiome in Inborn Errors of Immunity and Post-Infectious Conditions, the Canadian Institutes of Health Research (CIHR), the Fonds de Recherche du Québec (FRQ) . K.D.L. is supported by the IRCM Foundation. M.A. is supported by CIHR. E.D. and A.D. are supported bythe Fonds de Recherche du Québec (FRQ) . The work was supported by CIHR (PJT-191724), the Canada Research Chairs Program, the FRQ Clinical Research Scholars - Junior 1 Establishment Funds for Young Investigators, the John R. Evans Leaders Fund from the Canadian Foundation for Innovation (CFI), the J-Louis Lévesque Foundation Research Chair, the Mirella and Lino Saputo Foundation, and the IRCM Foundation. We thank members of the IRCM animal facility (Mariane Canuel, Eve-Marie Charbonneau and Jo-Anny Bisson), the IRCM Flow Cytometry Platform (Éric Massicotte and Julie Lord), and the McGill Genome Centre, Centre for Microbiome Research, for technical support. We are grateful to Kathy McCoy for providing germ-free mice. We thank Christian Charbonneau, Dr. Marianne Isaac and Melina Narlis of the IRIC Microscopy and Histology Core Facilities for guidance and for performing sagittal brain cryosections and H&E staining. We also thank Véronique Barrès and Liliane Meunier of the CRCHUM Molecular Pathology Core Facility for immunolabeling, slide scanning, and assistance with paraffin and OCT processing of colon, brain, spleen and Peyer’s patches, and Anabelle Bouchard-Bourque of the IRCM Histology Core Facility for additional paraffin and OCT sectioning. We acknowledge Dominic Filion and Mattew Duguay of the IRCM Imaging Platform for imaging support. We thank Dr. Mieczyslaw Marcinkiewicz, Dennis A. Drewnik, Dr. Valerio Piscopo for advice and assistance with brain immunofluorescence interpretation, fixation and embedding. We also thank Dr. Mélanie Dieudé, Charlotte Veilleux-Trihn, and Sandrine Julliard for providing NZB mouse serum. Author contributions K.D-L., M.A., J.P. and E.L.F. conceived the study and designed the research. K.D-L., M.A., F.G., L.L., L.F., A.D. and E.D. performed experiments. K.D-L., M.A., F.G., L.L., L.F., A.D., E.D., J.P. and E.L.F. analysed the data. K.D-L., J.P. and E.L.F. wrote the initial manuscript draft, and all authors contributed to manuscript revision. E.L.F. secured funding for the study. J.P. and E.L.F. supervised the research. Competing interests The authors declare no competing interest. Data availability Raw and processed sequencing data are available under SRA accession number PRJNA1230363 (submission: SUB15146657). Any additional data supporting the findings of this study are available from the corresponding author upon reasonable request. Code availability Custom code used for microbiota processing, statistical analyses, and figure generation will be deposited on GitHub and made publicly available upon publication. Scripts are available from the corresponding author upon reasonable request prior to release. References Ely, E. W., Brown, L. M., Fineberg, H. V., National Academies of Sciences, E. & Medicine Committee on Examining the Working Definition for Long, C. Long Covid Defined. N Engl J Med 391 , 1746-1753 (2024). https://doi.org/10.1056/NEJMsb2408466 World Health, O. Post COVID-19 condition (long COVID) , (2025). Davis, H. E., McCorkell, L., Vogel, J. M. & Topol, E. J. Long COVID: major findings, mechanisms and recommendations. Nat Rev Microbiol 21 , 133-146 (2023). https://doi.org/10.1038/s41579-022-00846-2 Davis, H. E. et al. Characterizing long COVID in an international cohort: 7 months of symptoms and their impact. EClinicalMedicine 38 , 101019 (2021). https://doi.org/10.1016/j.eclinm.2021.101019 National Academies of Sciences, E. & Medicine. A Long COVID Definition: A Chronic, Systemic Disease State with Profound Consequences . (The National Academies Press, 2024). Giron, L. B. et al. Markers of fungal translocation are elevated during post-acute sequelae of SARS-CoV-2 and induce NF-kappaB signaling. JCI Insight 7 (2022). https://doi.org/10.1172/jci.insight.160989 Mackay, F. & Browning, J. L. BAFF: A fundamental survival factor for B cells. Nature Reviews Immunology 2 , 465-475 (2002). https://doi.org/10.1038/nri844 Furie, R. et al. Two-Year, Randomized, Controlled Trial of Belimumab in Lupus Nephritis. N Engl J Med 383 , 1117-1128 (2020). https://doi.org/10.1056/NEJMoa2001180 World Health, O. A clinical case definition of post COVID-19 condition by a Delphi consensus. (World Health Organization, Geneva, 2021). Cai, M., Xie, Y., Topol, E. J. & Al-Aly, Z. Three-year outcomes of post-acute sequelae of COVID-19. Nat Med 30 , 1564-1573 (2024). https://doi.org/10.1038/s41591-024-02987-8 World Health, O. A clinical case definition for post COVID-19 condition in children and adolescents. (World Health Organization, Geneva, 2023). Al-Aly, Z., Xie, Y. & Bowe, B. High-dimensional characterization of post-acute sequelae of COVID-19. Nature 594 , 259-264 (2021). https://doi.org/10.1038/s41586-021-03553-9 Zhang, H. et al. Data-driven identification of post-acute SARS-CoV-2 infection subphenotypes. Nat Med 29 , 226-235 (2023). https://doi.org/10.1038/s41591-022-02116-3 Al-Aly, Z. & Topol, E. Solving the puzzle of Long Covid. Science 383 , 830-832 (2024). https://doi.org/10.1126/science.adl0867 Thaweethai, T. et al. Development of a Definition of Postacute Sequelae of SARS-CoV-2 Infection. JAMA 329 , 1934-1946 (2023). https://doi.org/10.1001/jama.2023.8823 Geng, L. N. et al. 2024 Update of the RECOVER-Adult Long COVID Research Index. Jama 333 , 694-700 (2025). https://doi.org/10.1001/jama.2024.24184 Liew, F. et al. Large-scale phenotyping of patients with long COVID post-hospitalization reveals mechanistic subtypes of disease. Nature Immunology 25 , 607-621 (2024). https://doi.org/10.1038/s41590-024-01778-0 Talla, A. et al. Persistent serum protein signatures define an inflammatory subcategory of long COVID. Nat Commun 14 , 3417 (2023). https://doi.org/10.1038/s41467-023-38682-4 Peluso, M. J. & Deeks, S. G. Mechanisms of long COVID and the path toward therapeutics. Cell 187 , 5500-5529 (2024). https://doi.org/10.1016/j.cell.2024.07.054 Chang, R. et al. Risk of autoimmune diseases in patients with COVID-19: A retrospective cohort study. EClinicalMedicine 56 , 101783 (2023). https://doi.org/10.1016/j.eclinm.2022.101783 Son, K. et al. Circulating anti-nuclear autoantibodies in COVID-19 survivors predict long COVID symptoms. Eur Respir J 61 (2023). https://doi.org/10.1183/13993003.00970-2022 Rong, Z. et al. Persistence of spike protein at the skull-meninges-brain axis may contribute to the neurological sequelae of COVID-19. Cell Host Microbe 32 , 2112-2130.e2110 (2024). https://doi.org/10.1016/j.chom.2024.11.007 Yeoh, Y. K. et al. Gut microbiota composition reflects disease severity and dysfunctional immune responses in patients with COVID-19. Gut 70 , 698-706 (2021). https://doi.org/10.1136/gutjnl-2020-323020 Liu, Q. et al. Gut microbiota dynamics in a prospective cohort of patients with post-acute COVID-19 syndrome. Gut 71 , 544-552 (2022). https://doi.org/10.1136/gutjnl-2021-325989 Zollner, A., Meyer, M., Jukic, A., Adolph, T. & Tilg, H. The Intestine in Acute and Long COVID: Pathophysiological Insights and Key Lessons. Yale J Biol Med 97 , 447-462 (2024). https://doi.org/10.59249/pmie8461 Prasad, R. et al. Plasma Microbiome in COVID-19 Subjects: An Indicator of Gut Barrier Defects and Dysbiosis. Int J Mol Sci 23 (2022). https://doi.org/10.3390/ijms23169141 Greene, C. et al. Blood–brain barrier disruption and sustained systemic inflammation in individuals with long COVID-associated cognitive impairment. Nature Neuroscience 27 , 421-432 (2024). https://doi.org/10.1038/s41593-024-01576-9 Vrettou, C. S. et al. A Prospective Study on Neural Biomarkers in Patients with Long-COVID Symptoms. J Pers Med 14 (2024). https://doi.org/10.3390/jpm14030313 Zhang, Y. et al. B cell-activating factor and its targeted therapy in autoimmune diseases. Cytokine Growth Factor Rev 64 , 57-70 (2022). https://doi.org/10.1016/j.cytogfr.2021.11.004 Cerutti, A., Cols, M. & Puga, I. Marginal zone B cells: virtues of innate-like antibody-producing lymphocytes. Nat Rev Immunol 13 , 118-132 (2013). https://doi.org/10.1038/nri3383 Doyon-Laliberté, K. et al. NR4A Expression by Human Marginal Zone B-Cells. Antibodies (Basel) 8 , E50 (2019). https://doi.org/10.3390/antib8040050 Fontaine, J. et al. High expression levels of B lymphocyte stimulator (BLyS) by dendritic cells correlate with HIV-related B-cell disease progression in humans. Blood 117 , 145-155 (2011). https://doi.org/10.1182/blood-2010-08-301887 Doyon-Laliberte, K. et al. Excess BAFF Alters NR4As Expression Levels and Breg Function of Human Precursor-like Marginal Zone B-Cells in the Context of HIV-1 Infection. Int J Mol Sci 23 (2022). https://doi.org/10.3390/ijms232315142 Doyon-Laliberte, K., Aranguren, M., Poudrier, J. & Roger, M. Marginal Zone B-Cell Populations and Their Regulatory Potential in the Context of HIV and Other Chronic Inflammatory Conditions. Int J Mol Sci 23 (2022). https://doi.org/10.3390/ijms23063372 Doyon-Laliberté, K. et al. Excess BAFF May Impact HIV-1-Specific Antibodies and May Promote Polyclonal Responses Including Those from First-Line Marginal Zone B-Cell Populations. Curr Issues Mol Biol 46 , 25-43 (2023). https://doi.org/10.3390/cimb46010003 Zevin, A. S., McKinnon, L., Burgener, A. & Klatt, N. R. Microbial translocation and microbiome dysbiosis in HIV-associated immune activation. Curr Opin HIV AIDS 11 , 182-190 (2016). https://doi.org/10.1097/COH.0000000000000234 Ellis, R. J. et al. Markers of Gut Barrier Function and Microbial Translocation Associate with Lower Gut Microbial Diversity in People with HIV. Viruses 13 (2021). https://doi.org/10.3390/v13101891 Nazli, A. et al. Exposure to HIV-1 directly impairs mucosal epithelial barrier integrity allowing microbial translocation. PLoS Pathog 6 , e1000852 (2010). https://doi.org/10.1371/journal.ppat.1000852 Brenchley, J. M. et al. Microbial translocation is a cause of systemic immune activation in chronic HIV infection. Nat Med 12 , 1365-1371 (2006). https://doi.org/10.1038/nm1511 Jiang, W. et al. Plasma levels of bacterial DNA correlate with immune activation and the magnitude of immune restoration in persons with antiretroviral-treated HIV infection. J Infect Dis 199 , 1177-1185 (2009). https://doi.org/10.1086/597476 Ramendra, R. et al. Circulating LPS and (1-->3)-beta-D-Glucan: A Folie a Deux Contributing to HIV-Associated Immune Activation. Front Immunol 10 , 465 (2019). https://doi.org/10.3389/fimmu.2019.00465 Marchetti, G. et al. Microbial translocation is associated with sustained failure in CD4+ T-cell reconstitution in HIV-infected patients on long-term highly active antiretroviral therapy. AIDS 22 , 2035-2038 (2008). https://doi.org/10.1097/QAD.0b013e3283112d29 Kowalczyk-Quintas, C. et al. Antibodies That Block or Activate Mouse B Cell Activating Factor of the Tumor Necrosis Factor (TNF) Family (BAFF), Respectively, Induce B Cell Depletion or B Cell Hyperplasia. J Biol Chem 291 , 19826-19834 (2016). https://doi.org/10.1074/jbc.M116.725929 Kaku, H., Cheng, K. F., Al-Abed, Y. & Rothstein, T. L. A novel mechanism of B cell-mediated immune suppression through CD73 expression and adenosine production. J Immunol 193 , 5904-5913 (2014). https://doi.org/10.4049/jimmunol.1400336 Tesch, F. et al. Incident autoimmune diseases in association with SARS-CoV-2 infection: a matched cohort study. Clin Rheumatol 42 , 2905-2914 (2023). https://doi.org/10.1007/s10067-023-06670-0 Jernbom, A. F. et al. Prevalent and persistent new-onset autoantibodies in mild to severe COVID-19. Nature Communications 15 , 8941 (2024). https://doi.org/10.1038/s41467-024-53356-5 Vincent, F. B., Saulep-Easton, D., Figgett, W. A., Fairfax, K. A. & Mackay, F. The BAFF/APRIL system: emerging functions beyond B cell biology and autoimmunity. Cytokine Growth Factor Rev 24 , 203-215 (2013). https://doi.org/10.1016/j.cytogfr.2013.04.003 Hu, S.-Z. et al. The emerging role of BLyS/APRIL in autoimmune diseases: Biological characteristics, functions, and therapeutic potential. Journal of Autoimmunity 149 , 103329 (2024). https://doi.org/https://doi.org/10.1016/j.jaut.2024.103329 Liu, Y. et al. High APRIL Levels Are Associated With Slow Disease Progression and Low Immune Activation in Chronic HIV-1-Infected Patients. Frontiers in Medicine Volume 7 - 2020 (2020). https://doi.org/10.3389/fmed.2020.00299 Knox, J. J., Myles, A. & Cancro, M. P. T-bet+ memory B cells: Generation, function, and fate. Immunological Reviews 288 , 149-160 (2019). https://doi.org/https://doi.org/10.1111/imr.12736 Song, W. et al. Development of Tbet- and CD11c-expressing B cells in a viral infection requires T follicular helper cells outside of germinal centers. Immunity 55 , 290-307.e295 (2022). https://doi.org/https://doi.org/10.1016/j.immuni.2022.01.002 Woodruff, M. C. et al. Extrafollicular B cell responses correlate with neutralizing antibodies and morbidity in COVID-19. Nat Immunol 21 , 1506-1516 (2020). https://doi.org/10.1038/s41590-020-00814-z Groom, J. et al. Association of BAFF/BLyS overexpression and altered B cell differentiation with Sjogren's syndrome. J Clin Invest 109 , 59-68 (2002). https://doi.org/10.1172/JCI14121 Salazar-Camarena, D. C. et al. Association of BAFF, APRIL serum levels, BAFF-R, TACI and BCMA expression on peripheral B-cell subsets with clinical manifestations in systemic lupus erythematosus. Lupus 25 , 582-592 (2016). https://doi.org/10.1177/0961203315608254 Li, L. et al. The role of B cell-activating factor system in autoimmune diseases: mechanisms, disease implications, and therapeutic advances. Frontiers in Immunology Volume 16 - 2025 (2025). https://doi.org/10.3389/fimmu.2025.1538555 Gupta, K. et al. BAFF blockade in experimental autoimmune encephalomyelitis reduces inflammation in the meninges and synaptic and neuronal loss in adjacent brain regions. J Neuroinflammation 20 , 229 (2023). https://doi.org/10.1186/s12974-023-02922-7 Min, J. W. et al. BAFF Inhibition Effectively Suppresses the Development of Anti-HLA.A2 Antibody in the Highly Sensitized Mouse Model. Int J Mol Sci 22 (2021). https://doi.org/10.3390/ijms22020861 Uzzan, M., Colombel, J. F., Cerutti, A., Treton, X. & Mehandru, S. B Cell-Activating Factor (BAFF)-Targeted B Cell Therapies in Inflammatory Bowel Diseases. Dig Dis Sci 61 , 3407-3424 (2016). https://doi.org/10.1007/s10620-016-4317-9 Couzin-Frankel, J. in ScienceInsider (Science.org, 2021). COVID-19 WHOWccdwgop, condition. A clinical case definition of post COVID-19 condition by a Delphi consensus (2021). https://www.who.int/publications/i/item/WHO-2019-nCoV-Post_COVID-19_condition-Clinical_case_definition-2021.1 Liang, Y. et al. Systematic Analysis of Impact of Sampling Regions and Storage Methods on Fecal Gut Microbiome and Metabolome Profiles. mSphere 5 (2020). https://doi.org/10.1128/mSphere.00763-19 Poudrier, J. et al. The AIDS Disease of CD4C/HIV Transgenic Mice Shows Impaired Germinal Centers and Autoantibodies and Develops in the Absence of IFN-γ and IL-6. Immunity 15 , 173-185 (2001). https://doi.org/https://doi.org/10.1016/S1074-7613(01)00177-7 FastQC: a quality control tool for high throughput sequence data (2010). Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30 , 2114-2120 (2014). https://doi.org/10.1093/bioinformatics/btu170 Masella, A. P., Bartram, A. K., Truszkowski, J. M., Brown, D. G. & Neufeld, J. D. PANDAseq: paired-end assembler for illumina sequences. BMC Bioinformatics 13 , 31 (2012). https://doi.org/10.1186/1471-2105-13-31 Mlaga, K. D. et al. HCK and ABAA: A Newly Designed Pipeline to Improve Fungi Metabarcoding Analysis. Front Microbiol 12 , 640693 (2021). https://doi.org/10.3389/fmicb.2021.640693 Fu, L., Niu, B., Zhu, Z., Wu, S. & Li, W. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics 28 , 3150-3152 (2012). https://doi.org/10.1093/bioinformatics/bts565 Lu, J. & Salzberg, S. L. Ultrafast and accurate 16S rRNA microbial community analysis using Kraken 2. Microbiome 8 , 124 (2020). https://doi.org/10.1186/s40168-020-00900-2 McMurdie, P. J. & Holmes, S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One 8 , e61217 (2013). https://doi.org/10.1371/journal.pone.0061217 Whitmore, N. R for Conservation and Development Projects A Primer for Practitioners . (Chapman and Hall / CRC Press, 2022). Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26 , 139-140 (2010). https://doi.org/10.1093/bioinformatics/btp616 Cao, Y. et al. microbiomeMarker: an R/Bioconductor package for microbiome marker identification and visualization. Bioinformatics 38 , 4027-4029 (2022). https://doi.org/10.1093/bioinformatics/btac438 Dixon, P. VEGAN, a package of R functions for community ecology. Journal of vegetation science 14 , 927-930 (2003). https://doi.org/10.1111/j.1654-1103.2003.tb02228.x Meijer, P., Sobas, F. & Tsiamyrtzis, P. Assessment of accuracy of laboratory testing results, relative to peer group consensus values in external quality control, by bivariate z-score analysis: the example of D-Dimer. Clin Chem Lab Med 62 , 1548-1556 (2024). https://doi.org/10.1515/cclm-2023-0835 Kravitz, Y. (University of Colorado Denver, 2024). Wu, J. Advances in K-means Clustering . (Springer Berlin, Heidelberg, 2014). Additional Declarations There is NO Competing Interest. Supplementary Files DoyonExtendedDataTable1.docx Extended Data Table 1 DoyonExtendedDataTable2.docx Extended Data Table 2 DoyonExtendedDataTable3.docx Extended Data Table 3 DoyonExtendedDataTable4.docx Extended Data Table 4 DoyonExtendedDataTable5.docx Extended Data Table 5 DoyonExtendedDataTable6.docx Extended Data Table 6 Ext.Figure.1CorrelationsMonosFinal.pdf Extended Data Fig. 1 | Markers of microbial translocation and intracellular spike-positive monocyte subsets in long COVID a, Correlations between plasma zonulin and b-D-glucan (left) or lipopolysaccharide-binding protein (LBP; right) at 12 months in participants with long COVID (LC). b, Intermediate monocytes (HLA-DR + CD11c + CD14 + CD16 + ): membrane B-cell-activating factor (mBAFF) geometric mean fluorescence intensity (GeoMFI) in pandemic controls and LC participants at 3-6, 12 and 24 months (left); frequencies of intracellular spike 1 (S1) + intermediate monocytes in pandemic controls and LC participants at 12 and 24 months with dashed lines indicating thresholds defined using COVID-19-positive (upper dashed line) and pre-pandemic control (lower dashed line) samples (middle); paired S1 + intermediate monocyte frequencies in matched LC participants at 12 to 24 months (right). c, Non-classical monocytes (HLA-DR + CD11c + CD14 dim/low CD16 ++ ): mBAFF GeoMFI in pandemic controls and LC participants at 3-6, 12 and 24 months (left); frequencies of intracellular S1 + non-classical monocytes in pandemic controls and LC participants at 12 and 24 months (middle) with thresholds as in b; paired S1 + non-classical monocyte frequencies in matched LC participants at 12 to 24 months (right). d, Correlations between plasma BAFF and circulating spike protein (left), and between S1 + frequency and mBAFF frequency in intermediate (middle) and non-classical (right) monocytes. Each dot represents one participant; lines connect matched longitudinal samples. Spearman r and P values are shown; significance is indicated in the figure (* P < 0.05; ** P < 0.01; *** P < 0.001; **** P < 0.0001; ns, not significant). Ext.Figure.2GatingStrategyFinal.pdf Extended Data Fig. 2 | Flow-cytometry gating strategy for circulating B-cell subsets and marginal zone precursor B cell phenotyping a, Live, singlet gating strategy. Lymphocytes were identified by forward- and side-scatter, doublets excluded by FSC-H versus FSC-W and SSC-H versus SSC-W (H, height; W, width), and live cells selected using a fixable viability dye. b, Identification of CD19 + B cells and gating of naïve and transitional immature B cells using CD20, CD1c, CD27 and CD21/CD10 expression (representative plots). c, Gating of memory B cells and resting switched memory B cells. Memory B cells were identified by CD27 expression and resting switched memory cells were defined by CD21 and IgM expression (representative plots). d, Gating of marginal zone (MZ) and marginal zone precursor-like (MZp) B cells within the CD1c + CD27 + IgM + compartment, with MZp defined by CD10 expression. Representative gates for NR4A3 + MZp and T-bet + MZp are shown. e, Gating of B-cell blasts and isotype-positive blast subsets. Blasts were identified within CD19 + cells as CD20 - CD38 + , and IgG + and IgM + blast gates are shown (representative plots). Ext.Figure.3IgsBAFFandZonulinStatus.pdf Extended Data Fig. 3 | Serum immunoglobulin isotypes stratified by BAFF and zonulin status in long COVID Serum concentrations of IgA and IgG subclasses (IgG1-IgG3) in individuals with long COVID (LC), stratified by plasma BAFF status or serum zonulin status (low, intermediate and high). a, BAFF status at 3-6 months after infection. b, BAFF status at 12 months. c, Zonulin status at 3-6 months. d, Zonulin status at 12 months. Each dot represents one participant; violin plots show the distribution with median and interquartile range. BAFF and zonulin strata were defined by data-driven clustering of ELISA values. P values are shown in the figure; ns, not significant. Ext.Figure.4IgBlastBAFFAPRILZonulinStatus.pdf Extended Data Fig. 4 | Isotype distribution of circulating Ig + B-cell blasts stratified by BAFF, APRIL and zonulin status in long COVID a, Frequencies of IgA + , IgG + and IgM + B-cell blasts in individuals with long COVID (LC) at 3-6 months (left set of plots) and 12 months (right set of plots), stratified by plasma B-cell-activating factor (BAFF) status (low, intermediate and high). b, As in a, stratified by plasma a proliferation-inducing ligand (APRIL) status. c, As in a, stratified by serum zonulin status. B-cell blasts were defined by flow cytometry (see gating strategy in Extended Data Fig. 2). Each dot represents one participant; violin plots show distribution with median and interquartile range. Status strata were defined by data-driven clustering of ELISA values. P values are shown in the figure; * P < 0.05. Ext.Figure.5AntiBAFF.pdf Extended Data Fig. 5 | Additional barrier and microbiota readouts following anti-BAFF treatment in gnotobiotic mice colonized with severe long COVID microbiota a, Haematoxylin and eosin (H&E)-stained sagittal brain section indicating regions analysed for immunofluorescence (cortex, hippocampus and pons/medulla). b, Colon length in anti-BAFF- and isotype-treated gnotobiotic mice; each dot represents one mouse, and the P value is shown. c, Representative immunofluorescence images of cortical vasculature stained for zonula occludens-1 (ZO-1; green) and nuclei (DAPI; blue) in isotype- (left) and anti-BAFF-treated (right) gnotobiotic mice. Boxed regions indicate areas shown at higher magnification; arrows highlight discontinuous ZO-1 staining patterns. Scale bars, 200mm (overview) and 100mm (insets). P values are shown where applicable. 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Created in BioRender (Falcone, E.; https://BioRender.com/phcx6gb). \u003cstrong\u003eb\u003c/strong\u003e, Plasma SARS-CoV-2 spike (left) and nucleocapsid (right) protein levels in long COVID (LC) participants at 3-6, 12 and 24 months post-infection, shown as cross-sectional (left subpanels) and paired longitudinal (right subpanels) measurements. Dashed line indicates detection threshold defined using COVID-19-positive control samples. \u003cstrong\u003ec\u003c/strong\u003e, Plasma zonulin levels in pandemic controls (Pand Ctrl) at enrolment and in LC participants at 3-6, 12 and 24 months (left), and correlation between zonulin and soluble BAFF levels at 12 months in LC (right). \u003cstrong\u003ed\u003c/strong\u003e, Correlations between soluble B-cell maturation antigen (BCMA) and soluble transmembrane activator and CAML interactor (TACI) (left), and between a proliferation-inducing ligand (APRIL) and B-cell-activating factor (BAFF) (right), shown at 3-6, 12 and 24 months post-infection.\u0026nbsp; \u003cstrong\u003ee\u003c/strong\u003e, Correlation matrices (left) and corresponding network visualizations (right) summarizing relationships among soluble TACI, BCMA, zonulin, APRIL, lipopolysaccharide binding protein (LBP), β-D-glucan and BAFF in LC participants at 3-6, 12 and 24 months. Numbers indicate Spearman correlation coefficients (r); colour and line width reflect correlation direction and magnitude. \u003cstrong\u003ef\u003c/strong\u003e, Flow cytometry gating strategy for monocyte subsets and representative histograms of membrane BAFF (mBAFF) staining. \u003cstrong\u003eg\u003c/strong\u003e, Frequency of mBAFF\u003csup\u003e+\u003c/sup\u003e cells (left) and geometric mean fluorescence intensity (GeoMFI; right) for classical, intermediate and non-classical monocytes in pandemic controls and LC participants at 3-6, 12 and 24 months. \u003cstrong\u003eh\u003c/strong\u003e, Representative histograms (top) and frequency of mBAFF+ cells (bottom left) and GeoMFI (bottom right) on circulating T cells and B cells in pandemic control and LC participants at 3–6, 12 and 24 months. \u003cstrong\u003ei\u003c/strong\u003e, Proposed model linking intestinal barrier dysfunction and microbial translocation with persistent viral antigen exposure and sustained BAFF/APRIL dysregulation in LC. Created in BioRender. Chandrasekaran, P. (2026) https://BioRender.com/lwqmq5p. Each dot represents one participant; violin plots show median and interquartile range; lines connect paired longitudinal samples. \u003cem\u003eP\u003c/em\u003e values are shown in the figure; * \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05; ** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01; *** \u003cem\u003eP \u003c/em\u003e\u0026lt; 0.001; **** \u003cem\u003eP\u003c/em\u003e \u0026lt;\u0026nbsp;0.0001; ns, not significant.\u003c/p\u003e","description":"","filename":"Figure1Final.png","url":"https://assets-eu.researchsquare.com/files/rs-8876163/v1/bd358ea4d98e568ba6da7582.png"},{"id":105443544,"identity":"b58fdf0e-25c7-42c0-9f7f-4d402e4c0744","added_by":"auto","created_at":"2026-03-26 06:42:18","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":619311,"visible":true,"origin":"","legend":"\u003cp\u003eExpansion and altered phenotype of marginal zone precursor B cells in long COVID\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e, Schematic overview of circulating B-cell subsets analysed by flow cytometry (top; created in BioRender. Chandrasekaran, P. (2026) https://BioRender.com/xlavvjc; gating definitions shown beneath each subset), with corresponding frequencies in pandemic controls (Pand Ctrl) and long COVID (LC) participants at 3-6, 12 and 24 months after infection (bottom). Total B cells are shown as a frequency of peripheral blood mononuclear cells (PBMCs); indicated subsets are shown as a frequency of total B cells. \u003cstrong\u003eb\u003c/strong\u003e, Pairwise Spearman correlation matrices showing relationships between marginal zone precursor (MZp) B cell frequencies, monocyte membrane B-cell-activating factor (mBAFF) expression (classical (C), intermediate (I) and non-classical (NC) monocytes) and soluble BAFF pathway components (B-cell maturation antigen (BCMA) and transmembrane activator and CAML interactor (TACI)) at 3-6, 12 and 24 months. Numbers indicate Sperman correlation coefficients (r); colour denotes direction and magnitude; asterisks indicate significance (as shown). \u003cstrong\u003ec,d,\u003c/strong\u003e Frequency of T-bet\u003csup\u003e+\u003c/sup\u003e MZp B cells in pandemic controls and LC participants stratified by plasma BAFF (\u003cstrong\u003ec\u003c/strong\u003e) or zonulin status (\u003cstrong\u003ed\u003c/strong\u003e) (low, intermediate and high) at 3-6, 12 and 24 months post-infection. \u003cstrong\u003ee,f\u003c/strong\u003e, NR4A3 expression in MZp B cells, shown as geometric mean fluorescence intensity (GeoMFI), in pandemic controls and LC participants stratified by plasma BAFF (\u003cstrong\u003ee\u003c/strong\u003e) or zonulin (\u003cstrong\u003ef\u003c/strong\u003e) status at 3-6, 12 and 24 months. Each dot represents one participant; violin plots show distribution with median and interquartile range. BAFF and zonulin strata were defined by data-driven clustering of plasma ELISA values. \u003cem\u003eP\u003c/em\u003e values are shown in the figure; * \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05; ** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01; *** \u003cem\u003eP \u003c/em\u003e\u0026lt; 0.001; **** \u003cem\u003eP\u003c/em\u003e \u0026lt;\u0026nbsp;0.0001; ns, not significant.\u003c/p\u003e","description":"","filename":"Figure2Final.png","url":"https://assets-eu.researchsquare.com/files/rs-8876163/v1/5be040d5f95fb3af18a21d95.png"},{"id":105566758,"identity":"29b2698a-017b-4222-a0d7-8f8680811a68","added_by":"auto","created_at":"2026-03-27 12:57:14","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":352951,"visible":true,"origin":"","legend":"\u003cp\u003eImmunoglobulin profiles, isotype-specific B-cell blasts and autoantibodies in long COVID.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e, Total serum immunoglobulin (Total Ig) concentrations in pandemic controls (Pand Ctrl) and individuals with long COVID (LC) at 3-6 and 12 months after infection (left). Paired serum concentrations of IgA and IgG subclasses (IgG1-IgG3) in matched LC participants at 3-6 and 12 months (right). \u003cstrong\u003eb\u003c/strong\u003e, Paired serum IgM concentrations in matched LC participants at 3-6 and 12 months. \u003cstrong\u003ec\u003c/strong\u003e,\u003cstrong\u003ed\u003c/strong\u003e, Serum IgM concentrations in LC participants stratified by plasma B-cell-activating factor (BAFF) status (\u003cstrong\u003ec\u003c/strong\u003e) or serum zonulin status (\u003cstrong\u003ed\u003c/strong\u003e) (low, intermediate and high) at 3-6 months (left) and 12 months (right). \u003cstrong\u003ee\u003c/strong\u003e, Paired serum IgG4 concentrations in matched LC participants at 3-6 and 12 months. \u003cstrong\u003ef\u003c/strong\u003e,\u003cstrong\u003eg\u003c/strong\u003e, Serum IgG4 concentrations in LC participants stratified by plasma BAFF status (\u003cstrong\u003ef\u003c/strong\u003e) or serum zonulin status (\u003cstrong\u003eg\u003c/strong\u003e) (low, intermediate and high) at 3-6 months (left) and 12 months (right). \u003cstrong\u003eh\u003c/strong\u003e, Frequencies of IgA\u003csup\u003e+\u003c/sup\u003e, IgG\u003csup\u003e+\u003c/sup\u003e and IgM\u003csup\u003e+\u003c/sup\u003e B-cell blasts at 3-6, 12 and 24 months in LC. \u003cstrong\u003ei\u003c/strong\u003e, Frequencies of IgG autoantibody positivity to DNA topoisomerase I, Mi-2 and SmD in pandemic controls and LC at 3-6, 12 and 24 months. Stacked bars show the distribution of participant groups among autoantibody-positive and -negative individuals. \u003cstrong\u003ej\u003c/strong\u003e, Autoantibody positivity at 12 months for the indicated nuclear antigens in pandemic controls and LC participants stratified by zonulin status (low, intermediate and high). Stacked bars show the distribution of zonulin strata among autoantibody-positive and -negative individuals. In \u003cstrong\u003ea\u003c/strong\u003e, \u003cstrong\u003eb\u003c/strong\u003e and \u003cstrong\u003ee\u003c/strong\u003e, each dot represents one participant, and lines connect matched longitudinal samples. In violin plots (\u003cstrong\u003ec\u003c/strong\u003e, \u003cstrong\u003ed\u003c/strong\u003e, \u003cstrong\u003ef-h\u003c/strong\u003e), each dot represents one participant; violins show distribution with median and interquartile range. BAFF and zonulin strata were defined by data-driven clustering of ELISA values. \u003cem\u003eP\u003c/em\u003e values are shown in the figure; *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001; ns, not significant.\u003c/p\u003e","description":"","filename":"Figure3Final.png","url":"https://assets-eu.researchsquare.com/files/rs-8876163/v1/7bafd76d26f9b7c6a5b3f632.png"},{"id":105443506,"identity":"323d6de9-c3d0-412e-b0c3-e48a65603b08","added_by":"auto","created_at":"2026-03-26 06:42:13","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2194367,"visible":true,"origin":"","legend":"\u003cp\u003eMicrobiota from individuals with severe long COVID promotes intestinal barrier disruption, BAFF upregulation and splenic B-cell activation in gnotobiotic mice\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e, Experimental design for faecal microbiota transfer (FMT). Germ-free (GF) C57BL/6 mice (6-8 weeks old; n = 5 per human donor microbiota) were colonized by oral gavage with faecal suspensions from human donors with mild or severe long COVID (LC), followed by an engraftment period, acclimation and tissue harvest at the indicated time points. Created in BioRender. Doyon-Laliberté, K. (2026) https://BioRender.com/cmprnje. \u003cstrong\u003eb\u003c/strong\u003e, Colon length in recipient mice colonized with microbiota from mild and severe LC donors. \u003cstrong\u003ec\u003c/strong\u003e, Serum levels of lipopolysaccharide-binding protein (LBP; left) and B-cell-activating factor (BAFF) (right) in recipient mice. \u003cstrong\u003ed\u003c/strong\u003e, Representative immunofluorescence images of colon sections stained for zonula occludens-1 (ZO-1; green) and nuclei (DAPI; blue) in mice colonized with microbiota from mild or severe LC donors. Insets show magnified regions; arrows indicate disrupted or punctate ZO-1 staining. Scale bars, 200mm. \u003cstrong\u003ee\u003c/strong\u003e, Frequency of BAFF-expressing cells among CD45\u003csup\u003e+\u003c/sup\u003e immune cells in the lamina propria (top) and spleen (bottom) of recipient mice. \u003cstrong\u003ef\u003c/strong\u003e, Representative immunofluorescence images of spleen sections stained for peanut agglutinin (PNA; green) and IgM (red) (left) or CD169 (green) and IgM (red) (right) in mice colonized with mild or severe LC microbiota. Arrow indicate PNA\u003csup\u003e+\u003c/sup\u003e foci. Scale bars, 700mm (PNA/IgM) and 400mm (CD169/IgM). \u003cstrong\u003eg\u003c/strong\u003e, Frequency of CD19\u003csup\u003e+\u003c/sup\u003eCD1d\u003csup\u003e+\u003c/sup\u003eIgM\u003csup\u003ebright\u003c/sup\u003e B cells among total splenic B cells (top) and frequency of IgM\u003csup\u003e+\u003c/sup\u003e cells among splenic B-cell blasts (bottom). \u003cstrong\u003eh\u003c/strong\u003e, Representative immunofluorescence images of Peyer’s patches stained for B220 (red) and IgA (green) in mice colonized with mild or severe LC microbiota. Boxes highlight regions enriched for IgA\u003csup\u003e+\u003c/sup\u003eB220\u003csup\u003e+\u003c/sup\u003e double-positive cells within Peyer’s patches. Scale bars, 400mm. Data are representative of 3 independent experiments using distinct mild and severe LC microbiota donors. Each dot represents one mouse; violin plots show the distribution with median and interquartile range. * \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05; ** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01; *** \u003cem\u003eP \u003c/em\u003e\u0026lt; 0.001; **** \u003cem\u003eP\u003c/em\u003e \u0026lt;\u0026nbsp;0.0001; ns, not significant.\u003c/p\u003e","description":"","filename":"Figure4Final.png","url":"https://assets-eu.researchsquare.com/files/rs-8876163/v1/abbcc06bb76aa4906f1c4b11.png"},{"id":105566860,"identity":"001385cf-192b-489b-9f78-a8b4d9789d34","added_by":"auto","created_at":"2026-03-27 12:57:34","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2306980,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e, Experimental design. Germ-free (GF) mice were colonized with faecal microbiota from a donor with severe long COVID (LC), treated by intraperitoneal injection with anti-BAFF monoclonal antibody (Sandy-2; n=5) or isotype control (n=5), and euthanized for tissue harvest at the indicated time points. Created in BioRender. Doyon-laliberté, K. (2026) https://BioRender.com/kgwbhd4. \u003cstrong\u003eb\u003c/strong\u003e, Serum anti-nuclear antibody (ANA) levels in anti-BAFF- and isotype-treated mice. \u003cstrong\u003ec\u003c/strong\u003e, Serum BAFF concentration (left), frequency of membrane BAFF (mBAFF)\u003csup\u003e+\u003c/sup\u003e cells among CD45\u003csup\u003e+\u003c/sup\u003e splenocytes (middle) and mBAFF geometric mean fluorescence intensity (GeoMFI) on CD45\u003csup\u003e+\u003c/sup\u003e splenocytes (right). \u003cstrong\u003ed\u003c/strong\u003e, Frequency of total B cells among CD45\u003csup\u003e+\u003c/sup\u003e splenocytes (left) and CD73 GeoMFI on CD19\u003csup\u003e+\u003c/sup\u003eCD1d\u003csup\u003e+\u003c/sup\u003eIgM\u003csup\u003ebright \u003c/sup\u003esplenic B cells (right). \u003cstrong\u003ee\u003c/strong\u003e, Frequency of total splenic blasts among CD45\u003csup\u003e+\u003c/sup\u003e cells (left) and isotype composition of the blast compartment (IgA\u003csup\u003e+\u003c/sup\u003e, IgM\u003csup\u003e+\u003c/sup\u003e and IgG\u003csup\u003e+\u003c/sup\u003e; middle to right). \u003cstrong\u003ef\u003c/strong\u003e, Representative immunofluorescence images of colon epithelium stained for zonula occludens-1 (ZO-1; green) and nuclei (DAPI; blue) in isotype- and anti-BAFF-treated mice; boxed regions indicate areas shown at higher magnification (insets). \u003cstrong\u003eg\u003c/strong\u003e, Representative brain immunofluorescence images showing glial fibrillary acidic protein (GFAP; green) and ionized calcium-binding adaptor molecule 1 (IBA1; green) with DAPI (blue) in anti-BAFF- and isotype-treated mice; insets show magnified regions. Bottom, quantification of GFAP and IBA1 integrated density. \u003cstrong\u003eh\u003c/strong\u003e, Faecal microbiota alpha diversity metrics (Chao1 richness, Shannon diversity and Pielou evenness). \u003cstrong\u003ei\u003c/strong\u003e, Principal coordinates analysis (PCoA) of Bray-Curtis dissimilarity (beta diversity) for anti-BAFF- and isotype-treated mice; ellipses indicate group clustering and pairwise statistics are shown. \u003cstrong\u003ej\u003c/strong\u003e, Volcano plot of differential genus abundance comparing anti-BAFF-treated versus isotype-treated mice (log\u003csub\u003e2\u003c/sub\u003efold change versus isotype plotted against -log\u003csub\u003e10\u003c/sub\u003eadjusted P value); significantly altered genera are highlighted as indicated. \u003cstrong\u003ek\u003c/strong\u003e, Heat tree visualization summarizing taxa that differ between anti-BAFF- and isotype-treated mice, with node size and colour indicating relative abundance and direction/magnitude of change as shown. \u003cstrong\u003el\u003c/strong\u003e, Linear discriminant analysis effect size (LEfSe) identifying genera differentially enriched between anti-BAFF and isotype-treated groups (ordered by LDA score as shown). Each dot represents one mouse. Violin/box plots depict distribution with centre and spread as shown in the figure; \u003cem\u003eP\u003c/em\u003e values are indicated in the panels; ns, not significant; *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05; **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01; ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001; ****\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001.\u003c/p\u003e\n\u003cp\u003eBAFF blockade attenuates long COVID-like immune, barrier and neuroinflammatory features and is associated with microbiota restructuring in gnotobiotic mice.\u003c/p\u003e","description":"","filename":"Figure5Final.png","url":"https://assets-eu.researchsquare.com/files/rs-8876163/v1/9c41db791a1ac3db7af54a9e.png"},{"id":105571641,"identity":"2f4f825c-7a08-4a00-8468-d6f839069f85","added_by":"auto","created_at":"2026-03-27 13:23:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8155061,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8876163/v1/da464c01-addd-4da1-972b-fa49dadc842c.pdf"},{"id":105443500,"identity":"4ca47fcb-4958-4134-bd91-1796a84d4832","added_by":"auto","created_at":"2026-03-26 06:42:08","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":19063,"visible":true,"origin":"","legend":"\u003cp\u003eExtended Data Table 1\u003c/p\u003e","description":"","filename":"DoyonExtendedDataTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8876163/v1/0d040537fcd2682f21d2efde.docx"},{"id":105443566,"identity":"82d6bbf4-6e13-4e9a-8022-d54d6fddff70","added_by":"auto","created_at":"2026-03-26 06:42:25","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":22807,"visible":true,"origin":"","legend":"\u003cp\u003eExtended Data Table 2\u003c/p\u003e","description":"","filename":"DoyonExtendedDataTable2.docx","url":"https://assets-eu.researchsquare.com/files/rs-8876163/v1/029467802ed463b1a72a6053.docx"},{"id":105443545,"identity":"5d4011e6-06ee-4194-89a4-f4eea73947e6","added_by":"auto","created_at":"2026-03-26 06:42:19","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":20502,"visible":true,"origin":"","legend":"\u003cp\u003eExtended Data Table 3\u003c/p\u003e","description":"","filename":"DoyonExtendedDataTable3.docx","url":"https://assets-eu.researchsquare.com/files/rs-8876163/v1/285a57931e1a2b88d3ed5814.docx"},{"id":105443547,"identity":"bc3b10b2-97c9-438e-85be-e27cf7e4e8e7","added_by":"auto","created_at":"2026-03-26 06:42:21","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":30226,"visible":true,"origin":"","legend":"\u003cp\u003eExtended Data Table 4\u003c/p\u003e","description":"","filename":"DoyonExtendedDataTable4.docx","url":"https://assets-eu.researchsquare.com/files/rs-8876163/v1/0ab6c951f19349ee30ecf512.docx"},{"id":105443516,"identity":"8f13bf7b-b5cb-481f-b765-fe191529f98e","added_by":"auto","created_at":"2026-03-26 06:42:16","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":21621,"visible":true,"origin":"","legend":"\u003cp\u003eExtended Data Table 5\u003c/p\u003e","description":"","filename":"DoyonExtendedDataTable5.docx","url":"https://assets-eu.researchsquare.com/files/rs-8876163/v1/f4bd6dcd4b77380a289843e8.docx"},{"id":105443515,"identity":"1ef25517-91a0-4237-ad92-c1b89115b039","added_by":"auto","created_at":"2026-03-26 06:42:16","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":22159,"visible":true,"origin":"","legend":"\u003cp\u003eExtended Data Table 6\u003c/p\u003e","description":"","filename":"DoyonExtendedDataTable6.docx","url":"https://assets-eu.researchsquare.com/files/rs-8876163/v1/8dc5a90a04b01f6463135c40.docx"},{"id":105443518,"identity":"d349283c-7406-4a33-a314-f596570daa14","added_by":"auto","created_at":"2026-03-26 06:42:17","extension":"pdf","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":306867,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExtended Data Fig. 1 | \u003c/strong\u003eMarkers of microbial translocation and intracellular spike-positive monocyte subsets in long COVID\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e, Correlations between plasma zonulin and b-D-glucan (left) or lipopolysaccharide-binding protein (LBP; right) at 12 months in participants with long COVID (LC). \u003cstrong\u003eb\u003c/strong\u003e, Intermediate monocytes (HLA-DR\u003csup\u003e+\u003c/sup\u003eCD11c\u003csup\u003e+\u003c/sup\u003eCD14\u003csup\u003e+\u003c/sup\u003eCD16\u003csup\u003e+\u003c/sup\u003e): membrane B-cell-activating factor (mBAFF) geometric mean fluorescence intensity (GeoMFI) in pandemic controls and LC participants at 3-6, 12 and 24 months (left); frequencies of intracellular spike 1 (S1)\u003csup\u003e+\u003c/sup\u003e intermediate monocytes in pandemic controls and LC participants at 12 and 24 months with dashed lines indicating thresholds defined using COVID-19-positive (upper dashed line) and pre-pandemic control (lower dashed line) samples (middle); paired S1\u003csup\u003e+\u003c/sup\u003e intermediate monocyte frequencies in matched LC participants at 12 to 24 months (right). \u003cstrong\u003ec\u003c/strong\u003e, Non-classical monocytes (HLA-DR\u003csup\u003e+\u003c/sup\u003eCD11c\u003csup\u003e+\u003c/sup\u003eCD14\u003csup\u003edim/low\u003c/sup\u003eCD16\u003csup\u003e++\u003c/sup\u003e): mBAFF GeoMFI in pandemic controls and LC participants at 3-6, 12 and 24 months (left); frequencies of intracellular S1\u003csup\u003e+\u003c/sup\u003e non-classical monocytes in pandemic controls and LC participants at 12 and 24 months (middle) with thresholds as in \u003cstrong\u003eb\u003c/strong\u003e; paired S1\u003csup\u003e+\u003c/sup\u003e non-classical monocyte frequencies in matched LC participants at 12 to 24 months (right). \u003cstrong\u003ed\u003c/strong\u003e, Correlations between plasma BAFF and circulating spike protein (left), and between S1\u003csup\u003e+\u003c/sup\u003e frequency and mBAFF frequency in intermediate (middle) and non-classical (right) monocytes. Each dot represents one participant; lines connect matched longitudinal samples. Spearman r and \u003cem\u003eP\u003c/em\u003e values are shown; significance is indicated in the figure (*\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05; **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01; ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001; ****\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.0001; ns, not significant).\u003c/p\u003e","description":"","filename":"Ext.Figure.1CorrelationsMonosFinal.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8876163/v1/09b16f45ec72e62358d83b3f.pdf"},{"id":105566735,"identity":"223fcda8-3404-4601-a89e-018978ee18e2","added_by":"auto","created_at":"2026-03-27 12:57:09","extension":"pdf","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":568885,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExtended Data Fig. 2 | \u003c/strong\u003eFlow-cytometry gating strategy for circulating B-cell subsets and marginal zone precursor B cell phenotyping\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea, \u003c/strong\u003eLive, singlet gating strategy. Lymphocytes were identified by forward- and side-scatter, doublets excluded by FSC-H versus FSC-W and SSC-H versus SSC-W (H, height; W, width), and live cells selected using a fixable viability dye. \u003cstrong\u003eb\u003c/strong\u003e, Identification of CD19\u003csup\u003e+\u003c/sup\u003e B cells and gating of naïve and transitional immature B cells using CD20, CD1c, CD27 and CD21/CD10 expression (representative plots). \u003cstrong\u003ec\u003c/strong\u003e, Gating of memory B cells and resting switched memory B cells. Memory B cells were identified by CD27 expression and resting switched memory cells were defined by CD21 and IgM expression (representative plots). \u003cstrong\u003ed\u003c/strong\u003e, Gating of marginal zone (MZ) and marginal zone precursor-like (MZp) B cells within the CD1c\u003csup\u003e+\u003c/sup\u003eCD27\u003csup\u003e+\u003c/sup\u003eIgM\u003csup\u003e+\u003c/sup\u003e compartment, with MZp defined by CD10 expression. Representative gates for NR4A3\u003csup\u003e+\u003c/sup\u003e MZp and T-bet\u003csup\u003e+\u003c/sup\u003e MZp are shown. \u003cstrong\u003ee\u003c/strong\u003e, Gating of B-cell blasts and isotype-positive blast subsets. Blasts were identified within CD19\u003csup\u003e+\u003c/sup\u003e cells as CD20\u003csup\u003e-\u003c/sup\u003eCD38\u003csup\u003e+\u003c/sup\u003e, and IgG\u003csup\u003e+\u003c/sup\u003e and IgM\u003csup\u003e+\u003c/sup\u003e blast gates are shown (representative plots).\u003c/p\u003e","description":"","filename":"Ext.Figure.2GatingStrategyFinal.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8876163/v1/8823c964a3f1a9f7f22326a8.pdf"},{"id":105443519,"identity":"b2e9e91e-b98d-425b-b79f-e3e0729a6541","added_by":"auto","created_at":"2026-03-26 06:42:18","extension":"pdf","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":613669,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExtended Data Fig. 3 | \u003c/strong\u003eSerum immunoglobulin isotypes stratified by BAFF and zonulin status in long COVID\u003c/p\u003e\n\u003cp\u003eSerum concentrations of IgA and IgG subclasses (IgG1-IgG3) in individuals with long COVID (LC), stratified by plasma BAFF status or serum zonulin status (low, intermediate and high). \u003cstrong\u003ea\u003c/strong\u003e, BAFF status at 3-6 months after infection. \u003cstrong\u003eb\u003c/strong\u003e, BAFF status at 12 months. \u003cstrong\u003ec\u003c/strong\u003e, Zonulin status at 3-6 months. \u003cstrong\u003ed\u003c/strong\u003e, Zonulin status at 12 months. Each dot represents one participant; violin plots show the distribution with median and interquartile range. BAFF and zonulin strata were defined by data-driven clustering of ELISA values. \u003cem\u003eP\u003c/em\u003e values are shown in the figure; ns, not significant.\u003c/p\u003e","description":"","filename":"Ext.Figure.3IgsBAFFandZonulinStatus.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8876163/v1/a9cba577cae856494ea9bf1c.pdf"},{"id":105443521,"identity":"de03f9b7-b992-4fe0-b044-a3edb8c2994d","added_by":"auto","created_at":"2026-03-26 06:42:18","extension":"pdf","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":577855,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExtended Data Fig. 4 | \u003c/strong\u003eIsotype distribution of circulating Ig\u003csup\u003e+\u003c/sup\u003e B-cell blasts stratified by BAFF, APRIL and zonulin status in long COVID\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e, Frequencies of IgA\u003csup\u003e+\u003c/sup\u003e, IgG\u003csup\u003e+\u003c/sup\u003e and IgM\u003csup\u003e+\u003c/sup\u003e B-cell blasts in individuals with long COVID (LC) at 3-6 months (left set of plots) and 12 months (right set of plots), stratified by plasma B-cell-activating factor (BAFF) status (low, intermediate and high). \u003cstrong\u003eb\u003c/strong\u003e, As in \u003cstrong\u003ea\u003c/strong\u003e, stratified by plasma a proliferation-inducing ligand (APRIL) status. \u003cstrong\u003ec\u003c/strong\u003e, As in \u003cstrong\u003ea\u003c/strong\u003e, stratified by serum zonulin status. B-cell blasts were defined by flow cytometry (see gating strategy in Extended Data Fig. 2). Each dot represents one participant; violin plots show distribution with median and interquartile range. Status strata were defined by data-driven clustering of ELISA values. \u003cem\u003eP \u003c/em\u003evalues are shown in the figure; *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"Ext.Figure.4IgBlastBAFFAPRILZonulinStatus.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8876163/v1/0dfe671952e02092c59c5887.pdf"},{"id":105443503,"identity":"df726c52-5e1f-4435-a60e-21a7431380e7","added_by":"auto","created_at":"2026-03-26 06:42:09","extension":"pdf","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":1580722,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExtended Data Fig. 5 | \u003c/strong\u003eAdditional barrier and microbiota readouts following anti-BAFF treatment in gnotobiotic mice colonized with severe long COVID microbiota\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea, \u003c/strong\u003eHaematoxylin and eosin\u003cstrong\u003e (\u003c/strong\u003eH\u0026amp;E)-stained sagittal brain section indicating regions analysed for immunofluorescence (cortex, hippocampus and pons/medulla). \u003cstrong\u003eb\u003c/strong\u003e, Colon length in anti-BAFF- and isotype-treated gnotobiotic mice; each dot represents one mouse, and the \u003cem\u003eP\u003c/em\u003e value is shown. \u003cstrong\u003ec\u003c/strong\u003e, Representative immunofluorescence images of cortical vasculature stained for zonula occludens-1 (ZO-1; green) and nuclei (DAPI; blue) in isotype- (left) and anti-BAFF-treated (right) gnotobiotic mice. Boxed regions indicate areas shown at higher magnification; arrows highlight discontinuous ZO-1 staining patterns. Scale bars, 200mm (overview) and 100mm (insets). \u003cem\u003eP\u003c/em\u003evalues are shown where applicable.\u003c/p\u003e","description":"","filename":"Ext.Figure.5AntiBAFF.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8876163/v1/043c0b9f9f3eadf7e543e443.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Microbiota-induced intestinal barrier disruption drives BAFF-mediated B-cell dysregulation and autoimmunity in long COVID","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSince the start of the coronavirus disease 2019 (COVID-19) pandemic, a substantial proportion of individuals infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have developed post-COVID-19 condition or long COVID (LC), defined as symptoms typically beginning within 3 months of acute infection and lasting for ≥2 months, not explained by alternative diagnoses\u003csup\u003e1,9\u003c/sup\u003e. LC can be disabling, last for years\u003csup\u003e10\u003c/sup\u003e, and occurs across ages\u003csup\u003e1,9,11\u003c/sup\u003e. Although the risk of LC increases with the severity of the acute infection, many LC cases follow an initially mild infection\u003csup\u003e12\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eLC is heterogeneous across organ systems, but convergent analyses now suggest phenotypic and biological subtypes rather than a single syndrome\u003csup\u003e13\u003c/sup\u003e. A common, often disabling phenotype, which is more prevalent among women, features post-exertional malaise, profound fatigue, cognitive dysfunction (“brain fog”), and symptoms of cardiac and/or neurological dysautonomia\u003csup\u003e14-16\u003c/sup\u003e. Recent large-scale immune phenotyping highlights mechanistic clusters that plausibly reflect distinct pathobiology\u003csup\u003e17,18\u003c/sup\u003e. Multiple, potentially interacting pathways are under investigation, including viral antigen persistence, immune dysregulation and autoimmunity, coagulation abnormalities, and gut-brain-axis dysfunction\u003csup\u003e19\u003c/sup\u003e. Consistent with this, several cohorts report increased autoreactivity and higher rates of new or persistent autoimmune diagnoses in patients with LC compared to controls\u003csup\u003e20,21\u003c/sup\u003e. Emerging data indicate that SARS-CoV-2 proteins can persist at tissue interfaces leading to neuroinflammation\u003csup\u003e22\u003c/sup\u003e, while independent studies report sustained alterations in the intestinal microbiome\u003csup\u003e23,24\u003c/sup\u003e and biomarkers of impaired epithelial barriers and microbial translocation\u003csup\u003e6,25,26\u003c/sup\u003e. Consistent with a neuroimmune axis, several cohorts report elevations of astroglial injury markers (e.g., glial fibrillary acidic protein [GFAP]) and blood-brain barrier disruption signatures in LC subsets\u003csup\u003e27,28\u003c/sup\u003e. Together, these observations support a model in which barrier disruption enables persistent antigen exposure and chronic innate and adaptive immune activation. Whether intestinal barrier injury contributes to B-cell dysregulation and autoimmunity in LC remains unclear.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eB-cell-activating factor (BAFF) provides essential survival and differentiation cues to B cell populations; excess BAFF can erode tolerance and favour polyreactivity and autoreactivity, a pattern observed across autoimmune diseases\u003csup\u003e29\u003c/sup\u003e. Marginal zone (MZ) B cells are particularly BAFF-dependent, respond predominantly to T-independent antigens and harbour a B-cell receptor repertoire biased towards polyreactivity, a feature linked with autoreactivity\u003csup\u003e30\u003c/sup\u003e. In our prior work, we identified a MZ “precursor-like” population (MZp) with high B-cell regulatory capacity\u003csup\u003e31\u003c/sup\u003e that becomes impaired under BAFF excess, acquiring an exhaustion-associated CD11c⁺T-bet⁺ phenotype, polyclonal immunoglobulin (Ig) responses and autoimmune potential\u003csup\u003e32-35\u003c/sup\u003e. We therefore hypothesized that intestinal barrier dysfunction could sustain BAFF signalling and drive a T-bet-skewed, regulatory-impaired MZp state that promotes autoreactivity in LC.\u003c/p\u003e\n\u003cp\u003eHere, we longitudinally profiled adults with LC in the \u003cem\u003eInstitut de Recherches Cliniques\u003c/em\u003e \u003cem\u003ede Montréal\u003c/em\u003e (IRCM) post-COVID-19 (IPCO) cohort at 3–6, 12, and 24 months after infection. We measured circulating indicators of intestinal barrier dysfunction and microbial translocation, and related them to symptom severity, BAFF levels, and B-cell subset dynamics, including MZp B cells, and autoantibody reactivity. Markers of intestinal barrier injury were elevated in LC compared with pandemic controls and were associated with higher BAFF levels, MZp perturbation, and features of autoreactivity. To test causality, we transferred faecal microbiota from individuals with severe LC into germ-free mice, which reproduced barrier disruption and BAFF-linked immune abnormalities that were attenuated by BAFF blockade. Together, these findings implicate a microbiota-intestinal barrier-BAFF axis in LC-associated B-cell dysregulation and autoimmunity and identify BAFF as a candidate therapeutic target.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eViral antigen persistence, intestinal barrier dysfunction and sustained BAFF/APRIL pathway modulation in LC\u003c/p\u003e\n\u003cp\u003eWe established the IPCO longitudinal sampling framework incorporating pandemic controls and individuals with LC evaluated at 3-6, 12 and 24 months after SARS-CoV-2 infection (Fig.1a). \u0026nbsp;Participant characteristics are summarized in Extended Data Table 1. We quantified circulating SARS-CoV-2 antigens across this time course. Plasma spike protein was detectable in a subset of participants with LC, increased between 3-6 to 12 months in paired analyses, and declined by 24 months (Fig.1b, left). In contrast, plasma nucleocapsid protein was highest at 3-6 months and decreased thereafter (Fig.1b, right), indicating distinct antigen kinetics over the course of LC.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo assess intestinal barrier integrity, we measured serum zonulin. Zonulin concentrations were higher in LC than in pandemic controls across time points, with the greatest separation at 24 months (Fig.1c, left). Zonulin correlated positively with markers of microbial translocation, including ꞵ-D-glucan and lipopolysaccharide-binding protein (LBP), most prominently at 12 months (Extended Data Fig.1a). Zonulin also correlated positively with circulating BAFF at 12 months (Fig.1c, right), linking barrier disruption with modulation of the BAFF axis.\u003c/p\u003e\n\u003cp\u003eGiven reported connections between intestinal barrier dysfunction, chronic viral antigen exposure and dysregulation of the BAFF/a proliferation-inducing ligand (APRIL) \u0026nbsp;pathway\u003csup\u003e32,36-42\u003c/sup\u003e, we longitudinally profiled circulating BAFF/APRIL pathway components. Soluble B-cell maturation antigen (BCMA) and soluble transmembrane activator and CAML interactor (TACI) were strongly correlated at 3-6 and 12 months, but this coupling was attenuated by 24 months (Fig.1d, left). In parallel, BAFF and APRIL displayed an inverse relationship that was most evident at 3-6 and 12 months (Fig.1d, right). Correlation matrices and network visualizations further highlighted coordinated relationships between zonulin, markers of microbial translocation (LBP and ꞵ-D-glucan) and BAFF/APRIL pathway measures, with denser connectivity at 3-6 and 12 months and partial loosening by 24 months (Fig.1e).\u003c/p\u003e\n\u003cp\u003eTo identify cellular sources of BAFF and connect BAFF upregulation with viral antigen persistence, we quantified membrane BAFF (mBAFF) expression and intracellular spike protein in circulating monocyte subsets. mBAFF staining was increased in LC relative to pandemic controls across classical, intermediate and non-classical monocytes, with particularly prominent differences at 12 months (Fig.1f,g). \u0026nbsp;Intracellular spike was rare in classical monocytes and, when detected, occurred at low frequency. In contrast, spike-positive cells were detectable within intermediate and non-classical monocytes at 12 months and declined by 24 months in paired analyses (Extended Data Fig.1b,c). Circulating spike and BAFF levels were positively correlated (Extended Data Fig.1d, left). Across intermediate and non-classical subsets, frequencies of spike-positive cells tracked with mBAFF expression (Extended Data Fig.1d, right).\u003c/p\u003e\n\u003cp\u003eFinally, mBAFF expression was also increased on circulating T cells and B cells in LC compared to pandemic controls across time points (Fig.1h). Collectively, these data support a model in which prolonged viral antigen exposure and intestinal barrier disruption are associated with sustained remodelling of BAFF/APRIL pathway signalling in LC.\u003c/p\u003e\n\u003cp\u003eB cell compartment dysregulation in people with LC\u003c/p\u003e\n\u003cp\u003eBased on our observed longitudinally remodelling of BAFF/APRIL pathway measures, we performed high-dimensional immunophenotyping of circulating B cells in pandemic controls and individuals with LC at 3-6, 12 and 24 months after infection (gating strategy, Extended Data Fig.2). Across time points, the overall frequency of circulating CD19\u003csup\u003e+\u0026nbsp;\u003c/sup\u003eB cells was broad similar between groups (Fig.2a). However, LC was associated with time-dependent shifts in B cell subset composition. Naïve and transitional immature B cells were increased at 3-6 and 12 months in LC and converged toward control levels by 24 months, whereas total memory and resting switched memory B cells were increased at 24 months relative to earlier time points in LC (Fig.2a) In contrast to MZ B cells, which showed little separation between groups, MZp B cells were increased in LC and remained elevated through 24 months (Fig.2a).\u003c/p\u003e\n\u003cp\u003eWe next related this sustained MZp B cell expansion to BAFF/APRIL pathway activation. Correlation analyses across time points revealed coordinated relationships between MZp frequencies and BAFF/APRIL pathway measures, including soluble TACI and BCMA and monocyte subset mBAFF expression, most prominently at 3-6 and 12 months, with attenuation by 24 months (Fig.2b). These data support a model in which early BAFF-axis activation is linked to expansion of the MZp compartment, which persists despite partial relaxation of the broader correlation patterns over time.\u003c/p\u003e\n\u003cp\u003eTo probe the functional state of MZp cells, we assessed markers previously associated with inflammatory versus regulatory programmes, focusing on T-bet and NR4A3, respectively\u003csup\u003e33\u003c/sup\u003e. In individuals with LC, Tbet\u003csup\u003e+\u003c/sup\u003e MZp B cells were increased at earlier time points in a BAFF- and intestinal barrier-context-dependent manner. Stratifying LC participants by circulating BAFF levels (low/intermediate/high) revealed higher T-bet\u003csup\u003e+\u003c/sup\u003e MZp frequencies in those with higher BAFF at 3-6 and 12 months, with no clear separation by 24 months (Fig.2c). A similar pattern was observed upon stratification by zonulin status, with elevated T-bet\u003csup\u003e+\u003c/sup\u003e MZp frequencies most evident at earlier time points (Fig.2d). Conversely, NR4A3 expression within MZp cells was reduced in LC, particularly among individuals with higher BAFF and/or zonulin at 3-6 and 12 months, and differences were less pronounced by 24 months (Fig.2e,f).\u003c/p\u003e\n\u003cp\u003eTogether, these data define a sustained expansion of the MZp compartment in LC coupled to an early inflammatory skew, characterized by increased T-bet and reduced NR4A3 in the setting of elevated BAFF and markers of barrier disruption, that partially normalizes over time while MZp abundance remains increased.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eExcess BAFF and intestinal barrier dysfunction associate with altered immunoglobulin profiles and circulating autoantibodies\u003c/p\u003e\n\u003cp\u003eTo connect B-cell compartment dysregulation with humoral outputs, we quantified serum immunoglobulins in pandemic controls and individuals with LC, focusing on 3-6 and 12 months after infection, when BAFF and zonulin perturbations were most prominent. Total immunoglobulin levels were higher in LC at 12 months, and paired analyses showed an increase between 3-6 and 12 months that was most evident for IgA (Fig.3a). In contrast, IgG1-IgG3 varied modestly across time and across BAFF or zonulin strata (Extended Data Fig.3). IgM increased from 3-6 to 12 months in paired analyses (Fig.3b) and was most clearly linked to barrier dysfunction as IgM differed across zonulin strata at both 3-6 and 12 months, with higher concentrations in the high zonulin group (Fig.3d). By comparison, associations between IgM and BAFF strata were most apparent at 3–6 months and less evident at 12 months (Fig. 3c). We additionally observed a modest increase in IgG4 between 3–6 and 12 months (Fig. 3e), with higher IgG4 among individuals with higher BAFF at 12 months (Fig. 3f).\u003c/p\u003e\n\u003cp\u003eTo relate these systemic Ig profiles to the antibody-secreting compartment, we quantified circulating Ig\u003csup\u003e+\u003c/sup\u003e B cell blasts over time. Across 3-6 and 12 months, stratifying LC participants by BAFF, APRIL and zonulin status revealed generally modest shifts in the isotype composition of Ig\u003csup\u003e+\u0026nbsp;\u003c/sup\u003eblasts, with the clearest separation for IgM\u003csup\u003e+\u003c/sup\u003e blasts across BAFF strata at 3-6 months (Extended Data Fig.4). Over the longitudinal time course, frequencies of IgG\u003csup\u003e+\u003c/sup\u003e and IgM\u003csup\u003e+\u003c/sup\u003e B-cell blasts increased at 24 months, whereas IgA+ blasts showed little change (Fig.3h). Together, these data indicate sustained remodelling of the antibody-secreting compartment in a subset of individuals with LC.\u003c/p\u003e\n\u003cp\u003eTo assess autoreactivity, we measured IgG autoantibodies to a panel of nuclear antigens at 3–6, 12 and 24 months. LC was associated with increased seropositivity for several antigens including DNA topoisomerase I, Mi-2 and SmD, with the strongest enrichment at 3–6 and 12 months post-infection (Fig. 3i). Stratifying LC participants by zonulin status further showed that seropositivity across multiple autoantibodies was enriched among individuals with higher zonulin (Fig. 3j), consistent with an association between barrier dysfunction with increased systemic autoreactivity in LC.\u003c/p\u003e\n\u003cp\u003eLC faecal microbiota transfer reproduces barrier disruption and BAFF-associated B cell dysregulation in germ-free mice\u003c/p\u003e\n\u003cp\u003eTo test whether the LC-associated intestinal microbiota contribute causally to intestinal barrier dysfunction and immune perturbations observed in the IPCO cohort, we performed faecal microbiota transplantation (FMT) into germ-free mice using stool from human donors selected on the basis of neurological symptom burden together with serum zonulin and plasma BAFF levels (Extended Data Table 6). “Severe” donors were defined by the presence of neurological symptoms (listed in Extended Data Table 2) and elevated zonulin/BAFF, whereas “mild” donors lacked neurological symptoms and had low zonulin/BAFF levels (Fig.4a; Extended Data Table 6). All donor stool samples except 1 had undetectable SARS-CoV-2 spike, reducing the likelihood that transferable faecal viral antigen accounts for the observed phenotypes (Extended Data Table 6).\u003c/p\u003e\n\u003cp\u003eFollowing engraftment, mice colonized with severe LC microbiota showed a trend toward shorter colon length relative to mice receiving mild LC microbiota (Fig.4b). In contrast, serum LBP was increased in mice colonized with severe LC microbiota (Fig.4c, left), consistent with increased microbial translocation. Immunostaining for the tight-junction protein zonula occludens-1 (ZO-1) further revealed more discontinuous and punctate epithelial junctional staining in the colons of mice colonized with severe LC microbiota relative to mild LC microbiota recipients (Fig.4d), indicative of impaired barrier integrity.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBarrier disruption was accompanied by heightened BAFF activity in vivo. Severe LC-microbiota recipients exhibited increased serum BAFF (Fig.4c, right) together with higher frequencies of BAFF-expressing CD45\u003csup\u003e+\u003c/sup\u003e immune cells in both the colonic LP and spleen (Fig.4e). In the spleen, immunofluorescence revealed fewer discrete peanut agglutinin (PNA)\u003csup\u003e+\u003c/sup\u003e foci, diminished CD169 signal intensity on marginal zone metallophilic macrophages, altered CD169\u003csup\u003e+\u0026nbsp;\u003c/sup\u003emarginal zone architecture, and a more diffuse IgM\u003csup\u003ebright\u003c/sup\u003e staining pattern extending across marginal zone, follicular and extra-follicular areas in severe LC microbiota recipients (Fig.4f), suggesting disruption of splenic organization and reduced germinal-centre-like structures. Consistent with a shift toward IgM-biased B-cell responses, flow cytometry showed increased frequencies of CD19\u003csup\u003e+\u003c/sup\u003eCD1d\u003csup\u003e+\u003c/sup\u003eIgM\u003csup\u003ebright\u003c/sup\u003e B cells and increased frequencies of IgM\u003csup\u003e+\u003c/sup\u003e cells within the blast compartment in mice colonized with severe LC-microbiota (Fig.4g). In Peyer’s patches, regions of IgA\u003csup\u003e+\u003c/sup\u003eB220\u003csup\u003e+\u003c/sup\u003e double-positive cells were more prominent in mice colonized with mild LC microbiota and reduced in severe LC-microbiota recipients (Fig.4h), suggesting an alteration in IgA mucosal B-cell features in the severe LC-microbiota condition. Together, these findings indicate that microbiota from severe LC donors is sufficient to induce gut barrier disruption and microbial translocation alongside systemic BAFF upregulation and remodelling of B-cell organization and isotype output in gnotobiotic mice.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBAFF antagonism ameliorates LC-like immune and intestinal barrier features in gnotobiotic mice\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGiven the convergence of BAFF-axis perturbation, intestinal barrier disruption and B-cell dysregulation in the human cohort and FMT model, we asked whether BAFF blockade could modify the LC-like phenotype. Germ-free mice colonized with faecal microbiota from a severe LC donor received a single intraperitoneal dose of anti-mouse BAFF monoclonal antibody (Sandy-2)\u003csup\u003e43\u003c/sup\u003e or isotype control and were analysed 15 days later (Fig.5a). Anti-BAFF treatment markedly reduced circulating BAFF concentrations and decreased mBAFF expression on CD45\u003csup\u003e+\u003c/sup\u003e cells (Fig.5c). Consistent with the known dependence of peripheral B cells on BAFF, anti-BAFF treatment reduced the frequency of B cells and decreased the frequency of total blasts (Fig.5d). Within the CD19\u003csup\u003e+\u003c/sup\u003eCD1d\u003csup\u003e+\u003c/sup\u003eIgM\u003csup\u003ebright\u0026nbsp;\u003c/sup\u003ecompartment, anti-BAFF increased CD73 expression (Fig.5d), a marker previously linked to immunoregulatory programmes\u003csup\u003e44\u003c/sup\u003e. Anti-BAFF also impacted the immunoglobulin isotype composition of the blast compartment, significantly increasing proportions of IgA\u003csup\u003e+\u003c/sup\u003e and IgM\u003csup\u003e+\u0026nbsp;\u003c/sup\u003eblasts, whereas IgG\u003csup\u003e+\u003c/sup\u003e blasts did not change significantly (Fig.5e). In parallel, serum anti-nuclear antibody (ANA) levels were reduced in anti-BAFF-treated mice (Fig.5b), consistent with attenuation of systemic autoreactivity.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAt the tissue level, anti-BAFF treatment was associated with improved epithelial junctional organization, with more continuous ZO-1 staining in colonic epithelium compared with isotype-treated controls (Fig.5f), and colon length showed a non-significant trend toward increase in anti-BAFF-treated mice (Extended Data Fig.5b). Given the severe LC-donor microbiota was selected from individuals with neurological symptom burden, we also evaluated neuroinflammatory and blood-brain barrier associated readouts across specific brain regions (Extended Data Fig.5a). Anti-BAFF treatment reduced glial fibrillary acidic protein (GFAP) staining in the hippocampus and decreased ionized calcium-binding adapter molecule 1 (IBA1) signal in the pons/medulla (Fig.5g). In representative sections, astrocytes appeared less hypertrophic/bushy and microglia less amoeboid in anti-BAFF-treated mice than in isotype controls, consistent with attenuated neuroinflammatory features. Anti-BAFF treatment was also associated with more linear ZO-1 staining along the cortical vasculature (Extended Data Fig.5c), suggestive of improved junctional organization and possibly blood-brain barrier integrity.\u003c/p\u003e\n\u003cp\u003eBAFF blockade is associated with microbiota restructuring\u003c/p\u003e\n\u003cp\u003eTo determine whether BAFF antagonism was accompanied by changes in the engrafted microbial community, we profiled faecal microbiota composition in gnotobiotic mice following treatment. Alpha diversity metrics were largely unchanged, with a trend toward higher Shannon diversity (Fig.5h), whereas beta diversity analyses showed separation between anti-BAFF-treated and isotype-treated mice (Fig.5i). Differential-abundance analyses identified multiple genera contributing to this shift (Fig. 5j–l). Thus, while BAFF blockade does not appear to broadly increase community richness, it is associated with a compositional restructuring of the microbiota in this gnotobiotic setting, along with reduced BAFF-linked immune remodelling and improvements in barrier-associated features and neuroinflammatory signatures.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this prospective cohort focused on non-hospitalized adults with LC, we identified a signature of intestinal barrier dysfunction linked to BAFF/APRIL pathway remodelling, BAFF-biased B-cell dysregulation and systemic autoreactivity that is most pronounced within the first year after SARS-CoV-2 infection and only partially relaxes by 24 months. Using gnotobiotic mouse models, we showed that faecal microbiota from individuals with severe LC was sufficient to induce barrier disruption and microbial translocation alongside elevated BAFF and B-cell compartment remodelling, including systemic autoreactivity. Several of these features were attenuated by BAFF blockade. Together, these findings support a model in which dysbiosis-associated barrier disruption sustains a BAFF-dependent, marginal zone-skewed B-cell response linked to autoreactivity and multi-organ consequences in LC while identifying BAFF signalling as a potential therapeutic axis.\u003c/p\u003e\n\u003cp\u003eOther independent cohorts have reported persistent intestinal dysbiosis\u003csup\u003e24\u003c/sup\u003e, microbial translocation and epithelial barrier injury in LC\u003csup\u003e6\u003c/sup\u003e, as well as increased rates of autoimmune disease\u003csup\u003e20,45\u003c/sup\u003e and sustained autoantibody responses\u003csup\u003e21,46\u003c/sup\u003e. In our cohort, LC participants exhibited higher serum zonulin levels and markers of microbial translocation than pandemic controls, with differences persisting at 24 months. This intestinal barrier phenotype was tightly associated with modulation of the BAFF/APRIL system as demonstrated by increased soluble BAFF, elevated mBAFF on monocytes and T cells, higher levels of soluble TACI and BCMA, and an inverse relationship between BAFF and APRIL. These patterns mirror BAFF/APRIL dysregulation described in systemic autoimmune diseases\u003csup\u003e47,48\u003c/sup\u003e and chronic viral infections\u003csup\u003e32,33,35,49\u003c/sup\u003e, and are notable here in a cohort of individuals with LC who experienced mild acute SARS-CoV-2 infection.\u003c/p\u003e\n\u003cp\u003eWithin this context, we observed sustained alterations of the B-cell compartment centred on MZp B cells. Although overall CD19\u003csup\u003e+\u003c/sup\u003e B-cell frequencies and classical MZ B-cell frequencies were largely preserved, MZp cells were expanded in LC for at least 24 months and were enriched among individuals with higher BAFF and higher zonulin. This expansion was accompanied at earlier time points by increased T-bet expression and reduced NR4A3 in MZp cells, features previously associated with chronic activation, diminished regulatory programmes and extra-follicular, lower-affinity responses\u003csup\u003e33,35,50-52\u003c/sup\u003e. The association between MZp frequency, BAFF, zonulin and soluble TACI, together with an IgM-skewed humoral profile and nuclear autoreactivity, is consistent with a BAFF-driven shift of this BAFF-sensitive population toward a more inflammatory state\u003csup\u003e31,34,35\u003c/sup\u003e. While our data are correlative in humans, they align with work implicating BAFF and T-bet\u003csup\u003e+\u003c/sup\u003e age-associated B-cell-like populations in loss of tolerance and autoimmunity, and suggest that MZp dysfunction may be a feature of post-infection chronic inflammatory conditions\u003csup\u003e34\u003c/sup\u003e. The serological data further support this model. LC participants showed rising immunoglobulin levels over the first year after infection, with IgM most clearly associated with zonulin status. We also detected IgG autoreactivity to selected nuclear antigens, including Mi-2, SmD and topoisomerase I, which were enriched in individuals with LC, particularly those with high zonulin, whereas pandemic controls were consistently negative. Although the breadth of our autoantibody panel was limited and we did not assess antigen specificity at the single-cell level, these findings position a subset of individuals with LC within the spectrum of BAFF- and barrier compromise-associated autoimmune risk states.\u003c/p\u003e\n\u003cp\u003eOur gnotobiotic experiments provide orthogonal support for a contribution of the LC-associated intestinal microbiota in shaping intestinal barrier and immune phenotypes. Germ-free mice colonized with microbiota from donors with severe LC, selected based on neurological symptom burden together with higher BAFF and zonulin, developed features consistent with barrier compromise and microbial translocation, including disrupted colonic ZO-1 organization and elevated LBP, alongside increased systemic BAFF and a higher frequency of BAFF-expressing CD45\u003csup\u003e+\u003c/sup\u003e cells in gut and spleen. These changes were accompanied by remodelling of the B-cell compartments, with expansion of CD19\u003csup\u003e+\u003c/sup\u003eCD1d\u003csup\u003e+\u003c/sup\u003eIgM\u003csup\u003ebright\u0026nbsp;\u003c/sup\u003eB cells, an increased fraction of IgM\u003csup\u003e+\u003c/sup\u003e cells within the blast compartment and reduced IgA\u003csup\u003e+\u003c/sup\u003eB220\u003csup\u003e+\u003c/sup\u003e regions in Peyer’s patches, together with alterations in splenic organization suggestive of IgM\u003csup\u003ebright\u003c/sup\u003e extra-follicular foci, altered MZ and follicular areas with reduced germinal-centre-like structures. In contrast, mice colonized with mild LC microbiota showed comparatively preserved barrier integrity and lymphoid architecture. Notably, most donor stool samples had undetectable SARS-CoV-2 spike and BAFF, reducing the likelihood that passive transfer of faecal viral antigen or human BAFF accounts for the observed phenotypes and instead implicating the microbial community and/or its products as upstream drivers (Extended Data Table 6). Although these experiments do not establish that identical mechanisms operate in humans, they show that a severe LC-associated microbiota can be sufficient to induce an LC-like barrier-BAFF-B-cell axis in vivo.\u003c/p\u003e\n\u003cp\u003eBAFF neutralization in severe-LC-microbiota-colonized mice supports a functional role for BAFF signalling in linking intestinal barrier perturbation to downstream immune and neuroinflammatory features. Beyond the expected suppression of BAFF availability and BAFF-dependent B-cell compartments, anti-BAFF treatment was accompanied by reduced ANA titres and improved intestinal epithelial junctional organization, as well as attenuation of astrocytic and microglial activation together with more linear vascular ZO-1 staining in the cerebral cortex. Collectively, these observations are consistent with BAFF acting at the intersection of a barrier-immune-neuroinflammation axis in this model, although the intervening cellular pathways and tissue specificity remain to be defined.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBecause mucosal immunity can reciprocally shape microbial community structure, we asked whether BAFF blockade altered the engrafted microbiota. BAFF neutralization was associated with restructuring of the microbial community: alpha diversity metrics changed modestly, whereas beta diversity separated anti-BAFF-treated mice from isotype controls with differential abundance of multiple taxa. This finding raises the possibility of bidirectional coupling, in which BAFF-driven immune and barrier changes feed back to shape microbial ecology, rather than BAFF acting solely downstream of dysbiosis. Together with the established roles of BAFF in sustained autoreactive B-cell responses and extra-follicular responses, and with the clinical efficacy of BAFF inhibition in systemic lupus erythematosus and related autoimmune diseases\u003csup\u003e7,8,35,47,48,53-58\u003c/sup\u003e, these data provide preclinical proof-of-concept that targeting the BAFF pathway can ameliorate LC-like features induced by human LC microbiota, while motivating future work to uncover direct versus indirect effects on the microbiota.\u003c/p\u003e\n\u003cp\u003eOur findings should be interpreted in the context of certain limitations. This is a single-centre cohort with a defined sample size, enriched for women (as is the case for most LC cohorts), and restricted to non-hospitalized individuals who provided stool samples. Associations between BAFF, zonulin, MZp features and autoreactivity in humans are correlative and could be influenced by comorbidities or concomitant medications, although we controlled for SARS-CoV-2 reinfection and vaccination timing. The autoantibody panel was targeted and does not capture the full breadth of potential autoreactivities, nor did we resolve specificity at the level of individual B-cell clones. Donors selected for gnotobiotic experiments were female representing defined severe or mild LC phenotypes. Therefore, microbiota-driven effects may not generalize to all LC endotypes. Finally, BAFF-blockade was evaluated using a short-term, single-dose regimen in a defined gnotobiotic setting. As such, response duration, safety and the optimal therapeutic window would need to be defined before translation into clinical trials.\u003c/p\u003e\n\u003cp\u003eDespite these caveats, the convergence of longitudinal human profiling, networked biomarker relationships, microbiota-transfer experiments and pharmacologic BAFF inhibition provides a coherent framework for LC pathogenesis centered on a gut-BAFF-B-cell axis. In this framework, dysbiosis and intestinal barrier disruption promote microbial translocation and prolong inflammatory cues, including persistent viral antigen exposure, thereby sustaining BAFF production by myeloid and stromal compartments. Elevated BAFF would be expected to favour expansion and reprogramming of MZp and related innate-like B-cell subsets, biasing humoral outputs toward IgM while still permitting class-switching, given that MZ populations can undergo isotype switching\u003csup\u003e30\u003c/sup\u003e, and promoting extra-follicular and autoreactive responses\u003csup\u003e52\u003c/sup\u003e with potential consequences for mucosal, vascular and neurological compartments. This model integrates several leading hypotheses for LC pathogenesis (e.g., intestinal barrier failure, viral antigen persistence, autoimmunity and neuroimmune dysregulation) into a pathway that can be therapeutically targeted.\u003c/p\u003e\n\u003cp\u003eOur data highlight several translational opportunities. First, combining profiling of BAFF/APRIL outputs with zonulin and markers of microbial translocation, together with MZp features and autoantibody profiles, could help define a BAFF-biased, barrier-injury endotype of LC and support biomarker-guided stratification in future studies. Second, longitudinal interventional studies are needed to determine how antiviral therapy, microbiota-directed strategies and BAFF/APRIL pathway modulation interact over time, and whether particular treatment sequences or combinations are required for durable benefit. Third, carefully designed trials of BAFF pathway inhibitors, potentially combined with approaches to restore intestinal barrier integrity or reshape the microbiota, should be pursued in well-characterized LC populations, while monitoring safety, infection risk and effects on vaccine responsiveness.\u003c/p\u003e\n\u003cp\u003eIn conclusion, our study connects intestinal barrier dysfunction with BAFF/APRIL pathway remodelling, marginal-zone-skewed B-cell perturbations and systemic autoreactivity in LC and shows that an LC-like phenotype induced by severe LC microbiota is modifiable by BAFF blockade in vivo. These findings provide mechanistic support for a gut-immune axis in LC and identify BAFF signalling as a target for precision therapeutics in this emerging post-infectious chronic disease.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eStudy design and population\u003c/p\u003e\n\u003cp\u003eThe \u003cem\u003eInstitut de Recherches Cliniques\u003c/em\u003e \u003cem\u003ede Montréal\u0026nbsp;\u003c/em\u003e(IRCM) post-coronavirus disease 2019 (COVID-19) research clinic integrates clinical care with a prospective observational cohort and biobank (IPCO; protocol #2021-1092; ClinicalTrials.gov\u0026nbsp;\u003cu\u003eNCT04736732\u003c/u\u003e)\u003csup\u003e59\u003c/sup\u003e. Between 12 February 2021 and 25 July 2022, Quebec residents aged 18 to 100 years were enrolled after written informed consent. The study was approved by the IRCM Research Ethics Board.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe cohort comprised individuals meeting the WHO clinical case definition\u003csup\u003e60\u003c/sup\u003e of post-COVID-19 condition (long COVID; LC), and pandemic control participants who had never tested positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and did not report symptoms consistent with acute COVID-19. LC participants had PCR-confirmed SARS-CoV-2 infection and were enrolled at least 3 months after infection. Study visits with biobanking were scheduled at 3-6, 12 and 24 months after infection, although participants could enrol at any time after infection and contributed samples at the nearest scheduled time point(s).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor the present analyses, we included participants who provided stool samples and did not experience severe acute COVID-19 (defined as requiring supplemental oxygen, presenting to the emergency department or being hospitalized). This subset included 103 LC participants (36 males, 67 females) and 17 pandemic controls (9 males, 8 females) (Fig. 1a). Clinical and demographic characteristics are summarized in Extended Data Table 1.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eClinical procedures\u003c/p\u003e\n\u003cp\u003eDemographic data were collected using a self-administered questionnaire. Clinical information was recorded by a healthcare professional using a standardized case-report form. At each visit, participants underwent a structured assessment of 51 symptoms associated with LC along with documentation on vaccination status (Extended Data Tables 1,2). Blood samples were collected at IRCM. Clinical laboratory testing was performed at the \u003cem\u003eCentre Hospitalier de l'Université de Montréal\u003c/em\u003e (CHUM) Department of Laboratory Medicine according to standard operating procedures.\u003c/p\u003e\n\u003cp\u003eSample collection and processing\u003c/p\u003e\n\u003cp\u003eWhole blood and stool samples were collected at 3-6, 12 and 24 months after infection. Blood was drawn into ethylenediaminetetraacetic acid (EDTA) and acid citrate dextrose (ACD) tubes for plasma and peripheral blood mononuclear cell (PBMC) isolation, and into serum separator tubes (SST) for serum, and processed within 6 h of collection. Whole blood was centrifuged at 400g for 10 min at room temperature to separate plasma and serum, which were aliquoted and stored at -80°C. PBMCs were isolated using SepMate tubes (STEMCELL Technologies) according to the manufacturer’s instructions and cryopreserved in freezing medium (10% dimethyl sulfoxide (DMSO) in heat inactivated foetal bovine serum (FBSi)) in liquid nitrogen. \u0026nbsp;Sample availability at each time point are shown in Extended Data Table 3.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eStool was self-collected into sterile, dry tubes, stored immediately at −20°C by participants, and transferred to −80°C upon receipt at the clinic\u003csup\u003e61\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSoluble biomarkers in blood\u003c/p\u003e\n\u003cp\u003eHumans\u003c/p\u003e\n\u003cp\u003ePlasma B-cell activating factor (BAFF), a proliferation inducing ligand (APRIL), lipopolysaccharide-binding protein (LBP), and \u0026nbsp;ꞵ-D-glucan were quantified by enzyme-linked immunosorbent assay (ELISA) using commercial kits (Human BAFF/BLyS, R\u0026amp;D systems; human APRIL, Thermo Fisher Scientific; human LBP, Abcam; ꞵ-D-glucan, Glucatell, Associates of Cape Cod) according to the manufacturers’ instructions. Serum zonulin was quantified by ELISA using a commercial kit (human Zonulin, Elabscience) according to the manufacturer’s instructions. Serum soluble transmembrane activator and calcium-modulating cyclophilin ligand interactor (TACI) and B-cell maturation antigen (BCMA) were measured using Meso Scale Discovery (MSD) assays (R-PLEX human TACI; U-PLEX human BCMA) according to the manufacturer’s instructions.\u0026nbsp;Plasma SARS-CoV-2 spike and nucleocapsid (N) proteins were quantified using S-PLEX SARS-CoV-2 Spike and S-PLEX SARS-CoV-2 Nucleocapsid assays (MSD).\u0026nbsp;MSD\u0026nbsp;data were analysed using Discovery Workbench software (MSD).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMice\u003c/p\u003e\n\u003cp\u003eSerum BAFF and LBP were measured using mouse-specific ELISA kits (BAFF/BlyS, R\u0026amp;D Systems; LBP, Abcam) according to the manufacturers’ instructions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eImmunoglobulin isotypes and autoantibody measurements\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHumans\u003c/p\u003e\n\u003cp\u003eSerum immunoglobulin isotypes were quantified with the MILLIPLEX MAP Human Immunoglobulin Isotyping Magnetic Bead Panel (MilliporeSigma), with data acquisition on a Luminex MAGPIX system and analysis using the manufacturer’s recommended settings.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSerum IgG autoantibodies were measured using an electrochemiluminescence-based multiplex assay (MSD U-PLEX). Briefly, pooled biotinylated antigens (double stranded DNA (dsDNA), DNA topoisomerase I, histidyl-tRNA-synthetase, Mi-2, SmD, U1-snRNP, U1-snRNP A, Ro/SS-A, La/SS-B and Ro/SS-A52; Surmodics IVD) were prepared according to the MSD Biotinylation QuickGuide and coupled to U-PLEX linkers (final coating concentration, 66 nM per antigen). A fixed positivity threshold of 1,000 electrochemiluminescence (ECL) counts was used, selected a priori as threefold the blank (background) signal to prioritize specificity. Data were analysed in Discovery Workbench (MSD).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMice\u003c/p\u003e\n\u003cp\u003eSerum anti-nuclear IgG antibodies (ANA) were quantified by ELISA using a commercial kit (US Biological Life Sciences) according to the manufacturer’s instructions.\u003c/p\u003e\n\u003cp\u003eMulticolour flow cytometry\u003c/p\u003e\n\u003cp\u003eHumans\u003c/p\u003e\n\u003cp\u003eCryopreserved PBMCs were thawed, washed with Iscove's Modified Dulbecco's Medium (IMDM; Gibco) and phosphate-buffered saline (PBS), and stained for viability using LIVE/DEAD Fixable Aqua (Invitrogen). Non-specific binding was blocked in FACS buffer (PBS, 2% heat-inactivated foetal bovine serum (FBSi; Gibco) and 0.1% sodium azide) supplemented with 20% FBSi, mouse IgG (50µg; Sigma-Aldrich) and Human BD Fc Block (7µL per 10\u003csup\u003e6\u003c/sup\u003e cells; BD Biosciences).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSurface staining was performed using fluorochrome-conjugated mouse anti-human monoclonal antibodies against CD19, IgM, CD10, CD83, CD27, IgG, CD14, CD20, CD21, CD1c, CD38, CD16, CD11c, BAFF, CD3, CD66b and IgA. For intracellular antigen detection, cells were fixed and permeabilized using Cytofix/Cytoperm(BD Biosciences) and stained with Alexa Fluor 700 anti-SARS-CoV-2 Spike S1 (R\u0026amp;D Systems). For intranuclear staining, cells were processed with the FoxP3/Transcription Factor Staining Buffer Set (Thermo Fisher Scientific) and stained with antibodies against NR4A3 and T-bet (BioLegend). Fluorescence-minus-one (FMO) controls were used to define gates, and anti-mouse Igκ compensation beads (Thermo Fisher Scientific) were used for compensation. Antibody details are provided in Extended Data Table 4a.\u003c/p\u003e\n\u003cp\u003eMice\u003c/p\u003e\n\u003cp\u003eSingle-cell suspensions from spleen and lamina propria (LP) were prepared fresh (see below). Cells were blocked in FACS buffer supplemented with 20% FBSi, anti-Mouse CD16/CD32 (2µL per 10\u003csup\u003e6\u003c/sup\u003e cells; eBioscience), and normal goat and rat serum (2µL each per well). Surface staining was performed using fluorochrome-conjugated antibodies against CD45, CD19, CD138, CD11b, Ly6C, Ly6G, CD3, IgM, CD1d, CD73, IgA, IgG, CD11c, MHC class II, F4/80 and BAFF. For intranuclear staining, cells were processed using the FoxP3/Transcription Factor Staining Buffer Set (Thermo Fisher Scientific) and stained with antibodies against FoxP3, T-bet and RORℽt. Antibody details are provided in Extended Data Table 4b.\u003c/p\u003e\n\u003cp\u003eFor all experiments, cells were fixed in 1.25% paraformaldehyde at 4°C before acquisition on a BD LSRFortessa. Data were analysed using FlowJo v10.8.1 and GraphPad Prism v10.3.1.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMice (housing and ethics)\u003c/p\u003e\n\u003cp\u003eFemale C57BL/6 mice (5-7 weeks old) were obtained from the University of Calgary Germ-Free and Gnotobiotic Platform and housed in the IRCM Germ-Free and Gnotobiotic Facility in sterile cages within flexible-film isolators (Class Biologically Clean) at 22±1°C on a 12-h light/dark cycle with autoclaved chow and water provided \u003cem\u003ead libitum\u003c/em\u003e.Mice were routinely monitored for microbial contamination. All procedures were performed under sterile conditions in a biosafety cabinet, in accordance with Canadian Council of Animal Care guidelines, and were approved by the IRCM Animal Care Committee.\u003c/p\u003e\n\u003cp\u003eFaecal microbiota transplantation into germ-free mice\u003c/p\u003e\n\u003cp\u003eHuman stool aliquots were resuspended anaerobically in brain-heart infusion (BHI) broth containing 30% glycerol (200mg stool per 1mL), passed through a 70-µm cell strainer and kept on ice until administration. After a 2-week acclimation period, germ-free mice received 200µL of faecal suspension by oral gavage on three occasions over 1 week (every other day), followed by a 3-week engraftment period. Three independent experiments were performed using stool from donors with mild versus severe LC (all mice were germ-free and female; n=5 per group). Experimental group sizes and donor group assignment for each experiment are summarized in Extended Data Table 5. Stool donor characteristics are shown in Extended Data Table 6.\u003c/p\u003e\n\u003cp\u003eAnti-BAFF treatment in the gnotobiotic long COVID model\u003c/p\u003e\n\u003cp\u003eGnotobiotic C57BL/6 mice colonized with stool from a donor with severe LC received a single intraperitoneal dose of an anti-mouse BAFF monoclonal antibody (Sandy-2) or mouse IgG1 isotype control (AdipoGen Life Sciences; n=5 per group). Tissues were harvested 18 days after treatment, as described previously\u003csup\u003e43\u003c/sup\u003e. This experiment was performed twice; experimental group sizes and treatments are summarized in Extended Data Table 5.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMouse tissue harvest, processing and immunofluorescence\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMice were sedated with ketamine and euthanized by terminal cardiac puncture. Blood was allowed to clot, and serum was isolated and stored at -80°C. Colon, Peyer's patches, spleen and brain were collected into ice-cold Hank’s Balanced Salt Solution (HBSS; Multicell). Colons were measured and divided: one half was fixed in 10% neutral-buffered formalin and the other was cut into 2-cm segments for colon epithelial cell (CEC) isolation and LP cell preparation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCECs were isolated by washing in Ca\u003csup\u003e2+\u003c/sup\u003e/Mg\u003csup\u003e2+\u003c/sup\u003e-free HBSS supplemented with 25mM HEPES, followed by incubation in pre-warmed HBSS containing 15mM HEPES, 10% FBSi, 5mM EDTA, and 1mM dithiothreitol (DTT) for 15min at 37°C with intermittent shaking. Cell suspensions were passed through 100-μm strainers. LP cells were obtained by enzymatic digestion in 5mL IMDM supplemented with 1% penicillin-streptomycin-glutamine (Gibco), Liberase and DNase I (Roche) for 1h at 37°C with agitation every 15min, followed by sequential filtration (100- and 40-μm strainers), washing in PBS and downstream flow-cytometry staining. Spleens were divided, with one portion processed for flow cytometry and the other reserved for imaging.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSpleens and Peyer’s patches were embedded in optimal cutting temperature (OCT) compound (Scigen) and stored at -80℃. Serial 10µm cryosections were fixed in cold acetone (-20℃), air dried and stored at -80℃ until immunolabelling, as described previously\u003csup\u003e62\u003c/sup\u003e. Formalin-fixed colons were paraffin-embedded and sectioned at 10µm (IRCM Histology Core). Brains were fixed in 4% paraformaldehyde, cryoprotected in 30% sucrose, embedded in OCT, sectioned at 14µm and stored at -80℃ (IRIC Histology core).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eImmunolabelling was performed at the CRCHUM Molecular Pathology Core using a Discovery Ultra automated stainer (Roche). \u0026nbsp;For paraffin sections, antigen retrieval was performed with Cell Conditioning 1 (Tris-EDTA, pH 7.8) for 60min at 95°C. Slides were blocked in PBS containing 1% bovine serum albumin (BSA) (30min, room temperature), incubated with primary and secondary antibodies (2h each, room temperature), counterstained with DAPI (1:3,000; 10min) and washed in PBS. Autofluorescence was reduced with 0.1% Sudan Black B in 70% ethanol (15min). Slides were mounted with Fluoromount (Sigma) and scanned on an Aperio VERSA 200 (20×/0.8 NA; 0.275mm per pixel; Leica Biosystems). Images were reviewed in Aperio ImageScope (Leica Biosystems). Reagents used for immunolabelling included biotinylated peanut agglutinin (PNA), biotinylated anti-IgA and fluorophore-conjugated secondary antibodies/streptavidin, together with primary antibodies against IgM, B220 (CD45R), zonula occludens-1 (ZO-1), glial fibrillary acidic protein (GFAP) and ionized calcium-binding adaptor molecule 1 (IBA1). Full reagent details (including supplier, catalogue number, clone (where applicable) and dilution) are provided in Extended Data Table 4c.\u003c/p\u003e\n\u003cp\u003eMouse faecal microbiome analysis\u003c/p\u003e\n\u003cp\u003eMouse faecal pellets were collected into sterile, dry tubes on dry ice and stored at -80°C. DNA was extracted using the Dneasy 96 PowerSoil Pro QIAcube HT Kit on a QIAcube 96 system (QIAGEN). The V4 region of the bacterial 16S rRNA genes was amplified and sequenced on an Illumina MiSeq platform (McGill Centre for Microbiome Research).\u0026nbsp;Read quality metrics (including Phred scores and duplicate rate) were assessed with FastQC\u003csup\u003e63\u003c/sup\u003e. Adapters and primer sequences were trimmed with Trimmomatic\u003csup\u003e64\u003c/sup\u003e (paired-end; LEADING=3, TRAILING=3, SLIDINGWINDOW=4:15). Paired reads were merged with PANDAseq\u003csup\u003e65\u003c/sup\u003e (minimum overlap, 30 nt; minimum length, 50 nt; sequences containing ambiguous bases were removed). Non-specific reads and chimeras were filtered as described previously\u003csup\u003e66\u003c/sup\u003e. Sequences were clustered with CD-HIT-EST\u003csup\u003e67\u003c/sup\u003e and taxonomically classified using Kraken2\u003csup\u003e68\u003c/sup\u003e against an in-house RefSeq-based database (updated November 2023). Downstream analyses were performed in R (v4.2) using phyloseq\u003csup\u003e69\u003c/sup\u003e and tidyverse\u003csup\u003e70\u003c/sup\u003e. Alpha diversity was quantified using the Shannon index; two-group comparisons were performed using t-tests and comparisons across \u0026gt;2 groups were performed using Kruskal-Wallis tests. \u0026nbsp;Differential abundance was assessed using linear discriminant effect size (LEfSe) implemented with edgeR\u003csup\u003e71\u003c/sup\u003e and microbiomeMarker\u003csup\u003e72\u003c/sup\u003e (LDA ≥3.5; Kruskal-Wallis \u003cem\u003eP\u003c/em\u003e ≤0.05). \u0026nbsp;Beta diversity was evaluated by principal coordinate analysis (PCoA) using Bray-Curtis distances; overall differences between groups were tested by PERMANOVA (adonis2, vegan v2.6-6.1) with 1,000 permutations. Pairwise PERMANOVA comparisons used an adapted \u003cem\u003epairwise.adonis\u003c/em\u003e procedure\u003csup\u003e73\u003c/sup\u003e with Bonferroni adjustment. Unless otherwise stated, \u003cem\u003eP\u003c/em\u003e values were two-sided and \u003cem\u003eP\u003c/em\u003e ≤0.05 were considered statistically significant.\u003c/p\u003e\n\u003cp\u003eStatistical analysis\u003c/p\u003e\n\u003cp\u003eFor clustering of plasma BAFF and zonulin concentrations, outliers were removed by Z-score filtering\u003csup\u003e74\u003c/sup\u003e. The maximum number of clusters for each analyte was estimated using the silhouette method, and k-means clustering was performed with 25 random starts and a maximum of 50 iterations\u003csup\u003e75,76\u003c/sup\u003e. For each analyte, clusters were subsequently consolidated and labelled as low, intermediate (when present) or high based on the observed ranges.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGroup comparisons were performed using one-way ANOVA with Tukey’s post hoc test for normally distributed data or Kruskal-Wallis tests with Dunn’s post hoc test for non-normally distributed data. For two-group comparisons, paired t-tests were used for normally distributed paired data and Wilcoxon matched-pairs signed-rank tests were used otherwise. Normality was assessed with the Shapiro-Wilk test. Correlations were assessed using Pearson’s correlation for normally distributed data and Spearman’s rank correlation otherwise. Unless otherwise stated, statistical tests were two-sided. Analyses were performed in GraphPad Prism v10.3.1, and\u003cem\u003e\u0026nbsp;P\u003c/em\u003e ≤0.05 were considered statistically significant.\u003cbr clear=\"all\"\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eE.L.F is supported by a Tier 2 Canada Research Chair in Role of the Microbiome in Inborn Errors of Immunity and Post-Infectious Conditions, the Canadian Institutes of Health Research (CIHR), the\u0026nbsp;\u003cem\u003eFonds de Recherche du Québec (FRQ)\u003c/em\u003e. K.D.L. is supported by the IRCM Foundation. M.A. is supported by CIHR. E.D. and A.D. are supported bythe \u003cem\u003eFonds de Recherche du Québec (FRQ)\u003c/em\u003e. The work was supported by CIHR\u0026nbsp;(PJT-191724), the Canada Research Chairs Program, the FRQ Clinical Research Scholars - Junior 1 Establishment Funds for Young Investigators, the John R. Evans Leaders Fund from the Canadian Foundation for Innovation (CFI), the J-Louis Lévesque Foundation Research Chair, the Mirella and Lino Saputo Foundation, and the IRCM Foundation.\u003c/p\u003e\n\u003cp\u003eWe thank members of the IRCM animal facility (Mariane Canuel, Eve-Marie Charbonneau and Jo-Anny Bisson), the IRCM Flow Cytometry Platform (Éric Massicotte and Julie Lord), and the McGill Genome Centre, Centre for Microbiome Research, for technical support. We are grateful to Kathy McCoy for providing germ-free mice. We thank Christian Charbonneau, Dr. Marianne Isaac and Melina Narlis of the IRIC Microscopy and Histology Core Facilities for guidance and for performing sagittal brain cryosections and H\u0026amp;E staining. We also thank Véronique Barrès and Liliane Meunier of the CRCHUM Molecular Pathology Core Facility for immunolabeling, slide scanning, and assistance with paraffin and OCT processing of colon, brain, spleen and Peyer’s patches, and Anabelle Bouchard-Bourque of the IRCM Histology Core Facility for additional paraffin and OCT sectioning. We acknowledge Dominic Filion and Mattew Duguay of the IRCM Imaging Platform for imaging support. We thank Dr. Mieczyslaw Marcinkiewicz, Dennis A. Drewnik, Dr. Valerio Piscopo for advice and assistance with brain immunofluorescence interpretation, fixation and embedding. We also thank Dr. Mélanie Dieudé, Charlotte Veilleux-Trihn, and Sandrine Julliard for providing NZB mouse serum.\u003c/p\u003e\n\u003cp\u003eAuthor contributions\u003c/p\u003e\n\u003cp\u003eK.D-L., M.A., J.P. and E.L.F. conceived the study and designed the research. K.D-L., M.A., F.G., L.L., L.F., A.D. and E.D. performed experiments. K.D-L., M.A., F.G., L.L., L.F., A.D., E.D., J.P. and E.L.F. analysed the data. K.D-L., J.P. and E.L.F. wrote the initial manuscript draft, and all authors contributed to manuscript revision. E.L.F. secured funding for the study. J.P. and E.L.F. supervised the research.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interest.\u003c/p\u003e\n\u003cp\u003eData availability\u003c/p\u003e\n\u003cp\u003eRaw and processed sequencing data are available under SRA accession number PRJNA1230363 (submission: SUB15146657). Any additional data supporting the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003eCode availability\u003c/p\u003e\n\u003cp\u003eCustom code used for microbiota processing, statistical analyses, and figure generation will be deposited on GitHub and made publicly available upon publication. Scripts are available from the corresponding author upon reasonable request prior to release.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eEly, E. W., Brown, L. M., Fineberg, H. V., National Academies of Sciences, E. \u0026amp; Medicine Committee on Examining the Working Definition for Long, C. Long Covid Defined. \u003cem\u003eN Engl J Med\u003c/em\u003e\u003cstrong\u003e391\u003c/strong\u003e, 1746-1753 (2024). https://doi.org/10.1056/NEJMsb2408466\u003c/li\u003e\n\u003cli\u003eWorld Health, O. \u003cem\u003ePost COVID-19 condition (long COVID)\u003c/em\u003e, \u0026lt;https://www.who.int/news-room/fact-sheets/detail/post-covid-19-condition-%28long-covid%29\u0026gt; (2025).\u003c/li\u003e\n\u003cli\u003eDavis, H. E., McCorkell, L., Vogel, J. M. \u0026amp; Topol, E. J. Long COVID: major findings, mechanisms and recommendations. \u003cem\u003eNat Rev Microbiol\u003c/em\u003e\u003cstrong\u003e21\u003c/strong\u003e, 133-146 (2023). https://doi.org/10.1038/s41579-022-00846-2\u003c/li\u003e\n\u003cli\u003eDavis, H. E.\u003cem\u003e et al.\u003c/em\u003e Characterizing long COVID in an international cohort: 7 months of symptoms and their impact. \u003cem\u003eEClinicalMedicine\u003c/em\u003e\u003cstrong\u003e38\u003c/strong\u003e, 101019 (2021). https://doi.org/10.1016/j.eclinm.2021.101019\u003c/li\u003e\n\u003cli\u003eNational Academies of Sciences, E. \u0026amp; Medicine. \u003cem\u003eA Long COVID Definition: A Chronic, Systemic Disease State with Profound Consequences\u003c/em\u003e. (The National Academies Press, 2024).\u003c/li\u003e\n\u003cli\u003eGiron, L. B.\u003cem\u003e et al.\u003c/em\u003e Markers of fungal translocation are elevated during post-acute sequelae of SARS-CoV-2 and induce NF-kappaB signaling. \u003cem\u003eJCI Insight\u003c/em\u003e\u003cstrong\u003e7\u003c/strong\u003e (2022). https://doi.org/10.1172/jci.insight.160989\u003c/li\u003e\n\u003cli\u003eMackay, F. \u0026amp; Browning, J. L. BAFF: A fundamental survival factor for B cells. \u003cem\u003eNature Reviews Immunology\u003c/em\u003e\u003cstrong\u003e2\u003c/strong\u003e, 465-475 (2002). https://doi.org/10.1038/nri844\u003c/li\u003e\n\u003cli\u003eFurie, R.\u003cem\u003e et al.\u003c/em\u003e Two-Year, Randomized, Controlled Trial of Belimumab in Lupus Nephritis. \u003cem\u003eN Engl J Med\u003c/em\u003e\u003cstrong\u003e383\u003c/strong\u003e, 1117-1128 (2020). https://doi.org/10.1056/NEJMoa2001180\u003c/li\u003e\n\u003cli\u003eWorld Health, O. A clinical case definition of post COVID-19 condition by a Delphi consensus. (World Health Organization, Geneva, 2021).\u003c/li\u003e\n\u003cli\u003e Cai, M., Xie, Y., Topol, E. J. \u0026amp; Al-Aly, Z. Three-year outcomes of post-acute sequelae of COVID-19. \u003cem\u003eNat Med\u003c/em\u003e\u003cstrong\u003e30\u003c/strong\u003e, 1564-1573 (2024). https://doi.org/10.1038/s41591-024-02987-8\u003c/li\u003e\n\u003cli\u003e World Health, O. A clinical case definition for post COVID-19 condition in children and adolescents. (World Health Organization, Geneva, 2023).\u003c/li\u003e\n\u003cli\u003e Al-Aly, Z., Xie, Y. \u0026amp; Bowe, B. High-dimensional characterization of post-acute sequelae of COVID-19. \u003cem\u003eNature\u003c/em\u003e\u003cstrong\u003e594\u003c/strong\u003e, 259-264 (2021). https://doi.org/10.1038/s41586-021-03553-9\u003c/li\u003e\n\u003cli\u003e Zhang, H.\u003cem\u003e et al.\u003c/em\u003e Data-driven identification of post-acute SARS-CoV-2 infection subphenotypes. \u003cem\u003eNat Med\u003c/em\u003e\u003cstrong\u003e29\u003c/strong\u003e, 226-235 (2023). https://doi.org/10.1038/s41591-022-02116-3\u003c/li\u003e\n\u003cli\u003e Al-Aly, Z. \u0026amp; Topol, E. Solving the puzzle of Long Covid. \u003cem\u003eScience\u003c/em\u003e\u003cstrong\u003e383\u003c/strong\u003e, 830-832 (2024). https://doi.org/10.1126/science.adl0867\u003c/li\u003e\n\u003cli\u003e Thaweethai, T.\u003cem\u003e et al.\u003c/em\u003e Development of a Definition of Postacute Sequelae of SARS-CoV-2 Infection. \u003cem\u003eJAMA\u003c/em\u003e\u003cstrong\u003e329\u003c/strong\u003e, 1934-1946 (2023). https://doi.org/10.1001/jama.2023.8823\u003c/li\u003e\n\u003cli\u003e Geng, L. N.\u003cem\u003e et al.\u003c/em\u003e 2024 Update of the RECOVER-Adult Long COVID Research Index. \u003cem\u003eJama\u003c/em\u003e\u003cstrong\u003e333\u003c/strong\u003e, 694-700 (2025). https://doi.org/10.1001/jama.2024.24184\u003c/li\u003e\n\u003cli\u003e Liew, F.\u003cem\u003e et al.\u003c/em\u003e Large-scale phenotyping of patients with long COVID post-hospitalization reveals mechanistic subtypes of disease. \u003cem\u003eNature Immunology\u003c/em\u003e\u003cstrong\u003e25\u003c/strong\u003e, 607-621 (2024). https://doi.org/10.1038/s41590-024-01778-0\u003c/li\u003e\n\u003cli\u003e Talla, A.\u003cem\u003e et al.\u003c/em\u003e Persistent serum protein signatures define an inflammatory subcategory of long COVID. \u003cem\u003eNat Commun\u003c/em\u003e\u003cstrong\u003e14\u003c/strong\u003e, 3417 (2023). https://doi.org/10.1038/s41467-023-38682-4\u003c/li\u003e\n\u003cli\u003e Peluso, M. J. \u0026amp; Deeks, S. G. Mechanisms of long COVID and the path toward therapeutics. \u003cem\u003eCell\u003c/em\u003e\u003cstrong\u003e187\u003c/strong\u003e, 5500-5529 (2024). https://doi.org/10.1016/j.cell.2024.07.054\u003c/li\u003e\n\u003cli\u003e Chang, R.\u003cem\u003e et al.\u003c/em\u003e Risk of autoimmune diseases in patients with COVID-19: A retrospective cohort study. \u003cem\u003eEClinicalMedicine\u003c/em\u003e\u003cstrong\u003e56\u003c/strong\u003e, 101783 (2023). https://doi.org/10.1016/j.eclinm.2022.101783\u003c/li\u003e\n\u003cli\u003e Son, K.\u003cem\u003e et al.\u003c/em\u003e Circulating anti-nuclear autoantibodies in COVID-19 survivors predict long COVID symptoms. \u003cem\u003eEur Respir J\u003c/em\u003e\u003cstrong\u003e61\u003c/strong\u003e (2023). https://doi.org/10.1183/13993003.00970-2022\u003c/li\u003e\n\u003cli\u003e Rong, Z.\u003cem\u003e et al.\u003c/em\u003e Persistence of spike protein at the skull-meninges-brain axis may contribute to the neurological sequelae of COVID-19. \u003cem\u003eCell Host Microbe\u003c/em\u003e\u003cstrong\u003e32\u003c/strong\u003e, 2112-2130.e2110 (2024). https://doi.org/10.1016/j.chom.2024.11.007\u003c/li\u003e\n\u003cli\u003e Yeoh, Y. K.\u003cem\u003e et al.\u003c/em\u003e Gut microbiota composition reflects disease severity and dysfunctional immune responses in patients with COVID-19. \u003cem\u003eGut\u003c/em\u003e\u003cstrong\u003e70\u003c/strong\u003e, 698-706 (2021). https://doi.org/10.1136/gutjnl-2020-323020\u003c/li\u003e\n\u003cli\u003e Liu, Q.\u003cem\u003e et al.\u003c/em\u003e Gut microbiota dynamics in a prospective cohort of patients with post-acute COVID-19 syndrome. \u003cem\u003eGut\u003c/em\u003e\u003cstrong\u003e71\u003c/strong\u003e, 544-552 (2022). https://doi.org/10.1136/gutjnl-2021-325989\u003c/li\u003e\n\u003cli\u003e Zollner, A., Meyer, M., Jukic, A., Adolph, T. \u0026amp; Tilg, H. The Intestine in Acute and Long COVID: Pathophysiological Insights and Key Lessons. \u003cem\u003eYale J Biol Med\u003c/em\u003e\u003cstrong\u003e97\u003c/strong\u003e, 447-462 (2024). https://doi.org/10.59249/pmie8461\u003c/li\u003e\n\u003cli\u003e Prasad, R.\u003cem\u003e et al.\u003c/em\u003e Plasma Microbiome in COVID-19 Subjects: An Indicator of Gut Barrier Defects and Dysbiosis. \u003cem\u003eInt J Mol Sci\u003c/em\u003e\u003cstrong\u003e23\u003c/strong\u003e (2022). https://doi.org/10.3390/ijms23169141\u003c/li\u003e\n\u003cli\u003e Greene, C.\u003cem\u003e et al.\u003c/em\u003e Blood\u0026ndash;brain barrier disruption and sustained systemic inflammation in individuals with long COVID-associated cognitive impairment. \u003cem\u003eNature Neuroscience\u003c/em\u003e\u003cstrong\u003e27\u003c/strong\u003e, 421-432 (2024). https://doi.org/10.1038/s41593-024-01576-9\u003c/li\u003e\n\u003cli\u003e Vrettou, C. S.\u003cem\u003e et al.\u003c/em\u003e A Prospective Study on Neural Biomarkers in Patients with Long-COVID Symptoms. \u003cem\u003eJ Pers Med\u003c/em\u003e\u003cstrong\u003e14\u003c/strong\u003e (2024). https://doi.org/10.3390/jpm14030313\u003c/li\u003e\n\u003cli\u003e Zhang, Y.\u003cem\u003e et al.\u003c/em\u003e B cell-activating factor and its targeted therapy in autoimmune diseases. \u003cem\u003eCytokine Growth Factor Rev\u003c/em\u003e\u003cstrong\u003e64\u003c/strong\u003e, 57-70 (2022). https://doi.org/10.1016/j.cytogfr.2021.11.004\u003c/li\u003e\n\u003cli\u003e Cerutti, A., Cols, M. \u0026amp; Puga, I. Marginal zone B cells: virtues of innate-like antibody-producing lymphocytes. \u003cem\u003eNat Rev Immunol\u003c/em\u003e\u003cstrong\u003e13\u003c/strong\u003e, 118-132 (2013). https://doi.org/10.1038/nri3383\u003c/li\u003e\n\u003cli\u003e Doyon-Lalibert\u0026eacute;, K.\u003cem\u003e et al.\u003c/em\u003e NR4A Expression by Human Marginal Zone B-Cells. \u003cem\u003eAntibodies (Basel)\u003c/em\u003e\u003cstrong\u003e8\u003c/strong\u003e, E50 (2019). https://doi.org/10.3390/antib8040050\u003c/li\u003e\n\u003cli\u003e Fontaine, J.\u003cem\u003e et al.\u003c/em\u003e High expression levels of B lymphocyte stimulator (BLyS) by dendritic cells correlate with HIV-related B-cell disease progression in humans. \u003cem\u003eBlood\u003c/em\u003e\u003cstrong\u003e117\u003c/strong\u003e, 145-155 (2011). https://doi.org/10.1182/blood-2010-08-301887\u003c/li\u003e\n\u003cli\u003e Doyon-Laliberte, K.\u003cem\u003e et al.\u003c/em\u003e Excess BAFF Alters NR4As Expression Levels and Breg Function of Human Precursor-like Marginal Zone B-Cells in the Context of HIV-1 Infection. \u003cem\u003eInt J Mol Sci\u003c/em\u003e\u003cstrong\u003e23\u003c/strong\u003e (2022). https://doi.org/10.3390/ijms232315142\u003c/li\u003e\n\u003cli\u003e Doyon-Laliberte, K., Aranguren, M., Poudrier, J. \u0026amp; Roger, M. Marginal Zone B-Cell Populations and Their Regulatory Potential in the Context of HIV and Other Chronic Inflammatory Conditions. \u003cem\u003eInt J Mol Sci\u003c/em\u003e\u003cstrong\u003e23\u003c/strong\u003e (2022). https://doi.org/10.3390/ijms23063372\u003c/li\u003e\n\u003cli\u003e Doyon-Lalibert\u0026eacute;, K.\u003cem\u003e et al.\u003c/em\u003e Excess BAFF May Impact HIV-1-Specific Antibodies and May Promote Polyclonal Responses Including Those from First-Line Marginal Zone B-Cell Populations. \u003cem\u003eCurr Issues Mol Biol\u003c/em\u003e\u003cstrong\u003e46\u003c/strong\u003e, 25-43 (2023). https://doi.org/10.3390/cimb46010003\u003c/li\u003e\n\u003cli\u003e Zevin, A. S., McKinnon, L., Burgener, A. \u0026amp; Klatt, N. R. Microbial translocation and microbiome dysbiosis in HIV-associated immune activation. \u003cem\u003eCurr Opin HIV AIDS\u003c/em\u003e\u003cstrong\u003e11\u003c/strong\u003e, 182-190 (2016). https://doi.org/10.1097/COH.0000000000000234\u003c/li\u003e\n\u003cli\u003e Ellis, R. J.\u003cem\u003e et al.\u003c/em\u003e Markers of Gut Barrier Function and Microbial Translocation Associate with Lower Gut Microbial Diversity in People with HIV. \u003cem\u003eViruses\u003c/em\u003e\u003cstrong\u003e13\u003c/strong\u003e (2021). https://doi.org/10.3390/v13101891\u003c/li\u003e\n\u003cli\u003e Nazli, A.\u003cem\u003e et al.\u003c/em\u003e Exposure to HIV-1 directly impairs mucosal epithelial barrier integrity allowing microbial translocation. \u003cem\u003ePLoS Pathog\u003c/em\u003e\u003cstrong\u003e6\u003c/strong\u003e, e1000852 (2010). https://doi.org/10.1371/journal.ppat.1000852\u003c/li\u003e\n\u003cli\u003e Brenchley, J. M.\u003cem\u003e et al.\u003c/em\u003e Microbial translocation is a cause of systemic immune activation in chronic HIV infection. \u003cem\u003eNat Med\u003c/em\u003e\u003cstrong\u003e12\u003c/strong\u003e, 1365-1371 (2006). https://doi.org/10.1038/nm1511\u003c/li\u003e\n\u003cli\u003e Jiang, W.\u003cem\u003e et al.\u003c/em\u003e Plasma levels of bacterial DNA correlate with immune activation and the magnitude of immune restoration in persons with antiretroviral-treated HIV infection. \u003cem\u003eJ Infect Dis\u003c/em\u003e\u003cstrong\u003e199\u003c/strong\u003e, 1177-1185 (2009). https://doi.org/10.1086/597476\u003c/li\u003e\n\u003cli\u003e Ramendra, R.\u003cem\u003e et al.\u003c/em\u003e Circulating LPS and (1--\u0026gt;3)-beta-D-Glucan: A Folie a Deux Contributing to HIV-Associated Immune Activation. \u003cem\u003eFront Immunol\u003c/em\u003e\u003cstrong\u003e10\u003c/strong\u003e, 465 (2019). https://doi.org/10.3389/fimmu.2019.00465\u003c/li\u003e\n\u003cli\u003e Marchetti, G.\u003cem\u003e et al.\u003c/em\u003e Microbial translocation is associated with sustained failure in CD4+ T-cell reconstitution in HIV-infected patients on long-term highly active antiretroviral therapy. \u003cem\u003eAIDS\u003c/em\u003e\u003cstrong\u003e22\u003c/strong\u003e, 2035-2038 (2008). https://doi.org/10.1097/QAD.0b013e3283112d29\u003c/li\u003e\n\u003cli\u003e Kowalczyk-Quintas, C.\u003cem\u003e et al.\u003c/em\u003e Antibodies That Block or Activate Mouse B Cell Activating Factor of the Tumor Necrosis Factor (TNF) Family (BAFF), Respectively, Induce B Cell Depletion or B Cell Hyperplasia. \u003cem\u003eJ Biol Chem\u003c/em\u003e\u003cstrong\u003e291\u003c/strong\u003e, 19826-19834 (2016). https://doi.org/10.1074/jbc.M116.725929\u003c/li\u003e\n\u003cli\u003e Kaku, H., Cheng, K. F., Al-Abed, Y. \u0026amp; Rothstein, T. L. A novel mechanism of B cell-mediated immune suppression through CD73 expression and adenosine production. \u003cem\u003eJ Immunol\u003c/em\u003e\u003cstrong\u003e193\u003c/strong\u003e, 5904-5913 (2014). https://doi.org/10.4049/jimmunol.1400336\u003c/li\u003e\n\u003cli\u003e Tesch, F.\u003cem\u003e et al.\u003c/em\u003e Incident autoimmune diseases in association with SARS-CoV-2 infection: a matched cohort study. \u003cem\u003eClin Rheumatol\u003c/em\u003e\u003cstrong\u003e42\u003c/strong\u003e, 2905-2914 (2023). https://doi.org/10.1007/s10067-023-06670-0\u003c/li\u003e\n\u003cli\u003e Jernbom, A. F.\u003cem\u003e et al.\u003c/em\u003e Prevalent and persistent new-onset autoantibodies in mild to severe COVID-19. \u003cem\u003eNature Communications\u003c/em\u003e\u003cstrong\u003e15\u003c/strong\u003e, 8941 (2024). https://doi.org/10.1038/s41467-024-53356-5\u003c/li\u003e\n\u003cli\u003e Vincent, F. B., Saulep-Easton, D., Figgett, W. A., Fairfax, K. A. \u0026amp; Mackay, F. The BAFF/APRIL system: emerging functions beyond B cell biology and autoimmunity. \u003cem\u003eCytokine Growth Factor Rev\u003c/em\u003e\u003cstrong\u003e24\u003c/strong\u003e, 203-215 (2013). https://doi.org/10.1016/j.cytogfr.2013.04.003\u003c/li\u003e\n\u003cli\u003e Hu, S.-Z.\u003cem\u003e et al.\u003c/em\u003e The emerging role of BLyS/APRIL in autoimmune diseases: Biological characteristics, functions, and therapeutic potential. \u003cem\u003eJournal of Autoimmunity\u003c/em\u003e\u003cstrong\u003e149\u003c/strong\u003e, 103329 (2024). https://doi.org/https://doi.org/10.1016/j.jaut.2024.103329\u003c/li\u003e\n\u003cli\u003e Liu, Y.\u003cem\u003e et al.\u003c/em\u003e High APRIL Levels Are Associated With Slow Disease Progression and Low Immune Activation in Chronic HIV-1-Infected Patients. \u003cem\u003eFrontiers in Medicine\u003c/em\u003e\u003cstrong\u003eVolume 7 - 2020\u003c/strong\u003e (2020). https://doi.org/10.3389/fmed.2020.00299\u003c/li\u003e\n\u003cli\u003e Knox, J. J., Myles, A. \u0026amp; Cancro, M. P. T-bet+ memory B cells: Generation, function, and fate. \u003cem\u003eImmunological Reviews\u003c/em\u003e\u003cstrong\u003e288\u003c/strong\u003e, 149-160 (2019). https://doi.org/https://doi.org/10.1111/imr.12736\u003c/li\u003e\n\u003cli\u003e Song, W.\u003cem\u003e et al.\u003c/em\u003e Development of Tbet- and CD11c-expressing B cells in a viral infection requires T follicular helper cells outside of germinal centers. \u003cem\u003eImmunity\u003c/em\u003e\u003cstrong\u003e55\u003c/strong\u003e, 290-307.e295 (2022). https://doi.org/https://doi.org/10.1016/j.immuni.2022.01.002\u003c/li\u003e\n\u003cli\u003e Woodruff, M. C.\u003cem\u003e et al.\u003c/em\u003e Extrafollicular B cell responses correlate with neutralizing antibodies and morbidity in COVID-19. \u003cem\u003eNat Immunol\u003c/em\u003e\u003cstrong\u003e21\u003c/strong\u003e, 1506-1516 (2020). https://doi.org/10.1038/s41590-020-00814-z\u003c/li\u003e\n\u003cli\u003e Groom, J.\u003cem\u003e et al.\u003c/em\u003e Association of BAFF/BLyS overexpression and altered B cell differentiation with Sjogren's syndrome. \u003cem\u003eJ Clin Invest\u003c/em\u003e\u003cstrong\u003e109\u003c/strong\u003e, 59-68 (2002). https://doi.org/10.1172/JCI14121\u003c/li\u003e\n\u003cli\u003e Salazar-Camarena, D. C.\u003cem\u003e et al.\u003c/em\u003e Association of BAFF, APRIL serum levels, BAFF-R, TACI and BCMA expression on peripheral B-cell subsets with clinical manifestations in systemic lupus erythematosus. \u003cem\u003eLupus\u003c/em\u003e\u003cstrong\u003e25\u003c/strong\u003e, 582-592 (2016). https://doi.org/10.1177/0961203315608254\u003c/li\u003e\n\u003cli\u003e Li, L.\u003cem\u003e et al.\u003c/em\u003e The role of B cell-activating factor system in autoimmune diseases: mechanisms, disease implications, and therapeutic advances. \u003cem\u003eFrontiers in Immunology\u003c/em\u003e\u003cstrong\u003eVolume 16 - 2025\u003c/strong\u003e (2025). https://doi.org/10.3389/fimmu.2025.1538555\u003c/li\u003e\n\u003cli\u003e Gupta, K.\u003cem\u003e et al.\u003c/em\u003e BAFF blockade in experimental autoimmune encephalomyelitis reduces inflammation in the meninges and synaptic and neuronal loss in adjacent brain regions. \u003cem\u003eJ Neuroinflammation\u003c/em\u003e\u003cstrong\u003e20\u003c/strong\u003e, 229 (2023). https://doi.org/10.1186/s12974-023-02922-7\u003c/li\u003e\n\u003cli\u003e Min, J. W.\u003cem\u003e et al.\u003c/em\u003e BAFF Inhibition Effectively Suppresses the Development of Anti-HLA.A2 Antibody in the Highly Sensitized Mouse Model. \u003cem\u003eInt J Mol Sci\u003c/em\u003e\u003cstrong\u003e22\u003c/strong\u003e (2021). https://doi.org/10.3390/ijms22020861\u003c/li\u003e\n\u003cli\u003e Uzzan, M., Colombel, J. F., Cerutti, A., Treton, X. \u0026amp; Mehandru, S. B Cell-Activating Factor (BAFF)-Targeted B Cell Therapies in Inflammatory Bowel Diseases. \u003cem\u003eDig Dis Sci\u003c/em\u003e\u003cstrong\u003e61\u003c/strong\u003e, 3407-3424 (2016). https://doi.org/10.1007/s10620-016-4317-9\u003c/li\u003e\n\u003cli\u003e Couzin-Frankel, J. in \u003cem\u003eScienceInsider\u003c/em\u003e (Science.org, 2021).\u003c/li\u003e\n\u003cli\u003e COVID-19 WHOWccdwgop, condition. A clinical case definition of post COVID-19 condition by a Delphi consensus (2021). https://www.who.int/publications/i/item/WHO-2019-nCoV-Post_COVID-19_condition-Clinical_case_definition-2021.1\u003c/li\u003e\n\u003cli\u003e Liang, Y.\u003cem\u003e et al.\u003c/em\u003e Systematic Analysis of Impact of Sampling Regions and Storage Methods on Fecal Gut Microbiome and Metabolome Profiles. \u003cem\u003emSphere\u003c/em\u003e\u003cstrong\u003e5\u003c/strong\u003e (2020). https://doi.org/10.1128/mSphere.00763-19\u003c/li\u003e\n\u003cli\u003e Poudrier, J.\u003cem\u003e et al.\u003c/em\u003e The AIDS Disease of CD4C/HIV Transgenic Mice Shows Impaired Germinal Centers and Autoantibodies and Develops in the Absence of IFN-\u0026gamma; and IL-6. \u003cem\u003eImmunity\u003c/em\u003e\u003cstrong\u003e15\u003c/strong\u003e, 173-185 (2001). https://doi.org/https://doi.org/10.1016/S1074-7613(01)00177-7\u003c/li\u003e\n\u003cli\u003e FastQC: a quality control tool for high throughput sequence data (2010).\u003c/li\u003e\n\u003cli\u003e Bolger, A. M., Lohse, M. \u0026amp; Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. \u003cem\u003eBioinformatics\u003c/em\u003e\u003cstrong\u003e30\u003c/strong\u003e, 2114-2120 (2014). https://doi.org/10.1093/bioinformatics/btu170\u003c/li\u003e\n\u003cli\u003e Masella, A. P., Bartram, A. K., Truszkowski, J. M., Brown, D. G. \u0026amp; Neufeld, J. D. PANDAseq: paired-end assembler for illumina sequences. \u003cem\u003eBMC Bioinformatics\u003c/em\u003e\u003cstrong\u003e13\u003c/strong\u003e, 31 (2012). https://doi.org/10.1186/1471-2105-13-31\u003c/li\u003e\n\u003cli\u003e Mlaga, K. D.\u003cem\u003e et al.\u003c/em\u003e HCK and ABAA: A Newly Designed Pipeline to Improve Fungi Metabarcoding Analysis. \u003cem\u003eFront Microbiol\u003c/em\u003e\u003cstrong\u003e12\u003c/strong\u003e, 640693 (2021). https://doi.org/10.3389/fmicb.2021.640693\u003c/li\u003e\n\u003cli\u003e Fu, L., Niu, B., Zhu, Z., Wu, S. \u0026amp; Li, W. CD-HIT: accelerated for clustering the next-generation sequencing data. \u003cem\u003eBioinformatics\u003c/em\u003e\u003cstrong\u003e28\u003c/strong\u003e, 3150-3152 (2012). https://doi.org/10.1093/bioinformatics/bts565\u003c/li\u003e\n\u003cli\u003e Lu, J. \u0026amp; Salzberg, S. L. Ultrafast and accurate 16S rRNA microbial community analysis using Kraken 2. \u003cem\u003eMicrobiome\u003c/em\u003e\u003cstrong\u003e8\u003c/strong\u003e, 124 (2020). https://doi.org/10.1186/s40168-020-00900-2\u003c/li\u003e\n\u003cli\u003e McMurdie, P. J. \u0026amp; Holmes, S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. \u003cem\u003ePLoS One\u003c/em\u003e\u003cstrong\u003e8\u003c/strong\u003e, e61217 (2013). https://doi.org/10.1371/journal.pone.0061217\u003c/li\u003e\n\u003cli\u003e Whitmore, N. \u003cem\u003eR for Conservation and Development Projects\u003c/em\u003e \u003cem\u003eA Primer for Practitioners\u003c/em\u003e. (Chapman and Hall / CRC Press, 2022).\u003c/li\u003e\n\u003cli\u003e Robinson, M. D., McCarthy, D. J. \u0026amp; Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. \u003cem\u003eBioinformatics\u003c/em\u003e\u003cstrong\u003e26\u003c/strong\u003e, 139-140 (2010). https://doi.org/10.1093/bioinformatics/btp616\u003c/li\u003e\n\u003cli\u003e Cao, Y.\u003cem\u003e et al.\u003c/em\u003e microbiomeMarker: an R/Bioconductor package for microbiome marker identification and visualization. \u003cem\u003eBioinformatics\u003c/em\u003e\u003cstrong\u003e38\u003c/strong\u003e, 4027-4029 (2022). https://doi.org/10.1093/bioinformatics/btac438\u003c/li\u003e\n\u003cli\u003e Dixon, P. VEGAN, a package of R functions for community ecology. \u003cem\u003eJournal of vegetation science\u003c/em\u003e\u003cstrong\u003e14\u003c/strong\u003e, 927-930 (2003). https://doi.org/10.1111/j.1654-1103.2003.tb02228.x\u003c/li\u003e\n\u003cli\u003e Meijer, P., Sobas, F. \u0026amp; Tsiamyrtzis, P. Assessment of accuracy of laboratory testing results, relative to peer group consensus values in external quality control, by bivariate z-score analysis: the example of D-Dimer. \u003cem\u003eClin Chem Lab Med\u003c/em\u003e\u003cstrong\u003e62\u003c/strong\u003e, 1548-1556 (2024). https://doi.org/10.1515/cclm-2023-0835\u003c/li\u003e\n\u003cli\u003e Kravitz, Y. (University of Colorado Denver, 2024).\u003c/li\u003e\n\u003cli\u003e Wu, J. \u003cem\u003eAdvances in K-means Clustering\u003c/em\u003e. (Springer Berlin, Heidelberg, 2014).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8876163/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8876163/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Long COVID (post-coronavirus disease 2019 (COVID-19) condition) is an infection-associated chronic condition that can follow severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and persist for months to years1-3. Symptoms can be severe and significantly impact functional status and quality of life2,4,5, but the mechanisms linking barrier dysfunction to systemic immune dysregulation remain unclear. Markers consistent with impaired intestinal mucosal barrier integrity and microbial translocation have been linked to long COVID6. Here we show that non-hospitalized individuals with long COVID have intestinal barrier dysfunction associated with increased B-cell activating factor (BAFF), perturbation of the B cell compartment and autoimmunity that peak at 12 months after infection and begin to resolve by 24 months. Transfer of faecal microbiota from individuals with severe long COVID into germ-free mice is sufficient to reproduce intestinal barrier dysfunction, systemic immune dysregulation with autoimmunity, and neuroinflammation. Treating recipient gnotobiotic mice with a BAFF-neutralizing monoclonal antibody, an approach supported by BAFF biology and clinical efficacy in autoantibody-mediated disease7,8, markedly improves these abnormalities. Together, these findings implicate microbiota-linked intestinal barrier disruption as a driver of autoimmunity and end-organ complications in long COVID and identify BAFF as a therapeutic target.","manuscriptTitle":"Microbiota-induced intestinal barrier disruption drives BAFF-mediated B-cell dysregulation and autoimmunity in long COVID","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-26 06:41:26","doi":"10.21203/rs.3.rs-8876163/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-immunology","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"ni","sideBox":"Learn more about [Nature Immunology](http://www.nature.com/ni/)","snPcode":"","submissionUrl":"","title":"Nature Immunology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Research","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"0eff4525-7313-43d9-8748-cb170dea43e7","owner":[],"postedDate":"March 26th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":63398167,"name":"Biological sciences/Immunology/Autoimmunity"},{"id":63398168,"name":"Biological sciences/Immunology/Mucosal immunology"},{"id":63398169,"name":"Health sciences/Diseases/Infectious diseases/Viral infection"},{"id":63398170,"name":"Biological sciences/Microbiology/Microbial communities/Microbiome"},{"id":63398171,"name":"Biological sciences/Immunology/Adaptive immunity/Humoral immunity/Antibodies"}],"tags":[],"updatedAt":"2026-03-26T06:41:26+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-26 06:41:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8876163","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8876163","identity":"rs-8876163","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}