CXCR3 is associated with T-cell-induced heart damage in acute rheumatic fever

Domain: biology
Published: 2026-03-31
Editorial outcome: minor_revision

Abstract
The pathogenesis of acute rheumatic fever (ARF) is poorly understood, limiting the development of immune-modulating therapies to treat disease and prevent progressive heart damage. Here, participants with definite ARF were compared to other severe acute paediatric conditions and matched healthy controls by profiling circulating immune molecules and cells to inform disease mechanisms and potential druggable pathways. ARF shared immunological similarities with other inflammatory conditions, including elevated serum IL-6 and an increased frequency of circulating CD4+ T cells. However, elevation of the chemokine CCL5 and immunoglobulin IgG3, along with reduced expression of the chemokine receptor CXCR3 in the T cell compartment distinguished ARF from all other groups. Immunofluorescence imaging of rheumatic valve tissue confirmed a role for CXCR3-mediated T cell tissue homing during inflammatory disease. Together with a reduced frequency of circulating regulatory T cells, these data underscore a perturbed T cell compartment and provide a rationale for exploring currently available immune-modulating therapies to treat ARF.

Introduction
Acute rheumatic fever (ARF) is an autoimmune sequela to Group A Streptococcus (Streptococcus pyogenes; Strep A) infection. Carditis is the most concerning symptom of ARF, as it can progress to chronic rheumatic heart disease (RHD), which is characterised by progressive damage to heart valves 1. ARF and RHD rates are highest in children and young adults in low- and middle- income countries and remain dramatically elevated in Indigenous and Pacific populations in Australia and Aotearoa New Zealand. These Strep A sequelae are major causes of global morbidity and mortality, with approximately 40.5 million people estimated to be living with RHD in 2019 and 306,000 annual deaths attributed to the disease 2.

The lack of a disease-specific biomarker or diagnostic test is a major barrier to effective management and epidemiologic surveillance of ARF, with a clinical algorithm known as the ‘Jones Criteria’ required for diagnosis 3. Additionally, there is no effective immunomodulatory treatment for ARF. Long-term penicillin prophylaxis is administered following diagnosis, and whilst this serves to prevent further Strep A infections from triggering recurrent ARF, there are no therapies available that can reduce immune-mediated damage and limit progression during an ARF episode. Underlying these major diagnostic and therapeutic gaps is an incomplete understanding of the pathogenesis of ARF and RHD.

ARF is characterised by autoimmune-mediated tissue damage following a Strep A infection. However, the mechanisms involved are poorly understood 4. The prevailing hypothesis is autoimmune ‘molecular mimicry’ whereby Strep A antigens, such as the M-protein share regions of similarity to self-antigens resulting in a cross-reactive immune response 5,6. Deposition of cross-reactive antibody in the heart is proposed to damage tissue and upregulate adhesion molecules allowing T cell infiltration 7-9. Recent evidence supports broad increases in autoantibody reactivity in ARF and a role for epitope spreading 10-12. Furthermore, the immunoglobulin G subclass 3 (IgG3) is elevated in ARF and antibody binding to the Strep A M-protein has been shown to be polarised towards an IgG3-dominant response 13,14.

T cells have been suggested as being integral for the perpetuation of valve damage 15, however there is limited research on the role of cellular immunity in ARF and RHD 16. Cross-reactive T cells have been identified in heart valve infiltrate and peripheral circulation, consistent with hypothesised extravasation of circulating T cells into heart valves 8,11,15,17-19. Despite this evidence for cell-mediated immune dysregulation, the circulating immune cell profile has not been extensively investigated. To date, immunophenotyping studies have shown increased inflammation-associated T helper 17 (Th17) cells and reduced T regulatory (Treg) cells in adults with RHD 20, while absolute numbers of mucosal-associated invariant T cells (MAIT) were observed to be elevated in ARF 21.

Cytokine and chemokine regulation (and dysregulation) of immune cells in autoimmune pathologies is widely recognised, and some of the most effective therapeutic strategies interrupt these signalling pathways 22. Cytokine and chemokine analyses in ARF and RHD suggest inflammatory cytokines are important regulating signals in ARF 23, with elevated interleukin-6 (IL-6) and tumour necrosis factor-α (TNFα) frequently reported 24. Coordination of cellular dynamics by chemokines has also been suggested as important in ARF and RHD, with valve-infiltrating T cell lines derived from RHD patients shown to migrate toward a CXCL9 gradient in vitro 16. Yet, there remains a lack of insight into the broad populations of circulating immune cells in ARF, and how the interplay between these immune cell populations, cytokines, and chemokines might inform new avenues for research into therapeutics.

This study aimed to improve understanding of the ARF immune landscape via the profiling of soluble serum factors including cytokines and chemokines, and peripheral blood mononuclear cell (PBMC) populations. It makes use of samples collected in the ‘Searching for a Technology Driven Acute Rheumatic Fever Test’ (START ) study, a multi-centre prospective cohort study that enrolled children and young adults in Australia and New Zealand 25. Imaging of infiltrating immune cells in explant valve tissue from a distinct cohort of children with RHD undergoing cardiac surgery was utilised to confirm observations in circulation.

Results
Rheumatic fever is characterised by elevated CCL5 and IgG3 levels

To profile circulating immune features in ARF, we initially analysed soluble serum factors in the START study cohort 25. This comprises patients with ARF as well as four control groups: serious streptococcal infections; other inflammatory conditions; non-inflammatory RHD; and matched healthy controls (Supp. Fig. 1, Supp. Table 1), thus enabling interpretation with respect to streptococcal and inflammatory driven disease responses as well as progression to RHD. Similarities in the soluble factor profile were observed between ARF and children with other inflammatory conditions, and these grouped separately from RHD, healthy, and other streptococcal infections using hierarchical clustering (Fig. 1A). Classic inflammatory factors IL-6 and TNFα were significantly elevated in ARF compared to both RHD and healthy control groups (Fig. 1B), while IL-1Ra, which is frequently elevated in infectious and immune-mediated conditions 26, was significantly elevated compared to healthy controls (Supp. Fig. 2). Overall, the levels of these inflammatory cytokines were similar in ARF and children with other inflammatory conditions in keeping with the broadly inflammatory nature of ARF (Fig. 1B, Supp. Fig. 2).

In contrast, elevated levels of the chemokine CCL5 and the immunoglobulin subclass IgG3 distinguish ARF from other inflammatory conditions (Fig. 1.B). Elevated IgG3 has been previously reported for ARF compared with children with Strep A pharyngitis 13,14; the observation here of significant elevation compared to other inflammatory conditions further suggests an association with ARF pathogenesis. Marked heterogeneity was evident in the concentrations of both CCL5 and IgG3 concentrations in ARF. To explore if variation in illness course contributes to this heterogeneity, C-reactive protein (CRP) was quantified (Supp. Fig. 3) and used to stratify patients into high (≥ 10 μg/ml, clinically significant inflammation) and low CRP (< 10 μg/ml, reduced inflammation) 27. This stratification was restricted to ARF and children with other streptococcal infections and inflammatory conditions given the lack of elevated CRP in RHD and healthy groups. Both CCL5 and IgG3 were significantly elevated in ARF patients with elevated CRP compared with low CRP (Fig. 1.C). However, this was not the case for children with other streptococcal infections and inflammatory conditions. In these groups where there was no difference in CCL5 and IgG3 by CRP status, suggesting a distinct role for these immune factors in ARF.

Circulating T cells have reduced CXCR3 expression in ARF

To explore whether the unique pattern of soluble immune molecules observed in ARF sera was also reflected in circulating immune cells, an in-depth analysis of the major immune cell populations was performed using a 33-colour flow cytometry panel. This was applied to the ex vivo immune cell compartment of participants from the START study for which PBMC were available (Supp. Fig. 1, Supp. Table 2).

Initial unsupervised clustering analysis enabled a broad and unbiased overview of immune cell populations. Uniform manifold proximation and projection (UMAP) representation of clusters identified by unsupervised FlowSOM clustering showed these cell populations were well-separated by phenotypic markers and confirmed the panel accurately identified major T cell (Fig. 2A,B and Supp. Fig. 4A,B) and non-T cell populations (Fig. 2C,D and Supp. Fig. 4C,D). Of the 10 T cell and 9 non-T cell populations defined, only Tregs (CD25-high, CD127-low) showed a significant difference in frequency in ARF and were reduced compared to children with other streptococcal infections (Fig. 2E). Indeed, ARF showed the lowest median frequency of circulating Tregs compared to all other groups.

To further elucidate potential functional differences and immune activation states of immune cells in ARF, the expression of chemokine and differentiation markers across the major populations was analysed. The expression (median fluorescence intensity (MFI)) of all the chemokine receptors (CXCR3, CCR5, CCR4, CXCR5, CCR6), as well as the activation marker PD-1 was measured across all T cell populations. A significant reduction in CXCR3 in 8 of the 10 major T cell populations was evident in ARF and the inflammatory group (Fig. 2F). This is of interest given the major role of CXCR3 in T cell trafficking to sites of inflammation 28. This reduction in CXCR3 expressing T cells was most pronounced in ARF, while the RHD group generally showed the highest level of CXCR3+ T cells. A similar assessment of non-T cells using the MFI of the same chemokine receptors, as well as additional activation and differentiation markers (CD27, CD56, CD11c, CD16 and HLA-DR) was performed. In contrast to the T cells, this showed no significant reduction in CXCR3 expression. Rather, in non-T cells there was a significant increase of CD11c on non-classical monocytes and HLA-DR on plasma B cells in ARF (Fig. 2G). This was also observed in children with other inflammatory conditions and is indicative of an activated B cell and monocyte compartment 29,30. Of note, there is reduced CXCR5 expression on both CD4+ memory T and mature B cells (Fig. 2F,G), suggestive of alteration in chemokine-mediated trafficking as this receptor guides migration to germinal centres 31.

Taken together, unsupervised clustering identified pronounced disruptions in the T cell compartment in ARF, most notably the reduction of CXCR3 expressing T cells, with changes in the non-T cell compartment suggestive of cell activation and possible changes in chemokine-mediated cell migration.

Manual gating confirms reduced CXCR3 contributes to the ARF immune phenotype

To gain finer resolution of the disruption in the T cell compartment observed in ARF through unsupervised clustering, manual gating was performed (Supp. Fig. 5B). This gating identified 12 distinct T cell populations among CD3+ T cells. Mature CD4+ T cells were subdivided into T central memory (Tcm), T effector memory (Tem), and T follicular helper (Tfh) cells, and mature CD8 T cells into Tcm, Tem, and terminal effector memory T (Temra) cells. Each T cell population was first assessed for change in cell frequency in ARF, with three of the 12 found to be significantly different compared with at least one control group (p< 0.05). The cell frequencies for these three populations (CD4+ Tcm cells, Tfh cells, and Treg cells) are shown (Fig. 3A), with UMAP representation for T cells illustrating differences in cell density in ARF (Fig. 3B).

The reduction in Tfh cells (defined by CXCR5 expression) in ARF cases compared to RHD and healthy controls (Fig. 3A), improves the specificity of the decreased CXCR5 expression on bulk CD4+ memory T cells identified by unsupervised clustering (Fig. 2F). Alongside the significant increase in CD4+ Tcm cells observed (Fig. 3A), this provides evidence for an ongoing adaptive immune response in ARF. As with the unsupervised clustering (Fig. 2E), manual gating identified a reduction in Treg cells (CD25-high, CD127-low), further confirming this as an ARF phenotype. This supports previous published observations of reduced Tregs in ARF 20.

To further evaluate the phenotypes of the 12 manually gated T cell populations the expression of cytokine and chemokine receptors, activation and functional markers was compared following the same approach taken in the automated analysis. As already observed on the automated clusters (Fig. 2F), the chemokine receptor CXCR3 was significantly reduced in ARF across multiple T cell populations including γδ T cells, MAIT cells, natural killer T (NKT) cells, and conventional CD4+ and CD8+ T cells (Fig. 3C). Example histograms are shown for T cell populations with the largest reduction in CXCR3 expression in ARF amongst cytotoxic T cells (CD8+), helper (CD4+) T cells, and non-conventional T cells (Fig. 3D). Hierarchical clustering showed this reduction in CXCR3 signal was common between ARF and children with other inflammatory conditions (Fig. 3C), in keeping with observations at the broader level with automated clustering (Fig. 2F) and again pointing to the inflammatory nature of ARF immune dysregulation.

This analysis also identified significant changes in expression of CCR6 on NKT and Treg populations as well as PD-1 on Tcm cells (CD4+ and CD8+) (Fig. 3C). While CCR6 is important for T cell trafficking and PD-1 indicates recent cellular activation, these changes were limited to selected T cell populations. In contrast the changes in CXCR3 expression were observed across the T cell compartment in ARF.

Composite CXCR3 T cell score combined with circulating immune markers are prominent features of ARF immunopathology

The results thus far identify a distinct pattern of soluble immune molecules in ARF, together with a reduced T cell CXCR3 signature in circulation. To further evaluate the T cell signature, CXCR3 expression (MFI) across all T cell populations was combined into a composite score. This composite score was significantly reduced in ARF compared to all other study groups (Fig. 4A), including those with other inflammatory conditions, highlighting a cumulative effect of reduced CXCR3-expressing T cells as a defining feature of ARF.

To gain a global view of the immunological environment in ARF, the CXCR3 composite score was correlated to CRP (as a proxy for inflammatory status), the circulating serum factors that distinguished ARF from other inflammatory conditions, IgG3 and CCL5 (Fig. 1A,B), and the two chemokines measured that are ligands of CXCR3 (CXCL9 and IP-10). As before, this CRP-based analysis was restricted to ARF and children with other streptococcal infections and inflammatory conditions given the lack of elevated CRP in RHD and healthy groups. In ARF, CRP positively correlated with IgG3 and CCL5, which also showed a significant positive correlation with each other (Fig. 4B). These positive correlations were absent in other inflammatory conditions and streptococcal infections, suggesting a coordinated IgG3 and CCL5 response associated with inflammation occurs in ARF. This aligns with earlier observations of elevated CCL5 and IgG3 concentrations in ARF cases when stratified by high- and low-CRP (Fig. 1C). In addition, the T cell CXCR3 composite score shows a strong significant negative correlation to cognate ligand CXCL9 in both the inflammatory and streptococcal infection groups, but not in ARF (Fig. 4B,C). The striking lack of a CXCR3-CXCL9 correlation in ARF points to an uncoupling of the relationship between the chemokine receptor and a cognate ligand in circulating T cells.

Finally, for a combined measure of an "ARF immune phenotype" that includes both soluble markers and immune cells a receiver operator curve (ROC) analysis was performed using a logistic regression model comparing ARF to all control groups combined. Each of the four features (CRP, IgG3, CCL5 and the T cell CXCR3) distinguished ARF with relatively high areas under the curve (AUC’s) (Fig. 4D). Combining the circulating markers (CRP, IgG3 and CCL5) increased the AUC to 0.86 (CI, 0.79-0.92) and inclusion of the T cell CXCR3 composite score increased the AUC further to 0.9 (CI, 0.84-0.95) (Fig. 4D). This points to a distinct immunophenotype in ARF, characterized by increased IgG3 and CCL5 levels linked to inflammation (CRP), alongside a reduction in circulating CXCR3-expressing T cells.

Multiplex immunofluorescence imaging confirms CXCR3+ T cell infiltration of explant valve tissue

Having observed significant reductions in CXCR3-expression on circulating T cells in ARF, we hypothesised these cells were migrating out of circulation and into affected tissue. To investigate this, multiplex immunofluorescence microscopy was performed on explant rheumatic valve tissue from seven paediatric patients enrolled in a distinct, New Zealand only, study (Supp. Table 3).

Substantial CXCR3 expression was observed on both CD8+ T cells and CD8- T cells (likely CD4+ T cells) in aortic and mitral rheumatic valve tissue (Fig. 5A,B). Quantification of CXCR3-expressing T cells (by calculating CXCR3+ CD3+ cells as a proportion of total cells) demonstrated negligible CXCR3+ T cells in the two non-rheumatic valve tissues examined compared to the 7 rheumatic cases (Fig. 5C). Valve-infiltrating CXCR3+ T cells correlated significantly (p<0.01) with total infiltrating T cells, confirming this chemokine receptor is associated with T cell migration into rheumatic valves (Fig. 5D). The seven participants were of varying disease stage enabling infiltrating CXCR3+ T cell frequency to be compared between those with early/acute diagnoses (early ARF and fulminant RHD) and those with late/chronic diagnoses (ARF beyond the inflammatory stage and chronic RHD).

Early/acute participants (mean CRP 59.6 μg/ml) had significantly elevated infiltrating CXCR3+ T cells compared to late/chronic participants (mean CRP 6.5 μg/ml), supporting involvement of the CXCR3 chemokine axis in tissue infiltration during the inflammatory stage of ARF and RHD (Fig. 5E).

Methods
Study Design and Participant Details

START enrolled participants across three sites: Royal Darwin Hospital in the Northern Territory (NT) of Australia, and Middlemore Hospital and Starship Children’s Hospital both in Auckland, New Zealand 25. Blood samples were collected from participants between November 2018 and June 2021 in accordance with Northern Territories Department of Health and Menzies School of Health Research Ethics Committee (18/3126) and the New Zealand Health and Disability Ethics Committee (18/CEN/197) 25. Study participants were enrolled into study groups: ARF cases were assigned definite, probable, or possible status according to the 2015 revised Jones Criteria 3, and controls were grouped as non-ARF streptococcal infections or non-streptococcal acute inflammatory conditions requiring hospitalisation, RHD without ARF, and healthy. For this study only definite cases were included as ARF, and only samples collected at study enrolment were analysed across all groups. Participant samples were excluded from the analysis if >17 years of age at collection or were on steroid medications. Additional samples were excluded from PBMC analysis due to low sample quality and cell viability. Participant numbers for each of the analyses following application of these inclusion and exclusion criteria are shown (Supp. Fig. 1) as are demographics of the final cohorts (Supp. Table 1, Supp. Table 2).

The ‘Visualising the Immunology of rheumatic heart Valves’ (VIV) study enrolled participants undergoing cardiac valve repair or replacement at Starship Children’s Hospital in Auckland, New Zealand between October 2022 and January 2024. Explant cardiac valve tissue was collected in accordance with the New Zealand Health and Disability Ethics Committee (2022 FULL 11877). Ten participants were recruited, of which seven had valve tissue suitable for imaging (five male, two female). Details of VIV participants included in the imaging analysis are shown (Supp. Table 3).

All participants of the START and VIV studies (or their parents/guardians) provided informed consent prior to enrolment.

Serum and PBMC Processing

Blood was processed from sodium heparin blood tubes with an initial centrifugation step (500 x g for 10 minutes) and plasma was removed, the volume of which was replaced by RPMI 1640 Medium (Gibco™) with 1% Heparin. PBMC isolation was followed by density gradient centrifugation, with 1:1 dilution of blood with RPMI 1640 Medium with 2% Foetal Calf Serum and layering onto LymphoprepTM (STEMCELLTM Technologies) with centrifugation (400 x g for 30 minutes). Serum was obtained from blood collected in Vacutainer® Serum Tubes (BD) following clotting and centrifugation (2000 x g for 10 minutes). Plasma and serum were stored at -80° Celsius, and PBMC were stored in liquid nitrogen until use.

Circulating Analyte Quantification

Serum cytokines were analysed using custom bead-based multiplex assays (Luminex Human Discovery Assay) according to the manufacturer’s instructions, with CCL5 at a 1:50 serum dilution, and the remaining 19-plex (April, BAFF, CCL20, CXCL13, CXCL9, GMCSF, IFNγ, IL-10, IL-17, IL-18, IL-1β, IL-1Ra, IL-2, IL-4, IL-6, IL-7, IL-8, IP-10, TNFα) at a 1:2 dilution. Immunoglobulin subclass IgG3 was quantified using a single-plex assay (Merck Millipore) at a dilution of 1:50,000, as previously described 13. Bead-based assays were run on a Luminex 200 instrument (Luminex Corporation) and analysed with Belysa Immunoassay Curve Fitting Software (Version 1.2.0, Merck). CRP was analysed by use of electrochemiluminescent sandwich immunoassay (Meso Scale Discovery), read on the MESO QuickPlex SQ 120MM, and analysed using Methodical Mind software (https://msd.methodicalmind.com). Serum analytes were quantified for 241 participants (Supp. Fig. 1).

Spectral Flow Cytometry

A 33-colour spectral flow cytometry panel was developed to analyse PBMC. The panel was comprised of phenotypic markers to define T cells (conventional and non-conventional), B cells, Natural Killer (NK) cells, Monocytes, and Dendritic cells (DCs), as well as activation markers, and cytokine/chemokine receptors (Supp. Table 4).

PBMC were thawed rapidly and washed into RPMI 1640 Medium (Gibco™) before resting for 1hr at 37° Celsius. Cells were then blocked with Human TruStain FcX™ (BioLegend) and True-Stain Monocyte Blocker™ (BioLegend) and stained with Zombie NIR stain (BioLegend) for 10-minutes. Samples were stained with antibody cocktail #1 (10-minutes), followed by antibody cocktail #2 (20-minutes). PBMC samples were washed by centrifugation with FACS buffer and analysed using a 5-laser Aurora (Cytek). Data was pre-processed in FlowJo™ v10.10 Software (BD Life Sciences) with doublet exclusion, dead cell exclusion, and CD45+ gating performed (Supp. Fig. 5A). Axes were adjusted per marker to obtain optimal positive and negative signal, then CD45+ gates were exported as channel values. PBMC were excluded from the analysis if they failed quality control (trypan blue viability <80% and samples with excessive debris in forward and side scatter plot) resulting in data from 151 participants being included for analysis (Supp. Fig. 1).

OMIQ Analysis of cell populations

Pre-processed data were imported into OMIQ software from Dotmatics (www.omiq.ai, www.dotmatics.com) for manual gating, FlowSOM clustering, UMAP creation, and graphing MFI histograms. Two analysis strategies were used for the flow cytometry data. Initially, gating of CD3+ and CD3- populations was performed and unsupervised clustering analyses were performed on these two populations. Secondly, manual gating of CD3+ cells was conducted to further explore immune cell populations of interest and to validate results from the unsupervised clustering.

For unsupervised clustering, analysis was performed with FlowSOM in OMIQ. T cells were clustered using the cosine distance metric using the following features: CD3, CD4, CD8, CD45RA, CD127, CD161, CD25, CCR7, TCRγδ and TCR Vα7.2. Non-T-cells were clustered using the Euclidean distance metric using the following features: CD303, CD38, CD16, CD1c, CD11c, CD27, CD56, HLA-DR, CD14, IgD and CD19. Both FlowSOMs used 20 xdim, 20 ydim and 30 training iteration parameters. FlowSOM clusters were then combined to create final annotated clusters based on expression of markers (excluding ‘other’ T cells) as well as clustering in UMAPs. Population statistics were exported for subsequent data analysis. UMAP was used to visualise and validate unsupervised FlowSOM clustering. UMAPs for both T cells and non-T cells were generated using OMIQ on a subset of 5 million cells ensuring equal subsampling from each study group with the following parameters: 15 neighbours, 0.4 minimum distance, 2 components, cosine metric, S1 learning rate, 200 epochs and spectral embedding initialisation. The CXCR3 composite score was created for each group by summing the median CXCR3 MFI from each of the 10 T cell clusters per participant.

The manual gating strategy focused on CD3+ cells in OMIQ. The gating strategy is shown in Supp. Fig. 5B and resulted in 12 T cell populations including TCRγδ, MAIT, and NKT unconventional populations, and conventional CD8+ and CD4+ populations. UMAP was used to visualise manually gated differences between population groups. Population statistics were exported for subsequent data analysis.

Valve Tissue Processing and Imaging

Excised cardiac tissue was formalin-fixed and paraffin-imbedded before microtome sectioning to obtain 3μm thick sections. A multiplex immunofluorescent panel was developed to profile infiltrating T cell populations including CD3 and CD8 to identify T cells, and the chemokine receptor CXCR3 to evaluate expression on infiltrating cells (Supp. Table 5). Staining of two non-diseased heart valves commercially obtained (BioChain®) were included as non-rheumatic controls.

The immunofluorescent staining protocol comprised overnight baking of tissue sections (60 °C) before dewaxing and rehydration, followed by antigen retrieval using the 2100 Antigen Retriever (Aptum Biologics). Slides were blocked and incubated overnight with the primary antibody master-mix (4 °C), followed by the secondary antibody master-mix for 1 hour and DAPI for 5 minutes. Stained samples were imaged on the Phenoimager™ HT (Akoya Biosciences). A full slide scan was performed to allow region of interest selection followed by the acquisition of multispectral field images (MSIs) for quantification. MSIs were then spectrally unmixed using inForm® software and exported as component images.

For quantification of MSIs, unmixed component images were imported into HALO® 3.6 software (Indica Labs Quantitative Pathology). Iterative training of a tissue classifier and nuclei segmentation algorithm was performed by manually annotating rheumatic valve tissue samples based on DAPI expression. This allowed automated tissue segmentation for the accurate quantification of infiltrating T cells as a proportion of total cells present in each section. Intensity thresholds were then set to exclude non-specific signal and tested on a representative sample before quantification of MSIs was performed and data exported for graphing.

Statistics, Data Analysis and Visualisation

Unless otherwise stated, data analysis and graphing were performed in R (version 2023.03.0+386) in Rstudio (version 2024.09.1+394) using the following packages: tidyverse 54, mixOmics 55, rstatix, broom, corrplot, Hmisc, ggpubr, RColorBrewer, Viridis and pROC. For cytokine and PBMC analysis a linear regression model was employed to evaluate differences between study groups. The model included fixed effects for the primary variable of interest (study group) while adjusting for relevant covariates: age, ethnicity (Australian Aboriginal, New Zealand Māori, Pacific Peoples, Asian, Caucasian, Other), gender, PBMC analysis batch, and study site (Royal Darwin Hospital, Starship Children’s Hospital, and Middlemore Hospital). Age, sex, and ethnicity were compared between groups for serum and PBMC samples, with only age statistically significant between groups (Supp. Table 1, Supp. Table 2). All covariates were included simultaneously in the model without stepwise selection, as they were deemed biologically or methodologically important a priori. P-values from these comparisons were corrected for multiple testing using the Benjamini-Hochberg false discovery rate method. Heatmaps were created with Morpheus (https://software.broadinstitute.org/morpheus) or ComplexHeatmap with Euclidean hierarchical clustering used where indicated. Box plots show the median (line), interquartile range (box), and variability (whiskers extending to values within 1.5×IQR). Pearson correlation analysis was performed, and the correlation coefficient (Pearson's R) was reported to assess relationships between variables.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Overall Evaluation\nThis paper investigates the mechanism by which the CXCR3 chemokine axis mediates T cell-induced cardiac damage in\nacute rheumatic fever (ARF), representing a rigorously designed study with comprehensive data. Through a multicenter\nprospective cohort study, combined with techniques such as flow cytometry, serum factor analysis, and tissue\nimmunofluorescence, the authors systematically revealed the immune phenotype specific to ARF. The study identified the\npivotal role of CXCR3+ T cells in the pathogenesis of ARF, providing a crucial theoretical foundation for developing\nimmunomodulatory therapies.\nKey Strengths\nComprehensive Study Design: The authors employed a multi-group control design (including other streptococcal infections,\nother inflammatory diseases, non-inflammatory RHD, and healthy controls), effectively distinguishing ARF-specific immune\nfeatures. This multi-tiered control design enhances the reliability of the findings.\nTechnological Innovation:\n• Utilized 33-color flow cytometry for in-depth immune phenotyping analysis\n• Developed a CXCR3 composite scoring system to quantify T cell characteristics\n• Integrated circulating immune markers with tissue infiltration analysis, providing a complete evidence chain from peripheral\nblood to target organs\nKey Findings:\n• Identified ARF-specific immune hallmarks: elevated CCL5 and IgG3, reduced CXCR3+ T cells\n• Revealed abnormal regulation of the CXCR3-CXCL9 axis in ARF\n• Confirmed CXCR3+ T cell infiltration through valve tissue analysis\nClinical Translational Value: The proposed immune signatures (CRP, IgG3, CCL5, and CXCR3 composite score)\ndemonstrate potential diagnostic value with an AUC of 0.9. Simultaneously provides theoretical basis for clinical application\nof existing immunomodulatory drugs (e.g., IL-6 inhibitors).\nAreas for Improvement\n1. Methodological details require supplementation\n(1) Sample characteristics: Information provided in the supplementary table is incomplete. Recommend adding comparisons\nof age, gender, and ethnicity distribution across groups, and whether these factors were included as covariates in analyses.\n(2) Flow Cytometry: Although a 33-color panel was used, the paper does not explicitly describe the gating strategy or cell\npopulation definition criteria. It is recommended to supplement with flow cytometry gating strategy diagrams or typical gating\nexamples.\n(3) Tissue Analysis: Only 7 rheumatic valve cases and 2 controls were analyzed, representing a small sample size. It is\nrecommended to explain the rationale for determining the sample size and discuss potential limitations arising from the\nsmall sample size.\n2. Data analysis requires clarification\n(1) Multiple comparison correction: The paper mentions using Benjamini-Hochberg correction but does not specify whether\ncorrection was applied to all tests or only selectively to some results. We recommend clearly stating the correction strategy.\n(2) Correlation Analysis: In the correlation matrix shown in Figure 4B, some correlations appear weak yet remain statistically\nsignificant. It is recommended to supplement the correlation coefficients with confidence intervals to aid in assessing the\nprecision of these correlations.\n(3) Composite Score Construction: The calculation method for the CXCR3 composite score requires more detailed\ndescription. Is it a simple summation or a weighted score? Do all T cell subsets contribute equally to the total score?\n3. Interpretation of Results Could Be More In-Depth\nMechanistic Discussion: Although CXCR3+ T cell depletion and peripheral CXCR3-CXCL9 decoupling were observed, the\nspecific mechanism underlying this phenomenon remains under-explained. Does it involve receptor downregulation,\ninternalization, or other regulatory mechanisms?\nTemporal dynamics: As a cross-sectional study, it is impossible to determine whether the observed changes are causal or\nconsequential. We recommend a more thorough discussion of this limitation and propose a design for future longitudinal\nstudies.\nClinical relevance: The analysis of correlations between immune profiles and clinical manifestations (e.g., severity of\nmyocarditis, extent of valvular damage) is insufficient. We suggest supplementing these analyses to enhance clinical\nsignificance.\nMinor Issues\nAbbreviation Usage: Full forms should be provided for abbreviations at their first mention (e.g., “Strep A” on line 47 remains\nunexplained).\nStatistical Reporting: Some statistical results report only p-values without effect sizes (e.g., magnitude of differences\nbetween groups). Supplementing effect size measures (e.g., mean difference, Cohen's d) is recommended.\nReferences: Some key references are outdated (e.g., reference 5 on molecular modeling was published in 2014). It is\nrecommended to update with recent relevant research advances.\nConclusions and Recommendations\nThis study makes significant contributions to understanding the immunopathogenesis of ARF, particularly by elucidating the\nrole of CXCR3+ T cells in the disease. The experimental design is sound, data analysis is comprehensive, and conclusions\nare supported by multi-platform data. Addressing the above issues will substantially enhance the paper's completeness and\nimpact.", "reviewer_2": "The manuscript ‘The CXCR3 Chemokine Axis Mediates T Cell Induced Heart Damage in Acute Rheumatic Fever’ by\nMiddelton et al. assess immune changes in peripheral blood of patients with ARF and contrasts these to patients with RHD,\nStrepA infection, other inflammatory conditions and healthy controls. The methods are well described and the analysis\nrobustly documented. Results are well presented and interpreted (with the two exceptions mentioned below under ‘Major’\nissues). The entire study would be hard to replicate but is sufficiently well described to allow others to attempt such\nmammoth effort (their cohort design is in fact what makes this study stand out). The manuscript is very well written (outside of\n3 minor suggestions (see below under 'Minor')).\nNoteworthy in particular are the findings of the lower (at first sight ‘counter-intuitive’) concentration of the CXCR3 ligand\nCXCL9 in peripheral blood, lower expression of CXCR3 on peripheral blood T cells but elevated presence of this receptor\non T cells infiltrating RHD valves. While parts of this axis have been found to be altered in ARF/RHD as described by others\nbefore, the combined analysis of peripheral blood and valve tissue (even though not from the same subjects) and the\naccompanying contrast across clinically relevant differential diagnoses, elevates this observation into a potential diagnostic\nbiomarker. The other findings the authors emphasize in the manuscript, such as elevation of CCL5 and IgG3, are not as\nnovel.\nAs the authors correctly state, the field of ARF/RHD has been hampered by lack of diagnostic biomarkers for far too long.\nThe findings presented here offer the hope that the CXCR3/CXCL9 assessment will provide such marker. With better\ndiagnostics, the upstream cause-effect relationships driving the underlying pathogenesis are more likely to be deciphered,\nand with that potential therapeutic and/or prophylactic (in high risk groups) modalities evaluated.\nMAJOR\n- Overstated title. The detailed assessment and comprehensive study design as well as advanced analysis provide a solid\ncharacterisation of inflammation particular to ARF. However, the data presented are not necessarily directly linked to the\nunderlying pathogenesis leading to ARF. The markers identified as ‘specific’ to ARF are best described as ‘associated with\nARF’ as the authors correctly do (e.g. line 118) or presented as a working hypothesis (as the authors do (line 361)). Their\ncurrent title however implies causality (‘mediates’). I would suggest to rephrase the title to something like ‘The CXCR3\nChemokine Axis is associated with T Cell Induced Heart Damage in Acute Rheumatic Fever’.\n- Overstated therapeutic implications. The suggestion of using generic immune modulators (mentioned in this manuscript are\nIL6 blockade and T-cell modulation using hydrochloroquine) to treat inflammation in ARF are not new (as the authors\ncorrectly identify). Nor does this approach take into account the novel findings presented here. Specifically, the authors\ncould instead hypothesize that targeting the CXCR3/CXCL9 axis in acute stages of ARF as described for other clinical\nscenarios (e.g. PMID:33510341; 39931812; 22233170) should be considered for future interventions.\nMINOR\n- Line 39: delete ‘in’ ARF\n- Line 278: consider inserting comma (,) after ‘this’\n- Line 312/313: I understand your sentence to mean ‘Reduced numbers/percentages of selected CXCR3+ T cells’ … I\nbelieve ‘expression’ is incorrect in this context."}, "author_response": "We thank the reviewers for their comments and queries on our manuscript. We have\nresponded to these point by point below.\nReviewer #1\nOverall Evaluation\nThis paper investigates the mechanism by which the CXCR3 chemokine axis mediates T cell-\ninduced cardiac damage in acute rheumatic fever (ARF), representing a rigorously designed\nstudy with comprehensive data. Through a multi-centre prospective cohort study, combined\nwith techniques such as flow cytometry, serum factor analysis, and tissue\nimmunofluorescence, the authors systematically revealed the immune phenotype specific to\nARF. The study identified the pivotal role of CXCR3+ T cells in the pathogenesis of ARF, providing\na crucial theoretical foundation for developing immunomodulatory therapies.\nThank you for the positive comments on our paper including the key strengths and clinical\nimplications, as well as highlighting areas for improvement.\n1. Methodological details require supplementation\n(1) Sample characteristics: Information provided in the supplementary table is incomplete.\nRecommend adding comparisons of age, gender, and ethnicity distribution across groups, and\nwhether these factors were included as covariates in analyses.\nWe have now compared age, sex, and ethnicity between study groups with appropriate\nstatistics in Supplementary Table 1 and Supplementary Table 2, with only age statistically\nsignificantly different between groups. Each of these were included as covariates in analyses\nthroughout the paper, with this detailed in the methods in lines 444 - 447.\n(2) Flow Cytometry: Although a 33-color panel was used, the paper does not explicitly describe\nthe gating strategy or cell population definition criteria. It is recommended to supplement with\nflow cytometry gating strategy diagrams or typical gating examples.\nThe gating strategy and cell population definition criteria are shown in Supplementary Figure 5.\nHowever we appreciate that this could have been more clearly sign-posted from the results\nsection of the manuscript. We now refer to this in line 171 in the results (as well as in the\nmethods lines 420 - 422).\n(3) Tissue Analysis: Only 7 rheumatic valve cases and 2 controls were analyzed, representing a\nsmall sample size. It is recommended to explain the rationale for determining the sample size\nand discuss potential limitations arising from the small sample size.\nWe agree that the small sample of heart valves is a limitation of this analysis and we have added\na comment to more clearly reflect this in the discussion. The reason for this is the difficulty in\nrecruiting paediatric patients with rheumatic heart disease before surgery to allow collection of\nany tissue that is removed. This study was funded for a 12 months recruitment period at our\nlocal tertiary hospital, and the number of suitable surgeries in that year was limited.\nFurthermore, sometimes only small fragments are removed and are not suitable for multi-\ncolour microscopy. Thus, in this study these valve samples were used to further explore a\nmechanistic link from circulation to tissue, rather than in a discovery immune profiling manner.\nChanges:\n Added text to discussion: “This study is also limited by the number of rheumatic valves\nanalysed, reflecting the scale of valve surgery in our setting. Future longitudinal studies\nshould be adequately powered to allow correlation of immunological profiles to clinical\nmanifestations such as carditis and would ideally include more in-depth analysis of\nheart valve tissue.” - Lines 323 - 326\n2. Data analysis requires clarification\n(1) Multiple comparison correction: The paper mentions using Benjamini-Hochberg correction\nbut does not specify whether correction was applied to all tests or only selectively to some\nresults. We recommend clearly stating the correction strategy.\nEvery figure that presents multiple t-tests requiring correction now has had a statement added\naffirming that the Benjamini-Hochberg correction method was used.\n(2) Correlation Analysis: In the correlation matrix shown in Figure 4B, some correlations appear\nweak yet remain statistically significant. It is recommended to supplement the correlation\ncoefficients with confidence intervals to aid in assessing the precision of these correlations.\nR value, confidence intervals, and p-values have now been provided for this figure in the data\nsummary file.\n(3) Composite Score Construction: The calculation method for the CXCR3 composite score\nrequires more detailed description. Is it a simple summation or a weighted score? Do all T cell\nsubsets contribute equally to the total score?\nDetails describing the CXCR3 composite score calculation have now been added in the ‘OMIQ\nAnalysis of cell populations’ subheading in the methods section. As the reviewer suggests this\nscore represents the simple summation of median CXCR3 MFI from every T cell population so\nthat all T cell subsets contribute equally to the score, and it is not weighted.\nChanges:\n Added text to materials and methods: “The CXCR3 composite score was created for\neach group by summing the median CXCR3 MFI from each of the 10 T cell clusters per\nparticipant.” – Lines 418 - 419\n3. Interpretation of Results Could Be More In-Depth\nMechanistic Discussion: Although CXCR3+ T cell depletion and peripheral CXCR3-CXCL9\ndecoupling were observed, the specific mechanism underlying this phenomenon remains\nunder-explained. Does it involve receptor downregulation, internalization, or other regulatory\nmechanisms?\nWe have discussed a potential mechanism regarding the decoupling in the discussion (line 274\n- 277), and propose that “…cumulative reduction of multiple circulating CXCR3+ T cell\npopulations in ARF…suggests T cell migration continues to occur after triggering Strep A\ninfections in ARF, resulting in immune dysregulation”. However, we agree that there are other\nmechanisms that could explain or contribute to the observed CXCR3+ T cell depletion and have\nadded these for a more detailed discussion.\nChanges:\n Added text to discussion: “Alternative mechanisms such as reduced CXCR3 expression\nand/or internalisation might also contribute to the reduction in circulating CXCR3+ T cell\npopulations.” – Lines 277 - 279\nTemporal dynamics: As a cross-sectional study, it is impossible to determine whether the\nobserved changes are causal or consequential. We recommend a more thorough discussion of\nthis limitation and propose a design for future longitudinal studies.\nWe agree with cross-sectional design being a limitation and have expanded the discussion on\nthis in the limitations section. – Lines 319 - 326\nClinical relevance: The analysis of correlations between immune profiles and clinical\nmanifestations (e.g., severity of myocarditis, extent of valvular damage) is insufficient. We\nsuggest supplementing these analyses to enhance clinical significance.\nWe agree that analysis by clinical presentation (e.g. severity of carditis) would be informative,\nhowever this is somewhat undermined by the known heterogenous clinical presentation of ARF,\nthat is also reflected in the START study cohort. Once participants without PBMC samples were\nexcluded, too few samples are left for sufficient statistical power in the current work. Of the 62\nARF participants with PBMCs, these are split three ways into no carditis (22), subclinical carditis\n(14), and clinical carditis (26). Thus, investigating an association of immune profiles with clinical\nmanifestations should be a focus for future work in larger cohorts with an eye to improved\nstatistical power. A comment has been added to the discussion to reflect this.\nChanges:\n Added text to discussion: “Future longitudinal studies should be adequately powered to\nallow correlation of immunological profiles to clinical manifestations such as carditis” –\nLines 324 - 326\nMinor Issues\nAbbreviation Usage: Full forms should be provided for abbreviations at their first mention (e.g.,\n“Strep A” on line 47 remains unexplained).\nStrep A is used as an abbreviation of Group A Streptococcus and indicated at first mention on\nlines 47 - 48. See “Group A Streptococcus (Streptococcus pyogenes; Strep A)”.\nStatistical Reporting: Some statistical results report only p-values without effect sizes (e.g.,\nmagnitude of differences between groups). Supplementing effect size measures (e.g., mean\ndifference, Cohen's d) is recommended.\nWe agree with the reviewer and have added effect sizes with p-values in the data summary\nprovided for each figure for better interpretability.\nReferences: Some key references are outdated (e.g., reference 5 on molecular modeling was\npublished in 2014). It is recommended to update with recent relevant research advances.\nChanges:\nThe following more recent references have been added:\nKarthikeyan G, Guilherme L. Acute rheumatic fever. The Lancet. 2018;392(10142):161-74.\n Line 67\nKirvan CA, Canini H, Swedo SE, Hill H, Veasy G, Jankelow D, et al. IgG2 rules: N-acetyl-β-D-\nglucosamine-specific IgG2 and Th17/Th1 cooperation may promote the pathogenesis of acute\nrheumatic heart disease and be a biomarker of the autoimmune sequelae of Streptococcus\npyogenes. Frontiers in Cardiovascular Medicine. 2023;9:919700.\n Line 69\nPassos LS, Jha PK, Becker-Greene D, Blaser MC, Romero D, Lupieri A, et al. Prothymosin Alpha:\nA Novel Contributor to Estradiol Receptor Alpha–Mediated CD8+ T-Cell Pathogenic Responses\nand Recognition of Type 1 Collagen in Rheumatic Heart Valve Disease. Circulation.\n2022;145(7):531-48.\n Lines 73 and 76\nReviewer #2\nThe manuscript ‘The CXCR3 Chemokine Axis Mediates T Cell Induced Heart Damage in Acute\nRheumatic Fever’ by Middelton et al. assess immune changes in peripheral blood of patients\nwith ARF and contrasts these to patients with RHD, StrepA infection, other inflammatory\nconditions and healthy controls. The methods are well described and the analysis\nrobustly documented. Results are well presented and interpreted (with the two exceptions mentioned\nbelow under ‘Major’ issues). The entire study would be hard to replicate but is sufficiently well\ndescribed to allow others to attempt such mammoth effort (their cohort design is in fact what\nmakes this study stand out). The manuscript is very well written (outside of 3 minor suggestions\n(see below under 'Minor')).\nWe appreciate the positive comments of our manuscript as well as the suggestions for\nimprovements.\nMAJOR\nOverstated title…I would suggest to rephrase the title to something like ‘The CXCR3 Chemokine\nAxis is associated with T Cell Induced Heart Damage in Acute Rheumatic Fever’.\nThe title has been amended as suggested to: “The CXCR3 Chemokine Axis is Associated with T\nCell Induced Heart Damage in Acute Rheumatic Fever.”\nOverstated therapeutic implications. The suggestion of using generic immune modulators\n(mentioned in this manuscript are IL6 blockade and T-cell modulation using hydrochloroquine)\nto treat inflammation in ARF are not new (as the authors correctly identify). Nor does this\napproach take into account the novel findings presented here. Specifically, the authors could\ninstead hypothesize that targeting the CXCR3/CXCL9 axis in acute stages of ARF as described\nfor other clinical scenarios (e.g. PMID:33510341; 39931812; 22233170) should be considered\nfor future interventions.\nWe agree that targeting the CXCR3/CXCL9 axis as a potential therapeutic is a future possibility,\nand exploring the value this for the CXCR3 axis has now been added to the discussion, together\nwith the suggested references.\nChanges:\n Added text to discussion: “With specific inhibitors of the CXCR3 chemokine axis in\npreclinical development 47-49, this could also be explored as a therapeutic target for\nrheumatic fever in the future.” Lines – 309 - 311\nMINOR\n- Line 39: delete ‘in’ ARF\n- Line 278: consider inserting comma (,) after ‘this’\n- Line 312/313: I understand your sentence to mean ‘Reduced numbers/percentages of\nselected CXCR3+ T cells’ … I believe ‘expression’ is incorrect in this context.\nAll minor corrections have been made."}, {"round": 2, "reviewer_comments": {"reviewer_1": "Reviewer #1\nOverall Evaluation\nThis paper investigates the mechanism by which the CXCR3 chemokine axis mediates T cell-\ninduced cardiac damage in acute rheumatic fever (ARF), representing a rigorously designed\nstudy with comprehensive data. Through a multi-centre prospective cohort study, combined\nwith techniques such as flow cytometry, serum factor analysis, and tissue\nimmunofluorescence, the authors systematically revealed the immune phenotype specific to\nARF. The study identified the pivotal role of CXCR3+ T cells in the pathogenesis of ARF, providing\na crucial theoretical foundation for developing immunomodulatory therapies.\nThank you for the positive comments on our paper including the key strengths and clinical\nimplications, as well as highlighting areas for improvement.\n1. Methodological details require supplementation\n(1) Sample characteristics: Information provided in the supplementary table is incomplete.\nRecommend adding comparisons of age, gender, and ethnicity distribution across groups, and\nwhether these factors were included as covariates in analyses.\nWe have now compared age, sex, and ethnicity between study groups with appropriate\nstatistics in Supplementary Table 1 and Supplementary Table 2, with only age statistically\nsignificantly different between groups. Each of these were included as covariates in analyses\nthroughout the paper, with this detailed in the methods in lines 444 - 447.\n(2) Flow Cytometry: Although a 33-color panel was used, the paper does not explicitly describe\nthe gating strategy or cell population definition criteria. It is recommended to supplement with\nflow cytometry gating strategy diagrams or typical gating examples.\nThe gating strategy and cell population definition criteria are shown in Supplementary Figure 5.\nHowever we appreciate that this could have been more clearly sign-posted from the results\nsection of the manuscript. We now refer to this in line 171 in the results (as well as in the\nmethods lines 420 - 422).\n(3) Tissue Analysis: Only 7 rheumatic valve cases and 2 controls were analyzed, representing a\nsmall sample size. It is recommended to explain the rationale for determining the sample size\nand discuss potential limitations arising from the small sample size.\nWe agree that the small sample of heart valves is a limitation of this analysis and we have added\na comment to more clearly reflect this in the discussion. The reason for this is the difficulty in\nrecruiting paediatric patients with rheumatic heart disease before surgery to allow collection of\nany tissue that is removed. This study was funded for a 12 months recruitment period at our\nlocal tertiary hospital, and the number of suitable surgeries in that year was limited.\nFurthermore, sometimes only small fragments are removed and are not suitable for multi-\ncolour microscopy. Thus, in this study these valve samples were used to further explore a\nmechanistic link from circulation to tissue, rather than in a discovery immune profiling manner.\nChanges:\n Added text to discussion: “This study is also limited by the number of rheumatic valves\nanalysed, reflecting the scale of valve surgery in our setting. Future longitudinal studies\nshould be adequately powered to allow correlation of immunological profiles to clinical\nmanifestations such as carditis and would ideally include more in-depth analysis of\nheart valve tissue.” - Lines 323 - 326\n2. Data analysis requires clarification\n(1) Multiple comparison correction: The paper mentions using Benjamini-Hochberg correction\nbut does not specify whether correction was applied to all tests or only selectively to some\nresults. We recommend clearly stating the correction strategy.\nEvery figure that presents multiple t-tests requiring correction now has had a statement added\naffirming that the Benjamini-Hochberg correction method was used.\n(2) Correlation Analysis: In the correlation matrix shown in Figure 4B, some correlations appear\nweak yet remain statistically significant. It is recommended to supplement the correlation\ncoefficients with confidence intervals to aid in assessing the precision of these correlations.\nR value, confidence intervals, and p-values have now been provided for this figure in the data\nsummary file.\n(3) Composite Score Construction: The calculation method for the CXCR3 composite score\nrequires more detailed description. Is it a simple summation or a weighted score? Do all T cell\nsubsets contribute equally to the total score?\nDetails describing the CXCR3 composite score calculation have now been added in the ‘OMIQ\nAnalysis of cell populations’ subheading in the methods section. As the reviewer suggests this\nscore represents the simple summation of median CXCR3 MFI from every T cell population so\nthat all T cell subsets contribute equally to the score, and it is not weighted.\nChanges:\n Added text to materials and methods: “The CXCR3 composite score was created for\neach group by summing the median CXCR3 MFI from each of the 10 T cell clusters per\nparticipant.” – Lines 418 - 419\n3. Interpretation of Results Could Be More In-Depth\nMechanistic Discussion: Although CXCR3+ T cell depletion and peripheral CXCR3-CXCL9\ndecoupling were observed, the specific mechanism underlying this phenomenon remains\nunder-explained. Does it involve receptor downregulation, internalization, or other regulatory\nmechanisms?\nWe have discussed a potential mechanism regarding the decoupling in the discussion (line 274\n- 277), and propose that “…cumulative reduction of multiple circulating CXCR3+ T cell\npopulations in ARF…suggests T cell migration continues to occur after triggering Strep A\ninfections in ARF, resulting in immune dysregulation”. However, we agree that there are other\nmechanisms that could explain or contribute to the observed CXCR3+ T cell depletion and have\nadded these for a more detailed discussion.\nChanges:\n Added text to discussion: “Alternative mechanisms such as reduced CXCR3 expression\nand/or internalisation might also contribute to the reduction in circulating CXCR3+ T cell\npopulations.” – Lines 277 - 279\nTemporal dynamics: As a cross-sectional study, it is impossible to determine whether the\nobserved changes are causal or consequential. We recommend a more thorough discussion of\nthis limitation and propose a design for future longitudinal studies.\nWe agree with cross-sectional design being a limitation and have expanded the discussion on\nthis in the limitations section. – Lines 319 - 326\nClinical relevance: The analysis of correlations between immune profiles and clinical\nmanifestations (e.g., severity of myocarditis, extent of valvular damage) is insufficient. We\nsuggest supplementing these analyses to enhance clinical significance.\nWe agree that analysis by clinical presentation (e.g. severity of carditis) would be informative,\nhowever this is somewhat undermined by the known heterogenous clinical presentation of ARF,\nthat is also reflected in the START study cohort. Once participants without PBMC samples were\nexcluded, too few samples are left for sufficient statistical power in the current work. Of the 62\nARF participants with PBMCs, these are split three ways into no carditis (22), subclinical carditis\n(14), and clinical carditis (26). Thus, investigating an association of immune profiles with clinical\nmanifestations should be a focus for future work in larger cohorts with an eye to improved\nstatistical power. A comment has been added to the discussion to reflect this.\nChanges:\n Added text to discussion: “Future longitudinal studies should be adequately powered to\nallow correlation of immunological profiles to clinical manifestations such as carditis” –\nLines 324 - 326\nMinor Issues\nAbbreviation Usage: Full forms should be provided for abbreviations at their first mention (e.g.,\n“Strep A” on line 47 remains unexplained).\nStrep A is used as an abbreviation of Group A Streptococcus and indicated at first mention on\nlines 47 - 48. See “Group A Streptococcus (Streptococcus pyogenes; Strep A)”.\nStatistical Reporting: Some statistical results report only p-values without effect sizes (e.g.,\nmagnitude of differences between groups). Supplementing effect size measures (e.g., mean\ndifference, Cohen's d) is recommended.\nWe agree with the reviewer and have added effect sizes with p-values in the data summary\nprovided for each figure for better interpretability.\nReferences: Some key references are outdated (e.g., reference 5 on molecular modeling was\npublished in 2014). It is recommended to update with recent relevant research advances.\nChanges:\nThe following more recent references have been added:\nKarthikeyan G, Guilherme L. Acute rheumatic fever. The Lancet. 2018;392(10142):161-74.\n Line 67\nKirvan CA, Canini H, Swedo SE, Hill H, Veasy G, Jankelow D, et al. IgG2 rules: N-acetyl-β-D-\nglucosamine-specific IgG2 and Th17/Th1 cooperation may promote the pathogenesis of acute\nrheumatic heart disease and be a biomarker of the autoimmune sequelae of Streptococcus\npyogenes. Frontiers in Cardiovascular Medicine. 2023;9:919700.\n Line 69\nPassos LS, Jha PK, Becker-Greene D, Blaser MC, Romero D, Lupieri A, et al. Prothymosin Alpha:\nA Novel Contributor to Estradiol Receptor Alpha–Mediated CD8+ T-Cell Pathogenic Responses\nand Recognition of Type 1 Collagen in Rheumatic Heart Valve Disease. Circulation.\n2022;145(7):531-48.\n Lines 73 and 76\nReviewer #2\nThe manuscript ‘The CXCR3 Chemokine Axis Mediates T Cell Induced Heart Damage in Acute\nRheumatic Fever’ by Middelton et al. assess immune changes in peripheral blood of patients\nwith ARF and contrasts these to patients with RHD, StrepA infection, other inflammatory\nconditions and healthy controls. The methods are well described and the analysis robustly\ndocumented. Results are well presented and interpreted (with the two exceptions mentioned\nbelow under ‘Major’ issues). The entire study would be hard to replicate but is sufficiently well\ndescribed to allow others to attempt such mammoth effort (their cohort design is in fact what\nmakes this study stand out). The manuscript is very well written (outside of 3 minor suggestions\n(see below under 'Minor')).", "reviewer_2": "The authors have satisfactorily responded to all queries from reviewers.", "reviewer_3": "Thank you for your thoughtful comments back to the reviewers and for the clarifications provided in the manuscript. All concerns from the prior review have adequately resolved and I do not have any further substantial comments."}, "author_response": "We thank the reviewers for confirming that all comments on the prior version have been\naddressed. We have separately responded to the editorial queries and corrections and this is\ndetailed in the checklist provided."}]

Decision letter