Recombination junctions from antibody isotype switching classify immune and DNA repair dysfunction

Domain: biology
Published: 2025-12-19
Editorial outcome: major_revision

Abstract
Personalized assessment of immunocompetence and DNA double-strand break (DSB) repair requires methods that are sensitive to genetic and molecular complexity beyond the well-known monogenic disorders. Inspired by decades of research using B cells to study DNA repair processes, here we present SWIBRID (SWItch junction Breakpoint Repertoire IDentification), a tool to systematically profile genomic junctions generated in vivo during antibody class switch recombination (CSR) in B cells. As CSR junctions reflect immune diversity and DNA repair proficiency, SWIBRID detects phenotypic manifestations of deficiencies via a highly scalable, blood-based PCR followed by long-read sequencing and bioinformatic analysis. We show that specific DNA repair defects, including cancer-associated mutations, exhibit distinct CSR junction patterns. Notably, SWIBRID distinguishes different types of DSB repair knockouts and identifies the respective genetic defect in cell lines. In 68 patients, we detect immunodeficiencies and DNA repair defects with high accuracy (area under the curve 0.99 and 0.84, respectively), and identify previously uncharacterized patient groups as well as patient-specific CSR junction signatures. With SWIBRID, we seek to advance the identification of pathogenic defects, support early diagnosis, and address molecular heterogeneity that drives variable clinical outcomes.

Introduction
Adaptive immune responses rely on a diverse repertoire of immune cells generated by somatic diversification of antigen receptors1. The underlying molecular processes include the programmed induction and repair of DNA double-strand breaks (DSB), which involves a complex network of repair pathways2,3. Defects in such pathways can negatively impact the adaptive immune system and jeopardize genome integrity, potentially leading to cancer, radiation sensitivity, premature ageing, neurodegenerative diseases, or developmental disorders4–9. To this end, numerous technical approaches have been developed to uncover defects in immunity or DSB repair, and to inform clinical decisions for treatment and prevention.

Newborn screening tests such as T-cell receptor excision circles (TREC) and κ-deleting recombination excision circles (KREC) have revolutionized the early identification of inborn errors of immunity (IEI) related to T and B cell defects10–12. However, these assays can fail to identify defective antibody responses as observed in common variable immunodeficiency (CVID)13,14.

Genetic testing identifies monogenic defects predisposing to cancer and impacting its treatment15,16. Beyond cancer, such testing can also benefit patients with immunodeficiencies, offering targeted treatment options. For instance, gene therapy has emerged as a promising treatment for defects in the DNA repair enzyme Artemis17.

Given that single, high-risk mutations are infrequent in the population while multifactorial diseases are often driven by many combined low-risk variants18–21, methods assessing deficiencies in single genes or proteins are unlikely to reflect the overall performance of the immune system and the DNA repair landscape. Therefore, more holistic approaches have addressed DNA repair performance in primary samples by directly measuring repair outcome, for instance, analyzing the quality and quantity of genomic alterations in whole genome sequencing (WGS) data22,23. While such methods avoid indirect in vitro assays, WGS data comes with considerable costs and technical challenges, as well as data privacy concerns. Alternative methods to assess immune health are based on high-dimensional phenotypic readouts such as transcriptomics and proteomics24.

In contrast, we reasoned that deep characterization of natural DSB hotspots in B cells holds the potential for a targeted, yet highly versatile solution to assess the quality of DNA repair and immunocompetence. Namely, the intron of the Immunoglobulin (Ig) heavy chain (IGH) locus accumulates DSB repair events in switch (S) regions. Such events support the diversification of the antibody effector repertoire in a process called class switch recombination (CSR)25,26. B cells have therefore been used for decades to study DNA repair phenotypes of specific genetic knockouts (KO)27–31.

During CSR, B cells rearrange the IGH locus to change the isotype of the resulting antibody from IgM to IgG, IgA, or IgE32. The process results in the replacement of the Cµ constant region exons with the Cγ, α, or ε exons located 100 to 200 kb downstream. S regions are GC-rich, repetitive intronic sequences located upstream of C exons encoding for constant domains. S regions are recombined with each other upon introduction of DNA lesions by activation-induced cytidine deaminase (AID). Lesions are converted into DSBs and repaired by several distinct pathways, resulting in CSR junctions. The dominant DNA repair pathway active during CSR is nonhomologous end joining (NHEJ), followed by microhomology-mediated end-joining (MMEJ), which is linked to aberrant repair and chromosomal translocations31,33–35. CSR junctions resulting from NHEJ are characterized by direct joining or linked to microhomologies of a few base pairs, while MMEJ is associated with longer stretches of homology, resections, and deletions36. Homologous recombination (HR) repairs DSBs with high accuracy, restoring the original sequence, and is considered mostly dispensable for CSR37. However, deficiencies in HR repair factors, such as BRCA1 (Breast cancer type 1 susceptibility protein), have been found to alter the sequence patterns surrounding CSR junctions in human38.

Here, we hypothesize that the repertoire of CSR junctions in class-switched memory B cells captures the diversity of antibody effector components and the performance of diverse DNA repair pathways. We introduce SWIBRID (SWItch junction Breakpoint Repertoire IDentification), an approach that systematically characterizes in vivo generated CSR junctions with the aim of aiding DNA repair research and improving the identification of immunocompromised individuals.

Results
A methodology to systematically profile class switch recombination (CSR) junctions

To evaluate isolated CSR junctions of different isotypes in a single reaction, we optimized a PCR amplification using 4 barcoded primers. In the human IGH locus, the forward (FW) primer binds 565 bp upstream of the human Sµ region39 while reverse (RV) primers for Sγ-, Sα-, and Sε bind 775 bp (γ3), 775 bp (γ1), 63 (γ2), 777 bp (γ4), 706 (α1), 169 (α2), and 161 bp (ε) downstream of the respective S regions (Fig. 1a). FW and RV primer binding sites are multiple kilobases apart in the genome, ensuring that primers exclusively amplify IGH genes from switched memory B cells. The number of PCR cycles (25) was kept as low as possible to minimize technical noise while still ensuring sufficient representation of the biological diversity. We subjected amplicons from blood-derived B cells to MinION long-read sequencing and designed a dedicated bioinformatics pipeline (see “Methods”, Supplementary Fig. 1a). Amplicons ranged from 1000 to 4000 bp in size (Supplementary Fig. 1b–e). Reads meeting our quality criteria were mapped to the genome and hierarchically clustered using similarity between individual alignment patterns. To assess and suppress technical noise likely caused by long-read sequencing and mapping artefacts, we analyzed reads from a linearized Sµ region-containing plasmid and subsequently ignored small indels for clustering (Supplementary Note 1 and Supplementary Fig. 1f, g). We performed extensive benchmarking experiments on simulated data to optimize our method with regard to a clustering cutoff value and a downstream cluster filtering strategy (Supplementary Note 1). We next applied our pipeline to human B cell samples with defined clonality. As expected, analysis of an in-house monoclonal cell line revealed two main CSR clones in accordance with 2 switched alleles (Fig. 1b, c). We observed some heterogeneity in the monoclonal cell line, where subtle sequence variations around the breakpoint (Supplementary Fig. 1h) formed independent clusters in the read plot, leading to a slight overestimation of diversity for this sample. PCR may introduce such sequence variation, especially in highly repetitive DNA, but a similar level of heterogeneity in both PCR-amplified samples and the non-PCR plasmid control (Supplementary Fig. 1g) suggests that most of the variation arises from clustering, mapping, and sequencing errors. An oligoclonal line showed several switched alleles, and a polyclonal sample of primary human B cells displayed a high and evenly distributed diversity. We also adapted our primer design, PCR protocol, and bioinformatical pipeline to analyze mouse CSR junctions and obtained amplicons from 2500 to 4000 bp in size (Supplementary Fig. 1i, j). We used the mouse CH12 cell line and splenocytes to validate our assay (Supplementary Fig. 1k, l).

To further confirm an adequate quantification of CSR clones and understand the limitations of the pipeline, we assessed different numbers of (i) in silico generated CSR junctions representing diverse clonotypes and (ii) primary B cells to address the impact of the input cell number on output SWIBRID clusters. First, we analyzed simulated reads generated using the coordinates of CSR junctions derived from B cell sequencing data (31 human donors). The sequences were further diversified by adding mutations, insertions, and deletions according to the observed technical noise characteristics (see “Methods”). As long as the input read number was sufficiently large, the number of output clusters correlated strongly with the input clonality (Fig. 1d; R2 = 0.98-1 for 10,000 to 100,000 reads). Second, we tested the effect of distinct B cell numbers using class-switched memory B cells of three independent donors in triplicates. When using a maximum of 50,000 reads of increasing amounts of switched memory B cells, a solid correlation was observed (Fig. 1e). A saturation of the resulting diversity was noticed beyond an input of 20,000 sorted B cells. We therefore used no more than 200,000 peripheral blood mononuclear cells (PBMC) per sample for further analysis, typically containing 5–10% total B cells and around 10,000 switched B cells. To benchmark the reproducibility of our assay, we devised a meta-clustering strategy to trace recurring clones across donors (Supplementary Note 2 and Supplementary Data 1). We observed matching clusters much more often in sample pairs from the same donor than from different donors. A baseline number of shared clusters between different donors likely reflects both the presence of public clones and a limited resolution of our approach (Fig. 1f). To compare SWIBRID with the standard approach for B cell analysis, we evaluated whether our genomic findings are reflected at the RNA level. Thus, we analyzed IGHA and IGHG isotype frequencies between CSR junction alleles and BCR mRNAs from 50,000 to 100,000 circulating B cells from three donors. Both approaches obtained similar outcomes regarding the percentage of IGHA (Fig. 1g). In conclusion, we have established a high-throughput pipeline to profile and quantify the diversity of CSR-derived clones from human blood samples.

Gathering CSR junction characteristics reflective of immunocompetence and DNA repair in human and mouse

To deeply profile the CSR junctions as a result of DSB repair and diversification, we established a strategy to condense a full dataset into a vector of 68 features related to 5 main categories (Figs. 1a and 2a and Supplementary Fig. 2a–e): (i) structural aberrations (n=5) such as duplications, templated inserts, and the frequency and size of inversions, which we define as stretches within S regions with reversed orientation (Supplementary Fig. 2d); (ii) sequence context (n=17) such as microhomologies, untemplated nucleotides, and sequence motifs directly adjacent to the junctions (Supplementary Fig. 2a, b, e); (iii) the breakpoint matrix (n=28) of all donor- and acceptor S regions to quantify frequencies of breaks within or between regions, as well as the dispersion of breakpoint locations, the fragment length, and GC content (Supplementary Fig. 2c); (iv) diversity measures (n=9) such as cluster number and mean size, and the entropy and other statistics of the size distribution; and (v) isotype composition (n=9). While groups (i) and (ii) are expected to reflect the performance of distinct DNA repair pathways, group (iii) represents a combined measure of DNA repair pathway engagement and immune performance. Groups (iv) and (v) should mirror the capacity of the immune system to respond to threats. We evaluated these features on a large dataset of 98 samples from 40 donors across 7 batches (cohort C1). A batch of 10 samples was sequenced with MinION as well as PacBio HiFi technology, giving very similar results (Supplementary Note 2 and Supplementary Data 1).

To assess the robustness of our features across different batches or sequencing methods, and their ability to discriminate between donors, we used variance partitioning analysis to verify that for most features, the bulk of the variability is explained by donor identity (Fig. 2b, c and Supplementary Fig. 2f). This indicates that our quantification yields accurate and reliable output. An independently collected and processed set of random human blood donors (cohort C2) with 41 samples from 14 individuals (6 batches) showed very similar distributions of features across the donor sets (Fig. 2d). For all further analyses in this manuscript, we selected a smaller set of 44 robust features (Supplementary Data 2) with a high percentage (>25%) of variance explained by donor identity and not primarily attributed to technical batch effects or the sequencing method (<35% and <20%, respectively; Fig. 2c). Of note, most features were not strongly correlated with age or sex. As exceptions and in line with previous literature, we found that the size of the biggest clone tended to be higher in older donors (≥50 y)40, while the breakpoint dispersion in Sγ was slightly lower in females (Fig. 2e). We additionally attempted to call single-nucleotide variants in our data. We classified as likely somatic those that were not annotated in dbSNP (database of Single Nucleotide Polymorphisms), but had intermediate allele frequencies and did not segregate across clusters as would be expected for heterozygous germline variants (see “Methods”). However, features related to variants were the least robust in discriminating between donors and the most affected by sequencing technology (Supplementary Fig. 2f). In contrast, the selected set of robust features showed a high correlation across sequencing methods (Supplementary Fig. 2g). Since some of our features measure similar properties of the data in different ways (e.g., diversity), we assessed correlations between features over the C2 cohort. As expected, we found groups of highly correlated features, but also a strong association of isotype frequencies with multiple features related to length, GC content, homology, or motif occurrences (Supplementary Fig. 2h). However, calculating context features separately for each isotype rather than globally did not produce more robust outcomes. We also confirmed increased robustness by calculating diversity features on subsets of 1000 reads and by averaging features first within clusters and then over the entire dataset (Supplementary Fig. 2i).

Beyond human, we used samples from spleens and lymph nodes of inbred immunized mice to demonstrate that our assay can be used in different mouse tissues with comparable results (Fig. 2f). To further benchmark the detection of CSR-related signatures of defective DNA repair, we re-analyzed a public dataset of Sµ-Sγ1 junction reads from splenic B cells of specific-pathogen-free mice generated by Vincendeau et al.30 (see “Methods”). This study investigated the role of Shieldin1 (Shld1) in preserving end-protection of AID-mediated DSB by restricting resection in NHEJ-proficient and NHEJ-deficient (Xlf−/−) B cells. We recognized that many features cannot easily be compared between human and mouse data, or between data sets with different primer designs (Supplementary Fig. 2j, Fig. 1a, and Supplementary Fig. 1i). Still, within the dataset of Vincendeau et al., we observed strong differences between different genotypes and not only for features directly connected to resection as observed in their work30 (Fig. 2f and Supplementary Fig. 2j). Moreover, in a principal component analysis (PCA) of that dataset, all KO samples separated well from WT controls and from each other, while Xlf−/− was most similar to WT and Shld1−/−Xlf−/− most dissimilar (Fig. 2g). We next created a scatter plot of standardized mean features to compare features between pairs of genotypes and highlight the most distinctive ones (Fig. 2h). Specifically, SWIBRID confirmed a hyper-resection phenotype for the Shld1−/−Xlf−/− double KO, as features that depend on read length and break dispersion along the Sγ1 region in CSR junctions were significantly altered. Compared to Xlf−/−, Shld1−/− strongly impacted diversity features such as cluster distribution, size, and entropy, in accordance with strongly decreased serum levels of IgG and significantly reduced frequencies of IgG class-switched B cells in these mice30. We also identified additional genotype-specific features, such as the expansion of single clones in the repertoire (occupancy_big_clusters) and a lower GC content in the acceptor side of the break (acceptor_score_W) for the KO of Shld1 in the presence or absence of Xlf.

Collectively, we show that SWIBRID delivers a reproducible and informative quantification of CSR junction characteristics that can be applied in diverse B cell-containing tissues to deeply explore the contribution of genetic factors on DSB repair and immune diversity in a single analysis.

CSR junction footprints serve for genotype prediction of DNA repair knockouts

Next, we aimed to better map the landscape of DSB repair at CSR junctions. We applied SWIBRID to CH12 mouse cell lines carrying monogenic KOs of further DNA repair factors, namely (i) Lig4 (DNA Ligase IV), which joins DSBs during NHEJ36, (ii) Trp53bp1 (P53-binding protein 1), which acts as a platform to protect DNA ends from resection by recruiting pro-NHEJ factors upon Ataxia Telangiectasia Mutated (ATM) kinase phosphorylation41, (iii) Rif1 (Rap1-interacting factor 1), which partners with Trp53bp1 to promote NHEJ42, and (iv) Brca1, which plays a key role in precise HR repair and competes for the DNA break site with Trp53bp1 and Rif143. CH12 cells were stimulated for two days with a cocktail of interleukin-4, anti-CD40 antibody, and TGFβ1, which induces CSR from IgM to IgA in vitro44 (Fig. 3a). A PCA combining the data from CH12 with spleen and lymph node samples from Fig. 2f revealed that the cell lines were not directly comparable to primary mouse samples that underwent CSR in vivo (Fig. 3b). The difference was only partly explained by the restriction of CH12 to IgA (Fig. 3b). Notably, splenocyte and lymph node samples were more variable and globally more distinct from CH12 samples with diverse KOs. However, with few exceptions, the CH12 samples clustered according to their genotype in a heatmap of repair outcomes represented by n=32 features (of the 44 robust features defined in a human dataset in Fig. 2, we excluded features missing in the mouse cell lines, such as features related to Sγ, isotype frequencies, and sequential breaks; Fig. 3c and Supplementary Data 2). For every genotype, we could identify features that were significantly different from WT samples (Fig. 3d). For instance, the direct switching from Sµ to any other S region (“pct_direct_switch”) was significantly higher for Brca1 KO compared to WT, and the mean length of PCR products, inversely reflective of resection, was significantly lower for Trp53bp1 and Lig4 KO and higher in Brca1 KO. The variability of CSR junction positions across the Sα region (“break_dispersion_SA”) was significantly increased in Trp53bp1 KO and Lig4 KO, while reduced in Brca1 KO. As previously reported45, CSR breaks leading to inversions of S regions were found to be substantially higher in Lig4 KO but decreased for Rif1 and Brca1 KO. Frequencies of templated insertions46 were highest for Rif1 KO. As expected, mean cluster size for all KOs was significantly increased, reflecting a reduced ability of B cells to diversify. All genotypes separated well in a PCA, despite some overlap of Rif1 KO with Trp53bp1 KO samples (Fig. 3e), which may be expected as they interact to protect DSBs against nucleolytic processing47,48.

Next, we re-analyzed published CSR junction data from mouse CH12 cells and splenocytes with KOs in various DSB repair proteins, including Lig4 and Trp53bp127. Using our bioinformatic pipeline on data generated by linear amplification and short-read sequencing, we recapitulated previous findings such as reduced mean length or an increased use of >6nt homology in the Trp53bp1 mutant (Supplementary Fig. 3a–d). Additionally, we identified a higher frequency of intra-switch inversions in Lig4 KO, which was even more pronounced in Trp53bp1 KO (Supplementary Fig. 3c). We also observed a striking decrease in GC content at the acceptor side of CSR junctions for both KOs. As expected, the bioinformatics pipeline of SWIBRID showed improved performance for breakpoint matrix features when long reads were available. Nonetheless, the assessment of sequence context features like 1–3 nt microhomology or blunt ends benefits from sequencing techniques with low error rates and experimental designs that preferentially amplify CSR junctions from high-complexity regions.

Finally, we applied machine learning based on a multinomial ridge regression model trained on 49 samples to predict defects in a newly generated set of 36 additional CH12 samples (“Methods”). Notably, the correct genotype was predicted for 35 out of 36 independent samples (for 32 of them with high confidence; Fig. 3f, g). Only one Brca1 KO sample was wrongly assigned to a Rif1 KO, showing Brca1 KO only as a second possible hit. Cross-validation using either one batch or 25% of the samples for testing and the rest for training, resulted in 96% and 94% prediction accuracy, respectively.

Taken together, SWIBRID confirms previously reported impacts of specific DSB repair KO on CSR phenotypes. Moreover, our attempt to globally define CSR junction characteristics identified novel features, such as donor/acceptor sequence motifs and templated insertions. Importantly, our data suggest that genetic defects affecting different pathways of DSB repair can be identified by SWIBRID.

Identification and classification of CVID patients by SWIBRID

No single clinical feature or laboratory test can definitively confirm the diagnosis of CVID18,49,50. We therefore examined whether SWIBRID could reliably identify CVID patients. We obtained two to three technical replicates each from 21 patients with CVID or related diagnoses and 14 controls for analysis by SWIBRID (“CVID training” dataset; Fig. 4a). As expected, the variance of CSR features was mainly explained by donor identity and diagnosis but not by age, sex, sequencing batch, or treatment such as Ig supplementation (Fig. 4b and Supplementary Fig. 4a). We pooled reads from technical replicates to increase read numbers and rescue samples failing quality control. Various features significantly differed between CVID patients and controls including the fraction of single breaks (pct_direct_switch), the amount of sequence microhomology surrounding breaks (homology), the size of CSR clones (cluster_size_mean), and the fraction of clusters with Sγ2 or Sγ3 assigned isotypes (pct_clusters_SG2/SG3) (Fig. 4c). Interestingly, we found higher usage of Sγ3 and lower usage of Sα1 in CVID patients that did not elicit IgG titers after vaccination (Supplementary Fig. 4b). Similar to the reduced frequency of somatic hypermutation events in VDJ sequencing data from CVID patients51, we found fewer somatic variants (somatic_variants) in our CVID patients than in the controls (Fig. 4c). In a PCA using the robust features, the controls separated well from CVID patients, as well as CVID-like disorders caused by specific IEI, such as deficiencies in KMT2D (Kabuki syndrome), ICOS (Inducible T cell costimulator), AICDA (encoding AID), NFκB2 (nuclear factor kappa B subunit 2), and APDS2 (Activated PI3K-delta Syndrome Type 2) caused by loss-of-function mutations in the regulatory subunit of PI3K (PIK3R1) (Fig. 4d). The distinction between controls and immunodeficient samples, as seen in PC1, is for instance characterized by a high percentage of direct switch events without consecutive switches (pct_direct_switch), and a low percentage of Sγ3 clusters (frac_clusters_SG3; Supplementary Fig. 4c). As part of PC2, we observed increased direct switching to Sγ3 and increased Sα1 usage (pct_breaks_SM_SG, pct_clusters_SA1), low homology (homology), and a low percentage of Sγ4 clusters (pct_clusters_SG4). Note that two patients (13 and 21), which were diagnosed as atypical CVID, have a potential failure in the plasmablast compartment, as they show low Igs in blood, but normal switched memory B cells. As expected, these samples clustered together with controls in the PC analysis, as did patients diagnosed with a subclass deficiency—except for an IgG4-deficient patient (Fig. 4d). Subclass deficient patients showed a significant reduction in the number of Sγ2 CSR clones (Supplementary Fig. 4d) but were otherwise comparable to controls.

Next, we additionally obtained from the same laboratory 12 controls and 14 patients for a “CVID testing” dataset, including patients with mutations in TNFRSF13B (Tumor Necrosis Factor Receptor Superfamily Member 13B) encoding TACI (Transmembrane activator and CAML interactor), plus two additional biological replicates from control 09 and patient 32. From a different laboratory, we obtained the “DNA repair” set with 12 patients, including children, three of whom had a deficiency in ATM, three in BRCA1, one in LIG4, and one in NIPBL (Nipped-B-like protein). In addition, 9 age-matched controls were obtained. Projection of the “CVID testing” (Fig. 4e) and “DNA repair” data (Fig. 4f) into the PCA of the “CVID training” samples validated a separation of control and patient samples of the two independent datasets.

Machine learning with ridge regression almost perfectly separated CVID and CVID-like patients from control samples of the “CVID testing” dataset, excluding replicate samples from training donors (Fig. 4g). Additional cross-validation with a 75:25 training:testing split resulted in a mean prediction accuracy of 0.92. Despite training on CVID data, DNA repair-related defects in the “DNA repair” samples were identified with an AUC score of 0.84, emphasizing a high degree of sensitivity and specificity of our predictor (Fig. 4g). Surprisingly, the IgA fraction measured by flow cytometry and SWIBRID correlated better in controls than in CVID samples, suggesting a potential alteration in B cell receptor (BCR) expression in CVID B cells (Supplementary Figs. 4e and 5).

Clustering of the “CVID training” and “CVID testing” cohorts resulted in three different groups (Fig. 4h and Supplementary Fig. 4f), with cluster 1 comprising mostly controls, atypical, and subclass deficient samples, and cluster 2 and 3 containing patients. Cluster 2, for instance, showed an increased frequency of untemplated inserts when compared to controls, as well as a very low amount of Sα CSR clones and high Sγ1, but otherwise less severely affected isotypes and diversity measures. Instead, cluster 3 showed a decrease for all Sγ isotype ratios and overall poor diversity measures, such as a low number of CSR clones and entropy (Fig. 4i). An interpretation of cluster 2 as immunocompromised versus cluster 3 as potentially DNA repair deficient was supported by shared features of cluster 3 and ATM deficiencies, such as decreased Sγ1, untemplated insertions and intra-switch deletions, as well as increased homology (Fig. 4i). This was further supported by inspecting PCA loadings and regression coefficients (Supplementary Fig. 4g, h) and by feature-class scores, which aggregate features from the initial categories (i) structural aberrations, (ii) sequence context, (iii) the breakpoint matrix, (iv) diversity measures and (v) isotype composition (Supplementary Fig. 4f). While PC analysis and clustering raises the impression that monogenic defects in e.g., AICDA, TNFRSF13B, or ICOS are not different from CVID patients, it is important to note that the CSR junction phenotypes are highly individualized (Fig. 4h). Although we did not establish a longitudinal cohort, samples from two individuals (09 and 32) collected over periods of up to four years clustered tightly in the heatmap and in PCA space (Fig. 4e), indicating a stable SWIBRID profile over time.

In conclusion, SWIBRID reliably identified CVID patients and monogenic defects, unravelling patient-specific CSR junction signatures, which allowed patient assignment to phenotypic clusters.

Methods
Mouse and human samples
All mouse and human samples in this manuscript were obtained as part of studies approved by the ethical commissions mentioned in the sections below.

CH12 B cells: cultivation, stimulation, and DNA extraction
The generation of CH12 cell lines64 and knockout lines is described elsewhere: Rif1 and Lig465, Trp53bp166, Brca144. CH12 cells were cultured using B cell complete medium that contained: 10% FBS (PAN Biotech GMBH, #P30-3302), 1% non-essential amino acids (Life Technologies, #11140035), 1% sodium pyruvate (Life Technologies, #11360039), 1% GlutaMAX (Life Technologies, #35050038), 1% Penicillin-Streptomycin (Life Technologies, #15140163), 0.1% mercaptoethanol (Life Technologies, #31350010), 100 μg/ml Kanamycin (Serva Electrophoresis, #26897.01), and 0.002% transferrin (Merck-Milipore, #616397).
For in vitro activation, 200,000 CH12 cells per milliliter were seeded in a 12-well plate (SARSTEDT, #833921). On day 0, 25 ng or 400 ng interleukin-4 (recombinant), 4300 ng of anti-mouse CD40 (BIOZOL, #BLD-102902), and 8.6 ng of mTGFβ1 (R&D Systems, #7666-MB-005/CF) were added to each well to induce B cell activation.
Cells were harvested on day 2 and gDNA isolation was performed using the ReliaPrep™ Blood gDNA Miniprep System (Promega, #A5082).

Humans samples and ethic votes
We used random 500 ml blood donations that were collected by the DRK-Blutspendedienst Nord-Ost GmbH to assess the diversity of CSR junction features in the population. The blood was tested to be free of HIV, HCV, and HBV. The use of these samples was approved by the Charité ethical commission in the ethics vote EA1/149/23. The corresponding consent requires us to use a de-identification strategy (randomize donor identity across different features) when making this data publicly available.
CVID patients and controls were obtained from the Uniklinikum of Freiburg and CVID patients fulfilled the diagnostic criteria for CVID (www.ESID.org). Genetic diagnosis for Kabuki syndrome, APDS2 (PI3KR1), AICDA, TNFRSF13B, and ICOS deficiencies were defined after whole exome or targeted sequencing. All experiments were performed under the ethical approval of local authorities 251/13 and 254/19 according to the Declaration of Helsinki. Some patients were undergoing treatments including: prednisone (n=3), co-trixomazol (n=2), cortisone (n=1), steroids (n=1), sirolimus (n=1), and infliximab (n=1). Ig supplementation was administered either subcutaneously (n=24) or intravenously (n=8).
Samples from patients with DNA repair defects were obtained from the Karolinska Institutet with the approval of the Institutional Review Board of Karolinska Institutet and the Ethics Committee of Tehran University of Medical Sciences (IR.TUMS.CHMC.REC.1398.030). The samples included previously studied donors with deficiencies in ATM67,68, BRCA138, as well as patients with mutations in AICDA, LIG436, and NIPBL69 encoding genes and controls of similar age and sex67,70. Detailed information for all donors is provided in the Supplementary Data 3.
Written informed consent was obtained from all patients, healthy individuals, or their parents.
As stated in the results section, we pooled reads from up to 3 technical replicates of 86 samples to rescue samples failing quality control by increasing read numbers. Of note, beyond the samples described in the results section, we obtained samples from 5 additional patients (1 “CVID testing”, 4 “DNA repair”) that could not be rescued, likely due to extremely low B cell counts.

Primary human blood cells: purification, sorting, and isolation of genomic DNA
PBMCs from 500 ml blood donations were isolated using Ficoll (Carl Roth GmbH, #0642.2) and centrifuging for 25 min at 500 × g with an acceleration/break of 3/0 (Centrifuge Eppendorf 5910R). Primary human B cells were isolated from PBMCs using CD19 Microbeads (Miltenyi Biotech, #130-050-301) following the manufacturers protocol. When B cell subpopulations were needed, class-switched memory B cells were FACS-sorted (CD27+ IgM− IgG+ IgA+). The following antibodies were used for staining: CD27-PE (dilution: 1:170, Miltenyi Biotec, #130-114-156, clone M-T271); IgD-PE-Cy7 (dilution: 1:80, Miltenyi Biotec, #130-098-584), IgM-AF488 (dilution: 1:1000, Life Technologies, #A21215); IgG-AF647 (dilution: 1:500, Dianova, #109-606-170); IgA-AF647 (dilution: 1:500, Dianova, #109-606-01); IgA-APC-Vio770 (dilution: 1:170, Miltenyi Biotec, #130-113-473, clone IS11-8), CD19-Brilliant Violet 605 (dilution: 1.160, Becton Dickinson, #562653); CD20-PerCP-Vio700 (dilution: 1:170, Miltenyi Biotec, #130-113-377, clone LT20). Gating strategies used to sort memory human B cells can be found in Supplementary Fig. 6. PBMCs from CVID patients and controls were also isolated from fresh EDTA blood by Ficoll density gradient centrifugation following standard protocols. PBMCs were frozen in Iscove’s Modified Dulbecco’s Medium, 40% FBS, and 10% DMSO until further processing.
Frozen samples were gently thawed prior to DNA isolation. The thawing process involved the rapid melting of the cells using a water bath at 37 °C; and immediately after, the addition of cold B cell complete medium drop-wise into the vial to minimize temperature and osmotic shock. Thawed cells were resuspended in B cell complete medium at twice the original frozen volume. Cells were centrifuged and counted for further processing.
One million PBMCs were collected from the samples of C1 and CVID samples from the University of Freiburg for DNA isolation. DNA isolation was performed following the protocol of the ReliaPrep™ Blood gDNA Miniprep System (Promega, #A5082). DNA from one million PBMCs were eluted in 50 μl.
Genomic DNA (gDNA) was obtained from the DNA repair defects samples from the Karolinska Institutet following the “salting out procedure”71.

Mouse splenocytes and lymph nodes: isolation of cells and genomic DNA
Twelve female mice aged 9 to 10 weeks were immunized using HIV glycoprotein (gp) 140 on days 1, 21, and 42. On day 56, the mice were sacrificed and cells from spleen and lymph nodes were collected. This process was performed by Experimental Pharmacology & Oncology Berlin-Buch GmbH (Ther. E0023/23, study number: 18906). Between 900,000 and 2.5 million cells were collected per sample for DNA isolation. gDNA isolation from these cells was done following the protocol of the ReliaPrep™ Blood gDNA Miniprep System (Promega, #A5082).

CSR junction PCR
The human CSR junction PCR included in one reaction 3 μl mM dNTPs each (Roboklon, #E0501-01), 5 μl LongAmpTaq Buffer, 1 μl LongAmpTaq polymerase (NEB, #M0323), nuclease-free water, primers and template until a total volume of 25 μl, 1 μl of 10 μM Sµ FW, Sα RV and Sγ RV primers39, and optionally Sε RV, were added (Supplementary Table 1 and Supplementary Fig. 1b). The template used was either 200,000 PBMCs, in C1 and the CVID patients from the University of Freiburg; or 30 ng, from the DNA repair defects from Karolinska Institutet. The PCR protocol consisted in 25 cycles of 95 °C for 40 s, 60 °C for 30 s, 65 °C for 3 min; and the last elongation step at 65 °C for 10 min with a ramp speed of 4 °C/s using Biometra TAdvance (Analytik Jena). The use of a high-fidelity polymerase, along with optimized primer design, cycle number, and ramp speed, was carefully adjusted to minimize nonspecific binding, reduce the impact of secondary structures, and enable efficient amplification of highly repetitive, GC-rich switch joints.
The mouse CSR junction PCR included the same mixture of LongAmpTaq reagents and dNTPs as the human CSR junction PCR. However, mouse CSR junction PCR was performed separately for different combinations of forward and reversed primers (Supplementary Table 1 and Supplementary Fig. 1i). Thus, mSµ FW primer72 was used in combination of mSα REV, mSγ2bc REV, mSγ3 REV, or mSγ1 REV in separate reactions, respectively. 100 ng of mouse gDNA from splenocytes, lymph nodes or CH12 was used in each PCR reaction. The PCR protocol consisted of 30 cycles of 95 °C for 40 s, 60 °C for 30 s, 65 °C for 3 min; and the last elongation step at 65 °C for 10 min with a ramp speed of 2 °C/s using Biometra TAdvance (Analytik Jena).
Amplicons were observed in an agarose gel and sporadically in the TapeStation using High Sensitivity D5000 ScreenTape (Agilent, #5067-5592) (Supplementary Figs. 1c–f, 7, and 8).

BCR transcript analysis
BCR sequencing was performed as described in Turchaninova et al.73 with several modifications. All primers used during this procedure are described in Supplementary Table 2. In brief, RNA was extracted from 50,000 or 100,000 memory B cells and used for cDNA synthesis with SMARTScribe RT (TaKaRa, #639538), using a mixture of constant domain primers at 1 μM final concentration and a SmartNNNext template-switch adapter. cDNA was purified with 1.8 volumes of AMPure XP beads (Beckman Coulter, #A63881) and eluted in 1 volume of milli-Q water (mQ). Purified cDNA was used as a template for 25 cycles of the first PCR with a mixture of nested constant domain primers and M1ss primer annealing to the template-switch adapter sequence at 0.2 μM. The PCR was purified with 1.2 volumes of ProNex beads (Promega, #NG2001) and eluted in 1 volume of mQ. Purified PCR was used as a template for 5 cycles of a second PCR with a mixture of Illumina adapters-bearing primers at 0.2 μM final concentration. The second PCR was purified with 1.2 volumes of ProNex beads and eluted in 1 volume of mQ. Purified PCR was used as a template for 5 cycles of indexing PCR with Illumina Nextera adapters. The products were pooled, purified with 1.2 volumes of ProNex beads, eluted in 1 volume of mQ, and analyzed by BioAnalyzer High Sensitivity dsDNA kit (Agilent Technologies, #5067-4626). The library was sequenced with MiSeq 600 cycles kit (Illumina, #MS-102-3003). Base-called data and demultiplexed data were analyzed with MiXCR74 using the “generic-bcr-amplicon-umi” preset with the tag pattern “^N{22}(UMI:N{16})N{7}(R1:*)\^(R2:*)”. The ratio of IGHA to IGHG was extracted from the allCHitsWithScore column in the final MiXCR output.

Sequencing
CSR junction PCR products were purified using ProNex beads in a 1:1 ratio following manufacturer protocol and eluted in 16 μl, and 30% of amplicons were sequenced using MinION Nanopore (SQK-LSK109 and FLO-MIN106D) or PacBio Technologies (Genomics Facility, Max-Delbrück-Center, Berlin) following the manufacturers protocol.

Bioinformatic pipeline
SWIBRID’s bioinformatic pipeline is written in Python and Snakemake. It performs end-to-end processing of sequencing output (fastq files) to derived SWIBRID features. Code and documentation are available at bihealth.github.io/swibrid.
The pipeline consists of the following steps:
1) Demultiplexing
A custom demultiplexing script detects ONT barcodes as well as sequencing primers in the reads using BLAST with parameters optimized for short matches, and then distributes input reads into sample-specific output files according to sample barcodes, while collecting information of read length, quality, primers and barcodes detected at the read ends or internally (more than 100 nt from the ends) as well as other meta-data included in the input fastq headers into a separate csv file. The script features a “–split_reads” mode to detect clusters of multiple barcodes and primers in close proximity and split presumably concatenated reads into parts.
2) Alignment
Read alignment is performed using LAST (default; “last-train” followed by “lastal” and “last-split”), minimap2, STAR, or bwa. Alignments statistics in terms of mismatches, insertions, and deletions are obtained from “last-train” output or directly collected from the bam output of the other aligners. Optionally, reads are also aligned against telomer repeats using BLAST.
3) Processing and filtering
Reads are inspected for alignments to the switch (S) region or elsewhere (potential inserts). Isotypes are determined by overlap of the right-most alignment block with any of the annotated S regions. Reads are filtered out,
– that are too short
– that don’t contain forward and reverse primers
– that contain internal primers
– that don’t contain at least two separate alignments to the S region (with large tree height of cluster root nodes)
– if mapped regions on the genome are much shorter than mapped parts of the reads
– if there is too much overlap between different alignments on the genome or on the read
– if too little of the read maps (less than 90%)
– if alignments to the S region are in the wrong order, or
– if an isotype cannot be determined.
(Templated) inserts are detected as alignment blocks to regions outside of the S region (or to the telomer repeats) flanked by alignments to the S region. Read orientation, coverage, fraction of read sequence mapping to the same genomic region, mapped S region segments, and filtered inserts are recorded. Additionally, the aligned read sequence (including gaps but ignoring insertions) is written out for construction of a (pseudo) multiple sequence alignment. Finally, 40 nt of sequence around each break between alignment blocks are taken from the read (20 nt upstream and 20 nt downstream) and re-aligned against 40 nt of genomic sequence of donor and acceptor S region, using the PairwiseAligner routine from BioPython with penalties as used by minimap2 for MinION or pacBio reads, respectively. Untemplated nucleotides are counted as contiguous bases in the read near the break that don’t align to either donor or acceptor region, homologous nucleotides are counted as contiguous bases near the break that align to both donor and acceptor region.
4) Construction of a (pseudo) multiple sequence alignment (MSA)
To facilitate efficient clustering of tens of thousands of reads of several kb length, we transform the reads aligned to selected S regions into a sparse coverage matrix of dimensions n_reads × n_bases. From hereon, we use a reduced coordinate system of concatenated S regions (about n_bases = 30 kb in total) extended to the nearest multiple of 500 to allow for unambiguous binning.
The matrix values m encode coverage and consensus nucleotide identity, such that m%10 encodes nucleotide identity (with A=1, C=2, G=3, T=4 and gaps encoded as missing or zero values), and m//10 encodes coverage of genomic regions by one read (typical values are 1, values of 2 or higher indicate tandem duplications, negative values an inversion). n_reads is typically constrained to at most 50,000 to limit computational resource usage and ensure that different samples are comparable.
5) Detection of gaps and junction breaks
Gaps in the MSA are detected and saved into arrays containing the read index, left and right position of the gap (in the reduced coordinates) and gap size.
6) Construction of a linkage by hierarchical clustering
Hierarchical clustering is performed using cosine metric and average linkage (UPGMA) on cleaned coverage patterns, i.e., the coverage values m//10 of the MSA with gaps smaller than a cutoff value (typically 75 nt) filled in to reduce noise from sequencing and mapping artefacts. Clustering can be done using the Python implementation of fastcluster on the dense version of the MSA, or by hierarchical agglomerative clustering with approximate average linkage on a nearest-neighbor graph, allowing sparse input and with improved time complexity and memory requirements (see Supplementary Note 1).
7) Defining and filtering clusters
Clusters are defined by cutting the dendrogram at a certain cutoff, which is typically fixed at 0.01 but can also be automatically determined by scanning cutoff values in a pre-determined range and finding an inflection point in the curve that shows the cluster number as function of cutoff (either by distance from the straight line connecting the extremes or from the intersection of a double-exponential fit). Small and isolated clusters are flagged such that remaining clusters contain at least 95% of the reads (see Supplementary Note 1).
8) Calling single-nucleotide variants
We implemented a routine to call single-nucleotide variants in the MSA by comparing observed nucleotide frequencies at a position with what is expected given the reference nucleotide and average mismatch frequencies estimated during the alignment step using a chi square test. Variants are excluded if
– they are near gaps (more than 70% gap frequency in a 10 nt window)
– the minor allele frequency is lower than expected by a poisson model
– the strand bias is too high (p-value < 0.05 in a chi square test and relative difference of read numbers in positive vs. negative direction > 0.125)
– no cluster of at least 10 reads has an allele frequency larger than 0.4, or if the total allele frequency is below 0.4.
Variants are annotated using dbSNP. Next, filtered clusters with at least 10 reads are again grouped into “haplotypes” using weighted non-negative matrix factorization (wNMF package) of cluster-wise alternative allele counts with 2 components and cluster sizes at each position as weights. Co-segregating read clusters (i.e., putative haplotypes) are inferred from the component matrix, and variants are classified as likely homozygous germline if their allele frequency is >0.6 in both components, heterozygous germline if the allele frequency is >0.6 in one component and <0.6 in the other, and likely somatic otherwise. wNMF is run 100 times and a consistency score is derived by counting how often clusters are sorted into the same component.
9) Calling structural rearrangements
Structural variants (rearrangements) are called from the MSA by finding regions with coverage >1 (duplications) or <0 (inversions) without gaps bigger than 25 nt. Arrays of read indices, left and right positions, and sizes are returned for duplications and inversions separately.
10) Analysis of clustering results
Aggregate statistics obtained from the clustering results:
– cluster size (number of reads)
– cluster isotype (most frequent)
– average read fraction mapped per cluster
– average read fraction mapped to same region multiple times
– average read fraction ignored because it maps outside the defined switch regions
– consensus sequence
– length of consensus
– GC content of consensus
– spread of breakpoints (number of bases with intermediate coverage)
– size of inversions
– size of duplications
– frequency of insert-containing reads in cluster
– fractional overlap of inserts in cluster
– fractional overlap of insert-associated breakpoints
– average insert length
– average length of gaps around inserts
– average isotype associated to inserts
– number of homologous nucleotides around insert-associated breakpoints
– number of homologous nucleotides around switch region breakpoints
– number of untemplated nucleotides around insert-associated breakpoints
– number of untemplated nucleotides around switch region breakpoints
– length of different S regions covered in cluster
– cluster size adjusted for PCR length and GC bias
11) Downsampling of clustering
For more robust estimation of diversity measures, the clustering is downsampled 10 times on a subsample of typically 1000 reads, using fastcluster with cosine metric and average linkage and the same cutoff as used in the cluster definition step.
The following diversity measured are calculated:
– cluster_size_mean: average fraction of reads in clusters
– cluster_size_std: standard deviation of cluster size
– clusters: number of clusters after filtering
– clusters_eff: from the entropy of the cluster size distribution (pre-filtering)
– cluster_gini: Gini coefficient (post-filtering)
– cluster_entropy: entropy (post-filtering)
– cluster_inverse_simpson: inverse Simpson coefficient (post-filtering)
– occupancy_top_clone: fraction of reads in the biggest cluster
– occupancy_big_clones: fraction of reads in clusters with more than 1% of reads
12) Breakpoint statistics
A 2D breakpoint histogram matrix is generated from the detected gaps ≥75 nt counting the number of reads with donor and acceptor position in 50 nt bins along the reduced coordinate system. Reads are weighted according to cluster sizes such that all clusters contribute equally to the histogram unless specified otherwise. This will produce various aggregate statistics on breakpoints:
– breaks_normalized: number of breaks divided by number of clusters (or reads)
– pct_duplications: percentage of breaks leading to duplication events
– pct_inversions: percentage of breaks leading to inversion events
– pct_direct_switch: percentage of breaks from Sµ to another S region
– pct_sequential_switch: percentage of breaks between two different S regions, but not SM
– pct_intraswitch_deletion: percentage of intra-switch deletions within the same S region
– pct_intraswitch_inversions/duplications: percentage breaks with inversions or duplications within the same S regions
– pct_breaks_X_X: percentage of breaks connecting indicated S regions
– break_dispersion_XX: dispersion (standard deviation) of breakpoint positions in an S region
– homology_score_fw: homology in bins around breakpoint positions (same orientation)
– homology_score_rv: homology in bins around breakpoint positions (opposite orientation)
– homology_score_fw/rv_XX: homologies for breaks connecting indicated S regions
– donor/acceptor_score(_XX): motif scores for donor and acceptor breakpoints for different motifs (subdivided by S region)
– donor/acceptor_complexity(_XX): sequence complexity for donor and acceptor breakpoints (subdivided by S region).
Homology scores are calculated from 2D binned breakpoint frequencies weighted by homology of bins, motif scores are calculated from 1D binned breakpoint frequencies weighted by motif occurrences.
13) Read plots
Using an MSA and clustering results, this routine displays individual reads mapping over the S regions. Reads will be ordered as dictated by the linkage, or simply by isotype and cluster value. Reads can be colored by different variables (isotype, cluster, haplotype, coverage, strand, orientation, or other column present in an additional info file). Sidebars can display additional variables per read or cluster. Variant positions or breakpoint realignment statistics can also be indicated.
14) Summary statistics
This routine produces a summary of features derived from a sample. Statistics produced by process_alignments, find_clusters, and downsample_clustering are collected. Cluster-specific values from clustering analysis and breakpoint statistics are averaged over a sample. Number of reads/clusters per isotype, and statistics on variants (germline vs. somatic, transitions vs. transversions, etc.) are collected.

Generation of simulated switch junction reads
Simulated reads were generated using the “swibrid test” mode of the SWIBRID pipeline. In short, given a bed file with alignment coordinates of SWIBRID clones from a pool of 31 control donors on a truncated genome containing only 1 MB of chr14, the script will randomly choose n entries of the bed file, obtain the concatenated genomic sequence for each entry, create an average of k copies according to a specified distribution (default: poisson), and add mutations, insertions, and deletions according to parameters estimated by LAST from a non-PCR control plasmid sample.

Vincendeau et al. re-analysis
Sequencing data of CSR junctions generated by Vincendeau et al.30 were downloaded from SRA (accession number PRJNA831666) and processed with standard SWIBRID settings for mouse, after adjusting coordinates of the relevant switch region for different primer locations.

HTGTS data re-analysis
Sequencing data of CSR junctions generated with by HTGTS27 were downloaded from SRA (accession numbers SRR2104731-44 for spleen, SRR6293456-79 for CH12). Overlapping paired-end reads were merged using bbmerge and then subjected to SWIBRID analysis, using bwa-mem2 (v2.2.1) for alignment. We adapted parameters of the processing and filtering steps to extract reads with SA, SG1, or SE isotype and at least two S region matches, allowing for up to 10 nt overlap between alignments on the read. We then constructed the MSA over suitably adapted switch regions, did not remove gaps, but only re-aligned reads across junctions that were at least 100 nt apart.

Downstream analysis
Pipeline configuration files and analysis code for this manuscript are available at github.com/bihealth/swibrid_paper. Pipeline results for all human and mouse samples were connected with relevant meta-data. Samples with less than 500 final reads or otherwise failing visual post-pipeline quality check (QC) were removed.
For variance partitioning, we used the R package variancePartition75 (v1.32.5) and a design “~ (1 | donor) + (1 | age) + (1 | sex) + (1 | sequencing) + (1 | batch)” for Fig. 2, or “~ (1 | donor) + (1 | diagnosis) + (1 | sex) + age + (1 | batch) + (1 | Treatment) + (1 | IV)” for Fig. 4.
PCA were calculated on the selected robust features after z-score normalization using the prcomp R function, adapting isotype names between human and mouse, and removing irrelevant features for data from Vincendeau et al. (related to Sα or Sγ2/Sγ3/Sγ4) or from CH12 samples (related to Sγ, sequential breaks, isotype composition). Test or validation data were independently normalized and projected into the PCA.
Heatmaps were created with the ComplexHeatmap R package (v2.18.0)
Ridge regression was performed with the glmnet R package (v4.1-8), using the cv.glmnet function to estimate an optimal value of the regularizer. Training, test and validation datasets were independently normalized using z-scores. For the CH12 dataset, we used 33 features plus 49 samples for training and 36 samples for testing. For the CVID dataset, we used 44 features plus 29 samples for training, 26 samples for testing, and 21 samples for validation.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The authors PCR immunoglobulin class switch recombination (Ig CSR) regions from pools of human peripheral blood\nmononuclear cells (PBMC). They then do long-read sequencing with the goal of analyzing the Ig switch junctions for\nfeatures. They have a computational algorithm that combines junctional features and other parameters to allow them to plot\ntheir Principal Component (PCA) 1 versus PCA2. I have the following comments and suggestions.\n1. I commend the authors analytical skills, but the writing places this paper beyond the analytical ability of all but a fraction of\nthe bioinformatics community and beyond any molecular biologists. I would say the same thing about similarly informatically-\nintensive papers by others in other journals, but I do not review all such papers. I wish to be helpful to the authors because I\nrespect all the effort that they have put into this fine work.\n2. The PBMC pools consisted of up to 200,000 cells per analysis. Of such a pool, they estimate it contains 10,000 B cells.\nHow many cycles of PCR do they do prior to the sequence analysis step? I am worried about complications during the PCR\nsteps at regions of repetitive DNA. The switch regions are distinctive for their repetitive sequence. Please show me a PacBio\nread after a minimal number of cycles (e.g., 8 cycles) versus whatever number they normally do (I could not find a statement\nof the number of PCR cycles in the paper). Also, if they authors amplify a much smaller number of cells, how does this\ncompare to a larger number of cells from the same patient.\n3. If the authors take the PBMC from one patient and divide in half and the PCR, and sequence, please show how similar or\ndifferent the results are. For example, can they find the top 10 largest and top 10 smallest deletions in each of the two halves\nof this analysis?\n4. Beyond the one or a few actual sequence junction examples shown, please provide 20 actual human patient junctions\nand the sequence features of each of these (from the pooled PBMC samples). I request this from all bioinformatic DNA\nsequence studies.\n5. Because Ig CSR sequences are very repetitive, assessment of microhomology and perhaps even some other features\nmay be difficult.\n6. The use of the term ‘scar’ is inappropriate at all places in this manuscript. These are simply genomic alterations. Near the\nvery end of PMID 36587186, that author explains the inception of the phrase ‘information scar’, which was not to be\nconfused with any physical ‘scar’ in the DNA duplex structure. Since that inception, the dramatic use of the term ‘scar’ even\nfor simple mutations has become disturbingly frequent in the literature, even though this is inappropriate, especially for non-\ncoding regions such as the Ig CSR regions which do not encode information. I provide this comment for all papers that I\nreview that inappropriately use the phrase ‘genomic scar’.\n7. In the Supplementary file, lines 25-26, the authors write: ‘Given a high rate of sequencing errors, only the coverage of a\nposition (not nucleotide identity) was considered, and gaps smaller than 75nt were ignored.’ Given the repetitive nature of Ig\nCSR sequences, could the authors explain this to me a bit more in their responses?\n8. For CVID, it is my understanding that this has diverse causes that do not affect the actual DNA events of VDJ\nrecombination or class switch recombination. Perhaps in the Supplement, they authors could comment on how or why the\nanalysis gives a readout beyond simply the number of B cells that have an antigen receptor.\nAgain, I commend the authors for their impressive amount of work.", "reviewer_2": "The manuscript by Vázquez García, Obermayer et al., presents a novel method named SWIBRID (SWItch joint Breakpoint\nRepertoire IDentification) designed to characterize DNA double-strand break (DSB) repair outcomes during antibody class\nswitch recombination (CSR). Leveraging decades of foundational research into B-cell mediated DNA repair, this method\nuniquely profiles genomic scars resulting from CSR, allowing for assessment of immune diversity and DNA repair\nfunctionality from peripheral blood samples. Employing long-read sequencing and sophisticated bioinformatics pipelines,\nSWIBRID identifies distinct repair signatures in various DNA repair mutants and in patients with immunodeficiencies such\nas Common Variable Immunodeficiency (CVID) and cancer-associated DNA repair deficiencies. The authors demonstrate\ndiscriminatory power in distinguishing repair-deficient genotypes and patient cohorts, underscoring potential clinical utility in\npersonalized diagnostics and early detection of pathogenic defects. Some issues still requiring clarifications include:\n1- The methodology shows promise in distinguishing specific DNA repair deficiencies and CVID. Have the authors\nconsidered potential confounding factors such as age-related changes in B-cell repertoire or treatment-related effects on\nCSR in clinical samples?\n2- It remains unclear whether the CSR scar patterns detected by SWIBRID are stable over time in individual patients or\ninfluenced by transient immune activation events or infections. Are there any longitudinal patient data to draw such\nconclusions from in the analysed sample cohort?\n3- While the method categorizes patients effectively, it would be highly beneficial to correlate specific CSR signatures with\nclinical outcomes or disease severity, if possible. Such correlation would substantiate clinical relevance and utility.\n4- The manuscript introduces several CSR-related genomic scar features. However, the biological mechanisms underlying\nsome of these newly identified features (e.g., long templated insertions, certain sequence context motifs) require further\nexperimental validation to clarify their roles in DNA repair pathway preference or dysfunction, or at least additional\ndiscussion to their origins and potential mechanisms.\n5- The manuscript employs machine learning (ridge regression) to classify genotypes. Detailed clarification on model\nrobustness, cross-validation methods, and generalizability to external datasets would be ideal.\nMinor Concerns:\n1. Line 141–147: clarify units—reads or cells?\n2. Extended Data Fig. 1f,g legend missing information to be understandable.\n3. Figure quality: many diagrams use colour-only encoding (e.g., PCA). Or have overlapping color legends with such as fig\n2b.\n4. Methods: provide accession for training datasets (mouse Vincendeau et al.)\n5. Some sections, particularly those presenting comparisons and statistical significances, could benefit from clearer\nmethodological descriptions, including exact statistical tests used and justification for the chosen methods.\n6. The authors acknowledge technical noise from sequencing errors. More explicit discussion and mitigation strategies for\nthese technical limitations, including error rates of Nanopore versus PacBio technologies, are essential for readers to\ninterpret data quality accurately.\n7. Discussion on how SWIBRID might compare to existing clinical tests (e.g., TREC/KREC assays) could provide valuable\ncontext for clinical integration.\nIn conclusion, the manuscript significantly advances the field by introducing a powerful new method for profiling DNA repair\ncompetency and immunological health through CSR scars. Addressing the highlighted concerns will enhance the\nrobustness, clarity, and broader impact of this promising study.", "reviewer_3": "The authors of Garcia and Obermayer et al describe SWIBRID to identify the repertoire of switch joint breakpoints that uses\nPCR and long read sequencing from blood samples to detect DSB repair patterns driven by IGH class switch recombination\n(CSR) in the B cell subgroup. In surveying patient samples, the authors report immunodeficiencies, DNA repair defects, and\npatient-specific scarring signatures with high accuracy. The work looks to be well developed with many identified features\nthat may be useful for sub-group distinction across human and mouse samples but this reviewer was struggling to figure out\nwhich new features were the most frequent and most relevant to the CSR repair outcome repertoire. The manuscript could\nbe improved further by addressing the following comments below:\nMajor Comments\nMost of the 68 features in 5 categories are presented as fractional data, presumably to normalize for absolute variance\nmeasures. It would be very helpful to have all of the data outlined in the figures mirrored in absolute frequencies in\nsupplemental tables; this would be analogous to showing the films of blots. It is important for readership to evaluate which\nfeatures are weighted by high frequency, the rigor of the sequencing data, and to confirm any statistically significant shifts in\nproportional data are not influenced by low frequencies which may make some highlighted features too noisy for evaluation.\nAdding to the first comment, too many features will naturally yield some measurable difference that may not be related to\nbiology unless the magnitude of difference withstands the increased number of multiple comparisons. As the central point of\nthe manuscript is to highlight the numerous features, then proper statistical rigor needs to account for false discovery.\nFig2a is somewhat helpful for revealing the categories but a detailed description of the full 68 features should be outlined in\na supplemental figure/table so readers can fully evaluate the data presented. The feature names are bulky. Some terms\nwere not readily understood by this reviewer (i.e. not clear from only looking at the figures as a litmus test for\ncomprehension) and will certainly alienate most readers. Please reconsider renaming for efficiency and clarity that could\npair with detailed description of the filters/ranges used to define each feature. Fig2h and related would certainly benefit.\nFurthermore, for each experiment using the feature/category breakdown, a list of ranked features impacting the experiment\nshould be provided. If none are impacting, then indicate none (presume this is the case for Spleen Lig4 in EDFig3b? If not\nneed to indicate in figure legend why no Lig4 data for spleen). In this regard, it is difficult to follow the logic as to why 32\nfeatures were used for one set of knockouts and 39 features in another, what those features were and why others were\nexcluded; it would appear that the remaining 20 or so have little value if they are not used frequently.\nIn terms of PCA, you need to indicate which components distinguish each of your comparisons and which features overlap.\nThis is otherwise generically saying there are differences and similarities or they are separated from controls, which have no\nadded value to the field given the prior work on the genes implicated or to provide causal sources for distinction in\nCVID/CVID-like samples; this latter part is partially done but needs to be applied to earlier parts.\nRelated to point #5 is that a laundry list of features are indicated but needs to be distilled down to what those robust affecting\nfeatures mean in aggregate.\nMethods section does not describe machine learning and more detail is needed beyond multinomial ridge regression for\neach experiment they were used. For independent reproduction of findings, the specific features used for each prediction\nexperimental sets should be listed and any additional values, range for confidence, number of technical versus biological\nreplications, etc. in the model should be reported. If this software is not cited in MS, please make sure this is included.\nMinor Comments\nThis manuscript suffers from poorly framed figures with text and symbols that are either too crammed together, too small, or\nusing inappropriate axis labeling. For the latter, there are no such fractional n_homology measures and listing 0.5\nincrements only serves to cause confusion to the lay reader. Labeling format (e.g. n_homology, Fraction IGA VDJ) may be\nunderstandable as a programming label for the authors but is not for lay audience. This reviewer urges the authors to\ncarefully consider the context of the data shown and reformat the figure panels to make it more conceptually available to a\nbroader audience.\nNot clear what type of V(D)J sequencing was occurring in the study as it is noted in the manuscript. Fig 1F labeling is also\nnot clear. Was any variable region analysis performed and how was that integrated into SWBRID comparison? Line 152 is\nrelated to the same panel but in comparison to IGHA vs IGHG. There seems to be a disconnect in the text and figure.\nThere is a disconnect in feature numbers for iii on line 163 versus Fig 2a. Please clarify.\nLines 171-172. Presuming only data from 1 of the 10 batches are relevant given that was the only batch using long read\nsequencing, please make sure to break down that information when comparing this to C2. If using both sequencing formats,\nthen indicate as such for both cohorts. Only 6 of the 68 features are referenced; why those 6? Are they representing the\nmajority of the data? If not, then why only show a minority? Brief explanation in the main text or more detailed explanation in\nthe Supp. Note may be useful.\nPCAs need to have indicated in legend how many robust features were ultimately used since this seems to change across\nexperiments.\nFor Fig2h and EDFig3a graphs you are better off showing only those with FDR-adjusted significance as showing everything\ndespite some with larger symbols is uninterpretable and providing too much unnecessary information.\nLines 260-261: not clear whether this new observation of GC content on the acceptor side of the old data that is not shown is\nconserved in your experiential data on Lig4 deficiency in the prior paragraph.\nLines 287-288: Can the authors speculate in the discussion as to what the correlation means?\nV(D)J is more accurate than VDJ and symbolizes that some antigen receptor loci (i.e. lambda, kappa, alpha, gamma) only\nuse V-J recombination."}, "author_response": "Point-to-point response Vázquez García & Obermayer et al.\nWe would like to thank the reviewer for the overall positive evaluation of our work and for the\nvaluable feedback, which has helped us improve the manuscript. While we acknowledge that a\nsubstantial portion of the manuscript is focused on bioinformatics, we deliberately chose Nature\nCommunications over a dedicated bioinformatics journal due to the broad relevance of our\nmethod to the fields of DNA repair and immunology, as well as its direct applicability to patient\nsamples. We have carefully addressed all points raised, as outlined in our point-to-point response.\nReviewer #1:\n1. I commend the authors analytical skills, but the writing places this paper beyond the\nanalytical ability of all but a fraction of the bioinformatics community and beyond any\nmolecular biologists. I would say the same thing about similarly informatically-intensive\npapers by others in other journals, but I do not review all such papers. I wish to be helpful\nto the authors because I respect all the effort that they have put into this fine work.\nWhile we have tried to present our ideas as clearly as possible, we regret if our writing appears\nincomprehensible at places. We fully share the goal of making our analytical approach accessible\nto non-specialists, but face the challenge that high-throughput methods, such as ours, need rather\ncomplex bioinformatics pipelines to address systematic biases and derive robust output. Different\nfrom other fields in genomics, where highly non-trivial methods for variant calling, gene expression\nquantification, or differential expression testing have become standard, here we could not rely on\nestablished methods and needed to implement a completely de-novo approach.\nIn response to the comments from all three reviewers, we have revised the manuscript to better\nexplain our bioinformatics approach, as described below. For instance, we added additional\nexplanations on the features terminology (see TableS1_features and materials and methods line\n629-694). In addition, we better explained experimental setups and pipelines within the results\nsection (e.g. lines 136-139, 153-157, 178-182) and improved figure legends (e.g. legend of figures\n2 and 3, lines 821--824, 831-833, 840).\n2. The PBMC pools consisted of up to 200,000 cells per analysis. Of such a pool, they\nestimate it contains 10,000 B cells. How many cycles of PCR do they do prior to the\nsequence analysis step? I am worried about complications during the PCR steps at\nregions of repetitive DNA. The switch regions are distinctive for their repetitive sequence.\nPlease show me a PacBio read after a minimal number of cycles (e.g., 8 cycles) versus\nwhatever number they normally do (I could not find a statement of the number of PCR\ncycles in the paper). Also, if they authors amplify a much smaller number of cells, how\ndoes this compare to a larger number of cells from the same patient.\nThe reviewer raises an excellent point, and we agree that PCR amplification of repetitive, GC-rich\nregions is inherently biased due to their lower amplification efficiency. This bias can only be\npartially mitigated, and we carefully considered it when establishing our initial protocol. To reduce\nthe impact of secondary structures, we used a high-fidelity polymerase, optimized primer design,\nand adjusted the ramp speed to minimize nonspecific binding (which is now stated in the methods\nsection: “The use of a high-fidelity polymerase, along with optimized primer design, cycle number,\nand ramp speed, was carefully adjusted to minimize nonspecific binding, reduce the impact of\nsecondary structures, and enable efficient amplification of highly repetitive, GC-rich switch\njunctions.”). As the reviewer correctly notes, the number of PCR cycles was a critical parameter\nrequiring careful optimization. On the one hand, we needed sufficient amplification to enrich CSR\njunctions and generate enough material for the minION library preparation; on the other hand, we\naimed to keep the cycle number as low as possible to maintain diversity.\nWhen PCR is performed in regions of repetitive DNA, recombination can create artificial diversity,\nwhich in a monoclonal cell line would appear as heterogeneous outcomes. We do observe some\ndiversity after 25 PCR cycles (Figure X1A-B). However, our read plot (Figure X1A) and sequence\nalignment of 8 random reads (Figure X1C) still show considerable homology in a monoclonal\nsample with two switched alleles, including i) highly similar read alignments to the switch regions\n(Figure X1A) and ii) substantial nucleotide homology in the alignments (Figure X1C), enabling\nprecise breakpoint identification. As PCR-induced artifacts increase with cycle number, we\nconclude that 25 cycles under our optimized conditions introduce only minimal artificial diversity.\nA B\nSM SG3 SG1 SA1 SG2 SG4 SE SA2\nC\nSM - chr14: 105860498-105860568\nTAGA-GGGAGACAAAA-GATGG--AAGCCA--GCCTGGC-TGTGCAG/GAACCCGGCAATGAGATGGCTTTAGCTGAGA\n78761#TAGA-GGGAGACAAA--GATGG--AAGCTG--GGCTGAGTTG/--AG/T------TGAGCTGGGCTGCGCTGAGCTGGGCTG\n89973#TAGA-GGGAGACAAAAAGATGG--AAGCCA--GC-TG/-------AG/TTG-AGCTGAGCTGGGCTGCGCTGAGCTGGGCTG\n18861#T-GA-GGAGA-CA----GATGG--AAGCCA--GC-TG/-------AG/TTG-AGCTGAGCTGGGCTGCGCTGAGCTGGGCTG\n52648#TAAAGAGGAGACAAA--GATGG--AAGCCA--GC-TG/----------TTG-AGCTGAGCTGGGCTGCGCTGAGCTGGGCTG\n64523#TA---GGGAGACAAAA-GATGGGGAAGCCAAAGC-TG/-------AG/TTG-AGCTGAGCTGGGCTGCCTTGAGCTGGGCTG\n31578#TAGA-GG-AGACAAA--GATGG--AAGCCA--GC-TG/-------AG/TTGGAGCTGAGCTGGGCTGCGCTGAGCTGGGCTG\n11123#TAAGAGG-AGACAAAA-GATGG--AAGCCA--GC-TG/-------AG/TTG-AGCTGAGCTGGGCTGCGCTGAGCTGGGCTG\n6784#TAGAGGG-AGACAAA--GATGG--AAGCCA--GC-TG/-------AG/TTG-AGCTGAGCTGGGCTGCGCTGAGCTGGGCTG\nCTGGGCTGAGCTGAGCTGGGCTGAGCAAGGCTAGGCTGAG/CTG-AGCTGAGCTGGGCTGCGCTGAGCTGGGCTG\nSM - chr14: 105860022-105860094\nFigure X1. Clustering and sequence alignment of a monoclonal sample amplified with 25 cycles. A.\nReadplot of 2,500 reads after SWIBRID analysis of a monoclonal B cell line obtained from EBV\nimmortalization containing two switched alleles. B. Plot depicting clones from the samples in A. Every circle\nrepresents a clone with a unique CSR junction. The size of the circle corresponds to the number of reads.\nColours are matched with A. C. Manual analysis of 8 reads from the monoclonal sample. Break shows\nalignment in the big deletion found in switch µ region. Coordinates belong to hg38 genome assembly. A\nand B taken from Fig 1.\nA\ntop recurring clusters for PBMCs amplified with 15 PCR cycles\n4#4 bfdce8b4-b520-4ed9-8c0b-eaddc45ecb75\nchr14:105859750-105745082: n_homology=1, n_untemplated=0\n7#7 f2a2a3f3-3bea-4486-9d8a-44b33358c67a\nchr14:105709763-105859802: n_homology=10, n_untemplated=0\n6#6 eb726ffd-83a1-4586-91f7-22650a70f5ac\nchr14:105711309-105860019: n_homology=11, n_untemplated=0\n1#1 e0f49b46-1da4-40cc-bdda-93e19a94b755\nchr14:105710468-105860085: n_homology=6, n_untemplated=1\n2#2 94717bdf-9052-40a0-875e-9eb4181ba5ea\nchr14:105710617-105860120: n_homology=11, n_untemplated=1\n3#3 706cb6ad-ea94-4592-b387-5ee1f3aa4576\nchr14:105710925-105859803: n_homology=6, n_untemplated=2\n5#5 77024fc2-abe8-4b5a-ac12-b73f7057ef4a\nchr14:105646476-105859781: n_homology=0, n_untemplated=22\nSM SG3 SG1 SA1 SG2 SG4 SE SA2\ntop recurring clusters for PBMCs amplified with 20 PCR cycles\n4#4 78593ca6-fac6-44f3-9af0-d4b81dd2aa20\nchr14:105745093-105859750: n_homology=3, n_untemplated=2\n3#3 fd6e4807-4959-482e-867d-fd30af00232c\nchr14:105710947-105859824: n_homology=9, n_untemplated=0\n2#2 a5721f35-f9cc-4918-a0c3-e634ceef9fe8\nchr14:105860094-105710610: n_homology=9, n_untemplated=0\n1#1 9689cdbd-06c2-4ed1-94b3-aa3ac96ccc6c\nchr14:105710425-105860069: n_homology=1, n_untemplated=2\n6#6 56ea75fa-32e0-4fa0-b86b-f092fcbdba7b\nchr14:105860030-105711308: n_homology=9, n_untemplated=1\n7#7 7b755550-c6c6-4981-8c82-75071d6d8c7a\nchr14:105859776-105709762: n_homology=5, n_untemplated=0\n5#5 812b66dc-d55d-44e2-9ace-ebb89684d68c\nchr14:105859787-105646486: n_homology=1, n_untemplated=1\nSM SG3 SG1 SA1 SG2 SG4 SE SA2\ntop recurring clusters for PBMCs amplified with 25 PCR cycles\n4#\nc\n4\nh r1\n4\n4\n6\n:\n1\n1\n6\n0\n0\n5\n0\n7\nc\n4\n0\n5\n-\n1\n9\n3\nf\n2\n4\n-\nf\n1\n-\n0\n4\n5\n1\n8\nf\n5\nc\n9\n-\n7\n8\n5\n8\n1\n6\n:\n9 -3\nn\n6\n_\n9\nh\n3\no\n5\nm\n9\no\nf\nl\n6\no\n8\ng\n1\ny\nc\n=\ne\n1, n_untemplated=0\n5#5 ed7b6b32-bfcc-4587-97fc-cc86ac48d54a\nchr14:105859824-105646483: n_homology=0, n_untemplated=57\n7#7 d3d29578-ea5d-498f-946c-b87bbec3e028\nchr14:105859783-105709764: n_homology=4, n_untemplated=1\n3#3 21db69a2-a445-4af2-98dc-3017c178ae49\nchr14:105710923-105859791: n_homology=2, n_untemplated=1\n2#\nc\n2\nh r1\n2\n4\n4\n:\na\n1\na\n0\n1\n5\n4\n7\n9\n1\na\n0\n-\n6\n2\n3\nc\n0\n9\n-\n2\n1\n-\n0\n4\n5\n5\n8\n3\n6\n0\n0\n-\n0\n9\n9\n2\n8\n3\n:\n3 -8\nn\n7\n_\na\n2\nh\n2\no\n2\nm\n7\no\n3\nl\n6\no\n6\ng\n1\ny\nb\n=\n4\n4, n_untemplated=1\n6#\nc\n6\nh r1\n4\n4\n5\n:\ne\n1\ne\n0\n2\n5\n1\n8\nb\n6\ne\n0\n-\n0\ne\n2\n6\n6\n1\n-\nb\n1\n-\n0\n4\n5\n6\n7\n7\n1\nb\n1\n-\n3\n9\n6\nd\n1\n1\n:\n3 -6\nn\nb\n_\n3\nh\n8\no\n5\nm\n2\no\nc\nl\nb\no\nb\ng\nd\ny\n0\n=\na\n0, n_untemplated=0\n1#\nc\n1\nh r1\nb\n4\n5\n:\ne\n1\n5\n0\n7\n5\ne\n7\n9\n1\nf\n0\n-\n4\n5\n5\na\n1\n3\n-\n2\n1\n-\n0\n4\n5\n1\n8\nc\n6\n9\n0\n-\n0\nb\n6\n1\n9\n0\n:\n0 -9\nn\n3\n_\n8\nh\n0\no\ne\n5\nm\n0\nb\nl\n4\no\n9\ng\n4\ny\n5\n=\n9\n17, n_untemplated=1\nSM SG3 SG1 SA1 SG2 SG4 SE SA2\nB\nClone 1:\nSM - chr14: 105860018-105860108\nAAGGCTAG-/GCTGAGCTGAGCTGGGCTGAGCAAGGCTAGGCTGAGCTGAGCTGAGCTGGG/CTGCGCTGAGCTGGGCTGGGCT\nAAGGCTAG-/GCTGAGTTGGGCTGAGCTGGACTGGGCTAGGCTGAGCTGAGCTTGGTTGGG/TTGACCTGGGCTGAGCTGAGCT\nAAGGCTAGA/G----GCTGAGCTGAGCTGGGCTGAGGTGAGCT-TGCT-AG----------/TTGACCTGGGCTGAGCTGAGCT\nAAGGCTAG-/GCTGAGCTGAGCTGGGCTGAGGAAGGCTAGGCTGAGCTGAG----------/-----CTGGGCTGAGCTGAGTT\nTGGGCTGA-/GCTGAGCTGGGCTGAGCTGGACTGGGCTAGGCTGAGCTGAGCTTGGTTGGG/TTGACCTGGGCTGAGCTGAGCT\nSA1 - chr14: 105710403-105710483\nFigure X2. Cluster tracing in samples of one donor amplified using 15, 20 and 25 cycles. A.\nReadplots with top matching events (#1-#7) across samples highlighted in different colors. Boxes include\ninformation on read name, homology value, and the presence of untemplated inserts. B. Manual\nsequence alignments of reads covering the CSR junctions of clones #1 obtained after 15, 20 and 25\ncycles\nTo address the impact of cycle number on SWIBRID output in more diverse sample setting, we\nperformed an experiment with 200.000 PBMCs of six different donors analyszed after 5, 10, 15,\n20 and 25 cycles of PCR amplification. Samples with 5 to 15 cycles did not pass our regular\nquality threshold of 500 filtered reads. 25 cycles yielded enough reads for reliable clustering and\nwas consequently set as standard.\nTo compare reads and their represented clones within the same donor, we devised a meta-\nclustering strategy matching and tracing similar events in a single donor after low versus high\ncycle number amplifications (see Supplementary Note 2 and Figure X2). Thereby we identified 7\nevents with increased probability to represent B cells deriving from the same common ancestor\n(Figure X2A). The manual analysis of potential sister clones revealed that clone #1 was detected\nafter 15, 20, and 25 cycles, as it represented with high sequence homology and matching\nbreakpoint coordinates (51 nt of homology of sequences: SM chr14: 105860040-105860090 and\nSA1; chr14: 105711609-105712367) (Figure X1B).). We have now added a Supplementary Note\n2 that described how we estimate reproducibility by cluster tracing.\nImportantly, it must be considered that 200.000 PBMCs with approximately 10.000 B cells and\n5.000 switched cells will contain only minor overlap of clones with shared common ancestors.\nThe effect of cell number on SWBRID output is addressed in main Figure 1e and results section\nline 145-156. When titrating the number of cells further, we observed that at 500 input memory B\ncells, the diversity does not reach 500 clusters. Such low diversity would be at risks of leading to\nunreliable measurements of SWIBRID features that depend on event averaging.\n3. If the authors take the PBMC from one patient and divide in half and the PCR, and\nsequence, please show how similar or different the results are. For example, can they find\nthe top 10 largest and top 10 smallest deletions in each of the two halves of this analysis?\nThe reviewer raises an important concern, pointing to the comparability of two replicates from the\nsame donor regarding their outcome. As shown above in Figure X2, we indeed find shared clones\nacross samples deriving from the same individual with certain clones present in independent\ntriplicates (we have added the tracing plots in the new Supplementary Note 2).\nWe also analyzed how many clusters are shared when comparing replicates of 50,000 and\n100,000 of IgG+IgA+CD27+ B cells (see now Figure 1f in the paper). When using purified class\nswitched B cells as an input (increasing the likelihood to find shared clonotypes), samples clearly\nshared more clusters among replicates of the same donor (A-A, B-B and C-C), than among\ndifferent donors (mixed). More quantitatively, we observed up to 26% shared clones in duplicate\nsamples of 50,000 memory B cells from the same donor, while less than 20% of clusters were\nshared between samples from different donors (Figure X3 and new Figure 1f in the paper). The\nbaseline likely reflects both, the limited resolution of our clustering methods as well as the possible\npresence of “public” clones expected from preferential switching in certain genomic loci. Shared\nVDJ heavy chain clonotypes have been estimated to range between 1% and 6% between two\nsubjects (Soto, C., Bombardi, R.G., Branchizio, A. et al. Nature 566, 398–402 2019;\nhttps://www.nature.com/articles/s41586-019-0934-8). However, no comparable studies providing\nestimates for CSR clonotypes are currently available.\nnumber experiment, now Figure 1f. 50,000 (50k) and 100,000\n24\n(100k) CD27+IgA+IgG+ B cells were analyzed in replicates of donor\nA, B and C. Shown are % of shared clusters in replicates of donor\nC) or from different donors (“mixed”)\n20\n50k100k\n# cells\n4. Beyond the one or a few actual sequence junction examples shown, please provide 20\nactual human patient junctions and the sequence features of each of these (from the\npooled PBMC samples). I request this from all bioinformatic DNA\nsequence studies.\nWe thank the reviewer for this request and developed visualizations that show selected CSR\njunctions in the context of the read clustering, together with information on homologous or\nuntemplated nucleotides, which are now integrated in the Supplementary Note 2. Besides the\nvisualizations of the cell number experiment (see point 3 above), we also show a read clustering\nand junctions from random reads in the top 15 shared clusters from one healthy human donor, for\nwhich we sequenced amplicons both using minION and pacBio technology. Nucleotide homology\nand untemplated inserts are indicated in every read. This shows that the two methods give\nextremely similar results.\n5. Because Ig CSR sequences are very repetitive, assessment of microhomology and\nperhaps even some other features may be difficult.\nWe agree with the reviewer on this important point. There are two issues to address: i)\nmicrohomology might be falsely interpreted as repair-associated homology, while it derives from\nthe repetitive nature of the genomic loci, and ii) the applied methodology (including sequencing\ntechnology) hampers accurate assessment of microhomology.\nRegarding the first point, it is true that microhomology may be misidentified due to the repetitive\nnature of switch region sequences. However, our intention is not to compare microhomology in\nCSR junctions with that observed in other, non-repetitive genomic regions. Instead, we aim to\ncompare microhomology within CSR junctions across different donor contexts, specifically, in\nrelation to varying defects in DNA repair and immune function.\n5\nriap\nni\nsretsulc\\derahs\n%\nDonors\nA−A\nB−B\nC−C\nmixed\nPoint-to-point response Vázquez García & Obermayer et al.\nTo increase robustness and mitigate bias introduced by the methodology, including sequencing\nerrors, most of our features are derived by averaging many reads and often also by binning events\nalong the genome. The variance partitioning analysis in Figure 2b presents our approach to\nsystematically attribute the “noisiness” of each feature to “relevant” factors such as donor identity\nor likely technical artefacts such as batch or sequencing method. We are therefore confident that\nthe robust features we selected on an independent cohort of healthy donors can be quantified\nwith sufficient accuracy.\nMore specifically, we included some nucleotide-level features such as homology and untemplated\nnucleotides around CSR junctions or blunt ends if neither is observed (see the annotated read\nplots now shown in Supplementary Note 2). Since switch regions are not directly comparable to\nless repetitive genomic regions, we at least aimed to relate our findings to other studies that\nspecifically analyzed switch regions:\n- Vincendeau et al.: SM-SG1 junctions of mouse splenocytes amplified by classical PCR\nand sequenced via pacBio\n- Panchakshari et al.: CH12 mouse cell lines and mouse spleen samples enriched by linear\namplification (HTGTS) and sequenced by Illumina short reads\n- This study (SWIBRID): random human donors sequenced by minION and pacBio.\n- This study (SWIBRID): mouse CH12 cell lines, spleens, and lymph nodes sequenced by\nminION.\nTo avoid adding more technical detail to the paper, we prefer to show the following comparison of\nmethods to sequence CSR junctions just in this response.\nFigure X4: Comparison of sequencing error rate, average number of homologous nucleotides around\njunctions, homology score (derived from binned breakpoint positions), average number of untemplated\ninserts around junctions, percentage of blunt breaks, or average sequence complexity around donor\nbreakpoint positions among the different datasets: Vincendeau et al. (mouse/pacBio), Panchakshari et al.\n(HTGTS) and this study (minION).\nGlobally, we observe that despite very different effective sequencing error rates (accounting not\nonly for PCR or sequencing errors but also for mappability), our nucleotide-based estimate of\nhomology in mouse samples is comparable to those reported in other mouse studies. In contrast,\nhuman samples show a marked divergence from the mouse data for nucleotide-based estimates\nof homology, while this difference was less prominent for the binning-based homology score\n(Figure X4).\nIn contrast to homology, we observed a markedly higher frequency of untemplated inserts and a\nlower proportion of blunt ends in our mouse data compared to the findings of Vincendeau et al.\nand Panchakshari et al. We assume that this discrepancy stems from methodological differences,\nspecifically, whether the approaches used were capable of retrieving reads that span junctions in\nhigh- or low-complexity genomic regions (highly diverse or highly repetitive sequences). The\nextent to which such complex regions were captured varied considerably depending on the\nmethod applied (see Figure X5). We observed that the coverage of repetitive (low complexity)\nregions is influenced by primer design and methodology more than by sequencing technology.\nFor instance, HTGTS junctions are biased towards the non-repetitive (high-complexity) parts of\nSm and Sa, while SWIBRID allows to capture more junctions that originate from repetitive (low-\ncomplexity) regions (Figure X5).\n1.0\n0.8\n0.6\n0.4\n0.2\n0.0\nFigure X5: Sequence complexity (top) and breakpoint frequency (bottom) for the human (left) or mouse\n(right) IgH locus, using different datasets of healthy human donors with pacBio or minION sequencing, or\nWT mouse spleens, lymph nodes, or CH12 cells with minION sequencing, mouse splenocyte data with\npacBio sequencing from Vincendeau et al., or mouse CH12 or spleen data with short-read HTGTS\nsequencing. Junctions originating in low-complexity regions (red ellipses) in Sm or Sa are preferentially\ncaptured with our minION reads.\n6. The use of the term ‘scar’ is inappropriate at all places in this manuscript. These are\nsimply genomic alterations. Near the very end of PMID 36587186, that author explains the\ninception of the phrase ‘information scar’, which was not to be confused with any physical\n‘scar’ in the DNA duplex structure. Since that inception, the dramatic use of the term ‘scar’\neven for simple mutations has become disturbingly frequent in the literature, even though\nthis is inappropriate, especially for non-coding regions such as the Ig CSR regions which\n7\nytixelpmoc\n10 4\n10 6\n10 8\nSM SG3 SG1 SA1 SG2 SG4SESA2\nycneuqerf\ntniopkaerb\nmou(cid:40)e(cid:41)(cid:40)pleen(cid:34)min(cid:35)(cid:36)(cid:37) mou(cid:40)e(cid:34)pac(cid:38)io\nmou(cid:40)e(cid:41)(cid:42)(cid:37)(cid:34)min(cid:35)(cid:36)(cid:37) mou(cid:40)e(cid:41)(cid:43)(cid:44)12(cid:34)(cid:44)(cid:45)G(cid:45)S\n(cid:33)uman(cid:34)min(cid:35)(cid:36)(cid:37)\nmou(cid:40)e(cid:41)(cid:43)(cid:44)12(cid:34)min(cid:35)(cid:36)(cid:37) mou(cid:40)e(cid:41)(cid:40)pleen(cid:34)(cid:44)(cid:45)G(cid:45)S\n(cid:33)uman(cid:34)pac(cid:38)io\nSm S(cid:39)3 S(cid:39)1 S(cid:39)2b S(cid:39)2c Se Sa\nPoint-to-point response Vázquez García & Obermayer et al.\ndo not encode information. I provide this comment for all papers that I review that\ninappropriately use the phrase ‘genomic scar’.\nWe appreciate the reviewer’s comment and have replaced the term “scar” with “CSR junction”\nthroughout the manuscript. We also changed the title accordingly “Recombination junctions from\nantibody isotype switching classify immune and DNA repair dysfunction”.\n7. In the Supplementary file, lines 25-26, the authors write: ‘Given a high rate of sequencing\nerrors, only the coverage of a position (not nucleotide identity) was considered, and gaps\nsmaller than 75nt were ignored.’ Given the repetitive nature of Ig CSR sequences, could\nthe authors explain this to me a bit more in their responses?\nAs shown in pages 12-13 of the Supplementary Material, we observe for minION but not pacBio\nthat sequencing leads to an overrepresentation of small indels. This is even observed in a plasmid\nsample that did not undergo PCR amplification (Extended Data Figure 1g). When assigning\ndifferent reads to a cluster, we therefore aimed to remove this kind of technical noise from the\ndata, and opted for an approach where we only consider whether a certain genomic position is\ncovered by a given read. We thereby ignore sequencing errors (expected in every ~30th\nnucleotide, see Figure X4 above). To further increase robustness and group reads with the same\nCSR junctions regardless of sequencing-induced indels, we removed small coverage gaps < 75nt,\nwithout ignoring larger structural rearrangements expected to be induced by switching. Removing\nsmall gaps is not strictly necessary for successful clustering, but it results in clusters that are much\nmore clearly defined (i.e., reads in the same cluster are more similar, reads in different clusters\nare more distinct). We have expanded this important point in the Supplementary Note 1. “Further,\nlong-read sequencing is prone to create indels. Insertions are ignored in the pseudo multiple\nsequence alignment but are taken into account at other points in the pipeline if they are either\ntemplated and detected by mapping, or untemplated and occurring around larger break points, in\nwhich case they are identified in the breakpoint realignment step. We noticed that deletions of up\nto 75nt length appear frequently even in a non-PCR plasmid control, but much less often when\nusing pacBio instead of minION sequencing technology. To increase the robustness of the\nclustering by preferentially comparing the positions of larger breaks rather than likely random\nsmall deletions, we ignored all gaps smaller than 75nt.”\n8. For CVID, it is my understanding that this has diverse causes that do not affect the actual\nDNA events of VDJ recombination or class switch recombination. Perhaps in the Supplement,\nthe authors could comment on how or why the analysis gives a readout beyond simply the\nnumber of B cells that have an antigen receptor.\nThe main determinant of B cell phenotypes in CVID is the defect in maturation and terminal\ndifferentiation of B cells. While the majority of CVID patients exhibit normal VDJ\nrecombination and retain mature naïve B cells, memory B cells, especially switched memory B\ncells, are frequently reduced.\nImportantly, it is not only the number of switched memory B cells that is affected in CVID.\nMutations in receptors involved in B cell activation, B-T cell interaction, and cytokine signaling can\nsignificantly impact isotype composition, beyond simple numerical changes in B cell subsets. For\ninstance, certain CVID patients show a more pronounced IgA deficiency than IgG, a pattern often\nlinked to mutations in TACI (A. E. J. Poodt et al., Clin. Exp. Immunol. 156, 35–39 2009;\nhttps://doi.org/10.1111/j.1365-2249.2008.03863.x), while in contrast, BAFF-R mutations can\npresent with normal IgA but reduced IgG levels (K. Warnatz et al., Proc. Natl. Acad. Sci. 106,\n13945–13950 2009; https://www.pnas.org/doi/10.1073/pnas.0903543106).\nFurthermore, a subset of CVID cases is associated with defects in DNA repair pathways. While\nmost CVID patients do not present with classic monogenic DNA repair disorders, recent research\nindicates that such defects can contribute to CVID pathogenesis in selected individuals. Targeted\nsequencing studies have identified rare, likely pathogenic variants in key DNA repair genes,\nincluding ATM (ataxia-telangiectasia mutated), and components of the DNA double-strand break\n(DSB) repair machinery such as RAD50, NBS1, as well as mismatch repair proteins like MSH2\nand MLH1 (C. E. Hargreaves et al., J. Clin. Immunol. 41, 1315–1330 2021;\nhttps://pmc.ncbi.nlm.nih.gov/articles/PMC8310859/, S. M. Offer, Q. Pan-Hammarström, L.\nHammarström, R. S. Harris, PLoS ONE. 5, e12260 2010;\nhttps://doi.org/10.1371/journal.pone.0012260). For some of those factors such as MLH1, defects\nare expected to affect the sequence context surrounding CSR junctions (R. Chahwan et al., J.\nExp. Med. 209, 671–678 2012; https://pmc.ncbi.nlm.nih.gov/articles/PMC3328365/).\nIn conclusion, with SWIBRID, we do not exclusively assess the absolute number of B cells in\nCVID but subtle changes in isotype composition and DNA-repair deficiencies that specifically\nimpact CSR. However, lower B cell numbers translate into fewer reads and fewer clusters, which\nin turn affect diversity metrics as well as other features that are standardized to the number of\nreads or clusters.\nAs we considered it crucial to explain this point to our readers, we have now added an explanation\nin the discussion section of the main text line 383-391.\nAgain, I commend the authors for their impressive amount of work.\nWe thank the reviewer once again for the appreciation of our work.\nReviewer #2 (Remarks to the Author):\nThe manuscript by Vázquez García, Obermayer et al., presents a novel method named\nSWIBRID (SWItch junction Breakpoint Repertoire IDentification) designed to characterize\nDNA double-strand break (DSB) repair outcomes during antibody class switch\nrecombination (CSR). Leveraging decades of foundational research into B-cell mediated\nDNA repair, this method uniquely profiles genomic scars resulting from CSR, allowing for\nassessment of immune diversity and DNA repair functionality from peripheral blood\nsamples. Employing long-read sequencing and sophisticated bioinformatics pipelines,\nSWIBRID identifies distinct repair signatures in various DNA repair mutants and in patients\nwith immunodeficiencies such as Common Variable Immunodeficiency (CVID) and cancer-\nassociated DNA repair deficiencies. The authors demonstrate discriminatory power in\ndistinguishing repair-deficient genotypes and patient cohorts, underscoring potential\nclinical utility in personalized diagnostics and early detection of pathogenic defects. Some\nissues still requiring clarifications include:\nWe thank the reviewer for recognizing our technology as a powerful new tool and for the thoughtful\ncomments, which we found highly valuable, particularly in guiding us to strengthen the clinically\nrelevant aspects of our work. We have carefully considered each point and addressed all\ncomments in detail as outlined below.\n1- The methodology shows promise in distinguishing specific DNA repair deficiencies and\nCVID. Have the authors considered potential confounding factors such as age-related\nchanges in B-cell repertoire or treatment-related effects on CSR in clinical samples?\nWe thank the reviewer for this important comment. While we had shown that feature values do\nnot correlate with age or gender (Figure 2c; original manuscript, lines 181–182), we had not\nassessed the potential influence of treatment. We have now included this analysis in main Figure\n4B and in Extended Data Figure 4A. Treatments of patients are described in the methods section\nand Supplementary Table 2.\nThe vast majority of features were not correlated with drug treatment or immunoglobulin (Ig)\nsupplementation. Treatments assessed were those given shortly before or at the time of sample\ncollection and included: prednisone (n=3), cortisone (n=1), steroids (n=1), sirolimus (n=1), and\ninfliximab (n=1). Ig supplementation was administered either subcutaneously (n=24) or\nintravenously (n=8). Overall, the feature values did not correlate with the drug treatment of Ig\nsupplementation (Figure X6A), however, cluster entropy showed a difference between individuals\nreceiving subcutaneous and intravenous Ig versus without treatment (Figure X6B).\nIt is important to note that this cohort is not optimal for specifically evaluating the effects of these\ntreatments, as only one individual each received cortisone, steroids, sirolimus, or infliximab. For\nIg supplementation, where a sufficient number of treated individuals was analyzed, we observed\nsignificant alterations in only two features of CSR junction patterns: the distribution of breaks\nacross the SA1 and cluster entropy, an indicator of sample diversity.\nHowever, it remains unclear whether these discrepancies are attributable to the treatment itself\nor to higher overall disease severity in these patients. Determining this would require analysis of\nsamples obtained both before and after treatment, which was not possible for the donors included\nin this study.\n*\n***\n1.0 ****\n0.9\n0.8\n0.7\ne g g n o n ivi scI\nFigure X6. Influence of treatment on SWIBRID results. A. Box plots of percent explained variance by\ndonor, diagnosis, sex, age, batch, immunomodulatory treatment, or Ig administration for features in\nindicated categories. B. Box plots of cluster entropy values for CVID patients that received intravenous\n(ivig) or subcutaneous Ig (scIg) together with controls (black dots).\n2- It remains unclear whether the CSR scar patterns detected by SWIBRID are stable over\ntime in individual patients or influenced by transient immune activation events or\ninfections. Are there any longitudinal patient data to draw such conclusions from in the\nanalysed sample cohort?\nWhile we did not systematically establish a longitudinal cohort at this point, we happen to have\nsamples from the same two donors (one healthy: 09-0/1/2, one CVID-like: 32-0/1/2) at successive\ntime points several months apart (09_healthy: 28.07.2020, 12.01.2016, 03.11.2020; 32_CVID-\nlike:23.03.2023, 02.10.2023, 11.05.2020). These samples cluster closely together in the PCAs\n(indicated by connecting lines) and heatmap of Figure 4, but also individual features are largely\nstable (Figure X7). Figure X7 depicts the heatmap of main Figure 4h with these 6 samples\nseparated on the right.\nWe have now added a sentence in the main manuscript and expanded the legend of Figure 4\naccordingly. “Although we did not establish a longitudinal cohort, samples from two individuals\n(numbered 09 and 32) collected over periods of up to four years clustered tightly in the heatmap\nand in PCA space (Fig 4e), indicating a stable SWIBRID profile over time.”\nImportant exceptions among the features that were less stable over time (e.g. in the CVID-like\ndonor) relate to repertoire diversity, such as top clone occupancy or cluster entropy. While\noutcomes of DNA repair in an individual are generally expected to remain stable over time,\nexposure to pathogens (which can induce clonal bursts and cause temporarily skewed diversity)\nmay lead to fluctuations in diversity related measures. We consider this variability a key advantage\nof the technology, as it also allows tracking changes over time, particularly relevant for detecting\npathogenic clonal expansions associated with B cell lymphoma.\n11\nyportne_retsulc\n80\n60\n40\n20\necnairav\ndenialpxE\n%\nA B\nCVID + CVID-like\nsubclass\ncontrol\nPoint-to-point response Vázquez García & Obermayer et al.\n(cid:39)iagnosis\nSex\nAge\nComplication\nClass\nalpha_ratio_clusters\npct_clusters_SA1\npct_clusters_SG1\npct_clusters_SG2\npct_clusters_SG4\npct_clusters_SG3\nuntemplated_inserts\nhomology_score_rv\nacceptor_score_GAGCT\ndonor_score_GAGCT\nacceptor_score_GGGST\ndonor_score_GGGST\ndonor_score_W\ndonor_complexity\nacceptor_score_W\ndonor_score_WGCW\ndonor_score_S\nhomology\nhomology_score_fw\nacceptor_score_S\npct_(cid:33)rea(cid:34)s_S(cid:35)_SA\npct_direct_switch\nread_length_mean\nread_length_SA_mean\npct_(cid:33)rea(cid:34)s_SG_SG\npct_(cid:33)rea(cid:34)s_S(cid:35)_SG\nread_length_SG_mean\nread_length_SG_std\n(cid:33)rea(cid:34)_dispersion_SG4\npct_intraswitch_deletion\npct_(cid:33)rea(cid:34)s_SA_SA\npct_(cid:33)rea(cid:34)s_SG_SA\npct_se(cid:36)uential_switch\noccupancy_(cid:33)ig_clusters\ncluster_gini\noccupancy_top_cluster\ncluster_si(cid:37)e_std\ncluster_si(cid:37)e_mean\nclusters_e(cid:38)\nclusters\ncluster_inverse_simpson\ncluster_entropy\npct_duplications\npct_inversions\n(cid:40)C2\n(cid:40)C1\nFigure X7: Heatmap of main Figure 4H with successive samples from the same two donors\nvisually separated on the right. Replicates of donor 09: #1: 28.07.2020, #2: 12.01.2016, #3:\n03.11.2020. Replicates of donor 32: #1 23.03.2023, #2 02.10.2023, #3: 11.05.2020.\n12\n1_(cid:41)1 1_(cid:42)3 1_(cid:43)1 1_33 1_2(cid:44) 1_(cid:41)3 1_(cid:44)(cid:44) 1_24 1_44 1_(cid:44)(cid:43) 1_4(cid:43) 1_(cid:45)2 1_(cid:43)(cid:42) 1_(cid:44)1 1_41 1_(cid:44)(cid:42) 1_(cid:44)2 1_(cid:42)4 1_2(cid:43) 1_(cid:43)(cid:43) 1_2(cid:42) 1_(cid:41)(cid:42) 1_4(cid:42) 1_(cid:43)4 1_(cid:46)(cid:42) 1_21 1_(cid:45)(cid:43) 1_31 1_3(cid:42) 1_(cid:45)4 1_12 1_(cid:43)3 1_(cid:45)3 1_32 1_4(cid:44) 1_3(cid:43) 1_(cid:42)2 1_3(cid:44) 1_(cid:41)(cid:43) 1_1(cid:44) 1_13 1_43 1_11 1_(cid:42)1 1_(cid:42)(cid:43) 1_(cid:43)(cid:44) 1_(cid:44)4 1_(cid:46)(cid:43) 1_(cid:41)4 1_(cid:41)2 1_(cid:46)1 1_(cid:44)3 1_(cid:43)2 1_(cid:46)2 1_42 1_(cid:45)1 1_22 1_(cid:46)4 1_1(cid:42)\n(cid:42)(cid:45) 32\n3(cid:47)(cid:42)2(cid:42)2(cid:48)11 1(cid:47)(cid:42)2(cid:42)2(cid:48)(cid:46)(cid:42) 2(cid:47)(cid:44)1(cid:42)2(cid:48)1(cid:42) 2 3 1 (cid:47) (cid:47) (cid:47)(cid:42) 3 3 2 2 2 (cid:42) (cid:42) (cid:42) 2 2 2 (cid:48) (cid:48) (cid:48) (cid:43) (cid:42) 3 (cid:42) 1 (cid:42)\n(cid:39)iagnosis (cid:40)C\n(cid:43) Control\nSu(cid:33)class (cid:42)\nAtypical (cid:57)(cid:43)\nC(cid:49)(cid:50)(cid:39)\n(cid:50)C(cid:51)S\n(cid:52)a(cid:33)u(cid:34)i\nA(cid:50)(cid:39)\n(cid:50)gG4def\n(cid:53)(cid:54)(cid:52)(cid:55)2\n(cid:40)(cid:50)3(cid:52)\nT(cid:53)(cid:54)(cid:56)S(cid:54)13(cid:55)\nSex\nfemale\nmale\nAge\n(cid:41)(cid:42)\n(cid:44)(cid:42)\n4(cid:42)\n2(cid:42)\nComplication\nio\nc\nClass\nCluster1\nCluster2\nCluster3\n(cid:37)(cid:57)score\n2\n(cid:42)\n(cid:57)2\nPoint-to-point response Vázquez García & Obermayer et al.\n3- While the method categorizes patients effectively, it would be highly beneficial to\ncorrelate specific CSR signatures with clinical outcomes or disease severity, if possible.\nSuch correlation would substantiate clinical relevance and utility.\nWe thank the reviewer for this comment and fully agree that correlating SWIBRID outcomes with\ndisease severity is of great value. However, the limited size and pronounced clinical heterogeneity\nof our current cohort make it challenging to address this question conclusively.\nAmong the CVID and CVID-like patients in our study, 23 are reported to have complications, and\n10 are classified as “infection only”, of which two additionally have splenomegaly and one has\ndiarrhea. Patients with complications represent a broad spectrum of immune dysregulation.\nUnfortunately, the complication data are not integrated in a way that allows us to assign a clearly\ndefined severity score or value.\nTo address the question nevertheless, we evaluated whether SWIBRID feature-based patient\nclassification correlates with the presence or absence of complications. While we did not observe\na general rule, we noted that 15 out of 23 patients with complications appear in Cluster 3 (Figure\nX7). More specifically, 7 out of 9 patients with complex lung disease (Interstitial Lung Disease and\nGranulomatous-Lymphocytic Interstitial Lung Disease), and 8 out of 11 patients with more than\none complication, fall into Cluster 3. This suggests that patients with more severe disease tend to\ncluster in Cluster 3 (71% of severe cases). While this trend is promising, we acknowledge that\nadditional data will be needed to establish a definitive link between SWIBRID-based classification\nand disease severity.\nIn conclusion, to rigorously answer the reviewer’s question, a larger and clinically well-\ncharacterized cohort will be required. We have therefore decided not to comment on the\ncorrelation between SWIBRID features and disease severity in the main manuscript at this point.\n4- The manuscript introduces several CSR-related genomic scar features. However, the\nbiological mechanisms underlying some of these newly identified features (e.g., long\ntemplated insertions, certain sequence context motifs) require further experimental\nvalidation to clarify their roles in DNA repair pathway preference or dysfunction, or at least\nadditional discussion to their origins and potential mechanisms.\nWe have addressed templated insertions in detail in our previous publication (Lebedin et al.,\n2022), where we showed that such insertions fall into two main categories: i) those derived\nfrom telomere-proximal regions and R-loops (45.4%), arising prior to the mature naïve B cell\nstage, and ii) those from early replication fragile sites (ERFS), appearing post-\nactivation in memory B cells. Overall, 88% of inserts originated from mRNA-encoding regions,\nsuggesting a link between transcriptional activity and insert origin. Even though this is a\ncomprehensive study, yet another study would be necessary to identify specific molecular factors\nresponsible for insertions. We reference our findings in our discussion “Our previous studies\nshowed that antibodies can gain an extra domain from large templated insertions in the S region,\nbut the precise molecular factors contributing to these events remain elusive”.\nWe would also like to respectfully point out that this study was not designed to focus on a single\nmolecular mechanism, such as templated insertions. Rather, our aim was to develop and validate\na novel, de novo analytical framework that can be applied across mouse and human samples\n(including patient-derived material) ensuring both technical robustness and clinical applicability in\npatient contexts. We strongly agree with the reviewer that understanding the mechanisms behind\nspecific features is a valuable future application of this technology. Our method provides a\nplatform for studying DNA repair in human systems and may help reveal yet-unknown factors, as\nexemplified by recent discoveries like Shieldin and HMCES.\nWe nevertheless think that fully dissecting the underlying biology of individual CSR junction\nfeatures would require a dedicated project, involving genetic models, perturbation studies,\nand functional validation, and is thus beyond the scope of this work.\n5- The manuscript employs machine learning (ridge regression) to classify genotypes.\nDetailed clarification on model robustness, cross-validation methods, and generalizability\nto external datasets would be ideal.\nWe thank the reviewer for requesting more information on that important point and have expanded\nthe corresponding parts in the results and methods sections line 743-745. Specifically, we\nseparated our data into testing and training sets, constructed the regression model on the training\ndata and evaluated on the test data. Additional cross-validation was used to get additional\nmeasures of accuracy.\nAs our data presents the first of its kind and most of our features are strongly correlated to isotype\nfrequencies, we cannot apply our model on external published data generated with different\nmethods, especially regarding primer design. We therefore used a different cohort of DNA repair\ndeficient patients from a different hospital to test generalizability of the prediction model.\nMinor Concerns:\n1. Line 141–147: clarify units—reads or cells?\nWe apologize for this confusion. In lines 141-147 of the original manuscript we explain two\ndifferent approaches to address the performance of our analysis pipeline. To test the impact of\nreads and cells on SWIBRID output, we modified the number of reads in the Simulated Reads\ndata (main Figure 1f) and the number of cells in the cell number experiment (main Figure 1e). We\nhave now modified the main text for clarification “To further confirm an adequate quantification of\nCSR clones and understand the limitations of the pipeline, we assessed different numbers of i) in\nsilico generated CSR-reads representing diverse clonotypes and ii) primary B cells to address the\nimpact of the input number on output clusters.”\n2. Extended Data Fig. 1f,g legend missing information to be understandable.\nThank you for your comment. The Extended Data Figure 1 f–g presents data obtained from a\nlinearized plasmid containing the human switch µ region as explained in the materials and\nmethods section. Linearization of this plasmid via a restriction enzyme digest allowed us to\nanalyze a sample in absence of PCR amplification to judge the impact of PCR on our analysis.\nWe have not modified our legend to clarify: “f a plasmid containing the human Sµ region was\nlinearized via a restriction enzyme digest, representing an outcome in absence of PCR\namplification. g Depicted is a read plot obtained from the linearized plasmid with 2,500 reads.”\n3. Figure quality: many diagrams use colour-only encoding (e.g., PCA). Or have\noverlapping color legends with such as fig 2b.\nWe have aimed to use the same color palettes across the manuscript (coding for feature classes\nin Figure 2a, explanatory factors in Figure 2b, genotypes in Figure 2g and Figure 3c,d,e,\ntissues in Figure 3b). We agree that colors in Figure 2b can be confusing and have changed the\npalette.\n4. Methods: provide accession for training datasets (mouse Vincendeau et al.)\nTraining datasets are available in the documentation of SWIBRID in the github repository\n(bihealth.github.io/swibrid). We have now indicated it more precisely in the manuscript in the\nmethods section: “Vincendeau et al. re-analysis: Sequencing data of CSR junctions generated\nby Vincendeau et al.were downloaded from SRA (accession number PRJNA831666) and\nprocessed with standard SWIBRID settings for mouse, after adjusting coordinates of the relevant\nswitch region for different primer locations.”\n5. Some sections, particularly those presenting comparisons and statistical significances,\ncould benefit from clearer methodological descriptions, including exact statistical tests\nused and justification for the chosen methods.\nWe have expanded the description of tests used including multiple testing correction in the\ncaptions of Figure 2 and Extended Data Figure 3. In addition, a more extended explanation of the\nvariance partitioning analysis in the legends of Figure 2, the ridge regression in Figure 3-4, and\nthe number of features used for the different PCAs (see point 5 above) are now added. A more\ndetailed description of features used in the PCA can be found in the Supplementary Table 1.\n6. The authors acknowledge technical noise from sequencing errors. More explicit\ndiscussion and mitigation strategies for these technical limitations, including error rates\nof Nanopore versus PacBio technologies, are essential for readers to interpret data quality\naccurately.\nWe are aware that minION sequencing has a high error rate in comparison with other sequencing\ntechnologies (Figure X4). In response to reviewer 1 (point 5), we compared the impact of the\nsequencing technologies minION and pacBio on our results. SWIBRID feature values are\nconsistent across sequencing technology, as illustrated by the comparable nucleotide-based\nestimate of homology. In response to Reviewer 1 (point 4), we present read clustering and CSR\njunctions from random reads within the top 15 shared clusters of a healthy human donor, for whom\namplicons were sequenced using both minION and pacBio technologies, demonstrating that the\ntwo methods yield highly similar results. We have added the tracing plots in the new\nSupplementary Note 2. Extended Data Figure 2g shows an explicit comparison, while Figure 2b,\nd and Extended Data Figure 2f show the influence of sequencing technology on features.\n7. Discussion on how SWIBRID might compare to existing clinical tests (e.g., TREC/KREC\nassays) could provide valuable context for clinical integration.\nWe thank the reviewer for this comment and fully agree that comparing our method to existing\nTREC/KREC assays is relevant. While we do not consider SWIBRID to be an alternative to\nTREC/KREC, we regard it as a complementary diagnostic tool. TREC/KREC assays are well-\nestablished for assessing V(D)J recombination capacity, primarily used to identify severe\nimmunodeficiencies characterized by profoundly reduced T and/or B cell numbers early in life.\nIn contrast, SWIBRID is designed to detect immune disorders that are not well captured by these\nconventional assays, such as Common Variable Immunodeficiency (CVID). Our method\nspecifically enables the identification of defects in class switch recombination and the loss of\nclass-switched memory B cells, which are key features of CVID that may not be apparent through\nTREC/KREC testing.\nWe have now addressed this point more explicitly in the discussion section, to better emphasize\nthe distinct and complementary roles of SWIBRID and TREC/KREC in immunodiagnostics:\n”Overall, SWIBRID enables the identification and characterization of class switch recombination\ndefects, key features of CVID that may be missed by TREC/KREC testing.”\nIn conclusion, the manuscript significantly advances the field by introducing a powerful\nnew method for profiling DNA repair competency and immunological health through CSR\nscars. Addressing the highlighted concerns will enhance the robustness, clarity, and\nbroader impact of this promising study.\nWe once again thank the reviewer for this assessment and appreciate the recognition of our work\nas a valuable methodological contribution.\nReviewer #3 (Remarks to the Author):\nThe authors of Garcia and Obermayer et al describe SWIBRID to identify the repertoire of\nswitch junction breakpoints that uses PCR and long read sequencing from blood samples\nto detect DSB repair patterns driven by IGH class switch recombination (CSR) in the B cell\nsubgroup. In surveying patient samples, the authors report immunodeficiencies, DNA\nrepair defects, and patient-specific scarring signatures with high accuracy. The work looks\nto be well developed with many identified features that may be useful for sub-group\ndistinction across human and mouse samples but this reviewer was struggling to figure\nout which new features were the most frequent and most relevant to the CSR repair\noutcome repertoire. The manuscript could be improved further by addressing the following\ncomments below:\nWe thank the reviewer for acknowledging the dedicated effort that went into developing our\ntechnology and for the constructive criticism regarding the relevance of specific features for\nrepertoire outcomes. We have carefully addressed all comments and believe they were very\nhelpful to improve the manuscript.\nMajor Comments\n1. Most of the 68 features in 5 categories are presented as fractional data, presumably to\nnormalize for absolute variance measures. It would be very helpful to have all of the data\noutlined in the figures mirrored in absolute frequencies in supplemental tables; this would\nbe analogous to showing the films of blots. It is important for readership to evaluate which\nfeatures are weighted by high frequency, the rigor of the sequencing data, and to confirm\nany statistically significant shifts in proportional data are not influenced by low\nfrequencies which may make some highlighted features too noisy for evaluation.\nWe agree that sharing of raw data is essential for evaluation of reproducibility, which is why our\ngithub repository (bihealth.github.io/swibrid) does not only contain the code needed to create the\nfigures but also the raw output of the SWIBRID pipeline. Raw reads for mouse data are available\non the sequence read archive, while we cannot currently share human read data due to patient\nprivacy concerns.\nWe are not entirely certain what is meant by “absolute frequencies”, as most features in our\nanalysis are obtained by first averaging over all reads within a cluster, and then over all clusters\nin a sample. Given that most samples contain more than 10,000 reads, we posit that most features\nare relatively robust unless they measure rare events like templated inserts. To systematically\nevaluate the noisiness and robustness or our features, we used the variance partitioning analysis\nof Figure 2b, which lets us quantify exactly what proportion of the observed variability is\nattributable to inter-individual differences rather than technical effects like batch or sequencing\nmethod. We therefore believe that the resulting 44 robust features are not strongly affected by\nlow frequencies.\nWe have reformulated and expanded the corresponding section in the manuscript to better\nconvey this point (“To assess the robustness of our features across different batches or\nsequencing methods, and their ability to discriminate between donors, we used variance\npartitioning analysis to verify that for most features, the bulk of the variability is explained by donor\nidentity (Fig. 2b,c, Extended Data Fig. 2f). This indicates that our quantification yields accurate\nand reliable output.”)\n2. Adding to the first comment, too many features will naturally yield some measurable\ndifference that may not be related to biology unless the magnitude of difference withstands\nthe increased number of multiple comparisons. As the central point of the manuscript is\nto highlight the numerous features, then proper statistical rigor needs to account for false\ndiscovery.\nWe thank the reviewer for raising this important point about statistical methodology. We would like\nto emphasize that our central aim is not the identification of specific “differential” features but\nrather of systematic differences in the breakpoint patterns between conditions. The features are\njust a way of capturing multiple aspects of these patterns in ways that we hope are somewhat\nintuitive. Accordingly, we are not primarily using multiple hypothesis testing and p-value\nadjustment to identify specific differential features, but rather dimensionality reduction (PCA) and\nregression with a training / testing split, which is where statistical rigor primarily derives from. Even\nif some features would show spurious significance in an un-adjusted test, we would not expect to\nsee a systematic difference in a PCA or high performance of a regression model unless multiple\nfeatures show coordinated effects.\nNevertheless, we have added a multiple testing comparison to Figure 2h and Extended Data\nFigure 3a (where many features are shown), while statistical comparisons of features shown in\nbox plots (e.g., Figure 3d, 4c,j, Extended Data Figure 4b) are meant to highlight interesting or\ninformative features for interpretation of the systematic group differences observed in PCA or\nregression, but not to justify differences between groups in the first place. We are therefore less\nconcerned with false discovery here.\n3. Fig2a is somewhat helpful for revealing the categories but a detailed description of the\nfull 68 features should be outlined in a supplemental figure/table so readers can fully\nevaluate the data presented. The feature names are bulky. Some terms were not readily\nunderstood by this reviewer (i.e. not clear from only looking at the figures as a litmus test\nfor comprehension) and will certainly alienate most readers. Please reconsider renaming\nfor efficiency and clarity that could pair with detailed description of the filters/ranges used\nto define each feature. Fig2h and related would certainly benefit.\nWe thank the reviewer for this comment, as we aim to maximize the readability and clarity of our\nmanuscript, including the use of self-explanatory feature names. We now renamed the features\nto make them easier to understand. For example, we have improved clarity by adding the primary\nmeasure or unit, such as percent (pct_) or mean (mean_), by improving the understanding of the\nmeasure, such as distribution of breaks along the switch region (break_dispersion) and by naming\nfirst the most important aspect of the feature (occupancy). We adapted the text and the figures\naccordingly. Supplementary Table 1 has been updated and includes a description of each\nmeasure. Additional feature documentation is available at bihealth.github.io/swibrid accordingly.\n4. Furthermore, for each experiment using the feature/category breakdown, a list of ranked\nfeatures impacting the experiment should be provided. If none are impacting, then indicate\nnone (presume this is the case for Spleen Lig4 in EDFig3b? If not need to indicate in figure\nlegend why no Lig4 data for spleen). In this regard, it is difficult to follow the logic as to\nwhy 32 features were used for one set of knockouts and 39 features in another, what those\nfeatures were and why others were excluded; it would appear that the remaining 20 or so\nhave little value if they are not used frequently.\nWe thank the reviewer for this important remark.\nWe have now revised the legend of Figure 3 to clarify that knockouts of DNA repair factors were\nstudied only in CH12 cells, whereas splenocytes and lymph nodes were derived from wild-type\nmice: “Principal component analysis (PCA) of WT CH12 cells, CH12 cells with knockouts (KO) of\nBrca1, Lig4, Rif1, and Trp53bp1, as well as splenocytes, and lymph nodes from WT mice using\nrobust SWIBRID features.”\nThe features were not selected arbitrarily. First of all, we only use the most robust features as\nidentified in Figure 2 through the analysis of human samples, defining a robust set of 44 features.\nSecond, the captions mention the number of features used in each specific PCA. This number\ndepended on the dataset: mouse vs. human, CH12 vs. splenic B cells, our data vs. Vincendeau,\nwhich all contain different information. As an example, CH12 switch exclusively to IgA, which\nexcludes any features related to the IgG isotype. In contrast, Vincendeau and colleagues focused\nspecifically on IgG1, using primers that exclude all other isotypes. As a result, the design of each\ndataset limited the number of usable features. We apologize that this was not clearly explained in\nthe original manuscript and have now added a clarification to the corresponding figure legends of\nFigure 2 and 3.\n“PCA of re-analyzed Vincendeau data30 using 32 of 44 robust features, excluding features related\nto isotypes missing in the dataset and features missing in mouse compared to human.”\n“Left: including reads from all switch regions (42 of 44 robust features - excluding features missing\nin mice compared to human). Right: selecting reads with Sα primer (33 of 44 robust features –\nexcluding sg and human specific features).” Furthermore, the features used for every analysis\nare listed in Supplementary Table 1.\nTo address feature importance, we included the respective plots in Figure 3f, Extended Data\nFigure 4c and the new Extended Data Figure 4g (please see also our response to the next point).\nFinally, Panchakshari et al. spleen data did not contain a Lig4 KO, which is why this comparison\nis missing (Figure 1 in Panchakshari et al.).\n5. In terms of PCA, you need to indicate which components distinguish each of your\ncomparisons and which features overlap. This is otherwise generically saying there are\ndifferences and similarities or they are separated from controls, which have no added\nvalue to the field given the prior work on the genes implicated or to provide causal sources\nfor distinction in CVID/CVID-like samples; this latter part is partially done but needs to be\napplied to earlier parts.\nRelated to an earlier point raised by the reviewer, our main aim indeed is to identify fingerprints\nof specific DNA repair defects or immunodeficiencies as manifested in a CSR junction pattern. If\nsuch a fingerprint happens to be sufficiently well captured by individual features that easily lend\nthemselves to mechanistic or causal interpretation, that is of course a highly desirable outcome.\nNevertheless, our method provides a valuable biomarker even if that is not the case.\nEach of our PCAs is accompanied by other plots that highlight distinctive features (e.g., Figure 2h\nfor the PCA in 2g, Figure 3c,d,f for the PCA in Figure 3e, 4c,h,j and Extended Data Figure4c for\nthe PCAs in Figure 4d-f, Extended Data Figure 3a,c for the PCA of Extended Data Figure 3b).\nWe agree with the reviewer that for the patient samples this point was less clear. We therefore\nadded another plot (Extended Data Figure 4g, which is also Figure X8) showing the weights for\nthe regression model of Figure 4g to indicate which features are most useful in distinguishing\nCVID from healthy.\nFigure X8. Top 19 coefficients in a multinomial ridge\nregression model derived from the 29 training samples.\nFeatures are colored according to the classification of\nFigure 2a. Higher coefficients indicate higher values in\nthe CVID group.\n6. Related to point #5 is that a laundry list of features are indicated but needs to be distilled\ndown to what those robust affecting features mean in aggregate.\nWe appreciate the comment of the reviewer. In this case, we would point out that a high-\nthroughput approach such as our presents a more unbiased and comprehensive view of CSR\njunction patterns than more targeted methods aimed at elucidating specific effects in specific\nconditions. We agree that the resulting data is highly complex and therefore hard to summarize\nin an intuitive way. That is why the features were designed by leveraging what has been used in\nprevious studies, e.g., homology, plus, some other features that capture not yet recognized\naspects in this region or in this context, e.g., break dispersion. Importantly, not all of these features\nare easily interpretable or directly and causally linked to a specific immunodeficiency or DNA\nrepair defect, even though many of them show systematic and very specific differences between\ndifferent mouse genotypes or the different human patient groups we studied. While our\nphenomenological approach for now is more similar to other biomarkers of immune health that\ncoef\n0.05 (CVID)\n0.00\n−0.05 (control)\nPoint-to-point response Vázquez García & Obermayer et al.\nintegrate disparate datasets (e.g., R. Sparks et al., Nat. Med. 30, 2461–2472 2024;\nhttps://www.nature.com/articles/s41591-024-03092-6), we expect that future cohorts allow to\nrefine and extend our observations.\n7. Methods section does not describe machine learning and more detail is needed beyond\nmultinomial ridge regression for each experiment they were used. For independent\nreproduction of findings, the specific features used for each prediction experimental sets\nshould be listed and any additional values, range for confidence, number of technical\nversus biological replications, etc. in the model should be reported. If this software is not\ncited in MS, please make sure this is included.\nWe understand the reviewer’s concern and have expanded the methods section to more clearly\nstate that we use standard R models as well as their inputs: “For the CH12 dataset, we used 33\nfeatures plus 49 samples for training and 36 samples for testing. For the CVID dataset, we used\n44 features plus 29 samples for training, 26 samples for testing, and 21 samples for validation.”.\nIn the manuscript, details about the machine learning models can be found in: i) Figure. 3a and\n4a summarizing the inputs used for our machine learning models and ii) the repository at\ngithub.com/bihealth/swibrid_paper containing all code and data needed to reproduce the figures.\nMinor Comments\n8. This manuscript suffers from poorly framed figures with text and symbols that are either\ntoo crammed together, too small, or using inappropriate axis labeling. For the latter, there\nare no such fractional n_homology measures and listing 0.5 increments only serves to\ncause confusion to the lay reader. Labeling format (e.g. n_homology, Fraction IGA VDJ)\nmay be understandable as a programming label for the authors but is not for lay audience.\nThis reviewer urges the authors to carefully consider the context of the data shown and\nreformat the figure panels to make it more conceptually available to a broader audience.\nWe understand the concern of the reviewer and have worked to generate clearer figures. First,\nwe renamed features in pursue of a clearer definition and we now leave more space in the figures.\nThe updated names of the features can be found in Supplementary Table 1 in the particular case\nof n_homology, we changed it to “homology”, although it is calculated as an average over the\nentire sample, leading to fractional values. In the rest of the cases, we have added “pct” for\npercentage, instead of “frac” for fraction to ease the understanding of the features.\n9. Not clear what type of V(D)J sequencing was occurring in the study as it is noted in the\nmanuscript. Fig 1F labeling is also not clear. Was any variable region analysis performed\nand how was that integrated into SWBRID comparison? Line 152 is related to the same\npanel but in comparison to IGHA vs IGHG. There seems to be a disconnect in the text and\nfigure.\nIn the manuscript, we attempt to compare SWIBRID with the most standard method to analyze B\ncells, which in this case is BCR repertoire sequencing. Beside information on VDJ we can also\nretrieve the information of the respective isotype. To understand whether our genomic approach\nis reflected in the RNA, namely CSR junction alleles matching BCR-mRNA isotypes, we chose to\ncompare the frequencies of IGHA vs IGHG isotypes in SWIBRID versus VDJ-sequencing\ndatasets. We have now modified the respective sentence to make this clearer: “To compare\nSWIBRID with the standard approach for B cell analysis and evaluate whether our genomic\nfindings are reflected at the RNA level, we analyzed IGHA and IGHG isotype frequencies between\nCSR junction alleles and BCR mRNAs from circulating B cells. […] Both approaches obtained\nsimilar inter-donor differences in IGHA fraction”\nAs now explained in the methods section, the VDJ library is generated by template switch cDNA\nsynthesis, followed by the amplification of BCR transcripts using a mixture of constant primers\nand template switch sequence. Illumina sequencing was then performed and the BCR transcript\nanalysis with MiXCR using the “generic-bcr-amplicon-umi” preset with the tag pattern\n\"^N{22}(UMI:N{16})N{7}(R1:*)\\^(R2:*)\".\n10. There is a disconnect in feature numbers for iii on line 163 versus Fig 2a. Please clarify.\nWe thank the reviewer for identifying this error. We have corrected the information in Figure 2a. It\nis only 28 features and we have 2 more breakpoint dispersions for mouse Sg2b and Sg2c.\n11. Lines 171-172. Presuming only data from 1 of the 10 batches are relevant given that\nwas the only batch using long read sequencing, please make sure to break down that\ninformation when comparing this to C2. If using both sequencing formats, then indicate as\nsuch for both cohorts. Only 6 of the 68 features are referenced; why those 6? Are they\nrepresenting the majority of the data? If not, then why only show a minority? Brief\nexplanation in the main text or more detailed explanation in the Supp. Note may be useful.\nWe apologize if there is confusion about the sequencing method we used. All our data is\ngenerated using long-read sequencing, mostly with minION and one batch of 10 samples in C1\nwith minION and pacBio, as stated in line 171, now line 176-177. The variance partitioning allows\nto isolate the amount of variability attributable to sequencing method even if not all batches use\nboth sequencing methods. Figure 2d shows 6 exemplary features that we thought were useful to\nillustrate value ranges, while Extended Data Figure 2g shows agreement between sequencing\ntechnologies for all 44 robust features. The new supplementary note 2 also shows a direct\ncomparison of sequencing methods for individual CSR junctions.\n12. PCAs need to have indicated in legend how many robust features were ultimately used\nsince this seems to change across experiments.\nThe legends now mention how features were used as well as Supplementary Table 1. Please see\nalso response point 4.\nFor Fig2h and EDFig3a graphs you are better off showing only those with FDR-adjusted\nsignificance as showing everything despite some with larger symbols is uninterpretable\nand providing too much unnecessary information.\nWe now increased the contrast between significant (after adjustment) and non-significant features\nand added a multiple testing correction; however even non-significant features contribute to the\nreported correlation and are therefore not irrelevant.\nLines 260-261: not clear whether this new observation of GC content on the acceptor side\nof the old data that is not shown is conserved in your experiential data on Lig4 deficiency\nin the prior paragraph.\nWe apologize for the confusion and have added some details in the main text to improve\nreadability. The GC content on the acceptor side (acceptor_score_W) is shown in Extended Data\nFigure 3c. The data on Ligase 4 KO in the HTGTS data aligns with the data obtained with CH12,\nas shown in the heatmap (Figure 3c).\nLines 287-288: Can the authors speculate in the discussion as to what the correlation\nmeans\nWe prefer not to speculate on this correlation. There is currently no available literature to help\ninterpret this finding. Additionally, our cohort is clinically highly diverse, comprising patients with a\nwide range of complications and a unique spectrum of heterogeneous disease etiologies.\nV(D)J is more accurate than VDJ and symbolizes that some antigen receptor loci (i.e.\nlambda, kappa, alpha, gamma) only use V-J recombination.\nWe agree with the reviewer that V(D)J is more appropriate when referring to BCR repertoires and\nin all cases when both heavy and light chains are considered. However, in our study we analyzed\nonly the heavy chain sequences, all of which include the D element. Therefore, the use of VDJ is\ncorrect in our specific context.\n23"}, {"round": 2, "reviewer_comments": {"reviewer_1": "Reviewer 1.\nMy goal is to continue to support publication. I respect the acthors responses to my questions. Importantly, the authors are\napplying long-read sequencing to a repetitive, GC-rich region, as they acknowledge. So their experience with difficulties is\nthe VALUE of this paper. I strongly suggest (almost insist) that the authors include in their Supplement the figure that\nthey provided in their response to me (Fig. X1), along with the text (and legend) they wrote associated with X1: \"the number of\nPCR cycles was a critical parameter requiring careful optimization. On the one hand, we needed sufficient amplification to\nenrich CSR junctions and generate enough material for the minION library preparation; on the other hand, we aimed to keep\nthe cycle number as low as possible to maintain diversity. When PCR is performed in regions of repetitive DNA,\nrecombination can create artificial diversity, which in a monoclonal cell line would appear as heterogeneous outcomes. We\ndo observe some diversity after 25 PCR cycles (Figure X1A-B). However, our read plot (Figure X1A) and sequence\nalignment of 8 random reads (Figure X1C) still show considerable homology in a monoclonal sample with two switched\nalleles, including i) highly similar read alignments to the switch regions (Figure X1A) and ii) substantial nucleotide homology\nin the alignments (Figure X1C), enabling precise breakpoint identification. As PCR-induced artifacts increase with cycle\nnumber, we conclude that 25 cycles under our optimized conditions introduce only minimal artificial diversity.\"\nI think that the diversity in the black histogram peaks that is clear in Figure X1 (panel A, left upper edge) seems to indicate to\nmy naive mind that the amplification of this monoclonal cell line gives heterogeneous switch junctions at each of the two\nalleles, especially the upper one. Or do these authors have a different interpretation of this? Their comments on this are\nvaluable regardless of their response. They should rest assured that I am not going to use their response against them. But\nreaders, along with me, deserve clarity on this point, and I think these excellent authors also wish to be clear as well.", "reviewer_2": "The authors have satisfactorily addressed my concerns.", "reviewer_3": "The authors made substantial improvements throughout the manuscript. This reviewer has no additional constructive\ncomments to add."}, "author_response": "• Reviewer #1 (Remarks to the Author):\nMy goal is to continue to support publication. I respect the authors responses to my\nquestions. Importantly, the authors are applying long-read sequencing to a repetitive,\nGC-rich region, as they acknowledge. So, their experience with difficulties is part of the\nVALUE of this paper. I strongly suggest (almost insist) that the authors include in their\nSupplement the figure that they provided in their response to me (Fig. X1), along with\nthe text (and legend) they wrote associated with X1: \"the number of PCR cycles was a\ncritical parameter requiring careful optimization. On the one hand, we needed sufficient\namplification to enrich CSR junctions and generate enough material for the minION\nlibrary preparation; on the other hand, we aimed to keep the cycle number as low as\npossible to maintain diversity. When PCR is performed in regions of repetitive DNA,\nrecombination can create artificial diversity, which in a monoclonal cell line would\nappear as heterogeneous outcomes. We do observe some diversity after 25 PCR cycles\n(Figure X1A-B). However, our read plot (Figure X1A) and sequence alignment of 8\nrandom reads (Figure X1C) still show considerable homology in a monoclonal sample\nwith two switched alleles, including i) highly similar read alignments to the switch\nregions (Figure X1A) and ii) substantial nucleotide homology in the alignments (Figure\nX1C), enabling precise breakpoint identification. As PCR-induced artifacts increase with\ncycle number, we conclude that 25 cycles under our optimized conditions introduce only\nminimal artificial diversity.\"\nI think that the diversity in the black histogram peaks that is clear in Figure X1 (panel A,\nleft upper edge) seems to indicate to my naive mind that the amplification of this\nmonoclonal cell line gives heterogeneous switch junctions at each of the two alleles,\nespecially the upper one. Or do these authors have a different interpretation of this?\nTheir comments on this are valuable regardless of their response. They should rest\nassured that I am not going to use their response against them. But readers, along with\nme, deserve clarity on this point, and I think these excellent authors also wish to be clear\nas well.\nResponse\nWe appreciate the reviewer comment and support to make the manuscript more\ncomprehensive. We fully agree with the particular challenges that long-read sequencing of a\nrepetitive, GC-rich region presents. We emphasized this struggle adding a sentence for\nclarification “The number of PCR cycles (25) was kept as low as possible to minimize technical\nnoise while still ensuring sufficient representation of the biological diversity” (lines 118–119).\nReviewer 1 also raised a remaining concern regarding heterogeneity in switch junctions within\nour monoclonal cell line. We also agree with the concern towards the CSR junction\nheterogeneity in the monoclonal cell line (Fig. 1b). In the revised manuscript, we added the\nfollowing statement together with read alignments in Extended Data Fig. 1h “We observed\nsome heterogeneity in the monoclonal cell line, where subtle sequence variations around the\nbreakpoint (Extended Data Fig. 1h) formed independent clusters in the read plot, leading to a\nslight overestimation of diversity for this sample.” (lines 131-133)\nImportantly, our results do not suggest that PCR is the main driver of this heterogeneity. To\nmake this clearer, we have included our interpretation in the text “PCR may introduce such\nsequence variations, but a similar level of heterogeneity in both PCR-amplified samples and\nthe non-PCR plasmid control suggests that most of the variation arises from clustering,\nmapping, and sequencing errors.” (lines 133-136)\n• Reviewer #2 (Remarks to the Author):\nThe authors have satisfactorily addressed my concerns.\nThank you for the comment and the support on the manuscript.\n• Reviewer #3 (Remarks to the Author):\nThe authors made substantial improvements throughout the manuscript. This reviewer\nhas no additional constructive comments to add.\nThank you for the comment and the support on the manuscript."}, {"round": 3, "reviewer_comments": {"reviewer_1": "The authors seem to have responded to my comments, though in a somewhat minimal manner. I am ok with it."}, "author_response": "Reviewer #1 (Remarks to the Author):\nThe authors seem to have responded to my comments, though in a somewhat minimal\nmanner. I am ok with it.\nWe regret that the reviewer considers our reply minimal. To our understanding, all aspects of\nthe respective point-by-point comment have been incorporated into the revised main text (see\ncolour marked text below). The elements were distributed throughout the manuscript to\npreserve its flow. Only the inclusion of the exact Fig. 1X was not met, as it would reproduce\nelements from the main figures and create redundancies.\nRegarding PCR diversity, we still share the reviewer’s view that it is a general matter of\nconcern. However, as we observed similar heterogeneity in a PCR- and a non-PCR-amplified\nsample, we decided not to discuss this aspect in greater detail.\nPoint-to-Point:\nThe number of PCR cycles was a critical parameter requiring careful optimization. On the one hand, we\nneeded sufficient amplification to enrich CSR junctions and generate enough material for the minION\nlibrary preparation; on the other hand, we aimed to keep the cycle number as low as possible to maintain\ndiversity. When PCR is performed in regions of repetitive DNA, recombination can create artificial\ndiversity, which in a monoclonal cell line would appear as heterogeneous outcomes. We do observe\nsome diversity after 25 PCR cycles (Figure X1A-B). However, our read plot (Figure X1A) and sequence\nalignment of 8 random reads (Figure X1C) still show considerable homology in a monoclonal sample\nwith two switched alleles, including i) highly similar read alignments to the switch regions (Figure X1A)\nand ii) substantial nucleotide homology in the alignments (Figure X1C), enabling precise breakpoint\nidentification. As PCR-induced artifacts increase with cycle number, we conclude that 25 cycles under\nour optimized conditions introduce only minimal artificial diversity.\nManuscript:\nLines 118-119: The number of PCR cycles (25) was kept as low as possible to minimize technical noise\nwhile still ensuring sufficient representation of the biological diversity.\nLine 133-134: PCR may introduce such sequence variations specially in highly repetitive DNA\nLine 129-133: As expected, analysis of an in-house monoclonal cell line revealed two main CSR clones\nin accordance with 2 switched alleles (Fig. 1b, c). We observed some heterogeneity in the monoclonal\ncell line, where subtle sequence variations around the breakpoint (Extended Data Fig. 1h) formed\nindependent clusters in the read plot, leading to a slight overestimation of diversity for this sample."}]

Decision letter