CompIgS: A Computational Workflow for comparative analysis of related clonotypes within distinct antibody subclasses

CompIgS: A Computational Workflow for comparative analysis of related clonotypes within distinct antibody subclasses | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF software CompIgS: A Computational Workflow for comparative analysis of related clonotypes within distinct antibody subclasses Udoye Chinweike Christopher, Sahar Mehrabani Khasraghi, Pia Witt, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7048307/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Individual B cells produce antibodies, also known as immunoglobulins (Ig), that target specific antigens. Collectively, the B cells within an individual generate a diverse Ig-repertoire capable of recognizing a wide range of antigens, which underpins adaptive immunity. On a molecular level, this repertoire is created by somatic recombination of variable (V), diversity (D), and joining (J) gene segments during early B cell development, eventually forming a unique VDJ-sequence that encodes for the antigen-binding region of antibodies. Each individual VDJ-sequence defines a clonotype. The nearly indefinite number of individual antigen-binding regions are joined to a limited number of constant regions, that determine the antibody subclass and hence the effector function, such as complement activation, neutralization, and opsonization, among others. A clonotype may contain antibodies of different subclasses that can have different and even opposing functions. These B cell subclones can introduce hypermutations within their VDJ-sequences to alter the antigen-binding affinities affecting antibody properties and clonal selection. Recent advancements in next-generation sequencing enabled high-depth profiling of antibody repertoires. However, current analysis tools provide limited analysis of related Ig clones present within distinct Ig subclasses of the same sample. To address this, we present an open-source computational tool, designed to identify subclone pairs between two antibody subclasses from the sample. The CompIgS (Comparative Igs Analyzer) workflow processes immunoglobulin variable heavy-chain (VH) repertoire data, utilizing ImMunoGeneTics (IMGT) annotations. It incorporates IMGT/HighV-QUEST and IMGT/StatClonotype outputs to standardize V(D)J representations and identifies shared clonotypes present in two distinct antibody subclasses. This approach allows for comparative analysis of clonal expansion and hypermutation. As a case study, we analyzed related IgE and IgG1 clonotypes from a murine model of food allergy in which IgE promotes the development of allergic symptoms, while IgG1 can inhibit allergy development. CompIgS computed various repertoire metrics including clonotype counts, IgE/IgG1 copy number ratios, somatic hypermutation profiles and estimation of different IgE subpopulations in relation to IgG1. Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Immunoglobulin (Ig) monomers are composed of two identical heavy (H) chains and two identical light (L) chains. These protein molecules, commonly known as antibodies, have a "Y" shaped structure with small antigen binding sites located at their two tips. The regions of both the light and the heavy chains that do not bind to antigens contain amino acid (AA) sequences that show very little variation among different immunoglobulins. These regions are referred to as the “constant region. At the genetic level, the constant region is encoded by specific constant region genes while the antigen-binding site is determined by various combinations of variable (V), diversity (D), and joining (J) gene segments. During B cell development, these V, D and J segments undergo partial deletion and are randomly spliced together in a process know as VDJ recombination. This process gives each B cell a unique antigen-binding site ( 1 ). As a result, the collective B cell population is able to produce antibodies that cover a broad antigen binding repertoire, that can bind hundreds of thousands of different antigens. The fragment crystallizable region (Fc region), or “constant region” of antibodies do not participate in antigen-binding but is crucial for determining the effector function of the antibody. There are several “constant regions”, which determine the antibody subclass. In mice, several Ig classes are known: IgA, IgD, IgE, IgG1, IgG2a/b/c; IgG3, IgM. These Igs can be found as membrane-bound molecules on naïve B cells, memory B and to some extent, on plasma cells. Functioning as antigen-specific B cell receptors (BCR), antibodies of the distinct subclasses exhibit differential signaling strengths and signaling qualities ( 2 – 4 ), thereby controlling antigen-specific B cell responses on the level of individual B cell clones. Initially, naïve B cells (B cells that have not been activated by their cognate antigen yet) express IgM and IgD. Upon activation and proper co-stimulation, they can undergo class switch to other subclasses. Of note, class switching involves deleting all upstream Ig constant region genes. Except co-expression of IgD and IgM on naïve B cells, single B cells express only one Ig subclass. After differentiation into the final stage as plasma cells, antibodies are produced in a splice variant that allows secretion into the body fluids. As secreted molecules, antibodies can fulfil a broad range of distinct effector functions. Antibody affinities range between 10 6 up to 10 12 L/Mol, which has a strong impact on their function. This property is determined by the original VDJ-sequence but later in the B cell response modified by somatic hypermutation and selection processes in the germinal center reaction ( 5 , 6 ). This process can increase the antigen-binding affinities between 10 to more than 1000-fold ( 7 ). Antibody affinities are, e.g. relevant for their roles in protection against infection ( 8 ), autoimmune diseases ( 9 ) and allergy development ( 10 ). In humans and mice, IgE can promote allergic reactions. Highly hypermutated IgE clones (presumably of high affinity) were shown to be capable of triggering anaphylaxis, a severe and potentially lethal allergic symptom, whereas less mutated, low-affinity IgE failed to or even competitively inhibited anaphylaxis development ( 11 , 12 ). Allergen-specific murine IgG1 is clonally related to IgE, but can inhibit allergy development ( 13 ). Therefore, knowledge about quantitative and qualitative differences between clonally related IgE and IgG may aid our understanding of the development of allergenic and anti-allergenic antibodies within one individual. The analysis of the BCR repertoire has significantly advanced our understanding into the development and maintenance of individual B cell clones ( 14 , 15 ). However, standard repertoire analysis tools have limitations when it comes to comparing Ig clone pairs that are related but belong to distinct antibody subclasses. Most existing tools focus on metrics related to single isotope diversity, such as clonal frequency distributions, V, D, J usage and lineage tracing within a single isotype or sample. Tools such as Immunarch, IMGT/HighV-QUEST, and the Change-O toolkit provide robust frameworks for repertoire analysis ( 16 – 18 ). These platforms effectively quantify clonotype diversity and somatic hypermutation and are commonly used to analyze B-cell or T-cell receptor repertoires. However, they are not specifically designed for comparative analyses of identical clonotypes found in distinct antibody subclasses, such as clonally related murine IgE and IgG1. To address that gap, we introduce CompIgS, a tool designed to facilitate the analysis of Ig-repertoire data. CompIgS enables the identification and comparative analysis of subclone pairs represented across different antibody subclasses. The capabilities of the open-source computational tool are exemplified in analyzing the related IgE and IgG1 repertoires in murine food allergy. Methods Mice and Animal Experiments The rational for the following procedures of animal handling, anesthesia and sacrifice was based on the principle of minimal stress for the test animals. The protocols were developed in cooperation and approval with our local Committee on the Ethics of Animal Experiments in our institution. All animal experiments were conducted in accordance with German GVSOLAS and European FELASA guidelines and approved by the respective Committee on the Ethics of Animal Experiments of the state Schleswig-Holstein (Ministerium für Landwirtschaft, Umwelt und ländliche Räume des Landes Schleswig-Holstein) (permit numbers V 242-18318/2016 [29 − 3/16] and 122 − 39[46 − 6/18] Manz). Female BALB/c mice (7–8 weeks old) were purchased from a commercial supplier (Charles River Laboratory, Sulzfeld, Germany) were housed under specific pathogen-free conditions at the University of Lübeck animal facility. The EW/EYP solution (1.25 mg/mL) was prepared by mixing 1.25 mg egg white (EW) with 1 mL egg yolk plasma (EYP) and filtered through a 70 µm cell strainer. BALB/c mice were anesthetized (i.p.; 5 mg/mL Ketanest S, 1.5 mg/mL Rompun in DPBS) and sensitized intratracheally (i.t.) with 40 µL EW/EYP solution (50 µg EW) three times over two weeks. From weeks 3–8, mice were challenged three times weekly by oral gavage (o.g.) with 300 µL EW/EYP solution (100 mg EW). Baseline rectal temperatures were measured before each challenge using a rectal thermometer (Physitemp). Allergic responses (rectal temperature drop) were assessed every 15 min for 1 h post-challenge. At the final timepoint (> 7 weeks post-challenge), mice were anesthetized (i.p.; 5 mg/mL Ketanest S, 1.5 mg/mL Rompun in DPBS), blood was collected by cardiac puncture, mice were euthanized by cervical dislocation and bone marrow (BM) was harvested for BCR repertoire sequencing. Software architecture and implementation CompIgS is a Python-based analytical tool that includes an optional graphical user interface (GUI). It is implemented in Python 3.1 and utilizes several standard libraries: pandas ( 19 ) for data manipulation, NumPy ( 20 ) for numerical computations, matplotlib ( 21 ) and seaborn ( 22 ) for generating plots, and PyQt5 ( 23 ) for the GUI. Both the desktop GUI (Fig. 1 ) and the Google-colab script are built on the same underlying logic, which ensures identical results. The GUI was designed for ease of use, allowing users to load data files and run the entire analysis pipeline with minimal technical skills required. Internally, the tool is organized into modular steps that include input parsing, clone identification, comparative analysis, and visualization. It also employs multi-threading to maintain the responsiveness of the GUI during analyses. The source code and documentation are available in Google-colab and GitHub, respectively, ( https://colab.research.google.com/drive/1y45LgoJmTWHnUwAIn3RmcEkNPEeqHv07?usp=sharing ), ( https://github.com/Chrisjames1992/CompIgS ). A standalone executable version of CompIgS for Windows systems is available on Zenodo ( https://doi.org/10.5281/zenodo.15599810 ). Input data and preprocessing CompIgS utilizes two distinct Ig isotype repertoire sequencing datasets pre-annotated and sub-clonally grouped by IMGT/Highv-QUEST and IMGT/StatClonotype ( 24 , 25 ), respectively. As an illustration, the tool used IgE (Ig1) and IgG1 (Ig2) repertoires output data from the same sample for comparative analysis. From the IMGT/HighV-QUEST analysis, CompIgS requires IgE (Ig1) and IgG1 (Ig2) tab-delimited text files including: 8_V-REGION-nt-mutation-statistics, 5_AA-sequences, 7_V-REGION-mutation-and-AA-change-table. From the IMGT/StatClonotype output files, CompIgS requires the IgE and IgG1 IMGT StatClonotype tab-delimited text files. Additionally, the workflow needs the IMGT VH germline AA sequences. The IMGT/StatClonotype output files provided essential data for clonotype identification, the data includes: ( 1 ) V gene assignment, ( 2 ) D gene assignment, ( 3 ) J gene assignment, ( 4 ) translated Complementarity-determining region (CDR)3 AA sequence, ( 5 ) sequence identifier and ( 6 ) copy number of representative clones. From the IMGT/StatClonotype output data, CompIgS uses the IMGT sequence identifier to trace back the clones' detailed IMGT/HighV-QUEST annotations, including: ( 8 ) number of somatic mutations (e.g. number of nucleotide mutations in the V region, number of non-silent mutations in CDR regions) and ( 9 ) VH region germline AA sequence. To reconstruct the framework region 1 (FR1) and complementarity-determining region 1 (CDR1) AA sequences of various clones, the IMGT VH germline AA sequences are employed. This process involves aligning each clone's VH AA sequence against the entire set of germline sequences to identify the VH gene with the highest similarity. The identified germline sequence serves as a reference to accurately complete the FR1 and CDR1 regions of the clone. In our case study, IgE and IgG1 libraries were generated from mice bone marrow samples using the IRepertoire iR-Complete Dual index (MBHΙ) multiplex PCR kit and sequenced on an Illumina MiSeq platform (Fig. 2 A) (iRepertoire, Inc., AL, USA). The sequencing data were demultiplexed by the iRepertoire (iRepertoire, Inc., AL, USA)., aligned to the IMGT reference database for Mus musculus using the IMGT/HighV-QUEST platform ( 26 , 27 ) and annotated by IMGT/HighV-QUEST. Based on nucleotide similarity, IMGT/HighV-QUEST assigns the V, D, and J germline genes and alleles to each sequence and provides standardized annotation of FRs and CDRs. Subsequently, these annotated sequences were processed by IMGT/StatClonotype to group them into subclones based on unique V-(D)-J gene rearrangements and in-frame CDR3 AA sequences ( 16 , 28 ). The CompIgS pipeline performs a quality control step, The CompIgS pipeline performs quality control based on best practices in immune repertoire analysis, removing non-productive sequences (e.g., with stop codons or missing annotations) and those with incomplete V, D, or J gene assignments, which often reflect low-quality reads ( 29 ). Additionally, to minimize the impact of potential sequencing artifacts, low-frequency sequences are filtered out; by default, singleton reads (sequences represented by a single read) are excluded from downstream analyses ( 29 ).After filtering, basic repertoire metrics, including the total number of productive clones per isotype—are quantified using the custom script, CompIgS, providing the basis for subsequent comparative analysis (Fig. 2 B). Clonotype definition and matching A core task of CompIgS is to define B-cell clonotypes and identify which clonotypes are shared between IgE (Ig1) and IgG1(Ig2) repertoires. CompIgS defines clonotypes based on unique V–D–J gene rearrangements with conserved anchors. Specifically, for each sequence the tool constructs a clonotype identifier string by concatenating the annotated V gene, D gene, and J gene. For example, a sequence might be assigned the clonotype string "IGHV1-18_IGHD2-2_IGHJ4" representing its V, D, and J gene calls. This canonical string representation is used to collapse sequences into clonotypes and to compare between the IgE and IgG1 datasets. Two sequences (even from different isotypes) are considered to belong to the same clonotype if they share the identical V gene, D gene, and J gene assignments. Using this definition, CompIgS identifies shared clonotypes – those present in both the IgE and IgG1 repertoires of the same subject – versus unique clonotypes – those found only in one isotype. The comparison is implemented by set operations: after generating the clonotype lists for IgE and IgG1, the intersection (IgE ∩ IgG1) yields shared clonotypes, while the set differences (IgE \ IgG1 and IgG1 \ IgE) yield unique IgE and unique IgG1 clonotypes, respectively. The number and percentage of shared vs. unique clonotypes are then calculated. CompIgS reports these values and visualises them with simple bar graphs for each isotype (Fig. 3 A-B). Quantification of clonal expansion and bias For each shared clonotype, CompIgS compares the abundance of that clone in the IgE vs. IgG1 compartments. We define the abundance of a clone by the sum of clone copies for all member sequences of that clonotype. CompIgS calculates an IgE/IgG1 copy number ratio for every shared clones as: $$\:Rati{o}_{clone}=\frac{{N}_{IgE}}{{N}_{IgG1}}$$ where: \(\:{N}_{IgE}\) denotes the number of IgE sequences within a given clone, \(\:{N}_{IgG1}\) denotes the number of IgG1 sequences within the same clone. For convenience, the log10 of this ratio is used to span orders of magnitude. Clonotypes with positive log-ratio are IgE-biased clones (overrepresented in IgE relative to IgG1), whereas those with negative log-ratio are IgG1-biased (underrepresented in IgE). Clones detected in only one isotype are handled as extreme cases: a clone unique to IgE would have $$\:{N}_{IgG1}=0$$ and thus, an undefined ratio (in practice, CompIgS labels such cases separately as unique IgE clones), and vice versa for unique IgG1 clones. Additionally, CompIgS quantifies the overall clonal expansion characteristics in each isotype. For each repertoire, it computes the clonal size distribution. However, in the context of our study, we focused on comparing clone size characteristics between shared IgE (Ig1) and IgG1 (Ig2) (Fig. 3 G). CompIgS provides plots of clone size distribution for various IgE (Ig1) subsets (Figure S1 ). Somatic hypermutation and mutation profiling Somatic hypermutation (SHM) in immunoglobulin genes is a critical factor in affinity maturation. CompIgS analyzes the mutation profiles of shared IgE and IgG1 sequences to gain insights into clone maturity. The tool uses the IMGT annotations of V-region mutations. For each clone, the number of nucleotide mutations in the V region is obtained (often reported by IMGT as “V-REGION nb of mutations”), and the number of AA replacement (non-silent) changes in CDR3 is noted (from “CDR3-IMGT nb of non-silent mutations”). CompIgS first ensures these values are numeric, then bins each clone into different subpopulations. We categorized IgE and IgG1 shared clonotypes into mutation frequency bins: “low mutation” (0–5 nucleotide mutations in the V region), “moderate mutation” (6–11 mutations), and “high mutation” (≥ 12 mutations). CompIgS tallies the number of shared IgE and IgG1 clonotypes falling into each mutation bin. To facilitate interpretation, CompIgS generates a mutation distribution plot for shared clones, showing how many subclones in IgE vs. IgG1 have a given number of V-region mutations (Fig. 3 D). Finally, a key mutation-based analysis in CompIgS is the identification of divergent IgE clones. Which are shared IgE clones with non-silent CDR3 mutations that make the CDR3 AA unique and not present in clonally related IgG1. This is central to identifying IgE clones that qualitatively differ from their IgG1 counterpart. Identification of Divergent IgE clones We define divergent IgE clones operationally as IgE clonotypes that show clear evidence of having diverged in the affinity maturation process from an IgG1 lineage. Specifically, CompIgS flags an IgE clonotype as “divergent” if they share the same VDJ rearrangement as IgG1 but contain at least a certain number of AA replacements in the CDR3 region, indicating substantial mutation from the IgG1 precursor. In our analysis, we required ≥ 2 non-silent mutations in CDR3 for an IgE clone to be considered divergent. This threshold was chosen to exceed the likelihood of random sequencing error and to capture clones that underwent notable antigen-driven selection in the IgE compartment. The process of identifying divergent IgE clones involves the use of a custom computational algorithm implemented in Python. The algorithm assessed CDR3 mutation patterns and performed similarity searches between IgE and IgG1 repertoires using core data manipulation libraries. Python libraries and dependencies Our pipeline employed a Python-based algorithm to stratify shared IgE clonotypes by hypermutation levels and assess their divergence from the IgG1 repertoire. We first generated a reference set of unique CDR3 amino acid sequences from productive IgG1 clones, then stratified shared IgE clonotypes into three hypermutation categories: low (0–5 mutations), moderate (6–11 mutations), and high (≥ 12 mutations) based on V-region mutation counts. For each mutation category, we performed Boolean matching to identify IgE CDR3 sequences absent from the IgG1 reference set, classifying these as "divergent" clones. Non-divergent clones were defined as those with CDR3 AA sequences present in the IgG1 repertoire. To focus on functionally relevant differences, we applied additional filtering criteria requiring ≥ 2 non-silent mutations in the VH-CDR3 region for divergent clone classification. Statistical summaries including clone counts, mean copy numbers, and total copy numbers were computed for each stratified group using standard Pandas aggregation functions with appropriate handling of missing values. Detailed algorithmic workflow, code implementations, and statistical functions are provided in supplementary methods. Data visualization and output CompIgS automatically generates a comprehensive suite of publication-quality visualizations to facilitate interpretation of comparative immunoglobulin analysis results. The software produces three primary categories of analytical plots, each designed to highlight distinct aspects of clonal distribution and characteristics. Clonotype distribution analysis The software generates clonotype summary plots presenting bar chart representations of total productive, shared, and unique clonotypes across IgE and IgG1 populations (Fig. 3 ). These visualizations provide an immediate overview of clonal overlap and diversity between the two immunoglobulin subclasses, enabling rapid assessment of shared versus unique clonal responses. Subset-specific clonal characterization CompIgS produces detailed grouped bar charts that categorize clones based on multiple parameters, including mutational status and isotype bias. These plots distinguish between mutated and unmutated clones that exhibit IgE-biased, IgG1-biased, or unique expression patterns (Fig. 3 ). Clone size distribution profiles The software generates scatter plots displaying clone sizes, defined as the number of sequences per sub-clonotype, arranged by rank order. These distributions are typically presented in side-by-side comparisons for Ig1 versus Ig2 populations, with optional highlighting of mean or median clone sizes to facilitate statistical interpretation (Fig. 3 ). Output format and data management All visualizations are exported as high-resolution PNG images suitable for publication or detailed examination, with files automatically saved to the designated output directory. Complementing the graphical output, CompIgS generates comprehensive numerical summaries including clone counts, frequencies, and VH AA sequences for distinct IgE and IgG1 subsets. These quantitative data are compiled into CSV-formatted summary reports that aggregate key findings for each analyzed subject. Experimental data For demonstration and testing, we applied CompIgS to immunoglobulin heavy-chain repertoire data from a murine model of egg allergy. In this model, mice were repeatedly sensitized for and challenged with allergens to induce food allergy to Hen´s egg, as described in ( 13 ). High-throughput sequencing data of the IgE and IgG1 immunoglobulin heavy chain transcriptome from each mouse’s bone marrow were obtained as follows: After RNA isolation, BCR libraries were generated using the MBHI-M kit (iRepertoire, Inc., AL, USA), which employs a multiplex PCR approach with a mixture of V- and C-region primers specifically optimized for unbiased amplification of the murine IgH repertoire and compatibility with Illumina sequencing workflows. Paired-end sequencing was conducted on the Illumina MiSeq platform, yielding approximately 1–2 million reads per sample. Sequencing quality was assessed using FastQC ( 30 ). Samples were excluded from downstream analysis if the median Phred quality score fell below 20 at any base position. Demultiplexing of the raw reads into individual samples was performed by iRepertoire. The resulting cDNA FASTA sequences were further separated into IgE and IgG1 datasets for each mouse ( https://doi.org/10.5281/zenodo.15725748 ). The FASTA sequences were processed through IMGT/HighV-QUEST and IMGT/StatClonotype pipeline to obtain annotated repertoires. CompIgS was applied on paired IgE–IgG1 data from an individual mouse to extract the comparative metrics described. Results IgE clones are limited in clonotype diversity We applied CompIgS to paired IgE and IgG1 repertoire sequencing data from two mice with > 1◦C temperature drop after oral challenge to egg. The IgG1 repertoires were vast, comprising of thousands of unique clonotypes ( 13 ). In stark contrast, IgE repertoires were much smaller, with only a few hundred clonotypes detectable in each mouse. Nearly all IgE clonotypes had a corresponding IgG1 clonotype in the same animal’s repertoire ( 13 ). In a representative sick mouse, we observed 308 total IgE clonotypes, of which 292 (95%) were also present in the IgG1 repertoire (Fig. 3 A). Only 16 clonotypes (5%) were unique to the IgE repertoire. Conversely, the IgG1 repertoire of the same mouse contained 3369 clonotypes in total, with 3077 (91%) being unique to IgG1 and only the 292 clonotypes shared with IgE (Fig. 3 B). This trend was consistent in the second mouse: IgG1 repertoires showed orders-of-magnitude greater diversity, and IgE repertoires appeared to be essentially a small subset drawn from the IgG1 pool. The finding that > 90% of IgE clonotypes were shared with IgG1 is in line with the classic model of sequential switching through IgG1 + cells. The presence of a very small number of IgE-exclusive clonotypes suggests that direct IgE class switching (bypassing an IgG1 intermediate) may occur but is quite rare, at least in this model. IgE-biased vs. IgG1-biased clonal expansions CompIgS generates a ranked list of shared clonotypes based on their IgE/IgG1 ratio. This ranking helps identifying biologically interesting clonotypes, such as those predominantly expanded in the IgE compartment despite also being present in IgG1. A bar plot is created to visualize the distribution of log-ratios across all shared clonotypes. In our dataset, we observed that only a handful of shared clones were IgE-biased, and they tended to have modest fold differences, whereas many clones were heavily IgG1-biased (orders of magnitude more abundant in IgG1 than IgE). The distribution of log10(IgE/IgG1) ratios was heavily skewed toward negative values (Fig. 3 E), meaning most shared clones had far more abundant IgG1 than in IgE. We defined IgE-biased clones as those with a ratio ≥ 2 (IgE reads exceed IgG1 reads) and IgG1-biased clones as those with ratio ≤ 2. By this definition, only 12 out of 292 shared clonotypes were IgE-biased in the example (approximately 4%), whereas the remaining ~ 96% were IgG1-biased (Fig. 3 F). Across the two mice, IgE-biased shared clones were consistently rare (~ 5% of shared clones). Moreover, even the IgE-biased clones were not extremely skewed – the highest observed IgE/IgG1 ratio for a shared clone was on the order of 10 (i.e., 10 times as many IgE reads as IgG1 reads for that clone). In contrast, many IgG1-biased clones had ratios < 0.1 (IgE representing < 10% of clone’s total), and some had ratios as low as 0.001 or less, indicating the shared clones were dominated with the IgG1 repertoire. The clone size distributions of mutated IgE and IgG1 corroborated this imbalance. Figure 3 G plots sub-clone sizes for shared IgE vs. IgG1 clonotypes sorted by rank. The largest IgE sub-clonotype had ~ 300 reads, whereas the largest IgG1 sub-clonotype had over 12,000 reads. In fact, the top 5 IgG1 sub-clones each had thousands of reads, accounting for a substantial fraction of the IgG1 repertoire, while the entire IgE repertoire was relatively evenly spread among small clones of < 300 reads each. The mean clone size among shared clones was higher in IgG1 (mean ~ 40 sequences per clone) than in IgE (mean ~ 25 sequences per clone). Despite these differences in expansion, it is noteworthy that the average clone sizes were on the same order for IgE and IgG1 (tens of reads per clone). IgG1 had a long tail of large clones that raised its mean, but aside from those, the bulk of the clone size distribution overlapped between IgE and IgG1 in the lower size range (Fig. 3 G, zoomed panels). These observations imply that on average IgG1 B-cell clone expands massively more than their IgE counterpart, therefore generating more differentiated plasma cells. This is consistent with in vivo models where IgE + cells in germinal centers are rare, poorly expanded, transient and rapidly differentiate to IgE plasma cells. Furthermore, antigen-dependent BCR ligation of IgE plasma cells tends to drive the IgE + plasma cells toward apoptosis and cell death, further reducing the plasma cell numbers ( 31 ). Somatic mutation profiles of IgE and IgG1 clonotypes We next examined the somatic hypermutation status of clonotypes in each isotype. We found that the overwhelming majority of IgE clonotypes showed evidence of somatic mutation, despite the low overall diversity. As shown in Fig. 3 C, about 94% of IgE clonotypes in the representative mouse carried one or more V-region mutations (i.e., were not germline), whereas only ~ 6% were unmutated. The IgG1 clonotypes were similarly mostly mutated (~ 92.5% mutated vs. 7.5% unmutated). Thus, both IgE and IgG1 repertoires were largely composed of antigen-experienced B-cell clones. This is consistent with the animals having been actively immunized or exposed to allergens, such that even newly class-switched IgE cells had undergone germinal center maturation and accumulated mutations. The small subset of unmutated IgE clones could represent naive or recently activated B cells that class-switched to IgE early or IgE sourced from extrafollicular B cell response. Their low frequency in our dataset suggests that such sub-population, if they occur, result in minor clones that may not significantly contribute to the overall IgE pool in chronic allergen exposure. Furthermore, CompIgS estimates the mutation count distributions for shared IgE vs. IgG1(Fig. 3 D) clones: IgG1 (red curve) showed a peak in the 5–7 mutation range and a tail extending to > 15 mutations, whereas IgE (blue curve) had far fewer sequences overall, with a relatively flat distribution given the low counts. Importantly, we did not observe a distinct population of IgE sequences that were hypermutated beyond the IgG1 range – the IgE clones fell well within the mutation spectrum of IgG1 clones. This suggests that the IgE-producing cells largely shared a similar history of germinal center activity as the IgG1-producing cells. Next, we sought to investigate if there were IgE clones that were qualitatively different from their IgG1 counterpart at different mutation levels. Divergent Ig1 (IgE) Clones: Shared Ig1 (IgE) Clonotypes with unique CDR3 AA sequence Recently, IgE was found to be clonally associated with IgG4 precursors within the type 2 polarised memory B cell (MBC2) compartment in allergic patients ( 32 , 33 ). In murine allergies, IgE clones are clonally related to IgG1 progenitors. However, after class-switching, IgE clones can introduce new hypermutations to generate a related subclone divergent from its IgG progenitor. These new mutations may be relevant for the antigen-binding properties, antibody function and clonal selection of the B cell clone. CompIgS was designed to identify divergent subclones between antibody subclasses, such as divergent IgE clones – IgE clonotypes that, although related to IgG1 clonotypes, have acquired distinct mutations (≥ 2 CDR3 non-silent mutations) that make the shared IgE CDR3 AA (AA) sequence unique. Using the criteria described (shared IgE with distinct CDR3 AA sequence not seen in IgG1). In our dataset of an allergic mouse, there were divergent and non-divergent IgE clones in the low, moderate and highly mutated shared IgE (Fig. 4 ). The number of divergent IgE clones among different mutation bins per mouse was about 1–6 clones. The divergent IgE clones were less abundant than the non-divergent IgE clones, which ranged between 10–45 clones in either the low, moderate or highly mutated subset. The presence of divergent IgE in different mutation bins suggests that there is a qualitative difference between clonally related IgE and IgG1, and these clones might be a better predictor of disease than the traditional metrics, like total IgE clones or sequences. Finally, we compared CompIgS to four major software platforms for immunoglobulin sequence analysis: Change-O, IMGT/HighV-QUEST with IMGT/StatClonotype, BCrep, and Immunarch. Each of these tools has distinct capabilities and limitations for B-cell receptor repertoire studies. All platforms support essential functions such as clonal grouping, mutation profiling, and visualization capabilities. However, they differ in specialized analytical features. CompIgS stamds out by offering comprehensive comparative analysis of two immunoglobulin isotypes from the same sample, identifying various Ig subpopulations, and estimating divergent CDR3 Ig clones - capabilities not available in other platforms. These capabilities make CompIgS particularly valuable for studies examining class-switching dynamics and clonal relationships between different antibody isotypes (Table 1 ). Discussion While traditional repertoire tools like IMGT/HighV-QUEST, Change-O, and Immunarch ( 16 – 18 ) excel at characterizing single-sample diversity and clonal expansions, they partly perform cross-comparisons between isotypes. CompIgS addresses questions specific to isotype relationships and evolution, filling a crucial niche in immunoglobulin repertoire analysis by enabling direct comparison between paired antibody isotype repertoire from the same sample. The results obtained via CompIgS in our mouse model of egg white allergy can provide new insights into the IgE response in allergic disease. In our food allergy model, most IgE clones shared their VDJ-regions with IgG1, a finding that supports the notion that most IgE responses originate from the canonical germinal center pathway through an IgG1-intermediate. This finding aligns with studies in both mice and humans that highlight how IgE response often resides in IgG memory B cells ( 32 , 34 , 35 ). CompIgS compares related clonotypes within the IgE and IgG1 compartment in more detail. Our findings highlight a subset of IgE clones - which we term divergent IgE clones. These are shared IgE clones which have introduced non silent hypermutations into their antigen binding regions. Divergent IgE clones were present in low, moderate and high mutation bins, suggesting these clones are present in different maturation stages of the IgE response. We observed that the unique, IgE-specific mutations in divergent clones were non-silent and often occurred in positions of the CDR3 loop known to interact with antigens. These observations suggest that divergent IgE clones could be products of antigen-driven selection occurring specifically in the IgE arm, possibly derived from germinal centers or memory B cells. Increased hypermutation rates have been shown to be associated with the capacity to induce allergic anaphylaxis, a severe and potentially lethal symptom of food allergy in mouse models ( 11 ). Moreover, increased hypermutation rates in IgE were observed in allergic individuals than non-allergic patients ( 36 ). The CompIgS platform was designed for automated analysis of the clonal expansion rates, non-silent and silent hypermutations of clonally related Ig-subsets, which can provide insights into the clonal selection process within GCs, potentially leading to Ig clones of various pathogenicity. In case of our example, the highly hypermutated divergent IgE clones are expected to have greater potential to induce allergic anaphylaxis. A limitation of the method is the definition of divergent Ig clones (shared Ig1 with unique CDR3 AAs absent in clonally related Ig2 subclasses, due to ≥ 2 CDR3 non-silent mutations), might exclude Igs with a single divergent point mutation. Future iterations of CompIgS might allow more flexibility in defining the minimum number of non-silent mutations present in the divergent IgE clone. In summary, CompIgS enabled us to categorize the IgE repertoire of allergic mice into various subsets (shared, mutated vs. unmutated, IgE-biased vs. IgG1-biased, and divergent vs. non-divergent). This analysis workflow enables comparative analysis of related immunoglobulin (Ig) clones and can provide insights into the clonal selection processes underlying IgE-associated germinal center (GC) reactions. Abbreviations AA amino acid BCR B cell receptor CDR Complementarity-determining region CompIgS Comparative Igs Analyzer D diversity Fc region fragment crystallizable region GUI graphical user interface Ig Immunoglobulin IMGT ImMunoGeneTics J joining L light MBC memory B cell SHM Somatic hypermutation V variable VH variable heavy-chain Declarations Ethics approval and consent to participate Animal experiments were conducted following the 3Rs principles and approved by the Animal Welfare Committee of Schleswig-Holstein (permits 22-39_2016-06-17 and 122-39(46-6/18)) in accordance with German Animal Welfare Act guidelines and GV-SOLAS standards. Consent for publication Not applicable. Availability of data and materials All repertoire data analyzed in this study (IgE and IgG1 IMGT annotated datasets for each mouse) are available in a public repository (https://doi.org/10.5281/zenodo.15774119). The CompIgS is open source and the source code can be accessed via the project’s google colab and GitHub repository respectively, (https://colab.research.google.com/drive/1y45LgoJmTWHnUwAIn3RmcEkNPEeqHv07?usp=sharing), (https://github.com/Chrisjames1992/CompIgS). The repository includes the full Python source code, and example data files to reproduce the analysis. A standalone executable version of CompIgS (for Windows systems) is also provided for users who prefer not to install python (https://doi.org/10.5281/zenodo.15599810), (https://github.com/Chrisjames1992/CompIgS/releases/tag/v.01). The immune repertoire sequencing data from this study (from the murine allergy experiments) have been processed and summarized within the manuscript. The raw sequence reads are provided as example datasets in Zenodo (https://doi.org/10.5281/zenodo.15725748) and have been submitted to GEO (accession number pending). All intermediate and result data generated by CompIgS (including shared clone lists, mutation distributions, and divergent clone lists for example) are also included in the repository (https://doi.org/ 10.5281/zenodo.15725846 ). Competing interests The authors declare no competing interests. Funding This work was supported by the Deutsche Forschungsgemeinschaft (DFG) through multiple grants: the research training group RTG 2633 (S.B. and P.W.), the Collaborative Research Centre CRC1526, project no. 454193335 (S.M. and R.M.), and grant MA 2273/16-1, project no. 497070163 (C.U. and R.M.). Additional support was provided by the CCU Junior Program of the Medical Section, grant J06-2024. Authors' contributions U.C.C., R.M., and A.F. conceptualized the study, planned the experimental design, wrote, and edited the manuscript. S.M.K., P.W., and S.B. conducted the mouse experiments and performed library preparation. All authors reviewed and approved the final manuscript. Acknowledgements We thank all members of the laboratory for their technical support throughout this study. We are grateful for the excellent animal care provided by the institutional animal facility staff. We acknowledge the core facilities for their technical expertise and assistance with sample processing and sequencing services. Availability and Requirements Project name : CompIgS – Comparative Analysis of Clonotypes Across Antibody Subclasses Project home page : https://github.com/Chrisjames1992/CompIgS Operating system(s) : Platform independent Programming language : Python Other requirements : Python ≥ 3.1, Jupyter Notebook, pandas, numpy, matplotlib, seaborn, Biopython License : GNU GPL v3 Any restrictions to use by non-academics : None References Schatz DG, Swanson PC. V (D) J recombination: mechanisms of initiation. Annu Rev Genet. 2011;45(1):167–202. Nimmerjahn F, Ravetch JV. Divergent immunoglobulin g subclass activity through selective Fc receptor binding. Science. 2005;310(5753):1510–2. Noviski M, Zikherman J. Control of autoreactive B cells by IgM and IgD B cell receptors: maintaining a fine balance. Curr Opin Immunol. 2018;55:67–74. Yang Z, Robinson MJ, Chen X, Smith GA, Taunton J, Liu W et al. Regulation of B cell fate by chronic activity of the IgE B cell receptor. Elife. 2016;5. Allen D, Cumano A, Dildrop R, Kocks C, Rajewsky K, Rajewsky N, et al. Timing, genetic requirements and functional consequences of somatic hypermutation during B-cell development. Immunol Rev. 1987;96:5–22. Merkenschlager J, Pyo AG, Silva Santos GS, Schaefer-Babajew D, Cipolla M, Hartweger H, et al. Regulated somatic hypermutation enhances antibody affinity maturation. Nature. 2025;641(8062):495–502. Mishra AK, Mariuzza RA. Insights into the structural basis of antibody affinity maturation from next-generation sequencing. Front Immunol. 2018;9:117. Bellusci L, Grubbs G, Zahra FT, Forgacs D, Golding H, Ross TM, et al. Antibody affinity and cross-variant neutralization of SARS-CoV-2 Omicron BA. 1, BA. 2 and BA. 3 following third mRNA vaccination. Nat Commun. 2022;13(1):4617. Collin R, Dugas V, Chabot-Roy G, Salem D, Zahn A, Di Noia JM, et al. Autoimmunity and antibody affinity maturation are modulated by genetic variants on mouse chromosome 12. J Autoimmun. 2015;58:90–9. Udoye CC, Ehlers M, Manz RA. The B cell response and formation of allergenic and anti-allergenic antibodies in food allergy. Biology. 2023;12(12):1501. He JS, Subramaniam S, Narang V, Srinivasan K, Saunders SP, Carbajo D, et al. IgG1 memory B cells keep the memory of IgE responses. Nat Commun. 2017;8(1):1–12. Lim J, Lin EV, Hong JY, Vaidyanathan B, Erickson SA, Annicelli C et al. Induction of natural IgE by glucocorticoids. J Exp Med. 2022;219(10). Udoye CC, Rau CN, Freye SM, Almeida LN, Vera-Cruz S, Othmer K, et al. B-cell receptor physical properties affect relative IgG1 and IgE responses in mouse egg allergy. Mucosal Immunol. 2022;15(6):1375–88. Greiff V, Miho E, Menzel U, Reddy ST. Bioinformatic and statistical analysis of adaptive immune repertoires. Trends Immunol. 2015;36(11):738–49. Briney B, Inderbitzin A, Joyce C, Burton DR. Commonality despite exceptional diversity in the baseline human antibody repertoire. Nature. 2019;566(7744):393–7. Aouinti S, Malouche D, Giudicelli V, Kossida S, Lefranc MP. IMGT/HighV-QUEST statistical significance of IMGT clonotype (AA) diversity per gene for standardized comparisons of next generation sequencing immunoprofiles of immunoglobulins and T cell receptors. PLoS ONE [Internet]. 2015;10. Available from: https://doi.org/10.1371/journal.pone.0142353 Gupta NT, Vander Heiden JA, Uduman M, Gadala-Maria D, Yaari G, Kleinstein SH. Change-O: a toolkit for analyzing large-scale B cell immunoglobulin repertoire sequencing data. Bioinformatics. 2015;31(20):3356–8. Nazarov VI, Tsvetkov VO, Fiadziushchanka S, Rumynskiy E, Popov AA, Balashov I et al. immunarch: bioinformatics analysis of T-cell and B-cell immune repertoires. (No Title). 2020. McKinney W. Data structures for statistical computing in Python. SciPy. 2010;445(1):51–6. Harris CR, Millman KJ, Van Der Walt SJ, Gommers R, Virtanen P, Cournapeau D, et al. Array programming with NumPy. Nature. 2020;585(7825):357–62. Hunter JD, Matplotlib. A 2D graphics environment. Comput Sci Eng. 2007;9(03):90–5. Waskom ML. Seaborn: statistical data visualization. J Open Source Softw. 2021;6(60):3021. Riverbank Computing Limited. PyQt5: Python bindings for the Qt cross-platform application framework [Internet]. 2021. Available from: https://www.riverbankcomputing.com/software/pyqt/intro Alamyar E, Giudicelli V, Duroux P, Lefranc MP. In. IMGT/HighV-QUEST: A high-throughput system and Web portal for the analysis of rearranged nucleotide sequences of antigen receptors - High-throughput version of IMGT/V-QUEST. 2010. p. 60. Lefranc MP. IMGT, the international ImMunoGeneTics information system. Nucleic Acids Res [Internet]. 2009;37. Available from: https://doi.org/10.1093/nar/gkn838 Alamyar E, Duroux P, Lefranc MP, Giudicelli V. IMGT® tools for the nucleotide analysis of immunoglobulin (IG) and T cell receptor (TR) V-(D)-J repertoires, polymorphisms, and IG mutations: IMGT/V-QUEST and IMGT/HighV-QUEST for NGS. Immunogenetics: Methods and Applications in Clinical Practice. 2012;569–604. Brochet X, Lefranc MP, Giudicelli V. IMGT/V-QUEST: the highly customized and integrated system for IG and TR standardized V-J and V-D-J sequence analysis. Nucleic Acids Res [Internet]. 2008;36. Available from: https://doi.org/10.1093/nar/gkn316 Aouinti S. IMGT/StatClonotype for pairwise evaluation and visualization of NGS IG and TR IMGT clonotype (AA) diversity or expression from IMGT/HighV-QUEST. Front Immunol [Internet]. 2016;7. Available from: https://doi.org/10.3389/fimmu.2016.00339 Yaari G, Kleinstein SH. Practical guidelines for B-cell receptor repertoire sequencing analysis. Genome Med. 2015;7:1–14. Simon A. FastQC: a quality control tool for high throughput sequence data. Version 010. 2010;1. Wade-Vallance AK, Yang Z, Libang JB, Robinson MJ, Tarlinton DM, Allen CDC. B cell receptor ligation induces IgE plasma cell elimination. J Exp Med. 2023;220(4):e20220964–20220964. Koenig JFE, Knudsen NPH, Phelps A, Bruton K, Hoof I, Lund G, et al. Type 2–polarized memory B cells hold allergen-specific IgE memory. Sci Transl Med. 2024;16(733):eadi0944–0944. de Almeida LN, Petry J, Udoye CC. Type 2 IgG Memory B Cells as Precursors of IgE Plasma Cells. Allergy. 2025. Asrat S, Kaur N, Liu X, Ben LH, Kajimura D, Murphy AJ, et al. Chronic allergen exposure drives accumulation of long-lived IgE plasma cells in the bone marrow, giving rise to serological memory. Sci Immunol. 2020;5(43):eaav8402. Ota M, Hoehn KB, Fernandes-Braga W, Ota T, Aranda CJ, Friedman S, et al. CD23 + IgG1 + memory B cells are poised to switch to pathogenic IgE production in food allergy. Sci Transl Med. 2024;16(733):eadi0673–0673. Dahlke I, Nott DJ, Ruhno J, Sewell WA, Collins AM. Antigen selection in the IgE response of allergic and nonallergic individuals. J allergy Clin Immunol. 2006;117(6):1477–83. Tables Table 1 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table1.png Table 1: Comparison of CompIgS with other computational tools for immunoglobulin repertoire analysis. CompIgS was evaluated alongside five widley used computational tools for B cell immunoglobulin repertoire analysis across 13 features. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7048307","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"software","associatedPublications":[],"authors":[{"id":491593773,"identity":"10b78cd5-cefc-43ae-83ff-e51dcc6ef901","order_by":0,"name":"Udoye Chinweike Christopher","email":"data:image/png;base64,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","orcid":"","institution":"University of Lübeck","correspondingAuthor":true,"prefix":"","firstName":"Udoye","middleName":"Chinweike","lastName":"Christopher","suffix":""},{"id":491593775,"identity":"369630ff-abf0-42c5-b593-ddfd535f5b17","order_by":1,"name":"Sahar Mehrabani Khasraghi","email":"","orcid":"","institution":"University of Lübeck","correspondingAuthor":false,"prefix":"","firstName":"Sahar","middleName":"Mehrabani","lastName":"Khasraghi","suffix":""},{"id":491593777,"identity":"75b1c72b-d8f9-4ce6-a11d-a1216086723b","order_by":2,"name":"Pia Witt","email":"","orcid":"","institution":"University of Lübeck","correspondingAuthor":false,"prefix":"","firstName":"Pia","middleName":"","lastName":"Witt","suffix":""},{"id":491593778,"identity":"44db511c-0758-4c32-81b5-5587302498b1","order_by":3,"name":"Swayanka Biswas","email":"","orcid":"","institution":"University of Lübeck","correspondingAuthor":false,"prefix":"","firstName":"Swayanka","middleName":"","lastName":"Biswas","suffix":""},{"id":491593779,"identity":"fbd0d8c1-44f3-4e5a-b462-ae330c9128c9","order_by":4,"name":"Rudolf Manz","email":"","orcid":"","institution":"University of Lübeck","correspondingAuthor":false,"prefix":"","firstName":"Rudolf","middleName":"","lastName":"Manz","suffix":""},{"id":491593781,"identity":"3b8d70f9-cf30-4406-b550-0909f2ff2f6a","order_by":5,"name":"Anke Fähnrich","email":"","orcid":"","institution":"University of Lübeck","correspondingAuthor":false,"prefix":"","firstName":"Anke","middleName":"","lastName":"Fähnrich","suffix":""}],"badges":[],"createdAt":"2025-07-04 15:53:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7048307/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7048307/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87829607,"identity":"ac60661b-8956-4328-9636-3d774fde0885","added_by":"auto","created_at":"2025-07-29 12:13:53","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":47227,"visible":true,"origin":"","legend":"\u003cp\u003eGUI of CompIgS: CompIgS, provides a graphical user interface to guide scientists through a complete workflow. The interface displays input fields for analysis of IgE and IgG1 sequences and includes file upload options for IMGT StatClonotype outputs, V-region mutation statistics, AA sequences, and mutation tables. Status indicators and analysis control buttons are positioned at the bottom of the interface.\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-7048307/v1/c06c9a6414393075ef7016cf.png"},{"id":87829618,"identity":"53242120-f3c6-4be1-a59a-193c780f4eef","added_by":"auto","created_at":"2025-07-29 12:13:54","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3578164,"visible":true,"origin":"","legend":"\u003cp\u003eOverview of the experimental workflow and CompIgS. A: Experimental workflow for analyzing immunoglobulin sequences from mouse bone marrow samples, B: CompIgS workflow, which includes five main steps: input data integration, preprocessing, clonotype analysis, mutation analysis and result visualization.\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-7048307/v1/b3453bb7479f2123c7115a49.png"},{"id":87830307,"identity":"53c9d7a0-3e21-445f-a4be-51fcb6b00994","added_by":"auto","created_at":"2025-07-29 12:21:54","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1298076,"visible":true,"origin":"","legend":"\u003cp\u003eRepresentative visualization generated by CompIgS. CompIgS produces multiple plots to illustrate immunoglobulin repertoire characteristics. A: Number of shared and unique IgE clones in an allergic mouse B: Number of shared and unique IgG1 clones. C: Proportion of mutated and unmutated IgE and IgG1 clones in a representative sample. D: Distribution of somatic hypermutation counts in shared IgE (blue) vs. shared IgG1 (red) sequences. The x-axis is the number of V-region nucleotide mutations; y-axis is the count of Subclones. Vertical dashed lines indicate thresholds bins for low (≤5 mutations), moderate (6–11), and high (≥12) mutation categories. E: Distribution of clonal IgE/IgG1 abundance ratios among shared clonotypes. Bars above zero (green) denote clonotypes biased to IgE, whereas bars below zero (red) denote those biased to IgG1. F: Counts of mutated and unmutated IgE and IgG1, mutated and unmutated unique IgE, IgE-biased and IgG1-baised clones in an allergic mouse. G: Sub-clonal size distribution of shared IgE and IgG1 with corresponding average copy numbers.\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-7048307/v1/57e623029e506ae6f2648b0f.png"},{"id":87829609,"identity":"3de6ca94-26e8-4f58-bd27-61d4f1a6406b","added_by":"auto","created_at":"2025-07-29 12:13:53","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":112634,"visible":true,"origin":"","legend":"\u003cp\u003eDivergent and non-divergent IgE clones. CompIgS analyzed bone -marrow derived IgE and IgG1 datasets from two mice. From the two representative examples, we identified low, moderate and highly mutated divergent VH-IgE clones. These mice had a significant average temperature drop of 1.5 and 3.4, respectively, after the last 5 oral challenges.\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-7048307/v1/5cd4a13dae1fb6b006b30a90.png"},{"id":89922162,"identity":"ebc0bf00-ddfc-47a9-b6f5-468c7a17f387","added_by":"auto","created_at":"2025-08-26 12:54:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5288928,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7048307/v1/62f073aa-3cc2-4d15-9110-ef0f8b34562c.pdf"},{"id":87829612,"identity":"c3eef55d-3d89-47cb-839f-c781b166811b","added_by":"auto","created_at":"2025-07-29 12:13:53","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":327095,"visible":true,"origin":"","legend":"\u003cp\u003eTable 1: Comparison of CompIgS with other computational tools for immunoglobulin repertoire analysis. CompIgS was evaluated alongside five widley used computational tools for B cell immunoglobulin repertoire analysis across 13 features.\u003c/p\u003e","description":"","filename":"Table1.png","url":"https://assets-eu.researchsquare.com/files/rs-7048307/v1/b9078f3454790f80a4cc6222.png"},{"id":87830306,"identity":"3725d78f-5610-4c57-97da-5e4eb29dcf84","added_by":"auto","created_at":"2025-07-29 12:21:53","extension":"png","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":3932253,"visible":true,"origin":"","legend":"","description":"","filename":"S1.png","url":"https://assets-eu.researchsquare.com/files/rs-7048307/v1/89229ec08f31df44b67f8732.png"},{"id":87830302,"identity":"59cbe56e-8105-4342-b337-c53a13473f30","added_by":"auto","created_at":"2025-07-29 12:21:53","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":18676,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMethods.docx","url":"https://assets-eu.researchsquare.com/files/rs-7048307/v1/5e6f90a9898d6f1a69e7a376.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"CompIgS: A Computational Workflow for comparative analysis of related clonotypes within distinct antibody subclasses","fulltext":[{"header":"Introduction","content":"\u003cp\u003eImmunoglobulin (Ig) monomers are composed of two identical heavy (H) chains and two identical light (L) chains. These protein molecules, commonly known as antibodies, have a \"Y\" shaped structure with small antigen binding sites located at their two tips. The regions of both the light and the heavy chains that do not bind to antigens contain amino acid (AA) sequences that show very little variation among different immunoglobulins. These regions are referred to as the “constant region. At the genetic level, the constant region is encoded by specific constant region genes while the antigen-binding site is determined by various combinations of variable (V), diversity (D), and joining (J) gene segments. During B cell development, these V, D and J segments undergo partial deletion and are randomly spliced together in a process know as VDJ recombination. This process gives each B cell a unique antigen-binding site (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). As a result, the collective B cell population is able to produce antibodies that cover a broad antigen binding repertoire, that can bind hundreds of thousands of different antigens. The fragment crystallizable region (Fc region), or “constant region” of antibodies do not participate in antigen-binding but is crucial for determining the effector function of the antibody. There are several “constant regions”, which determine the antibody subclass.\u003c/p\u003e\u003cp\u003eIn mice, several Ig classes are known: IgA, IgD, IgE, IgG1, IgG2a/b/c; IgG3, IgM. These Igs can be found as membrane-bound molecules on naïve B cells, memory B and to some extent, on plasma cells. Functioning as antigen-specific B cell receptors (BCR), antibodies of the distinct subclasses exhibit differential signaling strengths and signaling qualities (\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e–\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e), thereby controlling antigen-specific B cell responses on the level of individual B cell clones. Initially, naïve B cells (B cells that have not been activated by their cognate antigen yet) express IgM and IgD. Upon activation and proper co-stimulation, they can undergo class switch to other subclasses. Of note, class switching involves deleting all upstream Ig constant region genes. Except co-expression of IgD and IgM on naïve B cells, single B cells express only one Ig subclass. After differentiation into the final stage as plasma cells, antibodies are produced in a splice variant that allows secretion into the body fluids. As secreted molecules, antibodies can fulfil a broad range of distinct effector functions.\u003c/p\u003e\u003cp\u003eAntibody affinities range between 10\u003csup\u003e6\u003c/sup\u003e up to 10\u003csup\u003e12\u003c/sup\u003e L/Mol, which has a strong impact on their function. This property is determined by the original VDJ-sequence but later in the B cell response modified by somatic hypermutation and selection processes in the germinal center reaction (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). This process can increase the antigen-binding affinities between 10 to more than 1000-fold (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Antibody affinities are, e.g. relevant for their roles in protection against infection (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e), autoimmune diseases (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) and allergy development (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). In humans and mice, IgE can promote allergic reactions. Highly hypermutated IgE clones (presumably of high affinity) were shown to be capable of triggering anaphylaxis, a severe and potentially lethal allergic symptom, whereas less mutated, low-affinity IgE failed to or even competitively inhibited anaphylaxis development (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Allergen-specific murine IgG1 is clonally related to IgE, but can inhibit allergy development (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Therefore, knowledge about quantitative and qualitative differences between clonally related IgE and IgG may aid our understanding of the development of allergenic and anti-allergenic antibodies within one individual.\u003c/p\u003e\u003cp\u003eThe analysis of the BCR repertoire has significantly advanced our understanding into the development and maintenance of individual B cell clones (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). However, standard repertoire analysis tools have limitations when it comes to comparing Ig clone pairs that are related but belong to distinct antibody subclasses. Most existing tools focus on metrics related to single isotope diversity, such as clonal frequency distributions, V, D, J usage and lineage tracing within a single isotype or sample. Tools such as Immunarch, IMGT/HighV-QUEST, and the Change-O toolkit provide robust frameworks for repertoire analysis (\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e–\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). These platforms effectively quantify clonotype diversity and somatic hypermutation and are commonly used to analyze B-cell or T-cell receptor repertoires. However, they are not specifically designed for comparative analyses of identical clonotypes found in distinct antibody subclasses, such as clonally related murine IgE and IgG1.\u003c/p\u003e\u003cp\u003eTo address that gap, we introduce CompIgS, a tool designed to facilitate the analysis of Ig-repertoire data. CompIgS enables the identification and comparative analysis of subclone pairs represented across different antibody subclasses. The capabilities of the open-source computational tool are exemplified in analyzing the related IgE and IgG1 repertoires in murine food allergy.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003eMice and Animal Experiments\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe rational for the following procedures of animal handling, anesthesia and sacrifice was based on the principle of minimal stress for the test animals. The protocols were developed in cooperation and approval with our local Committee on the Ethics of Animal Experiments in our institution. All animal experiments were conducted in accordance with German GVSOLAS and European FELASA guidelines and approved by the respective Committee on the Ethics of Animal Experiments of the state Schleswig-Holstein (Ministerium für Landwirtschaft, Umwelt und ländliche Räume des Landes Schleswig-Holstein) (permit numbers V 242-18318/2016 [29 − 3/16] and 122 − 39[46 − 6/18] Manz).\u003c/p\u003e\u003cp\u003eFemale BALB/c mice (7–8 weeks old) were purchased from a commercial supplier (Charles River Laboratory, Sulzfeld, Germany) were housed under specific pathogen-free conditions at the University of Lübeck animal facility.\u003c/p\u003e\u003cp\u003eThe EW/EYP solution (1.25 mg/mL) was prepared by mixing 1.25 mg egg white (EW) with 1 mL egg yolk plasma (EYP) and filtered through a 70 µm cell strainer. BALB/c mice were anesthetized (i.p.; 5 mg/mL Ketanest S, 1.5 mg/mL Rompun in DPBS) and sensitized intratracheally (i.t.) with 40 µL EW/EYP solution (50 µg EW) three times over two weeks.\u003c/p\u003e\u003cp\u003eFrom weeks 3–8, mice were challenged three times weekly by oral gavage (o.g.) with 300 µL EW/EYP solution (100 mg EW). Baseline rectal temperatures were measured before each challenge using a rectal thermometer (Physitemp). Allergic responses (rectal temperature drop) were assessed every 15 min for 1 h post-challenge. At the final timepoint (\u0026gt; 7 weeks post-challenge), mice were anesthetized (i.p.; 5 mg/mL Ketanest S, 1.5 mg/mL Rompun in DPBS), blood was collected by cardiac puncture, mice were euthanized by cervical dislocation and bone marrow (BM) was harvested for BCR repertoire sequencing.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSoftware architecture and implementation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eCompIgS is a Python-based analytical tool that includes an optional graphical user interface (GUI). It is implemented in Python 3.1 and utilizes several standard libraries: pandas (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) for data manipulation, NumPy (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) for numerical computations, matplotlib (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e) and seaborn (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e) for generating plots, and PyQt5 (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e) for the GUI. Both the desktop GUI (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and the Google-colab script are built on the same underlying logic, which ensures identical results. The GUI was designed for ease of use, allowing users to load data files and run the entire analysis pipeline with minimal technical skills required. Internally, the tool is organized into modular steps that include input parsing, clone identification, comparative analysis, and visualization. It also employs multi-threading to maintain the responsiveness of the GUI during analyses. The source code and documentation are available in Google-colab and GitHub, respectively, (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://colab.research.google.com/drive/1y45LgoJmTWHnUwAIn3RmcEkNPEeqHv07?usp=sharing\u003c/span\u003e\u003cspan address=\"https://colab.research.google.com/drive/1y45LgoJmTWHnUwAIn3RmcEkNPEeqHv07?usp=sharing\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/Chrisjames1992/CompIgS\u003c/span\u003e\u003cspan address=\"https://github.com/Chrisjames1992/CompIgS\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). A standalone executable version of CompIgS for Windows systems is available on Zenodo ( \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5281/zenodo.15599810\u003c/span\u003e\u003cspan address=\"10.5281/zenodo.15599810\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eInput data and preprocessing\u003c/b\u003e\u003c/p\u003e\u003cp\u003eCompIgS utilizes two distinct Ig isotype repertoire sequencing datasets pre-annotated and sub-clonally grouped by IMGT/Highv-QUEST and IMGT/StatClonotype (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e), respectively. As an illustration, the tool used IgE (Ig1) and IgG1 (Ig2) repertoires output data from the same sample for comparative analysis. From the IMGT/HighV-QUEST analysis, CompIgS requires IgE (Ig1) and IgG1 (Ig2) tab-delimited text files including: 8_V-REGION-nt-mutation-statistics, 5_AA-sequences, 7_V-REGION-mutation-and-AA-change-table. From the IMGT/StatClonotype output files, CompIgS requires the IgE and IgG1 IMGT StatClonotype tab-delimited text files. Additionally, the workflow needs the IMGT VH germline AA sequences. The IMGT/StatClonotype output files provided essential data for clonotype identification, the data includes: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) V gene assignment, (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) D gene assignment, (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) J gene assignment, (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) translated Complementarity-determining region (CDR)3 AA sequence, (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) sequence identifier and (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) copy number of representative clones. From the IMGT/StatClonotype output data, CompIgS uses the IMGT sequence identifier to trace back the clones' detailed IMGT/HighV-QUEST annotations, including: (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) number of somatic mutations (e.g. number of nucleotide mutations in the V region, number of non-silent mutations in CDR regions) and (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) VH region germline AA sequence. To reconstruct the framework region 1 (FR1) and complementarity-determining region 1 (CDR1) AA sequences of various clones, the IMGT VH germline AA sequences are employed. This process involves aligning each clone's VH AA sequence against the entire set of germline sequences to identify the VH gene with the highest similarity. The identified germline sequence serves as a reference to accurately complete the FR1 and CDR1 regions of the clone. In our case study, IgE and IgG1 libraries were generated from mice bone marrow samples using the IRepertoire iR-Complete Dual index (MBHΙ) multiplex PCR kit and sequenced on an Illumina MiSeq platform (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA) (iRepertoire, Inc., AL, USA).\u003c/p\u003e\u003cp\u003eThe sequencing data were demultiplexed by the iRepertoire (iRepertoire, Inc., AL, USA)., aligned to the IMGT reference database for \u003cem\u003eMus musculus\u003c/em\u003e using the IMGT/HighV-QUEST platform (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e) and annotated by IMGT/HighV-QUEST. Based on nucleotide similarity, IMGT/HighV-QUEST assigns the V, D, and J germline genes and alleles to each sequence and provides standardized annotation of FRs and CDRs. Subsequently, these annotated sequences were processed by IMGT/StatClonotype to group them into subclones based on unique V-(D)-J gene rearrangements and in-frame CDR3 AA sequences (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). The CompIgS pipeline performs a quality control step, The CompIgS pipeline performs quality control based on best practices in immune repertoire analysis, removing non-productive sequences (e.g., with stop codons or missing annotations) and those with incomplete V, D, or J gene assignments, which often reflect low-quality reads (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Additionally, to minimize the impact of potential sequencing artifacts, low-frequency sequences are filtered out; by default, singleton reads (sequences represented by a single read) are excluded from downstream analyses (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e).After filtering, basic repertoire metrics, including the total number of productive clones per isotype—are quantified using the custom script, CompIgS, providing the basis for subsequent comparative analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003e\u003cb\u003eClonotype definition and matching\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA core task of CompIgS is to define B-cell clonotypes and identify which clonotypes are shared between IgE (Ig1) and IgG1(Ig2) repertoires. CompIgS defines clonotypes based on unique V–D–J gene rearrangements with conserved anchors. Specifically, for each sequence the tool constructs a clonotype identifier string by concatenating the annotated V gene, D gene, and J gene. For example, a sequence might be assigned the clonotype string \"IGHV1-18_IGHD2-2_IGHJ4\" representing its V, D, and J gene calls. This canonical string representation is used to collapse sequences into clonotypes and to compare between the IgE and IgG1 datasets. Two sequences (even from different isotypes) are considered to belong to the same clonotype if they share the identical V gene, D gene, and J gene assignments.\u003c/p\u003e\u003cp\u003eUsing this definition, CompIgS identifies shared clonotypes – those present in both the IgE and IgG1 repertoires of the same subject – versus unique clonotypes – those found only in one isotype. The comparison is implemented by set operations: after generating the clonotype lists for IgE and IgG1, the intersection (IgE ∩ IgG1) yields shared clonotypes, while the set differences (IgE \\ IgG1 and IgG1 \\ IgE) yield unique IgE and unique IgG1 clonotypes, respectively. The number and percentage of shared vs. unique clonotypes are then calculated. CompIgS reports these values and visualises them with simple bar graphs for each isotype (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA-B).\u003c/p\u003e\u003cp\u003e\u003cb\u003eQuantification of clonal expansion and bias\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFor each shared clonotype, CompIgS compares the abundance of that clone in the IgE vs. IgG1 compartments. We define the abundance of a clone by the sum of clone copies for all member sequences of that clonotype. CompIgS calculates an IgE/IgG1 copy number ratio for every shared clones as:\u003c/p\u003e\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:Rati{o}_{clone}=\\frac{{N}_{IgE}}{{N}_{IgG1}}$$\u003c/div\u003e\u003c/div\u003e\u003cp\u003ewhere:\u003c/p\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{N}_{IgE}\\)\u003c/span\u003e\u003c/span\u003e denotes the number of IgE sequences within a given clone,\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{N}_{IgG1}\\)\u003c/span\u003e\u003c/span\u003edenotes the number of IgG1 sequences within the same clone.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003cp\u003eFor convenience, the log10 of this ratio is used to span orders of magnitude. Clonotypes with positive log-ratio are IgE-biased clones (overrepresented in IgE relative to IgG1), whereas those with negative log-ratio are IgG1-biased (underrepresented in IgE). Clones detected in only one isotype are handled as extreme cases: a clone unique to IgE would have\u003c/p\u003e\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:{N}_{IgG1}=0$$\u003c/div\u003e\u003c/div\u003e\u003cp\u003eand thus, an undefined ratio (in practice, CompIgS labels such cases separately as unique IgE clones), and vice versa for unique IgG1 clones.\u003c/p\u003e\u003cp\u003eAdditionally, CompIgS quantifies the overall clonal expansion characteristics in each isotype. For each repertoire, it computes the clonal size distribution. However, in the context of our study, we focused on comparing clone size characteristics between shared IgE (Ig1) and IgG1 (Ig2) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG). CompIgS provides plots of clone size distribution for various IgE (Ig1) subsets (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eSomatic hypermutation and mutation profiling\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSomatic hypermutation (SHM) in immunoglobulin genes is a critical factor in affinity maturation. CompIgS analyzes the mutation profiles of shared IgE and IgG1 sequences to gain insights into clone maturity. The tool uses the IMGT annotations of V-region mutations. For each clone, the number of nucleotide mutations in the V region is obtained (often reported by IMGT as “V-REGION nb of mutations”), and the number of AA replacement (non-silent) changes in CDR3 is noted (from “CDR3-IMGT nb of non-silent mutations”). CompIgS first ensures these values are numeric, then bins each clone into different subpopulations.\u003c/p\u003e\u003cp\u003eWe categorized IgE and IgG1 shared clonotypes into mutation frequency bins: “low mutation” (0–5 nucleotide mutations in the V region), “moderate mutation” (6–11 mutations), and “high mutation” (≥ 12 mutations). CompIgS tallies the number of shared IgE and IgG1 clonotypes falling into each mutation bin.\u003c/p\u003e\u003cp\u003eTo facilitate interpretation, CompIgS generates a mutation distribution plot for shared clones, showing how many subclones in IgE vs. IgG1 have a given number of V-region mutations (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). Finally, a key mutation-based analysis in CompIgS is the identification of divergent IgE clones. Which are shared IgE clones with non-silent CDR3 mutations that make the CDR3 AA unique and not present in clonally related IgG1. This is central to identifying IgE clones that qualitatively differ from their IgG1 counterpart.\u003c/p\u003e\u003cp\u003e\u003cb\u003eIdentification of Divergent IgE clones\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe define divergent IgE clones operationally as IgE clonotypes that show clear evidence of having diverged in the affinity maturation process from an IgG1 lineage. Specifically, CompIgS flags an IgE clonotype as “divergent” if they share the same VDJ rearrangement as IgG1 but contain at least a certain number of AA replacements in the CDR3 region, indicating substantial mutation from the IgG1 precursor. In our analysis, we required ≥ 2 non-silent mutations in CDR3 for an IgE clone to be considered divergent. This threshold was chosen to exceed the likelihood of random sequencing error and to capture clones that underwent notable antigen-driven selection in the IgE compartment.\u003c/p\u003e\u003cp\u003eThe process of identifying divergent IgE clones involves the use of a custom computational algorithm implemented in Python. The algorithm assessed CDR3 mutation patterns and performed similarity searches between IgE and IgG1 repertoires using core data manipulation libraries.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePython libraries and dependencies\u003c/b\u003e\u003c/p\u003e\u003cp\u003eOur pipeline employed a Python-based algorithm to stratify shared IgE clonotypes by hypermutation levels and assess their divergence from the IgG1 repertoire. We first generated a reference set of unique CDR3 amino acid sequences from productive IgG1 clones, then stratified shared IgE clonotypes into three hypermutation categories: low (0–5 mutations), moderate (6–11 mutations), and high (≥ 12 mutations) based on V-region mutation counts.\u003c/p\u003e\u003cp\u003eFor each mutation category, we performed Boolean matching to identify IgE CDR3 sequences absent from the IgG1 reference set, classifying these as \"divergent\" clones. Non-divergent clones were defined as those with CDR3 AA sequences present in the IgG1 repertoire. To focus on functionally relevant differences, we applied additional filtering criteria requiring ≥ 2 non-silent mutations in the VH-CDR3 region for divergent clone classification.\u003c/p\u003e\u003cp\u003eStatistical summaries including clone counts, mean copy numbers, and total copy numbers were computed for each stratified group using standard Pandas aggregation functions with appropriate handling of missing values.\u003c/p\u003e\u003cp\u003eDetailed algorithmic workflow, code implementations, and statistical functions are provided in supplementary methods.\u003c/p\u003e\u003cp\u003e\u003cb\u003eData visualization and output\u003c/b\u003e\u003c/p\u003e\u003cp\u003eCompIgS automatically generates a comprehensive suite of publication-quality visualizations to facilitate interpretation of comparative immunoglobulin analysis results. The software produces three primary categories of analytical plots, each designed to highlight distinct aspects of clonal distribution and characteristics.\u003c/p\u003e\u003cp\u003e\u003cb\u003eClonotype distribution analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe software generates clonotype summary plots presenting bar chart representations of total productive, shared, and unique clonotypes across IgE and IgG1 populations (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). These visualizations provide an immediate overview of clonal overlap and diversity between the two immunoglobulin subclasses, enabling rapid assessment of shared versus unique clonal responses.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSubset-specific clonal characterization\u003c/b\u003e\u003c/p\u003e\u003cp\u003eCompIgS produces detailed grouped bar charts that categorize clones based on multiple parameters, including mutational status and isotype bias. These plots distinguish between mutated and unmutated clones that exhibit IgE-biased, IgG1-biased, or unique expression patterns (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eClone size distribution profiles\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe software generates scatter plots displaying clone sizes, defined as the number of sequences per sub-clonotype, arranged by rank order. These distributions are typically presented in side-by-side comparisons for Ig1 versus Ig2 populations, with optional highlighting of mean or median clone sizes to facilitate statistical interpretation (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eOutput format and data management\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAll visualizations are exported as high-resolution PNG images suitable for publication or detailed examination, with files automatically saved to the designated output directory. Complementing the graphical output, CompIgS generates comprehensive numerical summaries including clone counts, frequencies, and VH AA sequences for distinct IgE and IgG1 subsets. These quantitative data are compiled into CSV-formatted summary reports that aggregate key findings for each analyzed subject.\u003c/p\u003e\u003cp\u003e\u003cb\u003eExperimental data\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFor demonstration and testing, we applied CompIgS to immunoglobulin heavy-chain repertoire data from a murine model of egg allergy. In this model, mice were repeatedly sensitized for and challenged with allergens to induce food allergy to Hen´s egg, as described in (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). High-throughput sequencing data of the IgE and IgG1 immunoglobulin heavy chain transcriptome from each mouse’s bone marrow were obtained as follows: After RNA isolation, BCR libraries were generated using the MBHI-M kit (iRepertoire, Inc., AL, USA), which employs a multiplex PCR approach with a mixture of V- and C-region primers specifically optimized for unbiased amplification of the murine IgH repertoire and compatibility with Illumina sequencing workflows. Paired-end sequencing was conducted on the Illumina MiSeq platform, yielding approximately 1–2\u0026nbsp;million reads per sample.\u003c/p\u003e\u003cp\u003eSequencing quality was assessed using FastQC (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Samples were excluded from downstream analysis if the median Phred quality score fell below 20 at any base position. Demultiplexing of the raw reads into individual samples was performed by iRepertoire. The resulting cDNA FASTA sequences were further separated into IgE and IgG1 datasets for each mouse (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5281/zenodo.15725748\u003c/span\u003e\u003cspan address=\"10.5281/zenodo.15725748\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The FASTA sequences were processed through IMGT/HighV-QUEST and IMGT/StatClonotype pipeline to obtain annotated repertoires. CompIgS was applied on paired IgE–IgG1 data from an individual mouse to extract the comparative metrics described.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eIgE clones are limited in clonotype diversity\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe applied CompIgS to paired IgE and IgG1 repertoire sequencing data from two mice with \u0026gt;\u0026thinsp;1◦C temperature drop after oral challenge to egg. The IgG1 repertoires were vast, comprising of thousands of unique clonotypes (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). In stark contrast, IgE repertoires were much smaller, with only a few hundred clonotypes detectable in each mouse. Nearly all IgE clonotypes had a corresponding IgG1 clonotype in the same animal\u0026rsquo;s repertoire (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). In a representative sick mouse, we observed 308 total IgE clonotypes, of which 292 (95%) were also present in the IgG1 repertoire (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Only 16 clonotypes (5%) were unique to the IgE repertoire. Conversely, the IgG1 repertoire of the same mouse contained 3369 clonotypes in total, with 3077 (91%) being unique to IgG1 and only the 292 clonotypes shared with IgE (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). This trend was consistent in the second mouse: IgG1 repertoires showed orders-of-magnitude greater diversity, and IgE repertoires appeared to be essentially a small subset drawn from the IgG1 pool.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe finding that \u0026gt;\u0026thinsp;90% of IgE clonotypes were shared with IgG1 is in line with the classic model of sequential switching through IgG1\u0026thinsp;+\u0026thinsp;cells. The presence of a very small number of IgE-exclusive clonotypes suggests that direct IgE class switching (bypassing an IgG1 intermediate) may occur but is quite rare, at least in this model.\u003c/p\u003e\u003cp\u003e\u003cb\u003eIgE-biased vs. IgG1-biased clonal expansions\u003c/b\u003e\u003c/p\u003e\u003cp\u003eCompIgS generates a ranked list of shared clonotypes based on their IgE/IgG1 ratio. This ranking helps identifying biologically interesting clonotypes, such as those predominantly expanded in the IgE compartment despite also being present in IgG1. A bar plot is created to visualize the distribution of log-ratios across all shared clonotypes. In our dataset, we observed that only a handful of shared clones were IgE-biased, and they tended to have modest fold differences, whereas many clones were heavily IgG1-biased (orders of magnitude more abundant in IgG1 than IgE).\u003c/p\u003e\u003cp\u003eThe distribution of log10(IgE/IgG1) ratios was heavily skewed toward negative values (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE), meaning most shared clones had far more abundant IgG1 than in IgE. We defined IgE-biased clones as those with a ratio\u0026thinsp;\u0026ge;\u0026thinsp;2 (IgE reads exceed IgG1 reads) and IgG1-biased clones as those with ratio\u0026thinsp;\u0026le;\u0026thinsp;2. By this definition, only 12 out of 292 shared clonotypes were IgE-biased in the example (approximately 4%), whereas the remaining\u0026thinsp;~\u0026thinsp;96% were IgG1-biased (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF). Across the two mice, IgE-biased shared clones were consistently rare (~\u0026thinsp;5% of shared clones). Moreover, even the IgE-biased clones were not extremely skewed \u0026ndash; the highest observed IgE/IgG1 ratio for a shared clone was on the order of 10 (i.e., 10 times as many IgE reads as IgG1 reads for that clone). In contrast, many IgG1-biased clones had ratios\u0026thinsp;\u0026lt;\u0026thinsp;0.1 (IgE representing\u0026thinsp;\u0026lt;\u0026thinsp;10% of clone\u0026rsquo;s total), and some had ratios as low as 0.001 or less, indicating the shared clones were dominated with the IgG1 repertoire.\u003c/p\u003e\u003cp\u003eThe clone size distributions of mutated IgE and IgG1 corroborated this imbalance. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG plots sub-clone sizes for shared IgE vs. IgG1 clonotypes sorted by rank. The largest IgE sub-clonotype had\u0026thinsp;~\u0026thinsp;300 reads, whereas the largest IgG1 sub-clonotype had over 12,000 reads. In fact, the top 5 IgG1 sub-clones each had thousands of reads, accounting for a substantial fraction of the IgG1 repertoire, while the entire IgE repertoire was relatively evenly spread among small clones of \u0026lt;\u0026thinsp;300 reads each. The mean clone size among shared clones was higher in IgG1 (mean\u0026thinsp;~\u0026thinsp;40 sequences per clone) than in IgE (mean\u0026thinsp;~\u0026thinsp;25 sequences per clone). Despite these differences in expansion, it is noteworthy that the average clone sizes were on the same order for IgE and IgG1 (tens of reads per clone). IgG1 had a long tail of large clones that raised its mean, but aside from those, the bulk of the clone size distribution overlapped between IgE and IgG1 in the lower size range (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG, zoomed panels).\u003c/p\u003e\u003cp\u003eThese observations imply that on average IgG1 B-cell clone expands massively more than their IgE counterpart, therefore generating more differentiated plasma cells. This is consistent with in vivo models where IgE\u0026thinsp;+\u0026thinsp;cells in germinal centers are rare, poorly expanded, transient and rapidly differentiate to IgE plasma cells. Furthermore, antigen-dependent BCR ligation of IgE plasma cells tends to drive the IgE\u0026thinsp;+\u0026thinsp;plasma cells toward apoptosis and cell death, further reducing the plasma cell numbers (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eSomatic mutation profiles of IgE and IgG1 clonotypes\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe next examined the somatic hypermutation status of clonotypes in each isotype. We found that the overwhelming majority of IgE clonotypes showed evidence of somatic mutation, despite the low overall diversity. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC, about 94% of IgE clonotypes in the representative mouse carried one or more V-region mutations (i.e., were not germline), whereas only\u0026thinsp;~\u0026thinsp;6% were unmutated. The IgG1 clonotypes were similarly mostly mutated (~\u0026thinsp;92.5% mutated vs. 7.5% unmutated). Thus, both IgE and IgG1 repertoires were largely composed of antigen-experienced B-cell clones. This is consistent with the animals having been actively immunized or exposed to allergens, such that even newly class-switched IgE cells had undergone germinal center maturation and accumulated mutations. The small subset of unmutated IgE clones could represent naive or recently activated B cells that class-switched to IgE early or IgE sourced from extrafollicular B cell response. Their low frequency in our dataset suggests that such sub-population, if they occur, result in minor clones that may not significantly contribute to the overall IgE pool in chronic allergen exposure.\u003c/p\u003e\u003cp\u003eFurthermore, CompIgS estimates the mutation count distributions for shared IgE vs. IgG1(Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD) clones: IgG1 (red curve) showed a peak in the 5\u0026ndash;7 mutation range and a tail extending to \u0026gt;\u0026thinsp;15 mutations, whereas IgE (blue curve) had far fewer sequences overall, with a relatively flat distribution given the low counts. Importantly, we did not observe a distinct population of IgE sequences that were hypermutated beyond the IgG1 range \u0026ndash; the IgE clones fell well within the mutation spectrum of IgG1 clones. This suggests that the IgE-producing cells largely shared a similar history of germinal center activity as the IgG1-producing cells. Next, we sought to investigate if there were IgE clones that were qualitatively different from their IgG1 counterpart at different mutation levels.\u003c/p\u003e\u003cp\u003e\u003cb\u003eDivergent Ig1 (IgE) Clones: Shared Ig1 (IgE) Clonotypes with unique CDR3 AA sequence\u003c/b\u003e\u003c/p\u003e\u003cp\u003eRecently, IgE was found to be clonally associated with IgG4 precursors within the type 2 polarised memory B cell (MBC2) compartment in allergic patients (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). In murine allergies, IgE clones are clonally related to IgG1 progenitors. However, after class-switching, IgE clones can introduce new hypermutations to generate a related subclone divergent from its IgG progenitor. These new mutations may be relevant for the antigen-binding properties, antibody function and clonal selection of the B cell clone.\u003c/p\u003e\u003cp\u003eCompIgS was designed to identify divergent subclones between antibody subclasses, such as divergent IgE clones \u0026ndash; IgE clonotypes that, although related to IgG1 clonotypes, have acquired distinct mutations (\u0026ge;\u0026thinsp;2 CDR3 non-silent mutations) that make the shared IgE CDR3 AA (AA) sequence unique. Using the criteria described (shared IgE with distinct CDR3 AA sequence not seen in IgG1). In our dataset of an allergic mouse, there were divergent and non-divergent IgE clones in the low, moderate and highly mutated shared IgE (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The number of divergent IgE clones among different mutation bins per mouse was about 1\u0026ndash;6 clones. The divergent IgE clones were less abundant than the non-divergent IgE clones, which ranged between 10\u0026ndash;45 clones in either the low, moderate or highly mutated subset. The presence of divergent IgE in different mutation bins suggests that there is a qualitative difference between clonally related IgE and IgG1, and these clones might be a better predictor of disease than the traditional metrics, like total IgE clones or sequences.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFinally, we compared CompIgS to four major software platforms for immunoglobulin sequence analysis: Change-O, IMGT/HighV-QUEST with IMGT/StatClonotype, BCrep, and Immunarch. Each of these tools has distinct capabilities and limitations for B-cell receptor repertoire studies. All platforms support essential functions such as clonal grouping, mutation profiling, and visualization capabilities. However, they differ in specialized analytical features. CompIgS stamds out by offering comprehensive comparative analysis of two immunoglobulin isotypes from the same sample, identifying various Ig subpopulations, and estimating divergent CDR3 Ig clones - capabilities not available in other platforms. These capabilities make CompIgS particularly valuable for studies examining class-switching dynamics and clonal relationships between different antibody isotypes (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWhile traditional repertoire tools like IMGT/HighV-QUEST, Change-O, and Immunarch (\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e) excel at characterizing single-sample diversity and clonal expansions, they partly perform cross-comparisons between isotypes. CompIgS addresses questions specific to isotype relationships and evolution, filling a crucial niche in immunoglobulin repertoire analysis by enabling direct comparison between paired antibody isotype repertoire from the same sample.\u003c/p\u003e\u003cp\u003eThe results obtained via CompIgS in our mouse model of egg white allergy can provide new insights into the IgE response in allergic disease. In our food allergy model, most IgE clones shared their VDJ-regions with IgG1, a finding that supports the notion that most IgE responses originate from the canonical germinal center pathway through an IgG1-intermediate. This finding aligns with studies in both mice and humans that highlight how IgE response often resides in IgG memory B cells (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eCompIgS compares related clonotypes within the IgE and IgG1 compartment in more detail. Our findings highlight a subset of IgE clones - which we term divergent IgE clones. These are shared IgE clones which have introduced non silent hypermutations into their antigen binding regions. Divergent IgE clones were present in low, moderate and high mutation bins, suggesting these clones are present in different maturation stages of the IgE response.\u003c/p\u003e\u003cp\u003eWe observed that the unique, IgE-specific mutations in divergent clones were non-silent and often occurred in positions of the CDR3 loop known to interact with antigens. These observations suggest that divergent IgE clones could be products of antigen-driven selection occurring specifically in the IgE arm, possibly derived from germinal centers or memory B cells. Increased hypermutation rates have been shown to be associated with the capacity to induce allergic anaphylaxis, a severe and potentially lethal symptom of food allergy in mouse models (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Moreover, increased hypermutation rates in IgE were observed in allergic individuals than non-allergic patients (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). The CompIgS platform was designed for automated analysis of the clonal expansion rates, non-silent and silent hypermutations of clonally related Ig-subsets, which can provide insights into the clonal selection process within GCs, potentially leading to Ig clones of various pathogenicity. In case of our example, the highly hypermutated divergent IgE clones are expected to have greater potential to induce allergic anaphylaxis.\u003c/p\u003e\u003cp\u003eA limitation of the method is the definition of divergent Ig clones (shared Ig1 with unique CDR3 AAs absent in clonally related Ig2 subclasses, due to \u0026ge;\u0026thinsp;2 CDR3 non-silent mutations), might exclude Igs with a single divergent point mutation. Future iterations of CompIgS might allow more flexibility in defining the minimum number of non-silent mutations present in the divergent IgE clone.\u003c/p\u003e\u003cp\u003eIn summary, CompIgS enabled us to categorize the IgE repertoire of allergic mice into various subsets (shared, mutated vs. unmutated, IgE-biased vs. IgG1-biased, and divergent vs. non-divergent). This analysis workflow enables comparative analysis of related immunoglobulin (Ig) clones and can provide insights into the clonal selection processes underlying IgE-associated germinal center (GC) reactions.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eamino acid\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBCR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eB cell receptor\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCDR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eComplementarity-determining region\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCompIgS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eComparative Igs Analyzer\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ediversity\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eFc region\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003efragment crystallizable region\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGUI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003egraphical user interface\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eIg\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eImmunoglobulin\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eIMGT\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eImMunoGeneTics\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eJ\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ejoining\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eL\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003elight\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMBC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ememory B cell\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSHM\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSomatic hypermutation\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eV\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003evariable\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eVH\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003evariable heavy-chain\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003ch3\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eAnimal experiments were conducted following the 3Rs principles and approved by the Animal Welfare Committee of Schleswig-Holstein (permits 22-39_2016-06-17 and 122-39(46-6/18)) in accordance with German Animal Welfare Act guidelines and GV-SOLAS standards.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eAll repertoire data analyzed in this study (IgE and IgG1 IMGT annotated datasets for each mouse) are available in a public repository (https://doi.org/10.5281/zenodo.15774119). The CompIgS\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eis open source and the source code can be accessed via the project\u0026rsquo;s google colab and GitHub repository respectively, (https://colab.research.google.com/drive/1y45LgoJmTWHnUwAIn3RmcEkNPEeqHv07?usp=sharing), (https://github.com/Chrisjames1992/CompIgS). The repository includes the full Python source code, and example data files to reproduce the analysis. A standalone executable version of CompIgS (for Windows systems) is also provided for users who prefer not to install python (https://doi.org/10.5281/zenodo.15599810), \u0026nbsp; \u0026nbsp;(https://github.com/Chrisjames1992/CompIgS/releases/tag/v.01).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe immune repertoire sequencing data from this study (from the murine allergy experiments) have been processed and summarized within the manuscript. The raw sequence reads are provided as example datasets in Zenodo (https://doi.org/10.5281/zenodo.15725748) and have been submitted to GEO (accession number pending). All intermediate and result data generated by CompIgS (including shared clone lists, mutation distributions, and divergent clone lists for example) are also included in the repository (https://doi.org/ 10.5281/zenodo.15725846 ).\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eThis work was supported by the Deutsche Forschungsgemeinschaft (DFG) through multiple grants: the research training group RTG 2633 (S.B. and P.W.), the Collaborative Research Centre CRC1526, project no. 454193335 (S.M. and R.M.), and grant MA 2273/16-1, project no. 497070163 (C.U. and R.M.). Additional support was provided by the CCU Junior Program of the Medical Section, grant J06-2024.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eU.C.C., R.M., and A.F. conceptualized the study, planned the experimental design, wrote, and edited the manuscript. S.M.K., P.W., and S.B. conducted the mouse experiments and performed library preparation. All authors reviewed and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank all members of the laboratory for their technical support throughout this study. We are grateful for the excellent animal care provided by the institutional animal facility staff. We acknowledge the core facilities for their technical expertise and assistance with sample processing and sequencing services.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eAvailability and Requirements\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eProject name\u003c/strong\u003e: CompIgS \u0026ndash; Comparative Analysis of Clonotypes Across Antibody Subclasses\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProject home page\u003c/strong\u003e: https://github.com/Chrisjames1992/CompIgS\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eOperating system(s)\u003c/strong\u003e: Platform independent\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eProgramming language\u003c/strong\u003e: Python\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Other requirements\u003c/strong\u003e: Python \u0026ge; 3.1, Jupyter Notebook, pandas, numpy, matplotlib, seaborn, Biopython\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLicense\u003c/strong\u003e: GNU GPL v3\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAny restrictions to use by non-academics\u003c/strong\u003e: None\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSchatz DG, Swanson PC. V (D) J recombination: mechanisms of initiation. Annu Rev Genet. 2011;45(1):167\u0026ndash;202.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNimmerjahn F, Ravetch JV. Divergent immunoglobulin g subclass activity through selective Fc receptor binding. Science. 2005;310(5753):1510\u0026ndash;2.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNoviski M, Zikherman J. Control of autoreactive B cells by IgM and IgD B cell receptors: maintaining a fine balance. Curr Opin Immunol. 2018;55:67\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYang Z, Robinson MJ, Chen X, Smith GA, Taunton J, Liu W et al. Regulation of B cell fate by chronic activity of the IgE B cell receptor. Elife. 2016;5.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAllen D, Cumano A, Dildrop R, Kocks C, Rajewsky K, Rajewsky N, et al. Timing, genetic requirements and functional consequences of somatic hypermutation during B-cell development. 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J allergy Clin Immunol. 2006;117(6):1477\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7048307/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7048307/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIndividual B cells produce antibodies, also known as immunoglobulins (Ig), that target specific antigens. Collectively, the B cells within an individual generate a diverse Ig-repertoire capable of recognizing a wide range of antigens, which underpins adaptive immunity. On a molecular level, this repertoire is created by somatic recombination of variable (V), diversity (D), and joining (J) gene segments during early B cell development, eventually forming a unique VDJ-sequence that encodes for the antigen-binding region of antibodies. Each individual VDJ-sequence defines a clonotype. The nearly indefinite number of individual antigen-binding regions are joined to a limited number of constant regions, that determine the antibody subclass and hence the effector function, such as complement activation, neutralization, and opsonization, among others. A clonotype may contain antibodies of different subclasses that can have different and even opposing functions. These B cell subclones can introduce hypermutations within their VDJ-sequences to alter the antigen-binding affinities affecting antibody properties and clonal selection. Recent advancements in next-generation sequencing enabled high-depth profiling of antibody repertoires. However, current analysis tools provide limited analysis of related Ig clones present within distinct Ig subclasses of the same sample. To address this, we present an open-source computational tool, designed to identify subclone pairs between two antibody subclasses from the sample. The CompIgS (Comparative Igs Analyzer) workflow processes immunoglobulin variable heavy-chain (VH) repertoire data, utilizing ImMunoGeneTics (IMGT) annotations. It incorporates IMGT/HighV-QUEST and IMGT/StatClonotype outputs to standardize V(D)J representations and identifies shared clonotypes present in two distinct antibody subclasses. This approach allows for comparative analysis of clonal expansion and hypermutation. As a case study, we analyzed related IgE and IgG1 clonotypes from a murine model of food allergy in which IgE promotes the development of allergic symptoms, while IgG1 can inhibit allergy development. CompIgS computed various repertoire metrics including clonotype counts, IgE/IgG1 copy number ratios, somatic hypermutation profiles and estimation of different IgE subpopulations in relation to IgG1.\u003c/p\u003e","manuscriptTitle":"CompIgS: A Computational Workflow for comparative analysis of related clonotypes within distinct antibody subclasses","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-29 12:13:48","doi":"10.21203/rs.3.rs-7048307/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"56e1c580-62b1-4921-a5c2-c7a7ebc8ec4d","owner":[],"postedDate":"July 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-26T12:53:16+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-29 12:13:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7048307","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7048307","identity":"rs-7048307","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}