Biology subjects

Ng, J. C.

Publications and source records attributed to Ng, J. C..

7 recordsLinked to original sources

bioRxiv2025

Multi-step antibody class switching in a primary human response is restricted after IGHG2 and dependent on B cell maturation stage.

Class switch recombination (CSR) allows the formation of functionally specialized antibodies. Understanding of CSR dynamics is key for better design and prediction of vaccines to protect mucosal surfaces. To investigate CSR in a primary human immune response under a controlled setting, we sampled healthy volunteers without COVID-19 history every other day during the first three weeks after SARS-CoV-2 vaccination, with additional time points up to six months. Leveraging bulk and single-cell B cell receptor repertoires, single-cell transcriptomics, and immunophenotyping, we uncover paradigm-shifting insights into CSR. Newly activated B cells produce sterile transcripts of all antibody isotype constant regions (IGHC) simultaneously up to IGHG2, challenging the view that sterile transcription occurs for only a single IGHC gene at a time. CSR follows a multistep progression along the IGHC locus; in this challenge vaccine-induced B cells switch from IGHM to IGHG3 and IGHG1, followed by a subsequent switching to IGHA1 and IGHG2 after secondary immunization. IGHA2 clones require pre-switching to IGHA1 clones. Notably, switching tendency, measured by IGHC sterile transcription, is memory B cell subtype dependent, particularly beyond IGHG2. Contrary to other vaccines, antigen-specific B cells are enriched in DN2, Cmem2 and DN4 subtypes after immunization. We also observe a temporal decoupling of CSR and somatic hypermutation (SHM), with the latter detectable only after six months post-immunization. Our data describes the dynamics between CSR, SHM, sterile transcription and B cell memory development during a human primary response, challenges textbook models of CSR and offers new insights to aid control of CSR direction. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=188 HEIGHT=200 SRC="FIGDIR/small/652638v1_ufig1.gif" ALT="Figure 1"> View larger version (49K): org.highwire.dtl.DTLVardef@785769org.highwire.dtl.DTLVardef@b2c54eorg.highwire.dtl.DTLVardef@b78c72org.highwire.dtl.DTLVardef@65b5aa_HPS_FORMAT_FIGEXP M_FIG C_FIG One Sentence SummaryClass switch recombination occurs independently of somatic hypermutation and in a multistep fashion up to IGHG2 during a primary response in humans.

immunology↗

bioRxiv2025

Tuning antibody stability and function by rational designs of framework mutations

Artificial intelligence and machine learning models have been developed to engineer antibodies for specific recognition of antigens, however these approaches often focus on the antibody complementarity determining region (CDR) whilst ignoring the immunoglobulin framework (FW) which provides structural rigidity and support for the flexible CDR loops. Here we present an integrated computational-experimental workflow, combining static structure analyses, molecular dynamics simulations and in vitro physicochemical and functional assays to generate rational designs of FW mutations for modulating antibody stability and activity. We first showed that recent antibody-specific language models lacked insights in FW mutagenesis, in comparison to approaches which utilised antibody structure information. Using the widely-used breast cancer therapeutic trastuzumab as a use case, we designed stabilising mutants which were distal to the CDR and preserved the antibodys functionality to engage its cognate antigen (HER2) and induce antibody-dependent cellular cytotoxicity (ADCC). Interestingly, guided by local backbone motions predicted using molecular dynamics simulations, we designed a FW mutation on the trastuzumab light chain which retained antigen-binding effects but lost Fab-mediated and Fc-mediated effector functions. This highlighted effects of FW on immunological functions engendered in distal areas of the antibody, and the importance to consider attributes other than binding affinity when assessing antibody function. Our approach incorporates interdomain dynamics and distal effects between FW and the Fc domains, expands the scope of antibody engineering beyond the CDR, and underscores the importance of a holistic perspective that considers the entire antibody structure as a whole in optimising antibody stability, developability and function.

immunology↗

bioRxiv2025

ImmunoMatch learns and predicts cognate pairing of heavy and light immunoglobulin chains

The development of stable antibodies formed by compatible heavy (H) and light (L) chain pairs is crucial in both the in vivo maturation of antibody-producing cells and the ex vivo designs of therapeutic antibodies. We present here a novel machine learning framework, ImmunoMatch, for deciphering the molecular rules governing the pairing of antibody chains. Fine-tuned on an antibody-specific language model, ImmunoMatch learns from paired H and L sequences from single human B cells to distinguish cognate H-L pairs and randomly paired sequences. We find that the predictive performance of ImmunoMatch can be augmented by training separate models on the two types of antibody L chains in humans,{kappa} and{lambda} , in line with the in vivo mechanism of B cell development in the bone marrow. Using ImmunoMatch, we illustrate that refinement of H-L chain pairing is a hallmark of B cell maturation in both healthy and disease conditions. We find further that ImmunoMatch is sensitive to sequence differences at the H-L interface. ImmunoMatch focusses on H-L chain pairing as a specific, under-explored problem in antibody developability, and facilitates the computational assessment and modelling of stably assembled immunoglobulins towards large-scale optimisation of efficacious antibody therapeutics.

bioinformatics↗

bioRxiv2024

VCAb: A web-tool for structure-guided antibody engineering

Effective responses against different immune challenges require secretion of antibodies with various isotypes performing specific effector functions. Structural information on these isotypes is essential to engineer antibodies with desired physico-chemical features of their antigen-binding properties, and optimal stability and developability as potential therapeutic antibodies. In silico mutational scanning profiles on antibody structures would further pinpoint candidate mutations for enhancing antibody stability and function. Although a number of antibody structure databases exist, a public data resource which provides clear, consistent annotation of isotypes, species coverage of 3D antibody structures and their deep mutation profiles is currently lacking. The V and C region bearing antibody (VCAb) web tool is established with the purpose to clarify these annotations and provide an accessible and easily consultable resource to facilitate antibody engineering. VCAb currently provides data on 6,948 experimentally determined antibody structures including both V and C regions from different species. Additionally, VCAb provides annotations of species and isotypes with both V and C region numbering schemes applied, which can be interactively queried or downloaded in batch. Multiple in silico mutational scanning methods are applied on VCAb structures to provide an easily accessible interface for querying the impact of mutations on antibody stability. These features are implemented in a R shiny application to enable interactive data interrogation. VCAb is freely accessible at https://fraternalilab.cs.ucl.ac.uk/VCAb/. The source code to generate the VCAb database and the online R shiny application is available at https://github.com/Fraternalilab/VCAb, enabling users to set up local VCAb instances.

bioinformatics↗

bioRxiv2024

TITINdb2 - Expanding Annotation and Structural Information for Protein Variants in the Giant Sarcomeric Protein Titin

Structured AbstractO_ST_ABSSummaryC_ST_ABSWe present TITINdb2, an update to the TITINdb database previously constructed to facilitate the identification of pathogenic missense variants in the giant protein titin, which are associated with a variety of skeletal and cardiac myopathies. The database and web portal have been substantially revised and include the following new features: (i) an increase in computational annotation from 4 to 20 variant impact predictors, available through a new custom data table dialogue; (ii) thorough structural coverage of single domains with AlphaFold2 predicted models; (iii) newly predicted domain-domain interface annotations; (iv) an expanded in silico saturation mutagenesis incorporating 4 variant impact predictors; (v) a comprehensive overhaul of available data, including population data sources and variants reported pathogenic in the literature; (vi) A curated mapping of existing protein, transcript and chromosomal sequence positions and a new variant conversion tool to translate variants in one format to any other format. Availability and ImplementationDatabase accessible via titindb.kcl.ac.uk/TITINdb/ ContactFranca Fraternali (f.fraternali@ucl.ac.uk) Supplementary InformationAvailable

bioinformatics↗

bioRxiv2023

sciCSR infers B cell state transition and predicts class-switch recombination dynamics using single-cell transcriptomic data

Class-switch recombination (CSR) is an integral part of B cell maturation. Steady-state analyses of isotype distribution (e.g. B cell receptor [BCR] repertoire analysis of snapshots during an immune response) do not directly measure CSR dynamics, which is crucial in understanding how B cell maturation is regulated across time. We present sciCSR (pronounced scissor, single-cell inference of class switch recombination), a computational pipeline which analyses CSR events and dynamics of B cells from single-cell RNA-sequencing (scRNA-seq) experiments. sciCSR re-analyses transcriptomic sequence alignments to differentiate productive heavy-chain immunoglobulin transcripts from germline "sterile" transcripts. From a snapshot of B cell scRNA-seq data, a Markov state model is built by the pipeline to infer the dynamics and direction of CSR. Applying sciCSR on SARS-CoV-2 vaccination time-course scRNA-seq data, we observe that sciCSR predicts, using data from an earlier timepoint in the collected time-course, the isotype distribution of BCR repertoires of subsequent timepoints with high accuracy (cosine similarity [~] 0.9). sciCSR also recapitulates CSR patterns in mouse models where B cell maturation was perturbed using gene knockouts. sciCSR infers cell state transitions using processes specific to B cells, identifies transitions which are often missed by conventional RNA velocity analyses, and can reveal insights into the regulation of CSR and the dynamics of B cell maturation during an immune response.

bioinformatics↗

bioRxiv2021

Pandemic, epidemic, endemic: B cell repertoire analysis reveals unique anti-viral responses to SARS-CoV-2, Ebola and Respiratory Syncytial Virus

Immunoglobulin gene heterogeneity reflects the diversity and focus of the humoral immune response towards different infections, enabling inference of B cell development processes. Detailed compositional and lineage analysis of long read IGH repertoire sequencing, combining examples of pandemic, epidemic and endemic viral infections with control and vaccination samples, demonstrates general responses including increased use of IGHV4-39 in both EBOV and COVID-19 infection cohorts. We also show unique characteristics absent in RSV infection or yellow fever vaccine samples: EBOV survivors show unprecedented high levels of class switching events while COVID-19 repertoires from acute disease appear underdeveloped. Despite the high levels of clonal expansion in COVID-19 IgG1 repertoires there is a striking lack of evidence of germinal centre mutation and selection. Given the differences in COVID-19 morbidity and mortality with age, it is also pertinent that we find significant differences in repertoire characteristics between young and old patients. Our data supports the hypothesis that a primary viral challenge can result in a strong but immature humoral response where failures in selection of the repertoire risks off-target effects.

immunology↗