Position-Dependent Differential Targeting of Somatic Hypermutation

Thrifty wide-context models of B cell receptor somatic hypermutation

Somatic hypermutation (SHM) is the diversity-generating process in antibody affinity maturation. Probabilistic models of SHM are needed for analyzing rare mutations, for understanding the selective forces guiding affinity maturation, and for understanding the underlying biochemical process. High throughput data offers the potential to develop and fit models of SHM on relevant data sets. In this paper we model SHM using modern frameworks. We are motivated by recent work suggesting the importance of a wider context for SHM, however, assigning an independent rate to each k-mer leads to an exponential proliferation of parameters. Thus, using convolutions on 3-mer embeddings, we develop "thrifty" models of SHM that have fewer free parameters than a 5-mer model and yet have a significantly wider context. These offer a slight performance improvement over a 5-mer model. We also find that a per-site effect is not necessary to explain SHM patterns given nucleotide context. Also, the two current methods for fitting an SHM model - on out-of-frame sequence data and on synonymous mutations - produce significantly different results, and augmenting out-of-frame data with synonymous mutations does not aid out-of-sample performance.

Interpretable deep learning reveals the role of an E-box motif in suppressing somatic hypermutation of AGCT motifs within human immunoglobulin variable regions

Introduction

Somatic hypermutation (SHM) of immunoglobulin variable (V) regions by activation induced deaminase (AID) is essential for robust, long-term humoral immunity against pathogen and vaccine antigens. AID mutates cytosines preferentially within WRCH motifs (where W=A or T, R=A or G and H=A, C or T). However, it has been consistently observed that the mutability of WRCH motifs varies substantially, with large variations in mutation frequency even between multiple occurrences of the same motif within a single V region. This has led to the notion that the immediate sequence context of WRCH motifs contributes to mutability. Recent studies have highlighted the potential role of local DNA sequence features in promoting mutagenesis of AGCT, a commonly mutated WRCH motif. Intriguingly, AGCT motifs closer to 5' ends of V regions, within the framework 1 (FW1) sub-region1, mutate less frequently, suggesting an SHM-suppressing sequence context.

Methods

Here, we systematically examined the basis of AGCT positional biases in human SHM datasets with DeepSHM, a machine-learning model designed to predict SHM patterns. This was combined with integrated gradients, an interpretability method, to interrogate the basis of DeepSHM predictions.

Results

DeepSHM predicted the observed positional differences in mutation frequencies at AGCT motifs with high accuracy. For the conserved, lowly mutating AGCT motifs in FW1, integrated gradients predicted a large negative contribution of 5'C and 3'G flanking residues, suggesting that a CAGCTG context in this location was suppressive for SHM. CAGCTG is the recognition motif for E-box transcription factors, including E2A, which has been implicated in SHM. Indeed, we found a strong, inverse relationship between E-box motif fidelity and mutation frequency. Moreover, E2A was found to associate with the V region locale in two human B cell lines. Finally, analysis of human SHM datasets revealed that naturally occurring mutations in the 3'G flanking residues, which effectively ablate the E-box motif, were associated with a significantly increased rate of AGCT mutation.

Discussion

Our results suggest an antagonistic relationship between mutation frequency and the binding of E-box factors like E2A at specific AGCT motif contexts and, therefore, highlight a new, suppressive mechanism regulating local SHM patterns in human V regions.

Altered somatic hypermutation patterns in COVID-19 patients classifies disease severity

Introduction

The success of the human body in fighting SARS-CoV2 infection relies on lymphocytes and their antigen receptors. Identifying and characterizing clinically relevant receptors is of utmost importance.

Methods

We report here the application of a machine learning approach, utilizing B cell receptor repertoire sequencing data from severely and mildly infected individuals with SARS-CoV2 compared with uninfected controls.

Results

In contrast to previous studies, our approach successfully stratifies non-infected from infected individuals, as well as disease level of severity. The features that drive this classification are based on somatic hypermutation patterns, and point to alterations in the somatic hypermutation process in COVID-19 patients.

Discussion

These features may be used to build and adapt therapeutic strategies to COVID-19, in particular to quantitatively assess potential diagnostic and therapeutic antibodies. These results constitute a proof of concept for future epidemiological challenges.

Vitamin C and its therapeutic potential in the management of COVID19

COVID19 has emerged as one of the worst pandemics in the history of mankind. Several vaccines have been approved by different government agencies worldwide, but data on their efficacy and safety are limited, and distribution remains a massive challenge. As per WHO, personal immunity is vital for protection against COVID19. Earlier, Vitamin C-mediated pathways have been shown to play critical role in boosting immunity attributed to its antioxidant properties. Recently, the involvement of such pathways in protection against COVID19 has been suggested. The controlled doses of Vitamin C administered through intravenous (IV) injections are being studied for determining its role in the prognosis of COVID19. In this article, we have discussed the potential role of Vitamin C in the management in COVID19 patients and presented recent clinical trials data. Additionally, we have elaborated the possibility of administering Vitamin C through inhalers in order to achieve local high concentration and the challenges of such approach.

Somatic Diversification of Rearranged Antibody Gene Segments by Intra- and Interchromosomal Templated Mutagenesis

The ability of the humoral immune system to generate Abs capable of specifically binding a myriad of Ags is critically dependent on the somatic hypermutation program. This program induces both templated mutations (i.e., gene conversion) and untemplated mutations. In humans, somatic hypermutation is widely believed to result in untemplated point mutations. In this study, we demonstrate detection of large-scale templated events that occur in human memory B cells and circulating plasmablasts. We find that such mutations are templated intrachromosomally from IGHV genes and interchromosomally from IGHV pseudogenes as well as other homologous regions unrelated to IGHV genes. These same donor regions are used in multiple individuals, and they predominantly originate from chromosomes 14, 15, and 16. In addition, we find that exogenous sequences placed at the IgH locus, such as LAIR1, undergo templated mutagenesis and that homology appears to be the major determinant for donor choice. Furthermore, we find that donor tracts originate from areas in proximity with open chromatin, which are transcriptionally active, and are found in spatial proximity with the IgH locus during the germinal center reaction. These donor sequences are inserted into the Ig gene segment in association with overlapping activation-induced cytidine deaminase hotspots. Taken together, these studies suggest that diversity generated during the germinal center response is driven by untemplated point mutations as well as templated mutagenesis using local and distant regions of the genome.

Deep learning model of somatic hypermutation reveals importance of sequence context beyond hotspot targeting

B cells undergo somatic hypermutation (SHM) of the Immunoglobulin (Ig) variable region to generate high-affinity antibodies. SHM relies on the activity of activation-induced deaminase (AID), which mutates C>U preferentially targeting WRC (W=A/T, R=A/G) hotspots. Downstream mutations at WA Polymerase η hotspots contribute further mutations. Computational models of SHM can describe the probability of mutations essential for vaccine responses. Previous studies using short subsequences (k-mers) failed to explain divergent mutability for the same k-mer. We developed the DeepSHM (Deep learning on SHM) model using k-mers of size 5-21, improving accuracy over previous models. Interpretation of DeepSHM identified an extended WWRCT motif with particularly high mutability. Increased mutability was further associated with lower surrounding G content. Our model also discovered a conserved AGYCTGGGGG (Y=C/T) motif within FW1 of IGHV3 family genes with unusually high T>G substitution rates. Thus, a wider sequence context increases predictive power and identifies features that drive mutational targeting.

Adaptive Immune Receptor Repertoire (AIRR) Community Guide to Repertoire Analysis

Adaptive immune receptor repertoires (AIRRs) are rich with information that can be mined for insights into the workings of the immune system. Gene usage, CDR3 properties, clonal lineage structure, and sequence diversity are all capable of revealing the dynamic immune response to perturbation by disease, vaccination, or other interventions. Here we focus on a conceptual introduction to the many aspects of repertoire analysis and orient the reader toward the uses and advantages of each. Along the way, we note some of the many software tools that have been developed for these investigations and link the ideas discussed to chapters on methods provided elsewhere in this volume.