Maturation of the Human Immunoglobulin Heavy Chain Repertoire With Age

Abstract

B cells play a central role in adaptive immune processes, mainly through the production of antibodies. The maturation of the B cell system with age is poorly studied. We extensively investigated age-related alterations of naïve and antigen-experienced immunoglobulin heavy chain (IgH) repertoires. The most significant changes were observed in the first 10 years of life, and were characterized by altered immunoglobulin gene usage and an increased frequency of mutated antibodies structurally diverging from their germline precursors. Older age was associated with an increased usage of downstream IgH constant region genes and fewer antibodies with self-reactive properties. As mutations accumulated with age, the frequency of germline-encoded self-reactive antibodies decreased, indicating a possible beneficial role of self-reactive B cells in the developing immune system. Our results suggest a continuous process of change through childhood across a broad range of parameters characterizing IgH repertoires and stress the importance of using well-selected, age-appropriate controls in IgH studies.

Deep Sequencing of B Cell Receptor Repertoires From COVID-19 Patients Reveals Strong Convergent Immune Signatures

“…IGHA and IGHG BCR sequencing yielded on average 154,717 unique sequences, and 24,043 clonotypes per sample ( Supplementary Table 1 ). To characterize the B cell response in COVID-19, we compared this BCR repertoire data to BCR repertoire data from healthy controls obtained in a separate study ( Supplementary Table 2 ), but amplified using the same primer set ( 22 ). Comparing IGHV gene segment usage revealed a significantly different IGHV gene usage in COVID-19 patients compared to the healthy controls, most notably with increases in the usage of IGHV2-70 (3.6× IGHA, 3.9× IGHG increase), IGHV3-30 (1.7× IGHA, 1.4× IGHG increase), IGHV5-51 (3.2× IGHA, 3.2× IGHG increase), and IGHV4-34 (1.2× IGHA, 2.2× IGHG increase) in the COVID-19 patients ( Figure 1A ).…”

“…A large number (1,254) of highly convergent clonotypes unique to COVID-19 were identified. Our approach of subtracting the convergent clonotypes also observed in healthy controls, and controls receiving influenza vaccination ( 22 – 24 ), allowed us to identify convergence specific to the disease cohort. The unbiased nature of the BCR repertoire analysis approach means that, while these convergent clonotypes are likely to include many antibodies to the spike protein and other parts of the virus they may also include other protective antibodies, including those to host proteins.…”

“…The healthy control BCR sequence dataset used here has been described previously ( 22 ). It was prepared using the same primer set that was used in the current study, so comparisons should not suffer from protocol-specific biases.…”

Single-cell sequencing of plasma cells from COVID-19 patients reveals highly expanded clonal lineages produce specific and neutralizing antibodies to SARS-CoV-2

“…While these studies performed bulk sequencing (no linkage of natural pairing of V H and V L ) on peripheral B cells (mixture of B cell subsets) and did not interrogate antigen-specific binding, they do however offer a valuable reference of repertoire features such as germline gene usage during SARS-CoV-2 infection. We therefore compared the germline profiles from our single-cell repertoire data to the bulk repertoire data from these two studies and observed very similar trends in IGHV gene usage, including an increased usage of IGHV3-30, IGHV3-30-3, IGHV3-33 and IGHV5-51 relative to a healthy control repertoire data set (36) ( Fig 3D ). While an increased IGHJ6 usage was observed for the dataset from Nielsen et al, the overall usage distribution is similar for all datasets ( Fig 3E ).…”

“…during SARS-CoV-2 infection. We therefore compared the germline profiles from our single-cell repertoire data to the bulk repertoire data from these two studies and observed very similar trends in IGHV gene usage, including an increased usage of IGHV3-30, IGHV3-30-3, IGHV3-33 and IGHV5-51 relative to a healthy control repertoire data set (36) (Fig 3D). While an increased IGHJ6 usage was observed for the dataset from Nielsen et al, the overall usage distribution is similar for all datasets (Fig 3E).…”