Germinal centres (GC) are lymphoid structures where B cells acquire affinity-enhancing somatic hypermutations (SHM), with surviving clones differentiating into memory B cells (MBCs) and long-lived bone marrow plasma cells (BMPCs) [1][2][3][4][5] . SARS-CoV-2 mRNA vaccination induces a persistent GC response that lasts for at least six months in humans [6][7][8] . The fate of responding GC B cells as well as the functional consequences of such persistence have not been elucidated. We detected SARS-CoV-2 spike (S)-specific MBCs in 42 individuals who had received two doses of BNT162b2, a SARS-CoV-2 mRNA vaccine six months earlier. S-specific IgG-secreting BMPCs were detected in 9 out of 11 participants. Using a combined approach of sequencing the B cell receptors of responding blood plasmablasts and MBCs, lymph node GC and plasma cells and BMPCs from eight individuals and expression of the corresponding monoclonal antibodies (mAbs), we tracked the evolution of 1540 S-specific B cell clones. We show that early blood S-specific plasmablasts -on averageexhibited the lowest SHM frequencies. In comparison, SHM frequencies of S-specific GC B cells increased by 3.5-fold within six months after vaccination. S-specific MBCs and BMPCs accumulated high levels of SHM, which corresponded with enhanced anti-S antibody avidity in blood and affinity as well as neutralization capacity of BMPC-derived mAbs. This study documents how the striking persistence of SARS-CoV-2 vaccination-induced GC reaction in humans culminates in affinity-matured long-term antibody responses that potently neutralize the virus. B cell response to mRNA vaccinationWe have previously shown that vaccination of humans with The Pfizer-BioNTech SARS-CoV-2 mRNA vaccine, BNT162b2 induces a robust but transient circulating plasmablast (PB) response and a persistent germinal centre (GC) reaction in the draining lymph nodes 6 . Whether these persistent GC responses lead to the generation of affinity-matured memory B cells (MBCs) and long-lived bone marrow-resident plasma cells (BMPCs) remains unclear. To address this question, we analyzed long-term B cell responses in the participants enrolled in our previously described observational study of 43 healthy participants (13 with a history of SARS-CoV-2 infection) who received two doses of BNT162b2 (Extended Data Tables 1) 6,7 . Long-term blood samples (n=42) and fine needle aspirates (FNAs) of the draining axillary lymph nodes (n=15) were collected 29 weeks post-vaccination (Fig. 1a). Bone marrow aspirates were collected 29 (n=11) and 40 weeks (n=2) post-vaccination, with the latter time point used only for B cell receptor (BCR) repertoire profiling (Fig. 1a). None of the participants who contributed FNA or bone marrow specimens had SARS-CoV-2 infection history. GC B cells were detected in FNAs from all 15 participants (Fig. 1b, c, left panels, Extended Data Fig. 1a, Extended Data Table 2). All 14 participants with FNAs collected prior to week 29 generated S-binding GC B cell responses of varying magnitudes (Fig 1b, c, r...

We have recently shown that two-dimensional (2D) and force-regulated kinetics of TCR–pMHC-I interactions predict responses of CD8+ T cells. To test whether these findings are applicable to CD4+ T cells, we analyzed the in situ 3.L2 TCR–pMHC-II interactions for a well-characterized panel of altered peptide ligands on the T-cell surface using the adhesion frequency assay with a micropipette and the thermal fluctuation and force-clamp assays with a biomembrane force probe. We found that the 2D effective TCR–pMHC-II affinity and off-rate correlate with, but better predict the T-cell response than, the corresponding measurements with the surface plasmon resonance in three dimensions (3D). The 2D affinity of the CD4 for MHCII was very low, approaching the detection limit, making it 1-2 orders of magnitude lower than the affinity of CD8 for MHC-I. In addition, the signal-dependent cooperation between TCR and co-receptor for pMHC binding previously observed for CD8 was not observed for CD4. Interestingly, force elicited TCR–pMHC-II catch-slip bonds for agonists but slip-only bonds for antagonists, thereby amplifying the power of discrimination between APLs. These results show that the force-regulated 2D binding kinetics of the 3.L2 TCR for pMHC-II determine functions of CD4+ T cells.

The molecular basis underlying the specificity of alloreactive T cells for peptide-major histocompatibility complex ligands has been elusive. Here we describe a screen of 60 I-E(k)-alloreactive T cells and 83 naturally processed peptides that identified 9 reactive T cells. Three of the T cells responded to multiple, distinct peptides that shared no sequence homology. These T cells recognized each peptide-major histocompatibility complex ligand specifically and used a distinct constellation of I-E(k) contact residues for each interaction. Our studies show that alloreactive T cells have a 'germline-encoded' capacity to recognize multiple, distinct ligands and thus show 'polyspecificity', not degeneracy. Our findings help to explain the high frequency of alloreactive T cells and provide insight into the nature of T cell specificity.

To better understand TCR discrimination of multiple ligands, we have analyzed the crystal structures of two Hb peptide/I-Ek complexes that differ by only a single amino acid substitution at the P6 anchor position within the peptide (E73D). Detailed comparison of multiple independently determined structures at 1.9 Å resolution reveals that removal of a single buried methylene group can alter a critical portion of the TCR recognition surface. Significant variance was observed in the peptide P5-P8 main chain as well as a rotamer difference at LeuP8, ∼10 Å distal from the substitution. No significant variations were observed in the conformation of the two MHC class II molecules. The ligand alteration results in two peptide/MHC complexes that generate bulk T cell responses that are distinct and essentially nonoverlapping. For the Hb-specific T cell 3.L2, substitution reduces the potency of the ligand 1000-fold. Soluble 3.L2 TCR binds the two peptide/MHC complexes with similar affinity, although with faster kinetics. These results highlight the role of subtle variations in MHC Ag presentation on T cell activation and signaling.

Background-Network analysis techniques allow a more accurate reflection of underlying systems biology to be realized than traditional unidimensional molecular biology approaches. Using gene coexpression network analysis, we define the gene expression network topology of cardiac hypertrophy and failure and the extent of recapitulation of fetal gene expression programs in failing and hypertrophied adult myocardium. Methods and Results-We assembled all myocardial transcript data in the Gene Expression Omnibus (nϭ1617). Because hierarchical analysis revealed species had primacy over disease clustering, we focused this analysis on the most complete (murine) dataset (nϭ478). Using gene coexpression network analysis, we derived functional modules, regulatory mediators, and higher-order topological relationships between genes and identified 50 gene coexpression modules in developing myocardium that were not present in normal adult tissue. We found that known gene expression markers of myocardial adaptation were members of upregulated modules but not hub genes. We identified ZIC2 as a novel transcription factor associated with coexpression modules common to developing and failing myocardium. Of 50 fetal gene coexpression modules, 3 (6%) were reproduced in hypertrophied myocardium and 7 (14%) were reproduced in failing myocardium. One fetal module was common to both failing and hypertrophied myocardium. Conclusions-Network modeling allows systems analysis of cardiovascular development and disease. Although we did not find evidence for a global coordinated program of fetal gene expression in adult myocardial adaptation, our analysis revealed specific gene expression modules active during both development and disease and specific candidates for their regulation. (Circ Cardiovasc Genet. 2011;4:26-35.)