Cattle produce Abs with an H chain ultralong CDR3 (40-70 aa). These Abs have been shown to have features such as broad neutralization of viruses and are investigated as human therapeutics. A common issue in sequencing the bovine BCR repertoire is the sequence length required to capture variable (V) and isotype gene information. This study aimed to assess the use of Oxford Nanopore Technologies\' MinION platform to perform IgM BCR repertoire sequencing to assess variation in the percentage of ultralong CDR3s among dairy cattle. Blood was collected from nine Holstein heifers. B cells were isolated using magnetic bead-based separation, RNA was extracted, and IgM+ transcripts were amplified using PCR and sequenced using a MinION R10.4 flow cell. The distribution of CDR3 lengths was trimodal, and the percentage of ultralong CDR3s ranged among animals from 2.32 to 20.13% in DNA sequences and 1.56% to 17.02% in productive protein sequences. V segment usage varied significantly among heifers. Segment IGHV1-7, associated with ultralong CDR3s, was used in 5.8-24.2% of sequences; usage was positively correlated with ultralong CDR3 production (r = 0.99, p < 0.01). To our knowledge, this is the first study to sequence the bovine BCR repertoire using Oxford Nanopore Technologies and demonstrates the potential for cost-efficient long-read repertoire sequencing in cattle without assembly. Findings from this study support literature describing the distribution of length and percentage of ultralong CDR3s. Future studies will investigate changes in the bovine BCR repertoire associated with age, antigenic exposure, and genetics.

BACKGROUND: The architecture and dynamics of T cell populations are critical in orchestrating the immune response to SARS-CoV-2. In our study, we used T Cell Receptor sequencing (TCRseq) to investigate TCR repertoires in 173 post-infection COVID-19 patients.
METHODS: The cohort included 98 mild and 75 severe cases with a median age of 53. We amplified and sequenced the TCR β chain Complementary Determining Region 3 (CDR3b) and performed bioinformatic analyses to assess repertoire diversity, clonality, and V/J allelic usage between age, sex and severity groups. CDR3b amino acid sequence inference was performed by clustering structural motifs and filtering validated reactive CDR3b to COVID-19.
RESULTS: Our results revealed a pronounced decrease in diversity and an increase in clonal expansion in the TCR repertoires of severe COVID-19 patients younger than 55 years old. These results reflect the observed trends in patients older than 55 years old (both mild and severe). In addition, we identified a significant reduction in the usage of key V alleles (TRBV14, TRBV19, TRBV15 and TRBV6-4) associated with disease severity. Notably, severe patients under 55 years old had allelic patterns that resemble those over 55 years old, accompanied by a skewed frequency of COVID-19-related motifs.
CONCLUSIONS: Present results suggest that severe patients younger than 55 may have a compromised TCR repertoire contributing to a worse disease outcome.

BACKGROUND: In this study, we comprehensively profiled the T-cell receptor (TCR) repertoire of the tumor and adjacent normal tissue in patients with HBV-associated hepatocellular carcinoma (HCC) and determined the baseline characteristics and clinical significance of TCR.
METHODS: High-throughput sequencing was used to determine the profile of complementarity-determining region 3 (CDR3) of the TCR-β chain variable (TRBV) in the tumor and normal tissue samples of 14 HCC patients. At the same time, TRBV diversity and differences in expression between tumor and normal tissues were investigated. The cumulative frequency of top 100 CDR3 (CF100), clonality, and Shannon entropy as indices to evaluate diversity, RESULTS: The diversity of TRBV CDR3 showed no significant difference between tumor and normal tissues. Of the 58 V gene segments in TRBV, TRBV16 and TRBV7-6 had a significantly higher frequency in the tumor group than in the normal group (p < 0.05). The frequency of 14 J gene segments showed no significant difference between tumor and normal tissues. In contrast, the frequency of 22 TRBVx/BJx combinations was significantly higher in the tumor than in the normal tissue. In addition, the length and type of TRBV CDR3 were similar in tumor and normal tissues, and a Gaussian distribution was observed in both groups.
CONCLUSIONS: This study provided a large amount of information about the TCR lineage in HBV-associated HCC, laying the foundation for further research. In addition, the fact that the immune repertoire (TRBV CDR3) hardly differs between tumor and adjacent normal tissue provides a new clue for exploring the mechanism of the liver as an organ with immune privileges.

OBJECTIVE: Wilms\' tumors are pediatric renal tumors that generally have a good prognosis and outcomes. Viral illnesses have been linked to development of neoplasms and should be considered as a factor that could modulate overall survival.
METHODS: We considered recently developed adaptive immune receptor, genomics and bioinformatics approaches to assess the potential impact of cytomegalovirus (CMV) infections in Wilms\' tumor.
RESULTS: T-cell receptor (TCR) complementarity determining region-3 (CDR3) amino acid sequences from Wilms\' tumor specimens represented by the Therapeutically Applicable Research to Generate Effective Treatments dataset were compared with known anti-CMV TCR CDR3s, indicating that cases representing the anti-CMV TCR CDR3s had worse outcomes. Then, a chemical complementarity scoring approach for the Wilms\' tumor, TCR CDR3s and a series of CMV antigens further indicated that cases representing a higher chemical complementarity to the CMV antigens had worse outcomes.
CONCLUSIONS: Overall, we present a potentially novel method to assess CMV infections and identify patients who could benefit from therapies that address such infections.

Traditional computational methods for antibody design involved random mutagenesis followed by energy function assessment for candidate selection. Recently, diffusion models have garnered considerable attention as cutting-edge generative models, lauded for their remarkable performance. However, these methods often focus solely on the backbone or sequence, resulting in the incomplete depiction of the overall structure and necessitating additional techniques to predict the missing component. This study presents Antibody-SGM, an innovative joint structure-sequence diffusion model that addresses the limitations of existing protein backbone generation models. Unlike previous models, Antibody-SGM successfully integrates sequence-specific attributes and functional properties into the generation process. Our methodology generates full-atom native-like antibody heavy chains by refining the generation to create valid pairs of sequences and structures, starting with random sequences and structural properties. The versatility of our method is demonstrated through various applications, including the design of full-atom antibodies, antigen-specific CDR design, antibody heavy chains optimization, validation with Alphafold3, and the identification of crucial antibody sequences and structural features. Antibody-SGM also optimizes protein function through active inpainting learning, allowing simultaneous sequence and structure optimization. These improvements demonstrate the promise of our strategy for protein engineering and significantly increase the power of protein design models.

Due to the high affinity and specificity of antibodies toward antigens, various antibody-based applications have been developed. Recently, variable antigen-binding domains of heavy-chain antibodies (VHH) have become an attractive alternative to conventional fragment antibodies due to their unique molecular characteristics. As an antibody-generating strategy, synthetic VHH libraries (including humanized VHH libraries) have been developed using distinct strategies to constrain the diversity of amino acid sequences. In this study, we designed and constructed several novel synthetic humanized VHH libraries based on biophysical analyses conducted using the complementarity determining region-grafting method and comprehensive sequence analyses of VHHs deposited in the protein data bank. We obtained VHHs from the libraries, and hit clones exhibited considerable thermal stability. We also found that VHHs from distinct libraries tended to have different epitopes. Based on our results, we propose a strategy for generating humanized VHHs with distinct epitopes toward various antigens by utilizing our library combinations.

B cell receptors (BCRs) play a crucial role in recognizing and fighting foreign antigens. High-throughput sequencing enables in-depth sampling of the BCRs repertoire after immunization. However, only a minor fraction of BCRs actively participate in any given infection. To what extent can we accurately identify antigen-specific sequences directly from BCRs repertoires? We present a computational method grounded on sequence similarity, aimed at identifying statistically significant responsive BCRs. This method leverages well-known characteristics of affinity maturation and expected diversity. We validate its effectiveness using longitudinally sampled human immune repertoire data following influenza vaccination and SARS-CoV-2 infections. We show that different lineages converge to the same responding Complementarity Determining Region 3, demonstrating convergent selection within an individual. The outcomes of this method hold promise for application in vaccine development, personalized medicine, and antibody-derived therapeutics.

Although γδ T cells are known to participate in immune dysregulation in solid tumors, their relevance to human microsatellite-stable (MSS) colorectal cancer (CRC) is still undefined. Here, using integrated gene expression analysis and T cell receptor sequencing, we characterized γδ T cells in MSS CRC, with a focus on Vδ1 + T cells. We identified Vδ1+ T cells with shared motifs in the third complementarity-determining region of the δ-chain, reflective of antigen recognition. Changes in gene and protein expression levels suggested a dysfunctional effector state of Vδ1+ T cells in MSS CRC, distinct from Vδ1+ T cells in microsatellite-instable (MSI). Interaction analysis highlighted an immunosuppressive role of fibroblasts in the dysregulation of Vδ1+ T cells in MSS CRC via the TIGIT-NECTIN2 axis. Blocking this pathway with a TIGIT antibody partially restored cytotoxicity of the dysfunctional Vδ1 phenotype. These results define an operative pathway in γδ T cells in MSS CRC.

Artificial Intelligence (AI) techniques have made great advances in assisting antibody design. However, antibody design still heavily relies on isolating antigen-specific antibodies from serum, which is a resource-intensive and time-consuming process. To address this issue, we propose a Pre-trained Antibody generative large Language Model (PALM-H3) for the de novo generation of artificial antibodies heavy chain complementarity-determining region 3 (CDRH3) with desired antigen-binding specificity, reducing the reliance on natural antibodies. We also build a high-precision model antigen-antibody binder (A2binder) that pairs antigen epitope sequences with antibody sequences to predict binding specificity and affinity. PALM-H3-generated antibodies exhibit binding ability to SARS-CoV-2 antigens, including the emerging XBB variant, as confirmed through in-silico analysis and in-vitro assays. The in-vitro assays validate that PALM-H3-generated antibodies achieve high binding affinity and potent neutralization capability against spike proteins of SARS-CoV-2 wild-type, Alpha, Delta, and the emerging XBB variant. Meanwhile, A2binder demonstrates exceptional predictive performance on binding specificity for various epitopes and variants. Furthermore, by incorporating the attention mechanism inherent in the Roformer architecture into the PALM-H3 model, we improve its interpretability, providing crucial insights into the fundamental principles of antibody design.

Overexpression of the secretory protein renalase-1 negatively impacts the survival of melanoma and pancreatic cancer patients, while inhibition of renalase-1 signaling drives tumor rejection by promoting T-cell activation. Thus, we investigated the chemical complementarity between melanoma-resident, T-cell receptor (TCR) complementarity-determining region 3 (CDR3) amino acid sequences (AAs) and the renalase-1 protein. Increasing complementarity of TCR CDR3s to renalase-1 AAs, as assessed by a chemical complementarity scoring algorithm, was associated with improved overall survival (OS) in melanoma patients. The expression levels of several immune signature genes were significantly, positively correlated with increasing TCR CDR3-renalase-1 complementarity scores. Additionally, the survival association observed with high complementarity of TCR CDR3s to renalase-1 AAs was more robust in cases with low renalase-1 gene expression levels. Mapping of TCR CDR3-renalase-1 in silico interaction sites identified major epitope candidates including RP220, the signaling module of the renalase-1 protein, consistent with the fact that a monoclonal antibody to RP220 is a potent inhibitor of melanoma growth. These findings indicate that renalase-1 is a potential antigen for TCR recognition in melanoma and could be considered as a target for immunotherapy.