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ABSTRACTAntibodies have increasingly been developed as drugs with over 100 now licensed in the US or EU. During development, it is often necessary to increase or reduce the affinity of an antibody and rational attempts to do so rely on having a structure of the antibody-antigen complex often obtained by modeling. The antigen-binding site consists primarily of six loops known as complementarity-determining regions (CDRs), and an open question has been whether these loops change their conformation when they bind to an antigen. Existing surveys of antibody-antigen complex structures have only examined CDR conformational change in case studies or small-scale surveys. With an increasing number of antibodies where both free and complexed structures have been deposited in the Protein Data Bank, a large-scale survey of CDR conformational change during binding is now possible. To this end, we built a dataset, AbAgDb, that currently includes 177 antibodies with high-quality CDRs, each of which has at least one bound and one unbound structure. We analyzed the conformational change of the Cα backbone of each CDR upon binding and found that, in most cases, the CDRs (other than CDR-H3) show minimal movement, while 70.6% and 87% of CDR-H3s showed global Cα RMSD ≤ 1.0Å and ≤ 2.0Å, respectively. We also compared bound CDR conformations with the conformational space of unbound CDRs and found most of the bound conformations are included in the unbound conformational space. In future, our results will contribute to developing insights into antibodies and new methods for modeling and docking.

Deep learning models have been shown to accurately predict protein structure from sequence, allowing researchers to explore protein space from the structural viewpoint. In this paper we explore whether "novel" features, such as distinct loop conformations can arise from these predictions despite not being present in the training data. Here we have used ABodyBuilder2, a deep learning antibody structure predictor, to predict the structures of ~1.5M paired antibody sequences. We examined the predicted structures of the canonical CDR loops and found that most of these predictions fall into the already described CDR canonical form structural space. We also found a small number of "new" canonical clusters composed of heterogeneous sequences united by a common sequence motif and loop conformation. Analysis of these novel clusters showed their origins to be either shapes seen in the training data at very low frequency or shapes seen at high frequency but at a shorter sequence length. To evaluate explicitly the ability of ABodyBuilder2 to extrapolate, we retrained several models whilst withholding all antibody structures of a specific CDR loop length or canonical form. These "starved" models showed evidence of generalisation across CDRs of different lengths, but they did not extrapolate to loop conformations which were highly distinct from those present in the training data. However, the models were able to accurately predict a canonical form even if only a very small number of examples of that shape were in the training data. Our results suggest that deep learning protein structure prediction methods are unable to make completely out-of-domain predictions for CDR loops. However, in our analysis we also found that even minimal amounts of data of a structural shape allow the method to recover its original predictive abilities. We have made the ~1.5 M predicted structures used in this study available to download at https://doi.org/10.5281/zenodo.10280181. [ABSTRACT FROM AUTHOR]

Deep learning models have been shown to accurately predict protein structure from sequence, allowing researchers to explore protein space from the structural viewpoint. In this paper we explore whether “novel” features, such as distinct loop conformations can arise from these predictions despite not being present in the training data. Here we have used ABodyBuilder2, a deep learning antibody structure predictor, to predict the structures of ~1.5M paired antibody sequences. We examined the predicted structures of the canonical CDR loops and found that most of these predictions fall into the already described CDR canonical form structural space. We also found a small number of “new” canonical clusters composed of heterogeneous sequences united by a common sequence motif and loop conformation. Analysis of these novel clusters showed their origins to be either shapes seen in the training data at very low frequency or shapes seen at high frequency but at a shorter sequence length. To evaluate explicitly the ability of ABodyBuilder2 to extrapolate, we retrained several models whilst withholding all antibody structures of a specific CDR loop length or canonical form. These “starved” models showed evidence of generalisation across CDRs of different lengths, but they did not extrapolate to loop conformations which were highly distinct from those present in the training data. However, the models were able to accurately predict a canonical form even if only a very small number of examples of that shape were in the training data. Our results suggest that deep learning protein structure prediction methods are unable to make completely out-of-domain predictions for CDR loops. However, in our analysis we also found that even minimal amounts of data of a structural shape allow the method to recover its original predictive abilities. We have made the ~1.5 M predicted structures used in this study available to download at https://doi.org/10.5281/zenodo.10280181.

BackgroundDevelopment of a diverse T-cell receptor β (TRB) repertoire is associated with immune recovery following hematopoietic cell transplantation (HCT) for severe combined immunodeficiency (SCID). High-throughput sequencing of the TRB repertoire allows evaluation of clonotype dynamics during immune reconstitution.ObjectivesWe investigated whether longitudinal analysis of the TRB repertoire would accurately describe T-cell receptor diversity and illustrate the quality of T-cell reconstitution following HCT or gene therapy for SCID.MethodsWe used high-throughput sequencing to study composition and diversity of the TRB repertoire in 27 infants with SCID at 3, 6, and 12 months and yearly posttreatment(s). Total RNA from peripheral blood was used as template to amplify TRB rearrangements.ResultsTRB sequence analysis showed poor diversity at 3 months, followed by significant improvement by 6 months after cellular therapies. Kinetics of development of TRB diversity were similar in patients with a range of underlying gene defects. However, in patients with RAG and DCLRE1C defects, HCT with no conditioning or immune suppression only resulted in lower diversity than did HCT with conditioning. HCT from a matched donor correlated with higher diversity than did HCT from a mismatched donor. Naive CD4+ T-cell count at 6 months post-HCT correlated with higher TRB diversity. A Shannon index of diversity of 5.2 or lower 3 months after HCT predicted a need for a second intervention.ConclusionsTRB repertoire after hematopoietic cell therapies for SCID provides a quantitative and qualitative measure of diversity of T-cell reconstitution and permits early identification of patients who may require a second intervention.

Preterm labor (PTL) is the leading cause of neonatal morbidity and mortality worldwide. Whereas many studies have investigated the maternal immune responses that cause PTL, fetal immune cell activation has recently been raised as an important contributor to the pathogenesis of PTL. In this study, we analyzed lymphocyte receptor repertoires in maternal and cord blood from 14 term and 10 preterm deliveries, hypothesizing that the high prevalence of infection in patients with PTL may result in specific changes in the T cell and B cell repertoires. We analyzed TCR β-chain (TCR-β) and IgH diversity, CDR3 lengths, clonal sharing, and preferential usage of variable and joining gene segments. Both TCR-β and IgH repertoires had shorter CDR3s compared with those in maternal blood. In cord blood samples, we found that CDR3 lengths correlated with gestational age, with shorter CDR3s in preterm neonates suggesting a less developed repertoire. Preterm cord blood displayed preferential usage of a number of genes. In preterm pregnancies, we observed significantly higher prevalence of convergent clones between mother/baby pairs than in term pregnancies. Together, our results suggest the repertoire of preterm infants displays a combination of immature features and convergence with maternal TCR-β clones compared with that of term infants. The higher clonal convergence in PTL could represent mother and fetus both responding to a shared stimulus like an infection. These data provide a detailed analysis of the maternal-fetal immune repertoire in term and preterm patients and contribute to a better understanding of neonate immune repertoire development and potential changes associated with PTL.

Using a targeted transcriptomics approach, we have analyzed resected brain tissue from a cohort of 53 pediatric epilepsy surgery cases, and have found that there is a spectrum of involvement of both the innate and adaptive immune systems as evidenced by the differential expression of immune-specific genes in the affected brain tissue. The specimens with the highest expression of immune-specific genes were from two Rasmussen encephalitis cases, which is known to be a neuro-immunological disease, but also from tuberous sclerosis complex (TSC), focal cortical dysplasia, and hemimegalencephaly surgery cases. We obtained T cell receptor (TCR) Vβ chain sequence data from brain tissue and blood from patients with the highest levels of T cell transcripts. The clonality indices and the frequency of the top 50 Vβ clonotypes indicated that T cells in the brain were clonally restricted. The top 50 Vβ clonotypes comprised both public and private (patient specific) clonotypes, and the TCR Vβ chain third complementarity region (CDR3) of the most abundant public Vβ clonotype in each brain sample was strikingly similar to a CDR3 that recognizes an immunodominant epitope in either human cytomegalovirus or Epstein Barr virus, or influenza virus A. We found that the frequency of 14 of the top 50 brain Vβ clonotypes from a TSC surgery case had significantly increased in brain tissue removed to control recurrent seizures 11 months after the first surgery. Conversely, we found that the frequency in the blood of 18 of the top 50 brain clonotypes from a second TSC patient, who was seizure free, had significantly decreased 5 months after surgery indicating that T cell clones found in the brain had contracted in the periphery after removal of the brain area associated with seizure activity and inflammation. However, the frequency of a public and a private clonotype significantly increased in the brain after seizures recurred and the patient underwent a second surgery. Combined single cell gene expression and TCR sequencing of brain-infiltrating leukocytes from the second surgery showed that the two clones were CD8 effector T cells, indicating that they are likely to be pathologically relevant.

Memory B cells (MBCs) are long-lived and produce high-affinity, generally, class-switched antibodies. Here, we use a multiparameter approach involving CD27 to segregate naïve B cells (NBC), IgD+ unswitched (unsw)MBCs and IgG+ or IgA+ class-switched (sw)MBCs from humans of different age, sex and race. Conserved antibody variable gene expression indicates that MBCs emerge through unbiased selection from NBCs. Integrative analyses of mRNAs, miRNAs, lncRNAs, chromatin accessibility and cis-regulatory elements uncover a core mRNA-ncRNA transcriptional signature shared by IgG+ and IgA+ swMBCs and distinct from NBCs, while unswMBCs display a transitional transcriptome. Some swMBC transcriptional signature loci are accessible but not expressed in NBCs. Profiling miRNAs reveals downregulated MIR181, and concomitantly upregulated MIR181 target genes such as RASSF6, TOX, TRERF1, TRPV3 and RORα, in swMBCs. Finally, lncRNAs differentially expressed in swMBCs cluster proximal to the IgH chain locus on chromosome 14. Our findings thus provide new insights into MBC transcriptional programs and epigenetic regulation, opening new investigative avenues on these critical cell elements in human health and disease.

The Consortium for Top-Down Proteomics (www.topdownproteomics.org) launched the present study to assess the current state of top-down mass spectrometry (TD MS) and middle-down mass spectrometry (MD MS) for characterizing monoclonal antibody (mAb) primary structures, including their modifications. To meet the needs of the rapidly growing therapeutic antibody market, it is important to develop analytical strategies to characterize the heterogeneity of a therapeutic product's primary structure accurately and reproducibly. The major objective of the present study is to determine whether current TD/MD MS technologies and protocols can add value to the more commonly employed bottom-up (BU) approaches with regard to confirming protein integrity, sequencing variable domains, avoiding artifacts, and revealing modifications and their locations. We also aim to gather information on the common TD/MD MS methods and practices in the field. A panel of three mAbs was selected and centrally provided to 20 laboratories worldwide for the analysis: Sigma mAb standard (SiLuLite), NIST mAb standard, and the therapeutic mAb Herceptin (trastuzumab). Various MS instrument platforms and ion dissociation techniques were employed. The present study confirms that TD/MD MS tools are available in laboratories worldwide and provide complementary information to the BU approach that can be crucial for comprehensive mAb characterization. The current limitations, as well as possible solutions to overcome them, are also outlined. A primary limitation revealed by the results of the present study is that the expert knowledge in both experiment and data analysis is indispensable to practice TD/MD MS.

Profiling immunoglobulin (Ig) receptor repertoires with specialized assays can be cost-ineffective and time-consuming. Here we report ImReP, a computational method for rapid and accurate profiling of the Ig repertoire, including the complementary-determining region 3 (CDR3), using regular RNA sequencing data such as those from 8,555 samples across 53 tissues types from 544 individuals in the Genotype-Tissue Expression (GTEx v6) project. Using ImReP and GTEx v6 data, we generate a collection of 3.6 million Ig sequences, termed the atlas of immunoglobulin repertoires (TAIR), across a broad range of tissue types that often do not have reported Ig repertoires information. Moreover, the flow of Ig clonotypes and inter-tissue repertoire similarities across immune-related tissues are also evaluated. In summary, TAIR is one of the largest collections of CDR3 sequences and tissue types, and should serve as an important resource for studying immunological diseases.

The T cell receptor (TCR) repertoire is diverse, thus allowing recognition of a wide range of pathogens by T cells. In humans, the study of the formation of TCR repertoires is problematic because of the difficulty in performing investigations in vivo. In this issue of the JCI, Khosravi-Maharlooei and colleagues describe a new humanized mouse model that allows direct investigations on this topic. Using high-throughput and single-cell TCR-complementarity-determining region 3 β (TCR-CDR3β) sequencing, the authors were able to demonstrate that human thymic selection is a major driver of TCR sequence sharing, also implicating a preferential selection of shared cross-reactive CDR3βs during repertoire formation.

BackgroundMutation of the IL2RG gene results in a form of severe combined immune deficiency (SCID-X1), which has been treated successfully with hematopoietic stem cell gene therapy. SCID-X1 gene therapy results in reconstitution of the previously lacking T cell compartment, allowing analysis of the roles of T cell immunity in humans by comparing before and after gene correction.MethodsHere we interrogate T cell reconstitution using four forms of high throughput analysis. (1) Estimation of the numbers of transduced progenitor cells by monitoring unique positions of integration of the therapeutic gene transfer vector. (2) Estimation of T cell population structure by sequencing of the recombined T cell receptor (TCR) beta locus. (3) Metagenomic analysis of microbial populations in oropharyngeal, nasopharyngeal, and gut samples. (4) Metagenomic analysis of viral populations in gut samples.ResultsComparison of progenitor and mature T cell populations allowed estimation of a minimum number of cell divisions needed to generate the observed populations. Analysis of microbial populations showed the effects of immune reconstitution, including normalization of gut microbiota and clearance of viral infections. Metagenomic analysis revealed enrichment of genes for antibiotic resistance in gene-corrected subjects relative to healthy controls, likely a result of higher healthcare exposure.ConclusionsThis multi-omic approach enables the characterization of multiple effects of SCID-X1 gene therapy, including T cell repertoire reconstitution, estimation of numbers of cell divisions between progenitors and daughter T cells, normalization of the microbiome, clearance of microbial pathogens, and modulations in antibiotic resistance gene levels. Together, these results quantify several aspects of the long-term efficacy of gene therapy for SCID-X1. This study includes data from ClinicalTrials.gov numbers NCT01410019, NCT01175239, and NCT01129544.

BackgroundDeep sequencing of lymphocyte receptor repertoires has made it possible to comprehensively profile the clonal composition of lymphocyte populations. This opens the door for novel approaches to diagnose and prognosticate diseases with a driving immune component by identifying repertoire sequence patterns associated with clinical phenotypes. Indeed, recent studies support the feasibility of this, demonstrating an association between repertoire-level summary statistics (e.g., diversity) and patient outcomes for several diseases. In our own prior work, we have shown that six codons in VH4-containing genes in B cells from the cerebrospinal fluid of patients with relapsing remitting multiple sclerosis (RRMS) have higher replacement mutation frequencies than observed in healthy controls or patients with other neurological diseases. However, prior methods to date have been limited to focusing on repertoire-level summary statistics, ignoring the vast amounts of information in the millions of individual immune receptors comprising a repertoire. We have developed a novel method that addresses this limitation by using innovative approaches for accommodating the extraordinary sequence diversity of immune receptors and widely used machine learning approaches. We applied our method to RRMS, an autoimmune disease that is notoriously difficult to diagnose.ResultsWe use the biochemical features encoded by the complementarity determining region 3 of each B cell receptor heavy chain in every patient repertoire as input to a detector function, which is fit to give the correct diagnosis for each patient using maximum likelihood optimization methods. The resulting statistical classifier assigns patients to one of two diagnosis categories, RRMS or other neurological disease, with 87% accuracy by leave-one-out cross-validation on training data (N = 23) and 72% accuracy on unused data from a separate study (N = 102).ConclusionsOur method is the first to apply statistical learning to immune repertoires to aid disease diagnosis, learning repertoire-level labels from the set of individual immune repertoire sequences. This method produced a repertoire-based statistical classifier for diagnosing RRMS that provides a high degree of diagnostic capability, rivaling the accuracy of diagnosis by a clinical expert. Additionally, this method points to a diagnostic biochemical motif in the antibodies of RRMS patients, which may offer insight into the disease process.

Understanding how broadly neutralizing antibodies (bnAbs) to HIV envelope (Env) develop during natural infection can help guide the rational design of an HIV vaccine. Here, we described a bnAb lineage targeting the Env V2 apex and the Ab-Env co-evolution that led to development of neutralization breadth. The lineage Abs bore an anionic heavy chain complementarity-determining region 3 (CDRH3) of 25 amino acids, among the shortest known for this class of Abs, and achieved breadth with only 10% nucleotide somatic hypermutation and no insertions or deletions. The data suggested a role for Env glycoform heterogeneity in the activation of the lineage germline B cell. Finally, we showed that localized diversity at key V2 epitope residues drove bnAb maturation toward breadth, mirroring the Env evolution pattern described for another donor who developed V2-apex targeting bnAbs. Overall, these findings suggest potential strategies for vaccine approaches based on germline-targeting and serial immunogen design.

Ciguatera poisoning (CP), caused by ciguatoxins (CTXs), is one of the most common food-borne diseases, affecting more than 50,000 people each year. In most cases, CP are managed with symptomatic and supportive remedies, and no specific treatment has been devised. In this study, toward the development of therapeutic antibodies for CP, we examined to humanize mouse anti-CTX3C antibody 10C9 (m10C9), which exhibited neutralizing activity against ciguatoxin in vitro and in vivo. The complementarity determining regions were grafted onto a human germline sequence with high sequence identity to m10C9, and the backmutations were examined to maintain the binding affinity. The optimized humanized antibody, Opt.h10C9Fab, showed a strong binding affinity to CTX3C with a high affinity (K D  = 19.0 nM), and only two backmutations of Arg L46 and Cys H94 in the framework regions were involved in determining the antigen binding affinity., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

VHH, i.e., VH domains of camelid single-chain antibodies, are very promising therapeutic agents due to their significant physicochemical advantages compared to classical mammalian antibodies. The number of experimentally solved VHH structures has significantly improved recently, which is of great help, because it offers the ability to directly work on 3D structures to humanise or improve them. Unfortunately, most VHHs do not have 3D structures. Thus, it is essential to find alternative ways to get structural information. The methods of structure prediction from the primary amino acid sequence appear essential to bypass this limitation. This review presents the most extensive overview of structure prediction methods applied for the 3D modelling of a given VHH sequence (a total of 21). Besides the historical overview, it aims at showing how model software programs have been shaping the structural predictions of VHHs. A brief explanation of each methodology is supplied, and pertinent examples of their usage are provided. Finally, we present a structure prediction case study of a recently solved VHH structure. According to some recent studies and the present analysis, AlphaFold 2 and NanoNet appear to be the best tools to predict a structural model of VHH from its sequence. [ABSTRACT FROM AUTHOR]

The molecular mechanism through which the interaction of a clonotypic αβ T-cell receptor (TCR) with a peptide-loaded major histocompatibility complex (p/MHC) leads to T-cell activation is not yet fully understood. Here we exploit a high-affinity TCR (B4.2.3) to examine the structural changes that accompany binding to its p/MHC ligand (P18-I10/H2-Dd). In addition to conformational changes in complementarity-determining regions (CDRs) of the TCR seen in comparison of unliganded and bound X-ray structures, NMR characterization of the TCR β-chain dynamics reveals significant chemical shift effects in sites removed from the MHC-binding site. Remodelling of electrostatic interactions near the Cβ H3 helix at the membrane-proximal face of the TCR, a region implicated in interactions with the CD3 co-receptor, suggests a possible role for an allosteric mechanism in TCR signalling. The contribution of these TCR residues to signal transduction is supported by mutagenesis and T-cell functional assays.

Proteases are frequent pharmacological targets, and their inhibitors are valuable drugs in multiple pathologies. The catalytic mechanism and the active-site fold, however, are largely conserved among the protease classes, making the development of the selective inhibitors exceedingly challenging. In our departure from the conventional strategies, we reviewed the structure of known camelid inhibitory antibodies, which block enzyme activities via their unusually long, convex-shaped paratopes. We synthesized the human Fab antibody library (over 1.25 × 109 individual variants) that carried the extended, 23- to 27-residue, complementarity-determining region (CDR)-H3 segments. As a proof of principle, we used the catalytic domain of matrix metalloproteinase-14 (MMP-14), a promalignant protease and a drug target in cancer, as bait. In our screens, we identified 20 binders, of which 14 performed as potent and selective inhibitors of MMP-14 rather than as broad-specificity antagonists. Specifically, Fab 3A2 bound to MMP-14 in the vicinity of the active pocket with a high 4.8 nM affinity and was similarly efficient (9.7 nM) in inhibiting the protease cleavage activity. We suggest that the convex paratope antibody libraries described here could be readily generalized to facilitate the design of the antibody inhibitors to many additional enzymes.

Studies of individual T cell antigen receptors (TCRs) have shed some light on structural features that underlie self-reactivity. However, the general rules that can be used to predict whether TCRs are self-reactive have not been fully elucidated. Here we found that the interfacial hydrophobicity of amino acids at positions 6 and 7 of the complementarity-determining region CDR3β robustly promoted the development of self-reactive TCRs. This property was found irrespective of the member of the β-chain variable region (Vβ) family present in the TCR or the length of the CDR3β. An index based on these findings distinguished Vβ2(+), Vβ6(+) and Vβ8.2(+) regulatory T cells from conventional T cells and also distinguished CD4(+) T cells selected by the major histocompatibility complex (MHC) class II molecule I-A(g7) (associated with the development of type 1 diabetes in NOD mice) from those selected by a non-autoimmunity-promoting MHC class II molecule I-A(b). Our results provide a means for distinguishing normal T cell repertoires versus autoimmunity-prone T cell repertoires.

Tumor infiltrating lymphocytes (TILs) have been associated with favorable prognosis in multiple tumor types. The Cancer Genome Atlas (TCGA) represents the largest collection of cancer molecular data, but lacks detailed information about the immune environment. Here, we show that exome reads mapping to the complementarity-determining-region 3 (CDR3) of mature T-cell receptor beta (TCRB) can be used as an immune DNA (iDNA) signature. Specifically, we propose a method to identify CDR3 reads in a breast tumor exome and validate it using deep TCRB sequencing. In 1,078 TCGA breast cancer exomes, the fraction of CDR3 reads was associated with TILs fraction, tumor purity, adaptive immunity gene expression signatures and improved survival in Her2+ patients. Only 2/839 TCRB clonotypes were shared between patients and none associated with a specific HLA allele or somatic driver mutations. The iDNA biomarker enriches the comprehensive dataset collected through TCGA, revealing associations with other molecular features and clinical outcomes.

Antibodies with conformational specificity are important for detecting and interfering with polypeptide aggregation linked to several human disorders. We are developing a motif-grafting approach for designing lead antibody candidates specific for amyloid-forming polypeptides such as the Alzheimer peptide (Aβ). This approach involves grafting amyloidogenic peptide segments into the complementarity-determining regions (CDRs) of single-domain (VH) antibodies. Here we have investigated the impact of polar mutations inserted at the edges of a large hydrophobic Aβ42 peptide segment (Aβ residues 17-42) in CDR3 on the solubility and conformational specificity of the corresponding VH domains. We find that VH expression and solubility are strongly enhanced by introducing multiple negatively charged or asparagine residues at the edges of CDR3, whereas other polar mutations are less effective (glutamine and serine) or ineffective (threonine, lysine, and arginine). Moreover, Aβ VH domains with negatively charged CDR3 mutations show significant preference for recognizing Aβ fibrils relative to Aβ monomers, whereas the same VH domains with other polar CDR3 mutations recognize both Aβ conformers. We observe similar behavior for a VH domain grafted with a large hydrophobic peptide from islet amyloid polypeptide (residues 8-37) that contains negatively charged mutations at the edges of CDR3. These findings highlight the sensitivity of antibody binding and solubility to residues at the edges of CDRs, and provide guidelines for designing other grafted antibody fragments with hydrophobic binding loops.

The antigen-binding sites in conventional antibodies are formed by hypervariable complementarity-determining regions (CDRs) from both heavy chains (HCs) and light chains (LCs). A deviation from this paradigm is found in a subset of bovine antibodies that bind antigens via an ultra-long CDR. The HCs bearing ultra-long CDRs pair with a restricted set of highly conserved LCs that convey stability to the antibody. Despite the importance of these LCs, their specific features remained unknown. Here, we show that the conserved bovine LC found in antibodies with ultra-long CDRs exhibits a distinct combination of favorable physicochemical properties such as good secretion from mammalian cells, strong dimerization, high stability, and resistance to aggregation. These physicochemical traits of the LCs arise from a combination of the specific sequences in the germline CDRs and a lambda LC framework. In addition to understanding the molecular architecture of antibodies with ultra-long CDRs, our findings reveal fundamental insights into LC characteristics that can guide the design of antibodies with improved properties., (© 2024 The Authors. Protein Science published by Wiley Periodicals LLC on behalf of The Protein Society.)

BackgroundRasmussen encephalitis (RE) is a rare neuroinflammatory disease characterized by intractable seizures and progressive atrophy on one side of the cerebrum. Perivascular cuffing and clusters of T cells in the affected cortical hemisphere are indicative of an active cellular immune response.MethodsPeripheral blood mononuclear cells (PBMCs) and brain-infiltrating lymphocytes (BILs) were isolated from 20 RE surgery specimens by standard methods, and CD3(+) T cell populations were analyzed by flow cytometry. Gamma delta T cell receptor spectratyping was carried out by nested PCR of reversed transcribed RNA extracted from RE brain tissue, followed by high resolution capillary electrophoresis. A MiSeq DNA sequencing platform was used to sequence the third complementarity determining region (CDR3) of δ1 chains.ResultsCD3(+) BILs from all of the RE brain specimens comprised both αβ and γδ T cells. The median αβ:γδ ratio was 1.9 (range 0.58-5.2) compared with a median ratio of 7.7 (range 2.7-40.8) in peripheral blood from the same patients. The αβ T cells isolated from brain tissue were predominantly CD8(+), and the majority of γδ T cells were CD4(-) CD8(-). Staining for the early activation marker CD69 showed that a fraction of the αβ and γδ T cells in the BILs were activated (median 42%; range 13-91%, and median 47%; range 14-99%, respectively). Spectratyping T cell receptor (TCR) Vδ1-3 chains from 14 of the RE brain tissue specimens indicated that the γδ T cell repertoire was relatively restricted. Sequencing δ1 chain PCR fragments revealed that the same prevalent CDR3 sequences were found in all of the brain specimens. These CDR3 sequences were also detected in brain tissue from 15 focal cortical dysplasia (FCD) cases.ConclusionNeuroinflammation in RE involves both activated αβ and γδ T cells. The presence of γδ T cells with identical TCR δ1 chain CDR3 sequences in all of the brain specimens examined suggests that a non-major histocompatibility complex (MHC)-restricted immune response to the same antigen(s) is involved in the etiology of RE. The presence of the same δ1 clones in CD brain implies the involvement of a common inflammatory pathway in both diseases.

Immunoglobulins (Ig) are produced by B lymphocytes as secreted antibodies or as part of the B-cell receptor. There is tremendous diversity of potential Ig transcripts (>1 × 10(12)) as a result of hundreds of germ-line gene segments, random nucleotide incorporation during joining of gene segments into a complete transcript, and the process of somatic hypermutation at individual nucleotides. This recombination and mutation process takes place in the maturing B cell and is responsible for the diversity of potential epitope recognition. Cancers arising from mature B cells are characterized by clonal production of Ig heavy (IGH@) and light chain transcripts, although whether the sequence has undergone somatic hypermutation is dependent on the maturation stage at which the neoplastic clone arose. Chronic lymphocytic leukemia (CLL) is the most common leukemia in adults and arises from a mature B cell with either mutated or unmutated IGH@ transcripts, the latter having worse prognosis and the assessment of which is routinely performed in the clinic. Currently, IGHV mutation status is assessed by Sanger sequencing and comparing the transcript to known germ-line genes. In this paper, we demonstrate that complete IGH@ V-D-J sequences can be computed from unselected RNA-seq reads with results equal or superior to the clinical procedure: in the only discordant case, the clinical transcript was out-of-frame. Therefore, a single RNA-seq assay can simultaneously yield gene expression profile, SNP and mutation information, as well as IGHV mutation status, and may one day be performed as a general test to capture multidimensional clinically relevant data in CLL.

Antibody phage display libraries combined with high-throughput selections have recently demonstrated tremendous promise to create the next generation of renewable, recombinant antibodies to study proteins and their many post-translational modification states; however, many challenges still remain, such as optimized antibody scaffolds. Recently, a single-chain fragment antigen binding (Fab) (scFab) format, in which the carboxy-terminus of the light chain is linked to the amino-terminus of the heavy chain, was described to potentially combine the high display levels of a single-chain fragment variable with the high stability of purified Fabs. However, this format required removal of the interchain disulfide bond to achieve modest display levels and subsequent bacterial expression resulted in high levels of aggregated scFab, hindering further use of scFabs. Here, we developed an improved scFab format that retains the interchain disulfide bond by increasing the linker length between the light and heavy chains to improve display and bacterial expression levels to 1-3 mg/L. Furthermore, rerouting of the scFab to the co-translational signal recognition particle pathway combined with reengineering of the signal peptide sequence results in display levels 24-fold above the original scFab format and 3-fold above parent Fab levels. This optimized scFab scaffold can be easily reformatted in a single step for expression in a bacterial or mammalian host to produce stable (Tm of 81 °C), predominantly monomeric (>90%) antibodies at a high yield. Ultimately, this new scFab format will advance high-throughput antibody generation platforms to discover the next generation of research and therapeutic antibodies.