Traditionally, immunoglobulin (Ig) expression has been attributed solely to B cells/plasma cells with well-documented and accepted regulatory mechanisms governing Ig expression in B cells. Ig transcription is tightly controlled by a series of transcription factors. However, increasing evidence has recently demonstrated that Ig is not only produced by B cell lineages but also by various types of non-B cells (non-B-Ig). Under physiological conditions, non-B-Ig not only exhibits antibody activity but also regulates cellular biological activities (such as promoting cell proliferation, adhesion, and cytoskeleton protein activity). In pathological conditions, non-B-Ig is implicated in the development of various diseases including tumour, kidney disease, and other immune-related disorders. The mechanisms underline Ig gene rearrangement and transcriptional regulation of Ig genes in non-B cells are not fully understood. However, existing evidence suggests that these mechanisms in non-B cells differ from those in B cells. For instance, non-B-Ig gene rearrangement occurs in an RAG-independent manner; and Oct-1 and Oct-4, rather than Oct-2, are required for the transcriptional regulation of non-B derived Igs. In this chapter, we will describe and compare the mechanisms of gene rearrangement and expression regulation between B-Ig and non-B-Ig.

Our case report is of an elderly male with a history of IgM κ lymphoplasmacytic lymphoma (LPL) presenting with generalized neuropathy and weakness. Due to his LPL history and worsening renal function, he underwent a renal biopsy revealing the presence of μ heavy and λ light chains, revealing a diagnosis of amyloidosis with unbound heavy & light chains (AHL), a rare type of amyloidosis. His bone marrow biopsy demonstrated κ light chain restriction by flow cytometry and amyloid deposition. The patient\'s serum had elevated free κ and λ light chains with a free light chain (FLC) ratio of 3.17. Serum immunofixation was positive for IgM κ and λ light chain clones. He completed six cycles of cyclophosphamide, bortezomib, dexamethasone, and rituximab (CyBorD+R), normalizing the FLC ratio. Still, he continued to present with persistently elevated M protein, IgM κ, and λ light chains on immunofixation. Thereafter, daratumumab, a human monoclonal antibody directed against CD38 expressed on myeloma cells was initiated, which led to a negative immunofixation study after two cycles accompanied by a reduction in protein excretion in the urine. The patient achieved a complete hematological response with daratumumab. To date, our case is the only reported μ heavy and λ light chain amyloidosis patient with bi-clonal (IgM κ and λ) gammopathy to be successfully treated with daratumumab.

Analysis of an individual\'s immunoglobulin (IG) gene repertoire requires the use of high-quality germline gene reference sets. When sets only contain alleles supported by strong evidence, AIRR sequencing (AIRR-seq) data analysis is more accurate and studies of the evolution of IG genes, their allelic variants and the expressed immune repertoire is therefore facilitated.
The Adaptive Immune Receptor Repertoire Community (AIRR-C) IG Reference Sets have been developed by including only human IG heavy and light chain alleles that have been confirmed by evidence from multiple high-quality sources. To further improve AIRR-seq analysis, some alleles have been extended to deal with short 3\' or 5\' truncations that can lead them to be overlooked by alignment utilities. To avoid other challenges for analysis programs, exact paralogs (e.g. IGHV1-69*01 and IGHV1-69D*01) are only represented once in each set, though alternative sequence names are noted in accompanying metadata.
The Reference Sets include less than half the previously recognised IG alleles (e.g. just 198 IGHV sequences), and also include a number of novel alleles: 8 IGHV alleles, 2 IGKV alleles and 5 IGLV alleles. Despite their smaller sizes, erroneous calls were eliminated, and excellent coverage was achieved when a set of repertoires comprising over 4 million V(D)J rearrangements from 99 individuals were analyzed using the Sets. The version-tracked AIRR-C IG Reference Sets are freely available at the OGRDB website (https://ogrdb.airr-community.org/germline_sets/Human) and will be regularly updated to include newly observed and previously reported sequences that can be confirmed by new high-quality data.

Previous investigators have used various anti-HLA-F monoclonal antibodies (mAbs) to demonstrate that the tissue distribution of HLA-F is highly restricted. Notably, these mAbs differed in their immunodiagnostic capabilities. Specifically, mAbs Fpep1.1 and FG1 detected HLA-F intracellularly in B cells but not on the cell surface, whereas mAb 3D11 detected HLA-F on the cell surface. The presence of HLA-F on T cells was recognized by mAb FG1 but not by mAb Fpep1.1. mAb 3D11 detected HLA-F on the cell surface of activated B cells and on peripheral blood lymphocytes, but not on the normal cells. Importantly, mAb 3D11 revealed that HLA-F exists as a heavy chain (HC) monomer, rather than as an HC associated with B2m. Although these mAbs are believed to be specific to HLA-F, their monospecificity has not been formally established, which is critical for immunodiagnostic and therapeutic purposes. Previously, we investigated the diversity of HLA class I reactivities of anti-HLA-E mAbs using HLA-I coated multiplex bead assays on a Luminex platform. We reported that more than 80% of the HLA-E mAbs were cross-reactive with other HLA-I molecules, with exceptionally few truly HLA-E-monospecific mAbs. In the present investigation, we generated IgG mAbs against HCs of HLA-F in Balb/C mice and examined the cross-reactivity of anti-HLA-F mAbs with other HLA-I alleles using a multiplex bead assay on the Luminex platform. Beads coated with an array of HLA homo- and heterodimers of different HLA-Ia (HLA-A, HLA-B, and HLA-C) and Ib (HLA-E, HLA-F, and HLA-G) alleles were used to examine the binding of the anti-HLA-F mAbs. Only two mAbs were HLA-F monospecific, and five were HLA-Ib restricted. Several anti-HLA-F mAbs cross-reacted with HLA-E (n = 4), HLA-G (n = 3), HLA-Ia alleles (n = 9), HLA-G and HLA-Ia (n = 2), and HLA-Ib and HLA-Ia (n = 6). This monospecificity and polyreactivity were corroborated by the presence of HLA-F monospecific and HLA-I-shared sequences. This study emphasizes the need to monitor the mono-specificity of HLA-F for reliable immunodiagnostics and passive immunotherapy.

As the world continues to grapple with infectious diseases, scientists are constantly searching for effective ways to combat these deadly pathogens. One promising avenue of research is the use of nanobodies as neutralization agents. These small proteins, derived from camelid antibodies, have several unique advantages over traditional antibodies, including their small size. Nanobodies are much smaller than conventional antibodies, typically weighing in at around 15 kDa compared to the 150 kDa of a typical human antibody. This small size allows them to penetrate into tight spaces that larger molecules cannot reach, such as the crevices on the surface of viruses or bacteria. This makes them highly effective at neutralizing viruses by binding to and blocking their key functional sites. In this mini-review we discuss the construction approaches of nanobodies, and some methods to increase the half-life of nanobodies. Moreover, we discuss Nanobodies and their therapeutic potential for infectious agents.

As a result of smallpox immunization programs that ended more than 40 years ago, a significant portion of the world\'s population is not immune. Moreover, due to the lack of anti-monkeypox drugs and vaccines against monkeypox, the spread of this virus may be the beginning of another challenge. In this study, novel antibodies against monkeypox virus were modeled based on a heavy chain of human antibody and a small peptide fragment. Docking of modeled antibodies with C19L protein showed the range of docking energy, and root-mean-square deviation (RMSD) was from -124 to -154 kcal/mL and 4-6 angstrom, respectively. Also, docking of modeled antibodies-C19L complex with gamma Fc receptor type I illustrated the range of docking energy, and RMSD was from -132 to -155 kcal/ml and 5-7 angstrom, respectively. Moreover, molecular dynamics simulation showed that antibody 62 had the highest stability with the lowest energy level and RMSD. Interestingly, no modeled antibodies had immunogenicity, allergenicity, and toxicity. Although all of them had good stability, only antibodies 25, 28, 54, and 62 had a half-life of >10 h. Moreover, the interaction between C19L protein and anti-C19L antibodies (wild-type and synthetic) was evaluated by the SPR method. We found that KD in synthetic antibodies was lower than wild antibody. In terms of δH°, TδS°, and δG°, the results were consistent with binding parameters. Here, the lowest value of thermodynamic parameters was obtained for antibody 62. These data show that the synthetic antibodies, especially antibody 62, had a higher affinity than the wild-type antibody.

The outbreak and persistence of coronavirus disease 2019 (COVID-19) threaten human health. B cells play a vital role in fighting the infections caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Despite many studies on the immune responses in COVID-19 patients, it is still unclear how B cell receptor (BCR) constituents, including immunoglobulin heavy (IGHs) and light chains (IGLs), respond to SARS-CoV-2 in patients with varying symptoms. In this study, we conducted complementarity-determining region 3 (CDR3) sequencing of BCR IGHs and IGLs from the peripheral blood of COVID-19 patients and healthy donors. The results showed significantly reduced clonal diversity, more expanded clones, and longer CDR3 lengths of IGH and IGL in COVID-19 patients than those in healthy individuals. The IGLs had a much higher percentage of VJ skew usage (47.83% IGLV and 42.86% IGLJ were significantly regulated) than the IGHs (12.09% IGHV and 0% IGHJ) between the healthy individuals and patients, which indicated the importance of BCR light chains. Furthermore, we found a largely expanded IGLV3-25 gene cluster mostly pairing with IGLJ1 and ILGJ2 in COVID-19 patients and a newly identified upregulated IGLJ1 gene and IGLJ2+IGLV13-21 recombination, both of which are potential sources of SARS-CoV-2-targeting antibodies. Our findings on specific immune B-cell signatures associated with COVID-19 have clinical implications for vaccine and biomarker development for disease diagnosis.

Serum anti-HLA-I IgG are present in non-alloimmunized males, cancer patients, and transplant recipients. Anti-HLA-I antibodies are also present in intravenous immunoglobulin (IVIg), prepared from the plasma of thousands of healthy donors. However, the HLA-Ia reactivity of IVIg diminishes markedly after passing through HLA-E HC-affinity columns, suggesting that the HLA-I reactivity is due to antibodies formed against HLA-E. Hence, we examined whether anti-HLA-E antibodies can react to HLA-I alleles. Monoclonal IgG antibodies (mAbs) against HCs of two HLA-E alleles were generated in Balb/C mice. The antibodies were analyzed using multiplex bead assays on a Luminex platform for HLA-I reactivity. Beads coated with an array of HLA heterodimers admixed with HCs (LABScreen) were used to examine the binding of IgG to different HLA-Ia (31-HLA-A, 50-HLA-B, and 16-HLA-C) and Ib (2-HLA-E, one each of HLA-F and HLA-G) alleles. A striking diversity in the HLA-Ia and/or HLA-Ib reactivity of mAbs was observed. The number of the mAbs reactive to (1) only HLA-E (n = 25); (2) all HLA-Ib isomers (n = 8); (3) HLA-E and HLA-B (n = 5); (4) HLA-E, HLA-B, and HLA-C (n = 30); (5) HLA-E, HLA-A*1101, HLA-B, and HLA-C (n = 83); (6) HLA-E, HLA-A, HLA-B, and HLA-C (n = 54); and (7) HLA-Ib and HLA-Ia (n = 8), in addition to four other minor groups. Monospecificity and polyreactivity were corroborated by HLA-E monospecific and HLA-I shared sequences. The diverse HLA-I reactivity of the mAbs are compared with the pattern of HLA-I reactivity of serum-IgG in non-alloimmunized males, cancer patients, and ESKD patients. The findings unravel the diagnostic potential of the HLA-E monospecific-mAbs and immunomodulatory potentials of IVIg highly mimicking HLA-I polyreactive-mAbs.

Leukocyte cell-surface HLA-I molecules, involved in antigen presentation of peptides to CD8+ T-cells, consist of a heavy chain (HC) non-covalently linked to β2-microglobulin (β2m) (Face-1). The HC amino acid composition varies across all six isoforms of HLA-I, while that of β2m remains the same. Each HLA-allele differs in one or more amino acid sequences on the HC α1 and α2 helices, while several sequences among the three helices are conserved. HCs without β2m (Face-2) are also observed on human cells activated by malignancy, viral transformation, and cytokine or chemokine-mediated inflammation. In the absence of β2m, the monomeric Face-2 exposes immunogenic cryptic sequences on these cells as confirmed by HLA-I monoclonal antibodies (LA45, L31, TFL-006, and TFL-007). Furthermore, such exposure enables dimerization between two Face-2 molecules by SH-linkage, salt linkage, H-bonding, and van der Waal forces. In HLA-B27, the linkage between two heavy chains with cysteines at position of 67 of the amino acid residues was documented. Similarly, several alleles of HLA-A, B, C, E, F and G express cysteine at 67, 101, and 164, and additionally, HLA-G expresses cysteine at position 42. Thus, the monomeric HC (Face-2) can dimerize with another HC of its own allele, as homodimers (Face-3), or with a different HC-allele, as heterodimers (Face-4). The presence of Face-4 is well documented in HLA-F. The post-translational HLA-variants devoid of β2m may expose several cryptic linear and non-linear conformationally altered sequences to generate novel epitopes. The objective of this review, while unequivocally confirming the post-translational variants of HLA-I, is to highlight the scientific and clinical importance of the four faces of HLA and to prompt further research to elucidate their functions and their interaction with non-HLA molecules during inflammation, infection, malignancy and transplantation. Indeed, these HLA faces may constitute novel targets for passive and active specific immunotherapy and vaccines.

Antibodies specifically bind to antigens and are an essential part of the immune system. Hence, antibodies are powerful tools in research and diagnostics. High-throughput sequencing technologies have promoted comprehensive profiling of the immune repertoire, which has resulted in large amounts of antibody sequences that remain to be further analyzed. In this study, antibodies were downloaded from IMGT/LIGM-DB and Sequence Read Archive databases. Contributing features from antibody heavy chains were formulated as numerical inputs and fed into an ensemble machine learning classifier to classify the antigen specificity of six classes of antibodies, namely anti-HIV-1, anti-influenza virus, anti-pneumococcal polysaccharide, anti-citrullinated protein, anti-tetanus toxoid and anti-hepatitis B virus. The classifier was validated using cross-validation and a testing dataset. The ensemble classifier achieved a macro-average area under the receiver operating characteristic curve (AUC) of 0.9246 from the 10-fold cross-validation, and 0.9264 for the testing dataset. Among the contributing features, the contribution of the complementarity-determining regions was 53.1% and that of framework regions was 46.9%, and the amino acid mutation rates occupied the first and second ranks among the top five contributing features. The classifier and insights provided in this study could promote the mechanistic study, isolation and utilization of potential therapeutic antibodies.