Immune repertoire mining for rapid affinity optimization of mouse monoclonal antibodies

Improvement of the sensitivity of lateral flow systems for detecting mycotoxins: Up-to-date strategies and future perspectives

Mycotoxins are dangerous human and animal health-threatening secondary fungal metabolites that can be found in various food and agricultural products. Several countries have established regulations to restrict their presence in food and agricultural products destined for human and animal consumption. Consequently, the need to develop highly sensitive and smart detection systems was recognized worldwide. Lateral flow assay possesses the advantages of easy operation, rapidity, stability, accuracy, and specificity, and it plays an important role in the detection of mycotoxins. Nevertheless, strategies to comprehensively improve the sensitivity of lateral flow assay to mycotoxins in food have rarely been highlighted and discussed. In this article, a comprehensive overview was presented on the application of lateral flow assay in mycotoxin detection in food samples by highlighting the principle of lateral flow assay, presenting a detailed discussion on various analytical performance-improvement strategies, such as the development of high-affinity recognition reagents, immunogen immobilization methods, and signal amplification. Additionally, a detailed discussion on the various signal analyzers and interpretation approaches was provided. Finally, current hurdles and future perspectives on the application of lateral flow assay in the detection of mycotoxins were discussed.

A novel complement-fixing IgM antibody targeting GPC1 as a useful immunotherapeutic strategy for the treatment of pancreatic ductal adenocarcinoma

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive cancers with a very low survival rate at 5 years. The use of chemotherapeutic agents results in only modest prolongation of survival and is generally associated with the occurrence of toxicity effects. Antibody-based immunotherapy has been proposed for the treatment of PDAC, but its efficacy has so far proved limited. The proteoglycan glypican-1 (GPC1) may be a useful immunotherapeutic target because it is highly expressed on the surface of PDAC cells, whereas it is not expressed or is expressed at very low levels in benign neoplastic lesions, chronic pancreatitis, and normal adult tissues. Here, we developed and characterized a specific mouse IgM antibody (AT101) targeting GPC1.

METHODS

We developed a mouse monoclonal antibody of the IgM class directed against an epitope of GPC1 in close proximity to the cell membrane. For this purpose, a 46 amino acid long peptide of the C-terminal region was used to immunize mice by an in-vivo electroporation protocol followed by serum titer and hybridoma formation.

RESULTS

The ability of AT101 to bind the GPC1 protein was demonstrated by ELISA, and by flow cytometry and immunofluorescence analysis in the GPC1-expressing "PDAC-like" BXPC3 cell line. In-vivo experiments in the BXPC3 xenograft model showed that AT101 was able to bind GPC1 on the cell surface and accumulate in the BXPC3 tumor masses. Ex-vivo analyses of BXPC3 tumor masses showed that AT101 was able to recruit immunological effectors (complement system components, NK cells, macrophages) to the tumor site and damage PDAC tumor tissue. In-vivo treatment with AT101 reduced tumor growth and prolonged survival of mice with BXPC3 tumor (p < 0.0001).

CONCLUSIONS

These results indicate that AT101, an IgM specific for an epitope of GPC1 close to PDAC cell surface, is a promising immunotherapeutic agent for GPC1-expressing PDAC, being able to selectively activate the complement system and recruit effector cells in the tumor microenvironment, thus allowing to reduce tumor mass growth and improve survival in treated mice.

Improved computational epitope profiling using structural models identifies a broader diversity of antibodies that bind to the same epitope

The function of an antibody is intrinsically linked to the epitope it engages. Clonal clustering methods, based on sequence identity, are commonly used to group antibodies that will bind to the same epitope. However, such methods neglect the fact that antibodies with highly diverse sequences can exhibit similar binding site geometries and engage common epitopes. In a previous study, we described SPACE1, a method that structurally clustered antibodies in order to predict their epitopes. This methodology was limited by the inaccuracies and incomplete coverage of template-based modeling. In addition, it was only benchmarked at the level of domain-consistency on one virus class. Here, we present SPACE2, which uses the latest machine learning-based structure prediction technology combined with a novel clustering protocol, and benchmark it on binding data that have epitope-level resolution. On six diverse sets of antigen-specific antibodies, we demonstrate that SPACE2 accurately clusters antibodies that engage common epitopes and achieves far higher dataset coverage than clonal clustering and SPACE1. Furthermore, we show that the functionally consistent structural clusters identified by SPACE2 are even more diverse in sequence, genetic lineage, and species origin than those found by SPACE1. These results reiterate that structural data improve our ability to identify antibodies that bind to the same epitope, adding information to sequence-based methods, especially in datasets of antibodies from diverse sources. SPACE2 is openly available on GitHub (https://github.com/oxpig/SPACE2).

KA-Search, a method for rapid and exhaustive sequence identity search of known antibodies

Antibodies with similar amino acid sequences, especially across their complementarity-determining regions, often share properties. Finding that an antibody of interest has a similar sequence to naturally expressed antibodies in healthy or diseased repertoires is a powerful approach for the prediction of antibody properties, such as immunogenicity or antigen specificity. However, as the number of available antibody sequences is now in the billions and continuing to grow, repertoire mining for similar sequences has become increasingly computationally expensive. Existing approaches are limited by either being low-throughput, non-exhaustive, not antibody specific, or only searching against entire chain sequences. Therefore, there is a need for a specialized tool, optimized for a rapid and exhaustive search of any antibody region against all known antibodies, to better utilize the full breadth of available repertoire sequences. We introduce Known Antibody Search (KA-Search), a tool that allows for the rapid search of billions of antibody variable domains by amino acid sequence identity across either the variable domain, the complementarity-determining regions, or a user defined antibody region. We show KA-Search in operation on the [Formula: see text]2.4 billion antibody sequences available in the OAS database. KA-Search can be used to find the most similar sequences from OAS within 30 minutes and a representative subset of 10 million sequences in less than 9 seconds. We give examples of how KA-Search can be used to obtain new insights about an antibody of interest. KA-Search is freely available at https://github.com/oxpig/kasearch .

Application of germline antibody features to vaccine development, antibody discovery, antibody optimization and disease diagnosis

Although the efficacy and commercial success of vaccines and therapeutic antibodies have been tremendous, designing and discovering new drug candidates remains a labor-, time- and cost-intensive endeavor with high risks. The main challenges of vaccine development are inducing a strong immune response in broad populations and providing effective prevention against a group of highly variable pathogens. Meanwhile, antibody discovery faces several great obstacles, especially the blindness in antibody screening and the unpredictability of the developability and druggability of antibody drugs. These challenges are largely due to poorly understanding of germline antibodies and the antibody responses to pathogen invasions. Thanks to the recent developments in high-throughput sequencing and structural biology, we have gained insight into the germline immunoglobulin (Ig) genes and germline antibodies and then the germline antibody features associated with antigens and disease manifestation. In this review, we firstly outline the broad associations between germline antibodies and antigens. Moreover, we comprehensively review the recent applications of antigen-specific germline antibody features, physicochemical properties-associated germline antibody features, and disease manifestation-associated germline antibody features on vaccine development, antibody discovery, antibody optimization, and disease diagnosis. Lastly, we discuss the bottlenecks and perspectives of current and potential applications of germline antibody features in the biotechnology field.

Anti-α-synuclein c-terminal antibodies block PFF uptake and accumulation of phospho-synuclein in preclinical models of Parkinson's disease

Parkinson's disease (PD), a neurodegenerative disease affecting dopaminergic (DA) neurons, is characterized by decline of motor function and cognition. Dopaminergic cell loss is associated with accumulation of toxic alpha synuclein aggregates. As DA neuron death occurs late in the disease, therapeutics that block the spread of alpha synuclein may offer functional benefit and delay disease progression. To test this hypothesis, we generated antibodies to the C terminal region of synuclein with high nanomolar affinity and characterized them in in vitro and in vivo models of spread. Interestingly, we found that only antibodies with high affinity to the distal most portion of the C-terminus robustly reduced uptake of alpha synuclein preformed fibrils (PFF) and accumulation of phospho (S129) alpha synuclein in cell culture. Additionally, the antibody treatment blocked the spread of phospho (S129) alpha synuclein associated-pathology in a mouse model of synucleinopathy. Blockade of neuronal PFF uptake by different antibodies was more predictive of in vivo activity than their binding potency to monomeric or oligomeric forms of alpha synuclein. These data demonstrate that antibodies directed to the C-terminus of the alpha synuclein have differential effects on target engagement and efficacy. Furthermore, our data provides additional support for the development of alpha synuclein antibodies as a therapeutic strategy for PD patients.

Structural Considerations in Affinity Maturation of Antibody-Based Biotherapeutic Candidates

Affinity maturation is an important stage in biologic drug discovery as is the natural process of generating an immune response inside the human body. In this chapter, we describe in silico approaches to affinity maturation via a worked example. Both advantages and limitations of the computational methods used are critically examined. Furthermore, construction of affinity maturation libraries and how their outputs might be implemented in an experimental setting are also described. It should be noted that structure-based design of biologic drugs is an emerging field and the tools currently available require further development. Furthermore, there are no standardized structure-based strategies yet for antibody affinity maturation as this research relies heavily on scientific logic as well as creative intuition.

A scalable model for simulating multi-round antibody evolution and benchmarking of clonal tree reconstruction methods

Affinity maturation (AM) of B cells through somatic hypermutations (SHMs) enables the immune system to evolve to recognize diverse pathogens. The accumulation of SHMs leads to the formation of clonal lineages of antibody-secreting b cells that have evolved from a common naïve B cell. Advances in high-throughput sequencing have enabled deep scans of B cell receptor repertoires, paving the way for reconstructing clonal trees. However, it is not clear if clonal trees, which capture microevolutionary time scales, can be reconstructed using traditional phylogenetic reconstruction methods with adequate accuracy. In fact, several clonal tree reconstruction methods have been developed to fix supposed shortcomings of phylogenetic methods. Nevertheless, no consensus has been reached regarding the relative accuracy of these methods, partially because evaluation is challenging. Benchmarking the performance of existing methods and developing better methods would both benefit from realistic models of clonal lineage evolution specifically designed for emulating B cell evolution. In this paper, we propose a model for modeling B cell clonal lineage evolution and use this model to benchmark several existing clonal tree reconstruction methods. Our model, designed to be extensible, has several features: by evolving the clonal tree and sequences simultaneously, it allows modeling selective pressure due to changes in affinity binding; it enables scalable simulations of large numbers of cells; it enables several rounds of infection by an evolving pathogen; and, it models building of memory. In addition, we also suggest a set of metrics for comparing clonal trees and measuring their properties. Our results show that while maximum likelihood phylogenetic reconstruction methods can fail to capture key features of clonal tree expansion if applied naively, a simple post-processing of their results, where short branches are contracted, leads to inferences that are better than alternative methods.

Similarity measurements of B cell receptor repertoire in baseline mice showed spectrum convergence of IgM

BACKGROUND

The B cell receptor (BCR) repertoire is highly diverse among individuals. Poor similarity of the spectrum among inbred baseline mice may limit the ability to discriminate true signals from those involving specific experimental factors. The repertoire similarity of the baseline status lacks intensive measurements.

RESULTS

We measured the repertoire similarity of IgH in blood and spleen samples from untreated BALB/c and C57BL/6J mice to investigate the baseline status of the two inbred strains. The antibody pool was stratified by the isotype of IgA, IgG and IgM. Between individuals, the results showed better convergence of CDR3 and clonal lineage profiles in IgM than in IgA and IgG, and better robustness of somatic mutation networks in IgM than in IgA and IgG. It also showed that the CDR3 clonotypes and clonal lineages shared better in the spleen samples than in the blood samples. The animal batch differences were detected in CDR3 evenness, mutated clonotype proportions, and maximal network degrees. A cut-off of 95% identity in the CDR3 nucleotide sequences was suitable for clonal lineage establishment.

CONCLUSIONS

Our findings reveal a natural landscape of BCR repertoire similarities between baseline mice and provide a solid reference for designing studies of mouse BCR repertoires.

Isolation of monoclonal antibodies from anti-synthetase syndrome patients and affinity maturation by recombination of independent somatic variants

The autoimmune disease known as Jo-1 positive anti-synthetase syndrome (ASS) is characterized by circulating antibody titers to histidyl-tRNA synthetase (HARS), which may play a role in modulating the non-canonical functions of HARS. Monoclonal antibodies to HARS were isolated by single-cell screening and sequencing from three Jo-1 positive ASS patients and shown to be of high affinity, covering diverse epitope space. The immune response was further characterized by repertoire sequencing from the most productive of the donor samples. In line with previous studies of autoimmune repertoires, these antibodies tended to have long complementarity-determining region H3 sequences with more positive-charged residues than average. Clones of interest were clustered into groups with related sequences, allowing us to observe different somatic mutations in related clones. We postulated that these had found alternate structural solutions for high affinity binding, but that mutations might be transferable between clones to further enhance binding affinity. Transfer of somatic mutations between antibodies within the same clonal group was able to enhance binding affinity in a number of cases, including beneficial transfer of a mutation from a lower affinity clone into one of higher affinity. Affinity enhancement was seen with mutation transfer both between related single-cell clones, and directly from related repertoire sequences. To our knowledge, this is the first demonstration of somatic hypermutation transfer from repertoire sequences to further mature in vivo derived antibodies, and represents an additional tool to aid in affinity maturation for the development of antibodies.

Trace Reconstruction Problems in Computational Biology

The problem of reconstructing a string from its error-prone copies, the trace reconstruction problem, was introduced by Vladimir Levenshtein two decades ago. While there has been considerable theoretical work on trace reconstruction, practical solutions have only recently started to emerge in the context of two rapidly developing research areas: immunogenomics and DNA data storage. In immunogenomics, traces correspond to mutated copies of genes, with mutations generated naturally by the adaptive immune system. In DNA data storage, traces correspond to noisy copies of DNA molecules that encode digital data, with errors being artifacts of the data retrieval process. In this paper, we introduce several new trace generation models and open questions relevant to trace reconstruction for immunogenomics and DNA data storage, survey theoretical results on trace reconstruction, and highlight their connections to computational biology. Throughout, we discuss the applicability and shortcomings of known solutions and suggest future research directions.

A computational method for immune repertoire mining that identifies novel binders from different clonotypes, demonstrated by identifying anti-pertussis toxoid antibodies

Due to their shared genetic history, antibodies from the same clonotype often bind to the same epitope. This knowledge is used in immune repertoire mining, where known binders are used to search bulk sequencing repertoires to identify new binders. However, current computational methods cannot identify epitope convergence between antibodies from different clonotypes, limiting the sequence diversity of antigen-specific antibodies that can be identified. We describe how the antibody binding site, the paratope, can be used to cluster antibodies with common antigen reactivity from different clonotypes. Our method, paratyping, uses the predicted paratope to identify these novel cross clonotype matches. We experimentally validated our predictions on a pertussis toxoid dataset. Our results show that even the simplest abstraction of the antibody binding site, using only the length of the loops involved and predicted binding residues, is sufficient to group antigen-specific antibodies and provide additional information to conventional clonotype analysis.

Abbreviations: BCR: B-cell receptor; CDR: complementarity-determining region; PTx: pertussis toxoid

Massively parallel single-cell B-cell receptor sequencing enables rapid discovery of diverse antigen-reactive antibodies

Obtaining full-length antibody heavy- and light-chain variable regions from individual B cells at scale remains a challenging problem. Here we use high-throughput single-cell B-cell receptor sequencing (scBCR-seq) to obtain accurately paired full-length variable regions in a massively parallel fashion. We sequenced more than 250,000 B cells from rat, mouse and human repertoires to characterize their lineages and expansion. In addition, we immunized rats with chicken ovalbumin and profiled antigen-reactive B cells from lymph nodes of immunized animals. The scBCR-seq data recovered 81% (n = 56/69) of B-cell lineages identified from hybridomas generated from the same set of B cells subjected to scBCR-seq. Importantly, scBCR-seq identified an additional 710 candidate lineages not recovered as hybridomas. We synthesized, expressed and tested 93 clones from the identified lineages and found that 99% (n = 92/93) of the clones were antigen-reactive. Our results establish scBCR-seq as a powerful tool for antibody discovery.