Cohort-specific serological recognition of SARS-CoV-2 variant RBD antigens

Background: Estimating the response of different cohorts (e.g. vaccinated or critically ill) to new SARS-CoV-2 variants is important to customize measures of control. Thus, our goal was to evaluate binding of antibodies from sera of infected and vaccinated people to different antigens expressed by SARS-CoV-2 variants. Methods: We compared sera from vaccinated donors with sera from four patient/donor cohorts: critically ill patients admitted to an intensive care unit (split in sera collected between 2 and 7 days after admission and more than ten days later), a NIBSC/WHO reference panel of SARS-CoV-2 positive individuals, and ambulatory or hospitalized (but not critically ill) positive donors. Samples were tested with an anti-SARS-CoV-2 IgG serological assay designed with microplates coated with a SARS-CoV-2 RBD recombinant antigen. The same sample sets were also tested with microplates coated with antigens harbouring RBD mutations present in eleven of the most widespread variants. Results: Sera from vaccinated individuals exhibited higher antibody binding (P<0.001) than sera from infected (but not critically ill) individuals when tested against the WT and each of 11 variants' RBD. The optical density generated by sera from non-critically ill convalescence individuals upon binding to variant's antigens was different (P<0.05) from that of the WT in some variants-noteworthy, Beta, Gamma, Delta, and Delta Plus variants. Conclusions: Understanding differences in binding and neutralizing antibody titers against WT vs variant RBD antigens from different donor cohorts can help design variant-specific immunoassays and complement other diagnostic and clinical data to evaluate the epidemiology of new variants. Key Words: COVID-19; SARS-CoV-2 vaccine; SARS-CoV-2 variants; RBD mutations; antibody specificity; critically ill, immunoassays, serology.

A framework for multiplex imaging optimization and reproducible analysis

Multiplex imaging technologies are increasingly used for single-cell phenotyping and spatial characterization of tissues; however, transparent methods are needed for comparing the performance of platforms, protocols and analytical pipelines. We developed a python software, jinxif, for reproducible image processing and utilize Jupyter notebooks to share our optimization of signal removal, antibody specificity, background correction and batch normalization of the multiplex imaging with a focus on cyclic immunofluorescence (CyCIF). Our work both improves the CyCIF methodology and provides a framework for multiplexed image analytics that can be easily shared and reproduced.

Production of Specific Polyclonal Antibody against the Larvae of Corallivorous Gastropod Drupella fragum for Prediction of Outbreaks

The term corallivorous gastropod refers to a group of snails that feeds on coral and inhabits coral communities worldwide. Outbreaks of these species cause serious damage to coral communities. There are various reasons behind the outbreaks; however, further clarifications are needed. It may be possible to predict outbreaks by measuring the number of floating larvae of corallivorous gastropods in seawater. Drupella fragum is the most damaging species in Japan, so we produced antibodies against D. fragum larvae in order to easily detect this species in the field. Antibody specificity analysis in aquarium-hatched corallivorous gastropods showed a higher specificity against D. fragum compared to D. cornus. A field study using the antibody showed that many D. fragum larvae were detected from June to November at all stations. The larvae at the Shirigai station were collected in June and July in large numbers compared to the other stations. Large groups of D. fragum were collected around the sampling point in Shirigai in September 2016. Our results imply that there is a possibility that outbreaks could be predicted using this antibody.

Engineered natural killer cells impede the immunometabolic CD73-adenosine axis in solid tumors

Immunometabolic reprogramming due to CD73-produced adenosine is a recognized immunosuppressive mechanism contributing to immune evasion in solid tumors. Adenosine is not only known to contribute to tumor progression, but it has specific roles in driving dysfunction of immune cells, including natural killer (NK) cells. Here, we engineered NK cells to directly target the CD73-adenosine axis by blocking the enzymatic activity of CD73. In doing so, the engineered NK cells not only impaired adenosinergic metabolism driven by the hypoxic uptake of ATP by cancer cells, but also mediated killing of tumor cells due to the specific recognition of overexpressed CD73. This results in a "single agent" immunotherapy that combines antibody specificity, blockade of purinergic signaling, and killing of targets mediated by NK cells. We also showed that CD73-targeted NK cells are potent in vivo and result in tumor arrest, while promoting NK cell infiltration into CD73+ tumors and enhanced intratumoral activation.

Template Theories, the Rule of Parsimony, and Disregard for Irreproducibility—The Example of Linus Pauling’s Research on Antibody Formation

In 1940, Linus Pauling proposed his template theory of antibody formation, one of many such theories that rejected Paul Ehrlich’s selective theory of preformed “receptors” (antibodies), assuming instead a direct molding of antibody shapes onto that of the antigen. Pauling believed that protein shapes—independently of amino acid sequences—determined antibody specificity and biological specificity in general. His theory was informed by his pioneering work on protein structure, and it was inspired by the intuitive “rule of parsimony” and simplicity. In 1942, Pauling published his alleged success in producing specific artificial antibodies through experiments based on his 1940 theory. However, his experiments could not be reproduced by prominent immunochemists at the time, and, later, it became generally accepted that antibody specificity was not generated according to Pauling’s and others’ “instruction” template theories. A citation analysis shows that Pauling’s papers on antibody generation continue to be cited as, among other things, pioneering studies of a chemical technology called “molecular imprinting.” The examples of Pauling and other protein chemists are used in this paper to demonstrate that scientific belief, philosophical concepts, and subjective theory preferences facilitated the occurrence of irreproducibility in immunochemistry and beyond. The article points to long-term consequences for the scientific community if irreproducible results are not acknowledged. It concludes by arguing that despite the risks, e.g., for the occurrence and perpetuation of irreproducible results that they entail, subjectivity and a commitment to scientific convictions have often been pre-requisites for the generation, and holding on to, scientific innovation in the face of doubt and rejection from the scientific community.

A billion synthetic 3D-antibody-antigen complexes enable unconstrained machine-learning formalized investigation of antibody specificity prediction

Machine learning (ML) is a key technology to enable accurate prediction of antibody-antigen binding, a prerequisite for in silico vaccine and antibody design. Two orthogonal problems hinder the current application of ML to antibody-specificity prediction and the benchmarking thereof: (i) The lack of a unified formalized mapping of immunological antibody specificity prediction problems into ML notation and (ii) the unavailability of large-scale training datasets. Here, we developed the Absolut! software suite that allows the parameter-based unconstrained generation of synthetic lattice-based 3D-antibody-antigen binding structures with ground-truth access to conformational paratope, epitope, and affinity. We show that Absolut!-generated datasets recapitulate critical biological sequence and structural features that render antibody-antigen binding prediction challenging. To demonstrate the immediate, high-throughput, and large-scale applicability of Absolut!, we have created an online database of 1 billion antibody-antigen structures, the extension of which is only constrained by moderate computational resources. We translated immunological antibody specificity prediction problems into ML tasks and used our database to investigate paratope-epitope binding prediction accuracy as a function of structural information encoding, dataset size, and ML method, which is unfeasible with existing experimental data. Furthermore, we found that in silico investigated conditions, predicted to increase antibody specificity prediction accuracy, align with and extend conclusions drawn from experimental antibody-antigen structural data. In summary, the Absolut! framework enables the development and benchmarking of ML strategies for biotherapeutics discovery and design.

Analytical and physiological validation of an enzyme immunoassay to measure oxytocin in dog, wolf, and human urine samples

Oxytocin (OT) promotes pro-sociality, bonding, and cooperation in a variety of species. Measuring oxytocin metabolite (OTM) concentrations in urine or saliva provides intriguing opportunities to study human and animal behaviour with minimal disturbance. However, a thorough validation of analytical methods and an assessment of the physiological significance of these measures are essential. We conducted an analytical validation of a commercial Enzyme Immunoassay (EIA; Arbor OT assay kit) to measure OTM concentrations in dog, wolf, and human urine samples. To test the assay’s ability to detect changes in OTM concentrations, we administered oxytocin intranasally to 14 dogs. Assay performance with regard to parallelism was acceptable. Assay accuracy and extraction efficiency for dog and wolf samples were comparable to a previously validated assay (Enzo OT assay kit) but variation was smaller for human samples. Binding sensitivity and antibody specificity were better in the Arbor assay. Average OTM concentrations were more than twice as high as in comparable samples measured with the Enzo assay, highlighting a lack of comparability of absolute values between different assays. Changes in OTM concentrations after intranasal treatment were detected reliably. The Arbor assay met requirements of a “fit-for-purpose” validation with improvement of several parameters compared to the Enzo assay.

Antibody Watch: Text mining antibody specificity from the literature

Antibodies are widely used reagents to test for expression of proteins and other antigens. However, they might not always reliably produce results when they do not specifically bind to the target proteins that their providers designed them for, leading to unreliable research results. While many proposals have been developed to deal with the problem of antibody specificity, it is still challenging to cover the millions of antibodies that are available to researchers. In this study, we investigate the feasibility of automatically generating alerts to users of problematic antibodies by extracting statements about antibody specificity reported in the literature. The extracted alerts can be used to construct an “Antibody Watch” knowledge base containing supporting statements of problematic antibodies. We developed a deep neural network system and tested its performance with a corpus of more than two thousand articles that reported uses of antibodies. We divided the problem into two tasks. Given an input article, the first task is to identify snippets about antibody specificity and classify if the snippets report that any antibody exhibits non-specificity, and thus is problematic. The second task is to link each of these snippets to one or more antibodies mentioned in the snippet. The experimental evaluation shows that our system can accurately perform the classification task with 0.925 weighted F1-score, linking with 0.962 accuracy, and 0.914 weighted F1 when combined to complete the joint task. We leveraged Research Resource Identifiers (RRID) to precisely identify antibodies linked to the extracted specificity snippets. The result shows that it is feasible to construct a reliable knowledge base about problematic antibodies by text mining.

From antibody specificity to T cell recognition

Landsteiner’s definition of human blood groups and the genetic rules that govern blood transfusion represents a milestone in human genetics and a historic event in public health. His research into the specificity of serological reactions, although less well known, has had a critical influence on the development of contemporary views on immune recognition, clonal selection, and immunological self-tolerance.