Clonal landscape of autoantibody-secreting plasmablasts in COVID-19 patients.

Whereas severe COVID-19 is often associated with elevated autoantibody titers, the underlying mechanism behind their generation has remained unclear. Here we report clonal composition and diversity of autoantibodies in humoral response to SARS-CoV-2. Immunoglobulin repertoire analysis and characterization of plasmablast-derived monoclonal antibodies uncovered clonal expansion of plasmablasts producing cardiolipin (CL)-reactive autoantibodies. Half of the expanded CL-reactive clones exhibited strong binding to SARS-CoV-2 antigens. One such clone, CoV1804, was reactive to both CL and viral nucleocapsid (N), and further showed anti-nucleolar activity in human cells. Notably, antibodies sharing genetic features with CoV1804 were identified in COVID-19 patient-derived immunoglobulins, thereby constituting a novel public antibody. These public autoantibodies had numerous mutations that unambiguously enhanced anti-N reactivity, when causing fluctuations in anti-CL reactivity along with the acquisition of additional self-reactivities, such as anti-nucleolar activity, in the progeny. Thus, potentially CL-reactive precursors may have developed multiple self-reactivities through clonal selection, expansion, and somatic hypermutation driven by viral antigens. Our results revealed the nature of autoantibody production during COVID-19 and provided novel insights into the origin of virus-induced autoantibodies.

Robust detection of infectious disease, autoimmunity, and cancer from the paratope networks of adaptive immune receptors.

Liquid biopsies based on peripheral blood offer a minimally invasive alternative to solid tissue biopsies for the detection of diseases, primarily cancers. However, such tests currently consider only the serum component of blood, overlooking a potentially rich source of biomarkers: adaptive immune receptors (AIRs) expressed on circulating B and T cells. Machine learning-based classifiers trained on AIRs have been reported to accurately identify not only cancers but also autoimmune and infectious diseases as well. However, when using the conventional "clonotype cluster" representation of AIRs, individuals within a disease or healthy cohort exhibit vastly different features, limiting the generalizability of these classifiers. This study aimed to address the challenge of classifying specific diseases from circulating B or T cells by developing a novel representation of AIRs based on similarity networks constructed from their antigen-binding regions (paratopes). Features based on this novel representation, paratope cluster occupancies (PCOs), significantly improved disease classification performance for infectious disease, autoimmune disease, and cancer. Under identical methodological conditions, classifiers trained on PCOs achieved a mean AUC of 0.893 when applied to new individuals, outperforming clonotype cluster-based classifiers (AUC 0.714) and the best-performing published classifier (AUC 0.777). Surprisingly, for cancer patients, we observed that "healthy-biased" AIRs were predicted to target known cancer-associated antigens at dramatically higher rates than healthy AIRs as a whole (Z scores >75), suggesting an overlooked reservoir of cancer-targeting immune cells that could be identified by PCOs.

The evolution of structural genomics.

Structural genomics began as a global effort in the 1990s to determine the tertiary structures of all protein families as a response to large-scale genome sequencing projects. The immediate outcome was an influx of tens of thousands of protein structures, many of which had unknown functions. At the time, the value of structural genomics was controversial. However, the structures themselves were only the most obvious output. In addition, these newly solved structures motivated the emergence of huge data science and infrastructure efforts, which, together with advances in Deep Learning, have brought about a revolution in computational molecular biology. Here, we review some of the computational research carried out at the Protein Data Bank Japan (PDBj) during the Protein 3000 project under the leadership of Haruki Nakamura, much of which continues to flourish today.