Development of LIBRA-seq for the guinea pig model system as a tool for the evaluation of antibody responses to multivalent HIV-1 vaccines.

Consistent elicitation of serum antibody responses that neutralize diverse clades of HIV-1 remains a primary goal of HIV-1 vaccine research. Prior work has defined key features of soluble HIV-1 Envelope (Env) immunogen cocktails that influence the neutralization breadth and potency of multivalent vaccine-elicited antibody responses including the number of Env strains in the regimen. We designed immunization groups that consisted of different numbers of SOSIP Env strains to be used in a cocktail immunization strategy: the smallest cocktail (group 2) consisted of a set of two Env strains, which were a subset of the three Env strains that made up group 3, which, in turn, were a subset of the six Env strains that made up group 4. Serum neutralizing titers were modestly broader in guinea pigs that were immunized with a cocktail of three Envs compared to cocktails of two and six, suggesting that multivalent Env immunization could provide a benefit but may be detrimental when the cocktail size is too large. We then adapted the LIBRA-seq platform for antibody discovery to be compatible with guinea pigs, and isolated several tier 2 neutralizing monoclonal antibodies. Three antibodies isolated from two separate guinea pigs were similar in their gene usage and CDR3s, establishing evidence for a guinea pig public clonotype elicited through vaccination. Taken together, this work investigated multivalent HIV-1 Env immunization strategies and provides a novel methodology for screening guinea pig B cell receptor antigen specificity at a high-throughput level using LIBRA-seq.IMPORTANCEMultivalent vaccination with soluble Env immunogens is at the forefront of HIV-1 vaccination strategies but little is known about the influence of the number of Env strains included in vaccine cocktails. Our results suggest that adding more strains is sometimes beneficial but may be detrimental when the number of strains is too high. In addition, we adapted the LIBRA-seq platform to be compatible with guinea pig samples and isolated several tier 2 neutralizing monoclonal antibodies, some of which share V and J gene usage and >70% CDR3 identity, thus establishing the existence of public clonotypes in guinea pigs elicited through vaccination.

SARS-CoV-2 antibodies from children exhibit broad neutralization and belong to adult public clonotypes.

From the beginning of the COVID-19 pandemic, children have exhibited different susceptibility to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, reinfection, and disease compared with adults. Motivated by the established significance of SARS-CoV-2-neutralizing antibodies in adults, here we characterize SARS-CoV-2-specific antibody repertoires in a young cohort of individuals aged from 5 months to 18 years old. Our results show that neutralizing antibodies in children possess similar genetic features compared to antibodies identified in adults, with multiple antibodies from children belonging to previously established public antibody clonotypes in adults. Notably, antibodies from children show potent neutralization of circulating SARS-CoV-2 variants that have cumulatively resulted in resistance to virtually all approved monoclonal antibody therapeutics. Our results show that children can rely on similar SARS-CoV-2 antibody neutralization mechanisms compared to adults and are an underutilized source for the discovery of effective antibody therapeutics to counteract the ever-evolving pandemic.

Negative Binomial Mixture Model for Identification of Noise in Antigen-Specificity Predictions by LIBRA-seq.

Motivation: LIBRA-seq (linking B cell receptor to antigen specificity by sequencing) provides a powerful tool for interrogating the antigen-specific B cell compartment and identifying antibodies against antigen targets of interest. Identification of noise in LIBRA-seq antigen count data is critical for improving antigen binding predictions for downstream applications including antibody discovery and machine learning technologies. Results: In this study, we present a method for denoising LIBRA-seq data by clustering antigen counts into signal and noise components with a negative binomial mixture model. This approach leverages the VRC01 negative control cells included in a recent LIBRA-seq study(Abu-Shmais et al.) to provide a data-driven means for identification of technical noise. We apply this method to a dataset of nine donors representing separate LIBRA-seq experiments and show that our approach provides improved predictions for in vitro antibody-antigen binding when compared to the standard scoring method used in LIBRA-seq, despite variance in data size and noise structure across samples. This development will improve the ability of LIBRA-seq to identify antigen-specific B cells and contribute to providing more reliable datasets for future machine learning based approaches to predicting antibody-antigen binding as the corpus of LIBRA-seq data continues to grow.

Archetype tasks link intratumoral heterogeneity to plasticity and cancer hallmarks in small cell lung cancer.

Small cell lung cancer (SCLC) tumors comprise heterogeneous mixtures of cell states, categorized into neuroendocrine (NE) and non-neuroendocrine (non-NE) transcriptional subtypes. NE to non-NE state transitions, fueled by plasticity, likely underlie adaptability to treatment and dismal survival rates. Here, we apply an archetypal analysis to model plasticity by recasting SCLC phenotypic heterogeneity through multi-task evolutionary theory. Cell line and tumor transcriptomics data fit well in a five-dimensional convex polytope whose vertices optimize tasks reminiscent of pulmonary NE cells, the SCLC normal counterparts. These tasks, supported by knowledge and experimental data, include proliferation, slithering, metabolism, secretion, and injury repair, reflecting cancer hallmarks. SCLC subtypes, either at the population or single-cell level, can be positioned in archetypal space by bulk or single-cell transcriptomics, respectively, and characterized as task specialists or multi-task generalists by the distance from archetype vertex signatures. In the archetype space, modeling single-cell plasticity as a Markovian process along an underlying state manifold indicates that task trade-offs, in response to microenvironmental perturbations or treatment, may drive cell plasticity. Stifling phenotypic transitions and plasticity may provide new targets for much-needed translational advances in SCLC. A record of this paper's Transparent Peer Review process is included in the supplemental information.

Unsupervised logic-based mechanism inference for network-driven biological processes.

Modern analytical techniques enable researchers to collect data about cellular states, before and after perturbations. These states can be characterized using analytical techniques, but the inference of regulatory interactions that explain and predict changes in these states remains a challenge. Here we present a generalizable, unsupervised approach to generate parameter-free, logic-based models of cellular processes, described by multiple discrete states. Our algorithm employs a Hamming-distance based approach to formulate, test, and identify optimized logic rules that link two states. Our approach comprises two steps. First, a model with no prior knowledge except for the mapping between initial and attractor states is built. We then employ biological constraints to improve model fidelity. Our algorithm automatically recovers the relevant dynamics for the explored models and recapitulates key aspects of the biochemical species concentration dynamics in the original model. We present the advantages and limitations of our work and discuss how our approach could be used to infer logic-based mechanisms of signaling, gene-regulatory, or other input-output processes describable by the Boolean formalism.

The Chemistry of Acylgermanes: Triacylgermenolates Represent Valuable Building Blocks for the Synthesis of a Variety of Germanium-Based Photoinitiators.

The formation of a stable triacylgermenolate 2 as a decisive intermediate was achieved by using three pathways. The first two methods involve the reaction of KOtBu or alternatively potassium with tetraacylgermane 1 yielding 2 via one electron transfer. The mechanism involves the formation of radical anions (shown by EPR). This reaction is highly efficient and selective. The third method is a classical salt metathesis reaction toward 2 in nearly quantitative yield. The formation of 2 was confirmed by NMR spectroscopy, UV-vis measurements, and X-ray crystallography. Germenolate 2 serves as a starting point for a wide variety of organo-germanium compounds. We demonstrate the potential of this intermediate by introducing new types of Ge-based photoinitiators 4b-4f. The UV-vis absorption spectra of 4b-4f show considerably increased band intensities due to the presence of eight or more chromophores. Moreover, compounds 4d-4f show absorption tailing up to 525 nm. The performance of these photoinitiators is demonstrated by spectroscopy (time-resolved EPR, laser flash photolysis (LFP), photobleaching (UV-vis)) and photopolymerization experiments (photo-DSC measurements).