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A human antibody reveals a conserved site on beta-coronavirus spike proteins and confers protection against SARS-CoV-2 infection
Broadly neutralizing antibodies (bnAbs) to coronaviruses (CoVs) are valuable in their own right as prophylactic and therapeutic reagents to treat diverse CoVs and as templates for rational pan-CoV vaccine design. We recently described a bnAb, CC40.8, from a CoV disease 2019 (COVID-19) convalescent donor that exhibits broad reactivity with human β-CoVs. Here, we showed that CC40.8 targets the conserved S2 stem helix region of the CoV spike fusion machinery. We determined a crystal structure of CC40.8 Fab with a SARS-CoV-2 S2 stem peptide at 1.6-Å resolution and found that the peptide adopted a mainly helical structure. Conserved residues in β-CoVs interacted with CC40.8 antibody, thereby providing a molecular basis for its broad reactivity. CC40.8 exhibited in vivo protective efficacy against SARS-CoV-2 challenge in two animal models. In both models, CC40.8-treated animals exhibited less weight loss and reduced lung viral titers compared to controls. Furthermore, we noted that CC40.8-like bnAbs are relatively rare in human COVID-19 infection, and therefore, their elicitation may require rational structure-based vaccine design strategies. Overall, our study describes a target on β-CoV spike proteins for protective antibodies that may facilitate the development of pan–β-CoV vaccines.
Modular adjuvant-free pan-HLA-DR-immunotargeting subunit vaccine against SARS-CoV-2 elicits broad sarbecovirus-neutralizing antibody responses
Subunit vaccines typically require co-administration with an adjuvant to elicit protective immunity, adding development hurdles that can impede rapid pandemic responses. To circumvent the need for adjuvant in a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) subunit vaccine, we engineer a thermostable immunotargeting vaccine (ITV) that leverages the pan-HLA-DR monoclonal antibody 44H10 to deliver the viral spike protein receptor-binding domain (RBD) to antigen-presenting cells. X-ray crystallography shows that 44H10 binds to a conserved epitope on HLA-DR, providing the basis for its broad HLA-DR reactivity. Adjuvant-free ITV immunization in rabbits and ferrets induces robust anti-RBD antibody responses that neutralize SARS-CoV-2 variants of concern and protect recipients from SARS-CoV-2 challenge. We demonstrate that the modular nature of the ITV scaffold with respect to helper T cell epitopes and diverse RBD antigens facilitates broad sarbecovirus neutralization. Our findings support anti-HLA-DR immunotargeting as an effective means to induce strong antibody responses to subunit antigens without requiring an adjuvant.
Immunization of cows with HIV envelope trimers generates broadly neutralizing antibodies to the V2-apex from the ultralong CDRH3 repertoire
The generation of broadly neutralizing antibodies (bnAbs) to conserved epitopes on HIV Envelope (Env) is one of the cornerstones of HIV vaccine research. The animal models commonly used for HIV do not reliably produce a potent broadly neutralizing serum antibody response, with the exception of cows. Cows have previously produced a CD4 binding site response by homologous prime and boosting with a native-like Env trimer. In small animal models, other engineered immunogens were shown to focus antibody responses to the bnAb V2-apex region of Env. Here, we immunized two groups of cows (n = 4) with two regimens of V2-apex focusing Env immunogens to investigate whether antibody responses could be generated to the V2-apex on Env. Group 1 was immunized with chimpanzee simian immunodeficiency virus (SIV)-Env trimer that shares its V2-apex with HIV, followed by immunization with C108, a V2-apex focusing immunogen, and finally boosted with a cross-clade native-like trimer cocktail. Group 2 was immunized with HIV C108 Env trimer followed by the same HIV trimer cocktail as Group 1. Longitudinal serum analysis showed that one cow in each group developed serum neutralizing antibody responses to the V2-apex. Eight and 11 bnAbs were isolated from Group 1 and Group 2 cows, respectively, and showed moderate breadth and potency. Potent and broad responses in this study developed much later than previous cow immunizations that elicited CD4bs bnAbs responses and required several different immunogens. All isolated bnAbs were derived from the ultralong CDRH3 repertoire. The finding that cow antibodies can target more than one broadly neutralizing epitope on the HIV surface reveals the generality of elongated structures for the recognition of highly glycosylated proteins. The exclusive isolation of ultralong CDRH3 bnAbs, despite only comprising a small percent of the cow repertoire, suggests these antibodies outcompete the long and short CDRH3 antibodies during the bnAb response.
The Making of a Metric: Co-Producing Decision-Relevant Climate Science
Developing decision-relevant science for adaptation requires the identification of climatic parameters that are both actionable for practitioners as well as tractable for modelers. In many sectors, these decision-relevant climatic metrics and the approaches that enable their identification remain largely unknown. “Co-production” of science with scientists and decision-makers is one potential way to identify these metrics, but there is little research describing specific and successful co-production approaches. This paper examines the negotiations and outcomes from Project Hyperion, wherein scientists and water managers jointly developed decision-relevant climatic metrics for adaptive water management. We identify successful co-production strategies by analyzing the project’s numerous back-and-forth engagements and tracing the evolution of the science during these engagements. We found that effective mediation between scientists and managers needed dedicated “boundary spanners” with significant modeling expertise. Translating practitioners’ information needs into tractable climatic metrics required direct and indirect methods of eliciting knowledge. We identified four indirect methods that were particularly salient for extracting tacitly held knowledge and enabling shared learning: developing a hierarchical framework linking management issues with metrics, starting discussions from the planning challenges, collaboratively exploring the planning relevance of new scientific capabilities, and using analogies of other “good” metrics. The decision-relevant metrics we developed provide insights into advancing adaptation-relevant climate science in the water sector. The co-production strategies we identified can be used to design and implement productive scientist–decision-maker interactions. Overall, the approaches and metrics we developed can help climate science to expand in new and more use-inspired directions.
Wabash CarbonSAFE Subtask 4.1 - Application of Policy Frameworks for Improved Carbon Capture and Storage Social Site Characterization & Stakeholder Engagement
Anthropogenic climate change threatens environmental and human health globally. Limiting these threats requires large-scale and innovative greenhouse gas mitigation responses across carbon-intensive energy and industrial sectors. Carbon capture and storage (CCS) technologies present potential opportunities for mitigating climate change while maintaining a diverse energy resource portfolio. Successful development of CCS physical sites requires effective and efficient project management solutions that elicit and incorporate the concerns and perspectives of diverse stakeholders. Due to the urgency of climate change mitigation technology implementation and the costs of CCS development, CCS project developers cannot risk setbacks by poor stakeholder assessment that concern management processes. Thus, this report presents four prominent policy frameworks and associated case studies as opportunities to improve CCS social site characterization and stakeholder engagement. After comparing the relative effectiveness and efficiencies of each framework with regard to CCS, this report concludes that the Advocacy Coalition Framework, Narrative Policy Framework, and Policy Conflict Framework can improve the CCS social site characterization process, while the Collaborative Governance Framework paired with the Q-Methodology provides an ideal framework for direct stakeholder engagement. Overall, this report finds that the Narrative Policy Framework and the Collaborative Governance Framework are most ideally suited for the purposes of CCS social site characterization and stakeholder engagement.
Oral delivery of a functional algal-expressed TGF-β mimic halts colitis in a murine DSS model
Inflammatory bowel disease (IBD) is a set of immunological disorders which can generate chronic pain and fatigue associated with the inflammatory symptoms. The treatment of IBD remains a significant hurdle with current therapies being only partially effective or having significant side effects, suggesting that new therapies that elicit different modes of action and delivery strategies are required. TGM1 is a TGF-β mimic that was discovered from the intestinal helminth parasite Heligmosomoides polygyrus and is thought to be produced by the parasite to suppress the intestinal inflammation response to help evade host immunity, making it an ideal candidate to be developed as a novel anti-inflammatory bio-therapeutic. Here we utilized the expression system of the edible green algae Chlamydomonas reinhardtii in order to recombinantly produce active TGM1 in a form that could be ingested. C. reinhardtii robustly expressed TGM1, and the resultant recombinant protein is biologically active as measured by regulatory T cell induction. When delivered orally to mice, the algal expressed TGM1 is able to ameliorate weight loss, lymphadenopathy, and disease symptoms in a mouse model of DSS-induced colitis, demonstrating the potential of this biologic as a novel treatment of IBD.
Trust in Artificial Intelligence: Meta-Analytic Findings
Objective The present meta-analysis sought to determine significant factors that predict trust in artificial intelligence (AI). Such factors were divided into those relating to (a) the human trustor, (b) the AI trustee, and (c) the shared context of their interaction. Background There are many factors influencing trust in robots, automation, and technology in general, and there have been several meta-analytic attempts to understand the antecedents of trust in these areas. However, no targeted meta-analysis has been performed examining the antecedents of trust in AI. Method Data from 65 articles examined the three predicted categories, as well as the subcategories of human characteristics and abilities, AI performance and attributes, and contextual tasking. Lastly, four common uses for AI (i.e., chatbots, robots, automated vehicles, and nonembodied, plain algorithms) were examined as further potential moderating factors. Results Results showed that all of the examined categories were significant predictors of trust in AI as well as many individual antecedents such as AI reliability and anthropomorphism, among many others. Conclusion Overall, the results of this meta-analysis determined several factors that influence trust, including some that have no bearing on AI performance. Additionally, we highlight the areas where there is currently no empirical research. Application Findings from this analysis will allow designers to build systems that elicit higher or lower levels of trust, as they require.
Resurfacing promotes antibacterial activity of a lipid A–binding nanobody
Nanobodies have been pursued as candidates for antimicrobial design due to their small size and versatile binding capacities, but direct antibacterial activity of a nanobody has yet to be described. Here, we employed a bacterial surface display platform to screen a synthetic library of nanobody variants for antimicrobial potential. We identified a candidate that binds the essential lipid A component of gram-negative lipopolysaccharide. Nonetheless, this nanobody required a weakened outer membrane to access its target and elicit its toxic activity. Borrowing from observations of innate immune proteins, we found that resurfacing nanobodies with positively charged residues enabled them to bind and perturb the gram-negative outer membrane, but this alone was not sufficient for toxic activity. However, when we resurface our lipid A-targeting nanobody, it gained the ability to disrupt the outer membrane and enact its antibacterial function against wild-type bacteria. This development of a dual-function nanobody that can reach and bind previously inaccessible gram-negative targets introduces a route for antimicrobial biologic advancement.
CyTRICS Impact-Based Prioritization Process
Cyber Testing for Resilient Industrial Control Systems™ (CyTRICS™) is the Department of Energy’s (DOE’s) program for cybersecurity vulnerability testing, digital subcomponent enumeration, and forensic assessment. CyTRICS leverages best-in-class test facilities and analytic capabilities at six DOE National Laboratories and strategic partnerships with key stakeholders including technology developers, manufacturers, asset owners and operators, and interagency partners. During the program’s development, CyTRICS established a unique methodology for prioritizing digital components within operational technology (OT) and industrial control systems (ICS) in the Energy Sector Industrial Base (ESIB) for cyber vulnerability testing. The CyTRICS prioritization process leverages multiple characteristics of systems, components, and their contextual deployment to calculate a quantification of individual digital components for CyTRICS testing. The initial version of the CyTRICS prioritization process was premised largely upon the impact which could result to an energy sector industrial control system if the digital component under testing was compromised, either through malicious means, faulty engineering, or other modes. CyTRICS has termed this process the “CyTRICS Impact-based Prioritization Process.” This paper describes the factors identified for use in the Impact-based Prioritization process and identifies the rationale for inclusion. During development, three National Laboratories piloted this prioritization process and generated prioritization scores for seven systems. Following the piloting of the process, laboratory subject matter experts (SME) validated that the numerical scores generated by the prioritization process were consistent with their knowledge of the impact that may occur should any of these systems be disrupted. The following document explains how to perform the prioritization process to generate prioritization scores for energy sector systems. After outlining assumptions required to conduct the process, it describes how to identify and elicit data which can be leveraged to evaluate a system and assign numerical values for each factor. The prioritization process uses different weights on different factors; rationale for each weight is included within the paper. Additionally, the paper includes some recommendations for future enhancements to prioritization, including lessons learned from developing and piloting the process. Finally, a comprehensive appendix includes example documents to be leveraged by those looking to execute the prioritization process.
Methods for R&D Portfolio Analysis and Evaluation (Workshop Report)
The Workshop on Methods for R&D Portfolio Analysis and Evaluation convened on 17–18 July 2019 at the National Renewable Energy Laboratory in Golden, Colorado, and examined strengths and weaknesses of the various methodologies applicable to R&D portfolio modeling, analysis, and decision support, given pragmatic constraints such as data availability, uncertainties in estimating the impact of R&D spending, and practical operational overheads. Participants employed their deep expertise in approaches such as stochastic optimization, real options, Monte-Carlo analysis, Bayesian networks, decision theory, complex systems analysis, deep uncertainty, and technology-evolution modeling to critique the initial example models developed by the project’s core team and to conduct thought experiments grounded in real-life technology models, progress data, expert elicitation, and portfolio information. This engagement of participants’ methodological expertise with the practical requirements of real-life portfolio decision support yielded ideas for improved approaches, alternative methodological hypotheses, and hybridization of methodologies that are well-grounded theoretically, computationally sound, and realistically executable given data availability and other practical constraints.
Methods for R&D Portfolio Analysis and Evaluation (Workshop Report)
The Workshop on Methods for R&D Portfolio Analysis and Evaluation convened on 17-18 July 2019 at the National Renewable Energy Laboratory in Golden, Colorado, and examined strengths and weaknesses of the various methodologies applicable to R&D portfolio modeling, analysis, and decision support, given pragmatic constraints such as data availability, uncertainties in estimating the impact of R&D spending, and practical operational overheads. Participants employed their deep expertise in approaches such as stochastic optimization, real options, Monte-Carlo analysis, Bayesian networks, decision theory, complex systems analysis, deep uncertainty, and technology-evolution modeling to critique the initial example models developed by the project’s core team and to conduct thought experiments grounded in real-life technology models, progress data, expert elicitation, and portfolio information. This engagement of participants’ methodological expertise with the practical requirements of real-life portfolio decision support yielded ideas for improved approaches, alternative methodological hypotheses, and hybridization of methodologies that are well-grounded theoretically, computationally sound, and realistically executable given data availability and other practical constraints.
Biosensors for the detection of chorismate and cis,cis -muconic acid in Corynebacterium glutamicum
Abstract Corynebacterium glutamicum ATCC 13032 is a promising microbial chassis for industrial production of valuable compounds, including aromatic amino acids derived from the shikimate pathway. In this work, we developed two whole-cell, transcription factor based fluorescent biosensors to track cis,cis-muconic acid (ccMA) and chorismate in C. glutamicum. Chorismate is a key intermediate in the shikimate pathway from which value-added chemicals can be produced, and a shunt from the shikimate pathway can divert carbon to ccMA, a high value chemical. We transferred a ccMA-inducible transcription factor, CatM, from Acinetobacter baylyi ADP1 into C. glutamicum and screened a promoter library to isolate variants with high sensitivity and dynamic range to ccMA by providing benzoate, which is converted to ccMA intracellularly. The biosensor also detected exogenously supplied ccMA, suggesting the presence of a putative ccMA transporter in C. glutamicum, though the external ccMA concentration threshold to elicit a response was 100-fold higher than the concentration of benzoate required to do so through intracellular ccMA production. We then developed a chorismate biosensor, in which a chorismate inducible promoter regulated by natively expressed QsuR was optimized to exhibit a dose-dependent response to exogenously supplemented quinate (a chorismate precursor). A chorismate–pyruvate lyase encoding gene, ubiC, was introduced into C. glutamicum to lower the intracellular chorismate pool, which resulted in loss of dose dependence to quinate. Further, a knockout strain that blocked the conversion of quinate to chorismate also resulted in absence of dose dependence to quinate, validating that the chorismate biosensor is specific to intracellular chorismate pool. The ccMA and chorismate biosensors were dually inserted into C. glutamicum to simultaneously detect intracellularly produced chorismate and ccMA. Biosensors, such as those developed in this study, can be applied in C. glutamicum for multiplex sensing to expedite pathway design and optimization through metabolic engineering in this promising chassis organism. One-Sentence Summary High-throughput screening of promoter libraries in Corynebacterium glutamicum to establish transcription factor based biosensors for key metabolic intermediates in shikimate and β-ketoadipate pathways.
Multiscale affinity maturation simulations to elicit broadly neutralizing antibodies against HIV
The design of vaccines against highly mutable pathogens, such as HIV and influenza, requires a detailed understanding of how the adaptive immune system responds to encountering multiple variant antigens (Ags). Here, we describe a multiscale model of B cell receptor (BCR) affinity maturation that employs actual BCR nucleotide sequences and treats BCR/Ag interactions in atomistic detail. We apply the model to simulate the maturation of a broadly neutralizing Ab (bnAb) against HIV. Starting from a germline precursor sequence of the VRC01 anti-HIV Ab, we simulate BCR evolution in response to different vaccination protocols and different Ags, which were previously designed by us. The simulation results provide qualitative guidelines for future vaccine design and reveal unique insights into bnAb evolution against the CD4 binding site of HIV. Our model makes possible direct comparisons of simulated BCR populations with results of deep sequencing data, which will be explored in future applications.
Expert Elicitation for Tidal Energy Levelized Cost of Energy: Present and Future
In accordance with the Government Performance and Results Act (GPRA), H2O annually assesses marine energy technology development resulting from government-funded research and development programs and strategy. For GPRA reporting, H2O uses the levelized cost of energy (LCOE) - which represents the total system cost per unit of energy produced - to measure the progression of marine energy technology development, assess the impact of their research and development programs, and identify future research priorities. To support H2O's GPRA reporting requirements and inform future strategy, the National Laboratory of the Rockies conducted a tidal energy LCOE expert elicitation study to estimate present and future LCOE[AB2.1]. This report describes the motivation and background for the elicitation study, the methodology used to conduct the study, and the study results. It also provides future recommendations for accelerated tidal energy LCOE reduction based on feedback from study participants.
A single, improbable B cell receptor mutation confers potent neutralization against cytomegalovirus
Cytomegalovirus (CMV) is a leading cause of infant hearing loss and neurodevelopmental delay, but there are no clinically licensed vaccines to prevent infection, in part due to challenges eliciting neutralizing antibodies. One of the most well-studied targets for CMV vaccines is the viral fusogen glycoprotein B (gB), which is required for viral entry into host cells. Within gB, antigenic domain 2 site 1 (AD-2S1) is a target of potently neutralizing antibodies, but gB-based candidate vaccines have yet to elicit robust responses against this region. We mapped the genealogy of B cells encoding potently neutralizing anti-gB AD-2S1 antibodies from their inferred unmutated common ancestor (UCA) and characterized the binding and function of early lineage ancestors. Surprisingly, we found that a single amino acid heavy chain mutation A33N, which was an improbable mutation rarely generated by somatic hypermutation machinery, conferred broad CMV neutralization to the non-neutralizing UCA antibody. Structural studies revealed that this mutation mediated key contacts with the gB AD-2S1 epitope. Collectively, these results provide insight into potently neutralizing gB-directed antibody evolution in a single donor and lay a foundation for using this B cell-lineage directed approach for the design of next-generation CMV vaccines.
Vaccination induces broadly neutralizing antibody precursors to HIV gp41
A key barrier to the development of vaccines that induce broadly neutralizing antibodies (bnAbs) against human immunodeficiency virus (HIV) and other viruses of high antigenic diversity is the design of priming immunogens that induce rare bnAb-precursor B cells. The high neutralization breadth of the HIV bnAb 10E8 makes elicitation of 10E8-class bnAbs desirable; however, the recessed epitope within gp41 makes envelope trimers poor priming immunogens and requires that 10E8-class bnAbs possess a long heavy chain complementarity determining region 3 (HCDR3) with a specific binding motif. We developed germline-targeting epitope scaffolds with affinity for 10E8-class precursors and engineered nanoparticles for multivalent display. Scaffolds exhibited epitope structural mimicry and bound bnAb-precursor human naive B cells in ex vivo screens, protein nanoparticles induced bnAb-precursor responses in stringent mouse models and rhesus macaques, and mRNA-encoded nanoparticles triggered similar responses in mice. Thus, germline-targeting epitope scaffold nanoparticles can elicit rare bnAb-precursor B cells with predefined binding specificities and HCDR3 features.
Estimating the Contributions to Human Error Probability from the Convolution of the Distribution of Time Available and Time Required
As part of their duties, Human Reliability Analysis must often evaluate if crews in nuclear power plants (NPPs) can complete tasks associated with a human-failure event within time limits. For example, the time required in NPP scenarios is determined by systematic and structured walkthroughs, feasibility studies, recorded times from training exercises, and interviews with experienced operators and experts. Typically, a point estimate is derived for the estimate (mean, maximum, or 95th percentile of time required). Using point-estimate values can mask the risk associated with variability among crews, plant conditions and set-up, environmental conditions, and other impact factors under which these actions are executed. While point estimates for time required and time available have served the industry well, without considering the uncertainty they could lead to biased understanding about the risk. The Integrated Human Event Analysis System - General Methodology (IDHEAS-G) model (developed by the US Nuclear Regulatory Commission, NRC) for human error probability calculates human error probability by summing two probabilities: insufficient time and cognitive error. As such, the model takes a more holistic approach by considering the full distributions for time required and time available to calculate the human error probability because the time available to complete the task is insufficient. In this study, we expand on the work of the NRC and discuss methods for estimating these time considerations. For example, for the time required, the impact of Performance Influencing Factors (PIFs) on the distribution was divided into impacts that are aleatory in nature, such as crew-to-crew variability, and those that are epistemic (i.e., the PIFs). Starting with the factors that introduce aleatory uncertainty, a first-order distribution was developed from a large set of time required (i.e., NPP task completion times) data for the range of operator actions that occur in the NPP control room under simulated accident conditions. The first-order distribution can then be adjusted to account for epistemic uncertainty using research associated with the impact of applicable PIFs on the time required. We also develop guidance for analysts to address the probability distributions for the time available. The guidance we developed on how to estimate time required and time available distributions is based on the identification of pertinent research and data, data analyses, and expert knowledge elicitation.