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At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Trivalent mosaic or consensus HIV immunogens prime humoral and broader cellular immune responses in adults

Mosaic and consensus HIV-1 immunogens provide two distinct approaches to elicit greater breadth of coverage against globally circulating HIV-1 and have shown improved immunologic breadth in nonhuman primate models. This double-blind randomized trial enrolled 105 healthy HIV-uninfected adults who received 3 doses of either a trivalent global mosaic, a group M consensus (CON-S), or a natural clade B (Nat-B) gp160 env DNA vaccine followed by 2 doses of a heterologous modified vaccinia Ankara–vectored HIV-1 vaccine or placebo. We performed prespecified blinded immunogenicity analyses at day 70 and day 238 after the first immunization. T cell responses to vaccine antigens and 5 heterologous Env variants were fully mapped. Env-specific CD4 + T cell responses were induced in 71% of the mosaic vaccine recipients versus 48% of the CON-S recipients and 48% of the natural Env recipients. The mean number of T cell epitopes recognized was 2.5 (95% CI, 1.2–4.2) for mosaic recipients, 1.6 (95% CI, 0.82–2.6) for CON-S recipients, and 1.1 (95% CI, 0.62–1.71) for Nat-B recipients. Mean breadth was significantly greater in the mosaic group than in the Nat-B group using overall (P = 0.014), prime-matched (P = 0.002), heterologous (P = 0.046), and boost-matched (P = 0.009) measures. Overall T cell breadth was largely due to Env-specific CD4 + T cell responses. Priming with a mosaic antigen significantly increased the number of epitopes recognized by Env-specific T cells and enabled more, albeit still limited, cross-recognition of heterologous variants. Mosaic and consensus immunogens are promising approaches to address global diversity of HIV-1.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

When to vaccinate for seasonal influenza: check the peak forecast

Background Seasonal influenza infects 5-20% of people every year in the United States, resulting in hospitalizations, deaths, and adverse economic impacts. To mitigate these impacts, influenza vaccines are developed and distributed annually; however, growing evidence suggests that vaccine effectiveness (VE) wanes over the course of a flu season. Delaying influenza vaccination for older adults has attracted attention as a potential public health strategy. However, given the uncertainties in seasonal peak, vaccine effectiveness, and waning rates, postponing vaccination could also lead to increased morbidity, motivating an evaluation of a range of potential scenarios. The aim of this study was to investigate favorable age group-specific vaccination schedules that could lead to the greatest disease burden reduction. Methods We systematically investigated a broad range of vaccination start times for five age groups under six combinations of initial effectiveness and waning rates, based on influenza cases and vaccine uptake data from 10 influenza seasons. We defined the most favorable vaccination schedule as the one that resulted in the greatest reduction in disease burden. Results In scenarios with fast waning, all age groups benefit from delaying vaccination regardless of initial VE and peak timing. In scenarios with slower waning, results are mixed. For the ≥65 group, high initial VE and slow waning suggests that in early-peaking seasons, early vaccination most effectively reduces disease burden, while in late-peaking seasons delaying vaccination is most effective. For the ≥65 group in medium and low initial VE, and slow waning scenarios, delaying vaccination appears to prevent the greatest number of cases, regardless of whether the season peaks early or late. Conclusion The most favorable vaccination schedule is sensitive to changes in initial VE, waning rate, and peak timing. Given estimates of these quantities from statistical and immunological models and observations, our methods can inform vaccination recommendations in order to most effectively reduce the annual disease burden caused by seasonal influenza. Specifically, accurate peak timing forecasts for the upcoming season have the potential to guide decisions on when to vaccinate.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Exposing new taxonomic variation with inflammation — a murine model-specific genome database for gut microbiome researchers

The murine CBA/J mouse model widely supports immunology and enteric pathogen research. This model has illuminated Salmonella interactions with the gut microbiome since pathogen proliferation does not require disruptive pretreatment of the native microbiota, nor does it become systemic, thereby representing an analog to gastroenteritis disease progression in humans. Despite the value to broad research communities, microbiota in CBA/J mice are not represented in current murine microbiome genome catalogs. Here we present the first microbial and viral genomic catalog of the CBA/J murine gut microbiome. Using fecal microbial communities from untreated and Salmonella-infected, highly inflamed mice, we performed genomic reconstruction to determine the impacts on gut microbiome membership and functional potential. From high depth whole community sequencing (~ 42.4 Gbps/sample), we reconstructed 2281 bacterial and 4516 viral draft genomes. Salmonella challenge significantly altered gut membership in CBA/J mice, revealing 30 genera and 98 species that were conditionally rare and unsampled in non-inflamed mice. Additionally, inflamed communities were depleted in microbial genes that modulate host anti-inflammatory pathways and enriched in genes for respiratory energy generation. Our findings suggest decreases in butyrate concentrations during Salmonella infection corresponded to reductions in the relative abundance in members of the Alistipes. Strain-level comparison of CBA/J microbial genomes to prominent murine gut microbiome databases identified newly sampled lineages in this resource, while comparisons to human gut microbiomes extended the host relevance of dominant CBA/J inflammation-resistant strains. This CBA/J microbiome database provides the first genomic sampling of relevant, uncultivated microorganisms within the gut from this widely used laboratory model. Using this resource, we curated a functional, strain-resolved view on how Salmonella remodels intact murine gut communities, advancing pathobiome understanding beyond inferences from prior amplicon-based approaches. Salmonella-induced inflammation suppressed Alistipes and other dominant members, while rarer commensals like Lactobacillus and Enterococcus endure. The rare and novel species sampled across this inflammation gradient advance the utility of this microbiome resource to benefit the broad research needs of the CBA/J scientific community, and those using murine models for understanding the impact of inflammation on the gut microbiome more generally.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

A computational model tracks whole-lung Mycobacterium tuberculosis infection and predicts factors that inhibit dissemination

Mycobacterium tuberculosis (Mtb), the causative infectious agent of tuberculosis (TB), kills more individuals per year than any other infectious agent. Granulomas, the hallmark of Mtb infection, are complex structures that form in lungs, composed of immune cells surrounding bacteria, infected cells, and a caseous necrotic core. While granulomas serve to physically contain and immunologically restrain bacteria growth, some granulomas are unable to control Mtb growth, leading to bacteria and infected cells leaving the granuloma and disseminating, either resulting in additional granuloma formation (local or non-local) or spread to airways or lymph nodes. Dissemination is associated with development of active TB. It is challenging to experimentally address specific mechanisms driving dissemination from TB lung granulomas. Herein, we develop a novel hybrid multi-scale computational model, MultiGran, that tracks Mtb infection within multiple granulomas in an entire lung. MultiGran follows cells, cytokines, and bacterial populations within each lung granuloma throughout the course of infection and is calibrated to multiple non-human primate (NHP) cellular, granuloma, and whole-lung datasets. We show that MultiGran can recapitulate patterns of in vivo local and non-local dissemination, predict likelihood of dissemination, and predict a crucial role for multifunctional CD8+ T cells and macrophage dynamics for preventing dissemination.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Antibody recognition of the Pneumovirus fusion protein trimer interface

Human metapneumovirus (hMPV) is a leading cause of viral respiratory infection in children, and can cause severe lower respiratory tract infection in infants, the elderly, and immunocompromised patients. However, there remain no licensed vaccines or specific treatments for hMPV infection. Although the hMPV fusion (F) protein is the sole target of neutralizing antibodies, the immunological properties of hMPV F remain poorly understood. To further define the humoral immune response to the hMPV F protein, we isolated two new human monoclonal antibodies (mAbs), MPV458 and MPV465. Both mAbs are neutralizing in vitro and were determined to target a unique antigenic site using competitive biolayer interferometry. We determined both MPV458 and MPV465 have higher affinity for monomeric hMPV F than trimeric hMPV F. MPV458 was co-crystallized with hMPV F, and the mAb primarily interacts with an alpha helix on the F2 region of the hMPV F protein. Surprisingly, the major epitope for MPV458 lies within the trimeric interface of the hMPV F protein, suggesting significant breathing of the hMPV F protein must occur for host immune recognition of the novel epitope. In addition, significant glycan interactions were observed with a somatically mutated light chain framework residue. The data presented identifies a novel epitope on the hMPV F protein for epitope-based vaccine design, and illustrates a new mechanism for human antibody neutralization of viral glycoproteins.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Nanobody screening and machine learning guided identification of cross-variant anti-SARS-CoV-2 neutralizing heavy-chain only antibodies

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) continues to persist, demonstrating the risks posed by emerging infectious diseases to national security, public health, and the economy. Development of new vaccines and antibodies for emerging viral threats requires substantial resources and time, and traditional development platforms for vaccines and antibodies are often too slow to combat continuously evolving immunological escape variants, reducing their efficacy over time. Previously, we designed a next-generation synthetic humanized nanobody (Nb) phage display library and demonstrated that this library could be used to rapidly identify highly specific and potent neutralizing heavy chain-only antibodies (HCAbs) with prophylactic and therapeutic efficacy in vivo against the original SARS-CoV-2. In this study, we used a combination of high throughput screening and machine learning (ML) models to identify HCAbs with potent efficacy against SARS-CoV-2 viral variants of interest (VOIs) and concern (VOCs). To start, we screened our highly diverse Nb phage display library against several pre-Omicron VOI and VOC receptor binding domains (RBDs) to identify panels of cross-reactive HCAbs. Using HCAb affinity for SARS-CoV-2 VOI and VOCs (pre-Omicron variants) and model features from other published data, we were able to develop a ML model that successfully identified HCAbs with efficacy against Omicron variants, independent of our experimental biopanning workflow. This biopanning informed ML approach reduced the experimental screening burden by 78% to 90% for the Omicron BA.5 and Omicron BA.1 variants, respectively. The combined approach can be applied to other emerging viruses with pandemic potential to rapidly identify effective therapeutic antibodies against emerging variants.

Antibodies↗
DOE OSTI2024

Plant Disease Detection Technology Assessment

Visual inspections by US Customs and Border Protection agriculture specialists have identified approximately 20,000 regulated, quarantined pests each year in agricultural products entering the United States. Most of these pests identified in the Agriculture Quarantine and Inspection (AQI) program are insects. Many pathogens are difficult to detect in agricultural products, particularly in early stages of infection. New technologies can help to detect plant pathogens and the diseases that they cause. This technology assessment was performed for the US Department of Homeland Security Science and Technology Directorate (DHS S&T) through the Food, Agriculture, and Veterinary Defense (FAV-D) program to identify emerging technologies that could address this hard problem. These emerging technologies differ in their diagnostic sensitivities and specificities, as well as in their measurement time and training requirements. New instruments that detect volatile organic compounds characteristic of plant disease or pathogens could provide a less invasive inspection method. Dogs, which can successfully detect many concealed agricultural products, have also been trained to detect some plant pests and pathogens. Simple immunological tests offer sensitive and specific detection of many pathogens at the point of use. Advanced imaging methods that use AI to sort fruits and vegetables and recognize anomalies at high speeds could be used in cooperation with exporters to improve food quality and reduce pests. Advances in nucleic acid–based detection methods that have become gold standards for confirmatory diagnostics are now making those methods available for faster, point-of-use detection. These new methods should be developed in the context of AQI operational requirements, which apply risk-based sampling protocols to protect agriculture and facilitate commerce and passenger transit.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Data Summarization and Inference at Scale

This is the final report for the DOE ASCR grant SC-0022260, Data Summarization and Inference at Scale, PI: Alex Pothen, Purdue University. The goal of the project was to solve data-intensive and compute-intensive problems in the physical sciences, engineering, information science, data science, etc. by designing and implementing new algorithms that could work with a subset of the data. The four subgoals were: (a) The solution of problems where the data is too large to be stored in the memory of a computer. In this streaming model of computation, the data arrives as a stream of elements to the computer, each element is processed as it arrives, and a decision is made to discard the data or to store it; only a small subset of the data proportional to the size of the output solution is stored, and when all the data has been streamed, a solution to the problem is computed from the stored subset. (b) The use of machine learning methods to compute solutions to data-intensive problems. The use of GPUs is critical to obtain high performance on machine learning tasks, but their memory sizes are smaller relative to that of CPUs. For large-scale problems, the data is sampled many times, and small samples are used with repetition, for robustness, to compute solutions to inference tasks. This sampling reduces the memory required to solve the problem, but attention is needed to avoid slow convergence to the solutions, and reduced accuracy of inference. We propose submodular optimization, Large Language Models, and physics-informed neural networks to enable GPU computations here. (c) Modeling and visualization of high-dimensional data using interpretable features. Clinical proteomic data sets from immunology for the detection of cancer and other diseases are temporal and high-dimensional, and algorithms for visualizing these data sets using clinically interpretable features are lacking. We propose methods that compute distances based on the optimal transportation problem and graph edit distances to address this problem. We also propose the use of optimal transport-based distances, spatial statistics, and network structure to classify image data sets, We apply these algorithms to electron micrographs of the peripheral nervous system in the digestive tract. (d) The design of data-intensive algorithms on emerging architectures, specifically, noisy, intermediate-scale quantum (NISQ) devices. Quantum computers offer the possibility of exploring large solution spaces due to the principle of superposition, but current quantum computers are limited by few qubits, short coherence times due to noise, poor interconections among the qubits, etc. We propose the use of the divide and conquer paradigm to solve large-scale problems, wherein collections of small subproblems are solved on the quantum devices, and the solutions to the subproblems are integrated into a solution for the original problem on a classical computer.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Methods integrating innate and adaptive immune responses in human in vitro immunization assays

Rapid vaccine development and innovative immunotherapeutics are critical in the fight against emerging outbreaks and global pandemic threats, yet the high costs and prolonged timelines for developing new vaccines underscore the urgent need for robust, predictive pre-clinical testing platforms. The rapid down-selection of vaccine candidates and identification of optimal vaccine formulations can be performed using human in vitro immunization (IVI) assays that recapitulate the complex interactions of the innate and adaptive human immune response. In this review, we present a comprehensive evaluation of three key IVI platforms: the whole blood assay (WBA), monocyte-derived dendritic cell (MoDC) assay with dendritic cell-T cell interface assay (DTI), and the microphysiological human tissue construct assay (HTC). The WBA offers a cost-effective and straightforward approach, while the MoDC + DTI system represents the current gold standard for balancing experimental efficiency with immunological complexity. The HTC assay, by mimicking both spatial and temporal aspects of immune interactions, provides enhanced physiological relevance. We discuss the methodological advantages and limitations of each platform, explore their roles in rapid vaccine candidate screening, and propose strategies for integrating these assays with complementary in vivo models. These insights pave the way for refining IVI assays and accelerating the translational pipeline for next-generation vaccines and immunotherapies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Early Immune Modulation in European Seabass (Dicentrarchus labrax) Juveniles in Response to Betanodavirus Infection

The early host–pathogen interaction between European seabass (Dicentrarchus labrax) and Betanodavirus was examined by using juvenile fish infected intramuscularly with RGNNV (red-spotted grouper nervous necrosis virus). The time course selected for sampling (0–144 h post-infection (hpi)) covered the early stages of infection, with hematological, antioxidant and immunological responses examined. Early activation of the host’s immune system was seen in the first few hours post-infection (6 to 9 hpi), as evidenced by an increase in tnfα, cd28 and c3 expression in the head kidney of infected fish. Most hematological parameters that were examined showed significant differences between sampling times, including differences in the number of thrombocytes and various leukocyte populations. The plasma lysozyme concentration decreased significantly over the course of the trial, and most antioxidant parameters examined in the liver showed significant differences over the infection period. At 144 hpi, peak expression of tnfα and il-1β coincided with the appearance of disease symptoms, peak levels of virus in the brain and high levels of fish mortality. The results of the study show the importance of analyzing the early interactions between European seabass and Betanodavirus to establish early indicators of infection to prevent more severe outcomes of the infection from occurring.

Vaz, Mariana↗
DOE OSTI2025

Modeling Sepsis: Establishment and Validation of a 72-Hour Swine Model of Penetrating Abdominal Trauma

Background/Objectives: Fecal peritonitis following penetrating abdominal trauma is a serious condition that often results in sepsis and organ failure. The aim of our study was to develop a novel conscious porcine model of sepsis and organ dysfunction caused by multiple penetrating injuries to the small and large intestines. Methods: Twelve female Yorkshire pigs (average weight 50.6 ± 6.5 kg) were divided into two groups: Penetrating Abdominal Trauma (PAT) (n = 8) and Control (n = 4). All surgical procedures were performed under anesthesia with adequate analgesia. In the PAT group, the small and large intestines were punctured, and feces mixed with saline were introduced into the abdominal cavity to induce peritonitis. The Control group received sham surgery with only saline solution. The animals were observed in a conscious state over a period of 72 h, vital parameters were recorded, and blood samples were taken regularly. We adapted a pig-specific SOFA score and developed pig-specific SIRS criteria and NEWS2 score to assess organ function. The model was validated by independent investigators. Results: The survival rate in the PAT group was 75%, with an average survival time of 58.5 h, while all animals in the Control group survived to euthanasia. Monitoring showed pathophysiological changes, such as tachycardia, leucopenia, and thrombocytopenia, indicative of sepsis and organ dysfunction. Blinded investigators independently confirmed the model’s validity. Conclusions: A new swine model of penetrating abdominal trauma and sepsis has been successfully developed that demonstrates significant physiological and immunologic changes comparable to human sepsis. This new model provides a realistic platform for future research into sepsis, its diagnostics, and the evaluation of therapeutic strategies.

General & Internal Medicine↗
DOE OSTI2023

Targeted Quantification of Protein Phosphorylation and Its Contributions towards Mathematical Modeling of Signaling Pathways

Post-translational modifications (PTMs) are key regulatory mechanisms that can control protein function. Of these, phosphorylation is the most common and widely studied. Because of its importance in regulating cell signaling, precise and accurate measurements of protein phosphorylation across wide dynamic ranges are crucial to understanding how signaling pathways function. Although immunological assays are commonly used to detect phosphoproteins, their lack of sensitivity, specificity, and selectivity often make them unreliable for quantitative measurements of complex biological samples. Recent advances in Mass Spectrometry (MS)-based targeted proteomics have made it a more useful approach than immunoassays for studying the dynamics of protein phosphorylation. Selected reaction monitoring (SRM)—also known as multiple reaction monitoring (MRM)—and parallel reaction monitoring (PRM) can quantify relative and absolute abundances of protein phosphorylation in multiplexed fashions targeting specific pathways. In addition, the refinement of these tools by enrichment and fractionation strategies has improved measurement of phosphorylation of low-abundance proteins. The quantitative data generated are particularly useful for building and parameterizing mathematical models of complex phospho-signaling pathways. Potentially, these models can provide a framework for linking analytical measurements of clinical samples to better diagnosis and treatment of disease.

mathematical modeling↗
DOE OSTI2021

Design, Synthesis, and Utility of Defined Molecular Scaffolds

A growing theme in chemistry is the joining of multiple organic molecular building blocks to create functional molecules. Diverse derivatizable structures—here termed “scaffolds” comprised of “hubs”—provide the foundation for systematic covalent organization of a rich variety of building blocks. This review encompasses 30 tri- or tetra-armed molecular hubs (e.g., triazine, lysine, arenes, dyes) that are used directly or in combination to give linear, cyclic, or branched scaffolds. Each scaffold is categorized by graph theory into one of 31 trees to express the molecular connectivity and overall architecture. Rational chemistry with exacting numbers of derivatizable sites is emphasized. The incorporation of water-solubilization motifs, robust or self-immolative linkers, enzymatically cleavable groups and functional appendages affords immense (and often late-stage) diversification of the scaffolds. Altogether, 107 target molecules are reviewed along with 19 syntheses to illustrate the distinctive chemistries for creating and derivatizing scaffolds. The review covers the history of the field up through 2020, briefly touching on statistically derivatized carriers employed in immunology as counterpoints to the rationally assembled and derivatized scaffolds here, although most citations are from the past two decades. The scaffolds are used widely in fields ranging from pure chemistry to artificial photosynthesis and biomedical sciences.

Sato, Daisuke (ORCID:0000000305472659)↗
DOE OSTI2020

The International Virus Bioinformatics Meeting 2020

The International Virus Bioinformatics Meeting 2020 was originally planned to take place in Bern, Switzerland, in March 2020. However, the COVID-19 pandemic put a spoke in the wheel of almost all conferences to be held in 2020. After moving the conference to 8–9 October 2020, we got hit by the second wave and finally decided at short notice to go fully online. On the other hand, the pandemic has made us even more aware of the importance of accelerating research in viral bioinformatics. Advances in bioinformatics have led to improved approaches to investigate viral infections and outbreaks. The International Virus Bioinformatics Meeting 2020 has attracted approximately 120 experts in virology and bioinformatics from all over the world to join the two-day virtual meeting. Despite concerns being raised that virtual meetings lack possibilities for face-to-face discussion, the participants from this small community created a highly interactive scientific environment, engaging in lively and inspiring discussions and suggesting new research directions and questions. The meeting featured five invited and twelve contributed talks, on the four main topics: (1) proteome and RNAome of RNA viruses, (2) viral metagenomics and ecology, (3) virus evolution and classification and (4) viral infections and immunology. Further, the meeting featured 20 oral poster presentations, all of which focused on specific areas of virus bioinformatics. This report summarizes the main research findings and highlights presented at the meeting.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1972

The effect of changing gravity and weightlessness of vasopressin control systems

An immunoassay to determine the effects of changing gravity and weightlessness on vasopressin control system is discussed. Seven extracts from persons known to have inappropriate ADH syndrome secondary to pulmonary oat cell cancer were examined. The extracts had previously been subject to bioassay in rats. The intent of the examination was to determine if the vasopressin content could be confirmed immunologically. The results compared favorably with the values obtained by biological assay.

Moran, W. H.↗