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DOE OSTI2021

Non-Thermal Plasma as a Novel Strategy for Treating or Preventing Viral Infection and Associated Disease

Pathogenic viruses cause many human, animal, and plant diseases that are associated with substantial morbidity, mortality and socio-economic impact. Although effective strategies for combatting virus transmission and associated disease are available, global outbreaks of viral pathogens such as the virus responsible for the COVID-19 pandemic demonstrate that there is still a critical need for new approaches that can be used to interrupt the chain of viral infection and mitigate virus-associated pathogenesis. Recent studies point to non-thermal plasma (NTP), a partly ionized gas comprised of a complex mixture of reactive oxygen and nitrogen species along with physical effectors, as the potential foundation for new antiviral approaches. A more thorough understanding of the antiviral properties and safety of NTP has stimulated explorations of NTP as the basis for treatments of viral diseases. The recently described immunomodulatory properties of NTP are also being evaluated for potential use in immunotherapies of viral diseases as well as in antiviral vaccination strategies. In this review, we present the current state-of-the-art in addition to compelling arguments that NTP merits further exploration for use in the prevention and management of viral infections and associated diseases.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

High‐Concentration Antibody Formulation via Solvent‐Based Dehydration

Abstract Although subcutaneous (SC) delivery is the preferred administration route for immunotherapies and other biologics for improved patient compliance and lower healthcare costs, it necessitates high‐concentration antibody formulations. However, high‐concentration antibody solutions face significant instabilities and prohibitively high viscosities. Other approaches for high‐concentration formulations have been developed, including non‐aqueous solutions, which can be irritating or painful, and antibody‐laden hydrogel microparticles, which require centrifugation and are limited to concentrations <300 mg mL −1 . This work presents a new formulation process wherein the antibody is concentrated and encapsulated into hydrogel microparticles via solvent‐based dehydration. The final dosage form is an aqueous particle suspension with a formulation concentration of 360 mg mL −1 . In this process, microparticles are synthesized continuously, and antibody precipitation is realized simultaneously to dehydration, which allows for higher antibody concentrations. Antibody phase behavior and precipitation–dehydration kinetics are analyzed. The antibody is structurally and functionally stable in the microparticle post‐processing and after 4 months. Injectability of the suspension meets clinical standards with glide force <20 N. For the first time, an aqueous antibody formulation at high concentrations comparable to non‐aqueous formulations is presented, ideal for subcutaneous administration. The process is envisioned to be generalizable as a platform for SC delivery in multiple clinical applications.

Zheng, Talia [Department of Chemical Engineering M↗

DOE OSTI2022

Development of a prediction model for radiotherapy response among patients with head and neck squamous cell carcinoma based on the tumor immune microenvironment and hypoxia signature

Abstract Introduction The immune system and hypoxia are major factors influencing radiosensitivity in patients with different cancer types. This study aimed at developing a model to predict radiotherapy response in patients with head and neck squamous cell carcinoma (HNSCC) based on the tumor immune microenvironment and hypoxia signature. Materials and Methods We first evaluated the hypoxia status and tumor immune microenvironment in the Cancer Genome Atlas (TCGA) cohort by using transcriptomic data. Differentially expressed genes (DEGs) were identified between the “high immunity and low hypoxia” and “low immunity and high hypoxia” groups and those DEGs significantly associated with disease‐specific survival in the univariate Cox regression analysis were selected as the prognostic DEGs. We selected the immune hypoxia–related genes (IHRGs) by intersecting prognostic DEGs with immune and hypoxia gene sets. We used the IHRGs to train a multivariate Cox regression model in the TCGA cohort, based on which we calculated the IHRG prognostic index (IHRGPI) for each patient and validated its efficacy in predicting radiotherapy response in the Gene Expression Omnibus cohorts. Furthermore, we explored potential mechanisms and effective combinational treatment strategies for different IHRGPI groups. Results Five IHRGs were used to construct the IHRGPI, which was used to dichotomize the cohorts. The patients with lower IHRGPI showed a better radiotherapy response across different cohorts and endpoints, including overall survival, progression‐free survival, and recurrence‐free survival ( p < 0.05). Patients with higher IHRGPI showed greater hypoxia and lesser immune cell infiltration. A lower IHRGPI indicated a better immunotherapy response, while a higher IHRGPI indicated a better chemotherapy response. Conclusions IHRGPI is promising for predicting radiotherapy response and guiding combinational treatment strategies in patients with HNSCC.

Zhu, Guang‐Li↗

DOE OSTI2022

Human Monoclonal IgE Antibodies—a Major Milestone in Allergy

This review provides an overview of this major milestone in allergy, the first atomic resolution structure of an authentic human IgE epitope. The molecular insights that IgE epitopes provide will allow for structure-based design approaches to the development of novel diagnostics, antibody therapeutics, and immunotherapies.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Proteogenomic insights suggest druggable pathways in endometrial carcinoma

We characterized a prospective endometrial carcinoma (EC) cohort containing 138 tumors and 20 enriched normal tissues using 10 different omics platforms. Targeted quantitation of two peptides can predict antigen processing and presentation machinery activity, and may inform patient selection for immunotherapy. Association analysis between MYC activity and metformin treatment in both patients and cell lines suggests a potential role for metformin treatment in non-diabetic patients with elevated MYC activity. PIK3R1 in-frame indels are associated with elevated AKT phosphorylation and increased sensitivity to AKT inhibitors. CTNNB1 hotspot mutations are concentrated near phosphorylation sites mediating pS45-induced degradation of β-catenin, which may render Wnt-FZD antagonists ineffective. Deep learning accurately predicts EC subtypes and mutations from histopathology images, which may be useful for rapid diagnosis. Overall, this study identified molecular and imaging markers that can be further investigated to guide patient stratification for more precise treatment of EC.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2020

Proteogenomic Characterization of Endometrial Carcinoma

We undertook a comprehensive proteogenomic characterization of 95 endometrial carcinomas, of which 83 were endometrioid tumors and 12 were serous tumors. This analysis revealed new consequences of perturbations to the p53 and Wnt/ß-catenin pathways, identified a potential role for circRNAs in the epithelial-mesenchymal transition, and provided new insights into proteomic markers of clinical and genomic tumor subgroups, some of which are in known druggable pathways. We performed a broad genome-wide acetylation survey, one of the first of its kind, yielding insights into regulatory mechanisms of histone acetylation. We also characterized the tumor immune landscape, including immunogenic alterations, neoantigens, common cancer/testis antigens, and the immune microenvironment, all of which can inform immunotherapy decisions. Collectively, our multi-omic analyses provide a valuable resource for researchers and clinicians, further our understanding of the molecular mechanisms underlying endometrial carcinoma, and identify new avenues for investigating potential therapeutic targets.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

The Cancer Microbiome: Distinguishing Direct and Indirect Effects Requires a Systemic View

The collection of microbes that live in and on the human body – the human microbiome – can impact on cancer initiation, progression, and response to therapy, including cancer immunotherapy. The mechanisms by which microbiomes impact on cancers can yield new diagnostics and treatments, but much remains unknown. The interactions between microbes, diet, host factors, drugs, and cell–cell interactions within the cancer itself likely involve intricate feedbacks, and no single component can explain all the behavior of the system. Understanding the role of host-associated microbial communities in cancer systems will require a multidisciplinary approach combining microbial ecology, immunology, cancer cell biology, and computational biology – a systems biology approach.

Xavier, Joao B.↗

DOE OSTI2021

Toward Guided Mutagenesis: Gaussian Process Regression Predicts MHC Class II Antigen Mutant Binding

Antigen-specific immunotherapies (ASI) require successful loading and presentation of antigen peptides into the major histocompatibility complex (MHC) binding cleft. One route of ASI design is to mutate native antigens for either stronger or weaker binding interaction to MHC. Exploring all possible mutations is costly both experimentally and computationally. To reduce experimental and computational expense, here we investigate the minimal amount of prior data required to accurately predict the relative binding affinity of point mutations for peptide-MHC class II (pMHCII) binding. Using data from different residue subsets, we interpolate pMHCII mutant binding affinities by Gaussian process (GP) regression of residue volume and hydrophobicity. We apply GP regression to an experimental data set from the Immune Epitope Database, and theoretical data sets from NetMHCIIpan and Free Energy Perturbation calculations. We find that GP regression can predict binding affinities of nine neutral residues from a six-residue subset with an average R 2 coefficient of determination value of 0.62 ± 0.04 (±95% CI), average error of 0.09 ± 0.01 kcal/mol (±95% CI), and with an receiver operating characteristic (ROC) AUC value of 0.92 for binary classification of enhanced or diminished binding affinity. Similarly, metrics increase to an R2 value of 0.69 ± 0.04, average error of 0.07 ± 0.01 kcal/mol, and an ROC AUC value of 0.94 for predicting seven neutral residues from an eight-residue subset. Our work finds that prediction is most accurate for neutral residues at anchor residue sites without register shift. This work holds relevance to predicting pMHCII binding and accelerating ASI design.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Determination of the Maturation Status of Dendritic Cells by Applying Pattern Recognition to High-Resolution Images

The maturation or activation status of dendritic cells (DCs) directly correlates with their behavior and immunofunction. A common means to determine the maturity of dendritic cells is from high-resolution images acquired via scanning electron microscopy (SEM) or atomic force microscopy (AFM). While direct and visual, the determination has been made by directly looking at the images by researchers. Here we report a machine learning approach using pattern recognition in conjunction with cellular biophysical knowledge of dendritic cells to determine the maturation status of dendritic cells automatically. The determination from AFM images reaches 100% accuracy. The results from SEM images reaches 94.9%. The results demonstrate the accuracy of using machine learning for accelerating data analysis, extracting information, and drawing conclusions from high-resolution cellular images, paving the way for future applications requiring high-throughput and automation, such as cellular sorting and selection based on morphology, quantification of cellular structure, and DC-based immunotherapy.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

National Cancer Institute Think-Tank Meeting Report on Proteomic Cartography and Biomarkers at the Single-Cell Level: Interrogation of Premalignant Lesions

A Think-Tank Meeting was convened by the National Cancer Institute (NCI) to solicit experts’ opinion on the development and application of multi-omic single-cell analyses, and especially single-cell proteomics, to improve the development of a new generation of biomarkers for cancer risk, early detection, diagnosis, prognosis as well as to discuss the discovery of new targets for prevention and therapy. It is anticipated that such markers and targets will be based on cellular, subcellular, molecular and functional aberrations within the lesion and within individual cells. Additionally, single-cell proteomic data will be essential for the establishment of new tools that with searchable and scalable features that include spatial and temporal cartographies of premalignant and malignant lesions. Challenges and potential solutions that were discussed included: • The best way/s to analyze single-cells from fresh and preserved tissue • Detection and analysis of secreted molecules and from single cells, especially from a tissue slice • Detection of new, previously undocumented cell type/s in the premalignant and early stage cancer tissue microenvironment • Multi-omic integration of data to support and inform proteomic measurements • Subcellular organelles – identifying abnormal structure, function, distribution and location within individual premalignant and malignant cells • How to improve the dynamic range of single-cell proteomic measurements for discovery of differentially expressed proteins and their post translational modifications (PTM) • The depth of coverage measured concurrently using single-cell techniques • Quantitation - absolute or semiquantitative? • Single methodology or multiplexed combinations? • Application of analytical methods for identification of biologically significant subsets • Data visualization of N-dimensional datasets • How to construct intercellular signaling networks in individual cells within premalignant tumor microenvironments (TME) • Associations between intrinsic cellular processes and extrinsic stimuli • How to predict cellular responses to stress inducing stimuli • Identification of new markers for prediction of progression from precursor, benign and localized lesions to invasive cancer, based on spatial and temporal changes within individual cells • Identification of new targets for immunoprevention or immunotherapy – identification of neoantigens and surfactome of individual cells within a lesion

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Comprehensive Detection of Single Amino Acid Variants and Evaluation of Their Deleterious Potential in a PANC-1 Cell Line

Identifying single amino acid variants (SAAVs) in cancer is critical for precision oncology. Several advanced algorithms are now available to identify SAAVs but attempts to combine different algorithms and optimize them on large datasets to achieve a more comprehensive coverage of SAAVs have not been implemented. Herein we report an expanded detection of SAAVs in the PANC-1 cell line using three different strategies, which results in identification of 540 SAAVs in the mass spectrometry data, the most comprehensive coverage yet reported. Among the set of 540 SAAVs, 79 are evaluated as deleterious SAAVs based on analysis using novel AssVar software in which one of the driver mutations found in each protein of KRAS, TP53 and SLC37A4 is further validated using independent selected reaction monitoring (SRM) analysis. Our study represents the most comprehensive discovery of SAAVs to date and the first large-scale detection of deleterious SAAVs in the PANC-1 cell line. This work may serve as the basis for future research in pancreatic cancer and personal immunotherapy and treatment.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Tyrosinase-Mediated Synthesis of Nanobody–Cell Conjugates

A convenient enzymatic strategy is reported for the modification of cell surfaces. Using a tyrosinase enzyme isolated from Agaricus bisporus, unique tyrosine residues introduced at the C-termini of nanobodies can be site-selectively oxidized to reactive o-quinones. These reactive intermediates undergo rapid modification with nucleophilic thiol, amine, and imidazole residues present on cell surfaces, producing novel nanobody–cell conjugates that display targeted antigen binding. We extend this approach toward the synthesis of nanobody–NK cell conjugates for targeted immunotherapy applications. The resulting NK cell conjugates exhibit targeted cell binding and elicit targeted cell death.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Molecular mechanism of phosphopeptide neoantigen immunogenicity

Abstract often leads to surface presentation of phosphopeptide neoantigens. However, their role in cancer immunogenicity remains unclear. Here we describe a mechanism by which an HLA-B*0702-specific acute myeloid leukemia phosphoneoantigen, pMLL 747–755 (EPR(pS)PSHSM), is recognized by a cognate T cell receptor named TCR27, a candidate for cancer immunotherapy. We show that the replacement of phosphoserine P 4 with serine or phosphomimetics does not affect pMHC conformation or peptide-MHC affinity but abrogates TCR27-dependent T cell activation and weakens binding between TCR27 and pMHC. Here we describe the crystal structures for TCR27 and cognate pMHC, map of the interface produced by nuclear magnetic resonance, and a ternary complex generated using information-driven protein docking. Our data show that non-covalent interactions between the epitope phosphate group and TCR27 are crucial for TCR specificity. This study supports development of new treatment options for cancer patients through target expansion and TCR optimization.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

The co-crystal structure of Cbl-b and a small-molecule inhibitor reveals the mechanism of Cbl-b inhibition

Cbl-b is a RING-type E3 ubiquitin ligase that is expressed in several immune cell lineages, where it negatively regulates the activity of immune cells. Cbl-b has specifically been identified as an attractive target for cancer immunotherapy due to its role in promoting an immunosuppressive tumor environment. A Cbl-b inhibitor, Nx-1607, is currently in phase I clinical trials for advanced solid tumor malignancies. Using a suite of biophysical and cellular assays, we confirm potent binding of C7683 (an analogue of Nx-1607) to the full-length Cbl-b and its N-terminal fragment containing the TKBD-LHR-RING domains. To further elucidate its mechanism of inhibition, we determined the co-crystal structure of Cbl-b with C7683, revealing the compound’s interaction with both the TKBD and LHR, but not the RING domain. Here, we provide structural insights into a novel mechanism of Cbl-b inhibition by a small-molecule inhibitor that locks the protein in an inactive conformation by acting as an intramolecular glue.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Diagnosis of Alzheimer’s disease using plasma biomarkers adjusted to clinical probability

Abstract Recently approved anti-amyloid immunotherapies for Alzheimer’s disease (AD) require evidence of amyloid-β pathology from positron emission tomography (PET) or cerebrospinal fluid (CSF) before initiating treatment. Blood-based biomarkers promise to reduce the need for PET or CSF testing; however, their interpretation at the individual level and the circumstances requiring confirmatory testing are poorly understood. Individual-level interpretation of diagnostic test results requires knowledge of disease prevalence in relation to clinical presentation (clinical pretest probability). Here, in a study of 6,896 individuals evaluated from 11 cohort studies from six countries, we determined the positive and negative predictive value of five plasma biomarkers for amyloid-β pathology in cognitively impaired individuals in relation to clinical pretest probability. We observed that p-tau217 could rule in amyloid-β pathology in individuals with probable AD dementia (positive predictive value above 95%). In mild cognitive impairment, p-tau217 interpretation depended on patient age. Negative p-tau217 results could rule out amyloid-β pathology in individuals with non-AD dementia syndromes (negative predictive value between 90% and 99%). Our findings provide a framework for the individual-level interpretation of plasma biomarkers, suggesting that p-tau217 combined with clinical phenotyping can identify patients where amyloid-β pathology can be ruled in or out without the need for PET or CSF confirmatory testing.

Cell Biology↗

DOE OSTI2020

Metabolite releasing polymers control dendritic cell function by modulating their energy metabolism

Metabolites control immune cell functions, and delivery of these metabolites in a sustained manner may be able to modulate function of the immune cells. In this study, alpha-ketoglutarate (aKG) and diol based polymeric-microparticles (termed paKG MPs) were synthesized to provide sustained release of aKG and promote an immunosuppressive cellular phenotype. Notably, after association with dendritic cells (DCs), paKG MPs modulated the intracellular metabolic-profile/pathways, and decreased glycolysis and mitochondrial respiration in vitro. These metabolic changes resulted in modulation of MHC-II, CD86 expression in DCs, and altered the frequency of regulatory T cells (Tregs), and T-helper type-1/2/17 cells in vitro. In conclusion, this unique strategy of intracellular delivery of key-metabolites in a sustained manner provides a new direction in immunometabolism field-based immunotherapy with potential applications in different diseases associated with immune disorders.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Engineered human antibodies for the opsonization and killing of Staphylococcus aureus

Gram-positive organisms with their thick envelope cannot be lysed by complement alone. Nonetheless, antibody-binding on the surface can recruit complement and mark these invaders for uptake and killing by phagocytes, a process known as opsonophagocytosis. The crystallizable fragment of immunoglobulins (Fcγ) is key for complement recruitment. The cell surface of S. aureus is coated with Staphylococcal protein A (SpA). SpA captures the Fcγ domain of IgG and interferes with opsonization by anti-S. aureus antibodies. In principle, the Fcγ domain of therapeutic antibodies could be engineered to avoid the inhibitory activity of SpA. However, the SpA-binding site on Fcγ overlaps with that of the neonatal Fc receptor (FcRn), an interaction that is critical for prolonging the half-life of serum IgG. This evolutionary adaptation poses a challenge for the exploration of Fcγ mutants that can both weaken SpA–IgG interactions and retain stability. Here, we use both wild-type and transgenic human FcRn mice to identify antibodies with enhanced half-life and increased opsonophagocytic killing in models of S. aureus infection and demonstrate that antibody-based immunotherapy can be improved by modifying Fcγ. Our experiments also show that by competing for FcRn-binding, staphylococci effectively reduce the half-life of antibodies during infection. These observations may have profound impact in treating cancer, autoimmune, and asthma patients colonized or infected with S. aureus and undergoing monoclonal antibody treatment.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Improved deep learning prediction of antigen–antibody interactions

Identifying antibodies that neutralize specific antigens is crucial for developing effective immunotherapies, but this task remains challenging for many target antigens. The rise of deep learning–based computational approaches presents a promising avenue to address this challenge. Here, we assess the performance of a deep learning approach through two benchmark tests aimed at predicting antibodies for the receptor-binding domain of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein. Three different strategies for constructing input sequence alignments are employed for predicting structural models of antigen–antibody complexes. In our initial testing set, which comprises known experimental structures, these strategies collectively yield a significant top-ranked prediction for 61% of cases and a success rate of 47%. Notably, one strategy that utilizes the sequences of known antigen binders outperforms the other two, achieving a precision of 90% in a subsequent test set of ~1,000 antibodies, balanced between true and control antibodies for the antigen, albeit with a lower recall of 25%. Our results underscore the potential of integrating deep learning methods with single B cell sequencing techniques to enhance the prediction accuracy of antigen–antibody interactions.

Science & Technology - Other Topics↗