A multivalent mRNA monkeypox virus vaccine (BNT166) protects mice and macaques from orthopoxvirus disease.

In response to the 2022 outbreak of mpox driven by unprecedented human-to-human monkeypox virus (MPXV) transmission, we designed BNT166, aiming to create a highly immunogenic, safe, accessible, and scalable next-generation vaccine against MPXV and related orthopoxviruses. To address the multiple viral forms and increase the breadth of immune response, two candidate multivalent mRNA vaccines were evaluated pre-clinically: a quadrivalent vaccine (BNT166a; encoding the MPXV antigens A35, B6, M1, H3) and a trivalent vaccine (BNT166c; without H3). Both candidates induced robust T cell responses and IgG antibodies in mice, including neutralizing antibodies to both MPXV and vaccinia virus. In challenge studies, BNT166a and BNT166c provided complete protection from vaccinia, clade I, and clade IIb MPXV. Furthermore, immunization with BNT166a was 100% effective at preventing death and at suppressing lesions in a lethal clade I MPXV challenge in cynomolgus macaques. These findings support the clinical evaluation of BNT166, now underway (NCT05988203).

The T-cell-directed vaccine BNT162b4 encoding conserved non-spike antigens protects animals from severe SARS-CoV-2 infection.

T cell responses play an important role in protection against beta-coronavirus infections, including SARS-CoV-2, where they associate with decreased COVID-19 disease severity and duration. To enhance T cell immunity across epitopes infrequently altered in SARS-CoV-2 variants, we designed BNT162b4, an mRNA vaccine component that is intended to be combined with BNT162b2, the spike-protein-encoding vaccine. BNT162b4 encodes variant-conserved, immunogenic segments of the SARS-CoV-2 nucleocapsid, membrane, and ORF1ab proteins, targeting diverse HLA alleles. BNT162b4 elicits polyfunctional CD4+ and CD8+ T cell responses to diverse epitopes in animal models, alone or when co-administered with BNT162b2 while preserving spike-specific immunity. Importantly, we demonstrate that BNT162b4 protects hamsters from severe disease and reduces viral titers following challenge with viral variants. These data suggest that a combination of BNT162b2 and BNT162b4 could reduce COVID-19 disease severity and duration caused by circulating or future variants. BNT162b4 is currently being clinically evaluated in combination with the BA.4/BA.5 Omicron-updated bivalent BNT162b2 (NCT05541861).

Rapid Estimation of Climate-Air Quality Interactions in Integrated Assessment Using a Response Surface Model.

Air quality and climate change are substantial and linked sustainability challenges, and there is a need for improved tools to assess the implications of addressing these challenges together. Due to the high computational cost of accurately assessing these challenges, integrated assessment models (IAMs) used in policy development often use global- or regional-scale marginal response factors to calculate air quality impacts of climate scenarios. We bridge the gap between IAMs and high-fidelity simulation by developing a computationally efficient approach to quantify how combined climate and air quality interventions affect air quality outcomes, including capturing spatial heterogeneity and complex atmospheric chemistry. We fit individual response surfaces to high-fidelity model simulation output for 1525 locations worldwide under a variety of perturbation scenarios. Our approach captures known differences in atmospheric chemical regimes and can be straightforwardly implemented in IAMs, enabling researchers to rapidly estimate how air quality in different locations and related equity-based metrics will respond to large-scale changes in emission policy. We find that the sensitivity of air quality to climate change and air pollutant emission reductions differs in sign and magnitude by region, suggesting that calculations of "co-benefits" of climate policy that do not account for the existence of simultaneous air quality interventions can lead to inaccurate conclusions. Although reductions in global mean temperature are effective in improving air quality in many locations and sometimes yield compounding benefits, we show that the air quality impact of climate policy depends on air quality precursor emission stringency. Our approach can be extended to include results from higher-resolution modeling and also to incorporate other interventions toward sustainable development that interact with climate action and have spatially distributed equity dimensions.

Systematic discovery and validation of T cell targets directed against oncogenic KRAS mutations.

Oncogenic mutations in KRAS can be recognized by T cells on specific class I human leukocyte antigen (HLA-I) molecules, leading to tumor control. To date, the discovery of T cell targets from KRAS mutations has relied on occasional T cell responses in patient samples or the use of transgenic mice. To overcome these limitations, we have developed a systematic target discovery and validation pipeline. We evaluate the presentation of mutant KRAS peptides on individual HLA-I molecules using targeted mass spectrometry and identify 13 unpublished KRASG12C/D/R/V mutation/HLA-I pairs and nine previously described pairs. We assess immunogenicity, generating T cell responses to nearly all targets. Using cytotoxicity assays, we demonstrate that KRAS-specific T cells and T cell receptors specifically recognize endogenous KRAS mutations. The discovery and validation of T cell targets from KRAS mutations demonstrate the potential for this pipeline to aid the development of immunotherapies for important cancer targets.

Sequence-based prediction of SARS-CoV-2 vaccine targets using a mass spectrometry-based bioinformatics predictor identifies immunogenic T cell epitopes.

BACKGROUND: The ongoing COVID-19 pandemic has created an urgency to identify novel vaccine targets for protective immunity against SARS-CoV-2. Early reports identify protective roles for both humoral and cell-mediated immunity for SARS-CoV-2. METHODS: We leveraged our bioinformatics binding prediction tools for human leukocyte antigen (HLA)-I and HLA-II alleles that were developed using mass spectrometry-based profiling of individual HLA-I and HLA-II alleles to predict peptide binding to diverse allele sets. We applied these binding predictors to viral genomes from the Coronaviridae family and specifically focused on T cell epitopes from SARS-CoV-2 proteins. We assayed a subset of these epitopes in a T cell induction assay for their ability to elicit CD8+ T cell responses. RESULTS: We first validated HLA-I and HLA-II predictions on Coronaviridae family epitopes deposited in the Virus Pathogen Database and Analysis Resource (ViPR) database. We then utilized our HLA-I and HLA-II predictors to identify 11,897 HLA-I and 8046 HLA-II candidate peptides which were highly ranked for binding across 13 open reading frames (ORFs) of SARS-CoV-2. These peptides are predicted to provide over 99% allele coverage for the US, European, and Asian populations. From our SARS-CoV-2-predicted peptide-HLA-I allele pairs, 374 pairs identically matched what was previously reported in the ViPR database, originating from other coronaviruses with identical sequences. Of these pairs, 333 (89%) had a positive HLA binding assay result, reinforcing the validity of our predictions. We then demonstrated that a subset of these highly predicted epitopes were immunogenic based on their recognition by specific CD8+ T cells in healthy human donor peripheral blood mononuclear cells (PBMCs). Finally, we characterized the expression of SARS-CoV-2 proteins in virally infected cells to prioritize those which could be potential targets for T cell immunity. CONCLUSIONS: Using our bioinformatics platform, we identify multiple putative epitopes that are potential targets for CD4+ and CD8+ T cells, whose HLA binding properties cover nearly the entire population. We also confirm that our binding predictors can predict epitopes eliciting CD8+ T cell responses from multiple SARS-CoV-2 proteins. Protein expression and population HLA allele coverage, combined with the ability to identify T cell epitopes, should be considered in SARS-CoV-2 vaccine design strategies and immune monitoring.

Defining HLA-II Ligand Processing and Binding Rules with Mass Spectrometry Enhances Cancer Epitope Prediction.

Increasing evidence indicates CD4+ T cells can recognize cancer-specific antigens and control tumor growth. However, it remains difficult to predict the antigens that will be presented by human leukocyte antigen class II molecules (HLA-II), hindering efforts to optimally target them therapeutically. Obstacles include inaccurate peptide-binding prediction and unsolved complexities of the HLA-II pathway. To address these challenges, we developed an improved technology for discovering HLA-II binding motifs and conducted a comprehensive analysis of tumor ligandomes to learn processing rules relevant in the tumor microenvironment. We profiled >40 HLA-II alleles and showed that binding motifs were highly sensitive to HLA-DM, a peptide-loading chaperone. We also revealed that intratumoral HLA-II presentation was dominated by professional antigen-presenting cells (APCs) rather than cancer cells. Integrating these observations, we developed algorithms that accurately predicted APC ligandomes, including peptides from phagocytosed cancer cells. These tools and biological insights will enable improved HLA-II-directed cancer therapies.

A Proteomics Approach to Profiling the Temporal Translational Response to Stress and Growth.

To quantify dynamic protein synthesis rates, we developed MITNCAT, a method combining multiplexed isobaric mass tagging with pulsed SILAC (pSILAC) and bio-orthogonal non-canonical amino acid tagging (BONCAT) to label newly synthesized proteins with azidohomoalanine (Aha), thus enabling high temporal resolution across multiple conditions in a single analysis. MITNCAT quantification of protein synthesis rates following induction of the unfolded protein response revealed global down-regulation of protein synthesis, with stronger down-regulation of glycolytic and protein synthesis machinery proteins, but up-regulation of several key chaperones. Waves of temporally distinct protein synthesis were observed in response to epidermal growth factor, with altered synthesis detectable in the first 15 min. Comparison of protein synthesis with mRNA sequencing and ribosome footprinting distinguished protein synthesis driven by increased transcription versus increased translational efficiency. Temporal delays between ribosome occupancy and protein synthesis were observed and found to correlate with altered codon usage in significantly delayed proteins.

On-demand dissolution of modular, synthetic extracellular matrix reveals local epithelial-stromal communication networks.

Methods to parse paracrine epithelial-stromal communication networks are a vital need in drug development, as disruption of these networks underlies diseases ranging from cancer to endometriosis. Here, we describe a modular, synthetic, and dissolvable extracellular matrix (MSD-ECM) hydrogel that fosters functional 3D epithelial-stromal co-culture, and that can be dissolved on-demand to recover cells and paracrine signaling proteins intact for subsequent analysis. Specifically, synthetic polymer hydrogels, modified with cell-interacting adhesion motifs and crosslinked with peptides that include a substrate for cell-mediated proteolytic remodeling, can be rapidly dissolved by an engineered version of the microbial transpeptidase Sortase A (SrtA) if the crosslinking peptide includes a SrtA substrate motif and a soluble second substrate. SrtA-mediated dissolution affected only 1 of 31 cytokines and growth factors assayed, whereas standard protease degradation methods destroyed about half of these same molecules. Using co-encapsulated endometrial epithelial and stromal cells as one model system, we show that the dynamic cytokine and growth factor response of co-cultures to an inflammatory cue is richer and more nuanced when measured from SrtA-dissolved gel microenvironments than from the culture supernate. This system employs accessible, reproducible reagents and facile protocols; hence, has potential as a tool in identifying and validating therapeutic targets in complex diseases.

Early signaling dynamics of the epidermal growth factor receptor.

Despite extensive study of the EGF receptor (EGFR) signaling network, the immediate posttranslational changes that occur in response to growth factor stimulation remain poorly characterized; as a result, the biological mechanisms underlying signaling initiation remain obscured. To address this deficiency, we have used a mass spectrometry-based approach to measure system-wide phosphorylation changes throughout the network with 10-s resolution in the 80 s after stimulation in response to a range of eight growth factor concentrations. Significant changes were observed on proteins far downstream in the network as early as 10 s after stimulation, indicating a system capable of transmitting information quickly. Meanwhile, canonical members of the EGFR signaling network fall into clusters with distinct activation patterns. Src homology 2 domain containing transforming protein (Shc) and phosphoinositol 3-kinase (PI3K) phosphorylation levels increase rapidly, but equilibrate within 20 s, whereas proteins such as Grb2-associated binder-1 (Gab1) and SH2-containing tyrosine phosphatase (SHP2) show slower, sustained increases. Proximity ligation assays reveal that Shc and Gab1 phosphorylation patterns are representative of separate timescales for physical association with the receptor. Inhibition of phosphatases with vanadate reveals site-specific regulatory mechanisms and also uncovers primed activating components in the network, including Src family kinases, whose inhibition affects only a subset of proteins within the network. The results presented highlight the complexity of signaling initiation and provide a window into exploring mechanistic hypotheses about receptor tyrosine kinase (RTK) biology.

Identification of Direct Kinase Substrates Using Analogue-Sensitive Alleles.

Identifying the substrates of protein kinases remains a major obstacle in the elucidation of eukaryotic signaling pathways. Promiscuity among kinases and their substrates coupled with the extraordinary plasticity of phosphorylation networks renders traditional genetic approaches or small-molecule inhibitors problematic when trying to determine the direct substrates of an individual kinase. Here we describe methods to label, enrich, and identify the direct substrates of analogue-sensitive kinases by exploiting their steric complementarity to artificial ATP analogues. Using calcium-dependent protein kinases of Toxoplasma gondii as a model for these approaches, this protocol brings together numerous advances that enable labeling of kinase targets in semi-permeabilized cells, quantification of direct labeling over background, and highly specific enrichment of targeted phosphopeptides.

Aurora B kinase phosphorylates and instigates degradation of p53.

Aurora B is a mitotic checkpoint kinase that plays a pivotal role in the cell cycle, ensuring correct chromosome segregation and normal progression through mitosis. Aurora B is overexpressed in many types of human cancers, which has made it an attractive target for cancer therapies. Tumor suppressor p53 is a genome guardian and important negative regulator of the cell cycle. Whether Aurora B and p53 are coordinately regulated during the cell cycle is not known. We report that Aurora B directly interacts with p53 at different subcellular localizations and during different phases of the cell cycle (for instance, at the nucleus in interphase and the centromeres in prometaphase of mitosis). We show that Aurora B phosphorylates p53 at S183, T211, and S215 to accelerate the degradation of p53 through the polyubiquitination-proteasome pathway, thus functionally suppressing the expression of p53 target genes involved in cell cycle inhibition and apoptosis (e.g., p21 and PUMA). Pharmacologic inhibition of Aurora B in cancer cells with WT p53 increased p53 protein level and expression of p53 target genes to inhibit tumor growth. Together, these results define a mechanism of p53 inactivation during the cell cycle and imply that oncogenic hyperactivation or overexpression of Aurora B may compromise the tumor suppressor function of p53. We have elucidated the antineoplastic mechanism for Aurora B kinase inhibitors in cancer cells with WT p53.