Chem Commun (Camb). 2023 Sep 8. doi: 10.1039/d3cc02909c. Online ahead of print.

ABSTRACT

Post-translational modifications (PTMs) are ubiquitous and key to regulating protein function. Understanding the dynamics of individual PTMs and their biological roles requires robust characterisation. Mass spectrometry (MS) is the method of choice for the identification and quantification of protein modifications. This article focusses on the MS-based analysis of those covalent modifications that induce a mass shift of +80 Da, notably phosphorylation and sulfation, given the challenges associated with their discrimination and pinpointing the sites of modification on a polypeptide chain. Phosphorylation in particular is highly abundant, dynamic and can occur on numerous residues to invoke specific functions, hence robust characterisation is crucial to understanding biological relevance. Showcasing our work in the context of other developments in the field, we highlight approaches for enrichment and site localisation of phosphorylated (canonical and non-canonical) and sulfated peptides, as well as modification analysis in the context of intact proteins (top down proteomics) to explore combinatorial roles. Finally, we discuss the application of native ion-mobility MS to explore the effect of these PTMs on protein structure and ligand binding.

PMID:37681662 | DOI:10.1039/d3cc02909c

Elife. 2023 Sep 8;12:e85126. doi: 10.7554/eLife.85126. Online ahead of print.

ABSTRACT

Antigen immunogenicity and the specificity of binding of T-cell receptors to antigens are key properties underlying effective immune responses. Here we propose diffRBM, an approach based on transfer learning and Restricted Boltzmann Machines, to build sequence-based predictive models of these properties. DiffRBM is designed to learn the distinctive patterns in amino-acid composition that, on the one hand, underlie the antigen's probability of triggering a response, and on the other hand the T-cell receptor's ability to bind to a given antigen. We show that the patterns learnt by diffRBM allow us to predict putative contact sites of the antigen-receptor complex. We also discriminate immunogenic and non-immunogenic antigens, antigen-specific and generic receptors, reaching performances that compare favorably to existing sequence-based predictors of antigen immunogenicity and T-cell receptor specificity.

PMID:37681658 | DOI:10.7554/eLife.85126

Biophys Rev. 2023 Jul 17;15(4):539-552. doi: 10.1007/s12551-023-01082-5. eCollection 2023 Aug.

ABSTRACT

Almost seventy years after its discovery, the sodium-potassium adenosine triphosphatase (the sodium pump) located in the cell plasma membrane remains a source of novel mechanistic and physiologic findings. A noteworthy feature of this enzyme/transporter is its robust stoichiometric ratio under physiological conditions: it sequentially counter-transports three sodium ions and two potassium ions against their electrochemical potential gradients per each hydrolyzed ATP molecule. Here we summarize some present knowledge about the sodium pump and its physiological roles, and speculate whether energetic constraints may have played a role in the evolutionary selection of its characteristic stoichiometric ratio.

PMID:37681108 | PMC:PMC10480117 | DOI:10.1007/s12551-023-01082-5

Biophys Rev. 2023 Jun 22;15(4):767-785. doi: 10.1007/s12551-023-01074-5. eCollection 2023 Aug.

ABSTRACT

Explaining the foundation of cognitive abilities in the processing of information by neural systems has been in the beginnings of biophysics since McCulloch and Pitts pioneered work within the biophysics school of Chicago in the 1940s and the interdisciplinary cybernetists meetings in the 1950s, inseparable from the birth of computing and artificial intelligence. Since then, neural network models have traveled a long path, both in the biophysical and the computational disciplines. The biological, neurocomputational aspect reached its representational maturity with the Distributed Associative Memory models developed in the early 70 s. In this framework, the inclusion of signal-signal multiplication within neural network models was presented as a necessity to provide matrix associative memories with adaptive, context-sensitive associations, while greatly enhancing their computational capabilities. In this review, we show that several of the most successful neural network models use a form of multiplication of signals. We present several classical models that included such kind of multiplication and the computational reasons for the inclusion. We then turn to the different proposals about the possible biophysical implementation that underlies these computational capacities. We pinpoint the important ideas put forth by different theoretical models using a tensor product representation and show that these models endow memories with the context-dependent adaptive capabilities necessary to allow for evolutionary adaptation to changing and unpredictable environments. Finally, we show how the powerful abilities of contemporary computationally deep-learning models, inspired in neural networks, also depend on multiplications, and discuss some perspectives in view of the wide panorama unfolded. The computational relevance of multiplications calls for the development of new avenues of research that uncover the mechanisms our nervous system uses to achieve multiplication.

PMID:37681105 | PMC:PMC10480136 | DOI:10.1007/s12551-023-01074-5

Transpl Int. 2023 Aug 23;36:11590. doi: 10.3389/ti.2023.11590. eCollection 2023.

ABSTRACT

The Banff community summoned the TMA Banff Working Group to develop minimum diagnostic criteria (MDC) and recommendations for renal transplant TMA (Tx-TMA) diagnosis, which currently lacks standardized criteria. Using the Delphi method for consensus generation, 23 nephropathologists (panelists) with >3 years of diagnostic experience with Tx-TMA were asked to list light, immunofluorescence, and electron microscopic, clinical and laboratory criteria and differential diagnoses for Tx-TMA. Delphi was modified to include 2 validations rounds with histological evaluation of whole slide images of 37 transplant biopsies (28 TMA and 9 non-TMA). Starting with 338 criteria in R1, MDC were narrowed down to 24 in R8 generating 18 pathological, 2 clinical, 4 laboratory criteria, and 8 differential diagnoses. The panelists reached a good level of agreement (70%) on 76% of the validated cases. For the first time in Banff classification, Delphi was used to reach consensus on MDC for Tx-TMA. Phase I of the study (pathology phase) will be used as a model for Phase II (nephrology phase) for consensus regarding clinical and laboratory criteria. Eventually in Phase III (consensus of the consensus groups) and the final MDC for Tx-TMA will be reported to the transplantation community.

PMID:37680648 | PMC:PMC10481335 | DOI:10.3389/ti.2023.11590

Front Immunol. 2023 Aug 23;14:1243689. doi: 10.3389/fimmu.2023.1243689. eCollection 2023.

ABSTRACT

INTRODUCTION: Persistent symptoms after COVID-19 infection ("long COVID") negatively affects almost half of COVID-19 survivors. Despite its prevalence, its pathophysiology is poorly understood, with multiple host systems likely affected. Here, we followed patients from hospital to discharge and used a systems-biology approach to identify mechanisms of long COVID.

METHODS: RNA-seq was performed on whole blood collected early in hospital and 4-12 weeks after discharge from 24 adult COVID-19 patients (10 reported post-COVID symptoms after discharge). Differential gene expression analysis, pathway enrichment, and machine learning methods were used to identify underlying mechanisms for post-COVID symptom development.

RESULTS: Compared to patients with post-COVID symptoms, patients without post-COVID symptoms had larger temporal gene expression changes associated with downregulation of inflammatory and coagulation genes over time. Patients could also be separated into three patient endotypes with differing mechanistic trajectories, which was validated in another published patient cohort. The "Resolved" endotype (lowest rate of post-COVID symptoms) had robust inflammatory and hemostatic responses in hospital that resolved after discharge. Conversely, the inflammatory/hemostatic responses of "Suppressive" and "Unresolved" endotypes (higher rates of patients with post-COVID symptoms) were persistently dampened and activated, respectively. These endotypes were accurately defined by specific blood gene expression signatures (6-7 genes) for potential clinical stratification.

DISCUSSION: This study allowed analysis of long COVID whole blood transcriptomics trajectories while accounting for the issue of patient heterogeneity. Two of the three identified and externally validated endotypes ("Unresolved" and "Suppressive") were associated with higher rates of post-COVID symptoms and either persistently activated or suppressed inflammation and coagulation processes. Gene biomarkers in blood could potentially be used clinically to stratify patients into different endotypes, paving the way for personalized long COVID treatment.

PMID:37680625 | PMC:PMC10482103 | DOI:10.3389/fimmu.2023.1243689

iScience. 2023 Aug 21;26(9):107700. doi: 10.1016/j.isci.2023.107700. eCollection 2023 Sep 15.

ABSTRACT

Pseudomonas aeruginosa (Pa) is a pathogen causing chronic pulmonary infections in patients with cystic fibrosis (CF). Manipulation of lipids is an important feature of Pa infection and on a tissue-level scale is poorly understood. Using a mouse model of acute Pa pulmonary infection, we explored the whole-lung phospholipid response using mass spectrometry imaging (MSI) and spatial lipidomics. Using a histology-driven analysis, we isolated airways and parenchyma from both mock- and Pa-infected lungs and used systems biology tools to identify enriched metabolic pathways from the differential phospholipid identities. Infection was associated with a set of 26 ions, with 11 unique to parenchyma and 6 unique to airways. Acyl remodeling was differentially enriched in infected parenchyma as the predominant biological function. These functions correlated with markers of polymorphonuclear (PMN) cell influx, a defining feature of the lung response to Pa infection, implicating enzymes active in phospholipid remodeling.

PMID:37680478 | PMC:PMC10480615 | DOI:10.1016/j.isci.2023.107700

iScience. 2023 Aug 18;26(9):107675. doi: 10.1016/j.isci.2023.107675. eCollection 2023 Sep 15.

ABSTRACT

To gain deeper insights into transcriptomes and epigenomes of organoids, liver organoids from two states (expandable and more differentiated) were subjected to single-cell RNA-seq (scRNA-seq) and single-cell ATAC-seq (scATAC-seq) analyses. Mitochondrial gene expression was higher in differentiated than in non-differentiated hepatocytes, with ATAC-seq peaks increasing near the mitochondrial control region. Differentiation of liver organoids resulted in the expression of transcription factors that act as enhancers and repressors. In addition, epigenetic mechanisms regulating the expression of alpha-fetoprotein (AFP) and albumin (ALB) differed in liver organoids and adult liver. Knockdown of PDX1, an essential transcription factor for pancreas development, led to the hepatic maturation of liver organoids through regulation of AFP and ALB expression. This integrative analysis of the transcriptomes and epigenomes of liver organoids at the single-cell level may contribute to a better understanding of the regulatory networks during liver development and the further development of mature in vitro human liver models.

PMID:37680467 | PMC:PMC10481365 | DOI:10.1016/j.isci.2023.107675

Front Genet. 2023 Aug 23;14:1264656. doi: 10.3389/fgene.2023.1264656. eCollection 2023.

ABSTRACT

Most high throughput genomic data analysis pipelines currently rely on over-representation or gene set enrichment analysis (ORA/GSEA) approaches for functional analysis. In contrast, topology-based pathway analysis methods, which offer a more biologically informed perspective by incorporating interaction and topology information, have remained underutilized and inaccessible due to various limiting factors. These methods heavily rely on the quality of pathway topologies and often utilize predefined topologies from databases without assessing their correctness. To address these issues and make topology-aware pathway analysis more accessible and flexible, we introduce the PSF (Pathway Signal Flow) toolkit R package. Our toolkit integrates pathway curation and topology-based analysis, providing interactive and command-line tools that facilitate pathway importation, correction, and modification from diverse sources. This enables users to perform topology-based pathway signal flow analysis in both interactive and command-line modes. To showcase the toolkit's usability, we curated 36 KEGG signaling pathways and conducted several use-case studies, comparing our method with ORA and the topology-based signaling pathway impact analysis (SPIA) method. The results demonstrate that the algorithm can effectively identify ORA enriched pathways while providing more detailed branch-level information. Moreover, in contrast to the SPIA method, it offers the advantage of being cut-off free and less susceptible to the variability caused by selection thresholds. By combining pathway curation and topology-based analysis, the PSF toolkit enhances the quality, flexibility, and accessibility of topology-aware pathway analysis. Researchers can now easily import pathways from various sources, correct and modify them as needed, and perform detailed topology-based pathway signal flow analysis. In summary, our PSF toolkit offers an integrated solution that addresses the limitations of current topology-based pathway analysis methods. By providing interactive and command-line tools for pathway curation and topology-based analysis, we empower researchers to conduct comprehensive pathway analyses across a wide range of applications.

PMID:37680201 | PMC:PMC10482229 | DOI:10.3389/fgene.2023.1264656

Biotechnol Biofuels Bioprod. 2023 Sep 7;16(1):133. doi: 10.1186/s13068-023-02384-2.

ABSTRACT

The use of marine microalgae in industrial systems is attractive for converting CO2 into value-added products using saline water and sunlight. The plant nature and demonstrated industrial potential facilitate Chlorella spp. as excellent model organisms for both basic research and commercial application. However, the transformation method has not been developed in marine Chlorella spp., thus genetic engineering is hindered in exploiting the industrial potentialities of these strains. In this study, we provided a transformation protocol for the marine Chlorella strain MEM25, which showed robust characteristics, including high production of proteins and polyunsaturated fatty acids in multiple cultivation systems over various spatial-temporal scales. We showed that transformants could be obtained in a dramatically time-saving manner (comparable to Saccharomyces cerevisiae) with four functional proteins expressed properly. The transgenes are integrated into the genome and can be successfully inherited for more than two years. The development of a marine Chlorella transformation method, in combination with the complete genome, will greatly facilitate more comprehensive mechanism studies and provide possibilities to use this species as chassis for synthetic biology to produce value-added compounds with mutual advantage in neutralization of CO2 in commercial scales.

PMID:37679828 | DOI:10.1186/s13068-023-02384-2

BMC Med. 2023 Sep 8;21(1):349. doi: 10.1186/s12916-023-03054-8.

ABSTRACT

BACKGROUND: Placental dysfunction, a root cause of common syndromes affecting human pregnancy, such as preeclampsia (PE), fetal growth restriction (FGR), and spontaneous preterm delivery (sPTD), remains poorly defined. These common, yet clinically disparate obstetrical syndromes share similar placental histopathologic patterns, while individuals within each syndrome present distinct molecular changes, challenging our understanding and hindering our ability to prevent and treat these syndromes.

METHODS: Using our extensive biobank, we identified women with severe PE (n = 75), FGR (n = 40), FGR with a hypertensive disorder (FGR + HDP; n = 33), sPTD (n = 72), and two uncomplicated control groups, term (n = 113), and preterm without PE, FGR, or sPTD (n = 16). We used placental biopsies for transcriptomics, proteomics, metabolomics data, and histological evaluation. After conventional pairwise comparison, we deployed an unbiased, AI-based similarity network fusion (SNF) to integrate the datatypes and identify omics-defined placental clusters. We used Bayesian model selection to compare the association between the histopathological features and disease conditions vs SNF clusters.

RESULTS: Pairwise, disease-based comparisons exhibited relatively few differences, likely reflecting the heterogeneity of the clinical syndromes. Therefore, we deployed the unbiased, omics-based SNF method. Our analysis resulted in four distinct clusters, which were mostly dominated by a specific syndrome. Notably, the cluster dominated by early-onset PE exhibited strong placental dysfunction patterns, with weaker injury patterns in the cluster dominated by sPTD. The SNF-defined clusters exhibited better correlation with the histopathology than the predefined disease groups.

CONCLUSIONS: Our results demonstrate that integrated omics-based SNF distinctively reclassifies placental dysfunction patterns underlying the common obstetrical syndromes, improves our understanding of the pathological processes, and could promote a search for more personalized interventions.

PMID:37679695 | DOI:10.1186/s12916-023-03054-8

Neurosci Bull. 2023 Sep 7. doi: 10.1007/s12264-023-01107-9. Online ahead of print.

NO ABSTRACT

PMID:37679533 | DOI:10.1007/s12264-023-01107-9

NPJ Syst Biol Appl. 2023 Sep 7;9(1):40. doi: 10.1038/s41540-023-00304-6.

ABSTRACT

Predicting evolution is a fundamental problem in biology with practical implications for treating antimicrobial resistance, which is a complex system-level phenomenon. In this perspective article, we explore the limits of predicting antimicrobial resistance evolution, quantitatively define the predictability and repeatability of microevolutionary processes, and speculate on how these quantities vary across temporal, biological, and complexity scales. The opportunities and challenges for predicting antimicrobial resistance in the context of systems biology are also discussed. Based on recent research, we conclude that the evolution of antimicrobial resistance can be predicted using a systems biology approach integrating quantitative models with multiscale data from microbial evolution experiments.

PMID:37679446 | DOI:10.1038/s41540-023-00304-6

Nat Genet. 2023 Sep;55(9):1448-1461. doi: 10.1038/s41588-023-01462-3. Epub 2023 Sep 7.

ABSTRACT

Conventional measurements of fasting and postprandial blood glucose levels investigated in genome-wide association studies (GWAS) cannot capture the effects of DNA variability on 'around the clock' glucoregulatory processes. Here we show that GWAS meta-analysis of glucose measurements under nonstandardized conditions (random glucose (RG)) in 476,326 individuals of diverse ancestries and without diabetes enables locus discovery and innovative pathophysiological observations. We discovered 120 RG loci represented by 150 distinct signals, including 13 with sex-dimorphic effects, two cross-ancestry and seven rare frequency signals. Of these, 44 loci are new for glycemic traits. Regulatory, glycosylation and metagenomic annotations highlight ileum and colon tissues, indicating an underappreciated role of the gastrointestinal tract in controlling blood glucose. Functional follow-up and molecular dynamics simulations of lower frequency coding variants in glucagon-like peptide-1 receptor (GLP1R), a type 2 diabetes treatment target, reveal that optimal selection of GLP-1R agonist therapy will benefit from tailored genetic stratification. We also provide evidence from Mendelian randomization that lung function is modulated by blood glucose and that pulmonary dysfunction is a diabetes complication. Our investigation yields new insights into the biology of glucose regulation, diabetes complications and pathways for treatment stratification.

PMID:37679419 | DOI:10.1038/s41588-023-01462-3

J Immunother Cancer. 2023 Sep;11(9):e006766. doi: 10.1136/jitc-2023-006766.

ABSTRACT

BACKGROUND: Phenotypic heterogeneity of melanoma cells contributes to drug tolerance, increased metastasis, and immune evasion in patients with progressive disease. Diverse mechanisms have been individually reported to shape extensive intra-tumor and inter-tumor phenotypic heterogeneity, such as IFNγ signaling and proliferative to invasive transition, but how their crosstalk impacts tumor progression remains largely elusive.

METHODS: Here, we integrate dynamical systems modeling with transcriptomic data analysis at bulk and single-cell levels to investigate underlying mechanisms behind phenotypic heterogeneity in melanoma and its impact on adaptation to targeted therapy and immune checkpoint inhibitors. We construct a minimal core regulatory network involving transcription factors implicated in this process and identify the multiple 'attractors' in the phenotypic landscape enabled by this network. Our model predictions about synergistic control of PD-L1 by IFNγ signaling and proliferative to invasive transition were validated experimentally in three melanoma cell lines-MALME3, SK-MEL-5 and A375.

RESULTS: We demonstrate that the emergent dynamics of our regulatory network comprising MITF, SOX10, SOX9, JUN and ZEB1 can recapitulate experimental observations about the co-existence of diverse phenotypes (proliferative, neural crest-like, invasive) and reversible cell-state transitions among them, including in response to targeted therapy and immune checkpoint inhibitors. These phenotypes have varied levels of PD-L1, driving heterogeneity in immunosuppression. This heterogeneity in PD-L1 can be aggravated by combinatorial dynamics of these regulators with IFNγ signaling. Our model predictions about changes in proliferative to invasive transition and PD-L1 levels as melanoma cells evade targeted therapy and immune checkpoint inhibitors were validated in multiple RNA-seq data sets from in vitro and in vivo experiments.

CONCLUSION: Our calibrated dynamical model offers a platform to test combinatorial therapies and provide rational avenues for the treatment of metastatic melanoma. This improved understanding of crosstalk among PD-L1 expression, proliferative to invasive transition and IFNγ signaling can be leveraged to improve the clinical management of therapy-resistant and metastatic melanoma.

PMID:37678920 | DOI:10.1136/jitc-2023-006766

Curr Opin Cell Biol. 2023 Sep 5;85:102233. doi: 10.1016/j.ceb.2023.102233. Online ahead of print.

ABSTRACT

Vimentin, an intermediate filament protein typically located in the cytoplasm of mesenchymal cells, can also be secreted as an extracellular protein. The organization of extracellular vimentin strongly determines its functions in physiological and pathological conditions, making it a promising target for future therapeutic interventions. The extracellular form of vimentin has been found to play a role in the interaction between host cells and pathogens. In this review, we first discuss the molecular biophysics of extracellular vimentin, including its structure, secretion, and adhesion properties. We then provide a general overview of the role of extracellular vimentin in mediating pathogen-host interactions, with a focus on its interactions with viruses and bacteria. We also discuss the implications of these findings for the development of new therapeutic strategies for combating infectious diseases.

PMID:37677998 | DOI:10.1016/j.ceb.2023.102233

Curr Opin Microbiol. 2023 Sep 5;75:102368. doi: 10.1016/j.mib.2023.102368. Online ahead of print.

ABSTRACT

Infections that involve interkingdom microbial communities, such as those between bacteria and yeast pathogens, are difficult to treat, associated with worse patient outcomes, and may be a source of antimicrobial resistance. In this review, we address co-occurrence and co-infections of Candida albicans and Pseudomonas aeruginosa, two pathogens that occupy multiple infection niches in the human body, especially in immunocompromised patients. The interaction between the pathogen species influences microbe-host interactions, the effectiveness of antimicrobials and even infection outcomes, and may thus require adapted treatment strategies. However, the molecular details of bacteria-fungal interactions both inside and outside the infection sites, are insufficiently characterised. We argue that comprehensively understanding the P. aeruginosa-C. albicans interaction network through integrated systems biology approaches will capture the highly dynamic and complex nature of these polymicrobial infections and lead to a more comprehensive understanding of clinical observations such as reshaped immune defences and low antimicrobial treatment efficacy.

PMID:37677865 | DOI:10.1016/j.mib.2023.102368

Science. 2023 Sep 8;381(6662):1112-1119. doi: 10.1126/science.adh3253. Epub 2023 Sep 7.

ABSTRACT

The cerebellum contains most of the neurons in the human brain and exhibits distinctive modes of development and aging. In this work, by developing our single-cell three-dimensional (3D) genome assay-diploid chromosome conformation capture, or Dip-C-into population-scale (Pop-C) and virus-enriched (vDip-C) modes, we resolved the first 3D genome structures of single cerebellar cells, created life-spanning 3D genome atlases for both humans and mice, and jointly measured transcriptome and chromatin accessibility during development. We found that although the transcriptome and chromatin accessibility of cerebellar granule neurons mature in early postnatal life, 3D genome architecture gradually remodels throughout life, establishing ultra-long-range intrachromosomal contacts and specific interchromosomal contacts that are rarely seen in neurons. These results reveal unexpected evolutionarily conserved molecular processes that underlie distinctive features of neural development and aging across the mammalian life span.

PMID:37676945 | DOI:10.1126/science.adh3253

J Chem Theory Comput. 2023 Sep 7. doi: 10.1021/acs.jctc.3c00787. Online ahead of print.

ABSTRACT

We evaluated the utility of a variant of the replica exchange method, a replica exchange with hybrid tempering (REHT), for all-atom explicit water biomolecular simulations and compared it with a more traditional replica exchange with the solute tempering (REST) algorithm. As a test system, we selected a 21-mer antimicrobial peptide PGLa binding to an anionic DMPC/DMPG lipid bilayer. Application of REHT revealed the following binding mechanism. Due to the strong hydrophobic moment, the bound PGLa adopts an extensive helical structure. The binding free energy landscape identifies two major bound states, a metastable surface bound state and a dominant inserted state. In both states, positively charged PGLa amino acids maintain electrostatic interactions with anionic phosphate groups by rotating the PGLa helix around its axis. PGLa binding causes an influx of anionic DMPG and an efflux of zwitterionic DMPC lipids from the peptide proximity. PGLa thins the bilayer and disorders the adjacent fatty acid tails. Deep invasion of water wires into the bilayer hydrophobic core is detected in the inserted peptide state. The analysis of charge density distributions indicated that peptide positive charges are nearly compensated for by lipid negative charges and water dipole ordering, whereas ions play no role in peptide binding. Thus, electrostatic interactions are the key energetic factor in binding cationic PGLa to an anionic DMPC/DMPG bilayer. Comparison of REHT and REST shows that due to exclusion of lipids from tempered partition, REST lags behind REHT in peptide equilibration, particularly, with respect to peptide insertion and helix acquisition. As a result, REST struggles to provide accurate details of PGLa binding, although it still qualitatively maps the bimodal binding mechanism. Importantly, REHT not only equilibrates PGLa in the bilayer faster than REST, but also with less computational effort. We conclude that REHT is a preferable choice for studying interfacial biomolecular systems.

PMID:37676235 | DOI:10.1021/acs.jctc.3c00787

J Med Chem. 2023 Sep 7. doi: 10.1021/acs.jmedchem.3c01252. Online ahead of print.

ABSTRACT

Protein or peptide cancer vaccines usually include immune potentiators, so-called adjuvants. However, it remains challenging to identify structurally simple, chemically accessible synthetic molecules that are effective and safe as vaccine adjuvant. Here, we present cholicamideβ (6), a self-assembling small-molecule vaccine adjuvant with an improved toxicity profile and proven efficacy in vivo. We demonstrate that cholicamideβ (6), which is less cytotoxic than its parent compound, forms virus-like particles to potently activate dendritic cells with the concomitant secretion of cytokines. When combined with a peptide antigen, cholicamideβ (6) potentiated the antigen presentation on dendritic cells to induce antigen-specific T cells. As a therapeutic cancer vaccine adjuvant in mice, a mixture of cholicamideβ (6) and a peptide antigen protected mice from the challenges of malignant cancer cells without overt toxicity. Cholicamideβ (6) may offer a translational opportunity as an unprecedented class of small-molecule cancer vaccine adjuvants.

PMID:37676021 | DOI:10.1021/acs.jmedchem.3c01252