Mol Nutr Food Res. 2023 Nov 12:e2300396. doi: 10.1002/mnfr.202300396. Online ahead of print.

ABSTRACT

SCOPE: For most substances, there are several routes of excretion from the human body. This study focuses on urinary excretion of dietary odorants and compares the results with previously obtained results on excretion into milk.

METHODS AND RESULTS: Lactating mothers (n = 18) are given a standardized curry dish and donate urine samples before and after the intervention. The odorants 1,8-cineole, linalool, cuminaldehyde, cinnamaldehyde, 4-hydroxy-2,5-dimethyl-3(2H)-furanone, sotolone, eugenol, vanillin, and γ-nonalactone are quantitatively analyzed. A significant transition of up to 6 µg g-1 creatinine into urine is observed for linalool, 1,8-cineole, and eugenol. Maximum concentrations are reached 1.5 h after the intervention for 1,8-cineole and eugenol as well as 2.5 h after the intervention for linalool. Comparison with previous results reveals that the excretion pattern of odorants into urine is divergent from the one into milk. In a second intervention study (n = 6), excretion of phase II metabolites into urine is studied using β-glucuronidase treatment. Linalool and eugenol concentrations are 23 and 77 times higher after treatment than before treatment with β-glucuronidase, respectively.

CONCLUSION: The study demonstrates transition of linalool, 1,8-cineole, and eugenol from the diet into urine and excretion of glucuronides in the case of linalool, eugenol, and vanillin.

PMID:37953385 | DOI:10.1002/mnfr.202300396

New Phytol. 2023 Nov 12. doi: 10.1111/nph.19364. Online ahead of print.

ABSTRACT

The emergence of new pathogens is an ongoing threat to human health and agriculture. While zoonotic spillovers received considerable attention, the emergence of crop diseases is less well studied. Here, we identify genomic factors associated with the emergence of Pseudomonas syringae bacterial blight of coffee. Fifty-three P. syringae strains from diseased Brazilian coffee plants were sequenced. Comparative and evolutionary analyses were used to identify loci associated with coffee blight. Growth and symptomology assays were performed to validate the findings. Coffee isolates clustered in three lineages, including primary phylogroups PG3 and PG4, and secondary phylogroup PG11. Genome-wide association study of the primary PG strains identified 37 loci, including five effectors, most of which were encoded on a plasmid unique to the PG3 and PG4 coffee strains. Evolutionary analyses support the emergence of coffee blight in PG4 when the coffee-associated plasmid and associated effectors derived from a divergent plasmid carried by strains associated with other hosts. This plasmid was only recently transferred into PG3. Natural diversity and CRISPR-Cas9 plasmid curing were used to show that strains with the coffee-associated plasmid grow to higher densities and cause more severe disease symptoms in coffee. This work identifies possible evolutionary mechanisms underlying the emergence of a new lineage of coffee pathogens.

PMID:37953378 | DOI:10.1111/nph.19364

Nucleic Acids Res. 2023 Nov 11:gkad1005. doi: 10.1093/nar/gkad1005. Online ahead of print.

ABSTRACT

The Human Phenotype Ontology (HPO) is a widely used resource that comprehensively organizes and defines the phenotypic features of human disease, enabling computational inference and supporting genomic and phenotypic analyses through semantic similarity and machine learning algorithms. The HPO has widespread applications in clinical diagnostics and translational research, including genomic diagnostics, gene-disease discovery, and cohort analytics. In recent years, groups around the world have developed translations of the HPO from English to other languages, and the HPO browser has been internationalized, allowing users to view HPO term labels and in many cases synonyms and definitions in ten languages in addition to English. Since our last report, a total of 2239 new HPO terms and 49235 new HPO annotations were developed, many in collaboration with external groups in the fields of psychiatry, arthrogryposis, immunology and cardiology. The Medical Action Ontology (MAxO) is a new effort to model treatments and other measures taken for clinical management. Finally, the HPO consortium is contributing to efforts to integrate the HPO and the GA4GH Phenopacket Schema into electronic health records (EHRs) with the goal of more standardized and computable integration of rare disease data in EHRs.

PMID:37953324 | DOI:10.1093/nar/gkad1005

Clin Immunol. 2023 Nov 10:109839. doi: 10.1016/j.clim.2023.109839. Online ahead of print.

ABSTRACT

BACKGROUND: Familial Mediterranean Fever (FMF) is a monogenic disease caused by gain-of-function mutations in the MEditerranean FeVer (MEFV) gene. The molecular dysregulations induced by these mutations and the associated causal mechanisms are complex and intricate.

OBJECTIVE: We sought to provide a computational model capturing the mechanistic details of biological pathways involved in FMF physiopathology and enabling the study of the patient's immune cell dynamics.

METHODS: We carried out a literature survey to identify experimental studies published from January 2000 to December 2020, and integrated its results into a molecular map and a mathematical model. Then, we studied the network of molecular interactions and the dynamic of monocytes to identify key players for inflammation phenotype in FMF patients.

RESULTS: We built a molecular map of FMF integrating in a structured manner the current knowledge regarding pathophysiological processes participating in the triggering and perpetuation of the disease flares. The mathematical model derived from the map reproduced patient's monocyte behavior, in particular its proinflammatory role via the Pyrin inflammasome activation. Network analysis and in silico experiments identified NF-κB and JAK1/TYK2 as critical to modulate IL-1β- and IL-18-mediated inflammation.

CONCLUSION: The in silico model of FMF monocyte proved its ability to reproduce in vitro observations. Considering the difficulties related to experimental settings and financial investments to test combinations of stimuli/perturbation in vitro, this model could be used to test complex hypotheses in silico, thus narrowing down the number of in vitro and ex vivo experiments to perform.

PMID:37952562 | DOI:10.1016/j.clim.2023.109839

Eur J Cancer. 2023 Nov 7;196:113421. doi: 10.1016/j.ejca.2023.113421. Online ahead of print.

ABSTRACT

Early diagnosis of cancer can significantly improve survival of cancer patients; however sensitive and highly specific biomarkers for cancer detection are currently lacking for most cancer types. Nullpeptides are short peptides that are absent from the human proteome. Here, we examined the emergence of nullpeptides during cancer development. We analyzed 3,600,964 somatic mutations across 10,064 whole exome sequencing tumor samples spanning 32 cancer types. We analyze RNA-seq data from primary tumor samples to identify the subset of nullpeptides that emerge in highly expresed genes. We show that nullpeptides, and particularly the subset that is highly recurrent across cancer patients, can be identified in tumor biopsy samples. We find that cancer genes show an excess of nullpeptides and detect nullpeptide hotspots in specific loci of oncogenes and tumor suppressors. We also observe that recurrent nullpeptides are more likely to be found in neoantigens, which have been shown to be effective targets for immunotherapy, suggesting that they can be used to prioritize candidates. Our findings provide evidence for the utility of nullpeptides as cancer detection and therapeutic biomarkers.

PMID:37952501 | DOI:10.1016/j.ejca.2023.113421

Sci Rep. 2023 Nov 11;13(1):19694. doi: 10.1038/s41598-023-46812-7.

ABSTRACT

Diffusion and mobility are essential for cellular functions, as molecules are usually distributed throughout the cell and have to meet to interact and perform their function. This also involves the cytosolic migration of cellular organelles. However, observing such diffusion and interaction dynamics is challenging due to the high spatial and temporal resolution required and the accurate analysis of the diffusional tracks. The latter is especially important when identifying anomalous diffusion events, such as directed motions, which are often rare. Here, we investigate the migration modes of peroxisome organelles in the cytosol of living cells. Peroxisomes predominantly migrate randomly, but occasionally they bind to the cell's microtubular network and perform directed migration, which is difficult to quantify, and so far, accurate analysis of switching between these migration modes is missing. We set out to solve this limitation by experiments and analysis with high statistical accuracy. Specifically, we collect temporal diffusion tracks of thousands of individual peroxisomes in the HEK 293 cell line using two-dimensional spinning disc fluorescence microscopy at a high acquisition rate of 10 frames/s. We use a Hidden Markov Model with two hidden states to (1) automatically identify directed migration segments of the tracks and (2) quantify the migration properties for comparison between states and between different experimental conditions. Comparing different cellular conditions, we show that the knockout of the peroxisomal membrane protein PEX14 leads to a decrease in the directed movement due to a lowered binding probability to the microtubule. However, it does not eradicate binding, highlighting further microtubule-binding mechanisms of peroxisomes than via PEX14. In contrast, structural changes of the microtubular network explain perceived eradication of directed movement by disassembly of microtubules by Nocodazole-treatment.

PMID:37951993 | DOI:10.1038/s41598-023-46812-7

J Cyst Fibros. 2023 Nov 9:S1569-1993(23)01669-7. doi: 10.1016/j.jcf.2023.10.019. Online ahead of print.

ABSTRACT

BACKGROUND: There have been dramatic clinical improvements in people with cystic fibrosis (PwCF) commenced on the cystic fibrosis conductance regulator (CFTR) modulator elexacaftor/tezacaftor/ivacaftor (ETI). Sputum proteomics is a powerful research technique capable of identifying important airway disease mechanisms. Using this technique, we evaluated how ETI changes the sputum proteome in PwCF.

METHODS: Sputum samples from 21 CF subjects pre- and post- ETI, 6 CF controls ineligible for ETI, and 15 healthy controls were analysed by liquid chromatography mass spectrometry.

RESULTS: Post-ETI, mean FEV1 % increased by 13.7 % (SD 7.9). Principal component and hierarchical clustering analysis revealed that the post-ETI proteome shifted to an intermediate state that was distinct from pre-ETI and healthy controls, even for those achieving normal lung function. Functional analysis showed incomplete resolution of neutrophilic inflammation. The CF control sputum proteome did not alter. At the protein-level many more proteins increased in abundance than decreased following ETI therapy (80 vs 30; adjusted p value <0.05), including many that have anti-inflammatory properties. Of those proteins that reduced in abundance many were pro-inflammatory neutrophil-derived proteins. Several important respiratory proteases were unchanged.

CONCLUSIONS: Sputum proteomics can provide insights into CF lung disease mechanisms and how they are modified by therapeutic intervention, in this case ETI. This study identifies imbalances in pro- and anti- inflammatory proteins in sputum that partially resolve with ETI even in those achieving normal spirometry values. This post-ETI intermediate state could contribute to ongoing airway damage and therefore its relevance to clinical outcomes needs to be established.

PMID:37951788 | DOI:10.1016/j.jcf.2023.10.019

Bioinformatics. 2023 Nov 10:btad678. doi: 10.1093/bioinformatics/btad678. Online ahead of print.

ABSTRACT

MOTIVATION: Dynamical properties of biochemical pathways (BPs) help in understanding the functioning of living cells. Their in silico assessment requires simulating a dynamical system with a large number of parameters such as kinetic constants and species concentrations. Such simulations are based on numerical methods that can be time-expensive for large BPs. Moreover, parameters are often unknown and need to be estimated.

RESULTS: We developed a framework for the prediction of dynamical properties of BPs directly from the structure of their graph representation. We represent BPs as Petri nets, which can be automatically generated, for instance, from standard SBML representations. The core of the framework is a neural network for graphs that extracts relevant information directly from the Petri net structure and exploits them to learn the association with the desired dynamical property. We show experimentally that the proposed approach reliably predicts a range of diverse dynamical properties (robustness, monotonicity, and sensitivity) while being faster than numerical methods at prediction time. In synergy with the neural network models, we propose a methodology based on Petri nets arc knock-out that allows the role of each molecule in the occurrence of a certain dynamical property to be better elucidated. The methodology also provides insights useful for interpreting the predictions made by the model. The results support the conjecture often considered in the context of systems biology that the BP structure plays a primary role in the assessment of its dynamical properties.

AVAILABILITY: https://github.com/marcopodda/petri-bio (code), https://zenodo.org/record/7610382 (data).

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID:37951586 | DOI:10.1093/bioinformatics/btad678

Toxicol Appl Pharmacol. 2023 Nov 9:116753. doi: 10.1016/j.taap.2023.116753. Online ahead of print.

ABSTRACT

Exposure to nickel, an environmental respiratory toxicant, is associated with lung diseases including asthma, pulmonary fibrosis, bronchitis and cancers. Our previous studies have shown that a majority of the nickel-induced transcriptional changes are persistent and do not reverse even after the termination of exposure. This suggested transcriptional memory, wherein the cell 'remembers' past nickel exposure. Transcriptional memory, due to which the cells respond more robustly to a previously encountered stimulus has been identified in a number of organisms. Therefore, transcriptional memory has been described as an adaptive mechanism. However, transcriptional memory caused by environmental toxicant exposures has not been well investigated. Moreover, how the transcriptional memory caused by an environmental toxicant might influence the outcome of exposure to a second toxicant has not been explored. In this study, we investigated whether nickel-induced transcriptional memory influences the outcome of the cell's response to a second respiratory toxicant, nicotine. Nicotine, an addictive compound in tobacco is associated with the development of chronic lung diseases including chronic obstructive pulmonary disease (COPD) and pulmonary fibrosis. Our results show that nicotine exposure upregulated a subset of genes only in the cells previously exposed to nickel. Furthermore, our analyses indicate robust activation of interferon (IFN) signaling in these cells. IFN signaling is a driver of inflammation, which is associated with many chronic lung diseases. Therefore, our results suggest that nicotine exposure of lung cells that retain the transcriptional memory of previous nickel exposure could result in increased susceptibility to developing chronic inflammatory lung diseases.

PMID:37951547 | DOI:10.1016/j.taap.2023.116753

Aging (Albany NY). 2023 Nov 9;15. doi: 10.18632/aging.205228. Online ahead of print.

ABSTRACT

5'-Adenosine monophosphate-activated protein kinase (AMPK) is a metabolic sensor that serves as a cellular housekeeper; it also controls energy homeostasis and stress resistance. Thus, correct regulation of this factor can enhance health and survival. AMPK signaling may have a critical role in aging-associated brain diseases. Some in vitro studies have shown that 1,5-anhydro-D-fructose (1,5-AF) induces AMPK activation. In the present study, we experimentally evaluated the effects of 1,5-AF on aging-associated brain diseases in vivo using an animal model of acute ischemic stroke (AIS), stroke-prone spontaneously hypertensive rats (SHRSPs), and the spontaneous senescence-accelerated mouse-prone 8 (SAMP8) model. In the AIS model, intraperitoneal injection of 1,5-AF reduced cerebral infarct volume, neurological deficits, and mortality. In SHRSPs, oral administration of 1,5-AF reduced blood pressure and prolonged survival. In the SAMP8 model, oral administration of 1,5-AF alleviated aging-related decline in motor cognitive function. Although aging reduced the expression levels of peroxisome proliferator-activated receptor-γ co-activator-1α (PGC-1α) and brain-derived neurotrophic factor (BDNF), we found that 1,5-AF activated AMPK, which led to upregulation of the PGC-1α/BDNF pathway. Our results suggest that 1,5-AF can induce endogenous neurovascular protection, potentially preventing aging-associated brain diseases. Clinical studies are needed to determine whether 1,5-AF can prevent aging-associated brain diseases.

PMID:37950725 | DOI:10.18632/aging.205228

New Phytol. 2023 Nov 10. doi: 10.1111/nph.19383. Online ahead of print.

ABSTRACT

Hypocotyl elongation is controlled by several signals and is a major characteristic of plants growing in darkness or under warm temperature. While already several molecular mechanisms associated with this process are known, protein degradation and associated E3 ligases have hardly been studied in the context of warm temperature. In a time-course phosphoproteome analysis on Arabidopsis seedlings exposed to control or warm ambient temperature, we observed reduced levels of diverse proteins over time, which could be due to transcription, translation, and/or degradation. In addition, we observed differential phosphorylation of the LRR F-box protein SLOMO MOTION (SLOMO) at two serine residues. We demonstrate that SLOMO is a negative regulator of hypocotyl growth, also under warm temperature conditions, and protein-protein interaction studies revealed possible interactors of SLOMO, such as MKK5, DWF1, and NCED4. We identified DWF1 as a likely SLOMO substrate and a regulator of warm temperature-mediated hypocotyl growth. We propose that warm temperature-mediated regulation of SLOMO activity controls the abundance of hypocotyl growth regulators, such as DWF1, through ubiquitin-mediated degradation.

PMID:37950543 | DOI:10.1111/nph.19383

Bioinformatics. 2023 Nov 9:btad676. doi: 10.1093/bioinformatics/btad676. Online ahead of print.

ABSTRACT

SUMMARY: In recent years, phased small interfering RNA has been found to play crucial roles in many biological processes in plants. However, efficiently predicting phasiRNA regulatory cascades with computational methods is still challenging. Here, we introduce PhasiHunter, a phasiRNA regulatory network prediction tool that has several distinctive features compared to existing tools. (i) PhasiHunter employs two major phasiRNA prediction algorithms, namely phase score and hypergeometric distribution-based methods, to ensure the integrity and accuracy of prediction. (ii) PhasiHunter can identify phasiRNAs and their regulatory networks based on multiple reference sequences and the predicted results can be automatically integrated. (iii) PhasiHunter can efficiently identify the phasiRNAs generated through alternative splicing (AS) events. (iv) The excellent data structure and parallel computing architecture allow PhasiHunter to predict phasiRNAs and their regulatory pathways with high efficiency.

AVAILABILITY AND IMPLEMENTATION: PhasiHunter is an open-source tool that is available at https://github.com/HuangLab-CBI/PhasiHunter.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID:37950456 | DOI:10.1093/bioinformatics/btad676

CPT Pharmacometrics Syst Pharmacol. 2023 Nov 10. doi: 10.1002/psp4.13076. Online ahead of print.

ABSTRACT

High drug development costs and the limited number of new annual drug approvals increase the need for innovative approaches for drug effect prediction. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the cause of coronavirus disease 2019 (COVID-19), led to a global pandemic with high morbidity and mortality. While effective preventive measures exist, there are few effective treatments for hospitalized patients with SARS-CoV-2 infection. Drug effect prediction are promising strategies that could shorten development time and reduce costs compared to de novo drug discovery. In this work, we present a machine learning framework to integrate a variety of target network features and physicochemical properties of compounds and analyze their influence on the therapeutic effects for SARS-CoV-2 infection and on host cell cytotoxic effects. The random forest models trained on compounds with known experimental effects on SARS-CoV-2 infection and subsequent feature importance analysis based on Shapely values provided insights into the determinants of drug efficacy and cytotoxicity, which can be incorporated into novel drug discovery approaches. Given the complexity of molecular mechanisms of drug action and limited sample sizes, our models achieve a reasonable mean ROC-AUC of 0.73 on our unseen validation set. To our knowledge, this is the first work to incorporate a combination of network and physicochemical features of compounds into a machine learning model to predict drug effects on SARS-CoV-2 infection. Our systems pharmacology-based machine learning framework can be used to classify other existing drugs for SARS-CoV-2 infection and can easily be adapted to drug effect prediction for future viral outbreaks.

PMID:37950385 | DOI:10.1002/psp4.13076

Genome Biol. 2023 Nov 10;24(1):259. doi: 10.1186/s13059-023-03100-x.

ABSTRACT

BACKGROUND: Feature selection is an essential task in single-cell RNA-seq (scRNA-seq) data analysis and can be critical for gene dimension reduction and downstream analyses, such as gene marker identification and cell type classification. Most popular methods for feature selection from scRNA-seq data are based on the concept of differential distribution wherein a statistical model is used to detect changes in gene expression among cell types. Recent development of deep learning-based feature selection methods provides an alternative approach compared to traditional differential distribution-based methods in that the importance of a gene is determined by neural networks.

RESULTS: In this work, we explore the utility of various deep learning-based feature selection methods for scRNA-seq data analysis. We sample from Tabula Muris and Tabula Sapiens atlases to create scRNA-seq datasets with a range of data properties and evaluate the performance of traditional and deep learning-based feature selection methods for cell type classification, feature selection reproducibility and diversity, and computational time.

CONCLUSIONS: Our study provides a reference for future development and application of deep learning-based feature selection methods for single-cell omics data analyses.

PMID:37950331 | DOI:10.1186/s13059-023-03100-x

Clin Epigenetics. 2023 Nov 10;15(1):181. doi: 10.1186/s13148-023-01594-7.

ABSTRACT

BACKGROUND: Puberty is a highly heritable and variable trait, with environmental factors having a role in its eventual timing and development. Early and late pubertal onset are both associated with various diseases developing later in life, and epigenetic characterisation of pubertal timing and development could lead to important insights. Blood DNA methylation, reacting to both genotype and environment, has been associated with puberty; however, such studies are relatively scarce. We investigated peripheral blood DNA methylation profiles (using Illumina 450 K and EPIC platforms) of 1539 young adult Finnish twins associated with pubertal development scale (PDS) at ages 12 and 14 as well as pubertal age (PA).

RESULTS: Fixed effect meta-analysis of the two platforms on 347,521 CpGs in common identified 58 CpG sites associated (p < 1 × 10-5) with either PDS or PA. All four CpGs associated with PA and 45 CpGs associated with PDS were sex-specific. Thirteen CpGs had a high heritability (h2: 0.51-0.98), while one CpG site (mapped to GET4) had a high shared environmental component accounting for 68% of the overall variance in methylation at the site. Utilising twin discordance analysis, we found 6 CpG sites (5 associated with PDS and 1 with PA) that had an environmentally driven association with puberty. Furthermore, genes with PDS- or PA-associated CpGs were consistently linked to various developmental processes and diseases such as breast, prostate and ovarian cancer, while methylation quantitative trait loci of associated CpG sites were enriched in immune pathways developing during puberty.

CONCLUSIONS: By identifying puberty-associated DNA methylation sites and examining the effects of sex, environment and genetics, we shed light on the intricate interplay between environment and genetics in the context of puberty. Through our comprehensive analysis, we not only deepen the understanding of the significance of both genetic and environmental factors in the complex processes of puberty and its timing, but also gain insights into potential links with disease risks.

PMID:37950287 | DOI:10.1186/s13148-023-01594-7

Nat Rev Nephrol. 2023 Nov 10. doi: 10.1038/s41581-023-00788-9. Online ahead of print.

NO ABSTRACT

PMID:37950018 | DOI:10.1038/s41581-023-00788-9

Cancer Gene Ther. 2023 Nov 10. doi: 10.1038/s41417-023-00685-0. Online ahead of print.

ABSTRACT

The replication-stress response is essential to ensure the faithful transmission of genetic information to daughter cells. Although several stress-resolution pathways have been identified to deal with replication stress, the precise regulatory mechanisms for replication fork stability are not fully understood. Our study identified Methyl-CpG Binding Domain 1 (MBD1) as essential for the maintaining genomic stability and protecting stalled replication forks in mammalian cells. Depletion of MBD1 increases DNA lesions and sensitivity to replication stress. Mechanistically, we found that loss of MBD1 leads to the dissociation of Poly(ADP-ribose) polymerase 1 (PARP1) from the replication fork, potentially accelerating fork progression and resulting in higher levels of transcription-replication conflicts (T-R conflicts). Using a proximity ligation assay combined with 5-ethynyl-2'-deoxyuridine, we revealed that the MBD1 and PARP1 proteins were recruited to stalled forks under hydroxyurea (HU) treatment. In addition, our study showed that the level of R-loops also increased in MBD1-delated cells. Without MBD1, stalled replication forks resulting from T-R conflicts were primarily degraded by the DNA2 nuclease. Our findings shed light on a new aspect of MBD1 in maintaining genome stability and providing insights into the mechanisms underlying replication stress response.

PMID:37949945 | DOI:10.1038/s41417-023-00685-0

Sci Rep. 2023 Nov 10;13(1):19653. doi: 10.1038/s41598-023-46392-6.

ABSTRACT

Personalised cancer screening before therapy paves the way toward improving diagnostic accuracy and treatment outcomes. Most approaches are limited to a single data type and do not consider interactions between features, leaving aside the complementary insights that multimodality and systems biology can provide. In this project, we demonstrate the use of graph theory for data integration via individual networks where nodes and edges are individual-specific. We showcase the consequences of early, intermediate, and late graph-based fusion of RNA-Seq data and histopathology whole-slide images for predicting cancer subtypes and severity. The methodology developed is as follows: (1) we create individual networks; (2) we compute the similarity between individuals from these graphs; (3) we train our model on the similarity matrices; (4) we evaluate the performance using the macro F1 score. Pros and cons of elements of the pipeline are evaluated on publicly available real-life datasets. We find that graph-based methods can increase performance over methods that do not study interactions. Additionally, merging multiple data sources often improves classification compared to models based on single data, especially through intermediate fusion. The proposed workflow can easily be adapted to other disease contexts to accelerate and enhance personalized healthcare.

PMID:37949935 | DOI:10.1038/s41598-023-46392-6

Adv Sci (Weinh). 2023 Nov 10:e2304076. doi: 10.1002/advs.202304076. Online ahead of print.

ABSTRACT

Effective anti-cancer immune responses require activation of one or more naïve T cells. If the correct naïve T cell encounters its cognate antigen presented by an antigen presenting cell, then the T cell can activate and proliferate. Here, mathematical modeling is used to explore the possibility that immune activation in lymph nodes is a rate-limiting step in anti-cancer immunity and can affect response rates to immune checkpoint therapy. The model provides a mechanistic framework for optimizing cancer immunotherapy and developing testable solutions to unleash anti-tumor immune responses for more patients with cancer. The results show that antigen production rate and trafficking of naïve T cells into the lymph nodes are key parameters and that treatments designed to enhance tumor antigen production can improve immune checkpoint therapies. The model underscores the potential of radiation therapy in augmenting tumor immunogenicity and neoantigen production for improved ICB therapy, while emphasizing the need for careful consideration in cases where antigen levels are already sufficient to avoid compromising the immune response.

PMID:37949675 | DOI:10.1002/advs.202304076

J Agric Food Chem. 2023 Nov 10. doi: 10.1021/acs.jafc.3c03099. Online ahead of print.

ABSTRACT

Recent developments in mass spectrometry-based metabolite profiling allow unprecedented qualitative coverage of complex biological extract composition. However, the electrospray ionization used in metabolite profiling generates multiple artifactual signals for a single analyte. This leads to thousands of signals per analysis without satisfactory means of filtering those corresponding to abundant constituents. Generic approaches are therefore needed for the qualitative and quantitative annotation of a broad range of relevant constituents. For this, we used an analytical platform combining liquid chromatography-mass spectrometry (LC-MS) with Charged Aerosol Detection (CAD). We established a generic metabolite profiling for the concomitant recording of qualitative MS data and semiquantitative CAD profiles. The MS features (recorded in high-resolution tandem MS) are grouped and annotated using state-of-the-art tools. To efficiently attribute features to their corresponding extracted and integrated CAD peaks, a custom signal pretreatment and peak-shape comparison workflow is built. This strategy allows us to automatically contextualize features at both major and minor metabolome levels, together with a detailed reporting of their annotation including relevant orthogonal information (taxonomy, retention time). Signals not attributed to CAD peaks are considered minor metabolites. Results are illustrated on an ethanolic extract of Swertia chirayita (Roxb.) H. Karst., a bitter plant of industrial interest, exhibiting the typical complexity of plant extracts as a proof of concept. This generic qualitative and quantitative approach paves the way to automatically assess the composition of single natural extracts of interest or broader collections, thus facilitating new ingredient registrations or natural-extracts-based drug discovery campaigns.

PMID:37949451 | DOI:10.1021/acs.jafc.3c03099