bioRxiv. 2023 Dec 13:2023.12.12.571327. doi: 10.1101/2023.12.12.571327. Preprint.

ABSTRACT

Mass spectrometry (MS) is a technique widely employed for the identification and characterization of proteins, personalized medicine, systems biology and biomedical applications. By combining MS with different proteomics approaches such as immunopurification MS, immunopeptidomics, and total protein proteomics, researchers can gain insights into protein-protein interactions, immune responses, cellular processes, and disease mechanisms. The application of MS-based proteomics in these areas continues to advance our understanding of protein function, cellular signaling, and complex biological systems. Data analysis for mass spectrometry is a critical process that includes identifying and quantifying proteins and peptides and exploring biological functions for these proteins in downstream analysis. To address the complexities associated with MS data analysis, we developed ProtPipe to streamline and automate the processing and analysis of high-throughput proteomics and peptidomics datasets. The pipeline facilitates data quality control, sample filtering, and normalization, ensuring robust and reliable downstream analysis. ProtPipe provides downstream analysis including identifying differential abundance proteins and peptides, pathway enrichment analysis, protein-protein interaction analysis, and MHC1-peptide binding affinity. ProtPipe generates annotated tables and diagnostic visualizations from statistical postprocessing and computation of fold-changes across pairwise conditions, predefined in an experimental design. ProtPipe is well-documented open-source software and is available at https://github.com/NIH-CARD/ProtPipe , accompanied by a web interface.

PMID:38168437 | PMC:PMC10760195 | DOI:10.1101/2023.12.12.571327

bioRxiv. 2023 Dec 13:2023.12.13.568471. doi: 10.1101/2023.12.13.568471. Preprint.

ABSTRACT

Enzymatic cleavage of C-F bonds in per- and polyfluoroalkyl substances (PFAS) is largely unknown but avidly sought to promote systems biology for PFAS bioremediation. Here, we report the reductive defluorination of α, β-unsaturated per- and polyfluorocarboxylic acids by Acetobacterium spp. Two critical molecular features in Acetobacterium species enabling reductive defluorination are (i) a functional fluoride efflux transporter (CrcB) and (ii) an electron-bifurcating caffeate reduction pathway (CarABCDE). The fluoride transporter was required for detoxification of released fluoride. Car enzymes were implicated in defluorination by the following evidence: (i) only Acetobacterium spp. with car genes catalyzed defluorination; (ii) caffeate and PFAS competed in vivo ; (iii) models from the X-ray structure of the electron-bifurcating reductase (CarC) positioned the PFAS substrate optimally for reductive defluorination; (iv) products identified by 19 F-NMR and high-resolution mass spectrometry were consistent with the model. Defluorination biomarkers identified here were found in wastewater treatment plant metagenomes on six continents.

PMID:38168399 | PMC:PMC10760045 | DOI:10.1101/2023.12.13.568471

J Neurovirol. 2024 Jan 2. doi: 10.1007/s13365-023-01191-7. Online ahead of print.

ABSTRACT

In recent years, we have seen the widespread devastations and serious health complications manifested by COVID-19 globally. Although we have effectively controlled the pandemic, uncertainties persist regarding its potential long-term effects, including prolonged neurological issues. To gain comprehensive insights, we conducted a meta-analysis of mass spectrometry-based proteomics data retrieved from different studies with a total of 538 COVID-19 patients and 523 healthy controls. The meta-analysis revealed that top-enriched pathways were associated with neurological disorders, including Alzheimer's (AD) and Parkinson's disease (PD). Further analysis confirmed a direct correlation in the expression patterns of 24 proteins involved in Alzheimer's and 23 proteins in Parkinson's disease with COVID-19. Protein-protein interaction network and cluster analysis identified SNCA as a hub protein, a known biomarker for Parkinson's disease, in both AD and PD. To the best of our knowledge, this is the first meta-analysis study providing proteomic profiling evidence linking COVID-19 to neurological complications.

PMID:38167982 | DOI:10.1007/s13365-023-01191-7

NPJ Breast Cancer. 2024 Jan 2;10(1):2. doi: 10.1038/s41523-023-00605-3.

ABSTRACT

Emerging data suggests that HER2 intratumoral heterogeneity (ITH) is associated with therapy resistance, highlighting the need for new strategies to assess HER2 ITH. A promising approach is leveraging multiplexed tissue analysis techniques such as cyclic immunofluorescence (CyCIF), which enable visualization and quantification of 10-60 antigens at single-cell resolution from individual tissue sections. In this study, we qualified a breast cancer-specific antibody panel, including HER2, ER, and PR, for multiplexed tissue imaging. We then compared the performance of these antibodies against established clinical standards using pixel-, cell- and tissue-level analyses, utilizing 866 tissue cores (representing 294 patients). To ensure reliability, the CyCIF antibodies were qualified against HER2 immunohistochemistry (IHC) and fluorescence in situ hybridization (FISH) data from the same samples. Our findings demonstrate the successful qualification of a breast cancer antibody panel for CyCIF, showing high concordance with established clinical antibodies. Subsequently, we employed the qualified antibodies, along with antibodies for CD45, CD68, PD-L1, p53, Ki67, pRB, and AR, to characterize 567 HER2+ invasive breast cancer samples from 189 patients. Through single-cell analysis, we identified four distinct cell clusters within HER2+ breast cancer exhibiting heterogeneous HER2 expression. Furthermore, these clusters displayed variations in ER, PR, p53, AR, and PD-L1 expression. To quantify the extent of heterogeneity, we calculated heterogeneity scores based on the diversity among these clusters. Our analysis revealed expression patterns that are relevant to breast cancer biology, with correlations to HER2 ITH and potential relevance to clinical outcomes.

PMID:38167908 | DOI:10.1038/s41523-023-00605-3

Nat Commun. 2024 Jan 2;15(1):8. doi: 10.1038/s41467-023-44568-2.

ABSTRACT

The secretion and quality control of glycosylphosphatidylinositol-anchored proteins (GPI-APs) necessitates post-attachment remodeling initiated by the evolutionarily conserved PGAP1, which deacylates the inositol in nascent GPI-APs. Impairment of PGAP1 activity leads to developmental diseases in humans and fatality and infertility in animals. Here, we present three PGAP1 structures (2.66-2.84 Å), revealing its 10-transmembrane architecture and product-enzyme interaction details. PGAP1 holds GPI-AP acyl chains in an optimally organized, guitar-shaped cavity with apparent energetic penalties from hydrophobic-hydrophilic mismatches. However, abundant glycan-mediated interactions in the lumen counterbalance these repulsions, likely conferring substrate fidelity and preventing off-target hydrolysis of bulk membrane lipids. Structural and biochemical analyses uncover a serine hydrolase-type catalysis with atypical features and imply mechanisms for substrate entrance and product release involving a drawing compass movement of GPI-APs. Our findings advance the mechanistic understanding of GPI-AP remodeling.

PMID:38167496 | DOI:10.1038/s41467-023-44568-2

Nat Commun. 2024 Jan 2;15(1):90. doi: 10.1038/s41467-023-44121-1.

ABSTRACT

Embryonic cells exhibit diverse metabolic states. Recent studies have demonstrated that metabolic reprogramming drives changes in cell identity by affecting gene expression. However, the connection between cellular metabolism and gene expression remains poorly understood. Here we report that glycolysis-regulated histone lactylation couples the metabolic state of embryonic cells with chromatin organization and gene regulatory network (GRN) activation. We found that lactylation marks genomic regions of glycolytic embryonic tissues, like the neural crest (NC) and pre-somitic mesoderm. Histone lactylation occurs in the loci of NC genes as these cells upregulate glycolysis. This process promotes the accessibility of active enhancers and the deployment of the NC GRN. Reducing the deposition of the mark by targeting LDHA/B leads to the downregulation of NC genes and the impairment of cell migration. The deposition of lactyl-CoA on histones at NC enhancers is supported by a mechanism that involves transcription factors SOX9 and YAP/TEAD. These findings define an epigenetic mechanism that integrates cellular metabolism with the GRNs that orchestrate embryonic development.

PMID:38167340 | DOI:10.1038/s41467-023-44121-1

Nat Commun. 2024 Jan 2;15(1):180. doi: 10.1038/s41467-023-44239-2.

ABSTRACT

Inactivating TP53 mutations leads to a loss of function of p53, but can also often result in oncogenic gain-of-function (GOF) of mutant p53 (mutp53) proteins which promotes tumor development and progression. The GOF activities of TP53 mutations are well documented, but the mechanisms involved remain poorly understood. Here, we study the mutp53 interactome and find that by targeting minichromosome maintenance complex components (MCMs), GOF mutp53 predisposes cells to replication stress and chromosomal instability (CIN), leading to a tumor cell-autonomous and cyclic GMP-AMP synthase (cGAS)-stimulator of interferon genes (STING)-dependent cytosolic DNA response that activates downstream non-canonical nuclear factor kappa light chain enhancer of activated B cell (NC-NF-κB) signaling. Consequently, GOF mutp53-MCMs-CIN-cytosolic DNA-cGAS-STING-NC-NF-κB signaling promotes tumor cell metastasis and an immunosuppressive tumor microenvironment through antagonizing interferon signaling and regulating genes associated with pro-tumorigenic inflammation. Our findings have important implications for understanding not only the GOF activities of TP53 mutations but also the genome-guardian role of p53 and its inactivation during tumor development and progression.

PMID:38167338 | DOI:10.1038/s41467-023-44239-2

BMC Pulm Med. 2024 Jan 2;24(1):2. doi: 10.1186/s12890-023-02789-7.

ABSTRACT

BACKGROUND: Chronic respiratory diseases, such as chronic obstructive pulmonary disease (COPD) and bronchiectasis, present significant threats to global health. Recent studies have revealed the crucial role of the lung microbiome in the development of these diseases. Pathogens have evolved complex strategies to evade the immune response, with the manipulation of host cellular epigenetic mechanisms playing a pivotal role. There is existing evidence regarding the effects of Pseudomonas on epigenetic modifications and their association with pulmonary diseases. Therefore, this study aims to directly assess the connection between Pseudomonas abundance and chronic respiratory diseases. We hope that our findings will shed light on the molecular mechanisms behind lung pathogen infections.

METHODS: We analyzed data from 366 participants, including individuals with COPD, acute exacerbations of COPD (AECOPD), bronchiectasis, and healthy individuals. Previous studies have given limited attention to the impact of Pseudomonas on these groups and their comparison with healthy individuals. Two independent datasets from different ethnic backgrounds were used for external validation. Each dataset separately analyzed bacteria at the genus level.

RESULTS: The study reveals that Pseudomonas, a bacterium, was consistently found in high concentrations in all chronic lung disease datasets but it was present in very low abundance in the healthy datasets. This suggests that Pseudomonas may influence cellular mechanisms through epigenetics, contributing to the development and progression of chronic respiratory diseases.

CONCLUSIONS: This study emphasizes the importance of understanding the relationship between the lung microbiome, epigenetics, and the onset of chronic pulmonary disease. Enhanced recognition of molecular mechanisms and the impact of the microbiome on cellular functions, along with a better understanding of these concepts, can lead to improved diagnosis and treatment.

PMID:38166878 | DOI:10.1186/s12890-023-02789-7

BMC Genom Data. 2024 Jan 2;25(1):3. doi: 10.1186/s12863-023-01177-8.

ABSTRACT

OBJECTIVES: The data presented in this study were collected with the aim of obtaining the complete genomes of specific strains of Bacillus bacteria, namely, Bacillus licheniformis T5. This strain was chosen based on its enzymatic activities, particularly amylolytic activity. In this study, nanopore sequencing technology was employed to obtain the genome sequences of this strain. It is important to note that these data represent a focused objective within a larger research context, which involves exploring the biochemical features of promising Bacilli strains and investigating the relationship between enzymatic activity, phenotypic features, and the microorganism's genome.

DATA DESCRIPTION: In this study, the whole-genome sequence was obtained from one Bacillus strain, Bacillus licheniformis T5, isolated from soil samples in Kazakhstan. Sample preparation and genomic DNA library construction were performed according to the Ligation sequencing gDNA kit (SQK-LSK109) protocol and NEBNext module. The prepared library was sequenced on a MinION instrument (Oxford Nanopore Technologies nanopore sequencer with a maximum throughput of up to 30 billion nucleotides per run and no limit on read length), using a flow cell for nanopore sequencing FLO-MIN106D. The genome de novo assembly was performed using the long sequencing reads generated by MinION Oxford Nanopore platform. Finally, one circular contig was obtained harboring a length of 4,247,430 bp with 46.16% G + C content and the mean contig 428X coverage. B. licheniformis T5 genome assembly annotation revealed 5391 protein-coding sequences, 81 tRNAs, 51 repeat regions, 24 rRNAs, 3 virulence factors and 53 antibiotic resistance genes. This sequence encompasses the complete genetic information of the strain, including genes, regulatory elements, and noncoding regions. The data reveal important insights into the genetic characteristics, phenotypic traits, and enzymatic activity of this Bacillus strain. The findings of this study have particular value to researchers interested in microbial biology, biotechnology, and antimicrobial studies. The genomic sequence offers a foundation for understanding the genetic basis of traits such as endospore formation, alkaline tolerance, temperature range for growth, nutrient utilization, and enzymatic activities. These insights can contribute to the development of novel biotechnological applications, such as the production of enzymes for industrial purposes. Overall, this study provides valuable insights into the genetic characteristics, phenotypic traits, and enzymatic activities of the Bacillus licheniformis T5 strain. The acquired genomic sequences contribute to a better understanding of this strain and have implications for various research fields, such as microbiology, biotechnology, and antimicrobial studies.

PMID:38166625 | DOI:10.1186/s12863-023-01177-8

Gut Microbes. 2024 Jan-Dec;16(1):2297860. doi: 10.1080/19490976.2023.2297860. Epub 2024 Jan 2.

ABSTRACT

The gut microbiome interacts with the host through complex networks that affect physiology and health outcomes. It is becoming clear that these interactions can be measured across many different omics layers, including the genome, transcriptome, epigenome, metabolome, and proteome, among others. Multi-omic studies of the microbiome can provide insight into the mechanisms underlying host-microbe interactions. As more omics layers are considered, increasingly sophisticated statistical methods are required to integrate them. In this review, we provide an overview of approaches currently used to characterize multi-omic interactions between host and microbiome data. While a large number of studies have generated a deeper understanding of host-microbiome interactions, there is still a need for standardization across approaches. Furthermore, microbiome studies would also benefit from the collection and curation of large, publicly available multi-omics datasets.

PMID:38166610 | DOI:10.1080/19490976.2023.2297860

BMC Bioinformatics. 2024 Jan 2;25(1):1. doi: 10.1186/s12859-023-05612-6.

ABSTRACT

Graph embedding techniques are using deep learning algorithms in data analysis to solve problems of such as node classification, link prediction, community detection, and visualization. Although typically used in the context of guessing friendships in social media, several applications for graph embedding techniques in biomedical data analysis have emerged. While these approaches remain computationally demanding, several developments over the last years facilitate their application to study biomedical data and thus may help advance biological discoveries. Therefore, in this review, we discuss the principles of graph embedding techniques and explore the usefulness for understanding biological network data derived from mass spectrometry and sequencing experiments, the current workhorses of systems biology studies. In particular, we focus on recent examples for characterizing protein-protein interaction networks and predicting novel drug functions.

PMID:38166530 | DOI:10.1186/s12859-023-05612-6

Gut Microbes. 2024 Jan-Dec;16(1):2297831. doi: 10.1080/19490976.2023.2297831. Epub 2024 Jan 2.

ABSTRACT

The prevalence of inflammatory bowel disease (IBD) is rising globally; however, its etiology is still not fully understood. Patient genetics, immune system, and intestinal microbiota are considered critical factors contributing to IBD. Preclinical animal models are crucial to better understand the importance of individual contributing factors. Among these, the dextran sodium sulfate (DSS) colitis model is the most widely used. DSS treatment induces gut inflammation and dysbiosis. However, its exact mode of action remains unclear. To determine whether DSS treatment induces pathogenic changes in the microbiota, we investigated the microbiota-modulating effects of DSS on murine microbiota in vitro. For this purpose, we cultured murine microbiota from the colon in six replicate continuous bioreactors. Three bioreactors were supplemented with 1% DSS and compared with the remaining PBS-treated control bioreactors by means of microbiota taxonomy and functionality. Using metaproteomics, we did not identify significant changes in microbial taxonomy, either at the phylum or genus levels. No differences in the metabolic pathways were observed. Furthermore, the global metabolome and targeted short-chain fatty acid (SCFA) quantification did not reveal any DSS-related changes. DSS had negligible effects on microbial functionality and taxonomy in vitro in the absence of the host environment. Our results underline that the DSS colitis mouse model is a suitable model to study host-microbiota interactions, which may help to understand how intestinal inflammation modulates the microbiota at the taxonomic and functional levels.

PMID:38165179 | DOI:10.1080/19490976.2023.2297831

J Phys Chem B. 2024 Jan 2. doi: 10.1021/acs.jpcb.3c05245. Online ahead of print.

ABSTRACT

Central to studying the conformational changes of a complex protein is understanding the dynamics and energetics involved. Phenomenologically, structural dynamics can be formulated using an overdamped Langevin model along an observable, e.g., the distance between two residues in the protein. The Langevin model is specified by the deterministic force (the potential of mean force, PMF) and stochastic force (characterized by the diffusion coefficient, D). It is therefore of great interest to be able to extract both PMF and D from an observable time series but under the same computational framework. Here, we approach this challenge in molecular dynamics (MD) simulations by treating it as a missing-data Bayesian estimation problem. An important distinction in our methodology is that the entire MD trajectory, as opposed to the individual data elements, is used as the statistical variable in Bayesian imputation. This idea is implemented through an eigen-decomposition procedure for a time-symmetrized Fokker-Planck equation, followed by maximizing the likelihood for parameter estimation. The mathematical expressions for the functional derivatives used in learning PMF and D also provide new physical insights for the manner by which the information on both the deterministic and stochastic forces is encoded in the dynamics data. An all-atom MD simulation of a nontrivial biomolecule case is used to illustrate the application of this approach. We show that, interestingly, the results of trajectory statistical learning can motivate new order parameters for an improved description of the kinetic bottlenecks in conformational changes. Complementing purely data-driven or black-box methods, this work underscores the advantages of physics-based machine learning in gaining chemical insights from quantitative parameter estimation.

PMID:38165090 | DOI:10.1021/acs.jpcb.3c05245

Front Genet. 2023 Dec 14;14:1345876. doi: 10.3389/fgene.2023.1345876. eCollection 2023.

NO ABSTRACT

PMID:38164513 | PMC:PMC10757914 | DOI:10.3389/fgene.2023.1345876

Front Pharmacol. 2023 Dec 14;14:1310135. doi: 10.3389/fphar.2023.1310135. eCollection 2023.

ABSTRACT

Protein kinases play vital roles in controlling cell behavior, and an array of kinase inhibitors are used successfully for treatment of disease. Typical drug development pipelines involve biological studies to validate a protein kinase target, followed by the identification of small molecules that effectively inhibit this target in cells, animal models, and patients. However, it is clear that protein kinases operate within complex signaling networks. These networks increase the resilience of signaling pathways, which can render cells relatively insensitive to inhibition of a single kinase, and provide the potential for pathway rewiring, which can result in resistance to therapy. It is therefore vital to understand the properties of kinase signaling networks in health and disease so that we can design effective multi-targeted drugs or combinations of drugs. Here, we outline how pharmacological and chemo-genetic approaches can contribute to such knowledge, despite the known low selectivity of many kinase inhibitors. We discuss how detailed profiling of target engagement by kinase inhibitors can underpin these studies; how chemical probes can be used to uncover kinase-substrate relationships, and how these tools can be used to gain insight into the configuration and function of kinase signaling networks.

PMID:38164473 | PMC:PMC10757940 | DOI:10.3389/fphar.2023.1310135

Front Nutr. 2023 Dec 18;10:1254983. doi: 10.3389/fnut.2023.1254983. eCollection 2023.

ABSTRACT

INTRODUCTION: Dietary patterns (DPs) are associated with overall nutritional status and may alter the clinical prognosis of tuberculosis. This interaction can be further intricated by dysglycemia (i.e., diabetes or prediabetes). Here, we identified DPs that are more common with tuberculosis-dysglycemia and depicted their association with tuberculosis treatment outcomes.

METHODS: A prospective cohort study of persons with tuberculosis and their contacts was conducted in Peru. A food frequency questionnaire and a multidimensional systems biology-based analytical approach were employed to identify DPs associated with these clinical groups. Potential independent associations between clinical features and DPs were analyzed.

RESULTS: Three major DPs were identified. TB-dysglycemia cases more often had a high intake of carbohydrates (DP1). Furthermore, DP1 was found to be associated with an increased risk of unfavorable TB outcomes independent of other factors, including dysglycemia.

CONCLUSION: Our findings suggest that the evaluation of nutritional status through DPs in comorbidities such as dysglycemia is a fundamental action to predict TB treatment outcomes. The mechanisms underlying the association between high intake of carbohydrates, dysglycemia, and unfavorable tuberculosis treatment outcomes warrant further investigation.

PMID:38164414 | PMC:PMC10757910 | DOI:10.3389/fnut.2023.1254983

Biosystems. 2023 Dec 30:105115. doi: 10.1016/j.biosystems.2023.105115. Online ahead of print.

ABSTRACT

Life is a natural phenomenon ineluctably subject to the laws and principles of physics. In this framework, thermodynamics has a crucial role, since living beings are structured on a molecular and cellular basis that can only be maintained with extensive energy consumption. This imposes that living beings are necessarily open systems. But the survival of each type of organism depends on the relative stability of certain essential variables, even in the presence of the disturbances to which they are subjected. The stability of these variables is relative in the sense that they have a narrow range of variation. This stability of the essential variables is a consequence of refined control mechanisms developed in the course of evolution, that lead to the condition called homeostasis. This homeostasis requires that control mechanisms process the various types of information related to the internal structure of the organism and its environment. Consequently, a biological system, through information processing aimed at guiding the mechanisms that maintain its homeostasis, manages the conditions imposed by the principles of thermodynamics, obtaining the most efficient use of energy possible and keeping entropic degradation controlled. In this article, we discuss the close links between thermodynamics, homeostasis and the information processing necessary to maintain homeostasis.

PMID:38163548 | DOI:10.1016/j.biosystems.2023.105115

Brain Commun. 2023 Oct 25;6(1):fcad293. doi: 10.1093/braincomms/fcad293. eCollection 2024.

ABSTRACT

Glioblastoma multiforme represents the most prevalent primary malignant brain tumour, while long non-coding RNA assumes a pivotal role in the pathogenesis and progression of glioblastoma multiforme. Nonetheless, the successful delivery of long non-coding RNA-based therapeutics to the tumour site has encountered significant obstacles attributable to inadequate biocompatibility and inefficient drug delivery systems. In this context, the use of a biofunctional surface modification of graphene oxide has emerged as a promising strategy to surmount these challenges. By changing the surface of graphene oxide, enhanced biocompatibility can be achieved, facilitating efficient transport of long non-coding RNA-based therapeutics specifically to the tumour site. This innovative approach presents the opportunity to exploit the therapeutic potential inherent in long non-coding RNA biology for treating glioblastoma multiforme patients. This study aimed to extract relevant genes from The Cancer Genome Atlas database and associate them with long non-coding RNAs to identify graphene therapy-related long non-coding RNA. We conducted a series of analyses to achieve this goal, including univariate Cox regression, least absolute shrinkage and selection operator regression and multivariate Cox regression. The resulting graphene therapy-related long non-coding RNAs were utilized to develop a risk score model. Subsequently, we conducted Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses on the identified graphene therapy-related long non-coding RNAs. Additionally, we employed the risk model to construct the tumour microenvironment model and analyse drug sensitivity. To validate our findings, we referenced the IMvigor210 immunotherapy model. Finally, we investigated differences in the tumour stemness index. Through our investigation, we identified four promising graphene therapy-related long non-coding RNAs (AC011405.1, HOXC13-AS, LINC01127 and LINC01574) that could be utilized for treating glioblastoma multiforme patients. Furthermore, we identified 16 compounds that could be utilized in graphene therapy. Our study offers novel insights into the treatment of glioblastoma multiforme, and the identified graphene therapy-related long non-coding RNAs and compounds hold promise for further research in this field. Furthermore, additional biological experiments will be essential to validate the clinical significance of our model. These experiments can help confirm the potential therapeutic value and efficacy of the identified graphene therapy-related long non-coding RNAs and compounds in treating glioblastoma multiforme.

PMID:38162904 | PMC:PMC10754320 | DOI:10.1093/braincomms/fcad293

iScience. 2023 Nov 23;26(12):108574. doi: 10.1016/j.isci.2023.108574. eCollection 2023 Dec 15.

ABSTRACT

The vectored Ebola vaccine rVSVΔG-ZEBOV-GP elicits protection against Ebola Virus Disease (EVD). In a study of forty-eight healthy adult volunteers who received either the rVSVΔG-ZEBOV-GP vaccine or placebo, we profiled intracellular microRNAs (miRNAs) from whole blood cells (WB) and circulating miRNAs from serum-derived extracellular vesicles (EV) at baseline and longitudinally following vaccination. Further, we identified early miRNA signatures associated with ZEBOV-specific IgG antibody responses at baseline and up to one year post-vaccination, and pinpointed target mRNA transcripts and pathways correlated to miRNAs whose expression was altered after vaccination by using systems biology approaches. Several miRNAs were differentially expressed (DE) and miRNA signatures predicted high or low IgG ZEBOV-specific antibody levels with high classification performance. The top miRNA discriminators were WB-miR-6810, EV-miR-7151-3p, and EV-miR-4426. An eight-miRNA antibody predictive signature was associated with immune-related target mRNAs and pathways. These findings provide valuable insights into early blood biomarkers associated with rVSVΔG-ZEBOV-GP vaccine-induced IgG antibody responses.

PMID:38162033 | PMC:PMC10755791 | DOI:10.1016/j.isci.2023.108574

iScience. 2023 Nov 23;26(12):108523. doi: 10.1016/j.isci.2023.108523. eCollection 2023 Dec 15.

ABSTRACT

Prostate cancer (PCa) is one of the most common malignant diseases of urinary system and has poor prognosis after progression to castration-resistant prostate cancer (CRPC), and increased cytosine methylation heterogeneity is associated with the more aggressive phenotype of PCa cell line. Activation-induced cytidine deaminase (AID) is a multifunctional enzyme and contributes to antibody diversification. However, the dysregulation of AID participates in the progression of multiple diseases and related with certain oncogenes through demethylation. Nevertheless, the role of AID in PCa remains elusive. We observed a significant upregulation of AID expression in PCa samples, which exhibited a negative correlation with E-cadherin expression. Furthermore, AID expression is remarkably higher in CRPC cells than that in HSPC cells, and AID induced the demethylation of CXCL12, which is required to stabilize the Wnt signaling pathway executor β-catenin and EMT procedure. Our study suggests that AID drives CRPC metastasis by demethylation and can be a potential therapeutic target for CRPC.

PMID:38162032 | PMC:PMC10755053 | DOI:10.1016/j.isci.2023.108523