Comput Struct Biotechnol J. 2023 Nov 14;21:5751-5764. doi: 10.1016/j.csbj.2023.11.018. eCollection 2023.

ABSTRACT

The incidence of lung cancer (LC) in Idiopathic Pulmonary Fibrosis (IPF) patients is more than twice that in non-IPF. This study aims to investigate IPF-to-LC pathogenesis and to develop a predictor for detecting IPF predisposing patients to LC. We conducted unsupervised clustering to detect high-risk subtypes from IPF to LC. Subsequently, we performed single-cell RNA-seq analysis to characterize high-risk IPF by examining the immune microenvironment. We identified 42 common immune function-related pathogenic genes between IPF and LC. We developed an LC risk classifier for IPF patients, comprising five genes: SPP1, MMP9, MMP12, FABP4, and IL1B. The five-gene classifier can successfully distinguish the high-risk population from IPF patients. High-risk IPF patients exhibited an immunosuppressive microenvironment with higher oncogene expression than low-risk patients. Single-cell analysis revealed that SPP1+ macrophages at the terminal of macrophages' developmental trajectory may promote the progression from IPF to LC. The strong crosstalk between SPP1+ macrophages and inflammation-related cancer-associated fibroblasts promoted the tumorigenic process in IPF. In vitro, assays showed that co-culturing macrophages overexpressing SPP1 with MRC-5 cells induced the transition of fibroblasts into cancer-associated fibroblasts. SPP1 produced by macrophages promoted epithelial-mesenchymal transition in alveolar epithelial cells via stimulating the upregulation of N-cadherin and Vimentin in MLE-12 cells. This study provided a novel method to identify the LC risk population from IPF, revealing the cellular interactions involved in the transition from IPF to LC. Our findings highlighted SPP1 as a critical driver in IPF progression, offering a potential target for therapy in fibrosis.

PMID:38074471 | PMC:PMC10708992 | DOI:10.1016/j.csbj.2023.11.018

Comput Struct Biotechnol J. 2023 Nov 23;21:5868-5876. doi: 10.1016/j.csbj.2023.11.039. eCollection 2023.

ABSTRACT

Selenzyme is a retrobiosynthesis tool that suggests candidate enzymes for user query reactions. Enzyme suggestions are based on identical reactions, as well as similar reactions, since enzymes are often capable of promiscuous substrate binding. Selenzyme is a user-friendly, widely used web-tool for ranking enzymes based on reaction similarity and additional features, including the phylogenetic distance between the source species of the enzyme and the intended host. While Selenzyme has proved invaluable in assisting with enzyme selection for known reactions, as well as many novel or orphan reactions, weaknesses have been exposed in its ability to rank functionally related enzymes. Within this update, we introduce a new reaction similarity scoring algorithm, which is used in conjunction with the previous similarity calculation, to improve the accuracy of enzyme suggestions based on non-identical similar reactions, across a range of EC reaction classes. This allows enzymes to be suggested for reactions not found within the database, even if the reaction is unbalanced. A database update was also carried out, to ensure that reaction and enzyme knowledge remains current. This update can be accessed at http://selenzymeRF.synbiochem.co.uk/.

PMID:38074466 | PMC:PMC10697999 | DOI:10.1016/j.csbj.2023.11.039

Adv Biomed Res. 2023 Oct 28;12:238. doi: 10.4103/abr.abr_185_23. eCollection 2023.

ABSTRACT

BACKGROUND: Gastric cancer (GC) is a common and deadly cancer worldwide. Molecular changes underlying the development of GC are not thoroughly understood. Therefore, we constructed and analyzed a novel four-component competing endogenous RNA (ceRNA) network to introduce plausible diagnostic and prognostic biomarkers in GC.

MATERIALS AND METHODS: Transcriptomics and circular RNA (circRNA) data were retrieved from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases, respectively. After batch effect correction, differential expression analysis, and interaction prediction, a ceRNA network including long noncoding RNAs (lncRNAs), circular RNAs (circRNAs), microRNAs (miRNAs), and messenger RNAs (mRNAs) was established. Enrichment analyses were performed, and a protein-protein interaction (PPI) network was constructed. Furthermore, a subnetwork was extracted, and using the quantitative real-time polymerase chain reaction (qRT-PCR) method, the expression changes of two hub ceRNAs were examined. Finally, survival analysis was performed to identify potential prognostic RNAs.

RESULTS: A four-component ceRNA network containing 822 nodes and 1365 edges was constructed. Enrichment analyses unveiled important signaling pathways and gene ontologies such as neuroactive ligand-receptor interaction and axonogenesis. The PPI network showed the interactions among mRNAs of the ceRNA network. qRT-PCR indicated downregulation of EPHA5 and SNAP91 mRNAs in GC compared to control tissues. Survival analyses revealed eight mRNAs and one lncRNA as potential prognostic biomarkers in GC.

CONCLUSION: The established four-component network of ceRNAs in GC reveals a comprehensive view of the molecular and cellular characteristics of GC progression, which can be considered as a basis to examine and validate potential diagnostic and prognostic biomarkers as well as therapeutic targets.

PMID:38073751 | PMC:PMC10699254 | DOI:10.4103/abr.abr_185_23

Front Neurol. 2023 Nov 22;14:1288740. doi: 10.3389/fneur.2023.1288740. eCollection 2023.

ABSTRACT

Neuroproteomics, an emerging field at the intersection of neuroscience and proteomics, has garnered significant attention in the context of neurotrauma research. Neuroproteomics involves the quantitative and qualitative analysis of nervous system components, essential for understanding the dynamic events involved in the vast areas of neuroscience, including, but not limited to, neuropsychiatric disorders, neurodegenerative disorders, mental illness, traumatic brain injury, chronic traumatic encephalopathy, and other neurodegenerative diseases. With advancements in mass spectrometry coupled with bioinformatics and systems biology, neuroproteomics has led to the development of innovative techniques such as microproteomics, single-cell proteomics, and imaging mass spectrometry, which have significantly impacted neuronal biomarker research. By analyzing the complex protein interactions and alterations that occur in the injured brain, neuroproteomics provides valuable insights into the pathophysiological mechanisms underlying neurotrauma. This review explores how such insights can be harnessed to advance personalized medicine (PM) approaches, tailoring treatments based on individual patient profiles. Additionally, we highlight the potential future prospects of neuroproteomics, such as identifying novel biomarkers and developing targeted therapies by employing artificial intelligence (AI) and machine learning (ML). By shedding light on neurotrauma's current state and future directions, this review aims to stimulate further research and collaboration in this promising and transformative field.

PMID:38073638 | PMC:PMC10703396 | DOI:10.3389/fneur.2023.1288740

J Am Soc Nephrol. 2023 Dec 11. doi: 10.1681/ASN.0000000000000277. Online ahead of print.

ABSTRACT

BACKGROUND: In autosomal dominant polycystic kidney disease (ADPKD) there is an unmet need for early markers of rapid disease progression to facilitate counseling and selection for kidney-protective therapy. Our aim was to identify markers for rapid disease progression in urinary extracellular vesicles (uEVs).

METHODS: Six paired case-control groups (n = 10-59/group) of cases with rapid disease progression and controls with stable disease were formed from two independent ADPKD cohorts with matching by age, sex, total kidney volume and genetic variant. Candidate uEV biomarkers were identified by mass spectrometry and further analyzed using immunoblotting and an enzyme-linked immunosorbent assay (ELISA). Single-nucleus RNA sequencing of healthy and ADPKD tissue was used to identify the cellular origin of the uEV biomarker.

RESULTS: In the discovery proteomics experiments, the protein abundance of matrix metalloproteinase-7 (MMP-7) was significantly higher in uEVs of patients with rapid disease progression compared to stable disease. In the validation groups, a significant >2-fold increase in uEV MMP-7 in patients with rapid disease progression was confirmed using immunoblotting. In contrast, no significant difference in MMP-7 was found in whole urine using ELISA. Compared to healthy kidney tissue, ADPKD tissue had significantly higher MMP-7 expression in proximal tubule and thick ascending limb cells with a pro-fibrotic phenotype.

CONCLUSION: Among patients with ADPKD, rapid disease progressors have higher uEV-associated MMP-7. Our findings also suggest that MMP-7 is a biologically plausible biomarker for more rapid disease progression.

PMID:38073039 | DOI:10.1681/ASN.0000000000000277

J Invest Dermatol. 2023 Dec 8:S0022-202X(23)03128-7. doi: 10.1016/j.jid.2023.11.009. Online ahead of print.

NO ABSTRACT

PMID:38072390 | DOI:10.1016/j.jid.2023.11.009

Metab Eng. 2023 Dec 8:S1096-7176(23)00174-X. doi: 10.1016/j.ymben.2023.12.001. Online ahead of print.

ABSTRACT

5-Aminovaleric acid (5-AVA), 5-hydroxyvalerate (5HV), copolymer P(3HB-co-5HV) of 3-hydroxybutyrate (3HB) and 5HV were produced from L-lysine as a substrate by recombinant Halomonas bluephagenesis constructed based on codon optimization, deletions of competitive pathway and L-lysine export protein, and three copies of davBA genes encoding L-lysine monooxygenase (DavB) and 5-aminovaleramide amidohydrolase (DavA) inserted into its genome to form H. bluephagenesis YF117ΔgabT1+2, which produced 16.4 g L-1 and 67.4 g L-1 5-AVA in flask cultures and in 7 L bioreactor, respectively. It was able to de novo synthesize 5-AVA from glucose by L-lysine-overproducing H. bluephagenesis TD226. Corn steep liquor was used instead of yeast extract for cost reduction during the 5-AVA production. Using promoter engineering based on Pporin mutant library for downstream genes, H. bluephagenesis YF117 harboring pSEVA341-Pporin42-yqhDEC produced 6 g L-1 5HV in shake flask growth, while H. bluephagenesis YF117 harboring pSEVA341-Pporin42-yqhDEC-Pporin278-phaCRE-abfT synthesized 42 wt% P(3HB-co-4.8 mol% 5HV) under the same condition. Thus, H. bluephagenesis was successfully engineered to produce 5-AVA and 5HV in supernatant and intracellular P(3HB-co-5HV) utilizing L-lysine as the substrate.

PMID:38072357 | DOI:10.1016/j.ymben.2023.12.001

Cell J. 2023 Nov 28;25(11):772-782. doi: 10.22074/cellj.2023.2004115.1320.

ABSTRACT

OBJECTIVE: Spinal cord injury (SCI) can disrupt membrane transmission by affecting transmembrane channels or neurotransmitter release. This study aimed to explore gene expression changes of transmembrane proteins underlying SCI through bioinformatics approaches and confirming in SCI model in rats.

MATERIALS AND METHODS: In this experimental study, the differentially expressed genes (DEGs) in acute and subacute SCI were obtained based on microarray data downloaded from the gene expression omnibus (GEO). Transmembrane proteins of DEGs were recognized by using the UniProt annotation and transmembrane helices prediction (TMHMM) methods. The model of SCI was established through a weight-dropping procedure in rats. To confirm the SCI model, hematoxylin and eosin (H and E) staining was performed. Total mRNA was extracted from spinal cord tissues, and the RNA expression profile of some of the significantly changed genes in the previous part that has been confirmed by real-time polymerase chain reaction (PCR). Blood was collected from rats before sacrificing. Extracellular vesicles (EVs) were isolated by high-speed centrifugation from plasma. For the assessment of protein expression, western blotting was used.

RESULTS: Based on bioinformatics analysis, we candidated a set of membrane proteins in SCI's acute and sub-acute phases, and confirmed significant upregulation in Grm1, Nrg1, CD63, Enpp3,and Cxcr4 between the acute and control groups and downregulation in Enpp3 between acute and subacute groups at the RNA level. Considering CD63 as an EV marker, we examined the protein expression of CD9 and CD63 in the plasma-derived EVs, and CD9 has significant expression between acute and control groups. We also demonstrate no significant CD63 and Cxcr4 expressions between groups.

CONCLUSION: Our results provide new insight into the relationship between candidate transmembrane protein expression and different stages of SCI using in-silico approaches. Also, results show the release of EVs in blood in each group after SCI helping enlarge strategies to enhance recovery following SCI.

PMID:38071409 | DOI:10.22074/cellj.2023.2004115.1320

BMC Bioinformatics. 2023 Dec 9;24(1):466. doi: 10.1186/s12859-023-05600-w.

ABSTRACT

BACKGROUND: Oscillatory behavior is critical to many life sustaining processes such as cell cycles, circadian rhythms, and notch signaling. Important biological functions depend on the characteristics of these oscillations (hereafter, oscillation characteristics or OCs): frequency (e.g., event timings), amplitude (e.g., signal strength), and phase (e.g., event sequencing). Numerous oscillating reaction networks have been documented or proposed. Some investigators claim that oscillations in reaction networks require nonlinear dynamics in that at least one rate law is a nonlinear function of species concentrations. No one has shown that oscillations can be produced for a reaction network with linear dynamics. Further, no one has obtained closed form solutions for the frequency, amplitude and phase of any oscillating reaction network. Finally, no one has published an algorithm for constructing oscillating reaction networks with desired OCs.

RESULTS: This is a theoretical study that analyzes reaction networks in terms of their representation as systems of ordinary differential equations. Our contributions are: (a) construction of an oscillating, two species reaction network [two species harmonic oscillator (2SHO)] that has no nonlinearity; (b) obtaining closed form formulas that calculate frequency, amplitude, and phase in terms of the parameters of the 2SHO reaction network, something that has not been done for any published oscillating reaction network; and (c) development of an algorithm that parameterizes the 2SHO to achieve desired oscillation, a capability that has not been produced for any published oscillating reaction network.

CONCLUSIONS: Our 2SHO demonstrates the feasibility of creating an oscillating reaction network whose dynamics are described by a system of linear differential equations. Because it is a linear system, we can derive closed form expressions for the frequency, amplitude, and phase of oscillations, something that has not been done for other published reaction networks. With these formulas, we can design 2SHO reaction networks to have desired oscillation characteristics. Finally, our sensitivity analysis suggests an approach to constructing a 2SHO for a biochemical system.

PMID:38071308 | DOI:10.1186/s12859-023-05600-w

Nat Commun. 2023 Dec 9;14(1):8170. doi: 10.1038/s41467-023-43991-9.

ABSTRACT

Human cancer cell lines have long served as tools for cancer research and drug discovery, but the presence and the source of intra-cell-line heterogeneity remain elusive. Here, we perform single-cell RNA-sequencing and ATAC-sequencing on 42 and 39 human cell lines, respectively, to illustrate both transcriptomic and epigenetic heterogeneity within individual cell lines. Our data reveal that transcriptomic heterogeneity is frequently observed in cancer cell lines of different tissue origins, often driven by multiple common transcriptional programs. Copy number variation, as well as epigenetic variation and extrachromosomal DNA distribution all contribute to the detected intra-cell-line heterogeneity. Using hypoxia treatment as an example, we demonstrate that transcriptomic heterogeneity could be reshaped by environmental stress. Overall, our study performs single-cell multi-omics of commonly used human cancer cell lines and offers mechanistic insights into the intra-cell-line heterogeneity and its dynamics, which would serve as an important resource for future cancer cell line-based studies.

PMID:38071219 | DOI:10.1038/s41467-023-43991-9

Crit Rev Oncol Hematol. 2023 Dec 7:104231. doi: 10.1016/j.critrevonc.2023.104231. Online ahead of print.

ABSTRACT

Natural killer (NK) cells are vital components of the human immune system, acting as innate lymphocytes and playing a crucial role in immune surveillance. Their unique ability to independently eliminate target cells without antigen contact or antibodies has sparked interest in immunological research. This review examines recent NK cell developments and applications, encompassing immune functions, interactions with target cells, genetic engineering techniques, pharmaceutical interventions, and implications in cancers. Insights into NK cell regulation emerge, with a focus on promising genetic engineering like CAR-engineered NK cells, enhancing specificity against tumors. Immune checkpoint inhibitors also enhance NK cells' potential in cancer therapy. Nanotechnology's emergence as a tool for targeted drug delivery to improve NK cell therapies is explored. In conclusion, NK cells are pivotal in immunity, holding exciting potential in cancer immunotherapy. Ongoing research promises novel therapeutic strategies, advancing immunotherapy and medical interventions.

PMID:38070841 | DOI:10.1016/j.critrevonc.2023.104231

Anal Biochem. 2023 Dec 7:115411. doi: 10.1016/j.ab.2023.115411. Online ahead of print.

ABSTRACT

We report a sensitive lateral flow assay (LFA) in which the assay colour change originated from reporter labels constructed from silica spheres (radius = 450 nm) coated with approximately 3.9 × 103 gold nanoparticles (radius = 8.5 nm). These reporter labels were modified with DNA and deposited in the conjugation area of an LFA device assembled on wax-patterned Fusion 5 paper. Test and control zones of the device were pre-loaded with capture probe formed by avidin-coated mesoporous silica nanoparticles attached with biotin-tagged DNA sequences. Proof-of-concept was demonstrated by the detection of a partial sequence of the actin gene of Colletotrichum truncatum. The DNA target could be detected with an LOD of 46 pM, which was 5 times lower than a comparative assay using gold nanoparticles alone. The assay showed good selectivity against the Colletotrichum species C. scovillei and C. gloeosporioides, as well as against DNA from the fungal genera Aspergillus niger and Alternaria alternata. There was negligible change in sensor response over storage for one month at room temperature. The LFA was used to detect PCR products following extraction from mycelium.

PMID:38070665 | DOI:10.1016/j.ab.2023.115411

J Exp Bot. 2023 Dec 9:erad491. doi: 10.1093/jxb/erad491. Online ahead of print.

ABSTRACT

The biosynthesis of the tetrapyrrole end-products chlorophyll (Chl) and heme depends on a multifaceted control mechanism, which acts primarily at the post-translational level upon the rate-limiting step of 5-aminolevulinic acid (ALA) synthesis, and the light-dependent protochlorophyllide oxidoreductase (POR). These regulatory processes require auxiliary factors that modulate the activity, stability, complex formation, and subplastidal localisation of the relevant proteins. Together, they ensure optimal metabolic flows during the day and at night. As an Arabidopsis homolog of the POR-interacting tetratricopeptide-repeat (TPR) protein (Pitt) first reported in Synechocystis, we characterize the Arabidopsis TTP1 (tetrapyrrole biosynthesis-regulating TPR protein1). TTP1 is a plastid-localized, membrane-bound factor that interacts with POR, the Mg protoporphyrin monomethylester cyclase CHL27, glutamyl-tRNA reductase (GluTR), GluTR-binding protein (GBP) and FLUORESCENCE IN BLUE LIGHT (FLU). Lack of TTP1 leads to accumulation of GluTR, enhanced ALA synthesis and lower levels of POR. Knockout mutants show enhanced sensitivity to reactive oxygen species and a slower greening of etiolated seedlings. Based on our studies, the interaction of TTP1 with GluTR and POR does not directly inhibit their enzymatic activity and contribute to the control of ALA synthesis. Instead, we propose that TTP1 sequesters a fraction of these proteins on the thylakoid membrane, and contributes to their stability.

PMID:38070484 | DOI:10.1093/jxb/erad491

Comput Biol Chem. 2023 Dec 5;108:107999. doi: 10.1016/j.compbiolchem.2023.107999. Online ahead of print.

ABSTRACT

Breast cancer continues to be a prominent cause for substantial loss of life among women globally. Despite established treatment approaches, the rising prevalence of breast cancer is a concerning trend regardless of geographical location. This highlights the need to identify common key genes and explore their biological significance across diverse populations. Our research centered on establishing a correlation between common key genes identified in breast cancer patients. While previous studies have reported many of the genes independently, our study delved into the unexplored realm of their mutual interactions, that may establish a foundational network contributing to breast cancer development. Machine learning algorithms were employed for sample classification and key gene selection. The best performance model further selected the candidate genes through expression pattern recognition. Subsequently, the genes common in all the breast cancer patients from India, China, Czech Republic, Germany, Malaysia and Saudi Arabia were selected for further study. We found that among ten classifiers, Catboost exhibited superior performance with an average accuracy of 92%. Functional enrichment analysis and pathway analysis revealed that calcium signaling pathway, regulation of actin cytoskeleton pathway and other cancer-associated pathways were highly enriched with our identified genes. Notably, we observed that these genes regulate each other, forming a complex network. Additionally, we identified PALMD gene as a novel potential biomarker for breast cancer progression. Our study revealed key gene modules forming a complex network that were consistently expressed in different populations, affirming their critical role and biological significance in breast cancer. The identified genes hold promise as prospective biomarkers of breast cancer prognosis irrespective of country of origin or ethnicity. Future investigations will expand upon these genes in a larger population and validate their biological functions through in vivo analysis.

PMID:38070457 | DOI:10.1016/j.compbiolchem.2023.107999

Biochem Biophys Res Commun. 2023 Dec 6;692:149364. doi: 10.1016/j.bbrc.2023.149364. Online ahead of print.

ABSTRACT

The periodontal ligament (PDL) is a critical component in maintaining tooth stability. It is composed of cells and an extracellular matrix (ECM), each with unique roles in tissue function and homeostasis. Secreted protein acidic and rich in cysteine (SPARC), a calcium-binding matricellular glycoprotein, plays a crucial role in regulating ECM assembly and turnover, alongside facilitating cellular-ECM interactions. In the present study, mass spectrometry-based proteomics was used to assess the impacts of Sparc-knockout (KO) on PDL-derived cells. Results demonstrated that Sparc-KO significantly reduces ECM production and alters its composition with increased levels of type I collagen. Despite this increase in Sparc-KO, type I collagen was not likely to be effectively integrated into the fibrils due to collagen cross-linking impairment. Furthermore, the pathway and process enrichment analyses suggested that SPARC plays a protective role against ECM degradation by antagonistically interacting with cell-surface collagen receptors. These findings provide detailed insights into the multifaceted role of SPARC in ECM organization, including its impact on ECM production, collagen regulation, and interactions with various cellular compartments. A better understanding of these complex mechanisms is crucial for comprehending the causes of periodontal disease and tissue regeneration, where precise control of ECM organization is necessary.

PMID:38070276 | DOI:10.1016/j.bbrc.2023.149364

Bioinformatics. 2023 Dec 9:btad745. doi: 10.1093/bioinformatics/btad745. Online ahead of print.

ABSTRACT

MOTIVATION: Target discovery and drug evaluation for diseases with complex mechanisms call for a streamlined chemical systems analysis platform. Currently available tools lack the emphasis on reaction kinetics, access to relevant databases, and algorithms to visualize perturbations on a chemical scale providing quantitative details as well streamlined visual data analytics functionality.

RESULTS: CytoCopasi, a Maven-based application for Cytoscape that combines the chemical systems analysis features of COPASI with the visualization and database access tools of Cytoscape and its plugin applications has been developed. The diverse functionality of CytoCopasi through ab initio model construction, model construction via pathway and parameter databases KEGG and BRENDA is presented. The comparative systems biology visualization analysis toolset is illustrated through a drug competence study on the cancerous RAF/MEK/ERK pathway.

AVAILABILITY: The COPASI files, simulation data, native libraries, and the manual are available on https://github.com/scientificomputing/CytoCopasi.

SUPPLEMENTARY INFORMATION: Supplementary data is available at Bioinformatics online.

PMID:38070155 | DOI:10.1093/bioinformatics/btad745

Chimia (Aarau). 2022 Feb 23;76(1-2):81-89. doi: 10.2533/chimia.2022.81.

ABSTRACT

Mass spectrometry-based proteomics has become an indispensable tool for system-wide protein quantification in systems biology, biomedical research, and increasing for clinical applications. In particular, targeted mass spectrometry offers the most sensitive and reproducible quantitative detection of proteins, peptides and post-translational modifications of any currently applied mass spectrometry technique and is therefore ideally suited to generate high quality quantitative datasets. Despite these apparent advantages, targeted mass spectrometry is only slowly gaining popularity in academia and pharmaceutical industries, mainly due to the additional efforts in assay generation and manual data validation. However, with the increasing accumulation of mass spectrometry data, advances in deep learning spectral prediction for automated assay development, these obstacles can and will be considerably reduced in the near future. Here, we describe the latest technological developments in this field and discuss the emerging importance of targeted mass spectrometry for systems biology research and potential key roles in bridging biomedical discovery and clinical implementation.

PMID:38069753 | DOI:10.2533/chimia.2022.81

Int J Mol Sci. 2023 Dec 3;24(23):17085. doi: 10.3390/ijms242317085.

ABSTRACT

Neuroblastoma (NB), a childhood cancer arising from the neural crest, poses significant clinical challenges, particularly in cases featuring amplification of the MYCN oncogene. Epigenetic factors play a pivotal role in normal neural crest and NB development, influencing gene expression patterns critical for tumorigenesis. This review delves into the multifaceted interplay between MYCN and known epigenetic modifications during NB genesis, shedding light on the intricate regulatory networks underlying the disease. We provide an extensive survey of known epigenetic mechanisms, encompassing DNA methylation, histone modifications, non-coding RNAs, super-enhancers (SEs), bromodomains (BET), and chromatin modifiers in MYCN-amplified (MNA) NB. These epigenetic changes collectively contribute to the dysregulated gene expression landscape observed in MNA NB. Furthermore, we review emerging therapeutic strategies targeting epigenetic regulators, including histone deacetylase inhibitors (HDACi), histone methyltransferase inhibitors (HMTi), and DNA methyltransferase inhibitors (DNMTi). We also discuss and summarize current drugs in preclinical and clinical trials, offering insights into their potential for improving outcomes for MNA NB patients.

PMID:38069407 | DOI:10.3390/ijms242317085

Int J Mol Sci. 2023 Dec 1;24(23):17040. doi: 10.3390/ijms242317040.

ABSTRACT

The continuous emergence of SARS-CoV-2 variants has led to a protracted global COVID-19 pandemic with significant impacts on public health and global economy. While there are currently available SARS-CoV-2 vaccines and therapeutics, most of the FDA-approved antiviral agents directly target viral proteins. However, inflammation is the initial immune pathogenesis induced by SARS-CoV-2 infection, there is still a need to find additional agents that can control the virus in the early stages of infection to alleviate disease progression for the next pandemic. Here, we find that both the spike protein and its receptor CD147 are crucial for inducing inflammation by SARS-CoV-2 in THP-1 monocytic cells. Moreover, we find that 3-epi-betulin, isolated from Daphniphyllum glaucescens, reduces the level of proinflammatory cytokines induced by SARS-CoV-2, consequently resulting in a decreased viral RNA accumulation and plaque formation. In addition, 3-epi-betulin displays a broad-spectrum inhibition of entry of SARS-CoV-2 pseudoviruses, including Alpha (B.1.1.7), Eplison (B.1.429), Gamma (P1), Delta (B.1.617.2) and Omicron (BA.1). Moreover, 3-epi-betulin potently inhibits SARS-CoV-2 infection with an EC50 of <20 μM in Calu-3 lung epithelial cells. Bioinformatic analysis reveals the chemical interaction between the 3-epi-betulin and the spike protein, along with the critical amino acid residues in the spike protein that contribute to the inhibitory activity of 3-epi-betulin against virus entry. Taken together, our results suggest that 3-epi-betulin exhibits dual effect: it reduces SARS-CoV-2-induced inflammation and inhibits virus entry, positioning it as a potential antiviral agent against SARS-CoV-2.

PMID:38069363 | DOI:10.3390/ijms242317040

Int J Mol Sci. 2023 Dec 1;24(23):17035. doi: 10.3390/ijms242317035.

ABSTRACT

Marrubium species have been used since ancient times as food additives and curative treatments. Their phytochemical composition and various pharmacological activities were the focus of a number of scientific investigations but no comprehensive metabolome profiling to identify the numerous primary and secondary metabolites has been performed so far. This study aimed to generate a comprehensive picture of the total metabolite content of two Marrubium species-M. peregrinum and M. friwaldskyanum-to provide detailed information about the main primary and secondary metabolites. In addition, the elemental composition was also evaluated. For this purpose, non-targeted metabolomic analyses were conducted using GC-MS, UPLC-MS/MS and ICP-MS approaches. Nearly 500 compounds and 12 elements were detected and described. The results showed a strong presence of phenolic acids, flavonoids and their glucosides, which are generally of great interest due to their various pharmacological activities. Furthermore, tissue-specific analyses for M. friwaldskyanum stem, leaves and flowers were carried out in order to outline the sources of potentially important bioactive molecules. The results generated from this study depict the Marrubium metabolome and reveal its dual scientific importance-from one side, providing information about the metabolites that is fundamental and vital for the survival of these species, and from the other side, defining the large diversity of secondary substances that are a potential source of phytotherapeutic agents.

PMID:38069358 | DOI:10.3390/ijms242317035