Recent insights into breast milk microRNA: their role as functional regulators.

Breast milk (BM) is a primary biofluid that plays a crucial role in infant development and the regulation of the immune system. As a class of rich biomolecules in BM, microRNAs (miRNAs) are regarded as active factors contributing to infant growth and development. Surprisingly, these molecules exhibit resilience in harsh conditions, providing an opportunity for infants to absorb them. In addition, many studies have shown that miRNAs in breast milk, when absorbed into the gastrointestinal system, can act as a class of functional regulators to effectively regulate gene expression. Understanding the absorption pattern of BM miRNA may facilitate the creation of formula with a more optimal miRNA balance and pave the way for novel drug delivery techniques. In this review, we initially present evidence of BM miRNA absorption. Subsequently, we compile studies that integrate both in vivo and in vitro findings to illustrate the bioavailability and biodistribution of BM miRNAs post-absorption. In addition, we evaluate the strengths and weaknesses of previous studies and discuss potential variables contributing to discrepancies in their outcomes. This literature review indicates that miRNAs can be absorbed and act as regulatory agents.

Machine Learning-Based Characterization and Identification of Tertiary Lymphoid Structures Using Spatial Transcriptomics Data.

Tertiary lymphoid structures (TLSs) are organized aggregates of immune cells in non-lymphoid tissues and are associated with a favorable prognosis in tumors. However, TLS markers remain inconsistent, and the utilization of machine learning techniques for this purpose is limited. To tackle this challenge, we began by identifying TLS markers through bioinformatics analysis and machine learning techniques. Subsequently, we leveraged spatial transcriptomic data from Gene Expression Omnibus (GEO) and built two support vector classifier models for TLS prediction: one without feature selection and the other using the marker genes. The comparable performances of these two models confirm the efficacy of the selected markers. The majority of the markers are immunoglobulin genes, demonstrating their importance in the identification of TLSs. Our research has identified the markers of TLSs using machine learning methods and constructed a model to predict TLS location, contributing to the detection of TLS and holding the promising potential to impact cancer treatment strategies.

Mechanism of Sophorae Flavescentis Radix (Kushen) in treating NSCLC: Insights from miRNA-mRNA network analysis.

ETHNOPHARMACOLOGICAL RELEVANCE: Sophorae Flavescentis Radix (Kushen) is the primary herb component of Compound Kushen Injection (CKI), an approved clinical treatment for tumors. Despite CKI's widespread use, the underlying mechanisms of Kushen regarding microRNA-target and pathway remain unclear in non-small cell lung cancer (NSCLC). AIM OF THE STUDY: This study aimed to elucidate the crucial miRNAs-targets and pathways responsible for the Kushen's impact on NSCLC. MATERIALS AND METHODS: CCK8, colony formation, and apoptosis assays were performed to assess the effects of Kushen on NSCLC cells. Subsequently, we treated the A549 cell line with varying concentrations of Kushen to obtain mRNA and miRNA expression profiles. A DE (differentially expressed) miRNAs-DEGs network was then constructed to identify the critical miRNA-mRNA interaction influenced by Kushen. Furthermore, we performed clinical significance and prognosis analyses of hub genes to narrow down key genes and their corresponding miRNAs. Finally, the effects of Kushen on critical miRNA-mRNA interaction and related pathway were verified by in vitro and in vivo experiments. RESULTS: In this study, we initially demonstrated that Kushen significantly inhibited cell proliferation, suppressed colony formation, and induced apoptosis in the A549 cells, PC9 cells, and the A549 zebrafish xenograft model. Through expression profile analysis, a DE miRs-DEGs network was constructed with 16 DE miRs and 68 DEGs. Through the network analysis and expression validation, we found Kushen could significantly down-regulate miR-183-5p expression and up-regulate EGR1 expression. Additionally, Kushen affected the PTEN/Akt pathway, increasing PTEN expression and decreasing pAkt expression. Finally, matrine, the essential active compound of Kushen, also inhibited cell growth, induced apoptosis, and regulated miR-183-5p/EGR1 and PTEN/AKT pathway. CONCLUSIONS: Altogether, these findings supported the critical role of miR-183-5p/EGR1 and the PTEN/AKT pathway in the beneficial effects of Kushen on NSCLC, highlighting the therapeutic potential of Kushen in NSCLC treatment.

Effects of Natural Products on Enzymes Involved in Ferroptosis: Regulation and Implications.

Ferroptosis is a form of regulated cell death that is characterized by the accumulation of iron-dependent lipid peroxides. The regulation of ferroptosis involves both non-enzymatic reactions and enzymatic mechanisms. Natural products have demonstrated potential effects on various enzymes, including GPX4, HO-1, NQO1, NOX4, GCLC, and GCLM, which are mainly involved in glutathione metabolic pathway or oxidative stress regulation, and ACSL3 and ACSL4, which mainly participate in lipid metabolism, thereby influencing the regulation of ferroptosis. In this review, we have provided a comprehensive overview of the existing literature pertaining to the effects of natural products on enzymes involved in ferroptosis and discussed their potential implications for the prevention and treatment of ferroptosis-related diseases. We also highlight the potential challenge that the majority of research has concentrated on investigating the impact of natural products on the expression of enzymes involving ferroptosis while limited attention is given to the regulation of enzyme activity. This observation underscores the considerable potential and scope for exploring the influence of natural products on enzyme activity.

A genetic basis of mitochondrial DNAJA3 in nonalcoholic steatohepatitis-related hepatocellular carcinoma.

BACKGROUND AND AIMS: NAFLD is the most common form of liver disease worldwide, but only a subset of individuals with NAFLD may progress to NASH. While NASH is an important etiology of HCC, the underlying mechanisms responsible for the conversion of NAFLD to NASH and then to HCC are poorly understood. We aimed to identify genetic risk genes that drive NASH and NASH-related HCC. APPROACH AND RESULTS: We searched genetic alleles among the 24 most significant alleles associated with body fat distribution from a genome-wide association study of 344,369 individuals and validated the top allele in 3 independent cohorts of American and European patients (N=1380) with NAFLD/NASH/HCC. We identified an rs3747579-TT variant significantly associated with NASH-related HCC and demonstrated that rs3747579 is expression quantitative trait loci of a mitochondrial DnaJ Heat Shock Protein Family (Hsp40) Member A3 ( DNAJA3 ). We also found that rs3747579-TT and a previously identified PNPLA3 as a functional variant of NAFLD to have significant additional interactions with NASH/HCC risk. Patients with HCC with rs3747579-TT had a reduced expression of DNAJA3 and had an unfavorable prognosis. Furthermore, mice with hepatocyte-specific Dnaja3 depletion developed NASH-dependent HCC either spontaneously under a normal diet or enhanced by diethylnitrosamine. Dnaja3 -deficient mice developed NASH/HCC characterized by significant mitochondrial dysfunction, which was accompanied by excessive lipid accumulation and inflammatory responses. The molecular features of NASH/HCC in the Dnaja3 -deficient mice were closely associated with human NASH/HCC. CONCLUSIONS: We uncovered a genetic basis of DNAJA3 as a key player of NASH-related HCC.

A Robust Drug-Target Interaction Prediction Framework with Capsule Network and Transfer Learning.

Drug-target interactions (DTIs) are considered a crucial component of drug design and drug discovery. To date, many computational methods were developed for drug-target interactions, but they are insufficiently informative for accurately predicting DTIs due to the lack of experimentally verified negative datasets, inaccurate molecular feature representation, and ineffective DTI classifiers. Therefore, we address the limitations of randomly selecting negative DTI data from unknown drug-target pairs by establishing two experimentally validated datasets and propose a capsule network-based framework called CapBM-DTI to capture hierarchical relationships of drugs and targets, which adopts pre-trained bidirectional encoder representations from transformers (BERT) for contextual sequence feature extraction from target proteins through transfer learning and the message-passing neural network (MPNN) for the 2-D graph feature extraction of compounds to accurately and robustly identify drug-target interactions. We compared the performance of CapBM-DTI with state-of-the-art methods using four experimentally validated DTI datasets of different sizes, including human (Homo sapiens) and worm (Caenorhabditis elegans) species datasets, as well as three subsets (new compounds, new proteins, and new pairs). Our results demonstrate that the proposed model achieved robust performance and powerful generalization ability in all experiments. The case study on treating COVID-19 demonstrates the applicability of the model in virtual screening.

Panax notoginseng alleviates oxidative stress through miRNA regulations based on systems biology approach.

BACKGROUND: Herbal medicine Sanqi (SQ), the dried root or stem of Panax notoginseng (PNS), has been reported to have anti-diabetic and anti-obesity effects and is usually administered as a decoction for Chinese medicine. Alternative to utilizing PNS pure compound for treatment, we are motivated to propose an unconventional scheme to investigate the functions of PNS mixture. However, studies providing a detailed overview of the transcriptomics-based signaling network in response to PNS are seldom available. METHODS: To explore the reasoning of PNS in treating metabolic disorders such as insulin resistance, we implemented a systems biology-based approach with RNA sequencing (RNA-seq) and miRNA sequencing data to elucidate key pathways, genes and miRNAs involved. RESULTS: Functional enrichment analysis revealed PNS up-regulating oxidative stress-related pathways and down-regulating insulin and fatty acid metabolism. Superoxide dismutase 1 (SOD1), peroxiredoxin 1 (PRDX1), heme oxygenase-1 (Hmox1) and glutamate cysteine ligase (GCLc) mRNA and protein levels, as well as related miRNA levels, were measured in PNS treated rat pancreatic ß cells (INS-1). PNS treatment up-regulated Hmox1, SOD1 and GCLc expression while down-regulating miR-24-3p and miR-139-5p to suppress oxidative stress. Furthermore, we verified the novel interactions between miR-139-5p and miR-24-3p with GCLc and SOD1. CONCLUSION: This work has demonstrated the mechanism of how PNS regulates cellular molecules in metabolic disorders. Therefore, combining omics data with a systems biology strategy could be a practical means to explore the potential function and molecular mechanisms of Chinese herbal medicine in the treatment of metabolic disorders.

Cinnamomi ramulus inhibits cancer cells growth by inducing G2/M arrest.

Introduction: Cinnamomi ramulus (CR) is one of the most widely used traditional Chinese medicine (TCM) with anti-cancer effects. Analyzing transcriptomic responses of different human cell lines to TCM treatment is a promising approach to understand the unbiased mechanism of TCM. Methods: This study treated ten cancer cell lines with different CR concentrations, followed by mRNA sequencing. Differential expression (DE) analysis and gene set enrichment analysis (GSEA) were utilized to analyze transcriptomic data. Finally, the in silico screening results were verified by in vitro experiments. Results: Both DE and GSEA analysis suggested the Cell cycle pathway was the most perturbated pathway by CR across these cell lines. By analyzing the clinical significance and prognosis of G2/M related genes (PLK1, CDK1, CCNB1, and CCNB2) in various cancer tissues, we found that they were up-regulated in most cancer types, and their down-regulation showed better overall survival rates in cancer patients. Finally, in vitro experiments validation on A549, Hep G2, and HeLa cells suggested that CR can inhibit cell growth by suppressing the PLK1/CDK1/ Cyclin B axis. Discussion: This is the first study to apply transcriptomic analysis to investigate the cancer cell growth inhibition of CR on various human cancer cell lines. The core effect of CR on ten cancer cell lines is to induce G2/M arrest by inhibiting the PLK1/CDK1/Cyclin B axis.

Quantitative model for genome-wide cyclic AMP receptor protein binding site identification and characteristic analysis.

Cyclic AMP receptor proteins (CRPs) are important transcription regulators in many species. The prediction of CRP-binding sites was mainly based on position-weighted matrixes (PWMs). Traditional prediction methods only considered known binding motifs, and their ability to discover inflexible binding patterns was limited. Thus, a novel CRP-binding site prediction model called CRPBSFinder was developed in this research, which combined the hidden Markov model, knowledge-based PWMs and structure-based binding affinity matrixes. We trained this model using validated CRP-binding data from Escherichia coli and evaluated it with computational and experimental methods. The result shows that the model not only can provide higher prediction performance than a classic method but also quantitatively indicates the binding affinity of transcription factor binding sites by prediction scores. The prediction result included not only the most knowns regulated genes but also 1089 novel CRP-regulated genes. The major regulatory roles of CRPs were divided into four classes: carbohydrate metabolism, organic acid metabolism, nitrogen compound metabolism and cellular transport. Several novel functions were also discovered, including heterocycle metabolic and response to stimulus. Based on the functional similarity of homologous CRPs, we applied the model to 35 other species. The prediction tool and the prediction results are online and are available at: https://awi.cuhk.edu.cn/∼CRPBSFinder.

A genome-wide association study (GWAS) of the personality constructs in CPAI-2 in Taiwanese Hakka populations.

Here in this study we adopted genome-wide association studies (GWAS) to investigate the genetic components of the personality constructs in the Chinese Personality Assessment Inventory 2 (CPAI-2) in Taiwanese Hakka populations, who are likely the descendants of a recent admixture between a group of Chinese immigrants with high emigration intention and a group of the Taiwanese aboriginal population generally without it. A total of 279 qualified participants were examined and genotyped by an Illumina array with 547,644 SNPs to perform the GWAS. Although our sample size is small and that unavoidably limits our statistical power (Type 2 error but not Type 1 error), we still found three genomic regions showing strong association with Enterprise, Diversity, and Logical vs. Affective Orientation, respectively. Multiple genes around the identified regions were reported to be nervous system related, which suggests that genetic variants underlying the certain personalities should indeed exist in the nearby areas. It is likely that the recent immigration and admixture history of the Taiwanese Hakka people created strong linkage disequilibrium between the emigration intention-related genetic variants and their neighboring genetic markers, so that we could identify them despite with only limited statistical power.

Holistic similarity-based prediction of phosphorylation sites for understudied kinases.

Phosphorylation is an essential mechanism for regulating protein activities. Determining kinase-specific phosphorylation sites by experiments involves time-consuming and expensive analyzes. Although several studies proposed computational methods to model kinase-specific phosphorylation sites, they typically required abundant experimentally verified phosphorylation sites to yield reliable predictions. Nevertheless, the number of experimentally verified phosphorylation sites for most kinases is relatively small, and the targeting phosphorylation sites are still unidentified for some kinases. In fact, there is little research related to these understudied kinases in the literature. Thus, this study aims to create predictive models for these understudied kinases. A kinase-kinase similarity network was generated by merging the sequence-, functional-, protein-domain- and 'STRING'-related similarities. Thus, besides sequence data, protein-protein interactions and functional pathways were also considered to aid predictive modelling. This similarity network was then integrated with a classification of kinase groups to yield highly similar kinases to a specific understudied type of kinase. Their experimentally verified phosphorylation sites were leveraged as positive sites to train predictive models. The experimentally verified phosphorylation sites of the understudied kinase were used for validation. Results demonstrate that 82 out of 116 understudied kinases were predicted with adequate performance via the proposed modelling strategy, achieving a balanced accuracy of 0.81, 0.78, 0.84, 0.84, 0.85, 0.82, 0.90, 0.82 and 0.85, for the 'TK', 'Other', 'STE', 'CAMK', 'TKL', 'CMGC', 'AGC', 'CK1' and 'Atypical' groups, respectively. Therefore, this study demonstrates that web-like predictive networks can reliably capture the underlying patterns in such understudied kinases by harnessing relevant sources of similarities to predict their specific phosphorylation sites.

Identification of species-specific RNA N6-methyladinosine modification sites from RNA sequences.

N6-methyladinosine (m6A) modification is the most abundant co-transcriptional modification in eukaryotic RNA and plays important roles in cellular regulation. Traditional high-throughput sequencing experiments used to explore functional mechanisms are time-consuming and labor-intensive, and most of the proposed methods focused on limited species types. To further understand the relevant biological mechanisms among different species with the same RNA modification, it is necessary to develop a computational scheme that can be applied to different species. To achieve this, we proposed an attention-based deep learning method, adaptive-m6A, which consists of convolutional neural network, bi-directional long short-term memory and an attention mechanism, to identify m6A sites in multiple species. In addition, three conventional machine learning (ML) methods, including support vector machine, random forest and logistic regression classifiers, were considered in this work. In addition to the performance of ML methods for multi-species prediction, the optimal performance of adaptive-m6A yielded an accuracy of 0.9832 and the area under the receiver operating characteristic curve of 0.98. Moreover, the motif analysis and cross-validation among different species were conducted to test the robustness of one model towards multiple species, which helped improve our understanding about the sequence characteristics and biological functions of RNA modifications in different species.

Extraction of microRNA-target interaction sentences from biomedical literature by deep learning approach.

MicroRNA (miRNA)-target interaction (MTI) plays a substantial role in various cell activities, molecular regulations and physiological processes. Published biomedical literature is the carrier of high-confidence MTI knowledge. However, digging out this knowledge in an efficient manner from large-scale published articles remains challenging. To address this issue, we were motivated to construct a deep learning-based model. We applied the pre-trained language models to biomedical text to obtain the representation, and subsequently fed them into a deep neural network with gate mechanism layers and a fully connected layer for the extraction of MTI information sentences. Performances of the proposed models were evaluated using two datasets constructed on the basis of text data obtained from miRTarBase. The validation and test results revealed that incorporating both PubMedBERT and SciBERT for sentence level encoding with the long short-term memory (LSTM)-based deep neural network can yield an outstanding performance, with both F1 and accuracy being higher than 80% on validation data and test data. Additionally, the proposed deep learning method outperformed the following machine learning methods: random forest, support vector machine, logistic regression and bidirectional LSTM. This work would greatly facilitate studies on MTI analysis and regulations. It is anticipated that this work can assist in large-scale screening of miRNAs, thereby revealing their functional roles in various diseases, which is important for the development of highly specific drugs with fewer side effects. Source code and corpus are publicly available at https://github.com/qi29.

KinasePhos 3.0: Redesign and Expansion of the Prediction on Kinase-specific Phosphorylation Sites.

The purpose of this work is to enhance KinasePhos, a machine learning-based kinase-specific phosphorylation site prediction tool. Experimentally verified kinase-specific phosphorylation data were collected from PhosphoSitePlus, UniProtKB, the GPS 5.0, and Phospho.ELM. In total, 41,421 experimentally verified kinase-specific phosphorylation sites were identified. A total of 1380 unique kinases were identified, including 753 with existing classification information from KinBase and the remaining 627 annotated by building a phylogenetic tree. Based on this kinase classification, a total of 771 predictive models were built at the individual, family, and group levels, using at least 15 experimentally verified substrate sites in positive training datasets. The improved models demonstrated their effectiveness compared with other prediction tools. For example, the prediction of sites phosphorylated by the protein kinase B, casein kinase 2, and protein kinase A families had accuracies of 94.5%, 92.5%, and 90.0%, respectively. The average prediction accuracy for all 771 models was 87.2%. For enhancing interpretability, the SHapley Additive exPlanations (SHAP) method was employed to assess feature importance. The web interface of KinasePhos 3.0 has been redesigned to provide comprehensive annotations of kinase-specific phosphorylation sites on multiple proteins. Additionally, considering the large scale of phosphoproteomic data, a downloadable prediction tool is available at https://awi.cuhk.edu.cn/KinasePhos/download.html or https://github.com/tom-209/KinasePhos-3.0-executable-file.

STING as an emerging therapeutic target for drug discovery: Perspectives from the global patent landscape.

BACKGROUND: The STimulator of INterferon Genes (STING) plays an essential role in the innate immune system by inducing the expression of type I interferons (IFNs) and inflammatory cytokines upon sensing cytosolic DNA. Although modulating STING has shown promise as a potential treatment for cancers and inflammatory and autoimmune diseases in substantial pre-clinical studies, current preliminary clinical results of STING agonists have demonstrated limited anti-tumor efficacy. Currently, there is ongoing R&D targeting STING and focusing on the delivery of next-generation therapeutics. Whereas no comprehensive analysis on the STING patent landscape has been conducted to fill the gap between basic research progress and drug development and commercialization. AIM OF REVIEW: This study summarized the current agents in the clinical stage and global patenting profiles to help identify the current status, development trends, and emerging technologies of the nascent field of STING modulation. KEY SCIENTIFIC CONCEPTS OF REVIEW: Rapidly increasing R&D efforts and outcomes targeting STING were indicated by the recently increasing number and pharmacologic classes of drug candidates in clinic as well as in emergent technological patenting activities. Despite the overall fragmental ownership of patents, several pioneers that have advanced the clinical evaluation of novel STING agonists have established the basis of STING-relevant inventions through their influential patents in the field. These patents also facilitated progress on novel STING modulators, relevant delivery systems, pharmaceutical compositions, and combination strategies with the potential for further enhancing therapeutic outcomes by targeting STING.

MED1 Regulates BMP/TGF-ß in Endothelium: Implication for Pulmonary Hypertension.

BACKGROUND: Dysregulated BMP (bone morphogenetic protein) or TGF-ß (transforming growth factor beta) signaling pathways are imperative in idiopathic and familial pulmonary arterial hypertension (PAH) as well as experimental pulmonary hypertension (PH) in rodent models. MED1 (mediator complex subunit 1) is a key transcriptional co-activator and KLF4 (Krüppel-like factor 4) is a master transcription factor in endothelium. However, MED1 and KLF4 epigenetic and transcriptional regulations of the BMP/TGF-ß axes in pulmonary endothelium and their dysregulations leading to PAH remain elusive. We investigate the MED1/KLF4 co-regulation of the BMP/TGF-ß axes in endothelium by studying the epigenetic regulation of BMPR2 (BMP receptor type II), ETS-related gene (ERG), and TGFBR2 (TGF-ß receptor 2) and their involvement in the PH. METHODS: High-throughput screening involving data from RNA-seq, MED1 ChIP-seq, H3K27ac ChIP-seq, ATAC-seq, and high-throughput chromosome conformation capture together with in silico computations were used to explore the epigenetic and transcriptional regulation of BMPR2, ERG, and TGFBR2 by MED1 and KLF4. In vitro experiments with cultured pulmonary arterial endothelial cells (ECs) and bulk assays were used to validate results from these in silico analyses. Lung tissue from patients with idiopathic PAH, animals with experimental PH, and mice with endothelial ablation of MED1 (EC-MED1-/-) were used to study the PH-protective effect of MED1. RESULTS: Levels of MED1 were decreased in lung tissue or pulmonary arterial endothelial cells from idiopathic PAH patients and rodent PH models. Mechanistically, MED1 acted synergistically with KLF4 to transactivate BMPR2, ERG, and TGFBR2 via chromatin remodeling and enhancer-promoter interactions. EC-MED1-/- mice showed PH susceptibility. In contrast, MED1 overexpression mitigated the PH phenotype in rodents. CONCLUSIONS: A homeostatic regulation of BMPR2, ERG, and TGFBR2 in ECs by MED1 synergistic with KLF4 is essential for the normal function of the pulmonary endothelium. Dysregulation of MED1 and the resulting impairment of the BMP/TGF-ß signaling is implicated in the disease progression of PAH in humans and PH in rodent models.

Air pollution and blood pressure in the elderly: evidence from a panel study in Nanjing, China.

Background: Air pollution is known to have notable negative effects on human health. Recently, the effect of air pollution on blood pressure among the elderly has attracted researchers' attention. However, the existing evidence is not consistent, given that positive, null, and negative outcomes are presented in the literature. In this study, we investigated the relationship between blood pressure (BP) and indices of air pollutants (PM2.5, PM10, and air quality index) in a specific elderly population through a panel study to address this knowledge gap. Methods: We obtained repeated BP measurements from January 2017 to May 2019 in a panel of 619 elderly with a total of 5106 records in Nanjing, China. Data on daily indices of ambient air pollutants, including fine particulate matter with an aerodynamic diameter of ≤ 2.5 µ m (PM2.5), ≤ 10 µ m (PM10), and air quality index (AQI) of the same period were obtained. We evaluated the association between BP and average concentrations of air pollutants in the past one-week, two-week, and four-week lags before measuring the BP. The non-linear panel regression models were used with fixed- and mixed-effects to control age, gender, and temperature. Results: In the non-linear panel fixed-effects model, the average concentration of PM2.5 is significantly associated with systolic BP (SBP) at all lags but is only significantly correlated with diastolic BP (DBP) at a one-week lag. An interquartile range (IQR) increase of one-week average moving PM2.5 (38.86 µg/m3) of our sample increases the SBP and DBP by 7.68% and 6.9%, respectively. PM10 shows the same pattern of effect on BP as PM2.5. AQI shows less significant associations with BP. In the non-linear mixed-effects model, the average concentrations of PM2.5 and PM10 are significantly associated with SBP at all lags but have no significant effect on DBP at one- and two-week lags. AQI is only significantly associated with DBP at a one-week lag. Conclusions: Exposures to ambient particulate matter (PM2.5 and PM10) were associated with increased BP among older people, indicating a potential link between air pollution and the high prevalence of hypertension. Air pollution is a well-recognized risk factor for future cardiovascular diseases and should be reduced to prevent hypertension among the elderly.

On the force field optimisation of [Formula: see text]-lactam cores using the force field Toolkit.

When employing molecular dynamics (MD) simulations for computer-aided drug design, the quality of the used force fields is highly important. Here we present reparametrisations of the force fields for the core molecules from 9 different [Formula: see text]-lactam classes, for which we utilized the force field Toolkit and Gaussian calculations. We focus on the parametrisation of the dihedral angles, with the goal of reproducing the optimised quantum geometry in MD simulations. Parameters taken from CGenFF turn out to be a good initial guess for the multiplicity of each dihedral angle, but the key to a successful parametrisation is found to lie in the phase shifts. Based on the optimised quantum geometry, we come up with a strategy for predicting the phase shifts prior to the dihedral potential fitting. This allows us to successfully parameterise 8 out of the 11 molecules studied here, while the remaining 3 molecules can also be parameterised with small adjustments. Our work highlights the importance of predicting the dihedral phase shifts in the ligand parametrisation protocol, and provides a simple yet valuable strategy for improving the process of parameterising force fields of drug-like molecules.

DNMT3b protects centromere integrity by restricting R-loop-mediated DNA damage.

This study used DNA methyltransferase 3b (DNMT3b) knockout cells and the functional loss of DNMT3b mutation in immunodeficiency-centromeric instability-facial anomalies syndrome (ICF) cells to understand how DNMT3b dysfunction causes genome instability. We demonstrated that R-loops contribute to DNA damages in DNMT3b knockout and ICF cells. More prominent DNA damage signal in DNMT3b knockout cells was due to the loss of DNMT3b expression and the acquirement of p53 mutation. Genome-wide ChIP-sequencing mapped DNA damage sites at satellite repetitive DNA sequences including (peri-)centromere regions. However, the steady-state levels of (peri-)centromeric R-loops were reduced in DNMT3b knockout and ICF cells. Our analysis indicates that XPG and XPF endonucleases-mediated cleavages remove (peri-)centromeric R-loops to generate DNA beaks, causing chromosome instability. DNMT3b dysfunctions clearly increase R-loops susceptibility to the cleavage process. Finally, we showed that DNA double-strand breaks (DSBs) in centromere are probably repaired by error-prone end-joining pathway in ICF cells. Thus, DNMT3 dysfunctions undermine the integrity of centromere by R-loop-mediated DNA damages and repair.

The Changing Global Landscape in the Development of Artemisinin-Based Treatments: A Clinical Trial Perspective.

Artemisinin and its derivatives (ARTs), due to their potent antimalarial activities, are widely used as frontline antimalarials across the world. Although the large-scale deployment of ARTs has significantly contributed to a substantial decline in malaria deaths, the global malaria burden is still high. New antimalarial treatments need to be developed to manage the growing artemisinin resistance. Understanding the status of ART development is crucial for developing strategies for new alternatives and identifying opportunities to develop ART-based treatments. This study sampled ART clinical trials from the past two decades to gain an overview of the global ART-development landscape. A total of 768 trials were collected to analyze the disease focuses, activity trends, development status, geographic distribution, and combination treatment profiles of ART trials. The findings highlighted the constant focus of ARTs on malaria, the evolving combination research focus, the distinctions between ART development preferences across global regions, the urgent demands for treatments for artemisinin-resistant malaria, and the unavoidable need to consider ART combinations in the development of new antimalarials.