Database Mining Research Articles

Data mining redefines the timeline and geographic spread of cotton leafroll dwarf virus.

Cotton leafroll dwarf virus (CLRDV), a threat to the cotton industry, was first reported in the United States (US) as an emergent pathogen in 2017. Phylogenetic analysis supports the hypothesis that US CLRDV strains are genetically distinct from strains in South America and elsewhere, which is not consistent with the hypothesis that the virus is newly introduced into the country. Using database mining, we evaluated the timeline and geographic distribution of CLRDV in the country. We uncovered evidence that shows CLRDV had been in the US for over a decade before its official first report. CLRDV sequences were detected in datasets derived from Mississippi in 2006, Louisiana in 2015, and California in 2018. Additionally, through field surveys of upland cotton in 2023, we confirmed that CLRDV is present in California, which had no prior reports of the virus. Viral sequences from these old and new datasets exhibited high nucleotide identities (>98%) with recently characterized US isolates, and phylogenetic analyses with their homologs placed these sequences within a US-specific clade, further supporting the earlier presence of CLRDV in the country. Moreover, potential new hosts, including another fiber crop, flax, were determined through data mining. Retrospective analysis suggests CLRDV has been present in the US since at least 2006 (Mississippi). Our findings challenge the current understanding of the arrival and spread of CLRDV in the US, highlight the power of data mining for virus discovery, and underscore the need for further investigation into CLRDV's impact on US cotton.

Network Pharmacology and In Vivo Experimental Verification of the Mechanism of the Qing'e Pill for Treating Intervertebral Disc Degeneration.

The Qing'e Pill (QEP) is widely used to alleviate low back pain and sciatica caused by Intervertebral Disc Degeneration (IDD). However, its active components, key targets, and molecular mechanisms are not fully understood. The aim of this study is to elucidate the molecular mechanisms through which the QEP improves IDD using database mining techniques. Active components and candidate targets of the QEP were identified using the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform and the Bioinformatics Analysis Tool for Molecular Mechanisms of Traditional Chinese Medicine. IDD-related targets were obtained from the GeneCards database, and liver- and kidney-specific genes were retrieved from the BioGPS database. The intersection of these candidate targets was analyzed to identify potential targets for the QEP in IDD. A protein-protein interaction network analysis was performed using STRING and Cytoscape 3.7.2 software. Core targets were further analyzed through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Molecular docking was used to assess the binding affinity of active components to candidate targets, and animal experiments were conducted for validation. We identified 65 potentially active components of the QEP that corresponded to 1,093 candidate targets, 2,108 IDD-related targets, and 1,113 liver- and kidney-specific genes. Key components included quercetin, berberine, isorhamnetin, and emodin. The primary candidate targets were Wnt5A, CTNNB1, IL-1β, MAPK14, MMP9, and MMP3. The GO and KEGG analyses revealed the involvement of these targets in Wnt signaling, TNF signaling, Wnt receptor activation, Frizzled binding, and Wnt-protein interactions. Molecular docking showed strong binding between these components and their targets. Animal experiments demonstrated that the QEP treatment significantly reduced the expression of Wnt5A, CTNNB1, IL-1β, MAPK14, MMP9, and MMP3 at high, medium, and low doses compared with the model group. The QEP alleviated IDD by modulating the Wnt/MAPK/MMP signaling pathways and reducing the release and activation of key factors.

The Environmental Neuroactive Chemicals List of Prioritized Substances for Human Biomonitoring and Neurotoxicity Testing: A Database and High-Throughput Toxicokinetics Approach

There is a diversity of chemicals to which humans are potentially exposed. Few of these chemicals have linked human biomonitoring data, and most have very limited neurotoxicity testing. Of particular concern are environmental exposures impacting children, who constitute a population of heightened susceptibility due to rapid neural growth and plasticity, yet lack biomonitoring data compared to other age/population subgroups. This study set out to develop a prioritized list of neuroactive substances, titled the Environmental NeuRoactIve CHemicals (ENRICH) list, to be used as a defined screening library in the evaluation of human biological samples, with emphasis on early childhood-relevant environmental exposures. In silico database mining approaches were used to prioritize chemicals based upon likelihood of neuroactivity, human exposure, and feasible detection in biological samples. Evidence of neuroactivity was compiled across in vitro high-throughput screening, animal testing, and/or human epidemiological findings. Chemicals were considered for their likelihood of human exposure and detection presence in biological samples (including metabolites), with additional evidence indicating presence within child-relevant products. The resulting list of 1827 chemicals were ranked using a Chemical Prioritization Index. Manual inclusion/exclusion criteria were employed for the top-ranking chemical candidates to ensure that chemicals were within the study's scope (i.e., environmentally relevant) and, for the purposes of biomonitoring, had properties amenable to mass spectrometry methods. These elements were combined to produce the ENRICH list of 250 top-ranking chemicals, spanning pesticides and those used in home maintenance, personal care, cleaning products, vehicles, arts and crafts, and consumer electronics, among other sources. Chemicals were additionally evaluated for high-throughput toxicokinetics to predict how much of a chemical and/or its metabolite(s) may reach urine, as an example biological matrix for practical use in biomonitoring efforts. This novel study couples databases and in silico-based predictions to prioritize chemicals in the environment with potential neurological impacts for future study.

In Silico Prediction of Quercetin Analogs for Targeting Death-Associated Protein Kinase 1 (DAPK1) Against Alzheimer's Disease.

Alzheimer's Disease (AD) is a progressive neurodegenerative disorder that greatly affects the health and life quality of the elderly population. Existing drugs mainly alleviate symptoms but fail to halt disease progression, underscoring the urgent need for the development of novel drugs. Based on the neuroprotective effects of flavonoid quercetin in AD, this study was designed to identify potential AD-related targets for quercetin and perform in silico prediction of promising analogs for the treatment of AD. Database mining suggested death-associated protein kinase 1 (DAPK1) as the most promising AD-related target for quercetin among seven protein candidates. To achieve better biological effects for the treatment of AD, we devised a series of quercetin analogs as ligands for DAPK1, and molecular docking analyses, absorption, distribution, metabolism, and excretion (ADME) predictions, as well as molecular dynamics (MD) simulations, were performed. The energy for drug-protein interaction was predicted and ranked. As a result, quercetin-A1a and quercetin-A1a1 out of 19 quercetin analogs exhibited the lowest interaction energy for binding to DAPK1 than quercetin, and they had similar dynamics performance with quercetin. In addition, quercetin-A1a and quercetin-A1a1 were predicted to have better water solubility. Thus, quercetin-A1a and quercetin-A1a1 could be promising agents for the treatment of AD. Our findings paved the way for further experimental studies and the development of novel drugs.

Unveiling the dual anti-viral and anti-bacterial potential of Tridax procumbens: integrating system biology and molecular modeling for therapeutic insights

This study evaluates the dual anti-viral and anti-bacterial activity of Tridax procumbens using system biology and molecular docking targeting the tumor necrosis factor (TNF) signaling pathway. The bioactive potential of Tridax procumbens was studied using phytochemical databases, and the essential biomolecules were selected for the study. The principle of geneVenn diagrams and protein target prediction was used to identify overlapping molecular targets with viral and bacterial diseases, which was later validated using various database mining tools like GeneCards and OMIM. Cytoscape software was applied to construct protein-protein interaction networks, which showed that TNF, AKT1, EGFR, SRC, and ESR1 are the hub genes of the networks. The gene ontology (GO) enrichment analysis also recapitulated the modulated biological processes, cellular components, and molecular functions of Tridax procumbens compounds, including their action on the TNF signaling in inflammation and immune responses. Molecular docking studies showed strong binding affinities of compounds such as cynaroside and 5,7,2,3,4-Pentahydroxy-3,6-dimethoxyflavone 7-glucoside to TNF, and molecular dynamics simulations confirmed these interactions’ stability. The findings suggest that Tridax procumbens exerts its therapeutic effects by modulating the TNF signaling pathway, offering significant anti-viral and anti-bacterial properties. This integrative approach provides insights into the Tridax procumbens’s mechanisms of action, supporting its potential as a source of novel therapeutic agents against infectious and bacterial diseases. There is still a need for further experimental research to define the full spectrum of Tridax procumbens’ therapeutic application.

Portal hypertension as an independent risk factor for cirrhotic cardiomyopathy: An investigation on its mechanism based on bioinformatics analysis

AbstractAimsDespite being commonly found in patients with various etiologies of liver cirrhosis, cirrhotic cardiomyopathy (CCM) is often overlooked, and its mechanism remains unknown. This study aimed to illuminate the epidemiological data and risk factors of CCM and to explore its potential mechanisms through a bioinformatics analysis.MethodsClinical data were collected from January 2008 to December 2022 retrospectively, and patients were divided into the CCM and non‐CCM groups based on the criteria proposed by the 2005 World Congress of Gastroenterology. Independent risk factors for CCM were identified through a multivariate logistic regression analysis. CCM‐related genes were explored using the GeneCards database, and enrichment analysis was performed. Protein–protein interaction analysis was conducted using the Search Tool for the Retrieval of Interacting Genes (STRING) database, and hub genes were identified using Cytoscape software. The Transcriptional Regulatory Relationships Unraveled by Sentence‐based Text Mining (TRRUST) database was utilized to explore transcription factors (TFs), and a network regulatory diagram of TF‐gene pathways was constructed. Finally, potential drugs were predicted using the Drug Gene Interaction Database.ResultsA total of 965 patients with liver cirrhosis were included, among whom 563 (58.3%) were diagnosed with CCM. Portal hypertension (PHT) was identified as the only independent risk factor for CCM. The hub genes included IL6, TNF, TGFB1, IL1B, MMP9, and IL10. Enrichment analysis revealed that Forkhead box O (FoxO), mitogen‐activated protein kinase (MAPK), hypoxia‐inducible factor (HIF‐1), phosphoinositide 3‐kinase‐protein kinase B (PI3K‐AKT), interleukin (IL)‐17, tumor necrosis factor (TNF), vascular endothelial growth factor (VEGF), mammalian target of rapamycin (mTOR), Janus kinase/signal transducers and activators of transcription (JAK‐STAT), and transforming growth factor (TGF)‐beta pathways were enriched. TFs such as CEBPB, NFKB1, STAT1, STAT3, and SP1 were also implicated. Potential drugs included pravastatin and atorvastatin.ConclusionsThe morbidity associated with CCM was high, and PHT was the only independent risk factor. The mechanism may be related to inflammation, and statins may hold therapeutic promise.

The rice genome annotation project: an updated database for mining the rice genome.

Rice (Oryza sativa L.) is a major cereal crop that provides calories across the world. With a small genome, rice has been used extensively as a model for genetic and genomic studies in the Poaceae. Since the release of the first rice genome sequence in 2002, an improved reference genome assembly, multiple whole genome assemblies, extensive gene expression profiles, and resequencing data from over 3000 rice accessions have been generated. To facilitate access to the rice genome for plant biologists, we updated the Rice Genome Annotation Project database (RGAP; https://rice.uga.edu) with new datasets including 16 whole genome rice assemblies and sequence variants generated from multiple rice pan-genome projects including the 3000 Rice Genomes Project. We updated gene expression abundance data with 80 RNA-sequencing datasets and to facilitate gene function discovery, performed gene coexpression resulting in 39 coexpression modules that capture highly connected sets of co-regulated genes. To facilitate comparative genome analyses, 32335 syntelogs were identified between the Nipponbare reference genome and other rice genomes and 19371 syntelogs were identified between Nipponbare and four other Poaceae genomes. Infrastructure improvements to the RGAP database include an upgraded genome browser and data access portals, enhanced website securityand increased performance of the website.

Ginsenoside Rg3 attenuates the stemness of breast cancer stem cells by activating the hippo signaling pathway

Ginsenoside Rg3 (Rg3), a bioactive compound from ginseng, is gaining attention for its potential in targeting cancer stem cells in cancer therapy. The therapeutic effect of Rg3 on breast cancer stem cells (BCSCs) has not been systematically explored using a suitable approach. Our study leverages a multi-faceted strategy, including network pharmacology, molecular docking, and in vitro experiments validation, to explore the effect of Rg3 against BCSCs. We identified 38 common targets of Rg3 and BCSCs through public databases mining. The analysis of protein-protein interaction network revealed Myc, Stat3, Bcl2, Cdh1, Egf, Il6, Egfr, Nfkb1, Sox2 and Sirt1 as the top 10 potential targets. Molecular docking further validated Rg3 has robust binding potential with these targets. Utilizing the BCSC-enriched MCF-7 and MDA-MB-231 mammosphere model, in vitro experiments substantiated Rg3's ability to induce apoptosis, suppress proliferation, and inhibit mammospheres formation of BCSCs. Rg3 also decreased the ALDHhigh and CD44+/CD24−/low subpopulations and downregulated the expression of cancer stem cell markers such as c-MYC, ALDH1A1, NANOG in BCSCs. After Rg3 treatment, most of the top 10 genes in BCSC-enriched MCF-7 mammospheres showed a significant reduction in expression, with Cdh1 (E-cadherin) being the most markedly downregulated. The E-cadherin/catenin complex acts as an upstream regulator of the Hippo signaling pathway, which is crucial for BCSC function and is among the top 20 enriched pathways identified by KEGG analysis. Mechanistically, Rg3 attenuates the stemness of BCSCs by activating the Hippo signaling pathway. This study provides a comprehensive evaluation of Rg3 as a promising therapeutic agent against BCSCs.

Sequence-Activity Relationship of Angiotensin-Converting Enzyme Inhibitory Peptides Derived from Food Proteins, Based on a New Deep Learning Model.

Food-derived peptides are usually safe natural drug candidates that can potentially inhibit the angiotensin-converting enzyme (ACE). The wet experiments used to identify ACE inhibitory peptides (ACEiPs) are time-consuming and costly, making it important and urgent to reduce the scope of experimental validation through bioinformatics methods. Here, we construct an ACE inhibitory peptide predictor (ACEiPP) using optimized amino acid descriptors (AADs) and long- and short-term memory neural networks. Our results show that combined-AAD models exhibit more efficient feature transformation ability than single-AAD models, especially the training model with the optimal descriptors as the feature inputs, which exhibits the highest predictive ability in the independent test (Acc = 0.9479 and AUC = 0.9876), with a significant performance improvement compared to the existing three predictors. The model can effectively characterize the structure-activity relationship of ACEiPs. By combining the model with database mining, we used ACEiPP to screen four ACEiPs with multiple reported functions. We also used ACEiPP to predict peptides from 21,249 food-derived proteins in the Database of Food-derived Bioactive Peptides (DFBP) and construct a library of potential ACEiPs to facilitate the discovery of new anti-ACE peptides.

Chemokine ligands and receptors regulate macrophage polarization in atherosclerosis: a comprehensive database mining study

Chemokine ligands and receptors regulate macrophage polarization in atherosclerosis: a comprehensive database mining study

Signaling Molecules in Pseudomonas aeruginosa Response to Antibiotics at Sub-Inhibitory Concentrations

Signaling Molecules are N-acylhomoserine lactones (AHLs) that form the Pseudomonas aeruginosa cell information system which determines gene expressions in a population dependent manner called quorum sensing (QS). Signal molecules, which are chemically varied, control genes expressions to antibiotics resistance, pathogenicity, motility, biofilm development, bioluminescence, secondary metabolite production, and plasmid transfer. This research was aimed to identify the signaling molecules in Pseudomonas aeruginosa response to antibiotics at sub-inhibitory concentrations. One hundred and fifty (150) clinical swab specimens were collected from urinary catheter wound and ear infection of patients and the swabs inoculated using standard microbiology method. The isolates were characterized based on the bacteriological methods such as morphology and biochemical tests. The isolates were further confirmed by species specific PCR by amplification of 16S rRNA and the amplicons were analyzed by gel electrophoresis; and further genomic sequencing was done and blast with NCBI database mining. Disc diffusion method was applied for the antibiotics susceptibility pattern. The isolates cell suspensions were analyzed by Gas Chromatography-Mass Spectrometry (GC-MS). The result of the isolation showed 22(59.46%) from wound infections, 12(32.43%) from ear infections and 3(8.11%) from urinary catheter. The isolates were Gram negative, produced β-hemolysis on blood agar and the morphology is small pigmented circular in form. The isolates showed positive result to catalase, oxidase, citrate, nitrate and indole tests. The amplification of 16S rRNA gene region resulted in the band size of 1500bp PCR product and the BLAST analysis gave 99% similarity. The resistant antibiotics susceptibility showed that Pseudomonas aeruginosa is multidrug resistant bacteria. The GC-MS results obtained from urinary catheter isolates showed four (4) signaling molecules such as N-Octanoyl-L-homoserine Lactone, N-Decanoyl-DL-Homoserine Lactone, N-Dodecanoyl-DL-Homoserine Lactone and N-Tetradecanoyl-L-Homoserine Lactone. Three (3) signaling molecules such as N-Octanoyl-L-homoserine Lactone, N-Decanoyl-DL-Homoserine Lactone and N-Tetradecanoyl-L-Homoserine Lactone were obtained from wound isolates. N-Tetradecanoyl-L-Homoserine Lactone was the only signaling molecule obtained from ear isolates. Further research should be done to develop rapid tests that could be possible to detect acyl homoserine specific to Pseudomonsa aeruginosa present to human samples and deliver results in real time. Signaling molecules inhibitors (SMI) are possible potential therapeutics that could produce the subsequent class of antibacterial drugs. The fundamental role of each signal molecule in the production of biofilms and virulent factors should also be investigated, as well as whether Pseudomonas aeruginosa needs one or more signaling molecules to form quorum sensing. Identification of the signals molecules (AHLs) of P. aeruginosa and developing signaling molecules inhibitors (SMI) can have an important prognostic, diagnostic and therapeutic value in human medicine for the treatment of P. aeruginosa infections.

MIR937 amplification potentiates ovarian cancer progression by attenuating FBXO16 inhibition on ULK1-mediated autophagy

High-grade serous ovarian carcinoma (HGSOC) is one of the most lethal gynecological cancer. Genetic studies have revealed gene copy number alterations (CNAs) frequently occurred in HGSOC pathogenesis, however the function and mechanism of CNAs for microRNAs are still not fully understood. Here, we show the dependence on gene copy number amplification of MIR937 that enhances cell autophagy and dictates HGSOC proliferative activity. Data mining of TCGA database revealed MIR937 amplification is correlated with increased MIR937 expression and cell proliferation of HGSOC. Deletion of MIR937 in HGSOC cells led to impaired autophagy and retarded cell proliferation, and the extent for its inhibitory effects scaled with the degree of MIR937 copy loss. Rescue assay confirmed miR-937-5p, a mature product of MIR937, was sufficient to restore its oncogenic function. Mechanistically, MIR937 amplification raised the expression of miR-937-5p, enhanced its binding to 3′ UTR of FBXO16 transcript, and thereby restricting FBXO16 degradative effects on ULK1. Our results demonstrate that MIR937 amplification augments cell autophagy and proliferation, and suggest an alternative strategy of MIR937/FBXO16/ULK1 targeting for HGSOC treatment.

FAS gene expression, prognostic significance and molecular interactions in lung cancer.

FAS has been implicated in the development of various cancers, but its involvement in lung cancer has not been systematically characterized. In this study, we performed data mining in online tumor databases to investigate the expression, methylation, alterations, protein interactions, co-expression and prognostic significance of FAS in lung cancer. The expression, prognostic significance and molecular interactions of FAS in lung cancer was mined and analyzed using GENT2, GEPIA2, UALCAN, cBioPortal, STRING, GeneMANIA, UCSC Xena, Enrichr, and OSluca databases. FAS expression was subsequently investigated at the protein level in samples from 578 lung cancer patients to understand its protein-level expression. In vitro validation of FAS gene expression was performed on H1299, H1993, A549 and HBE cell lines. We found that the expression of FAS was significantly downregulated in both lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) compared to normal lung tissue. In addition, we observed a higher level of FAS promoter methylation in LUSC tissue than in normal tissue. FAS alterations were rare (1.9%) in lung cancer samples, with deep deletions being more common than missense mutations, which occurred mainly in the TNFR-like cysteine-rich domain and the death domain. We also identified a list of proteins interacting with FAS and genes co-expressed with FAS, with LUAD having 11 co-expressed genes and LUSC having 90 co-expressed genes. Our results also showed that FAS expression has limited prognostic significance (HR=1.302, 95% CI=0.935-1.139, P=0.530). Protein level investigation revealed that FAS expression varied among individuals, with nTPM values ranging from 5.2 to 67.2. This study provides valuable insights into the involvements and characteristics of FAS in lung cancer. Further studies are needed to investigate the clinical significance of FAS alterations in lung cancer and to explore the potential of targeting FAS for therapeutic intervention.

Transcriptomic analyses in the gametophytes of the apomictic fern Dryopteris affinis

Main conclusionA novel genomic map of the apogamous gametophyte of the fern Dryopteris affinis unlocks oldest hindrance with this complex plant group, to gain insight into evo-devo approaches.The gametophyte of the fern Dryopteris affinis ssp. affinis represents a good model to explore the molecular basis of vegetative and reproductive development, as well as stress responses. Specifically, this fern reproduces asexually by apogamy, a peculiar case of apomixis whereby a sporophyte forms directly from a gametophytic cell without fertilization. Using RNA-sequencing approach, we have previously annotated more than 6000 transcripts. Here, we selected 100 of the inferred proteins homolog to those of Arabidopsis thaliana, which were particularly interesting for a detailed study of their potential functions, protein–protein interactions, and distance trees. As expected, a plethora of proteins associated with gametogenesis and embryogenesis in angiosperms, such as FERONIA (FER) and CHROMATING REMODELING 11 (CHR11) were identified, and more than a dozen candidates potentially involved in apomixis, such as ARGONAUTE family (AGO4, AGO9, and AGO 10), BABY BOOM (BBM), FASCIATED STEM4 (FAS4), FERTILIZATION-INDEPENDENT ENDOSPERM (FIE), and MATERNAL EFFECT EMBRYO ARREST29 (MEE29). In addition, proteins involved in the response to biotic and abiotic stresses were widely represented, as shown by the enrichment of heat-shock proteins. Using the String platform, the interactome revealed that most of the protein–protein interactions were predicted based on experimental, database, and text mining datasets, with MULTICOPY SUPPRESSOR OF IRA4 (MSI4) showing the highest number of interactions: 16. Lastly, some proteins were studied through distance trees by comparing alignments with respect to more distantly or closely related plant groups. This analysis identified DCL4 as the most distant protein to the predicted common ancestor. New genomic information in relation to gametophyte development, including apomictic reproduction, could expand our current vision of evo-devo approaches.

What Do We Mean by Sharing of Patient Data? DaSH - A Data Sharing Hierarchy of Privacy and Ethical Challenges.

Sharing of clinical data is common and necessary for patient care, research, public health, and innovation. The term "data sharing," however, is often ambiguous in its many facets and complexities-each of which involves ethical, legal, and social issues. To our knowledge there is no extant hierarchy of data sharing that assesses these issues. Develop a hierarchy explicating the risks and ethical complexities of data sharing with particular focus on patient data privacy. We surveyed the available peer-reviewed and gray literature, and with our combined extensive experience in bioethics and medical informatics, created this hierarchy. We present six ways data are shared and provide a tiered Data Sharing Hierarchy (DaSH) of risks, showing increasing threats to patients' privacy and to clinicians and organizations as one progresses up the hierarchy from data sharing for direct patient care, public health and safety, scientific research, commercial purposes, complex combinations of the preceding efforts, and among networked third parties. We offer recommendations to enhance benefits of data sharing while mitigating risks and protecting patients' interests by: improving consenting; developing better policies and procedures; clarifying, simplifying, and updating regulation to include all health-related data regardless of source; expanding the scope of bioethics for information technology; and increasing ongoing monitoring and research. Data sharing, while essential for patient care, is increasingly complex, opaque, and perhaps perilous for patients, clinicians and healthcare institutions. Risks increase with advances in technology and with more encompassing patient data from wearables and artificial intelligence database mining. Data sharing places responsibilities on all parties: patients, clinicians, researchers, educators, risk managers, attorneys, informaticists, bioethicists, institutions, and policy makers.

Mining unique cysteine synthetases and computational study on thoroughly eliminating feedback inhibition through tunnel engineering.

L-cysteine is an essential component in pharmaceutical and agricultural industries, and synthetic biology has made strides in developing new metabolic pathways for its production, particularly in archaea with unique O-phosphoserine sulfhydrylases (OPSS) as key enzymes. In this study, we employed database mining to identify a highly catalytic activity OPSS from Acetobacterium sp. (AsOPSS). However, it was observed that the enzymatic activity of AsOPSS suffered significant feedback inhibition from the product L-cysteine, exhibiting an IC50 value of merely 1.2 mM. A semi-rational design combined with tunnel analysis strategy was conducted to engineer AsOPSS. The best variant, AsOPSSA218R was achieved, totally eliminating product inhibition without sacrificing catalytic efficiency. Molecular docking and molecular dynamic simulations indicated that the binding conformation of AsOPSSA218R with L-cys was altered, leading to a reduced affinity between L-cysteine and the active pocket. Tunnel analysis revealed that the AsOPSSA218R variant reshaped the landscape of the tunnel, resulting in the construction of a new tunnel. Furthermore, random acceleration molecular dynamics simulation and umbrella sampling simulation demonstrated that the novel tunnel improved the suitability for product release and effectively separated the interference between the product release and substrate binding processes. Finally, more than 45 mM of L-cysteine was produced in vitro within 2 h using the AsOPSSA218R variant. Our findings emphasize the potential for relieving feedback inhibition by artificially generating new product release channels, while also laying an enzymatic foundation for efficient L-cysteine production.

Metabolic engineering of Escherichia coli for de novo synthesis of 1,4-butanediol

1,4-butanediol is an important intermediate widely used in chemical, agricultural, and pharmaceutical industries. This study constructed a new short path for the production of 1,4-butanediol with glucose as the substrate by combining enzyme engineering and metabolic engineering. Firstly, a novel path catalyzed by α-ketoglutarate decarboxylase (SucA), carboxylate reductase (Car), and alcohol dehydrogenase (YqhD) was designed by database mining, and the de novo synthesis of 1,4-butanediol was achieved after introduction of the path into Escherichia coli W3110 (K-12) chassis cells. To further improve the synthesis efficiency of this path, we deleted the genes encoding lactate dehydrogenase A (LdhA) and pyruvate formate lyase B (PflB) to block the metabolic bypass. Furthermore, the expression of citrate synthase (GltAR163L) was up-regulated to increase the α-ketoglutarate metabolic flux. In addition, we improved the synthesis of the key cofactor NADPH and up-regulated the expression of sucA, car, and yqhD by substituting with strong promoters to increase the efficiency of supplying precursors to 1,4-butanediol synthesis. Eventually, the recombinant strain produced up to 770 mg/L of 1,4-butanediol within 48 h in a shake flask, and 4.22 g/L of 1,4-butanediol within 60 h in a 5 L fermenter with a yield of 12.46 mg/g glucose. Compared with the previously reported method, the novel path designed in this study for the de novo synthesis of 1,4-butanediol does not need acetyl coenzyme A and avoids the byproduct acetate or the addition of ammonia. Therefore, the outcome is expected to provide a new idea for the metabolic engineering of microbial chassis for the production of 1,4-butanediol and its high-value derivatives.

The Molecular Comorbidity Network of Periodontal Disease.

Periodontal disease, a multifactorial inflammatory condition affecting the supporting structures of the teeth, has been increasingly recognized for its association with various systemic diseases. Understanding the molecular comorbidities of periodontal disease is crucial for elucidating shared pathogenic mechanisms and potential therapeutic targets. In this study, we conducted comprehensive literature and biological database mining by utilizing DisGeNET2R for extracting gene-disease associations, Romin for integrating and modeling molecular interaction networks, and Rentrez R libraries for accessing and retrieving relevant information from NCBI databases. This integrative bioinformatics approach enabled us to systematically identify diseases sharing associated genes, proteins, or molecular pathways with periodontitis. Our analysis revealed significant molecular overlaps between periodontal disease and several systemic conditions, including cardiovascular diseases, diabetes mellitus, rheumatoid arthritis, and inflammatory bowel diseases. Shared molecular mechanisms implicated in the pathogenesis of these diseases and periodontitis encompassed dysregulation of inflammatory mediators, immune response pathways, oxidative stress pathways, and alterations in the extracellular matrix. Furthermore, network analysis unveiled the key hub genes and proteins (such as TNF, IL6, PTGS2, IL10, NOS3, IL1B, VEGFA, BCL2, STAT3, LEP and TP53) that play pivotal roles in the crosstalk between periodontal disease and its comorbidities, offering potential targets for therapeutic intervention. Insights gained from this integrative approach shed light on the intricate interplay between periodontal health and systemic well-being, emphasizing the importance of interdisciplinary collaboration in developing personalized treatment strategies for patients with periodontal disease and associated comorbidities.

Naturally Occurring Plant-Based Anticancerous Candidates as Potential ERK2 Inhibitors: In-Silico Database Mining and Molecular Dynamics Simulations.

The evolutionarily conserved extracellular signal-regulated kinase 2 (ERK2) is involved in regulating cellular signaling in both normal and pathological conditions. ERK2 expression is critical for human development, while hyperactivation is a major factor in tumor progression. Up to now, there have been no approved inhibitors that target ERK2, and as such, here we report on screening of a naturally occurring plant-based anticancerous compound-activity-target (NPACT) database for prospective ERK2 inhibitors. More than 1,500 phytochemicals were screened using in-silico molecular docking and molecular dynamics (MD) approaches. NPACT compounds with a docking score lower than a co-crystallized LHZ inhibitor (calc. -10.5 kcal/mol) were subjected to MD simulations. Binding energies (ΔGbinding) of inhibitor-ERK2 complexes over the MD course were estimated using an MM-GBSA approach. Based on MM-GBSA//100 ns MD simulations, the steroid zhankuic acid C (NPACT01034) demonstrated greater binding affinity against ERK2 protein than LHZ, with ΔGbinding values of -50.0 and -47.7 kcal/mol, respectively. Structural and energetical analyses throughout the MD course demonstrated stabilization of zhankuic acid C complexed with ERK2 protein. The anticipated ADMET properties of zhankuic acid C indicated minimal toxicity. Moreover, in-silico evaluation of fourteen ERK2 inhibitors in clinical trials demonstrated the higher binding affinity of zhankuic acid C towards ERK2 protein.

A real-world disproportionality analysis of baloxavir marboxil: post-marketing pharmacovigilance data

ABSTRACT Objective Baloxavir marboxil (hereafter referred to as baloxavir) is the only cap-dependent endonuclease inhiabitor approved for the treatment and prevention of influenza. However, as a new drug marketed in 2018, the long-term safety of baloxavir in large sample population was unclear. This study aims to evaluate baloxavir-associated adverse events (AEs) through data mining of the international pharmacovigilance database of US FDA Adverse Event Reporting System (FAERS). Methods Disproportionality analysis was conducted to assess the association between baloxavir and its AEs. Data were collected from FAERS from March 2018 to June 2023. After standardizing the data, signal quantification techniques including ROR, PRR, BCPNN and MGPS were used for analysis. Results A total of 49 significant baloxavir-related preferred terms (PTs) in 20 system organ classes (SOCs) were identified in our data analysis. Compared to baloxavir’s FDA label, some new PTs emerged, with the top 10 being pneumonia, loss of consciousness, rhabdomyolysis, seizure, altered state of consciousness, hepatic function abnormal, delirium, depressed level of consciousness, encephalopathy and cardio-respiratory arrest. Conclusion In clinical application of baloxavir, attention should be paid to the new AE signals in addition to the those recorded in the labels, so as to ensure the safety of the patients.