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This document is an erratum for an article titled "The quaternary question: Determining allostery in spastin through dynamics classification learning and bioinformatics." The authors found errors in the network and community analysis section of the methods, specifically in the calculation of DCCM maps. As a result, figures 3-5, which report results from the network analysis, have been updated. The correction also led to changes in the analysis of network centrality and optimal and sub-optimal paths between allosterically important positions in the results and discussion section. The updated tables and figures can be found in the supplementary material. [Extracted from the article]

BACKGROUND: In the process of exploring the mechanism of Alzheimer’s disease, the important role of bioinformatics for common target screening has been revealed, enabling the use of its screening results as a basis for exploring the therapeutic effects of drugs on the disease. OBJECTIVE: To predict the targets of liraglutide, a glucagon-like peptide-1 receptor agonist, in the treatment of Alzheimer’s disease by bioinformatics and molecular biology. METHODS: DisGeNET database and SEA database were used to obtain the common genes of Alzheimer’s disease and liraglutide. GO/KEGG enrichment analysis of common targets was conducted using DAVID online database. Protein-protein interaction networks were constructed using STRING database. The optimal dosage of liraglutide was determined using cell counting kit-8 assay. Expression of key proteins was analyzed using immunofluorescence and immunoblotting techniques. The mouse hippocampal neuron HT22 cell line was used for ex vivo experiments, and the cells were randomly divided into three groups: HT22 group, HT22+Aβ group, and HT22+Aβ+Lir group. No special treatment was done in the HT22 group, while Aβ1-42 was used to intervene in the HT22 cell line for 24 hours to construct an Aβ injury cell model in the HT22+Aβ group. In additional to modeling, liraglutide was added to the HT22+Aβ+Lir group for 12 hours. RESULTS AND CONCLUSION: A total of 3 333 genes associated with Alzheimer’s disease were screened from DisGeNET database. Then 147 potential targets of liraglutide were obtained from SEA database. Finally, 64 common targets of Alzheimer’s disease and Liraglutide were determined using R packets. GO/ KEGG analysis of common targets using DAVID online database suggested that common targets were mainly enriched in the following biological processes: neuroactive ligand-receptor interaction, renin-angiotensin system, bladder cancer, endopeptidase activity, peptide receptor activity, G protein-coupled peptide receptor activity, and transport vesicles. The obtained 64 common target proteins were imported into SRTING online database for protein-protein interaction network construction, and the top three genes, matrix metalloproteinases 2, 9 and interleukin 1β, were obtained. The activity of cultured cells was detected by the cell counting kit-8 kit. Liraglutide at 100 nmol/L was the optimal concentration for antagonizing Aβ1-42. In the western blot and immunofluorescence assays, the expression of matrix metalloproteinases 2, 9 and interleukin 1β was significantly increased in the HT22+Aβ group compared with the HT22 group (P < 0.05) but significantly decreased in the HT22+Aβ+Lir group compared with the HT22+Aβ group (P < 0.05). To conclude, the above bioinformatics data and secondary validation of differential genes in the GEO database suggest that both matrix metalloproteinases 2,9 and interleukin 1β could be potential targets of liraglutide in the treatment of Alzheimer’s disease. [ABSTRACT FROM AUTHOR]

BACKGROUND: Ferroptosis-related genes have been found to play an important role in the pathogenesis of rheumatoid arthritis. However, there is currently a lack of immune expression of ferroptosis-related signature genes in rheumatoid arthritis and the construction of competing endogenous RNA (CeRNA) interaction networks. Machine learning, as a powerful signature gene selection algorithm based on bioinformatics, can more accurately identify ferroptosisrelated signature genes that dominate the pathogenesis of rheumatoid arthritis. OBJECTIVE: To screen ferroptosis-related signature genes in rheumatoid arthritis using bioinformatics and machine learning methods, and to analyze the correlation between ferroptosis-related signature genes and immune infiltration and the construction of CeRNA network of ferroptosis-related signature genes. METHODS: Rheumatoid arthritis-related microarrays were obtained from the GEO database, and ferroptosis-related genes and their differential gene expression were extracted using R language. The differentially expressed genes were screened using machine learning methods. The LASSO regression and SVM-RFE methods were used for signature gene screening, and the genes filtered by both were re-intersected to finally obtain the signature genes in rheumatoid arthritis. Receiver operating characteristic curves were used to assess the accuracy of the screened signature genes for disease diagnosis. Immune infiltration of rheumatoid arthritis and normal synovial tissues was analyzed using the CIBERSORT algorithm, and the correlation between the signature genes and immune cells was analyzed. Finally, the CeRNA network of ferroptosis-related signature genes for rheumatoid arthritis was constructed and the disease signature genes were validated. RESULTS AND CONCLUSION: A total of 150 ferroptosis-related genes in rheumatoid arthritis were obtained, including 55 up-regulated genes and 95 downregulated genes. GO and KEGG enrichment analyses identified 18 GO significantly correlated entries and 30 KEGG entries respectively, mainly involving metal ion homeostasis, ferric ion homeostasis and oxidative stress response. Machine learning analysis finally identified disease signature genes GABARAPL1 and SAT1. GSEA analysis found that adipocytokine signaling pathway, drug metabolism cytochrome P450, fatty acid metabolism, PPAR signaling pathway, tyrosine metabolism were mainly concentrated when GABARAPL1 was highly expressed, and chemokine signaling pathway, intestinal immune network on IGA production were mainly concentrated when SAT1 was highly expressed. Immune infiltration analysis found that nine immune cells were significantly different in rheumatoid arthritis and normal synovial tissues, in which plasma cells, T-cell CD8, and T-cell follicular helper were highly expressed and the rest were lowly expressed in the disease group. Single gene and immune cell correlation analysis found that GABARAPL1 was positively correlated with dendritic resting cells, activated NK cells, and macrophage M1, with the most significant correlation with dendritic resting cells, while SAT1 was positively correlated with T cell CD4 and γδ T cells and negatively correlated with NK resting cells. GSVA analysis found that SAT1 was upregulated in ascorbic acid and aldehyde metabolism, while downregulated in B-cell receptor signaling pathway, Toll-like receptor signaling pathway, T-cell receptor signaling pathway, and natural killer cell-mediated cytotoxicity. GABARAPL1 showed a down-regulation trend in PPAR signaling pathway, metabolism of nicotinate and nicotinamide, tryptophan metabolism, fatty acid metabolism, and steroid biosynthesis. Sixty long non-code RNAs may play a key role in the development of rheumatoid arthritis. To conclude, the occurrence of rheumatoid arthritis is significantly correlated with the abnormal expression of rheumatoid arthritis-induced ferroptosis-related signature genes, and the signature genes induce disease development via relevant signaling pathways. By analyzing rheumatoid arthritis-related long non-code RNAs-mediated ceRNA networks, potential therapeutic targets and signaling pathways can be identified to further elucidate its pathogenesis and provide a reference basis for subsequent experimental studies. [ABSTRACT FROM AUTHOR]

Hoogsteen base pairing enables the association of an RNA strand with a DNA double-helix, allowing the formation of RNA-DNA triplexes, which now have been identified to occur in vivo. Such identifications require specific bioinformatic, biochemical, and biophysical methods, offering experimental opportunities, but also render the field complex. Triplex formation has been best studied for long non-coding RNAs (lncRNAs). Numerous lncRNAs fine-tune gene expression through triplex formation. Many aspects of triplex biology still await clarification, making this research field highly dynamic. Interactions of RNA with DNA are principles of gene expression control that have recently gained considerable attention. Among RNA–DNA interactions are R-loops and RNA-DNA hybrid G-quadruplexes, as well as RNA-DNA triplexes. It is proposed that RNA-DNA triplexes guide RNA-associated regulatory proteins to specific genomic locations, influencing transcription and epigenetic decision making. Although triplex formation initially was considered solely an in vitro event, recent progress in computational, biochemical, and biophysical methods support in vivo functionality with relevance for gene expression control. Here, we review the central methodology and biology of triplexes, outline paradigms required for triplex function, and provide examples of physiologically important triplex-forming long non-coding RNAs. [ABSTRACT FROM AUTHOR]

Objective: Somatic variants in UBA1 cause VEXAS, a recently described, systemic autoinflammatory disease. Research on VEXAS has largely focused on highly symptomatic patients. We sought to determine the prevalence of canonical, VEXAS‐associated somatic variants and their disease penetrance in a diverse, unselected population. Methods: We analyzed clinical‐grade whole genome sequencing data from 245,368 participants in the All of Us Research Program. We compared persons carrying a canonical VEXAS‐associated somatic variant to age, sex, and ancestry matched controls across the domains of diagnoses, medications, and laboratory values. Results: 74 participants were identified who carry one VEXAS‐defining somatic variant, UBA1 c.121A>C, p.Met41Leu. The variant allele fraction ranged from 4.5% to 33%. No other canonical VEXAS‐associated variants were identified. Of the 74 carriers, 62 (84%) were women, 20 (27%) were African American, and 14 (19%) were American Admixed/Latino. There was no statistically significant association between case/control status and any VEXAS‐associated diagnosis code, medication prescription, or laboratory value. Conclusion: We report the largest cohort to date of persons with the VEXAS‐associated p.Met41Leu somatic variant. This cohort differed substantially from reported cohorts of patients with clinical VEXAS, having a higher proportion of persons who were young, female, and of diverse ancestry. Variant allele fractions were lower than reported in clinical VEXAS cohorts, and bioinformatic analysis detected no clinical manifestations of VEXAS. Thus, the UBA1 p.Met41Leu somatic variant displayed incomplete penetrance for VEXAS. Further study is needed to determine the natural history of VEXAS‐associated somatic variants in the predisease phase. [ABSTRACT FROM AUTHOR]

Objective: The specific role of fibroblast‐like synoviocytes (FLSs) in the pathogenesis of rheumatoid arthritis (RA) is still not fully elucidated. This study aimed to explore the molecular mechanisms of epigenetic pathways, including three epigenetic factors, microRNA (miRNA)‐22 (MIR22), ten‐eleven translocation methylcytosine dioxygenase 3 (TET3), and MT‐RNR2 like 2 (MTRNR2L2), in RA‐FLSs. Methods: The expression of MIR22, TET3, and MTRNR2L2 in the synovium of patients with RA and arthritic mice were determined by fluorescence in situ hybridization, quantitative polymerase chain reaction (qPCR), immunohistochemistry, and Western blot. Mir22−/− and Tet3+/− mice were used to establish a collagen antibody–induced arthritis (CAIA) model. Mir22 angomir and Tet3 small interfering RNA (siRNA) were used to illustrate the therapeutic effects on arthritis using a collagen‐induced (CIA) model. Bioinformatics, luciferase reporter assay, 5‐hydroxymethylcytosine (5hmC) dot blotting, chromatin immunoprecipitation–qPCR, and hydroxymethylated DNA immunoprecipitation were conducted to show the direct repression of MIR22 on the TET3 and transcriptional activation of TET3 on MTRNR2L2. Results: The Mir22−/− CAIA model and RA‐FLS–related in vitro experiments demonstrated the inhibitory effect of MIR22 on inflammation. MIR22 can directly inhibit the translation of TET3 in RA‐FLSs by binding to its 3′ untranslated region in TET3. The Tet3+/− mice–established CAIA model showed less severe symptoms of arthritis in vivo. In vitro experiments further confirmed the proinflammatory effect of TET3 in RA. In addition, the CIA model was used to validate the therapeutic effects of Mir22 angomir and Tet3 siRNA. Finally, TET3 exerts its proinflammatory effect by promoting 5hmC production in the promoter of its target MTRNR2L2 in RA‐FLSs. Conclusion: The key role of the MIR22‐TET3‐MTRNR2L2 pathway in RA‐FLSs provided an experimental basis for further studies into the pathogenesis and related targets of RA from the perspective of FLSs. [ABSTRACT FROM AUTHOR]

To explore the interaction of TGFβ regulatory microRNAs (miRNAs) with different severities of diabetic kidney disease (DKD). According to different UACR (30 and 300 mg/g), 436 subjects were included, and high glucose induced RMCs were cultured. Real-time PCR, ELISA, and automatic biochemical analysis were used to measure miRNAs, TGFβ1, and other biochemical indicators in serum and RMCs. Target genes of miRNA were predicted and visualised by bioinformatics. HbA1c, TGFβ1, miR-217, and miR-224 in T2DM patients increased with UACR, while miR-192 and miR-216a decreased. Ln UACR was positively correlated with HbA1c, TGFβ1, miR-217, and miR-224, and negatively correlated with miR-192 and miR-216a. High glucose and TGFβ1 affected miRNAs and these miRNAs affected each other. The miRNA target genes mainly revolve around PTEN, PI3K/Akt, and MAPK signalling pathways. TGFβ regulatory miRNAs and different severity of DKD have a potential interaction regulating fibrosis through PTEN, PI3K/Akt, and MAPK pathways. [ABSTRACT FROM AUTHOR]

Introduction The global disease burden may be exacerbated by exposure to passive smoking (SHS), with the workplace being a primary location for such exposure. Numerous epidemiological studies have identified SHS as a risk factor for diseases affecting various systems, including cardiovascular, respiratory, immune, endocrine, and nervous systems. The conventional observational study has certain methodological constraints that can be circumvented through a Mendelian randomization (MR) study. Our MR study intends to investigate the causal link between workplace exposure to SHS and the potential associated diseases. Aim and Methods Summary statistics data involving European participants were sourced from three databases: the UK Biobank, the FinnGen study, and the European Bioinformatics Institute. Genetic variants linked with exposure to SHS in the workplace were identified as instrumental variables. The MR was carried out using inverse variance weighted (IVW), MR-Egger, and weighted median methods. Sensitivity tests were also undertaken within the MR to evaluate the validity of the causality. Results According to the IVW model, genetically determined atrial fibrillation (AF) and stroke (p = 6.64E−04 and 5.68E−07, odds ratio = 2.030 and 2.494, 95% confidence interval = 1.350 to 3.051 and 1.743 to 3.569) were robustly associated with exposure to SHS in the workplace. Suggestive associations were found between workplace SHS and myocardial infarction (MI), asthma, and depression. Conclusions The MR study demonstrates that exposure to SHS in the workplace is a significant risk factor for AF and stroke in European individuals. Whether workplace exposure to SHS influences other diseases and the causality between them requires further exploration. Implications This study explored the causality between exposure to SHS in the workplace and potential associated diseases in multiple systems, including MI, AF, stroke, lung cancer, asthma, allergic disease, type 2 diabetes, and depression, using an MR study. The MR study can circumvent the methodological constraints of observational studies and establish a causal relationship. The two-sample MR analysis provides evidence supporting the causal association of frequent workplace SHS with AF and stroke. Individuals exposed to SHS in the workplace may also have a heightened risk of MI, asthma, and depression. However, whether SHS affects other diseases and the causality between them requires further investigation. To our knowledge, this is the first two-sample MR study to determine the causal relationship between SHS and potential diseases. Exposure to SHS in the workplace is a prevalent issue and may contribute to a global disease burden. The reduction of exposure following the introduction of smoke-free laws has led to a decrease in the admission rate for cardiac events and an improvement in health indicators. It is crucial to further advance smoke-free policies and their implementation. [ABSTRACT FROM AUTHOR]

Background: Atherosclerosis is a chronic inflammatory disease characterized by abnormal lipid deposition in the arteries. Programmed cell death is involved in the inflammatory response of atherosclerosis, but PANoptosis, as a new form of programmed cell death, is still unclear in atherosclerosis. This study explored the key PANoptosis-related genes involved in atherosclerosis and their potential mechanisms through bioinformatics analysis. Methods: We evaluated differentially expressed genes (DEGs) and immune infiltration landscape in atherosclerosis using microarray datasets and bioinformatics analysis. By intersecting PANoptosis-related genes from the GeneCards database with DEGs, we obtained a set of PANoptosis-related genes in atherosclerosis (PANoDEGs). Functional enrichment analysis of PANoDEGs was performed and protein–protein interaction (PPI) network of PANoDEGs was established. The machine learning algorithms were used to identify the key PANoDEGs closely linked to atherosclerosis. Receiver operating characteristic (ROC) analysis was used to assess the diagnostic potency of key PANoDEGs. CIBERSORT was used to analyze the immune infiltration patterns in atherosclerosis, and the Spearman method was used to study the relationship between key PANoDEGs and immune infiltration abundance. The single gene enrichment analysis of key PANoDEGs was investigated by GSEA. The transcription factors and target miRNAs of key PANoDEGs were predicted by Cytoscape and online database, respectively. The expression of key PANoDEGs was validated through animal and cell experiments. Results: PANoDEGs in atherosclerosis were significantly enriched in apoptotic process, pyroptosis, necroptosis, cytosolic DNA-sensing pathway, NOD-like receptor signaling pathway, lipid and atherosclerosis. Four key PANoDEGs (ZBP1, SNHG6, DNM1L, and AIM2) were found to be closely related to atherosclerosis. The ROC curve analysis demonstrated that the key PANoDEGs had a strong diagnostic potential in distinguishing atherosclerotic samples from control samples. Immune cell infiltration analysis revealed that the proportion of initial B cells, plasma cells, CD4 memory resting T cells, and M1 macrophages was significantly higher in atherosclerotic tissues compared to normal tissues. Spearman analysis showed that key PANoDEGs showed strong correlations with immune cells such as T cells, macrophages, plasma cells, and mast cells. The regulatory networks of the four key PANoDEGs were established. The expression of key PANoDEGs was verified in further cell and animal experiments. Conclusions: This study evaluated the expression changes of PANoptosis-related genes in atherosclerosis, providing a reference direction for the study of PANoptosis in atherosclerosis and offering potential new avenues for further understanding the pathogenesis and treatment strategies of atherosclerosis. [ABSTRACT FROM AUTHOR]

Plant root-associated environments such as the rhizosphere, rhizoplane, and endosphere, are notably different from non-root-associated soil environments. However, the microbial dynamics in these spatially divided compartments remain unexplored. In this study, we propose a combinational analysis of single-cell genomics with 16S rRNA gene sequencing. This method enabled us to understand the entire soil microbiome and individual root-associated microorganisms. We applied this method to soybean microbiomes and revealed that their composition was different between the rhizoplane and rhizosphere in the early growth stages, but became more similar as growth progressed. In addition, a total of 610 medium- to high-quality single-amplified genomes (SAGs) were acquired, including plant growth-promoting rhizobacteria (PGPR) candidates while genomes with high GC content tended to be missed by SAGs. The whole-genome analyses of the SAGs suggested that rhizoplane-enriched Flavobacterium solubilizes organophosphate actively and Bacillus colonizes roots more efficiently. Single-cell genomics, together with 16S rRNA gene sequencing, enabled us to connect microbial taxonomy and function, and assess microorganisms at a strain resolution even in the complex soil microbiome. [ABSTRACT FROM AUTHOR]

Background: Osteoporosis (OP) and type 2 diabetes (T2D) are commonly encountered metabolic disorders in clinical practice, but the comorbidity mechanism has not been clarified. This study explored the underlying mechanisms for utilizing bioinformatics methods. Furthermore, it predicted traditional Chinese medicines (TCMs) with preventive and therapeutic effects. Materials and Methods: GSE35958 and GSE43950 were retrieved and downloaded from the GEO database, and differential expression analysis was performed to identify differentially expressed genes (DEGs) with similar expression patterns in OP and T2D. Then, the common DEGs were uploaded to the STRING database to construct a protein interaction network. Enrichment analysis of the screened genes was conducted using R language packages. Relevant TCMs were searched and screened based on gene targets using the Encyclopedia of traditional Chinese medicine (ETCM) database. Molecular docking of active ingredients of the TCMs and related gene targets was performed using AutoDock Vina software. Results: By analyzing the gene expression microarrays, GSE35958 and GSE43950, 34 genes with the same expression pattern shared by OP and T2D were identified. Among these genes, 32 were upregulated and two were downregulated. Protein interaction network analysis revealed that tumor necrosis factor, vascular endothelial growth factor A, and CD44 might play key roles in the co-pathogenesis of T2D and OP. TCMs, including Wolfberry (枸杞), Ginseng (人参), and Yam (山药), were screened based on key genes. Molecular docking results demonstrated binding activity between all active ingredients and the related gene targets. Conclusion: This study explored the potential molecular co-pathogenesis of OP and T2D through bioinformatic analysis and preliminarily predicted traditional herbal medicines that may have preventive and therapeutic effects. [ABSTRACT FROM AUTHOR]

Background: Codonopsis Radix (CR), a renowned traditional Chinese medicine (TCM) formula, has been widely applied for its immunomodulatory, antitumor, antioxidant, neuroprotective, and antiviral effects. However, the multitarget mechanism of CR in ovarian cancer (OC) remains to be elucidated. Objectives: We applied bioinformatics and molecular docking techniques to explore possible pharmacological targets, bioactivities, and molecular mechanisms of CR for OC treatment. Materials and Methods: We identified 40 common genes associated with CR and OC and obtained core genes through a protein–protein interaction network. Results: Enrichment analysis revealed that mitochondrial electron transport was the key biological process involved. Based on the analysis, we selected estrogen receptor 1 (ESR1) and phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha (PIK3CA) as the key target genes for molecular docking. In the final verification analysis, we evaluated the effect of the PIK3CA mutation on the survival rate of patients with OC and determined that the phosphoinositide 3-kinase/protein kinase B (PI3K/AKT) pathway was the key pathway in the OC treatment. Conclusion: These results suggest that CR inhibits the activity of mitochondrial complex II, reduces adenosine triphosphate (ATP) production by mitochondrial electron transport, inhibits PI3K/AKT phosphorylation, and promotes apoptosis in OC cells. [ABSTRACT FROM AUTHOR]

Background: Cervical cancer (CC), closely linked to persistent human papillomavirus infection, represents a major health problem for women worldwide. The objective of this study is to elucidate KIF23's role in the development of CC and its regulatory mechanism. Methods: The bioinformatics methods were utilized to extract pyroptosis‐associated differentially expressed genes (DEGs) and pivot genes from the GSE9750 and GSE63678 datasets, followed by immune infiltration analysis and quantification of these genes' expression. The effects of kinesin family member 23 (KIF23) were verified through functional experiments in vitro and a mouse xenograft model. The NLPR3 activator, nigericin, was applied for further analyzing the potential regulatory mechanism of KIF23 in CC. Results: A total of 8 pyroptosis‐related DEGs were screened out, among which 4 candidate core genes were identified as candidate hub genes and confirmed upregulation in CC tissues and cells. These genes respectively showed a positive correlation with the infiltration of distinct immune cells or tumor purity. Downregulation of KIF23 could suppress the proliferation, migration, and invasion abilities in CC cells and tumorigenesis through enhancing pyroptosis. Conversely, KIF23 overexpression accelerated the malignant phenotypes of CC cells and inhibited pyroptosis activation, which was blocked by nigericin treatment. Conclusions: KIF23 may play an oncogenic role in CC progression via inhibition of the NLRP3‐mediated pyroptosis pathway. [ABSTRACT FROM AUTHOR]

Clear cell renal cell carcinoma (ccRCC) is a malignant tumor of the urinary system. To explore the potential mechanisms of DHODH in ccRCC, we analyzed its molecular characteristics using public databases. TCGA pan-cancer dataset was used to analyze DHODH expression in different cancer types and TCGA ccRCC dataset was used to assess differential expression, prognosis correlation, immune infiltration, single-gene, and functional enrichment due to DHODH. The GSCALite and CellMiner databases were employed to explore drugs and perform molecular docking analysis with DHODH. Protein–protein interaction networks and ceRNA regulatory networks of DHODH were constructed using multiple databases. The effect of DHODH on ccRCC was confirmed in vitro. DHODH was highly expressed in ccRCC. Immune infiltration analysis revealed that DHODH may be involved in regulating the infiltration of immunosuppressive cells such as Tregs. Notably, DHODH influenced ccRCC progression by forming regulatory networks with molecules, such as hsa-miR-26b-5p and UMPS and significantly enhanced the malignant characteristics of ccRCC cells. Several drugs, such as lapatinib, silmitasertib, itraconazole, and dasatinib, were sensitive to DHODH expression and exhibited strong molecular binding with it. Thus, DHODH may promote ccRCC progression and is a candidate effective therapeutic target for ccRCC. [ABSTRACT FROM AUTHOR]

Brucellosis is a zoonotic disease with significant economic and healthcare costs. Despite the eradication efforts, the disease persists. Vaccines prevent disease in animals while antibiotics cure humans with limitations. This study aims to design vaccines and drugs for brucellosis in animals and humans, using protein modeling, epitope prediction, and molecular docking of the target proteins (BvrR, OMP25, and OMP31). Tertiary structure models of three target proteins were constructed and assessed using RMSD, TM-score, C-score, Z-score, and ERRAT. The best models selected from AlphaFold and I-TASSER due to their superior performance according to CASP 12 – CASP 15 were chosen for further analysis. The motif analysis of best models using MotifFinder revealed two, five, and five protein binding motifs, however, the Motif Scan identified seven, six, and eight Post-Translational Modification sites (PTMs) in the BvrR, OMP25, and OMP31 proteins, respectively. Dominant B cell epitopes were predicted at (44–63, 85–93, 126–137, 193–205, and 208–237), (26–46, 52–71, 98–114, 142–155, and 183–200), and (29–45, 58–82, 119–142, 177–198, and 222–251) for the three target proteins. Additionally, cytotoxic T lymphocyte epitopes were detected at (173–181, 189–197, and 202–210), (61–69, 91–99, 159–167, and 181–189), and (3–11, 24–32, 167–175, and 216–224), while T helper lymphocyte epitopes were displayed at (39–53, 57–65, 150–158, 163–171), (79–87, 95–108, 115–123, 128–142, and 189–197), and (39–47, 109–123, 216–224, and 245–253), for the respective target protein. Furthermore, structure-based virtual screening of the ZINC and DrugBank databases using the docking MOE program was followed by ADMET analysis. The best five compounds of the ZINC database revealed docking scores ranged from (− 16.8744 to − 15.1922), (− 16.0424 to − 14.1645), and (− 14.7566 to − 13.3222) for the BvrR, OMP25, and OMP31, respectively. These compounds had good ADMET parameters and no cytotoxicity, while DrugBank compounds didn't meet Lipinski's rule criteria. Therefore, the five selected compounds from the ZINC20 databases may fulfill the pharmacokinetics and could be considered lead molecules for potentially inhibiting Brucella's proteins. [ABSTRACT FROM AUTHOR]

Recombinant multiepitope proteins (RMPs) are a promising alternative for application in diagnostic tests and, given their wide application in the most diverse diseases, this review article aims to survey the use of these antigens for diagnosis, as well as discuss the main points surrounding these antigens. RMPs usually consisting of linear, immunodominant, and phylogenetically conserved epitopes, has been applied in the experimental diagnosis of various human and animal diseases, such as leishmaniasis, brucellosis, cysticercosis, Chagas disease, hepatitis, leptospirosis, leprosy, filariasis, schistosomiasis, dengue, and COVID-19. The synthetic genes for these epitopes are joined to code a single RMP, either with spacers or fused, with different biochemical properties. The epitopes' high density within the RMPs contributes to a high degree of sensitivity and specificity. The RMPs can also sidestep the need for multiple peptide synthesis or multiple recombinant proteins, reducing costs and enhancing the standardization conditions for immunoassays. Methods such as bioinformatics and circular dichroism have been widely applied in the development of new RMPs, helping to guide their construction and better understand their structure. Several RMPs have been expressed, mainly using the Escherichia coli expression system, highlighting the importance of these cells in the biotechnological field. In fact, technological advances in this area, offering a wide range of different strains to be used, make these cells the most widely used expression platform. RMPs have been experimentally used to diagnose a broad range of illnesses in the laboratory, suggesting they could also be useful for accurate diagnoses commercially. On this point, the RMP method offers a tempting substitute for the production of promising antigens used to assemble commercial diagnostic kits. [ABSTRACT FROM AUTHOR]

Background: Osteoporosis (OP), the "silent epidemic" of our century, poses a significant challenge to public health, predominantly affecting postmenopausal women and the elderly. It evolves from mild symptoms to pronounced severity, stabilizing eventually. Unique among OP's characteristics is the altered metabolic profile of affected cells, particularly in pyrimidine metabolism (PyM), a crucial pathway for nucleotide turnover and pyrimidine decomposition. While metabolic adaptation is acknowledged as a therapeutic target in various diseases, the specific role of PyM genes (PyMGs) in OP's molecular response remains to be clarified. Methods: In pursuit of elucidating and authenticating PyMGs relevant to OP, we embarked on a comprehensive bioinformatics exploration. This entailed the integration of Weighted Gene Co-expression Network Analysis (WGCNA) with a curated list of 37 candidate PyMGs, followed by the examination of their biological functions and pathways via Gene Set Variation Analysis (GSVA). The Least Absolute Shrinkage and Selection Operator (LASSO) technique was harnessed to identify crucial hub genes. We evaluated the diagnostic prowess of five PyMGs in OP detection and explored their correlation with OP's clinical traits, further validating their expression profiles through independent datasets (GSE2208, GSE7158, GSE56815, and GSE35956). Results: Our analytical rigor unveiled five PyMGs—IGKC, TMEM187, RPS11, IGLL3P, and GOLGA8N—with significant ties to OP. A deeper dive into their biological functions highlighted their roles in estrogen response modulation, cytosolic calcium ion concentration regulation, and GABAergic synaptic transmission. Remarkably, these PyMGs emerged as potent diagnostic biomarkers for OP, distinguishing affected individuals with substantial accuracy. Conclusions: This investigation brings to light five PyMGs intricately associated with OP, heralding new avenues for biomarker discovery and providing insights into its pathophysiological underpinnings. These findings not only deepen our comprehension of OP's complexity but also herald the advent of more refined diagnostic and therapeutic modalities. [ABSTRACT FROM AUTHOR]

Cancer is a disease that many multicellular organisms have faced for millions of years, and species have evolved various tumour suppression mechanisms to control oncogenesis. Although cancer occurs across the tree of life, cancer related mortality risks vary across mammalian orders, with Carnivorans particularly affected. Evolutionary theory predicts different selection pressures on genes associated with cancer progression and suppression, including oncogenes, tumour suppressor genes and immune genes. Therefore, we investigated the evolutionary history of cancer associated gene sequences across 384 mammalian taxa, to detect signatures of selection across categories of oncogenes (GRB2, FGL2 and CDC42), tumour suppressors (LITAF, Casp8 and BRCA2) and immune genes (IL2, CD274 and B2M). This approach allowed us to conduct a fine scale analysis of gene wide and site-specific signatures of selection across mammalian lineages under the lens of cancer susceptibility. Phylogenetic analyses revealed that for most species the evolution of cancer associated genes follows the species' evolution. The gene wide selection analyses revealed oncogenes being the most conserved, tumour suppressor and immune genes having similar amounts of episodic diversifying selection. Despite BRCA2's status as a key caretaker gene, episodic diversifying selection was detected across mammals. The site-specific selection analyses revealed that the two apoptosis associated domains of the Casp8 gene of bats (Chiroptera) are under opposing forces of selection (positive and negative respectively), highlighting the importance of site-specific selection analyses to understand the evolution of highly complex gene families. Our results highlighted the need to critically assess different types of selection pressure on cancer associated genes when investigating evolutionary adaptations to cancer across the tree of life. This study provides an extensive assessment of cancer associated genes in mammals with highly representative, and substantially large sample size for a comparative genomic analysis in the field and identifies various avenues for future research into the mechanisms of cancer resistance and susceptibility in mammals. [ABSTRACT FROM AUTHOR]

Amplicon sequencing is an effective and increasingly applied method for studying viral communities in the environment. Here, we present vAMPirus, a user‐friendly, comprehensive, and versatile DNA and RNA virus amplicon sequence analysis program, designed to support investigators in exploring virus amplicon sequencing data and running informed, reproducible analyses. vAMPirus intakes raw virus amplicon libraries and, by default, performs nucleotide‐ and amino acid‐based analyses to produce results such as sequence abundance information, taxonomic classifications, phylogenies and community diversity metrics. The vAMPirus analytical framework leverages 16 different opensource tools and provides optional approaches that can increase the ratio of biological signal‐to‐noise and thereby reveal patterns that would have otherwise been masked. Here, we validate the vAMPirus analytical framework and illustrate its implementation as a general virus amplicon sequencing workflow by recapitulating findings from two previously published double‐stranded DNA virus datasets. As a case study, we also apply the program to explore the diversity and distribution of a coral reef‐associated RNA virus. vAMPirus is streamlined within Nextflow, offering straightforward scalability, standardization and communication of virus lineage‐specific analyses. The vAMPirus framework is designed to be adaptable; community‐driven analytical standards will continue to be incorporated as the field advances. vAMPirus supports researchers in revealing patterns of virus diversity and population dynamics in nature, while promoting study reproducibility and comparability. [ABSTRACT FROM AUTHOR]

Background. Ankylosing spondylitis (AS) and inflammatory bowel disease (IBD) are prevalent autoimmune disorders that often co-occur, posing significant treatment challenges. This investigation adopts a multidisciplinary strategy, integrating bioinformatics, network pharmacology, molecular docking, and Mendelian randomization, to elucidate the relationship between AS and IBD and to investigate the potential mechanisms of traditional Chinese medicine formulations, represented by Qiangji Jianpi (QJJP) decoction, in treating these comorbid conditions. Methods. We utilized databases to pinpoint common targets among AS, IBD, and QJJP decoction's active compounds through intersection analysis. Through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses, we mapped a network in Cytoscape, isolating critical targets. Molecular docking with AutoDock validated the affinity between targets and compounds. ROC analysis and dataset validation assessed diagnostic performance, while Gene Set Enrichment Analysis (GSEA) offered pathway insights. Mendelian randomization explored the AS-IBD causal relationship. Results. Screening identified 105 targets for QJJP decoction, 414 for AS, and 2420 for IBD, with 85 overlapping. These targets predominantly participate in organismal responses and DNA transcription factor binding, with a significant cellular presence in the endoplasmic reticulum and vesicle lumen. Molecular docking, facilitated by Cytoscape, confirmed IL1A, IFNG, TGFB1, and EDN1 as critical targets, with IFNG demonstrating diagnostic potential through GEO dataset validation. The integration of GSEA with network pharmacology highlighted the therapeutic significance of the relaxin, osteoclast differentiation, HIF-1, and AGE-RAGE signaling pathways in QJJP decoction's action. Mendelian randomization analysis indicated a positive causal relationship between IBD and AS, pinpointing rs2193041 as a key SNP influencing IFNG. Conclusion. Based on the principle of "treating different diseases with the same method" in traditional Chinese medicine theory, we explored the intricate mechanisms through which QJJP decoction addresses AS and IBD comorbidity. Our research spotlighted the pivotal role of the IFNG gene. IFNG emerges not only as a key therapeutic target but also assumes significance as a potential diagnostic biomarker through its genetic underpinnings. This investigation establishes a solid base for subsequent experimental inquiries. Our findings introduce novel approaches for incorporating traditional Chinese medicine into the treatment of AS-IBD comorbidity, setting the stage for groundbreaking research directions. [ABSTRACT FROM AUTHOR]

Polycyclic aromatic hydrocarbons (PAHs) are highly toxic, carcinogenic substances. On soils contaminated with PAHs, crop cultivation, animal husbandry and even the survival of microflora in the soil are greatly perturbed, depending on the degree of contamination. Most microorganisms cannot tolerate PAH-contaminated soils, however, some microbial strains can adapt to these harsh conditions and survive on contaminated soils. Analysis of the metagenomes of contaminated environmental samples may lead to discovery of PAH-degrading enzymes suitable for green biotechnology methodologies ranging from biocatalysis to pollution control. In the present study, our goal was to apply a metagenomic data search to identify efficient novel enzymes in remediation of PAH-contaminated soils. The metagenomic hits were further analyzed using a set of bioinformatics tools to select protein sequences predicted to encode well-folded soluble enzymes. Three novel enzymes (two dioxygenases and one peroxidase) were cloned and used in soil remediation microcosms experiments. The experimental design of the present study aimed at evaluating the effectiveness of the novel enzymes on short-term PAH degradation in the soil microcosmos model. The novel enzymes were found to be efficient for degradation of naphthalene and phenanthrene. Adding the inorganic oxidant CaO2 further increased the degrading potential of the novel enzymes for anthracene and pyrene. We conclude that metagenome mining paired with bioinformatic predictions, structural modelling and functional assays constitutes a powerful approach towards novel enzymes for soil remediation. [ABSTRACT FROM AUTHOR]

Background: Glioblastoma multiforme (GBM) is a type of fast-growing brain glioma associated with a very poor prognosis. This study aims to identify key genes whose expression is associated with the overall survival (OS) in patients with GBM. Methods: A systematic review was performed using PubMed, Scopus, Cochrane, and Web of Science up to Journey 2024. Two researchers independently extracted the data and assessed the study quality according to the New Castle Ottawa scale (NOS). The genes whose expression was found to be associated with survival were identified and considered in a subsequent bioinformatic study. The products of these genes were also analyzed considering protein-protein interaction (PPI) relationship analysis using STRING. Additionally, the most important genes associated with GBM patients' survival were also identified using the Cytoscape 3.9.0 software. For final validation, GEPIA and CGGA (mRNAseq_325 and mRNAseq_693) databases were used to conduct OS analyses. Gene set enrichment analysis was performed with GO Biological Process 2023. Results: From an initial search of 4104 articles, 255 studies were included from 24 countries. Studies described 613 unique genes whose mRNAs were significantly associated with OS in GBM patients, of which 107 were described in 2 or more studies. Based on the NOS, 131 studies were of high quality, while 124 were considered as low-quality studies. According to the PPI network, 31 key target genes were identified. Pathway analysis revealed five hub genes (IL6, NOTCH1, TGFB1, EGFR, and KDR). However, in the validation study, only, the FN1 gene was significant in three cohorts. Conclusion: We successfully identified the most important 31 genes whose products may be considered as potential prognosis biomarkers as well as candidate target genes for innovative therapy of GBM tumors. [ABSTRACT FROM AUTHOR]

BackgroundMethodsResultsConclusionsOsteoarthritis (OA) is a debilitating joint disorder characterized by the progressive degeneration of articular cartilage. Although the role of ion channels in OA pathogenesis is increasingly recognized, diagnostic markers and targeted therapies remain limited.In this study, we analyzed the GSE48556 dataset to identify differentially expressed ion channel-related genes (DEGs) in OA and normal controls. We employed machine learning algorithms, least absolute shrinkage and selection operator(LASSO), and support vector machine recursive feature elimination(SVM-RFE) to select potential diagnostic markers. Then the gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were performed to explore the potential diagnostic markers’ involvement in biological pathways. Finally, weighted gene co-expression network analysis (WGCNA) was used to identify key genes associated with OA.We identified a total of 47 DEGs, with the majority involved in transient receptor potential (TRP) pathways. Seven genes (CHRNA4, GABRE, HTR3B, KCNG2, KCNJ2, LRRC8C, and TRPM5) were identified as the best characteristic genes for distinguishing OA from healthy samples. We performed clustering analysis and identified two distinct subtypes of OA, C1, and C2, with differential gene expression and immune cell infiltration profiles. Then we identified three key genes (PPP1R3D, ZNF101, and LOC651309) associated with OA. We constructed a prediction model using these genes and validated it using the GSE46750 dataset, demonstrating reasonable accuracy and specificity.Our findings provide novel insights into the role of ion channel-related genes in OA pathogenesis and offer potential diagnostic markers and therapeutic targets for the treatment of OA. [ABSTRACT FROM AUTHOR]

AbstractPDA is an aggressive cancer with a 5-year survival rate, which is very low. There is no effective prognosis or therapy for PDA because of the lack of target biomarkers. The objective of this article is to identify the target biomarkers for PDA using a bioinformatics approach. In this work, we have analysed the three microarray datasets from the NCBI GEO database. We used the Geo2R tool to analyse the microarray data with the Benjamini and Hochberg false discovery rate method, and the significance level cut-off was set to 0.05. We have identified 659 DEGs from the datasets. There are a total of 15 hub genes that were selected from the PPI network constructed using the STRING application. Furthermore, these 15 genes were evaluated on PDA patients using TCGA and GTEx databases in (GEPIA). The online tool DAVID was used to analyse the functional annotation information for the DEGs. The functional pathway enrichment was performed on the GO and KEGG. The hub genes were mainly enriched for cell division, chromosome segregation, protein binding and microtubule binding. Further, the gene alteration study was performed using the cBioportal tool and screened out six hub genes (ASPM, CENPF, BIRC5, TTK, DLGAP5, and TOP2A) with a high alteration rate in PDA samples. Furthermore, Kaplan–Meier survival analysis was performed on the six hub genes and identified poor-survival outcomes that may be involved in tumorigenesis and PDA development. So, this study concludes that, these six hub genes may be potential prognostic biomarkers for PDA. [ABSTRACT FROM AUTHOR]

BACKGROUND: Hypertrophic scar is a skin fibrosis disease characterized by excessive proliferation of fibroblasts, epidermal thickening, and stratum corneum dysfunction. At present, the pathogenesis of Hypertrophic scar is still unclear. OBJECTIVE: To screen the core (Hub) genes and important signaling pathways in hypertrophic scar-related datasets based on bioinformatics, and then verify them by cell experiments to predict small molecule drugs that may have therapeutic effects on hypertrophic scar. METHODS: Datasets related to hypertrophic scar were searched from Gene Expression Omnibus (GEO) database, and differentially expressed genes were identified by R software analysis. Gene ontology and KEGG enrichment analyses were performed for differentially expressed genes. Protein-protein interaction network of differentially expressed genes was constructed using String online platform. Then, the key genes and core modules in the protein-protein interaction network were screened by Cytohubba and MCODE plugin-in Cytoscape software respectively, and the Hub genes were obtained by the intersection of the above key genes and the genes that formed the core module. Real-time fluorescent quantitative PCR was used to verify the difference in Hub gene mRNA expression between human hypertrophic scar and normal skin epidermal stem cells. The histological data from the Human Protein Atlas were used to verify the differences in the expression and distribution of Hub gene-encoded proteins in the two kinds of human tissues. Finally, the potential drugs for hypertrophic scar were predicted by the connectivity map database. RESULTS AND CONCLUSION: Among the identified differentially expressed genes, 102 genes were up-regulated and 702 genes were down-regulated. Gene ontology and KEGG analysis showed that the enriched signaling pathways and biological processes were mainly involved in tight junction, arachidonic acid metabolism, extracellular matrix receptor interaction, epidermal development and keratinization. Eight Hub genes were found to be closely related to the mevalonate pathway that regulates cholesterol metabolism, including HMGCS1, DHCR7, MSMO1, FDPS, MVK, HMGCR, MVD and ACAT2. Compared with the normal skin group, the mRNA expression of HMGCS1, DHCR7, MSMO1, FDPS, HMGCR, MVD and ACAT2 in the hypertrophic scar group decreased significantly (P < 0.05), while MVK mRNA expression had no significant change (P > 0.05). Except for MVK, the expression levels of other Hub gene-encoded proteins in normal skin tissue were higher than those in hypertrophic scar tissue (P < 0.05). The top 10 candidate drugs included protein kinase A inhibitor (H-89), serine protease inhibitor (Dabigatran-Etexilate), FLT3 inhibitor (sunitinib), among which resveratrol and β-sitosterol are plant extracts. To conclude, Hub genes closely related to mevalonate metabolism may affect the structure and function of the epidermis by regulating lipid metabolism, which may an important pathogenesis of hypertrophic scar. The small-molecule compounds identified in this study can be used as candidate drugs for the treatment of hypertrophic scar. [ABSTRACT FROM AUTHOR]

Background/Objectives: In recent times, epigenetics alterations in Hidradenitis suppurativa (HS) have been explored and exploited translationally to guide investigation of new therapeutic approaches. On the other hand, long noncoding RNAs (LncRNAs), main regulators of the epigenetic status of the human genome, have been scarcely investigated, notwithstanding their potential relevance in broad pathogenesis comprehension. Here, we aim to explore the methylation pattern of lncRNAs in HS. Methods: In this case-control study, 24 HS patients and age-, sex- and BMI-matched controls were analyzed to characterize the methylome of lncRNA genes in peripheral blood cells. Gene ontology analysis (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, protein–protein interaction (PPI) network, and MCODE analysis were performed. Results: A set of fifteen lncRNA genes exhibited significantly differential methylation patterns, with ten of them showing hypomethylation and five displaying hypermethylation at specific CpG sites. The hypomethylated lncRNA genes were DLEU2, MESTIT1, CASC2, TUG1, KCNQ1DN, PSORS1C3, PCA3, DSCR8, RFPL1S, and PVT1, while the hypermethylated ones were HAR1A, FAM66B, SNHG9, HCG9, and HCP5. These lncRNA genes have been linked to various important biological processes, including cell proliferation, apoptosis, inflammation, chronic inflammatory skin diseases, and wound healing. Their altered methylation status suggests potential roles in regulating these processes, and may contribute to HS pathogenesis and healing mechanisms. Conclusions: This study revealed an interesting dysregulation pattern of definite lncRNAs in the methylome which is linked to both the development of HS and its comorbidities. Epigenetically altered lncRNAs genes could represent useful biomarkers, and could help in guiding innovative treatment strategies. [ABSTRACT FROM AUTHOR]

Simple Summary: This study delves into how the community of microorganisms residing in the gut, known as the gut microbiome, can aid in understanding and predicting colorectal cancer (CRC). Through the examination of fecal and blood samples from 142 patients with stage II/III CRC and 91 reference controls, we observed that patients with CRC exhibit distinct gut microbe compositions in comparison to the control group. Additionally, we pinpoint specific microbial DNA fragments in the blood of patients with CRC. These findings imply that the gut microbiome could potentially serve as a marker for detecting CRC and predicting its prognosis. This potential could pave the way for personalized treatment strategies for patients, potentially diminishing healthcare expenses and enhancing outcomes. Nonetheless, further research is necessary to validate these findings and understand how the gut microbiome affects CRC. Background: Colorectal cancer (CRC) significantly contributes to cancer-related mortality, necessitating the exploration of prognostic factors beyond TNM staging. This study investigates the composition of the gut microbiome and microbial DNA fragments in stage II/III CRC. Methods: A cohort of 142 patients with stage II/III CRC and 91 healthy controls underwent comprehensive microbiome analysis. Fecal samples were collected for 16S rRNA sequencing, and blood samples were tested for the presence of microbial DNA fragments. De novo clustering analysis categorized individuals based on their microbial profiles. Alpha and beta diversity metrics were calculated, and taxonomic profiling was conducted. Results: Patients with CRC exhibited distinct microbial composition compared to controls. Beta diversity analysis confirmed CRC-specific microbial profiles. Taxonomic profiling revealed unique taxonomies in the patient cohort. De novo clustering separated individuals into distinct groups, with specific microbial DNA fragment detection associated with certain patient clusters. Conclusions: The gut microbiota can differentiate patients with CRC from healthy individuals. Detecting microbial DNA fragments in the bloodstream may be linked to CRC prognosis. These findings suggest that the gut microbiome could serve as a prognostic factor in stage II/III CRC. Identifying specific microbial markers associated with CRC prognosis has potential clinical implications, including personalized treatment strategies and reduced healthcare costs. Further research is needed to validate these findings and uncover underlying mechanisms. [ABSTRACT FROM AUTHOR]

Simple Summary: As the precursor of pepsin, Pepsinogen C (PGC) is the final product of the maturation and differentiation of gastric mucosa cells. The latest bioinformatics analysis found that PGC may play a little-known role in the carcinogenic process, but there is no relevant experimental evidence to prove the effect of PGC on the biological function of cancer cells. In our research, we found PGC may act as a tumor suppressor in the development and metastasis of gastric cancer. PGC can downregulate its interacting protein IQGAP1 and inhibit the Rho-GTPase pathway, thereby participating in the inhibition of gastric cancer cell migration and invasion. Thus, PGC has the potential to be a new therapeutic target for gastric cancer. Aim: This study systematically explored the biological effects and mechanisms of PGC on gastric cancer (GC) cells in vitro and in vivo. Method: The critical biological roles of PGC in GC were assessed via EdU staining, Hoechst staining, flow cytometry, mouse models, CCK-8, wound healing, transwell, and sphere-forming assays. The interaction study with IQ-domain GTPase-activating protein 1 (IQGAP1) was used by Liquid chromatography-mass spectrometry co-immunoprecipitation, immunofluorescence staining, CHX-chase assay, MG132 assay, and qRT-PCR. Results: PGC inhibited the proliferation, viability, epithelial–mesenchymal transition, migration, invasion, and stemness of GC cells and promoted GC cell differentiation. PGC suppressed subcutaneous tumor growth and peritoneal dissemination in vivo. The interaction study found PGC inhibits GC cell migration and invasion by downregulating IQGAP1 protein and IQGAP1-mediated Rho-GTPase signaling suppression. In addition, PGC disrupts the stability of the IQGAP1 protein, promoting its degradation and significantly shortening its half-life. Moreover, the expression levels of PGC and IQGAP1 in GC tissues were significantly negatively correlated. Conclusion: PGC may act as a tumor suppressor in the development and metastasis of GC. PGC can downregulate its interacting protein IQGAP1 and inhibit the Rho-GTPase pathway, thereby participating in the inhibition of GC cell migration and invasion. [ABSTRACT FROM AUTHOR]

Simple Summary: Partial hepatectomy (PH) is a common clinical surgery for managing hepatocellular carcinoma (HCC). A common concern about PH is the high tumor recurrence rate following PH. Previous studies have reported that PH can promote the growth of transplanted HCC in rodents. In the current study, we have used an inducible krasG12V-driven zebrafish HCC model to investigate the effects of PH on the oncogene-induced de novo HCC development. We found that PH can significantly promote the development of krasG12V-induced HCC in zebrafish. This enhancing effect could be attributed to the increased oxidative stress and the enhanced deregulation of molecular factors. Our findings may provide references for the future development of novel therapeutic strategies. The purpose of this study was to investigate the effects of PH on the development of oncogenic krasG12V-induced HCC in zebrafish. The inducible HCC model in Tg(fabp10a:rtTA2s-M2; TRE2:EGFP-krasG12V) zebrafish was used. PH or sham surgery was performed before the induction of oncogenic krasG12V expression in the livers of transgenic zebrafish. Histological analysis was carried out to determine the progression of HCC and other HCC-associated features including hepatocyte proliferation, extracellular matrix production, and local oxidative stress. The similarity between the process of PH-induced liver regeneration and that of krasG12V-induced HCC development was further compared by RNA-Seq analysis. The results show that PH promotes the development of krasG12V-induced HCC in zebrafish possibly through enhancing neutrophil-mediated oxidative stress and promoting the upregulation of s100a1, and the downregulation of ribosome biogenesis. [ABSTRACT FROM AUTHOR]

Resistance to olaparib is the major obstacle in targeted therapy for ovarian cancer (OC) with poly(ADP-ribose) polymerase inhibitors (PARPis), prompting studies on novel combination therapies to enhance olaparib efficacy. Despite identifying various mechanisms, understanding how OC cells acquire PARPi resistance remains incomplete. This study investigated microRNA (miRNA) expression in olaparib-sensitive (PEO1, PEO4) and previously established olaparib-resistant OC cell lines (PEO1-OR) using high-throughput RT-qPCR and bioinformatic analyses. The role of miRNAs was explored regarding acquired resistance and resensitization with the ATR/CHK1 pathway inhibitors. Differentially expressed miRNAs were used to construct miRNA–mRNA regulatory networks and perform functional enrichment analyses for target genes with miRNet 2.0. TCGA-OV dataset was analyzed to explore the prognostic value of selected miRNAs and target genes in clinical samples. We identified potential processes associated with olaparib resistance, including cell proliferation, migration, cell cycle, and growth factor signaling. Resensitized PEO1-OR cells were enriched in growth factor signaling via PDGF, EGFR, FGFR1, VEGFR2, and TGFβR, regulation of the cell cycle via the G2/M checkpoint, and caspase-mediated apoptosis. Antibody microarray analysis confirmed dysregulated growth factor expression. The addition of the ATR/CHK1 pathway inhibitors to olaparib downregulated FGF4, FGF6, NT-4, PLGF, and TGFβ1 exclusively in PEO1-OR cells. Survival and differential expression analyses for serous OC patients revealed prognostic miRNAs likely associated with olaparib resistance (miR-99b-5p, miR-424-3p, and miR-505-5p) and resensitization to olaparib (miR-324-5p and miR-424-3p). Essential miRNA–mRNA interactions were reconstructed based on prognostic miRNAs and target genes. In conclusion, our data highlight distinct miRNA profiles in olaparib-sensitive and olaparib-resistant cells, offering molecular insights into overcoming resistance with the ATR/CHK1 inhibitors in OC. Moreover, some miRNAs might serve as potential predictive signature molecules of resistance and therapeutic response. [ABSTRACT FROM AUTHOR]

The oral mucosa functions as a physico-chemical and immune barrier to external stimuli, and an adequate width of the keratinized mucosa around the teeth or implants is crucial to maintaining them in a healthy and stable condition. In this study, for the first time, bulk RNA-seq analysis was performed to explore the gene expression of laser microdissected epithelium and lamina propria from mice, aiming to investigate the differences between keratinized and non-keratinized oral mucosa. Based on the differentially expressed genes (DEGs) and Gene Ontology (GO) Enrichment Analysis, bone morphogenetic protein 2 (BMP-2) was identified to be a potential regulator of oral mucosal keratinization. Monoculture and epithelial–mesenchymal cell co-culture models in the air–liquid interface (ALI) indicated that BMP-2 has direct and positive effects on epithelial keratinization and proliferation. We further performed bulk RNA-seq of the ALI monoculture stimulated with BMP-2 in an attempt to identify the downstream factors promoting epithelial keratinization and proliferation. Analysis of the DEGs identified, among others, IGF2, ID1, LTBP1, LOX, SERPINE1, IL24, and MMP1 as key factors. In summary, these results revealed the involvement of a well-known growth factor responsible for bone development, BMP-2, in the mechanism of oral mucosal keratinization and proliferation, and pointed out the possible downstream genes involved in this mechanism. [ABSTRACT FROM AUTHOR]

Introduction: Optimizing treatment with biological agents is an ideal goal for patients with ulcerative colitis (UC). Recent data suggest that mucosal inflammation patterns and serum cytokine profiles differ between patients who respond and those who do not. Ustekinumab, a monoclonal antibody targeting the p40 subunit of interleukin (IL)-12 and IL-23, has shown promise, but predicting treatment response remains a challenge. We aimed to identify prognostic markers of response to ustekinumab in patients with active UC, utilizing information from their mucosal transcriptome. Methods: We performed a prospective observational study of 36 UC patients initiating treatment with ustekinumab. Colonic mucosal biopsies were obtained before treatment initiation for a gene expression analysis using a microarray panel of 84 inflammatory genes. A differential gene expression analysis (DGEA), correlation analysis, and network centrality analysis on co-expression networks were performed to identify potential biomarkers. Additionally, machine learning (ML) models were employed to predict treatment response based on gene expression data. Results: Seven genes, including BCL6, CXCL5, and FASLG, were significantly upregulated, while IL23A and IL23R were downregulated in non-responders compared to responders. The co-expression analysis revealed distinct patterns between responders and non-responders, with key genes like BCL6 and CRP highlighted in responders and CCL11 and CCL22 in non-responders. The ML algorithms demonstrated a high predictive power, emphasizing the significance of the IL23R, IL23A, and BCL6 genes. Conclusions: Our study identifies potential biomarkers associated with ustekinumab response in UC patients, shedding light on its underlying mechanisms and variability in treatment outcomes. Integrating transcriptomic approaches, including gene expression analyses and ML, offers valuable insights for personalized treatment strategies and highlights avenues for further research to enhance therapeutic outcomes for patients with UC. [ABSTRACT FROM AUTHOR]

In contrast to the hypothesis that aging results from cell-autonomous deterioration processes, the programmed longevity theory proposes that aging arises from a partial inactivation of a "longevity program" aimed at maintaining youthfulness in organisms. Supporting this hypothesis, age-related changes in organisms can be reversed by factors circulating in young blood. Concordantly, the endocrine secretion of exosomal microRNAs (miRNAs) by hypothalamic neural stem cells (htNSCs) regulates the aging rate by enhancing physiological fitness in young animals. However, the specific molecular mechanisms through which hypothalamic-derived miRNAs exert their anti-aging effects remain unexplored. Using experimentally validated miRNA–target gene interactions and single-cell transcriptomic data of brain cells during aging and heterochronic parabiosis, we identify the main pathways controlled by these miRNAs and the cell-type-specific gene networks that are altered due to age-related loss of htNSCs and the subsequent decline in specific miRNA levels in the cerebrospinal fluid (CSF). Our bioinformatics analysis suggests that these miRNAs modulate pathways associated with senescence and cellular stress response, targeting crucial genes such as Cdkn2a, Rps27, and Txnip. The oligodendrocyte lineage appears to be the most responsive to age-dependent loss of exosomal miRNA, leading to significant derepression of several miRNA target genes. Furthermore, heterochronic parabiosis can reverse age-related upregulation of specific miRNA-targeted genes, predominantly in brain endothelial cells, including senescence promoting genes such as Cdkn1a and Btg2. Our findings support the presence of an anti-senescence mechanism triggered by the endocrine secretion of htNSC-derived exosomal miRNAs, which is associated with a youthful transcriptional signature. [ABSTRACT FROM AUTHOR]

Idiopathic recurrent pregnancy loss (RPL) is defined as at least two pregnancy losses before 20 weeks of gestation. Approximately 5% of pregnant couples experience idiopathic RPL, which is a heterogeneous disease with various causes including hormonal, chromosomal, and intrauterine abnormalities. Although how pregnancy loss occurs is still unknown, numerous biological factors are associated with the incidence of pregnancy loss, including genetic variants. Whole-exome sequencing (WES) was conducted on blood samples from 56 Korean patients with RPL and 40 healthy controls. The WES data were aligned by means of bioinformatic analysis, and the detected variants were annotated using machine learning tools to predict the pathogenicity of protein alterations. Each indicated variant was confirmed using Sanger sequencing. A replication study was also conducted in 112 patients and 114 controls. The Variant Effect Scoring Tool, Combined Annotation Dependent Depletion tool, Sorting Intolerant from Tolerant annotation tool, and various databases detected 10 potential variants previously associated with spontaneous abortion genes in patients by means of a bioinformatic analysis of WES data. Several variants were detected in more than one patient. Interestingly, several of the detected genes were functionally clustered, including some with a secretory function (mucin 4; MUC4; rs200737893 G>A and hyaluronan-binding protein 2; HABP2; rs542838125 G>T), in which growth arrest-specific 2 Like 2 (GAS2L2; rs140842796 C>T) and dynamin 2 (DNM2; rs763894364 G>A) are functionally associated with cell protrusion and the cytoskeleton. ATP Binding Cassette Subfamily C Member 6 (ABCC6) was the only gene with two variants. HABP2 (rs542838125 G>T), MUC4 (rs200737893 G>A), and GAS2L2 (rs140842796 C>T) were detected in only the patient group in the replication study. The combination of WES and machine learning tools is a useful method to detect potential variants associated with RPL. Using bioinformatic tools, we found 10 potential variants in 9 genes. WES data from patients are needed to better understand the causes of RPL. [ABSTRACT FROM AUTHOR]

The subcellular location of a protein provides valuable insights to bioinformaticians in terms of drug designs and discovery, genomics, and various other aspects of medical research. Experimental methods for protein subcellular localization determination are time-consuming and expensive, whereas computational methods, if accurate, would represent a much more efficient alternative. This article introduces an ab initio protein subcellular localization predictor based on an ensemble of Deep N-to-1 Convolutional Neural Networks. Our predictor is trained and tested on strict redundancy-reduced datasets and achieves 63% accuracy for the diverse number of classes. This predictor is a step towards bridging the gap between a protein sequence and the protein's function. It can potentially provide information about protein–protein interaction to facilitate drug design and processes like vaccine production that are essential to disease prevention. [ABSTRACT FROM AUTHOR]

Extracellular vesicles (EVs) have been found to have the characteristics of their parent cells. Based on the characteristics of these EVs, various studies on disease treatment using mesenchymal stem cell (MSC)-derived EVs with regenerative activity have been actively conducted. The therapeutic nature of MSC-derived EVs has been shown in several studies, but in recent years, there have been many efforts to functionalize EVs to give them more potent therapeutic effects. Strategies for functionalizing EVs include endogenous and exogenous methods. In this study, human umbilical cord MSC (UCMSC)-derived EVs were selected for optimum OA treatments with expectation via bioinformatics analysis based on antibody array. And we created a novel nanovesicle system called the IGF-si-EV, which has the properties of both cartilage regeneration and long-term retention in the lesion site, attaching positively charged insulin-like growth factor-1 (IGF-1) to the surface of the UCMSC-derived Evs carrying siRNA, which inhibits MMP13. The downregulation of inflammation-related cytokine (MMP13, NF-kB, and IL-6) and the upregulation of cartilage-regeneration-related factors (Col2, Acan) were achieved with IGF-si-EV. Moreover, the ability of IGF-si-EV to remain in the lesion site for a long time has been proven through an ex vivo system. Collectively, the final constructed IGF-si-EV can be proposed as an effective OA treatment through its successful MMP13 inhibition, chondroprotective effect, and cartilage adhesion ability. We also believe that this EV-based nanoparticle-manufacturing technology can be applied as a platform technology for various diseases. [ABSTRACT FROM AUTHOR]

Background: Gynostemma pentaphyllum (Thunb.) Makino, commonly known as "southern ginseng", contains high amounts of ginsenoside derivatives and exhibits similar biological activities with Panax ginseng (C. A. MEY) (ginseng), which is usually used as a low-cost alternative to ginseng. G. pentaphyllum has therapeutic effects on liver diseases. However, the mechanisms underlying its hepatoprotective action have not been fully elucidated. Methods: The protective effects of the ethanolic extract of G. pentaphyllum (GPE) were evaluated using an experimental carbon tetrachloride (CCl4)-induced liver disease model. Potential targets of GPE were predicted using the "Drug-Disease" bioinformatic analysis. Furthermore, comprehensive network pharmacology and transcriptomic approaches were employed to investigate the underlying mechanisms of GPE in the treatment of liver disease. Results: The pathological examinations showed that GPE significantly alleviated hepatocyte necrosis and liver injury. GPE significantly downregulated Bax and cleaved-PARP expression and upregulated Bcl-2 expression during CCl4-induced hepatocyte apoptosis. We compared the effects of four typical compounds in GPE -a ginsenoside (Rb3) shared by both GPE and ginseng and three unique gypenosides in GPE. Notably, Gypenoside A (GPA), a unique saponin in GPE, markedly reduced hepatocyte apoptosis. In contrast, ginsenoside Rb3 had a weaker effect. Network pharmacology and transcriptomic analyses suggested that this anti-apoptotic effect was achieved by upregulating the PI3K/Akt signaling pathway mediated by PDK1. Conclusions: These results suggested that G. pentaphyllum had a promising hepatoprotective effect, with its mechanism primarily involving the upregulation of the PDK1/Bcl-2 signaling pathway by GPA, thereby preventing cell apoptosis. [ABSTRACT FROM AUTHOR]

Background: Osteonecrosis of the femoral head caused by glucocorticoids (GIONFH) is a significant issue resulting from prolonged or excessive clinical glucocorticoid use. Astaxanthin, an orange-red carotenoid present in marine organisms, has been the focus of this study to explore its impact and mechanism on osteoblast apoptosis induced by dexamethasone (Dex) and GIONFH. Methods: In this experiment, bioinformatic prediction, molecular docking and dynamics simulation, cytotoxicity assay, osteogenic differentiation, qRT-PCR analysis, terminal uridine nickend labeling (TUNEL) assay, determination of intracellular ROS, mitochondrial function assay, immunofluorescence, GIONFH rat model construction, micro-computed tomography (micro-CT) scans were performed. Results: Our research demonstrated that a low dose of astaxanthin was non-toxic to healthy osteoblasts and restored the osteogenic function of Dex-treated osteoblasts by reducing oxidative stress, mitochondrial dysfunction, and apoptosis. Furthermore, astaxanthin rescued the dysfunction in poor bone quality, bone metabolism and angiogenesis of GIONFH rats. The mechanism behind this involves astaxanthin counteracting Dex-induced osteogenic damage by activating the Nrf2 pathway. Conclusion: Astaxanthin shields osteoblasts from glucocorticoid-induced oxidative stress and mitochondrial dysfunction via Nrf2 pathway activation, making it a potential therapeutic agent for GIONFH treatment. [ABSTRACT FROM AUTHOR]

Background Sputum-based testing is a barrier to increasing access to molecular diagnostics for tuberculosis (TB). Many people with TB are unable to produce sputum, and sputum processing increases assay complexity and cost. Tongue swabs are emerging as an alternative to sputum, but performance limits are uncertain. Methods From June 2022 to July 2023, we enrolled 397 consecutive adults with cough >2 weeks at 2 health centers in Kampala, Uganda. We collected demographic and clinical information, sputum for TB testing (Xpert MTB/RIF Ultra and 2 liquid cultures), and tongue swabs for same-day quantitative polymerase chain reaction (qPCR) testing. We evaluated tongue swab qPCR diagnostic accuracy versus sputum TB test results, quantified TB targets per swab, assessed the impact of serial swabbing, and compared 2 swab types (Copan FLOQSWAB and Steripack spun polyester). Results Among 397 participants, 43.1% were female, median age was 33 years, 23.5% were diagnosed with human immunodeficiency virus, and 32.0% had confirmed TB. Sputum Xpert Ultra and tongue swab qPCR results were concordant for 98.2% (95% confidence interval [CI]: 96.2–99.1) of participants. Tongue swab qPCR sensitivity was 92.6% (95% CI: 86.5 to 96.0) and specificity was 99.1% (95% CI: 96.9 to 99.8) versus microbiological reference standard. A single tongue swab recovered a 7-log range of TB copies, with a decreasing recovery trend among 4 serial swabs. Swab types performed equivalently. Conclusions Tongue swabs are a promising alternative to sputum for molecular diagnosis of TB, with sensitivity approaching sputum-based molecular tests. Our results provide valuable insights for developing successful tongue swab–based TB diagnostics. [ABSTRACT FROM AUTHOR]

Background: The selection of primer pairs in sequencing-based research can greatly influence the results, highlighting the need for a tool capable of analysing their performance in-silico prior to the sequencing process. We therefore propose PrimerEvalPy, a Python-based package designed to test the performance of any primer or primer pair against any sequencing database. The package calculates a coverage metric and returns the amplicon sequences found, along with information such as their average start and end positions. It also allows the analysis of coverage for different taxonomic levels. Results: As a case study, PrimerEvalPy was used to test the most commonly used primers in the literature against two oral 16S rRNA gene databases containing bacteria and archaea. The results showed that the most commonly used primer pairs in the oral cavity did not match those with the highest coverage. The best performing primer pairs were found for the detection of oral bacteria and archaea. Conclusions: This demonstrates the importance of a coverage analysis tool such as PrimerEvalPy to find the best primer pairs for specific niches. The software is available under the MIT licence at https://gitlab.citius.usc.es/lara.vazquez/PrimerEvalPy. [ABSTRACT FROM AUTHOR]

Motivation: Long non-coding RNAs (lncRNAs) are a class of molecules involved in important biological processes. Extensive efforts have been provided to get deeper understanding of disease mechanisms at the lncRNA level, guiding towards the detection of biomarkers for disease diagnosis, treatment, prognosis and prevention. Unfortunately, due to costs and time complexity, the number of possible disease-related lncRNAs verified by traditional biological experiments is very limited. Computational approaches for the prediction of disease-lncRNA associations allow to identify the most promising candidates to be verified in laboratory, reducing costs and time consuming. Results: We propose novel approaches for the prediction of lncRNA-disease associations, all sharing the idea of exploring associations among lncRNAs, other intermediate molecules (e.g., miRNAs) and diseases, suitably represented by tripartite graphs. Indeed, while only a few lncRNA-disease associations are still known, plenty of interactions between lncRNAs and other molecules, as well as associations of the latters with diseases, are available. A first approach presented here, NGH, relies on neighborhood analysis performed on a tripartite graph, built upon lncRNAs, miRNAs and diseases. A second approach (CF) relies on collaborative filtering; a third approach (NGH-CF) is obtained boosting NGH by collaborative filtering. The proposed approaches have been validated on both synthetic and real data, and compared against other methods from the literature. It results that neighborhood analysis allows to outperform competitors, and when it is combined with collaborative filtering the prediction accuracy further improves, scoring a value of AUC equal to 0966. Availability: Source code and sample datasets are available at: https://github.com/marybonomo/LDAsPredictionApproaches.git [ABSTRACT FROM AUTHOR]

Introduction: With over 360 blood group antigens in systems recognized, there are antigens, such as RhD, which demonstrate a quantitative reduction in antigen expression due to nucleotide variants in the non-coding region of the gene that result in aberrant splicing or a regulatory mechanism. This study aimed to evaluate bioinformatically predicted GATA1-binding regulatory motifs in the RHD gene for samples presenting with weak or apparently negative RhD antigen expression but showing normal RHD exons. Methods: Publicly available open chromatin region data were overlayed with GATA1 motif candidates in RHD. Genomic DNA from weak D, Del or D– samples with normal RHD exons (n = 13) was used to confirm RHD zygosity by quantitative PCR. Then, RHD promoter, intron 1, and intron 2 regions were amplified for Sanger sequencing to detect potential disruptions in the GATA1 motif candidates. Electrophoretic mobility shift assay (EMSA) was performed to assess GATA1-binding. Luciferase assays were used to assess transcriptional activity. Results: Bioinformatic analysis identified five of six GATA1 motif candidates in the promoter, intron 1 and intron 2 for investigation in the samples. Luciferase assays showed an enhancement in transcription for GATA1 motifs in intron 1 and for intron 2 only when the R2 haplotype variant (rs675072G>A) was present. GATA1 motifs were intact in 12 of 13 samples. For one sample with a Del phenotype, a novel RHD c.1–110A>C variant disrupted the GATA1 motif in the promoter which was supported by a lack of a GATA1 supershift in the EMSA and 73% transcriptional activity in the luciferase assay. Two samples were D+/D– chimeras. Conclusion: The bioinformatic predictions enabled the identification of a novel DEL allele, RHD c.1–110A>C, which disrupted the GATA1 motif in the proximal promoter. Although the majority of the samples investigated here remain unexplained, we provide GATA1 targets which may benefit future RHD regulatory investigations. [ABSTRACT FROM AUTHOR]

Background: Liver fibrosis is a widespread chronic liver ailment linked to substantial mortality and limited therapeutic options. An in-depth comprehension of the genetic underpinnings of liver fibrogenesis is crucial for the development of effective management and treatment approaches. Results: Using bioinformatics tools and the DisGeNET database, we pinpointed 105 genes significantly linked to liver fibrosis. Subsequently, we conducted functional assessments, incorporating gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and the STRING database, to construct protein–protein interaction networks (PPI) for these 105 liver fibrosis-associated genes. These analyses were executed via the WebGestalt 2019 online platform. We employed Cytoscape plugins, MCODE, and CytoHubba, to identify potential biomarker genes from these functional networks. Noteworthy hub genes encompassed TGF-β1, MMP2, CTNNB1, FGF2, IL6, LOX, CTGF, SMAD3, ALB, and VEGFA. TGF-β1 and MMP-2 exhibited substantial promise as liver fibrosis biomarkers, as denoted by their high systemic scores determined through the MCC algorithm in the CytoHubba methodology. Conclusions: In summary, this study presents a robust genetic biomarker strategy that may prove invaluable in the identification of potential liver fibrosis biomarkers. [ABSTRACT FROM AUTHOR]

Background: Autism spectrum disorder (ASD) is a complex neurological disability with multifactorial etiology. ASD is described by behavior, speech, language, and communication defects. CircRNA is a type of ceRNA that plays an important role in modulating microRNAs (miRNA) in several disorders. However, the potential role of the circRNA/miRNA/mRNA regulatory network in the pathogenesis of ASD is not fully understood. Therefore, this study aimed to create a circRNA/miRNA/mRNA network associated with ASD to cast light on the pathogenesis of ASD. Methods: CircRNA expression profile data were recruited from Gene Expression Omnibus datasets, and the differentially expressed circRNAs (DEcircRNAs) were identified. Then, miRNAs modulated by these circRNAs were predicted and overlapped with differentially expressed miRNAs. Next, the potentially involved genes were identified by overlapping predicted targets, and differentially expressed genes. The enrichment analysis was performed, and a PPI network was projected. Subsequently, ten key genes were selected from the network. Furthermore, a circRNA/miRNA/mRNA regulatory network was constructed, and probable molecules and drugs with potential anti-ASD effects were predicted. Results: 11 DEcircRNAs and 8 miRNAs regulated by 4 circRNAs were identified as being significantly involved. Subsequently, gene enrichment analysis of 71 overlapped mRNA regulated by these miRNAs showed that they are mostly associated with hippocampal synaptogenesis, neurogenesis, and axon guidance. Additionally, two high-score compounds, GSK3β inhibitor (SB216763) and dexamethasone, and three drugs (haloperidol, nystatin, paroxetine) were confirmed as potential therapeutic options for ASD. Conclusion: The results of this study may help gain deeper insight into the pathogenesis of the circRNA/miRNA/mRNA regulatory network in ASD, providing potential therapeutic management options. [ABSTRACT FROM AUTHOR]

Background : Melanomas are highly aggressive in nature known for metastasis and death. Melanocytes that gave rise to melanomas are neural crest progenitor cells. Our research was primarily concerned with uveal melanoma (UM) and cutaneous melanoma (CM), respectively. Although they both have the same melanocytic origin, the biology of their respective is different. Aim : The aim of our study was to recognize the common differentially expressed genes (DEGs) between UM and CM. Methodology: The gene expression profile was downloaded from the GEO and analyzed by GEO2R to recognize DEGs. By applying DAVID, GO, and KEGG, pathway enrichment analysis was performed. PPI of these DEGs was analyzed using STRING and visualized by Cytoscape and MCODE. Further, we utilized HPA and GEPIA to obtain Kaplan–Meier graph for survival analysis in order to assess the prognostic value of hub genes. Results : We examined the UM and CM datasets and discovered three common upregulated and eight downregulated DEGs based on computational analysis. HMGCS1 and ELOVL5 were shown to be enriched in a variety of altered molecular pathways and pathways in cancer. Overexpression of HMGCS1 and ELOVL5 was linked to a poor prognosis in CM. Conclusion : Computational evaluation found that HMGCS1 and ELOVL5 were upregulated in both melanomas. Enrichment analysis showed that these genes are involved in cancer metabolism pathway and associated with poor prognosis in CM. However, the molecular study of these genes in UM is limited. Therefore, a better understanding of the cancer metabolism pathways should be carried to pave the way for clinical benefits. [ABSTRACT FROM AUTHOR]

Background: Tubulointerstitial injury plays a pivotal role in the progression of diabetic kidney disease (DKD), yet the link between neutrophil extracellular traps (NETs) and diabetic tubulointerstitial injury is still unclear. Methods: We analyzed microarray data (GSE30122) from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs) associated with DKD's tubulointerstitial injury. Functional and pathway enrichment analyses were conducted to elucidate the involved biological processes (BP) and pathways. Weighted gene coexpression network analysis (WGCNA) identified modules associated with DKD. LASSO regression and random forest selected NET-related characteristic genes (NRGs) related to DKD tubulointerstitial injury. Results: Eight hundred ninety-eight DEGs were identified from the GSE30122 dataset. A significant module associated with diabetic tubulointerstitial injury overlapped with 15 NRGs. The hub genes, CASP1 and LYZ, were identified as potential biomarkers. Functional enrichment linked these genes with immune cell trafficking, metabolic alterations, and inflammatory responses. NRGs negatively correlated with glomerular filtration rate (GFR) in the Neph v5 database. Immunohistochemistry (IHC) validated increased NRGs in DKD tubulointerstitial injury. Conclusion: Our findings suggest that the CASP1 and LYZ genes may serve as potential diagnostic biomarkers for diabetic tubulointerstitial injury. Furthermore, NRGs involved in diabetic tubulointerstitial injury could emerge as prospective targets for the diagnosis and treatment of DKD. [ABSTRACT FROM AUTHOR]

Background: Sciatica is a phrase used to describe radiating leg discomfort. The most common cause is lumbar disc herniation (LDH), which is considered to start in the nucleus pulposus. Advancements in lipidomics and metabolomics have unveiled the complex role of fatty acid metabolism (FAM) in both healthy and pathological states. However, the specific roles of fatty acid metabolism-related genes (FAMGs) in shaping therapeutic approaches, especially in LDH, remain largely unexplored and are a subject of ongoing research. Methods: The junction of the weighted correlation network analysis (WGCNA) test with 6 FAMGs enabled the finding of FAMGs. Gene set variation analysis (GSVA) was used to identify the possible biological activities and pathways of FAMGs. LASSO was used to determine diagnostic effectiveness of the four FAMGs in diagnosing LDH. GSE124272, GSE147383, GSE150408, and GSE153761 were utilized to confirm the levels of expression of four FAMGs. Results: Four FAMGs were discovered [Acyl-CoA Thioesterase 4 (ACOT4), Cytochrome P450 Family 4 Subfamily A Member 11 (CYP4A11), Acyl-CoA Dehydrogenase Long Chain (ACADL), Enoyl-CoA Hydratase and 3-Hydroxyacyl CoA Dehydrogenase (EHHADH)] For biological function analysis, mhc class ib receptor activity, response to thyroxine, response to l phenylalanine derivative were emphasized. Conclusions: FAMGs can help with prognosis and immunology, and provide evidence for fatty acid metabolism-related targeted therapeutics. In LDH, FAMGs and their interactions with immune cells might be therapeutic targets. [ABSTRACT FROM AUTHOR]

Background: High-throughput sequencing (HTS) has become the gold standard approach for variant analysis in cancer research. However, somatic variants may occur at low fractions due to contamination from normal cells or tumor heterogeneity; this poses a significant challenge for standard HTS analysis pipelines. The problem is exacerbated in scenarios with minimal tumor DNA, such as circulating tumor DNA in plasma. Assessing sensitivity and detection of HTS approaches in such cases is paramount, but time-consuming and expensive: specialized experimental protocols and a sufficient quantity of samples are required for processing and analysis. To overcome these limitations, we propose a new computational approach specifically designed for the generation of artificial datasets suitable for this task, simulating ultra-deep targeted sequencing data with low-fraction variants and demonstrating their effectiveness in benchmarking low-fraction variant calling. Results: Our approach enables the generation of artificial raw reads that mimic real data without relying on pre-existing data by using NEAT, a fine-grained read simulator that generates artificial datasets using models learned from multiple different datasets. Then, it incorporates low-fraction variants to simulate somatic mutations in samples with minimal tumor DNA content. To prove the suitability of the created artificial datasets for low-fraction variant calling benchmarking, we used them as ground truth to evaluate the performance of widely-used variant calling algorithms: they allowed us to define tuned parameter values of major variant callers, considerably improving their detection of very low-fraction variants. Conclusions: Our findings highlight both the pivotal role of our approach in creating adequate artificial datasets with low tumor fraction, facilitating rapid prototyping and benchmarking of algorithms for such dataset type, as well as the important need of advancing low-fraction variant calling techniques. [ABSTRACT FROM AUTHOR]

BACKGROUND: Myelodysplastic syndrome has worse hazards of acute myeloid leukemia transformation, and some studies have revealed that immune infiltration plays a vital part in the two. Nevertheless, more studies are required to confirm the relationship between immune infiltration and related differentially expressed gene regulation. OBJECTIVE: To screen the differentially expressed genes with prognostic significance between myelodysplastic syndrome and acute myeloid leukemia by bioinformatics analysis and explore the possible roles and mechanisms among these differentially expressed genes and immune infiltration mechanisms in the occurrence and progression of diseases. METHODS: The differentially expressed genes were screened for bioinformatics analysis using the GEO datasets, and analyzed by DO, GO, KEGG and GSEA. The TCGA prognostic database was used to plot the K-M curves of differentially expressed genes and receiver operating characteristic curve analysis was applied to evaluate the clinical diagnostic performance. Finally, CIBERSORT analysis was used to intuitively demonstrate the correlation between critical prognostic genes and the distribution of immuno-infiltrated cells. RT-qPCR was employed to detect peripheral blood samples from healthy controls, myelodysplastic syndrome and acute myeloid leukemia patients so as to verify the crucial genes preliminarily. RESULTS AND CONCLUSION: (1) A total of 150 differentially expressed genes were obtained between myelodysplastic syndrome and acute myeloid leukemia, among which 16 genes were up-regulated and 134 were down-regulated. (2) The results of DO, GO, KEGG and GSEA analysis suggested that differentially expressed genes might promote the development of myelodysplastic syndrome to acute myeloid leukemia by regulating the immune response. CIBERSORT revealed the differences in immune infiltration between myelodysplastic syndrome and acute myeloid leukemia. The distribution of CD4+ T cells, monocytes, neutrophils and M1 macrophages decreased in acute myeloid leukemia patients. In contrast, the distribution of inflammatory suppressor cells M2 macrophages increased, suggesting that it may be related to the immunosuppression of acute myeloid leukemia. (3) K-M curve and receiver operating characteristic curve analysis of 150 differentially expressed genes screened out four genes relevant to immunity and prognosis with good diagnostic performance: MANSC1, FLT3, BMX and CXCR2. (4) The results of RT-qPCR exhibited that MANSC1, BMX and CXCR2 were low expressed, while FLT3 was highly expressed in acute myeloid leukemia patients. These findings verify that the differential expression of MANSC1, FLT3, BMX and CXCR2 in patients with myelodysplastic syndrome and acute myeloid leukemia is not only significantly correlated with the prognosis of patients but may also affect the occurrence and development of myelodysplastic syndrome and acute myeloid leukemia by regulating the immune infiltration of patients. They can be used as potential biomarkers and therapeutic targets of the transformation from myelodysplastic syndrome to acute myeloid leukemia, providing a new direction for clinical diagnosis and treatment of the transformation of myelodysplastic syndrome. [ABSTRACT FROM AUTHOR]