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Introduction: ERCC genes, responsible for encoding enzymes involved in base excision repair, have been implicated in various cancers, contributing to chemoresistance. However, a comprehensive analysis of the prognostic and therapeutic significance of this gene family in lung adenocarcinoma (LUAD) is lacking. Methods: This study conducted a multidimensional assessment of ERCC family genes in LUAD using bioinformatic approaches, including mRNA expression level, gene methylation, and copy number variation (CNV), as well as their correlations with clinical outcome, gene set variations, and tumor-infiltrating lymphocytes (TILs). In addition, We evaluated the anti-tumor effects of ERCC8 in cell lines, demonstrating its clinical potential on an experimental level. Results: Overall, the expression of ERCC genes exhibited a negative correlation with good prognosis, with ERCC6L and ERCC8 demonstrating the most reliable predictive performance. Gene methylation level and CNV increases of ERCC genes generally displayed negative and positive associations with their expression levels, respectively. Additionally, GSVA analysis suggested that ERCC expression was positively correlated with cell cycle and apoptosis pathways but negatively correlated to the TSC/mTOR pathway. Furthermore, the expression of ERCC genes exhibited a complex relationship with TILs and the response to anti-tumor drugs. The results of in vitro cellular experiments show that inhibiting ERCC8 can alleviate the malignant phenotype of LUAD cells. Discussion: Our study revealed the multifaceted biological and clinical significance of ERCC family members in LUAD. These findings provide new insights into the function of ERCC family genes in LUAD and their potential clinical applications. [ABSTRACT FROM AUTHOR]

Background: Dementia causes the loss of functional independence, resulting in a decrease in the quality of life of those who suffer from it. Aims: This study aimed to investigate the interactions influencing susceptibility to the development of dementia through multifactor dimensionality reduction (MDR). Methods: the study population was made up of 221 cases and 534 controls. We performed an MDR analysis as well as a bioinformatic analysis to identify interactions between the genes GSTO1_rs4925, AGER_rs2070600, and ESR1_rs3844508 associated with susceptibility to dementia. Results: We observed associations between the polymorphism of GSTO1 and risk of dementia for the site rs4925 with the recessive model (OR = 1.720, 95% CI = 1.166–2.537 p = 0.006). Similarly, the site AGER rs2070600 showed risk of dementia with an additive model of inheritance (OR = 7.278, 95% CI = 3.140–16.868; p < 0.001). Furthermore, we identified the best risk model with a high precision of 79.6% that, when combined with three environmental risk factors, did not give an OR = 26.662 95%CI (16.164–43.979) with p < 0.001. Conclusions: The MDR and bioinformatics results provide new information on the molecular pathogenesis of dementia, allowing identification of possible diagnostic markers and new therapeutic targets. [ABSTRACT FROM AUTHOR]

• Helianthus annuus L. is a significant oilseed plant with economic importance. • Secondary metabolites in plants have high potential to produce new drugs and bioactive compounds. • Compounds with anticancer properties such as taxadiene-5α-hydroxylase, parthenolide synthase, vinorine synthase etc. have been identified in H. annuus. • There are gene clusters categorized as polyketide, saccharide, saccharide-terpene, alkaloid, putative, and terpene types in H. annuus. Plant secondary metabolite gene clusters are regions within the plant genome that encode enzymes and proteins involved in the biosynthesis of secondary metabolites. Helianthus annuus L. is a significant oilseed plant with economic importance. This study aims to comprehensively identify secondary metabolite biosynthetic gene clusters within H. annuus using bioinformatics tools, shedding light on the functions of the enzymes involved. In this study, the plantiSMASH software was utilized to predict secondary metabolite biosynthetic gene clusters in H. annuus. The results from plantiSMASH were analyzed to identify the biosynthetic gene clusters for secondary metabolites on each chromosome. The biological and molecular functions of the enzymes within these clusters were predicted using data from the relevant articles. According to the obtained data, H. annuus lacks a biosynthetic gene cluster on chromosomes four, seven, and fifteen. The identified gene clusters in this plant are polyketide, saccharide, saccharide-terpene, alkaloid, putative, and terpene. The enzyme categories found in secondary metabolite biosynthetic gene clusters include Methyltransferase, Ketosynthase, Glycosyltransferase, BAHD acyltransferase, Dioxygenase, CoA-ligase, Epimerase, PRISE enzymes, Prenyltransferase, Oxidoreductase, Aminotransferase, Copper amine oxidase, Cytochrome P450, Terpene synthase, and Pictet-Spengler enzyme (Bet v1). This comprehensive analysis provides a foundation for further investigations into the biosynthesis of secondary metabolites in H. annuus , offering valuable insights for researchers exploring the medicinal and pharmaceutical potential of this plant species. [ABSTRACT FROM AUTHOR]

• Endometriosis is a common gynecological disease with a significant economic burden. • Endometriosis is affected by differentially expressed genes and non-coding RNA. • Bioinformatic is a valuable tool to unveil the molecular basis underlying endometriosis. • Bioinformatic analysis revealed the potential biomarkers of endometriosis. Endometriosis is a common gynecological disease with a significant economic burden. Growing evidence has suggested the role of aberrant gene expression and epigenetic mechanisms in the pathogenesis of endometriosis. This study aims to identify potential key genes, epigenetic features, and regulatory networks in endometriosis using an integrated bioinformatic approach. Six microarray and RNA-sequencing datasets (GSE23339, GSE7305, GSE25628, GSE51981, GSE120103, GSE87809) were downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) of each dataset were analyzed using the GEO2R tool, and their mRNA, miRNA, and lncRNA components were identified subsequently. The common DEGs between datasets were combined, and the Gene ontology (GO) and pathway enrichment were analyzed using the ShinyGo. The protein–protein interaction (PPI) network of DEGs, miRNA, and lncRNA was constructed using STRING and Cytoscape, and then the top 15 hub genes in the PPI network were identified using CytoHubba. A total of 551 common DEGs were identified from four or more studies, including 292 upregulated and 259 downregulated genes. Besides alterations in protein-coding genes (mRNA), 16 miRNA (5 upregulated and 11 downregulated) were identified from all studies, along with 12 lncRNA (10 upregulated and 2 downregulated) that were common in at least three studies. Enriched DEGs were mainly associated with extracellular matrix (ECM) interaction, P53 signaling pathway, and focal adhesion, which are suggested to play vital roles in the pathogenesis of endometriosis. Through PPI network construction of common DEGs, 178 nodes and 683 edges were obtained, from which 15 hub genes were identified, including CDK1, CCNB1, KIF11, CCNA2, BUB1B, DLGAP5, BUB1, TOP2A, ASPM, CEP55, CENPF, TPX2, CCNB2, KIFC, NCAPG. Our in-depth bioinformatics analysis reveals the critical molecular basis underlying endometriosis. The role of identified hub genes, miRNA, and lncRNA may also have an opportunity to be explored as potential biomarkers for endometriosis diagnosis and prognosis. [ABSTRACT FROM AUTHOR]

DNA is the macromolecule responsible for storing the genetic information of a cell and it has intrinsic properties such as deformability, stability and curvature. DNA Curvature plays an important role in gene transcription and, consequently, in the subsequent production of proteins, a fundamental process of cells. With recent advances in bioinformatics and theoretical biology, it became possible to analyze and understand the involvement of DNA Curvature as a discriminatory characteristic of gene-promoting regions. These regions act as sites where RNAp (ribonucleic acid-polymerase) binds to initiate transcription. This review aims to describe the formation of Curvature, as well as highlight its importance in predicting promoters. Furthermore, this article provides the potential of DNA Curvature as a distinguishing feature for promoter prediction tools, as well as outlining the calculation procedures that have been described by other researchers. This work may support further studies directed towards the enhancement of promoter prediction software. [ABSTRACT FROM AUTHOR]

Carcinogenesis is driven by complex molecular events, often involving key enzymes that regulate oxidative stress (OS). While classical enzymes such as SOD, catalase, and GPx have been extensively studied, other, non-classical oxidative stress-related enzymes (OSRE) may play critical roles in cancer progression. We aimed to explore the role of OSRE involved in an OS scenario and to assess their potential contribution to carcinogenesis in some of the most prevalent cancer types. Through data mining and bioinformatic analysis of gene and protein expression and mutation data, we identified OSRE with altered expression and mutations across cancer types. Functional pathways involving EGFR, MT-ND, GST, PLCG2, PRDX6, SRC, and JAK2 were investigated. Our findings reveal that enzymes traditionally considered peripheral to OS play significant roles in tumor progression. Those OSRE may contribute to cancer initiation and progression, as well as be involved with cancer hallmarks, such as EMT and invasion, proliferation, and ROS production. In addition, enzymes like SRC and JAK2 were found to have dual roles in both promoting ROS generation and being modulated by OS. OSRE also interact with key oncogenic signaling pathways, including Wnt/β-catenin and JAK2/STAT3, linking them to cancer aggressiveness and therapeutic resistance. Future research should focus on translating these findings into clinical applications, including the development of novel inhibitors or drugs targeting these non-classical enzymes. [ABSTRACT FROM AUTHOR]

Epigenetic alterations of non-coding RNA (ncRNA) are pivotal in the continuous activation and differentiation of fibroblasts in keloid. However, the epigenetic mechanism of circRNA in keloid is still not clear yet. In this study, we aimed to investigate the interplay among differentially expressed circRNAs, miRNAs, and mRNAs during wound healing in keloid-prone individuals, construct a competing endogenous RNA (ceRNA) network, and gain an in-depth insight into the pathophysiological mechanisms underlying keloid development. Utilizing bioinformatic methods, we analyzed the expression profiles from the GSE113621 database. We identified 29 differentially expressed circRNAs (DEcircRNAs) in keloid-prone individuals during wound healing, from which we constructed 14 ceRNA networks. Subsequently, we validated the expression of predicted DEcircRNAs in keloid tissues and elucidated the ceRNA network involving circ_064002 and fibronectin-1 (FN1) through competing miR-30a/b-5p. Knocking down circ_064002 led to down-regulation of FN1 expression and various cellular functions in keloid-derived fibroblasts (KFs), including cell viability, migration, invasion, and repair capacity. Our study introduces a novel approach to explore the presence of DEcircRNAs and the ceRNA regulatory network during wound healing in keloid-prone individuals through in-depth mining of GEO data and also proves the epigenetic regulatory mechanism of circ_064002 in KFs. Level of Evidence V: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266. [ABSTRACT FROM AUTHOR]

Background: The prognostic value and immune significance of T-cell proliferation regulators (TCRs) in hepatocellular carcinoma (HCC) have not been previously reported. This study aimed to develop a new prognostic model based on TCRs in patients with HCC. Method: This study used The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC) and International Cancer Genome Consortium-Liver Cancer-Riken, Japan (ICGC-LIRI-JP) datasets along with TCRs. Differentially expressed TCRs (DE-TCRs) were identified by intersecting TCRs and differentially expressed genes between HCC and non-cancerous samples. Prognostic genes were determined using Cox regression analysis and were used to construct a risk model for HCC. Kaplan-Meier survival analysis was performed to assess the difference in survival between high-risk and low-risk groups. Receiver operating characteristic curve was used to assess the validity of risk model, as well as for testing in the ICGC-LIRI-JP dataset. Additionally, independent prognostic factors were identified using multivariate Cox regression analysis and proportional hazards assumption, and they were used to construct a nomogram model. TCGA-LIHC dataset was subjected to tumor microenvironment analysis, drug sensitivity analysis, gene set variation analysis, and immune correlation analysis. The prognostic genes were analyzed using consensus clustering analysis, mutation analysis, copy number variation analysis, gene set enrichment analysis, and molecular prediction analysis. Results: Among the 18 DE-TCRs, six genes (DCLRE1B, RAN, HOMER1, ADA, CDK1, and IL1RN) could predict the prognosis of HCC. A risk model that can accurately predict HCC prognosis was established based on these genes. An efficient nomogram model was also developed using clinical traits and risk scores. Immune-related analyses revealed that 39 immune checkpoints exhibited differential expression between the high-risk and low-risk groups. The rate of immunotherapy response was low in patients belonging to the high-risk group. Patients with HCC were further divided into cluster 1 and cluster 2 based on prognostic genes. Mutation analysis revealed that HOMER1 and CDK1 harbored missense mutations. DCLRE1B exhibited an increased copy number, whereas RAN exhibited a decreased copy number. The prognostic genes were significantly enriched in tryptophan metabolism pathways. Conclusions: This bioinformatics analysis identified six TCR genes associated with HCC prognosis that can serve as diagnostic markers and therapeutic targets for HCC. [ABSTRACT FROM AUTHOR]

Background: The incidence of papillary thyroid cancer (PTC) has been increasing annually; however, early diagnosis can improve patient outcomes. Pyroptosis is a programmed cell death modality that has received considerable attention recently. However, no studies have reported using pyroptosis-related genes in PTC diagnosis. Methods: Analyzed 33 pyroptosis-related genes in PTC transcriptome data from the Gene Expression Omnibus database. Subsequently, used the Least Absolute Shrinkage and Selection Operator (LASSO) model to construct a PTC molecular diagnostic model. Furthermore, confirmed differences in the expression of five genes between PTC and non-tumor tissues using immunohistochemistry. Collected 338 PTC and control samples to construct a five-gene PTC diagnostic model, which was then validated using a training set and underwent correlation analysis with immune cell infiltration. Additionally, validated the biological functions of the core gene NOD1 in vitro. Results: The five-gene PTC diagnostic model demonstrated good diagnostic value for PTC. Moreover, identified three reliable subtypes of pyroptosis and found that NOD1 is involved in tumor-suppressive microenvironment formation. Notably, patients with high NOD1 expression had lower Progression-Free Survival (PFS). Additionally, NOD1 expression was positively correlated with immune markers such as CD47, CD68, CD3, and CD8. Lastly, inhibiting NOD1 showed significant anti-PTC activity in vitro. Conclusion: Our results suggest that pyroptosis-related genes can be used for PTC diagnosis, and NOD1 could be a promising therapeutic target. [ABSTRACT FROM AUTHOR]

Abstract Substantial advances have been made in the development of promising HIV vaccines to eliminate HIV-1 infection. For the first time, one hundred of the most submitted HIV subtypes and CRFs were retrieved from the LANL database, and the consensus sequences of the eleven HIV proteins were obtained to design vaccines for human and mouse hosts. By using various servers and filters, highly qualified B-cell epitopes, as well as HTL and CD8 + epitopes that were common between mouse and human alleles and were also located in the conserved domains of HIV proteins, were considered in the vaccine constructs. With 90% coverage worldwide, the human vaccine model covers a diverse allelic population, making it widely available. Codon optimization and in silico cloning in prokaryotic and eukaryotic vectors guarantee high expression of the vaccine models in human and E. coli hosts. Molecular dynamics confirmed the stable interaction of the vaccine constructs with TLR3, TLR4, and TLR9, leading to a substantial immunogenic response to the designed vaccine. Vaccine models effectively target the humoral and cellular immune systems in humans and mice; however, experimental validation is needed to confirm these findings in silico.

Abstract Aim Pancreatic ductal adenocarcinoma (PAAD) is recognized as an exceptionally aggressive cancer that both highly lethal and unfavorable prognosis. The mitochondrial metabolism pathway is intimately involved in oncogenesis and tumor progression, however, much remains unknown in this area. In this study, the bioinformatic tools have been used to construct a prognostic model with mitochondrial metabolism-related genes (MMRGs) to evaluate the survival, immune status, mutation profile, and drug sensitivity of PAAD patients. Method Univariate Cox regression and LASSO regression were used to screen the differentially expressed genes (DEGs), and multivariate Cox regression was used to develop the risk model. Kaplan–Meier estimator was employed to identify MMRGs signatures associated with overall survival (OS). ROC curves were utilized to evaluate the model's performance. Maftools, immunedeconv and CIBERSORT R packages were applied to analyze the gene mutation profiles and immune status. The corresponding sensitivity to pharmaceutical agents was assessed using oncoPredict R packages. Results A prognostic model with five MMRGs was developed, which defined the patients as high-risk showed lower survival rates. There was good consistency among individuals categorized as high-risk, showing elevated rates of genetic alterations, particularly in the TP53 and KRAS genes. Furthermore, these patients exhibited increased levels of immunosuppression, characterized by an increased presence of macrophages, neutrophils, Th2 cells, and regulatory T cells. Additionally, high-risk patients showed increased sensitivity to Sabutoclax and Venetoclax. Conclusion By utilizing a gene signature associated with mitochondrial metabolism, a prognostic model has been established which could be a highly efficient method for predicting the outcomes of PAAD patients.

Background: Neutrophil extracellular traps (NETs) cause delayed wound closed up in type 2 diabetes mellitus (T2DM), but the specific regulatory mechanism of NETs-related genes (NETs-RGs) in T2DM is unclear. Methods: We acquired GSE21321 and GSE15932 datasets from gene expression omnibus (GEO) database. First, differentially expressed genes (DEGs) between T2DM and control samples of GSE21321 dataset were sifted out by differential expression analysis. NETs scores were calculated for all samples in GSE21321 dataset, and key module genes associated with NETs scores were screened by constructing co-expression network. Then, DEGs and key module genes were intersected to yield intersection genes, and candidate genes were identified by constructing a protein protein interaction (PPI) network. Least absolute shrinkage and selection operator (LASSO) regression analysis was implemented on candidate genes to screen out diagnostic genes, and they were subjected to single sample gene set enrichment analysis (ssGSEA). Finally, immune characteristic analysis was carried out, and we constructed the gene-drug and transcription factor (TF)-miRNA-mRNA networks. Besides, we validated the expression of diagnostic genes by quantitative real-time polymerase chain reaction (qRT-PCR). Results: In total, 23 candidate genes were gained by PPI analysis. The 5 diagnostic genes, namely, inter-trypsin inhibitor heavy chain 3 (ITIH3), fibroblast growth factor 1 (FGF1), neuron cell adhesion molecule (NRCAM), advanced glycosylation end-product-specific receptor (AGER), and calcium voltage-gated channel subunit alpha1 C (CACNA1C), were identified via LASSO analysis, and they were involved in carboxylic acid transport, axonogenesis, etc. M2 Macrophage, Monocyte, Natural killer (NK) cell, and Myeloid dendritic cells (DC) were remarkably different between T2DM and control samples. Diagnostic genes had the strongest and the most significant positive correlation with B cells. The gene-drug network included CACNA1C-Isradipine, CACNA1C-Benidipine and other relationship pairs. Totally 76 nodes and 44 edges constituted the TFmiRNA-mRNA network, including signal transducer and activator of transcription 1(STAT1) -hsa-miR-3170-AGER, CCCTC-binding factor (CTCF)-hsa-miR-455-5p-CACNA1C, etc. Moreover, qRT-PCR suggested that the expression trends of FGF1 and AGER were in keeping with the results of bioinformatic analysis. FGF1 and AGER were markedly regulated downwards in the T2DM group. Conclusion: Through bioinformatic analysis, we identified NETs-related diagnostic genes (ITIH3, FGF1, NRCAM, AGER, CACNA1C) in T2DM, and explored their mechanism of action from different aspects, providing new ideas for the studies related to diagnosis and treatment of T2DM. [ABSTRACT FROM AUTHOR]

Simple Summary: Krüppel-like factors (KLFs) are essential transcription factors found in many eukaryotes. In this study, the focus is on identifying and analyzing the KLF gene family members in chickens using bioinformatic tools, and comparing them with representative classes such as fish, amphibians, birds, and mammals. The analysis includes the structural characterization, evolutionary relationship assessment, and functional predictions. Additionally, in this study, the impact of miRNA-22 is explored, associated with lipid metabolism, on the expression of KLF genes in the liver, heart, and muscle tissues of Qingyuan partridge chickens. The results indicate that the avian KLF gene family is evolutionarily closer to that of mammals, and all chicken KLFs are non-transmembrane proteins. Moreover, the effect of miRNA-22 on KLF expression varies across different tissues. These findings provide a scientific basis for further research into the functions of KLFs in chickens. Krüppel-like factors (KLFs) are a class of fundamental transcription factors that are widely present in various eukaryotes from nematodes to humans, named after their DNA binding domain which is highly homologous to the Krüppel factor in fruit flies. To investigate the composition, organization, and evolutionary trajectory of KLF gene family members in chickens, in our study, we leveraged conserved sequences of KLF genes from representative classes across fish, amphibians, birds, and mammals as foundational sequences. Bioinformatic tools were employed to perform homology alignment on the chicken genome database, ultimately identifying the KLF family members present in chickens. The gene structure, phylogenetic analysis, conserved base sequences, physicochemical properties, collinearity analysis, and protein structure were then analyzed using bioinformatic tools. Additionally, the impact of miRNA-22, related to poultry lipid metabolism, on the expression of the KLF gene family in the liver, heart, and muscle of Qingyuan partridge chickens was explored. The results showed that: (1) compared to fish, the KLF family in birds is more closely related to mammals and amphibians; (2) KLFs within the same subgroups are likely to be derived from a common ancestral gene duplication; (3) KLF3/8/12 in the same subgroup may have some similar or overlapping functions; (4) the motif 4 of KLF5 was most likely lost during evolution; (5) KLF9 may perform a similar function in chickens and pigs; (6) there are collinear relationships between certain KLF genes, indicating that there are related biomolecular functions between these KLF genes; (7) all members of the KLF family in chickens are non-transmembrane proteins; and (8) interference and overexpression of miRNA-22 in Qingyuan partridge chickens can affect the expression levels of KLF genes in liver, heart, and muscle. [ABSTRACT FROM AUTHOR]

Substantial advances have been made in the development of promising HIV vaccines to eliminate HIV-1 infection. For the first time, one hundred of the most submitted HIV subtypes and CRFs were retrieved from the LANL database, and the consensus sequences of the eleven HIV proteins were obtained to design vaccines for human and mouse hosts. By using various servers and filters, highly qualified B-cell epitopes, as well as HTL and CD8 + epitopes that were common between mouse and human alleles and were also located in the conserved domains of HIV proteins, were considered in the vaccine constructs. With 90% coverage worldwide, the human vaccine model covers a diverse allelic population, making it widely available. Codon optimization and in silico cloning in prokaryotic and eukaryotic vectors guarantee high expression of the vaccine models in human and E. coli hosts. Molecular dynamics confirmed the stable interaction of the vaccine constructs with TLR3, TLR4, and TLR9, leading to a substantial immunogenic response to the designed vaccine. Vaccine models effectively target the humoral and cellular immune systems in humans and mice; however, experimental validation is needed to confirm these findings in silico. [ABSTRACT FROM AUTHOR]

Aim: Pancreatic ductal adenocarcinoma (PAAD) is recognized as an exceptionally aggressive cancer that both highly lethal and unfavorable prognosis. The mitochondrial metabolism pathway is intimately involved in oncogenesis and tumor progression, however, much remains unknown in this area. In this study, the bioinformatic tools have been used to construct a prognostic model with mitochondrial metabolism-related genes (MMRGs) to evaluate the survival, immune status, mutation profile, and drug sensitivity of PAAD patients. Method: Univariate Cox regression and LASSO regression were used to screen the differentially expressed genes (DEGs), and multivariate Cox regression was used to develop the risk model. Kaplan–Meier estimator was employed to identify MMRGs signatures associated with overall survival (OS). ROC curves were utilized to evaluate the model's performance. Maftools, immunedeconv and CIBERSORT R packages were applied to analyze the gene mutation profiles and immune status. The corresponding sensitivity to pharmaceutical agents was assessed using oncoPredict R packages. Results: A prognostic model with five MMRGs was developed, which defined the patients as high-risk showed lower survival rates. There was good consistency among individuals categorized as high-risk, showing elevated rates of genetic alterations, particularly in the TP53 and KRAS genes. Furthermore, these patients exhibited increased levels of immunosuppression, characterized by an increased presence of macrophages, neutrophils, Th2 cells, and regulatory T cells. Additionally, high-risk patients showed increased sensitivity to Sabutoclax and Venetoclax. Conclusion: By utilizing a gene signature associated with mitochondrial metabolism, a prognostic model has been established which could be a highly efficient method for predicting the outcomes of PAAD patients. [ABSTRACT FROM AUTHOR]

ERβ has been assigned a tumor suppressor role in many cancer types. However, as conflicting findings emerge, ERβ's tissue-specific expression and functional role have remained elusive. There remains a notable gap in compact and comprehensive analyses of ESR2 mRNA expression levels across diverse tumor types coupled with an exploration of its potential gene network. In this study, we aim to address these gaps by presenting a comprehensive analysis of ESR2 transcriptomic data. We distinguished cancer types with significant changes in ESR2 expression levels compared to corresponding healthy tissue and concluded that ESR2 influences patient survival. Gene Set Enrichment Analysis (GSEA) distinguished molecular pathways affected by ESR2, including oxidative phosphorylation and epithelial–mesenchymal transition. Finally, we investigated genes displaying similar expression patterns as ESR2 in tumor tissues, identifying potential co-expressed genes that may exert a synergistic effect on clinical outcomes, with significant results, including the expression of ACIN1, SYNE2, TNFRSF13C, and MDM4. Collectively, our results highlight the significant influence of ESR2 mRNA expression on the transcriptomic landscape and the overall metabolism of cancerous cells across various tumor types. [ABSTRACT FROM AUTHOR]

Chickpeas (Cicer arietinum L.) are an important legume crop known for their rich nutrient content, including proteins, carbohydrates, and minerals. Thus, they are enjoyed by people worldwide. In recent years, the production scale of chickpeas has been growing gradually. The planting area of chickpeas represents roughly 35–36% of the total planting area, and the output of the beans is roughly 47–48%. However, the growth and development process of chickpeas is limited by a number of factors, including high temperature, drought, salt stress, and so forth. In particular, high temperatures can reduce the germination rate, photosynthesis, seed setting rate, and filling rate of chickpeas, restricting seed germination, plant growth, and reproductive growth. These changes lead to a decrease in the yield and quality of the crop. Heat shock proteins (HSPs) are small proteins that play an important role in plant defense against abiotic stress. Therefore, in the present study, HSP20 gene family members were identified based on the whole-genome data of chickpeas, and their chromosomal positions, evolutionary relationships, promoter cis-acting elements, and tissue-specific expression patterns were predicted. Subsequently, qRT-PCR was used to detect and analyze the expression characteristics of HSP20 genes under different temperature stress conditions. Ultimately, we identified twenty-one HSP20 genes distributed on seven chromosomes, and their gene family members were found to be relatively conserved, belonging to ten subfamilies. We also found that CaHSP20 promoter regions have many cis-acting elements related to growth and development, hormones, and stress responses. In addition, under high-temperature stress, the relative expression of CaHSP20-17, CaHSP20-20, CaHSP20-7, CaHSP20-3, and CaHSP20-12 increased hundreds or even thousands of times as the temperature increased from 25 °C to 42 °C. Among them, excluding CaHSP20-5, the other five genes all contain 1-2 ABA cis-regulatory elements. This finding indicates that CaHSP20s are involved in the growth and development of chickpeas under heat stress, and the mechanisms of their responses to high-temperature stress may be related to hormone regulation. The results of the present study lay the foundation for exploring HSP20 gene family resources and the molecular mechanisms of heat resistance in chickpeas. Our results can also provide a theoretical basis for breeding high-temperature-resistant chickpea varieties and provide valuable information for the sustainable development of the global chickpea industry. [ABSTRACT FROM AUTHOR]

In this paper we illustrate our bioinformatics system destined for an automatic insect classification in different taxonomic categories corresponding to different levels of precision according to their morphological characteristics such as: size; color; antennae; wings; mouthpart; etc. Our main goal is to automate the research; classification and to create an exhaustive database of insects in order to create a common directory of insects to save significant research time and facilitate classification. Our system is considered as a bioinformatics system, it uses artificial intelligence techniques to create an inference engine able to automatically recognize and classify a new insect. It also uses database creation techniques to create the knowledge and fact bases necessary for the proper functioning of the inference engine. Due to its interactive graphic interface, and by guiding its user, step by step, the system starts with the introduction of the morphological characteristics of the insect and the result is to automatically classify the insect in the database in the appropriate species. Our classification is focused on Hymenoptera precisely on the Apidae family, we have given a detailed classification of the Apidae family with its three subfamilies: Apinae, Xylocopinae and Nomadini. Our system is implemented completely in JAVA programming language using phpMyadmin as a database management system. [ABSTRACT FROM AUTHOR]

In genomic research, establishing a relationship among variables is usually of interest. Genomic research usually links factors. DNA microarray gene expression data introduces novel molecular biology and medical data analysis issues. In scientific data, Frequent Pattern Mining successfully applied association patterns discovered in microarray gene expression analysis. Discretization and association rule mining plays an important role in bioinformatics. Traditional pattern-finding approaches need many DNA sequence scans. The issue is recognizing intriguing patterns to make time-consuming and disagreeable judgments. The paper proposes a novel technique for identifying frequent patterns in DNA sequences and analyzing microarray gene expression profiling data. It first discovers frequent patterns from DNA sequences, then performs association rule mining and clustering discretization on the gene expression data using Fuzzy C-Means and PBMF index. A hybrid Diff-Eclat algorithm is employed to generate strong association rules and improves performance by microarray gene expression data's prediction. Finally, a GA-ANN (Genetic Algorithm-Artificial Neural Network) model is developed to predict biological knowledge from the discriminant rules. The method is implemented on a gene expression dataset and shows improved performance compared with SVM(Support Vector Machine), CNN(Convolutional Neural Network), RF(Random Forest) based on various metrics. The frequent patterns found in DNA sequence that were discovered during this approach have significant implications for medical data analyses like disease etiology, treatment analysis, mutation, and genetic analysis. [ABSTRACT FROM AUTHOR]

Background: Lipodystrophy is a rare disease that is poorly diagnosed due to its low prevalence and frequent phenotypic heterogeneity. The main therapeutic measures for patients with clinical lipodystrophy are aimed at improving general metabolic complications such as diabetes mellitus, insulin resistance, and hypertriglyceridemia. Therefore, there is an urgent need to find new biomarkers to aid in the diagnosis and targeted treatment of patients with congenital generalized lipodystrophy (CGL). Methods: Dataset GSE159337 was obtained via the Gene Expression Omnibus database. First, differentially expressed genes (DEGs) between CGL and control samples were yielded via differential expression analysis and were analyzed for Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment to explore the functional pathways. Next, protein–protein interaction analysis and the MCC algorithm were implemented to yield candidate genes, which were then subjected to receiver operating characteristic (ROC) analysis to identify biomarkers with an area under the curve value exceeding 0.8. Moreover, random forest (RF), logistic regression, and support vector machine (SVM) analyses were carried out to assess the diagnostic ability of biomarkers for CGL. Finally, the small-molecule drugs targeting biomarkers were predicted, and ibuprofen was further validated in lipodystrophy mice. Results: A total of 71 DEGs in GSE159337 were sifted out and were involved in immune receptor activity, immune response-regulating signaling pathway, and secretory granule membrane. Moreover, CXCR2, TNFSF10, NLRC4, CCR2, CEACAM3, TLR10, TNFAIP3, and JUN were considered as biomarkers by performing ROC analysis on 10 candidate genes. Meanwhile, RF, logistic regression, and SVM analyses further described that those biomarkers had an excellent diagnosis capability for CGL. Eventually, the drug–gene network included ibuprofen–CXCR1, ibuprofen–CXCR1, cenicriviroc–CCR2, fenofibrate–JUN, and other relationship pairs. Ibuprofen treatment was also validated to downregulate CXCR1 and CXCR2 in peripheral blood mononuclear cells (PBMCs) and improve glucose tolerance, hypertriglyceridemia, hepatic steatosis, and liver inflammation in lipodystrophy mice. Conclusion: Eight biomarkers, namely, CXCR2, TNFSF10, NLRC4, CCR2, CEACAM3, TLR10, TNFAIP3, and JUN, were identified through bioinformatic analyses, and ibuprofen targeting CXCR1 and CXCR2 in PBMCs was shown to improve metabolic disturbance in lipodystrophy, contributing to studies related to the diagnosis and treatment of lipodystrophy. [ABSTRACT FROM AUTHOR]

Objective To analyze and validate the potential molecular mechanisms involved in the formation of vulnerable atherosclerosis plaques through the miRNA mRNA regulatory network. Methods Morphological staining was used to validate the presence of atherosclerotic plaques in the aortic root and brachiocephalic trunk. Differentially ex- pressed miRNAs (DEMs) from the GSE34646 and GSE34647 datasets were analyzed, and their target genes (DEMS TGs) were predicted. Differentially expressed genes in advanced plaque (DEGSAP) were then screened from the GSE10000 dataset. The intersection of DEGSAP and DEMsTGs was taken to identify candidate genes. A protein protein interaction (PPI) network was constructed for the identified genes, and key genes were identified. An miRNA mRNA regulatory network was established, and the prognostic value of key genes was assessed using receiver operating characteristic (ROC) curves. Finally, RTqPCR was used to detect the predicted mRNA and miRNA levels in the aorta. Results Histopathological examination showed significant vulnerable plaques in the model group. Forty-four DEMs were identified in vulnerable plaques from the GSE34646 and GSE34647 datasets, predicting 3,171 and 8,075 miRNA target genes, respectively. The GSE10000 dataset identified 3,441 DEGsAP. The intersection of DEGS - AP and DEMS - TGS yielded 936 candidate genes. The PPI network identified 10 upregulated and 10 downregulated hub genes, which were used to construct the miRNA mRNA regulatory network. Conclusion Let-7g-3p-ADAM10/PIK3R1/C3AR1 may be the potential miRNA-mRNA regulatory axes for prognostication and diagnosis of advanced plaques by the RTqPCR. [ABSTRACT FROM AUTHOR]

Rice (Oryza sativa L.) is one of the most important food crops in China, and saline-alkali stress significantly affects the growth and development of rice. In this study, growth parameters were measured, and a metabolomics analysis technique was used to analyze differentially expressed metabolites in rice seedlings in response to saline-alkali stress. The results showed that growth, relative growth rate, and biomass of rice seedlings significantly decreased under saline-alkali stress. A total of 41 metabolites (16 up-regulated and 25 down-regulated) were significantly changed in leaves of rice seedlings under saline-alkali stress. There were 36 metabolic pathways associated with saline-alkali stress, of which starch and sucrose metabolism, glyoxylate and dicarboxylic metabolism, tricarboxylic acid cycle (TCA cycle), alanine, aspartate and glutamate metabolism, and pentose phosphate pathway were the most highly correlated. This study found that saline-alkali stress significantly reduced carbohydrate metabolism, respiratory metabolism, amino acid metabolism, and organic acid synthesis, while the increased amino acids may be the key metabolites for rice seedlings to adapt to saline-alkali stress. Our results provide new ideas for studying the metabolic mechanism of saline-alkali tolerance of rice seedlings. [ABSTRACT FROM AUTHOR]

IntroductionERCC genes, responsible for encoding enzymes involved in base excision repair, have been implicated in various cancers, contributing to chemoresistance. However, a comprehensive analysis of the prognostic and therapeutic significance of this gene family in lung adenocarcinoma (LUAD) is lacking.MethodsThis study conducted a multidimensional assessment of ERCC family genes in LUAD using bioinformatic approaches, including mRNA expression level, gene methylation, and copy number variation (CNV), as well as their correlations with clinical outcome, gene set variations, and tumor-infiltrating lymphocytes (TILs). In addition, We evaluated the anti-tumor effects of ERCC8 in cell lines, demonstrating its clinical potential on an experimental level.ResultsOverall, the expression of ERCC genes exhibited a negative correlation with good prognosis, with ERCC6L and ERCC8 demonstrating the most reliable predictive performance. Gene methylation level and CNV increases of ERCC genes generally displayed negative and positive associations with their expression levels, respectively. Additionally, GSVA analysis suggested that ERCC expression was positively correlated with cell cycle and apoptosis pathways but negatively correlated to the TSC/mTOR pathway. Furthermore, the expression of ERCC genes exhibited a complex relationship with TILs and the response to anti-tumor drugs. The results of in vitro cellular experiments show that inhibiting ERCC8 can alleviate the malignant phenotype of LUAD cells.DiscussionOur study revealed the multifaceted biological and clinical significance of ERCC family members in LUAD. These findings provide new insights into the function of ERCC family genes in LUAD and their potential clinical applications.

Objective: The VEGF function blockage effectively reduces the progression of diabetic retinopathy. Ranibizumab and bevacizumab are some anti-VEGF monoclonal antibodies (mAb). Considering the importance of affinity maturation of ranibizumab, we aimed to find the essential amino acids of the ranibizumab antibody (Ab). Materials and Methods: We tried to find the important amino acids of this antibody via Paratome, Meta-PPISP, and the WESA web server. Subsequently, these amino acids were mutated to improve the binding affinity of the Ab variants to antigen (Ag). In this regard, the ranibizumab anti-VEGF-A was mutated. The structural docking prediction of the ranibizumab-VEGF-A complex was used for the design and validation of ranibizumab with a higher affinity for binding to VEGF-A. Finally, we measured the binding affinity of Ab variants to Ag by computational docking. Results: Bioinformatic analyzes such as molecular docking and dynamics showed that several mutant variants successfully improved the properties of Ab binding compared to the wild-type Ab. Conclusion: Consistent with the use of anti-VEGF monoclonal antibodies in the treatment of diabetic retinopathy, the mutant variants of ranibizumab may be potential candidates for stronger affinity binding to VEGF, which may affect the specificity and sensitivity of the antibody.

Objective: Bullous pemphigoid is a rare autoimmune skin disorder characterized by blistering, urticarial lesions, which are sometimes associated with adverse drug reactions. Vildagliptin is an oral anti-diabetic agent that selectively inhibits the dipeptidyl peptidase-4 (DPP-4) enzyme. Materials and Methods: A 75-year-old female with a known case of type 2 Diabetes Mellitus, hypertension, and hypothyroidism for the last 10 years presented with pruriginous tense bullous skin lesions over her both palms and soles. There was no mucosal involvement. Further interrogation revealed that she started taking Vildagliptin 5 days ago which was prescribed due to high levels of post-prandial blood sugar level despite already intake of Glimepiride-4 mg and Metformin-3 gm. Results: Vildagliptin was immediately advised to be stopped. She was treated with antihistamines, steroids, and conservative management which led to remission of the blisters. Conclusion: Vildagliptin is the probable causative drug for developing bullous pemphigoid skin lesion which shows temporal association in this case as other concomitant drugs has no direct correlation. Therefore physicians must be aware of this rare life-threatening side effect of this medicine and advice patients to visit the hospital even the slightest cutaneous manifestation. Bullous pemphigoid can result in fatal life-threatening conditions if not treated early.

Abstract Background Stent restenosis is a relatively common phenomenon among patients with coronary heart disease undergoing percutaneous coronary intervention (PCI). It seems that a set of clinical, laboratory, and even genetic factors make people susceptible to such a phenomenon and in fact, this is multi-factorial. We aimed to first determine the underlying clinical and laboratory risk factors for the occurrence of stent re-stenosis after PCI based on a systematic review study, and after that, through a bioinformatics study, to evaluate the related genes and microRNAs with the occurrence of stent re-stenosis. Main text In the first step, the manuscript databases including Medline, Web of Knowledge, Google Scholar, Scopus, and Cochrane were deeply searched by the two blinded investigators for all eligible studies based on the considered keywords to introduce clinical and laboratory determinants of stent re-stenosis. In the bioinformatic phase, and following a review of the literature to identify genes and microRNAs involved in restenosis, the interaction of each gene with other genes associated with stent re-stenosis was determined by GeneMANIA network analysis and Cytoscape software. Overall, 67 articles (including 40,789 patients) on clinical and biochemical predictors for stent restenosis and 25 articles on genetic determinants of this event were eligible for the final analysis. The predictors for this event were categorized into four subgroups patient-based parameters including traditional cardiovascular risk profiles, stent-based parameters including type and diametric characteristics of the stents used, coronary lesion-based parameters including several two target lesions and coronary involvement severity and laboratory-based parameters particularly related to activation of inflammatory processes. In the bioinformatic phase, we uncovered 42 genes that have been described to be involved in such a phenomenon considering a special position for genes encoding inflammatory cytokines. Also, 12 microRNAs have been pointed to be involved in targeting genes involved in stent re-stenosis. Conclusions The incidence of stent re-stenosis will be the result of a complex interaction of clinical risk factors, laboratory factors mostly related to the activation of inflammatory processes, and a complex network of gene-to-gene interactions.

Summary: Objectives: The exostosins (EXT), which are responsible for heparan sulfate backbone synthesis and play a vital role in tissue homeostasis, have been reported to be correlated with prognosis of various cancers. However, the expression, prognostic value, and immune infiltration of EXT1 and EXT2 in head and neck squamous cell carcinoma (HNSC) remain uncertain. Methods: GEPIA, UALCAN, and Xiantao bioinformatics tools were used to explore the EXT1 and EXT2 expression level in HNSC. GEPIA and Sangerbox were utilised to obtain the prognostic value of EXT1 and EXT2 in HNSC. Genetic alterations, immune cell infiltration, and single-cell analysis were conducted in cBioPortal, TIMER, and TISCH2. In addition, the expressions of EXT1 and EXT2 were validated by real-time polymerase chain reaction (PCR) in HNSC samples. Results: EXT1 and EXT2 were highly expressed in HNSC, especially in malignant cells. Only EXT2 was significantly negatively correlated to the prognosis of patients with HNSC. EXT1 and EXT2 were found to be associated with focal adhesin and cell adhesin molecule binding. EXT1 expression levels were considerably connected with CD8+ T cell infiltrating levels, whilst EXT2 expression levels were considerably negatively connected with infiltrating levels of CD4+ T cells, macrophages, neutrophils, and dendritic cells in HNSC. The gene mutation rates of EXT1 and EXT2 in HNSC were 7% and 2.8%, respectively. Moreover, EXT2 was validated to be highly expressed in HNSC samples by real-time PCR. Conclusion: EXT2 was highly expressed and presented negative correlation with the prognosis and immune infiltration of HNSC, which might be a potential biomarker for HNSC.

Abstract Background The high mobility group A2 (HMGA2) gene is expressed extensively during early embryonic development but is inactivated in adulthood, and it is also reactivated in various benign and malignant tumors, including breast cancer. We first assessed the potential functional significance of the unstudied deletion polymorphism rs10573247 at the 3′UTR of HMGA2 on miRNA binding using bioinformatic tools, and subsequently, the association between this polymorphism and breast cancer susceptibility was investigated. Materials and methods We applied the RNAhybrid tool to predict the functional effects of polymorphism rs10573247 located within the 3’ UTR of the HMGA2 gene on miRNA binding. Then, following DNA extraction, 141 breast cancer patients and 123 healthy controls were genotyped for polymorphism rs10573247 using RFLP-PCR with the restriction enzyme Eam1104I. Results Our bioinformatic data have shown that polymorphism rs10573247 is located in the region that serves as a potential target site for eight miRNAs binding. Among them, miR-3125 exhibited decreased binding affinity for the allele delTT (MFE = -21.8) when compared to the allele TT (MFE = -23.9), but miR-4476 increased binding affinity for the allele delTT (MFE = -22.4) compared to the allele TT (MFE = -22.2). In addition, our results showed that the genotype TT/delTT (p = 0.005) and the genotype delTT/delTT (p = 0.029) were significantly associated with an increased risk of developing breast cancer compared to the genotype TT/TT using RFLP-PCR. Discussion and Conclusion Our findings suggest that polymorphism rs10573247 may contribute to the risk of breast cancer through the functional effect of this polymorphism on miRNA binding.

Jianmin Gong,1,2,&ast; Bo Shi,1,3,&ast; Ping Yang,1 Adeel Khan,4 Tao Xiong,2 Zhiyang Li1 1Department of Clinical Laboratory, Nanjing Drum Tower Hospital, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, 210000, People’s Republic of China; 2College of Life Science, Yangtze University, Jingzhou, 434025, People’s Republic of China; 3Department of Clinical Laboratory, Nanjing Jiangning Hospital of Chinese Medicine (CM), Nanjing, 211100, People’s Republic of China; 4Department of Biotechnology, University of Science and Technology Bannu, Bannu, 28100, Islamic Republic of Pakistan&ast;These authors contributed equally to this workCorrespondence: Tao Xiong, College of Life Science, Yangtze University, Jingzhou, 434025, People’s Republic of China, Tel +8613872387410, Email xiongtao@yangtzeu.edu.cn Zhiyang Li, Department of Clinical Laboratory, Nanjing Drum Tower Hospital, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, 210008, People’s Republic of China, Tel +8618951703765, Email lizhiyang@nju.edu.cnBackground: Hypertrophic cardiomyopathy (HCM) is a dominantly inherited disease associated with sudden immune cell associations that remain unclear. The aim of this study was to comprehensively screen candidate markers associated with HCM and immune cells and explore potential pathogenic pathways.Methods: First, download the GSE32453 dataset to identify differentially expressed genes (DEGs) and perform Gene Ontology and pathway enrichment analysis using DAVID and GSEA. Next, construct protein-protein interaction (PPI) networks using String and Cytoscape to identify hub genes. Afterward, use CIBERSORT to determine the proportion of immune cells attributed to key genes in HCM and conduct ROC analysis based on the external dataset GSE36961 to evaluate their diagnostic value. Finally, validate the expression of key genes in the hypertrophic cardiomyocyte model through qRT-PCR using data from the HPA database.Results: Comprehensive analysis revealed that there were 254 upregulated genes and 181 downregulated genes in HCM. The enrichment study underscored pathways of inflammatory signaling, including MAPK and PI3K-Akt pathways. Pathways abundant in genes associated with HCM encompassed myocardial contraction and NADH dehydrogenase activity. Additionally, the analysis of immune infiltration revealed a notable increase in macrophages, NK cells, and monocytes in the HCM group, showing statistically significant variances in CD4 memory resting T cell infiltration when compared to the healthy control group. Within the validation dataset GSE36961, the Area Under the Curve (AUC) scores for eight crucial genes (FOS, CD86, CD68, BDNF, PIK3R1, PLEK, RAC2, CCL2) each exceeded 0.8. The HPA database revealed the positioning traits and paths of these eight crucial genes in smooth muscle cells, myocardial cells, and fibroblasts. The outcomes of the qRT-PCR were aligned with the sequencing findings.Conclusion: Bioinformatics analysis unveiled pivotal genes, pathways, and immune involvement, illuminating the molecular underpinnings of HCM. These findings suggest promising therapeutic targets for clinical applications.Keywords: hypertrophic cardiomyopathy, bioinformatic, immune cells infiltration, biomarker

Abstract Pseudoalteromonas viridis strain BBR56 was isolated from seawater at Dutungan Island, South Sulawesi, Indonesia. Bacterial DNA was isolated using Promega Genomic DNA TM050. DNA purity and quantity were assessed using NanoDrop spectrophotometers and Qubit fluorometers. The DNA library and sequencing were prepared using Oxford Nanopore Technology GridION MinKNOW 20.06.9 with long read, direct, and comprehensive analysis. High accuracy base calling was assessed with Guppy version 4.0.11. Filtlong and NanoPlot were used for filtering and visualizing the FASTQ data. Flye (2.8.1) was used for de novo assembly analysis. Variant calls and consensus sequences were created using Medaka. The annotation of the genome was elaborated by DFAST. The assembled genome and annotation were tested using Busco and CheckM. Herein, we found that the highest similarity of the BBR56 isolate was 98.37% with the 16 S rRNA gene sequence of P. viridis G-1387. The genome size was 5.5 Mb and included chromosome 1 (4.2 Mbp) and chromosome 2 (1.3 Mbp), which encoded 61 pseudogenes, 4 noncoding RNAs, 113 tRNAs, 31 rRNAs, 4,505 coding DNA sequences, 4 clustered regularly interspaced short palindromic repeats, 4,444 coding genes, and a GC content of 49.5%. The sequence of the whole genome of P. viridis BBR56 was uploaded to GenBank under the accession numbers CP072425–CP072426, biosample number SAMN18435505, and bioproject number PRJNA716373. The sequence read archive (SRR14179986) was successfully obtained from NCBI for BBR56 raw sequencing reads. Digital DNA–DNA hybridization results showed that the genome of BBR56 had the potential to be a new species because no other bacterial genomes were similar to the sample. Biosynthetic gene clusters (BGCs) were assessed using BAGEL4 and the antiSMASH bacterial version. The genome harbored diverse BGCs, including genes that encoded polyketide synthase, nonribosomal peptide synthase, RiPP-like, NRP-metallophore, hydrogen cyanide, betalactone, thioamide-NRP, Lant class I, sactipeptide, and prodigiosin. Thus, BBR56 has considerable potential for further exploration regarding the use of its secondary metabolite products in the human and fisheries sectors.

Background: An excess of exosomes, nanovesicles released from all cells and key regulators of brain plasticity, is an emerging therapeutic target for stress-related mental illnesses. The effects of chronic stress on exosome levels are unknown; even less is known about molecular drivers of exosome levels in the stress response. Methods: We used our state-of-the-art protocol with 2 complementary strategies to isolate neuronal exosomes from plasma, ventral dentate gyrus, basolateral amygdala, and olfactory bulbs of male mice to determine the effects of chronic restraint stress (CRS) on exosome levels. Next, we used RNA sequencing and bioinformatic analyses to identify molecular drivers of exosome levels. Results: We found that CRS leads to an increase in the levels of neuronal exosomes but not total (i.e., not neuronally enriched) exosome levels assayed in plasma and the ventral dentate gyrus, whereas CRS leads to a decrease in neuronal exosome levels but not total exosome levels in the basolateral amygdala. There was a further specificity of effects as shown by a lack of changes in the levels of neuronal exosomes assayed in the olfactory bulbs. In pursuit of advancing translational applications, we showed that acetyl-L-carnitine administration restores the CRS-induced increase in neuronal exosome levels assayed in plasma (the most accessible specimen). Furthermore, the CRS-induced changes in neuronal exosome levels in the ventral dentate gyrus and basolateral amygdala mirrored the opposite pattern of CRS-induced transcriptional changes in these key brain areas, with β-estradiol signaling as a potential upstream driver of neuronal exosome levels. Conclusions: This study provides a foundation for future studies of new forms of local and distant communication in stress neurobiology by demonstrating specific relationships between neuronal exosome levels assayed in plasma and the brain and providing new candidate targets for the normalization of exosome levels.

Background: Stent restenosis is a relatively common phenomenon among patients with coronary heart disease undergoing percutaneous coronary intervention (PCI). It seems that a set of clinical, laboratory, and even genetic factors make people susceptible to such a phenomenon and in fact, this is multi-factorial. We aimed to first determine the underlying clinical and laboratory risk factors for the occurrence of stent re-stenosis after PCI based on a systematic review study, and after that, through a bioinformatics study, to evaluate the related genes and microRNAs with the occurrence of stent re-stenosis. Main text: In the first step, the manuscript databases including Medline, Web of Knowledge, Google Scholar, Scopus, and Cochrane were deeply searched by the two blinded investigators for all eligible studies based on the considered keywords to introduce clinical and laboratory determinants of stent re-stenosis. In the bioinformatic phase, and following a review of the literature to identify genes and microRNAs involved in restenosis, the interaction of each gene with other genes associated with stent re-stenosis was determined by GeneMANIA network analysis and Cytoscape software. Overall, 67 articles (including 40,789 patients) on clinical and biochemical predictors for stent restenosis and 25 articles on genetic determinants of this event were eligible for the final analysis. The predictors for this event were categorized into four subgroups patient-based parameters including traditional cardiovascular risk profiles, stent-based parameters including type and diametric characteristics of the stents used, coronary lesion-based parameters including several two target lesions and coronary involvement severity and laboratory-based parameters particularly related to activation of inflammatory processes. In the bioinformatic phase, we uncovered 42 genes that have been described to be involved in such a phenomenon considering a special position for genes encoding inflammatory cytokines. Also, 12 microRNAs have been pointed to be involved in targeting genes involved in stent re-stenosis. Conclusions: The incidence of stent re-stenosis will be the result of a complex interaction of clinical risk factors, laboratory factors mostly related to the activation of inflammatory processes, and a complex network of gene-to-gene interactions. [ABSTRACT FROM AUTHOR]

Objective: The VEGF function blockage effectively reduces the progression of diabetic retinopathy. Ranibizumab and bevacizumab are some anti-VEGF monoclonal antibodies (mAb). Considering the importance of affinity maturation of ranibizumab, we aimed to find the essential amino acids of the ranibizumab antibody (Ab). Materials and Methods: We tried to find the important amino acids of this antibody via Paratome, Meta-PPISP, and the WESA web server. Subsequently, these amino acids were mutated to improve the binding affinity of the Ab variants to antigen (Ag). In this regard, the ranibizumab anti-VEGF-A was mutated. The structural docking prediction of the ranibizumab-VEGF-A complex was used for the design and validation of ranibizumab with a higher affinity for binding to VEGF-A. Finally, we measured the binding affinity of Ab variants to Ag by computational docking. Results: Bioinformatic analyzes such as molecular docking and dynamics showed that several mutant variants successfully improved the properties of Ab binding compared to the wild-type Ab. Conclusion: Consistent with the use of anti-VEGF monoclonal antibodies in the treatment of diabetic retinopathy, the mutant variants of ranibizumab may be potential candidates for stronger affinity binding to VEGF, which may affect the specificity and sensitivity of the antibody. [ABSTRACT FROM AUTHOR]

Varicella-zoster virus (VZV), a common pathogen with humans as the sole host, causes primary infection and undergoes a latent period in sensory ganglia. The recurrence of VZV is often accompanied by severe neuralgia in skin tissue, which has a serious impact on the life of patients. During the acute infection of VZV, there are few related studies on the pathophysiological mechanism of skin tissue. In this study, transcriptome sequencing data from the acute response period within 2 days of VZV antigen stimulation of the skin were used to explore a model of the trajectory of skin tissue changes during VZV infection. It was found that early VZV antigen stimulation caused activation of mainly natural immune-related signaling pathways, while in the late phase activation of mainly active immune-related signaling pathways. JAK-STAT, NFκB, and TNFα signaling pathways are gradually activated with the progression of infection, while Hypoxia is progressively inhibited. In addition, we found that dendritic cell-mediated immune responses play a dominant role in the lesion damage caused by VZV antigen stimulation of the skin. This study provides a theoretical basis for the study of the molecular mechanisms of skin lesions during acute VZV infection. [ABSTRACT FROM AUTHOR]

Dipeptidyl peptidase-IV (DPPIV) inhibitory peptides are a class of antihyperglycemic drugs used in the treatment of type 2 diabetes mellitus, a metabolic disorder resulting from reduced levels of the incretin hormone GLP-1. Given that DPPIV degrades incretin, a key regulator of blood sugar levels, various antidiabetic medications that inhibit DPPIV, such as vildagliptin, sitagliptin, and linagliptin, are employed. However, the potential side effects of these drugs remain a matter of debate. Therefore, we aimed to investigate food-derived peptides from Cannabis sativa (hemp) seeds. Our developed bioinformatics pipeline was used to identify the putative hydrolyzed peptidome of three highly abundant proteins: albumin, edestin, and vicilin. These proteins were subjected to in silico digestion by different proteases (trypsin, chymotrypsin, and pepsin) and then screened for DPPIV inhibitory peptides using IDPPIV-SCM. To assess potential adverse effects, several prediction tools, namely, TOXINpred, AllerCatPro, and HemoPred, were employed to evaluate toxicity, allergenicity, and hemolytic effects, respectively. COPID was used to determine the amino acid composition. Molecular docking was performed using GalaxyPepDock and HPEPDOCK, 3D visualizations were conducted using the UCSF Chimera program, and MD simulations were carried out with AMBER20 MD software. Based on the predictive outcomes, FNVDTE from edestin and EAQPST from vicilin emerged as promising candidates for DPPIV inhibitors. We anticipate that our findings may pave the way for the development of alternative DPPIV inhibitors. [ABSTRACT FROM AUTHOR]

Background: The high mobility group A2 (HMGA2) gene is expressed extensively during early embryonic development but is inactivated in adulthood, and it is also reactivated in various benign and malignant tumors, including breast cancer. We first assessed the potential functional significance of the unstudied deletion polymorphism rs10573247 at the 3′UTR of HMGA2 on miRNA binding using bioinformatic tools, and subsequently, the association between this polymorphism and breast cancer susceptibility was investigated. Materials and methods: We applied the RNAhybrid tool to predict the functional effects of polymorphism rs10573247 located within the 3' UTR of the HMGA2 gene on miRNA binding. Then, following DNA extraction, 141 breast cancer patients and 123 healthy controls were genotyped for polymorphism rs10573247 using RFLP-PCR with the restriction enzyme Eam1104I. Results: Our bioinformatic data have shown that polymorphism rs10573247 is located in the region that serves as a potential target site for eight miRNAs binding. Among them, miR-3125 exhibited decreased binding affinity for the allele delTT (MFE = -21.8) when compared to the allele TT (MFE = -23.9), but miR-4476 increased binding affinity for the allele delTT (MFE = -22.4) compared to the allele TT (MFE = -22.2). In addition, our results showed that the genotype TT/delTT (p = 0.005) and the genotype delTT/delTT (p = 0.029) were significantly associated with an increased risk of developing breast cancer compared to the genotype TT/TT using RFLP-PCR. Discussion and Conclusion: Our findings suggest that polymorphism rs10573247 may contribute to the risk of breast cancer through the functional effect of this polymorphism on miRNA binding. [ABSTRACT FROM AUTHOR]

Following the milestone success of the Human Genome Project, the 'Encyclopedia of DNA Elements (ENCODE)' initiative was launched in 2003 to unearth information about the numerous functional elements within the genome. This endeavor coincided with the emergence of numerous novel technologies, accompanied by the provision of vast amounts of whole-genome sequences, high-throughput data such as ChIP-Seq and RNA-Seq. Extracting biologically meaningful information from this massive dataset has become a critical aspect of many recent studies, particularly in annotating and predicting the functions of unknown genes. The core idea behind genome annotation is to identify genes and various functional elements within the genome sequence and infer their biological functions. Traditional wet-lab experimental methods still rely on extensive efforts for functional verification. However, early bioinformatics algorithms and software primarily employed shallow learning techniques; thus, the ability to characterize data and features learning was limited. With the widespread adoption of RNA-Seq technology, scientists from the biological community began to harness the potential of machine learning and deep learning approaches for gene structure prediction and functional annotation. In this context, we reviewed both conventional methods and contemporary deep learning frameworks, and highlighted novel perspectives on the challenges arising during annotation underscoring the dynamic nature of this evolving scientific landscape. [ABSTRACT FROM AUTHOR]

Objective To identify the TMPRSS6 gene variants in Mexican patients with iron treatment refractoriness, to describe hematological and iron profile parameters, and to use bioinformatic prediction and protein modeling tools to assess a possible biological impact for the detected missense variants. Methods Nineteen patients referred with iron treatment refractoriness were studied. Peripheral blood was collected to determine hematic cytometry, iron profile, hemoglobin electrophoresis, and quantification. Molecular screening was carried out for exons 15 through 18 of the TMPRSS6 gene by Sanger sequencing and for frequent thalassemia variants by amplification-refractory mutation system-polymerase chain reaction (PCR) and gap-PCR. The biological impact of the detected missense variants was assessed using bioinformatic prediction and protein modeling tools. Results We found 5 genetic variants in the matriptase-2 catalytic domain: 1 at intron-15/exon-16 junction (rs60484081) and 4 exonic, 3 missense (rs377054987, p.Gly626Asp; rs1384127820, p.Ser672Thr; rs855791, p.Val727Ala) and 1 synonymous (rs2235321, p.Tyr730=), with frequencies ranging from 0.18 to 0.53. No significant differences were observed in the hematological parameters or iron profile, considering type and number of variants. Bioinformatic predictions suggested a possible biological impact only for rs377054987. Conclusions The TMPRSS6 variants observed in Mexican patients with oral iron treatment refractoriness have high frequencies; nevertheless, their relationship with hematological and iron profile parameters needs further research. The possible biological impact for rs377054987 is due to size and amino acid hydrophobicity changes and hydrogen bond modifications. [ABSTRACT FROM AUTHOR]

Pseudoalteromonas viridis strain BBR56 was isolated from seawater at Dutungan Island, South Sulawesi, Indonesia. Bacterial DNA was isolated using Promega Genomic DNA TM050. DNA purity and quantity were assessed using NanoDrop spectrophotometers and Qubit fluorometers. The DNA library and sequencing were prepared using Oxford Nanopore Technology GridION MinKNOW 20.06.9 with long read, direct, and comprehensive analysis. High accuracy base calling was assessed with Guppy version 4.0.11. Filtlong and NanoPlot were used for filtering and visualizing the FASTQ data. Flye (2.8.1) was used for de novo assembly analysis. Variant calls and consensus sequences were created using Medaka. The annotation of the genome was elaborated by DFAST. The assembled genome and annotation were tested using Busco and CheckM. Herein, we found that the highest similarity of the BBR56 isolate was 98.37% with the 16 S rRNA gene sequence of P. viridis G-1387. The genome size was 5.5 Mb and included chromosome 1 (4.2 Mbp) and chromosome 2 (1.3 Mbp), which encoded 61 pseudogenes, 4 noncoding RNAs, 113 tRNAs, 31 rRNAs, 4,505 coding DNA sequences, 4 clustered regularly interspaced short palindromic repeats, 4,444 coding genes, and a GC content of 49.5%. The sequence of the whole genome of P. viridis BBR56 was uploaded to GenBank under the accession numbers CP072425–CP072426, biosample number SAMN18435505, and bioproject number PRJNA716373. The sequence read archive (SRR14179986) was successfully obtained from NCBI for BBR56 raw sequencing reads. Digital DNA–DNA hybridization results showed that the genome of BBR56 had the potential to be a new species because no other bacterial genomes were similar to the sample. Biosynthetic gene clusters (BGCs) were assessed using BAGEL4 and the antiSMASH bacterial version. The genome harbored diverse BGCs, including genes that encoded polyketide synthase, nonribosomal peptide synthase, RiPP-like, NRP-metallophore, hydrogen cyanide, betalactone, thioamide-NRP, Lant class I, sactipeptide, and prodigiosin. Thus, BBR56 has considerable potential for further exploration regarding the use of its secondary metabolite products in the human and fisheries sectors. [ABSTRACT FROM AUTHOR]

Bone remodeling begins with bone resorption and culminates with the formation of new bone. Several variables influence bone remodeling, including oxidative stress, which results from an imbalance of oxidants, reactive oxygen species (ROS), and antioxidants. Herbal supplements may enhance interactions between antioxidants, growth factors, osteoclasts, and osteoblasts. The aim of this study was to investigate bioactive components in Stingless Bee's (Trigona biroi) propolis interact with antioxidants and biomarkers that enhance growth factor and osteoblastogenesis. The simulation of ligand interaction which attempts to identify a correlation between binding energy or binding affinity and ligand interaction patterns in the target domain. Bioinformatic approach employed is molecular docking screening, which screens compounds with the highest negative binding affinity using a grid that spans the full target area. PyRx v1.0.0 software with an academic license was used to simulate the binding of Stingless Bee's Propolis components to Heat Shock Protein (HSP)-10, HSP-70, Fibroblast Growth Factor-2 (FGF-2), Vascular Endothelial Growth Factor (VEGF), runt-related transcription factor-2 (RUNX2), alkaline phosphatase (ALP), Osteocalcin, and collagen type 1a1 (Coll1a1). Molecular docking simulations show that compounds from stingless bee's (T. biroi) propolis have the most negative binding affinity, such as 27-hydroxymangiferolic acid, which can trigger HSP-70 activity (-9.7 kcal/mol), 27-hydroxyisomangiferolic acid on FGF-2 (-8.3), VEGF (-7.3 kcal/mol), RUNX-2 (-7.7 kcal/mol), ALP (-7.9 kcal/mol), Osteocalcin (-7.3 kcal/mol), Coll1a1 (-5.7 kcal/mol), and (+)-Pinobaksin. 27-hydroxymangiferolic acid, 27-hydroxyisomangiferolic acid, and (+)-Pinobanksin in tingless bee (T. biroi) propolis are thought to activate targets and cause responses involving antioxidant activity, growth factors, and osteoblastogenesis. [ABSTRACT FROM AUTHOR]

Introduction: Rice, a key agricultural crop globally, is vital for feeding millions of people. Blast is one of the important diseases of rice and it causes severe damage to many rice fields every year. This disease is caused by the fungus Magnaporthe oryzae, which infects plant tissues and significantly reduces performance. The genus Oryza belongs to the Poaceae family and comprises 24 different species. Species within this genus are divided into two categories in terms of genome: diploid and tetraploid. The existence of synteny between genes specially those that cause resistance to plant diseases is considered a very suitable way to identify genes from other species. The pizh and pigm gene clusters play a role in rice blast disease resistance in rice. Synteny in pizh and pigm clusters was investigated in 11 species of Oryzaand three related species. Materials and methods: The pizh and pigm gene clusters were identified in 13 other species using the BLASTN tool. Subsequently, the genome sequences of these 13 species were obtained from the NCBI database then, all genes for all species were compared together by BLASN tool to construct the homology file (BLAST file). After, the genes were mapped to the respective species by Gmap software. Finally, gene blocks exhibiting synteny were identified through the utilization of the MCScanX software. Results: The results of synteny analysis of blast resistance genes in 14 studied species showed synteny only in five species: O. meridionalis, O. sativa indica, O. glumipatula, O. barthii, and O. glaberrima. Synteny was not observed in other species, including O. sativa japonica, regarding these gene clusters for blast resistance. According to the results, it could be concluded that the gene cluster involved in blast disease resistance, has not been spread and preserved between different chromosomes of studied Oryza species during evolution. In fact, they are located mainly on chromosomes Number 6 and 9. Micro synteny for the species including O. meridionalis, O.sativa indica, O. glumipatula, O. barthii and O. glaberrima were observed, but no synteny was observed for other five Oryza species and three wild relatives. It is necessary to mention that these genes are clustered on chromosome Number 6 for O. sativa japonica, however, they have no synteny with other species. Conclusion: Species including O. meridionalis, O.sativa indica, O. glumipatula, O. barthii and O. glaberrima may be used as a promising source of blast resistance for blast resistance breeding programs. [ABSTRACT FROM AUTHOR]

Abstract UBXD family (UBXDF), a group of proteins containing ubiquitin regulatory X (UBX) domains, play a crucial role in the imbalance of proliferation and apoptotic in cancer. In this study, we summarised bioinformatics proof on multi-omics databases and literature on UBXDF’s effects on cancer. Bioinformatics analysis revealed that Fas-associated factor 1 (FAF1) has the largest number of gene alterations in the UBXD family and has been linked to survival and cancer progression in many cancers. UBXDF may affect tumour microenvironment (TME) and drugtherapy and should be investigated in the future. We also summarised the experimental evidence of the mechanism of UBXDF in cancer, both in vitro and in vivo, as well as its application in clinical and targeted drugs. We compared bioinformatics and literature to provide a multi-omics insight into UBXDF in cancers, review proof and mechanism of UBXDF effects on cancers, and prospect future research directions in-depth. We hope that this paper will be helpful for direct cancer-related UBXDF studies.

Carcinogenesis is driven by complex molecular events, often involving key enzymes that regulate oxidative stress (OS). While classical enzymes such as SOD, catalase, and GPx have been extensively studied, other, non-classical oxidative stress-related enzymes (OSRE) may play critical roles in cancer progression. We aimed to explore the role of OSRE involved in an OS scenario and to assess their potential contribution to carcinogenesis in some of the most prevalent cancer types. Through data mining and bioinformatic analysis of gene and protein expression and mutation data, we identified OSRE with altered expression and mutations across cancer types. Functional pathways involving EGFR, MT-ND, GST, PLCG2, PRDX6, SRC, and JAK2 were investigated. Our findings reveal that enzymes traditionally considered peripheral to OS play significant roles in tumor progression. Those OSRE may contribute to cancer initiation and progression, as well as be involved with cancer hallmarks, such as EMT and invasion, proliferation, and ROS production. In addition, enzymes like SRC and JAK2 were found to have dual roles in both promoting ROS generation and being modulated by OS. OSRE also interact with key oncogenic signaling pathways, including Wnt/β-catenin and JAK2/STAT3, linking them to cancer aggressiveness and therapeutic resistance. Future research should focus on translating these findings into clinical applications, including the development of novel inhibitors or drugs targeting these non-classical enzymes.

Background: Rheumatoid arthritis (RA) is a chronic, systemic autoimmune disease, whose development is associated with immune cells and persistent inflammation. Exploring the biomarkers of RA holds immense significance in terms of the prevention, diagnosis, and treatment of RA. Material and methods: The differentially expressed genes (DEGs) in RA patients and the control group were screened by limma package. Through DEGs intersection overlapping 200 inflammatory response-related genes and 2498 immune-related genes, differentially expressed immune and inflammation-related genes (DE-IIRGs) were identified. Lasso regression analysis screened RA diagnostic biomarkers and constructed PPI networks. Finally, immune infiltration analysis and drug prediction were performed. Results: A total of 20 DE-IIRGs were identified by overlapping DEGs with 2498 immune-related genes and 200 inflammatory response-related genes. These DE-IIRGs were primarily enriched in the cytokine-cytokine receptor interaction and other biological processes, and then five biomarker genes (TNFSF10, IL1R1, CXCL9, ACVR1B, and IL15) were identified. It was found that the expression levels of CXCL9, IL15, and TNFSF10 in the disease samples were significantly higher than those in the control group. These biomarker genes have more effective diagnostic potential. The RA samples exhibited significantly higher levels of cell infiltration compared to the control samples. hsa-miR-199a-5p's connections to the ACVR1B and CCR7 genes were identified by creating ceRNA networks from 20 screened DE-IIRGs. There was a connection between CCL5 and AEMA4D and hsa-miR-214-3p. Conclusion: We identified immune- and inflammation-related biomarkers in RA based on bioinformatics analysis and screened TNFSF10, IL1R1, CXCL9, ACVR1B, and IL15 as diagnostic markers for RA. Key Points • TNFSF10, IL1R1, CXCL9, ACVR1B, and IL15 may be new diagnostic biomarkers for RA. • These findings may provide a theoretical basis for early RA diagnosis. [ABSTRACT FROM AUTHOR]