Database For Annotation Research Articles

Hsa‑miR‑455‑3P as a predictive biomarker of anemia in patients with non‑small cell lung cancer treated with carboplatin plus paclitaxel.

Lung cancer is the leading cause of cancer-related morbidity and mortality worldwide. The initial treatment of lung cancer depends on the definition of the tumor type and its staging. The most common treatment is chemotherapy, and the first-line treatment is a combination of carboplatin and paclitaxel. Although this treatment has good efficacy, there is a high prevalence of adverse events, particularly hematological reactions. Studies on new biomarkers related to these adverse events, such as circulating microRNAs (miRNAs/miRs), are important for optimizing the quality of life of patients. miRNAs have high stability in several biological fluids and they have specific expressions in different tissues or pathologies. Thus, the present study aimed to assess the relationship between circulating miRNAs and adverse hematologic reactions caused by treatment with carboplatin + paclitaxel in patients with lung cancer. Blood was collected from patients before and 15 days after chemotherapy for hematological adverse reaction analysis, microarray and quantitative (q)PCR validation. Adverse reactions were classified according to the Common Terminology Criteria for Adverse Events v4.0. Microarray analysis was performed using plasma from six patients without anemia and six patients with anemia, and nine miRNAs were differentially expressed. miR-1273g-3p, miR-3613-5p and miR-455-3p, identified using microarray, were assessed using qPCR in 20 patients without anemia and 26 patients with anemia. Bioinformatic analyses of miR-455-3p were performed using miRWalk, the Database for Annotation, Visualization and Integrated Discovery and GeneMania software. Microarray analysis of patients with and without anemia revealed nine significant differentially-expressed plasma miRNAs among these patients. Of these, miR-1273g-3p, miR-3613-5p and miR-455-3p were chosen for further assessment. Only miR-455-3p demonstrated a significant reduction in expression (P=0.04) between the groups before chemotherapy with carboplatin + paclitaxel. Bioinformatics analysis of miR-455-3p revealed a relationship between this miRNA and the hematopoietic pathway, particularly with respect to the RUNX family transcription factor 1 (RUNX1) and TAL bHLH transcription factor 1, erythroid differentiation factor (TAL1) genes. The most prevalent adverse reactions in patients with lung cancer treated with carboplatin + paclitaxel were hematological, particularly anemia. This adverse reaction, caused by dysfunction of the hematopoietic system, may be explained by a possible association between the important genes in this system, RUNX1 and TAL1, and hsa-miR-455-3p.

The Mechanism of Polygonum Hydropiper L-Coptis Chinensis in the Treatment of Ulcerative Colitis Based on Network Pharmacology and Experimental Validation.

Polygonum hydropiper L (PH) was widely used to treat dysentery, gastroenteritis, diarrhea and other diseases. Coptis chinensis (CC) had the effects of clearing dampness-heat, purging fire, and detoxifying. Study confirmed that flavonoids in PH and alkaloids in CC alleviated inflammation to inhibit the development of intestinal inflammation. However, how PH-CC affects UC was unclear. Therefore, the aim of this study is to analyze the mechanism of PH-CC on ulcerative colitis (UC) through network pharmacology and in vivo experiments. The active ingredients and targets of PH-CC and targets of UC were screened based on related databases. The core targets of PH-CC on UC was predicted by protein-protein interaction network (PPI), and then the Gene Ontology-biological processes (GO-BP) function enrichment analysis was conducted using the Database for Annotation, Visualization and Integrated Discovery (DAVID) database. The binding activity between pyroptosis proteins, core targets and effective ingredients were verified based on molecular docking technology. Finally, combined with the results of network pharmacology and literature research, the mechanism of PH-CC against UC was verified by in vivo experiments. There were 23 active components and 191 potential targets in PH-CC, 5275 targets in UC, and 141 co-targets. GO-BP functional analysis of 141 co-targets showed that the first 20 biological processes were closely related to inflammation and lipopolysaccharide (LPS) stimulation. Furthermore, core targets had good binding activity with the corresponding compounds. Animal experiment indicated that PH-CC effectively prevented weight loss in UC mice, reduced the disease activity index (DAI) score, maintained colon length, suppressed myeloperoxidase (MPO) activity, inhibited pyroptosis protein expression, and downregulated the levels of IL-18 and IL-1β to alleviate intestinal inflammation. The results of network pharmacology and animal experiments showed that PH-CC suppressed the inflammatory response, restored colon morphology, and inhibited pyroptosis in UC mice. Thus, PH-CC may improve UC by regulating the NOD-like receptor protein domain 3 (NLRP3)/Caspase-1 signaling pathway.

Small extracellular vesicle microRNAs in pediatric myasthenia gravis plasma and skeletal muscle.

The diagnosis of myasthenia gravis (MG) in children remains difficult. Circulating small extracellular vesicle (sEV)-derived miRNAs (sEV-miRNAs) have been recognized as biomarkers of various diseases and can be excreted by different cell types. These biomarker candidates also play a vital role in autoimmune diseases via intercellular communication. In the present study, we used sEV isolation and purification methods to extract the plasma-derived sEV-miRNAs from children with MG and healthy controls. A small RNA sequencing analysis confirmed the miRNA expression features in plasma-derived sEVs from MG patients. The miRNA expression analysis in vitro was determined using microarray analysis. The enrichment and network analyses of altered sEV-miRNAs were performed using miRNA databases and Database for Annotation, Visualization, and Integrated Discovery website. Quantitative real-time polymerase chain reaction was performed for validation of sEV-miRNA. The diagnostic power of altered sEV-miRNAs was evaluated using receiver operating characteristic curve analyses. Twenty-four sEV-miRNAs with altered expression level were identified between groups by DESeq2 method. The miRNAs were extracted from the sEVs, which were isolated from human primary skeletal muscle cell culture treated with mAb198. The target genes and enriched pathways of sEV-miRNAs partially overlapped between cell supernatant and plasma samples. The significantly downregulated miR-143-3p was validated in quantitative real-time polymerase chain reaction analysis. For the first time, we report that plasma-derived sEV-miRNAs may act as novel circulating biomarkers and therapeutic targets in pediatric MG.

Artificial intelligence in technologies for segmentation and classification of neuro-oncological lesions

The study is devoted to considering the effectiveness of modern approaches to the development of diagnostic technology for analyzing MRI images in neuro-oncology, based on artificial intelligence (AI) and computer vision. Such approaches are necessary for rapid and diagnostically effective analysis to implement the principle of individualized medicine. Material and methods. An analysis of the effectiveness of the choice of AI technologies for the formation of processes of segmentation and classification of neuro-oncological MRI images has been presented. AI was trained on its own annotated database (SBT Dataset), containing about 1000 clinical cases based on archival data from preoperative MRI studies at the Federal Neurosurgical Center (Novosibirsk, Russian Federation), in patients with astrocytoma, glioblastoma, meningioma, neuroma, and with metastases of somatic tumors, with histological and histochemical postoperative confirmation. Results and discussion. The effectiveness and efficiency of the developed technologies was tested during the international BraTS competition, in which it was proposed to segment and classify cases from a dataset of neuro-oncological patients prepared by the competition organizers. Conclusions. The methodological approaches proposed in the article in the development of diagnostic systems based on AI and the principles of computer vision have shown high efficiency at the level of dozens of world leaders and can be used to develop software and hardware systems for diagnostic neuroradiology with the functions of a “doctor’s assistant.”

Bioinformatic Sweeties: a unified portal for characterizing human proteins and their variants

Next-generation sequencing techniques provide an unprecedented characterisation of human Variants of Unknown Significance (VUS). Single-residue variations are collected in public databases and associated to diseases and phenotypes. However, for detailing at molecular level mechanisms involved in the onset of diseases, variants need structural and functional annotation. Here we propose a new portal called Bioinformatic Sweeties, collecting resources ranging from databases for human protein annotation to computational methods for predicting impact of variants. The tools, included in the portal, allow computing different protein properties, ranging from solvent accessible surface to stability and interactions and do not require login or installation. The portal, speeding up the variant characterisation process, is available at: https://bioinformaticsweeties.biocomp.unibo.it

The Treatment of Tubal Inflammatory Infertility using Yinjia Tablets through EGFR/MEK/ERK Signaling Pathway based on Network Pharmacology.

<p>Background: Salpingitis obstructive infertility (SOI) refers to infertility caused by abnormal conditions such as tubal adhesion and blockage caused by acute and chronic salpingitis. SOI has a serious impact on women's physical and mental health and family harmony, and it is a clinical problem that needs to be solved urgently. <p> Objective: The purpose of the present study was to explore the potential pharmacological mechanisms of the Yinjia tablets (Yin Jia Pian, YJP) on tubal inflammation. <p> Methods: Networks of YJP-associated targets and tubal inflammation-related genes were constructed through the STRING database. Potential targets and pathway enrichment analysis related to the therapeutic efficacy of YJP were identified using Cytoscape and Database for Annotation, Visualization, and Integrated Discovery (metascape). E. coli was used to establish a rat model of tubal inflammation and to validate the predictions of network pharmacology and the therapeutic efficacy of YJP. H&E staining was used to observe the pathological changes in fallopian tubes. TEM observation of the ultrastructure of the fallopian tubes. ELISA was used to detect the changes of IL-6 and TNF-α in fallopian tubes. Immunohistochemistry was used to detect the expression of ESR1. The changes of Bcl-2, ERK1/2, p-ERK1/2, MEK, p-MEK, EGFR, and p-EGFR were detected by western blot. <p> Results: Through database analysis, it was found that YJP shared 105 identical targets with the disease. Network pharmacology analysis showed that IL-6, TNF, and EGFR belong to the top 5 core proteins associated with salpingitis, and EGFR/MEK/ERK may be the main pathway involved. The E. coli-induced disease rat model of fallopian tube tissue showed damage, mitochondrial disruption, and increased levels of the inflammatory factors IL-6 and TNF-α. Tubal inflammatory infertility rats have increased expression of Bcl-2, p-ERK1/2, p-MEK, and p-EGFR, and decreased expression of ESR1. In vivo, experiments showed that YJP improved damage of tissue, inhibited shedding of tubal cilia, and suppressed the inflammatory response of the body. Furthermore, YJP inhibited EGFR/MEK/ERK signaling, inhibited the apoptotic protein Bcl-2, and upregulated ESR1. <p> Conclusion: This study revealed that YJP Reducing tubal inflammation and promoting tissue repair may be associated with inhibition of the EGFR/MEK/ERK signaling pathway.</p>.

Transcriptomic analysis of nitrogen metabolism pathways in Klebsiella aerogenes under nitrogen-rich conditions.

The acceleration of the nitrogen cycle and the nitrogen excess observed in some coastal waters has increased interest into understanding the biochemical and molecular basis of nitrogen metabolism in various microorganisms. To investigate nitrogen metabolism of a novel heterotrophic nitrification and aerobic denitrification bacterium Klebsiella aerogenes strain (B23) under nitrogen-rich conditions, we conducted physiological and transcriptomic high-throughput sequencing analyses on strain B23 cultured on potassium nitrate-free or potassium nitrate-rich media. Overall, K. aerogenes B23 assimilated 82.47% of the nitrate present into cellular nitrogen. Further, 1,195 differentially expressed genes were observed between K. aerogenes B23 cultured on potassium nitrate-free media and those cultured on potassium nitrate-rich media. Gene annotation and metabolic pathway analysis of the transcriptome were performed using a series of bioinformatics tools, including Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and Non-Redundant Protein Database annotation. Accordingly, the nitrogen metabolism pathway of K. aerogenes B23 was analyzed; overall, 39 genes were determined to be involved in this pathway. Differential expression analysis of the genes involved in the nitrogen metabolism pathway demonstrated that, compared to the control, FNR, NarK/14945, fdx, gshA, proB, proA, gapA, argH, artQ, artJ, artM, ArgR, GAT1, prmB, pyrG, glnS, and Ca1 were significantly upregulated in the nitrogen-treated K. aerogenes B23; these genes have been established to be involved in the regulation of nitrate, arginine, glutamate, and ammonia assimilation. Further, norV, norR, and narI were also upregulated in nitrogen-treated K. aerogenes B23; these genes are involved in the regulation of NO metabolism. These differential expression results are important for understanding the regulation process of key nitrogen metabolism enzyme genes in K. aerogenes B23. Therefore, this study establishes a solid foundation for further research into the expression regulation patterns of nitrogen metabolism-associated genes in K. aerogenes B23 under nitrogen-rich conditions; moreover, this research provides essential insight into how K. aerogenes B23 utilizes nutritional elements.

Prognostic gene biomarkers for c-Src inhibitor Si162 sensitivity in melanoma cells.

Early detection and treatment are crucial in combating malignant melanoma. Src is an important therapeutic target in melanoma due to its association with cancer progression. However, developing effective Src-targeting drugs remains challenging and personalized medicine relies on biomarkers and targeted therapies for precise and effective treatment. This study focuses on Si162, a newly synthesized c-Src inhibitor, to identify reliable biomarkers for predicting Si162 sensitivity and explore associated biological characteristics and pathways in melanoma cells. Primary melanoma cells (M1, M21, M24, M84, M133, M307, and M2025) were obtained from patients diagnosed with melanoma. Si162 cytotoxicity tests were performed using luminescent adenosine triphosphate detection and the half-maximal inhibitory concentration (IC50) values were calculated. Gene expression profiles were analyzed using microarray-based gene expression data. Differentially expressed genes between the resistant and sensitive groups were identified using Pearson correlation analysis. Gene coexpression, interactions, and pathways were investigated through clustering, network, and pathway analyses. Biological functions were examined using the Database for Annotation, Visualization, and Integrated Discovery. Molecular pathways associated with different responses to Si162 were identified using gene set enrichment analysis. The gene expressions were validated using reverse transcription-quantitative polymerase chain reaction. The cells revealed significant differences in response to Si162 based on the IC50 values (p < 0.05). A total of 36 differentially expressed genes associated with Si162 susceptibility were identified. Distinct expression patterns between the sensitive and resistant groups were observed in 9 genes (LRBA, MGMT, CAND1, ADD1, SETD2, CNTN6, FGF18, C18orf25, and RPL13). Coexpression among the differentially expressed genes was highlighted, and 9 genes associated with molecular pathways, including EMT, transforming growth factor-beta (TGF-β) signaling, and ribosomal protein synthesis, between groups. Genes involved in dysregulated immune response were observed in the resistant group. The involvement of 5 genes (ADD1, CNTN6, FGF18, C18orf25, and RPL13) in Si162 resistance was confirmed through qRT-PCR validation. These findings contribute to our understanding of the underlying biological differences among melanoma cells and suggest potential biomarkers and pathways associated with Si162 response and resistance.

Identification of anti-gastric cancer effects and molecular mechanisms of resveratrol: From network pharmacology and bioinformatics to experimental validation.

Gastric cancer (GC) is one of the most aggressive malignancies with limited therapeutic options and a poor prognosis. Resveratrol, a non-flavonoid polyphenolic compound found in a variety of Chinese medicinal materials, has shown excellent anti-GC effect. However, its exact mechanisms of action in GC have not been clarified. To identify the effects of resveratrol on GC progression and explore the related molecular mechanisms. Action targets of resveratrol and GC-related targets were screened from public databases. The overlapping targets between the two were confirmed using a Venn diagram, and a "Resveratrol-Target-GC" network was constructed using Cytoscape software version 3.9.1. The protein-protein interaction (PPI) network was constructed using STRING database and core targets were identified by PPI network analysis. The Database for Annotation, Visualization and Integrated Discovery database was used for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. A "Target-Pathway" network was created by using Cytoscape 3.9.1. The RNA and protein expression levels of core target genes were observed using the Cancer Genome Atlas and the Human Protein Atlas databases. DriverDBv3 and Timer2.0 databases were used for survival and immune infiltration analysis. Subsequently, the findings were further verified by molecular docking technology and in vitro experiments. A total of 378 resveratrol action targets and 2154 GC disease targets were obtained from public databases, and 181 intersection targets between the two were screened by Venn diagram. The top 20 core targets were identified by PPI network analysis of the overlapping targets. GO function analysis mainly involved protein binding, identical protein binding, cytoplasm, nucleus, negative regulation of apoptotic process and response to xenobiotic stimulus. KEGG enrichment analysis suggested that the involved signaling pathways mainly included PI3K-AKT signaling pathway, MAPK signaling pathway, IL-17 signaling pathway, TNF signaling pathway, ErbB signaling pathway, etc. FBJ murine osteosarcoma viral oncogene homolog (FOS) and matrix metallopeptidase 9 (MMP9) were selected by differential expression analysis, and they were closely associated with immune infiltration. Molecular docking results showed that resveratrol docked well with these two targets. Resveratrol treatment arrested the cell cycle at the S phase, induced apoptosis, and weakened viability, migration and invasion in a dose-dependent manner. Furthermore, resveratrol could exhibit anti-GC effect by regulating FOS and MMP9 expression. The anti-GC effects of resveratrol are related to the inhibition of cell proliferation, migration, invasion and induction of cell cycle arrest and apoptosis by targeting FOS and MMP9.

Exploring the therapeutic mechanisms and prognostic targets of Biochanin A in glioblastoma via integrated computational analysis and in vitro experiments

Glioblastoma (GBM) is the most aggressive brain tumor and is characterized by a poor prognosis and high recurrence and mortality rates. Biochanin A (BCA) exhibits promising clinical anti-tumor effects. In this study, we aimed to explore the pharmacological mechanisms by which BCA acts against GBM. Network pharmacology was employed to identify overlapping target genes between BCA and GBM. Differentially expressed genes from the Gene Expression Profiling Interactive Analysis 2 (GEPIA2) database were visualized using VolcaNose. Interactions among these overlapping genes were analyzed using the Search Tool for the Retrieval of Interacting Genes/Proteins database. Protein–protein interaction networks were constructed using Cytoscape 3.8.1. The Kyoto Encyclopedia of Genes and Genomes pathway and Gene Ontology enrichment analyses were conducted using the Database for Annotation, Visualization, and Integrated Discovery. Survival analyses for these genes were performed using the GEPIA2 database. The Chinese Glioma Genome Atlas database was used to study the correlations between key prognostic genes. Molecular docking was confirmed using the DockThor database and visualized with PyMol software. Cell viability was assessed via the CCK-8 assay, apoptosis and the cell cycle stages were examined using flow cytometry, and protein expression was detected using western blotting. In all, 63 genes were initially identified as potential targets for BCA in treating GBM. Enrichment analysis suggested that the pharmacological mechanisms of BCA primarily involved cell cycle inhibition, induction of cell apoptosis, and immune regulation. Based on these findings, AKT1, EGFR, CASP3, and MMP9 were preliminarily predicted as key prognostic target genes for BCA in GBM treatment. Furthermore, molecular docking analysis suggested stable binding of BCA to the target protein. In vitro experiments revealed the efficacy of BCA in inhibiting GBM, with an IC50 value of 98.37 ± 2.21 μM. BCA inhibited cell proliferation, induced cell apoptosis, and arrested the cell cycle of GBM cells. Furthermore, the anti-tumor effects of BCA on U251 cells were linked to the regulation of the target protein. We utilized integrated bioinformatics analyses to predict targets and confirmed through experiments that BCA possesses remarkable anti-tumor activities. We present a novel approach for multi-target treatment of GBM using BCA.

QTL-seq analysis identified the genomic regions of plant height and days to heading in high-latitude rice.

Introduction: Rice (Oryza sativa L.) is one of the most extensive crops in the world. China's Heilongjiang Province is the northernmost rice-growing region in the world. However, rice cultivars suitable for growth in low-latitude regions may not mature normally due to their distinct climate and short frost-free period. It is necessary to precisely determine the frost-free period for each region to make the best use of the rice growth stage so as to ensure the maturity and yield of different rice cultivars in Heilongjiang Province. The time span of the heading stage is a key parameter for evaluating the adaptability of a rice cultivar to a specific rice-growing region. Given the above facts, it is of high importance to study the associated genes and sites controlling days to heading (DH) and plant height (PH) of rice in Heilongjiang Province. Bulked segregant analysis (BSA) combined with high-throughput sequencing can effectively exclude interferences from background genomic differences, making it suitable for analyzing the associated sites of complex agronomic traits in early generations. Methods: In this study, an F3 segregating population was obtained by crossing two main cultivars that are grown under different temperatures and day-light conditions in Heilongjiang. Two pools of extreme phenotypes were built for the DH and PH of the population. For SNP and InDel variants obtained from whole-genome resequencing in the pools, an association analysis was performed using the Euclidean distance (ED) algorithm and the SNP/InDel index algorithm. Results: The intersection of SNP and InDel regions associated with the phenotypes was considered to obtain the final associated sites. After excluding interferences from the cloned genes on chromosomes 2 and 7, a total length of 6.34Mb on chromosomes 1, 3, and 10 and 3.16Mb on chromosomes 1 and 10 were left associated with PH and DH, respectively. Then, we performed a gene annotation analysis for candidate genes in the remaining regions using multiple genome annotation databases. Our research provides basic data for subsequent gene mapping and cloning. Discussion: By mining more genetic loci associated with the days to heading and plant height of rice, we may provide abundant genetic resources for refined molecular breeding in Heilongjiang Province.

Network pharmacology integrated molecular docking of fucoidan against oral cancer and in vitro evaluation- A study using GEO datasets

Oral cancer is a widespread health concern in rural India due to a lack of awareness, delayed diagnosis and limited access to affordable treatment options. The current chemotherapy has notable side effects, underscoring the need for new drug candidates with improved bioavailability and specificity. In this current research, fucoidan, a sulphated polysaccharide, was extracted from the brown algae Spatoglossum asperum, and shown to be cytotoxic in vitro against oral cancer cells (KB cell line) at an IC50 of 107.76 µg/ml, suggesting its potential as a drug candidate. This study further aimed to explore the potential therapeutic implications of fucoidan in managing oral cancer using network pharmacology. PharmMapper, Comparative Toxicogenomics Database and SuperPred were initially used to identify fucoidan protein targets. The identified targets were further screened against Gene Expression Omnibus (GSE23558, GSE25099 and GSE146483), OMIM, TCGA and GeneCards datasets to identify oral cancer-specific protein targets. The interactions between the selected proteins were visualised using STRING and Cytoscape. Subsequently, Database for Annotation, Visualization and Integrated Discovery was used for gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis of candidate targets. The cancer-related network was assessed using CancerGeneNet, while life expectancy based on the expression of the top 10 CytoHubba ranked hub genes was evaluated using Kaplan-Meier plots. Finally, EGFR, AKT1, HSP90AA1 and SRC were selected for docking and molecular dynamics simulation with fucoidan, using Maestro and GROMACS, respectively. Communicated by Ramaswamy H. Sarma

Transcriptome Analysis in Mexican Adults with Acute Lymphoblastic Leukemia.

Acute lymphoblastic leukemia (ALL) represents around 25% of adult acute leukemias. Despite the increasing improvement in the survival rate of ALL patients during the last decade, the heterogeneous clinical and molecular features of this malignancy still represent a major challenge for treatment and achieving better outcomes. To identify aberrantly expressed genes in bone marrow (BM) samples from adults with ALL, transcriptomic analysis was performed using Affymetrix Human Transcriptome Array 2.0 (HTA 2.0). Differentially expressed genes (DEGs) (±2-fold change, p-value < 0.05, and FDR < 0.05) were detected using the Transcriptome Analysis Console. Gene Ontology (GO), Database for Annotation, Visualization, and Integrated Discovery (DAVID), and Ingenuity Pathway Analysis (IPA) were employed to identify gene function and define the enriched pathways of DEGs. The protein-protein interactions (PPIs) of DEGs were constructed. A total of 871 genes were differentially expressed, and DNTT, MYB, EBF1, SOX4, and ERG were the top five up-regulated genes. Meanwhile, the top five down-regulated genes were PTGS2, PPBP, ADGRE3, LUCAT1, and VCAN. An association between ERG, CDK6, and SOX4 expression levels and the probability of relapse and death was observed. Regulation of the immune system, immune response, cellular response to stimulus, as well as apoptosis signaling, inflammation mediated by chemokines and cytokines, and T cell activation were among the most altered biological processes and pathways, respectively. Transcriptome analysis of ALL in adults reveals a group of genes consistently associated with hematological malignancies and underscores their relevance in the development of ALL in adults.

Identification of prognostic biomarkers of smoking-related lung cancer.

The early diagnosis and effective prognostic treatment measures for lung cancer are still limited, leading to a 5-year survival rate of less than 15% for these patients. Smoking is one of the causes of lung cancer, but it is not the initial carcinogenic factor. It is not clear what specific mechanism cigarette induces lung cancer, and there is a lack of research on the relationship between related genes and the prognosis of patients with smoking lung cancer. The objective of this study was to provide new theoretical evidence and potential therapeutic targets for the mechanisms of smoking-related lung cancer formation. The gene expression profile data from the GSE12428 dataset which includes 63 lung cancer and normal tissue pairs were downloaded from the Gene Expression Omnibus (GEO) database, and data from smokers with lung cancer [both lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC)] from The Cancer Genome Atlas (TCGA) database were analyzed. The differential genes in smokers with lung cancer were screened using the linear model for microarray data via R software. The differential gene enrichment analysis was performed using the online analysis software Database for Annotation, Visualization and Integrated Discovery (DAVID). The expression levels of differential genes and their correlation with patient tumor clinical stage were analyzed using gene expression profiling interactive analysis (GEPIA). The overall survival rate was analyzed using Kaplan-Meier curves. In the GSE12428 dataset, 225 upregulated genes and 565 downregulated genes were identified in cancer tissues; based on smoking status, 1 upregulated gene and 4 downregulated genes were identified. Among smokers who also had lung cancer, 4 genes were downregulated, namely CSH1, BPIFA1, SLPI, and SCGB3A1. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis revealed that these genes were mainly associated with biological functions such as antibacterial response, humoral immune response, and response to external stimuli. Among them, BPIFA1, SLPI, and SCGB3A1 expression was decreased in lung cancer tissues, with SCGB3A1 showing significant differences. Additionally, high expression of SCGB3A1 was associated with favorable prognosis in patients with lung cancer. Three genes BPIFA1, SLPI and SCGB3A1, were identified as being associated with smokers with lung cancer, with SCGB3A1 showing a close correlation with patient prognosis. These findings provide potential new targets for the treatment of lung cancer. Certainly, this study needs to more investigate the mechanism of these genes regulation.

PoMeLo: a systematic computational approach to predicting metabolic loss in pathogen genomes

BackgroundGenome streamlining, the process by which genomes become smaller and encode fewer genes over time, is a common phenomenon among pathogenic bacteria. This reduction is driven by selection for minimized energy expenditure in a nutrient-rich environment. As pathogens evolve to become more reliant on the host, metabolic genes and resulting capabilities are lost in favor of siphoning metabolites from the host. Characterizing genome streamlining, gene loss, and metabolic pathway degradation can be useful in assessing pathogen dependency on host metabolism and identifying potential targets for host-directed therapeutics.ResultsPoMeLo (Predictor of Metabolic Loss) is a novel evolutionary genomics-guided computational approach for identifying metabolic gaps in the genomes of pathogenic bacteria. PoMeLo leverages a centralized public database of high-quality genomes and annotations and allows the user to compare an unlimited number of genomes across individual genes and pathways. PoMeLo runs locally using user-friendly prompts in a matter of minutes and generates tabular and visual outputs for users to compare predicted metabolic capacity between groups of bacteria and individual species. Each pathway is assigned a Predicted Metabolic Loss (PML) score to assess the magnitude of genome streamlining. Optionally, PoMeLo places the results in an evolutionary context by including phylogenetic relationships in visual outputs. It can also initially compute phylogenetically-weighted mean genome sizes to identify genome streamlining events. Here, we describe PoMeLo and demonstrate its use in identifying metabolic gaps in genomes of pathogenic Treponema species.ConclusionsPoMeLo represents an advance over existing methods for identifying metabolic gaps in genomic data, allowing comparison across large numbers of genomes and placing the resulting data in a phylogenetic context. PoMeLo is freely available for academic and non-academic use at https://github.com/czbiohub-sf/pomelo.

Star allele search: a pharmacogenetic annotation database and user-friendly search tool of publicly available 1000 Genomes Project biospecimens

Here we describe a new public pharmacogenetic (PGx) annotation database of a large (n = 3,202) and diverse biospecimen collection of 1000 Genomes Project cell lines and DNAs. The database is searchable with a user friendly, web-based tool (www.coriell.org/StarAllele/Search). This resource leverages existing whole genome sequencing data and PharmVar annotations to characterize *alleles for each biospecimen in the collection. This new tool is designed to facilitate in vitro functional characterization of *allele haplotypes and diplotypes as well as support clinical PGx assay development, validation, and implementation.

Artificial intelligence and database for NGS-based diagnosis in rare disease.

Rare diseases (RDs) are rare complex genetic diseases affecting a conservative estimate of 300 million people worldwide. Recent Next-Generation Sequencing (NGS) studies are unraveling the underlying genetic heterogeneity of this group of diseases. NGS-based methods used in RDs studies have improved the diagnosis and management of RDs. Concomitantly, a suite of bioinformatics tools has been developed to sort through big data generated by NGS to understand RDs better. However, there are concerns regarding the lack of consistency among different methods, primarily linked to factors such as the lack of uniformity in input and output formats, the absence of a standardized measure for predictive accuracy, and the regularity of updates to the annotation database. Today, artificial intelligence (AI), particularly deep learning, is widely used in a variety of biological contexts, changing the healthcare system. AI has demonstrated promising capabilities in boosting variant calling precision, refining variant prediction, and enhancing the user-friendliness of electronic health record (EHR) systems in NGS-based diagnostics. This paper reviews the state of the art of AI in NGS-based genetics, and its future directions and challenges. It also compare several rare disease databases.

Turkey hen sperm storage tubule transcriptome response to artificial insemination and the presence of semen.

Introduction: Sperm storage within the uterovaginal junction (UVJ) of avian species occurs in specialized structures termed sperm storage tubules (SSTs) and allows for prolonged storage of semen, though the molecular mechanisms involved in semen preservation are not well understood. Little work has been done examining how function of the SSTs is impacted by insemination and by semen present in the SSTs. Methods: Transcriptome analysis was performed on isolated SSTs from turkey hens receiving no insemination (control), sham-insemination, or semen-insemination at three timepoints (D1, D30, and D90 post-insemination). Bioinformatic and functional annotation analyses were performed using CLC Genomics Workbench, Database for Annotation, Visualization, and Integrated Discovery (DAVID), and Ingenuity Pathway Analysis (IPA). Pairwise comparisons and k-medoids cluster analysis were utilized to decipher differential expression profiles in the treatment groups. Results: The SST transcriptome of the semen inseminated group exhibited the greatest differences within the group, with differences detectable for up to 90 days post insemination, while control and sham-inseminated groups were more similar. In the semen-inseminated samples, upregulation of pathways relating to classical and non-classical reproductive signaling, cytoskeletal remodeling, physiological parameters of the local UVJ environment, and cellular metabolism was observed. In the sham-inseminated samples, upregulation of immune pathways and non-reproductive endocrine hormones was observed. Discussion: This work provides insights into the molecular level changes of the SST in response to insemination as well as to the presence of semen. Results from this study may have direct implications on fertility rates as well as potential strategies for avian semen cryopreservation protocols.

GSR-DB: a manually curated and optimized taxonomical database for 16S rRNA amplicon analysis.

Amplicon-based 16S ribosomal RNA sequencing remains a widely used method to profile microbial communities, especially in low biomass samples, due to its cost-effectiveness and low-complexity approach. Reference databases are a mainstay for taxonomic assignments, which typically rely on popular databases such as SILVA, Greengenes, Genome Taxonomy Database (GTDB), or Ribosomal Database Project (RDP). However, the inconsistency of the nomenclature across databases and the presence of shortcomings in the annotation of these databases are limiting the resolution of the analysis. To overcome these limitations, we created the GSR database (Greengenes, SILVA, and RDP database), an integrated and manually curated database for bacterial and archaeal 16S amplicon taxonomy analysis. Unlike previous integration approaches, this database creation pipeline includes a taxonomy unification step to ensure consistency in taxonomical annotations. The database was validated with three mock communities, two real data sets, and a 10-fold cross-validation method and compared with existing 16S databases such as Greengenes, Greengenes 2, GTDB, ITGDB, SILVA, RDP, and MetaSquare. Results showed that the GSR database enhances taxonomical annotations of 16S sequences, outperforming current 16S databases at the species level, based on the evaluation of the mock communities. This was confirmed by the 10-fold cross-validation, except for Greengenes 2. The GSR database is available for full-length 16S sequences and the most commonly used hypervariable regions: V4, V1-V3, V3-V4, and V3-V5.IMPORTANCETaxonomic assignments of microorganisms have long been hindered by inconsistent nomenclature and annotation issues in existing databases like SILVA, Greengenes, Greengenes2, Genome Taxonomy Database, or Ribosomal Database Project. To overcome these issues, we created Greengenes-SILVA-RDP database (GSR-DB), accurate and comprehensive taxonomic annotations of 16S amplicon data. Unlike previous approaches, our innovative pipeline includes a unique taxonomy unification step, ensuring consistent and reliable annotations. Our evaluation analyses showed that GSR-DB outperforms existing databases in providing species-level resolution, especially based on mock-community analysis, making it a game-changer for microbiome studies. Moreover, GSR-DB is designed to be accessible to researchers with limited computational resources, making it a powerful tool for scientists across the board. Available for full-length 16S sequences and commonly used hypervariable regions, including V4, V1-V3, V3-V4, and V3-V5, GSR-DB is a go-to database for robust and accurate microbial taxonomy analysis.

Sargent Gloryvine Extract Modulates Nuclear Factor κκB Signaling Pathway to Improve Severe Pancreatitis

This study explored the mechanism of action of Sargent gloryvine extract in treating severe pancreatitis. We used the PubChem and SwissTargetPredicton databases to predict the potential targets. The database for Annotation, Visualization, and Integrated Discovery was used to perform Gene Ontology. The Kyoto Encyclopedia of Genes was used to perform Genomes enrichment analyses. These analyses, visualized via Cytoscape software followed by molecular docking validation, identified Salidroside, Lignans, Emodin, and Physcion as key active compounds in the extract. The protein-protein interaction network analysis highlighted tumor necrosis factor, interleukin-6, and nuclear factor κB as critical targets with 42 signaling pathways identified as relevant. The nuclear factor κB pathway emerged as especially significant in the extract's effect on severe pancreatitis. Enrichment analysis screened 42 related signaling pathways, among which the nuclear factor κB pathway may play a vital role in treating severe pancreatitis with Sargent gloryvine extract. The molecular docking results indicated an excellent binding activity between Sargent gloryvine extract and nuclear factor κB. In conclusion, the therapeutic approach of Sargent gloryvine extract in severe pancreatitis involves a complex interaction of multiple components, targets, and pathways, with a potential mechanism linked to the downregulation of the nuclear factor κB signaling pathway.