Search

Background: Breast cancer has become the most common malignant tumor in women worldwide. Mitotic catastrophe (MC) is a way of cell death that plays an important role in the development of tumors. However, the exact relationship between MC-related genes (MCRGs) and the development of breast cancer is still unclear, and further research is needed to elucidate this complexity. Methods: Transcriptome data and clinical data of breast cancer were downloaded from the Cancer Genome Atlas (TCGA) database and the Gene Expression Omnibus (GEO) database. We identified differential expression of MCRGs by comparing tumor tissue with normal tissue. Subsequently, we used COX regression analysis and LASSO regression analysis to construct the prognosis risk model of MCRGs. Kaplan–Meier survival curve and receiver operating characteristic (ROC) curve were used to evaluate the predictive ability of prognostic model. Moreover, the clinical relevance, gene set enrichment analysis (GSEA), immune landscape, tumor mutation burden (TMB), and immunotherapy and drug sensitivity analysis between high-risk and low-risk groups were systematically investigated. Finally, we validated the expression levels of genes involved in constructing the prognostic model through real-time quantitative polymerase chain reaction (RT-qPCR) at the cellular and tissue levels. Results: We identified 12 prognostic associated MCRGs, four of which were selected to construct prognostic model. The Kaplan-Meier analysis suggested that patients in the high-risk group had a shorter overall survival (OS). The Cox regression analysis and ROC analysis indicated that risk model had independent and excellent ability in predicting prognosis of breast cancer patients. Mechanistically, a remarkable difference was observed in clinical relevance, GSEA, immune landscape, TMB, immunotherapy response, and drug sensitivity analysis. RT-qPCR results showed that genes involved in constructing the prognostic model showed significant abnormal expressions and the expression change trends were consistent with the bioinformatics results. Conclusions: We established a prognosis risk model based on four MCRGs that had the ability to predict clinical prognosis and immune landscape, proposing potential therapeutic targets for breast cancer. [ABSTRACT FROM AUTHOR]

Background: As a powerful tool, bioinformatics analysis is playing an increasingly important role in many fields. Osteogenic differentiation is a complex biological process involving the fine regulation of numerous genes and signaling pathways. Method: Osteogenic differentiation-related genes are collected from the online databases. Then, we proposed two indexes Jaccard similarity and Sorensen-Dice similarity to measure the topological relevance of genes in the human PPI network. Furthermore, we selected three pathways involving osteoblast-related transcription factors, osteoblast differentiation, and RUNX2 regulation of osteoblast differentiation for investigation. Subsequently, we performed functional a enrichment analysis of these top-ranked genes to check whether these candidate genes identified by similarity-based metrics are enriched in some specific biological functions and states. we performed a permutation test to investigate the similarity score with four well-known osteogenic differentiation-related pathways including hedgehog signaling pathway, BMP signaling, ERK pathway, and Wnt signaling pathway to check whether these osteogenic differentiation-related pathways can be regulated by FOXA1. Lentiviral transfection was used to knockdown and overexpress gene FOXA1 in human bone mesenchymal stem cells (hBMSCs). Alkaline phosphatase (ALP) staining and Alizarin Red staining (ARS) were employed to investigate osteogenic differentiation of hBMSCs. Result: After data collection, human PPI network involving 19,344 genes is included in our analysis. After simplifying, we used Jaccard and Sorensen-Dice similarity to identify osteogenic differentiation-related genes and integrated into a final similarity matrix. Furthermore, we calculated the sum of similarity scores with these osteogenic differentiation-related genes for each gene and found 337 osteogenic differentiation-related genes are involved in our analysis. We selected three pathways involving osteoblast-related transcription factors, osteoblast differentiation, and RUNX2 regulation of osteoblast differentiation for investigation and performed functional enrichment analysis of these top-ranked 50 genes. The results collectively demonstrate that these candidate genes can indeed capture osteogenic differentiation-related features of hBSMCs. According to the novel analyzing method, we found that these four pathways have significantly higher similarity with FOXA1 than random noise. Moreover, knockdown FOXA1 significantly increased the ALP activity and mineral deposits. Furthermore, overexpression of FOXA1 dramatically decreased the ALP activity and mineral deposits. Conclusion: In summary, this study showed that FOXA1 is a novel significant osteogenic differentiation-related transcription factor. Moreover, our study has tightly integrated bioinformatics analysis with biological knowledge, and developed a novel method for analyzing the osteogenic differentiation regulatory network. [ABSTRACT FROM AUTHOR]

Link prediction (LP) is a task for the identification of potential, missing and spurious links in complex networks. Protein-protein interaction (PPI) networks are important for understanding the underlying biological mechanisms of diseases. Many complex networks have been constructed using LP methods; however, there are a limited number of studies that focus on disease-related gene predictions and evaluate these genes using various evaluation criteria. The main objective of the study is to investigate the effect of a simple ensemble method in disease related gene predictions. Local similarity indices (LSIs) based disease related gene predictions were integrated by a simple ensemble decision method, simple majority voting (SMV), on the PPI network to detect accurate disease related genes. Human PPI network was utilized to discover potential disease related genes using four LSIs for the gene prediction. LSIs discovered potential links between disease related genes, which were obtained from OMIM database for gastric, colorectal, breast, prostate and lung cancers. LSIs based disease related genes were ranked due to their LSI scores in descending order for retrieving the top 10, 50 and 100 disease related genes. SMV integrated four LSIs based predictions to obtain SMV based the top 10, 50 and 100 disease related genes. The performance of LSIs based and SMV based genes were evaluated separately by employing overlap analyses, which were performed with GeneCard disease-gene relation dataset and Gene Ontology (GO) terms. The GO-terms were used for biological assessment for the inferred gene lists by LSIs and SMV on all cancer types. Adamic-Adar (AA), Resource Allocation Index (RAI), and SMV based gene lists are generally achieved good performance results on all cancers in both overlap analyses. SMV also outperformed on breast cancer data. The increment in the selection of the number of the top ranked disease related genes also enhanced the performance results of SMV. [ABSTRACT FROM AUTHOR]

Background: Global public health is seriously threatened by the escalating issue of antimicrobial resistance (AMR). Antimicrobial peptides (AMPs), pivotal components of the innate immune system, have emerged as a potent solution to AMR due to their therapeutic potential. Employing computational methodologies for the prompt recognition of these antimicrobial peptides indeed unlocks fresh perspectives, thereby potentially revolutionizing antimicrobial drug development. Methods: In this study, we have developed a model named as deepAMPNet. This model, which leverages graph neural networks, excels at the swift identification of AMPs. It employs structures of antimicrobial peptides predicted by AlphaFold2, encodes residue-level features through a bi-directional long short-term memory (Bi-LSTM) protein language model, and constructs adjacency matrices anchored on amino acids' contact maps. Results: In a comparative study with other state-of-the-art AMP predictors on two external independent test datasets, deepAMPNet outperformed in accuracy. Furthermore, in terms of commonly accepted evaluation matrices such as AUC, Mcc, sensitivity, and specificity, deepAMPNet achieved the highest or highly comparable performances against other predictors. Conclusion: deepAMPNet interweaves both structural and sequence information of AMPs, stands as a high-performance identification model that propels the evolution and design in antimicrobial peptide pharmaceuticals. The data and code utilized in this study can be accessed at https://github.com/Iseeu233/deepAMPNet. [ABSTRACT FROM AUTHOR]

To better understand RNA-binding proteins in rice, a comprehensive investigation was conducted on the RRM1 gene family of rice. It encompassed genome-wide identification and exploration of its role in rice blast resistance. The physicochemical properties of the rice OsRRM1 gene family were analyzed. There genes were also analyzed for their conserved domains, motifs, location information, gene structure, phylogenetic trees, collinearity, and cis-acting elements. Furthermore, alterations in the expression patterns of selected OsRRM1 genes were assessed using quantitative real-time PCR (qRT-PCR). A total of 212 members of the OsRRM1 gene family were identified, which were dispersed across 12 chromosomes. These genes all exhibit multiple exons and introns, all of which encompass the conserved RRM1 domain and share analogous motifs. This observation suggests a high degree of conservation within the encoded sequence domain of these genes. Phylogenetic analysis revealed the existence of five subfamilies within the OsRRM1 gene family. Furthermore, investigation of the promoter region identified cis-regulatory elements that are involved in nucleic acid binding and interaction with multiple transcription factors. By employing GO and KEGG analyses, four RRM1 genes were tentatively identified as crucial contributors to plant immunity, while the RRM1 gene family was also found to have a significant involvement in the complex of alternative splicing. The qRT-PCR results revealed distinct temporal changes in the expression patterns of OsRRM1 genes following rice blast infection. Additionally, gene expression analysis indicates that the majority of OsRRM1 genes exhibited constitutive expressions. These findings enrich our understanding of the OsRRM1 gene family. They also provide a foundation for further research on immune mechanisms rice and the management of rice blast. [ABSTRACT FROM AUTHOR]

Background: FAR1/FHY3 transcription factors are derived from transposase, which play important roles in light signal transduction, growth and development, and response to stress by regulating downstream gene expression. Although many FAR1/FHY3 members have been identified in various species, the FAR1/FHY3 genes in maize are not well characterized and their function in drought are unknown. Method: The FAR1/FHY3 family in the maize genome was identified using PlantTFDB, Pfam, Smart, and NCBI-CDD websites. In order to investigate the evolution and functions of FAR1 genes in maize, the information of protein sequences, chromosome localization, subcellular localization, conserved motifs, evolutionary relationships and tissue expression patterns were analyzed by bioinformatics, and the expression patterns under drought stress were detected by quantitative real-time polymerase chain reaction (qRT-PCR). Results: A total of 24 ZmFAR members in maize genome, which can be divided into five subfamilies, with large differences in protein and gene structures among subfamilies. The promoter regions of ZmFARs contain abundant abiotic stress-responsive and hormone-respovensive cis-elements. Among them, drought-responsive cis-elements are quite abundant. ZmFARs were expressed in all tissues detected, but the expression level varies widely. The expression of ZmFARs were mostly down-regulated in primary roots, seminal roots, lateral roots, and mesocotyls under water deficit. Most ZmFARs were down-regulated in root after PEG-simulated drought stress. Conclusions: We performed a genome-wide and systematic identification of FAR1/FHY3 genes in maize. And most ZmFARs were down-regulated in root after drought stress. These results indicate that FAR1/FHY3 transcription factors have important roles in drought stress response, which can lay a foundation for further analysis of the functions of ZmFARs in response to drought stress. [ABSTRACT FROM AUTHOR]

Background: Sepsis, an infection-triggered inflammatory syndrome, poses a global clinical challenge with limited therapeutic options. Our study is designed to identify potential diagnostic biomarkers of sepsis onset in critically ill patients by bioinformatics analysis. Methods: Gene expression profiles of GSE28750 and GSE74224 were obtained from the Gene Expression Omnibus (GEO) database. These datasets were merged, normalized and de-batched. Weighted gene co-expression network analysis (WGCNA) was performed and the gene modules most associated with sepsis were identified as key modules. Functional enrichment analysis of the key module genes was then conducted. Moreover, differentially expressed gene (DEG) analysis was conducted by the "limma" R package. Protein-protein interaction (PPI) network was created using STRING and Cytoscape, and PPI hub genes were identified with the cytoHubba plugin. The PPI hub genes overlapping with the genes in key modules of WGCNA were determined to be the sepsis-related key genes. Subsequently, the key overlapping genes were validated in an external independent dataset and sepsis patients recruited in our hospital. In addition, CIBERSORT analysis evaluated immune cell infiltration and its correlation with key genes. Results: By WGCNA, the greenyellow module showed the highest positive correlation with sepsis (0.7, p = 2e − 19). 293 DEGs were identified in the merged datasets. The PPI network was created, and the CytoHubba was used to calculate the top 20 genes based on four algorithms (Degree, EPC, MCC, and MNC). Ultimately, LTF, LCN2, ELANE, MPO and CEACAM8 were identified as key overlapping genes as they appeared in the PPI hub genes and the key module genes of WGCNA. These sepsis-related key genes were validated in an independent external dataset (GSE131761) and sepsis patients recruited in our hospital. Additionally, the immune infiltration profiles differed significantly between sepsis and non-sepsis critical illness groups. Correlations between immune cells and these five key genes were assessed, revealing that plasma cells, macrophages M0, monocytes, T cells regulatory, eosinophils and NK cells resting were simultaneously and significantly associated with more than two key genes. Conclusion: This study suggests a critical role of LTF, LCN2, ELANE, MPO and CEACAM8 in sepsis and may provide potential diagnostic biomarkers and therapeutic targets for the treatment of sepsis. [ABSTRACT FROM AUTHOR]

Background: Breast carcinoma (BRCA) is a life-threatening malignancy in women and shows a poor prognosis. Cuproptosis is a novel mode of cell death but its relationship with BRCA is unclear. This study attempted to develop a cuproptosis-relevant prognostic gene signature for BRCA. Methods: Cuproptosis-relevant subtypes of BRCA were obtained by consensus clustering. Differential expression analysis was implemented using the 'limma' package. Univariate Cox and multivariate Cox analyses were performed to determine a cuproptosis-relevant prognostic gene signature. The signature was constructed and validated in distinct datasets. Gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA) were also conducted using the prognostic signature to uncover the underlying molecular mechanisms. ESTIMATE and CIBERSORT algorithms were applied to probe the linkage between the gene signature and tumor microenvironment (TME). Immunotherapy responsiveness was assessed using the Tumor Immune Dysfunction and Exclusion (TIDE) web tool. Real-time quantitative PCR (RT-qPCR) was performed to detect the expressions of cuproptosis-relevant prognostic genes in breast cancer cell lines. Results: Thirty-eight cuproptosis-associated differentially expressed genes (DEGs) in BRCA were mined by consensus clustering and differential expression analysis. Based on univariate Cox and multivariate Cox analyses, six cuproptosis-relevant prognostic genes, namely SAA1, KRT17, VAV3, IGHG1, TFF1, and CLEC3A, were mined to establish a corresponding signature. The signature was validated using external validation sets. GSVA and GSEA showed that multiple cell cycle-linked and immune-related pathways along with biological processes were associated with the signature. The results ESTIMATE and CIBERSORT analyses revealed significantly different TMEs between the two Cusig score subgroups. Finally, RT-qPCR analysis of cell lines further confirmed the expressional trends of SAA1, KRT17, IGHG1, and CLEC3A. Conclusion: Taken together, we constructed a signature for projecting the overall survival of BRCA patients and our findings authenticated the cuproptosis-relevant prognostic genes, which are expected to provide a basis for developing prognostic molecular biomarkers and an in-depth understanding of the relationship between cuproptosis and BRCA. [ABSTRACT FROM AUTHOR]

Background As a powerful tool, bioinformatics analysis is playing an increasingly important role in many fields. Osteogenic differentiation is a complex biological process involving the fine regulation of numerous genes and signaling pathways. Method Osteogenic differentiation-related genes are collected from the online databases. Then, we proposed two indexes Jaccard similarity and Sorensen-Dice similarity to measure the topological relevance of genes in the human PPI network. Furthermore, we selected three pathways involving osteoblast-related transcription factors, osteoblast differentiation, and RUNX2 regulation of osteoblast differentiation for investigation. Subsequently, we performed functional a enrichment analysis of these top-ranked genes to check whether these candidate genes identified by similarity-based metrics are enriched in some specific biological functions and states. we performed a permutation test to investigate the similarity score with four well-known osteogenic differentiation-related pathways including hedgehog signaling pathway, BMP signaling, ERK pathway, and Wnt signaling pathway to check whether these osteogenic differentiation-related pathways can be regulated by FOXA1. Lentiviral transfection was used to knockdown and overexpress gene FOXA1 in human bone mesenchymal stem cells (hBMSCs). Alkaline phosphatase (ALP) staining and Alizarin Red staining (ARS) were employed to investigate osteogenic differentiation of hBMSCs. Result After data collection, human PPI network involving 19,344 genes is included in our analysis. After simplifying, we used Jaccard and Sorensen-Dice similarity to identify osteogenic differentiation-related genes and integrated into a final similarity matrix. Furthermore, we calculated the sum of similarity scores with these osteogenic differentiation-related genes for each gene and found 337 osteogenic differentiation-related genes are involved in our analysis. We selected three pathways involving osteoblast-related transcription factors, osteoblast differentiation, and RUNX2 regulation of osteoblast differentiation for investigation and performed functional enrichment analysis of these top-ranked 50 genes. The results collectively demonstrate that these candidate genes can indeed capture osteogenic differentiation-related features of hBSMCs. According to the novel analyzing method, we found that these four pathways have significantly higher similarity with FOXA1 than random noise. Moreover, knockdown FOXA1 significantly increased the ALP activity and mineral deposits. Furthermore, overexpression of FOXA1 dramatically decreased the ALP activity and mineral deposits. Conclusion In summary, this study showed that FOXA1 is a novel significant osteogenic differentiation-related transcription factor. Moreover, our study has tightly integrated bioinformatics analysis with biological knowledge, and developed a novel method for analyzing the osteogenic differentiation regulatory network.

Background Breast cancer has become the most common malignant tumor in women worldwide. Mitotic catastrophe (MC) is a way of cell death that plays an important role in the development of tumors. However, the exact relationship between MC-related genes (MCRGs) and the development of breast cancer is still unclear, and further research is needed to elucidate this complexity. Methods Transcriptome data and clinical data of breast cancer were downloaded from the Cancer Genome Atlas (TCGA) database and the Gene Expression Omnibus (GEO) database. We identified differential expression of MCRGs by comparing tumor tissue with normal tissue. Subsequently, we used COX regression analysis and LASSO regression analysis to construct the prognosis risk model of MCRGs. Kaplan–Meier survival curve and receiver operating characteristic (ROC) curve were used to evaluate the predictive ability of prognostic model. Moreover, the clinical relevance, gene set enrichment analysis (GSEA), immune landscape, tumor mutation burden (TMB), and immunotherapy and drug sensitivity analysis between high-risk and low-risk groups were systematically investigated. Finally, we validated the expression levels of genes involved in constructing the prognostic model through real-time quantitative polymerase chain reaction (RT-qPCR) at the cellular and tissue levels. Results We identified 12 prognostic associated MCRGs, four of which were selected to construct prognostic model. The Kaplan-Meier analysis suggested that patients in the high-risk group had a shorter overall survival (OS). The Cox regression analysis and ROC analysis indicated that risk model had independent and excellent ability in predicting prognosis of breast cancer patients. Mechanistically, a remarkable difference was observed in clinical relevance, GSEA, immune landscape, TMB, immunotherapy response, and drug sensitivity analysis. RT-qPCR results showed that genes involved in constructing the prognostic model showed significant abnormal expressions and the expression change trends were consistent with the bioinformatics results. Conclusions We established a prognosis risk model based on four MCRGs that had the ability to predict clinical prognosis and immune landscape, proposing potential therapeutic targets for breast cancer.

Link prediction (LP) is a task for the identification of potential, missing and spurious links in complex networks. Protein-protein interaction (PPI) networks are important for understanding the underlying biological mechanisms of diseases. Many complex networks have been constructed using LP methods; however, there are a limited number of studies that focus on disease-related gene predictions and evaluate these genes using various evaluation criteria. The main objective of the study is to investigate the effect of a simple ensemble method in disease related gene predictions. Local similarity indices (LSIs) based disease related gene predictions were integrated by a simple ensemble decision method, simple majority voting (SMV), on the PPI network to detect accurate disease related genes. Human PPI network was utilized to discover potential disease related genes using four LSIs for the gene prediction. LSIs discovered potential links between disease related genes, which were obtained from OMIM database for gastric, colorectal, breast, prostate and lung cancers. LSIs based disease related genes were ranked due to their LSI scores in descending order for retrieving the top 10, 50 and 100 disease related genes. SMV integrated four LSIs based predictions to obtain SMV based the top 10, 50 and 100 disease related genes. The performance of LSIs based and SMV based genes were evaluated separately by employing overlap analyses, which were performed with GeneCard disease-gene relation dataset and Gene Ontology (GO) terms. The GO-terms were used for biological assessment for the inferred gene lists by LSIs and SMV on all cancer types. Adamic-Adar (AA), Resource Allocation Index (RAI), and SMV based gene lists are generally achieved good performance results on all cancers in both overlap analyses. SMV also outperformed on breast cancer data. The increment in the selection of the number of the top ranked disease related genes also enhanced the performance results of SMV.

Background: This work explored the characteristics of the WRKY transcription factor family in Rhododendron henanense subsp. lingbaoense (Rhl) and the expression patterns of these genes under abiotic stress by conducting bioinformatics and expression analyses. Methods: RhlWRKY genes were identified from a gene library of Rhl. Various aspects of these genes were analyzed, including genetic structures, conserved sequences, physicochemical properties, cis-acting elements, and chromosomal location. RNA-seq was employed to analyze gene expression in five different tissues of Rhl: roots, stems, leaves, flowers, and hypocotyls. Additionally, qRT-PCR was used to detect changes in the expression of five RhlWRKY genes under abiotic stress. Result: A total of 65 RhlWRKY genes were identified and categorized into three subfamilies based on their structural characteristics: Groups I, II, and III. Group II was further divided into five subtribes, with shared similar genetic structures and conserved motifs among members of the same subtribe. The physicochemical properties of these proteins varied, but the proteins are generally predicted to be hydrophilic. Most proteins are predicted to be in the cell nucleus, and distributed across 12 chromosomes. A total of 84 cis-acting elements were discovered, with many related to responses to biotic stress. Among the identified RhlWRKY genes, there were eight tandem duplicates and 97 segmental duplicates. The majority of duplicate gene pairs exhibited Ka/Ks values <1, indicating purification under environmental pressure. GO annotation analysis indicated that WRKY genes regulate biological processes and participate in a variety of molecular functions. Transcriptome data revealed varying expression levels of 66.15% of WRKY family genes in all five tissue types (roots, stems, leaves, flowers, and hypocotyls). Five RhlWRKY genes were selected for further characterization and there were changes in expression levels for these genes in response to various stresses. Conclusion: The analysis identified 65 RhlWRKY genes, among which the expression of WRKY_42 and WRKY_17 were mainly modulated by the drought and MeJA, and WRKY_19 was regulated by the low-temperature and high-salinity conditions. This insight into the potential functions of certain genes contributes to understanding the growth regulatory capabilities of Rhl. [ABSTRACT FROM AUTHOR]

Background: Osteosarcoma is the most common primary malignant bone tumor, but its pathogenesis remains unclear. Ubiquitin-specific processing peptidase 22 (USP22) is reported to be highly expressed and associated with tumor malignancy and prognosis in cancers. However, the role and mechanism of USP22 in osteosarcoma is not fully understood. This study aims to investigate the function and potential mechanism of USP22 in osteosarcoma using bioinformatics analysis combined with experimental validation. Methods: We first integrated transcriptomic datasets and clinical information of osteosarcoma from GEO and TCGA databases to assess the expression and prognostic value of USP22 in osteosarcoma. Then, differential expression analysis and weighted gene co-expression network analysis (WGCNA) were conducted to identify USP22-related co-expressed genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to explore the biological functions and signaling pathways of USP22 co-expressed genes. To validate the accuracy of bioinformatics analyses, we downregulated USP22 expression in osteosarcoma cell line Sao-2 using siRNA and assessed its effect on cell proliferation, migration, invasion, apoptosis, and regulation of key signaling pathways. Results: We found that USP22 was highly expressed in osteosarcoma tissues and correlated with poor prognosis in osteosarcoma patients. USP22 also showed potential as a diagnostic marker for osteosarcoma. In addition, 344 USP22-related co-expressed genes were identified, mainly involved in signaling pathways such as glycolysis, oxidative phosphorylation, spliceosome, thermogenesis, and cell cycle. The in vitro experiments confirmed the accuracy and reliability of bioinformatics analyses. We found that downregulation of USP22 could inhibit Sao-2 cell proliferation, migration, invasion, and induce apoptosis. Furthermore, downregulation of USP22 significantly reduced aerobic glycolysis levels in Sao-2 cells and inhibited the expression of key enzymes and transporters in aerobic glycolysis pathways such as HK2, PKM2, and GLUT1. Conclusions: USP22 plays a critical role in the occurrence, development, and prognosis of osteosarcoma. USP22 could influence Sao-2 cell proliferation, apoptosis, migration, and invasion by regulating the glycolysis pathway, thereby promoting osteosarcoma progression. Therefore, USP22 may be a potential therapeutic target for the treatment of osteosarcoma. [ABSTRACT FROM AUTHOR]

Deciphering the targets of microRNAs (miRNAs) in plants is crucial for comprehending their function and the variation in phenotype that they cause. As the highly cell-specific nature of miRNA regulation, recent computational approaches usually utilize expression data to identify the most physiologically relevant targets. Although these methods are effective, they typically require a large sample size and high-depth sequencing to detect potential miRNA-target pairs, thereby limiting their applicability in improving plant breeding. In this study, we propose a novel miRNA-target prediction framework named kmerPMTF (k-mer-based prediction framework for plant miRNA-target). Our framework effectively extracts the latent semantic embeddings of sequences by utilizing k-mer splitting and a deep self-supervised neural network. We construct multiple similarity networks based on k-mer embeddings and employ graph convolutional networks to derive deep representations of miRNAs and targets and calculate the probabilities of potential associations. We evaluated the performance of kmerPMTF on four typical plant datasets: Arabidopsis thaliana, Oryza sativa, Solanum lycopersicum, and Prunus persica. The results demonstrate its ability to achieve AUPRC values of 84.9%, 91.0%, 80.1%, and 82.1% in 5-fold cross-validation, respectively. Compared with several state-of-the-art existing methods, our framework achieves better performance on threshold-independent evaluation metrics. Overall, our study provides an efficient and simplified methodology for identifying plant miRNA-target associations, which will contribute to a deeper comprehension of miRNA regulatory mechanisms in plants. [ABSTRACT FROM AUTHOR]

The loblolly pine (Pinus taeda L.) is one of the most profitable forest species worldwide owing to its quick growth, high wood yields, and strong adaptability. The AP2/ERF gene family plays a widespread role in the physiological processes of plant defense responses and the biosynthesis of metabolites. Nevertheless, there are no reports on this gene family in loblolly pine (P. taeda). In this study, a total of 303 members of the AP2/ERF gene family were identified. Through multiple sequence alignment and phylogenetic analysis, they were classified into four subfamilies, including AP2 (34), RAV (17), ERF (251), and Soloist (1). An analysis of the conservation domains, conserved motifs, and gene structure revealed that every PtAP2/ERF transcription factor (TF) had at least one AP2 domain. While evolutionary conservation was displayed within the same subfamilies, the distribution of conserved domains, conserved motifs, and gene architectures varied between subfamilies. Cis-element analysis revealed abundant light-responsive elements, phytohormone-responsive elements, and stress-responsive elements in the promoter of the PtAP2/ERF genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of potential target genes showed that the AP2/ERF gene family might play a critical role in plant growth and development, the response to environmental stresses, and metabolite biosynthesis. Utilizing quantitative real-time PCR (qRT-PCR), we examined the expression patterns of 10 randomly selected genes from Group IX after 6 h of treatments with mechanical injury, ethephon (Eth), and methyl jasmonate (MeJA). The AP2/ERF gene family in the loblolly pine was systematically analyzed for the first time in this study, offering a theoretical basis for exploring the functions and applications of AP2/ERF genes. [ABSTRACT FROM AUTHOR]

Background & Aims. Small nuclear ribonucleoprotein U1 subunit 70 (SNRNP70) as one of the components of the U1 small nuclear ribonucleoprotein (snRNP) is rarely reported in cancers. This study aims to estimate the application potential of SNRNP70 in hepatocellular carcinoma (HCC) clinical practice. Methods. Based on the TCGA database and cohort of HCC patients, we investigated the expression patterns and prognostic value of SNRNP70 in HCC. Then, the combination of SNRNP70 and alpha-fetoprotein (AFP) in 278 HCC cases was analyzed. Next, western blotting and immunohistochemistry were used to detect the expression of SNRNP70 in nucleus and cytoplasm. Finally, Cell Counting Kit-8 (CCK-8) and scratch wound healing assays were used to detect the effect of SNRNP70 on the proliferation and migration of HCC cells. Results. SNRNP70 was highly expressed in HCC. Its expression was increasingly high during the progression of HCC and was positively related to immune infiltration cells. Higher SNRNP70 expression indicated a poor outcome of HCC patients. In addition, nuclear SNRNP70/AFP combination could be a prognostic biomarker for overall survival and recurrence. Cell experiments confirmed that knockdown of SNRNP70 inhibited the proliferation and migration of HCC cells. Conclusion. SNRNP70 may be a new biomarker for HCC progression and HCC diagnosis as well as prognosis. SNRNP70 combined with serum AFP may indicate the prognosis and recurrence status of HCC patients after operation. [ABSTRACT FROM AUTHOR]

The taxonomic characterization of ancient microbiomes is a key step in the rapidly growing field of paleomicrobiology. While PCR amplification of the 16S ribosomal RNA (rRNA) gene is a widely used technique in modern microbiota studies, this method has systematic biases when applied to ancient microbial DNA. Shotgun metagenomic sequencing has proven to be the most effective method in reconstructing taxonomic profiles of ancient dental calculus samples. Nevertheless, shotgun sequencing approaches come with inherent limitations that could be addressed through hybridization enrichment capture. When employed together, shotgun sequencing and hybridization capture have the potential to enhance the characterization of ancient microbial communities. Here, we develop, test, and apply a hybridization enrichment capture technique to selectively target 16S rRNA gene fragments from the libraries of ancient dental calculus samples generated with shotgun techniques. We simulated data sets generated from hybridization enrichment capture, indicating that taxonomic identification of fragmented and damaged 16S rRNA gene sequences was feasible. Applying this enrichment approach to 15 previously published ancient calculus samples, we observed a 334-fold increase of ancient 16S rRNA gene fragments in the enriched samples when compared to unenriched libraries. Our results suggest that 16S hybridization capture is less prone to the effects of background contamination than 16S rRNA amplification, yielding a higher percentage of on-target recovery. While our enrichment technique detected low abundant and rare taxa within a given sample, these assignments may not achieve the same level of specificity as those achieved by unenriched methods. [ABSTRACT FROM AUTHOR]

To better understand RNA-binding proteins in rice, a comprehensive investigation was conducted on the RRM1 gene family of rice. It encompassed genome-wide identification and exploration of its role in rice blast resistance. The physicochemical properties of the rice OsRRM1 gene family were analyzed. There genes were also analyzed for their conserved domains, motifs, location information, gene structure, phylogenetic trees, collinearity, and cis-acting elements. Furthermore, alterations in the expression patterns of selected OsRRM1 genes were assessed using quantitative real-time PCR (qRT-PCR). A total of 212 members of the OsRRM1 gene family were identified, which were dispersed across 12 chromosomes. These genes all exhibit multiple exons and introns, all of which encompass the conserved RRM1 domain and share analogous motifs. This observation suggests a high degree of conservation within the encoded sequence domain of these genes. Phylogenetic analysis revealed the existence of five subfamilies within the OsRRM1 gene family. Furthermore, investigation of the promoter region identified cis-regulatory elements that are involved in nucleic acid binding and interaction with multiple transcription factors. By employing GO and KEGG analyses, four RRM1 genes were tentatively identified as crucial contributors to plant immunity, while the RRM1 gene family was also found to have a significant involvement in the complex of alternative splicing. The qRT-PCR results revealed distinct temporal changes in the expression patterns of OsRRM1 genes following rice blast infection. Additionally, gene expression analysis indicates that the majority of OsRRM1 genes exhibited constitutive expressions. These findings enrich our understanding of the OsRRM1 gene family. They also provide a foundation for further research on immune mechanisms rice and the management of rice blast.

Background Global public health is seriously threatened by the escalating issue of antimicrobial resistance (AMR). Antimicrobial peptides (AMPs), pivotal components of the innate immune system, have emerged as a potent solution to AMR due to their therapeutic potential. Employing computational methodologies for the prompt recognition of these antimicrobial peptides indeed unlocks fresh perspectives, thereby potentially revolutionizing antimicrobial drug development. Methods In this study, we have developed a model named as deepAMPNet. This model, which leverages graph neural networks, excels at the swift identification of AMPs. It employs structures of antimicrobial peptides predicted by AlphaFold2, encodes residue-level features through a bi-directional long short-term memory (Bi-LSTM) protein language model, and constructs adjacency matrices anchored on amino acids’ contact maps. Results In a comparative study with other state-of-the-art AMP predictors on two external independent test datasets, deepAMPNet outperformed in accuracy. Furthermore, in terms of commonly accepted evaluation matrices such as AUC, Mcc, sensitivity, and specificity, deepAMPNet achieved the highest or highly comparable performances against other predictors. Conclusion deepAMPNet interweaves both structural and sequence information of AMPs, stands as a high-performance identification model that propels the evolution and design in antimicrobial peptide pharmaceuticals. The data and code utilized in this study can be accessed at https://github.com/Iseeu233/deepAMPNet.

Background FAR1/FHY3 transcription factors are derived from transposase, which play important roles in light signal transduction, growth and development, and response to stress by regulating downstream gene expression. Although many FAR1/FHY3 members have been identified in various species, the FAR1/FHY3 genes in maize are not well characterized and their function in drought are unknown. Method The FAR1/FHY3 family in the maize genome was identified using PlantTFDB, Pfam, Smart, and NCBI-CDD websites. In order to investigate the evolution and functions of FAR1 genes in maize, the information of protein sequences, chromosome localization, subcellular localization, conserved motifs, evolutionary relationships and tissue expression patterns were analyzed by bioinformatics, and the expression patterns under drought stress were detected by quantitative real-time polymerase chain reaction (qRT-PCR). Results A total of 24 ZmFAR members in maize genome, which can be divided into five subfamilies, with large differences in protein and gene structures among subfamilies. The promoter regions of ZmFARs contain abundant abiotic stress-responsive and hormone-respovensive cis-elements. Among them, drought-responsive cis-elements are quite abundant. ZmFARs were expressed in all tissues detected, but the expression level varies widely. The expression of ZmFARs were mostly down-regulated in primary roots, seminal roots, lateral roots, and mesocotyls under water deficit. Most ZmFARs were down-regulated in root after PEG-simulated drought stress. Conclusions We performed a genome-wide and systematic identification of FAR1/FHY3 genes in maize. And most ZmFARs were down-regulated in root after drought stress. These results indicate that FAR1/FHY3 transcription factors have important roles in drought stress response, which can lay a foundation for further analysis of the functions of ZmFARs in response to drought stress.

Background Breast carcinoma (BRCA) is a life-threatening malignancy in women and shows a poor prognosis. Cuproptosis is a novel mode of cell death but its relationship with BRCA is unclear. This study attempted to develop a cuproptosis-relevant prognostic gene signature for BRCA. Methods Cuproptosis-relevant subtypes of BRCA were obtained by consensus clustering. Differential expression analysis was implemented using the ‘limma’ package. Univariate Cox and multivariate Cox analyses were performed to determine a cuproptosis-relevant prognostic gene signature. The signature was constructed and validated in distinct datasets. Gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA) were also conducted using the prognostic signature to uncover the underlying molecular mechanisms. ESTIMATE and CIBERSORT algorithms were applied to probe the linkage between the gene signature and tumor microenvironment (TME). Immunotherapy responsiveness was assessed using the Tumor Immune Dysfunction and Exclusion (TIDE) web tool. Real-time quantitative PCR (RT-qPCR) was performed to detect the expressions of cuproptosis-relevant prognostic genes in breast cancer cell lines. Results Thirty-eight cuproptosis-associated differentially expressed genes (DEGs) in BRCA were mined by consensus clustering and differential expression analysis. Based on univariate Cox and multivariate Cox analyses, six cuproptosis-relevant prognostic genes, namely SAA1, KRT17, VAV3, IGHG1, TFF1, and CLEC3A, were mined to establish a corresponding signature. The signature was validated using external validation sets. GSVA and GSEA showed that multiple cell cycle-linked and immune-related pathways along with biological processes were associated with the signature. The results ESTIMATE and CIBERSORT analyses revealed significantly different TMEs between the two Cusig score subgroups. Finally, RT-qPCR analysis of cell lines further confirmed the expressional trends of SAA1, KRT17, IGHG1, and CLEC3A. Conclusion Taken together, we constructed a signature for projecting the overall survival of BRCA patients and our findings authenticated the cuproptosis-relevant prognostic genes, which are expected to provide a basis for developing prognostic molecular biomarkers and an in-depth understanding of the relationship between cuproptosis and BRCA.

Background Sepsis, an infection-triggered inflammatory syndrome, poses a global clinical challenge with limited therapeutic options. Our study is designed to identify potential diagnostic biomarkers of sepsis onset in critically ill patients by bioinformatics analysis. Methods Gene expression profiles of GSE28750 and GSE74224 were obtained from the Gene Expression Omnibus (GEO) database. These datasets were merged, normalized and de-batched. Weighted gene co-expression network analysis (WGCNA) was performed and the gene modules most associated with sepsis were identified as key modules. Functional enrichment analysis of the key module genes was then conducted. Moreover, differentially expressed gene (DEG) analysis was conducted by the “limma” R package. Protein-protein interaction (PPI) network was created using STRING and Cytoscape, and PPI hub genes were identified with the cytoHubba plugin. The PPI hub genes overlapping with the genes in key modules of WGCNA were determined to be the sepsis-related key genes. Subsequently, the key overlapping genes were validated in an external independent dataset and sepsis patients recruited in our hospital. In addition, CIBERSORT analysis evaluated immune cell infiltration and its correlation with key genes. Results By WGCNA, the greenyellow module showed the highest positive correlation with sepsis (0.7, p = 2e − 19). 293 DEGs were identified in the merged datasets. The PPI network was created, and the CytoHubba was used to calculate the top 20 genes based on four algorithms (Degree, EPC, MCC, and MNC). Ultimately, LTF, LCN2, ELANE, MPO and CEACAM8 were identified as key overlapping genes as they appeared in the PPI hub genes and the key module genes of WGCNA. These sepsis-related key genes were validated in an independent external dataset (GSE131761) and sepsis patients recruited in our hospital. Additionally, the immune infiltration profiles differed significantly between sepsis and non-sepsis critical illness groups. Correlations between immune cells and these five key genes were assessed, revealing that plasma cells, macrophages M0, monocytes, T cells regulatory, eosinophils and NK cells resting were simultaneously and significantly associated with more than two key genes. Conclusion This study suggests a critical role of LTF, LCN2, ELANE, MPO and CEACAM8 in sepsis and may provide potential diagnostic biomarkers and therapeutic targets for the treatment of sepsis.

Objective. Cerebral infarction is the main cause of death in patients with cerebrovascular diseases. Our research aimed to screen and validate pyroptosis-related genes in cerebral infarction for the targeted therapy of cerebral infarction. Methods and results. A total of 1,517 differentially expressed genes (DEGs) were obtained by DESeq2 software analysis. Gene set enrichment analysis results indicated that genes of middle cerebral artery occlusion (MCAO) mice aged 3 months and 18 months were enriched in pyroptosis, respectively. Differentially expressed pyroptosis-related genes (including Aim2, Casp8, Gsdmd, Naip2, Naip5, Naip6 and Trem2) were obtained through intersection of DEGs and genes from pyroptosis Gene Ontology Term (GO:0070269), and they were up-regulated in the brain tissues of MCAO mice in GSE137482. In addition, Casp8, Gsdmd, and Trem2 were verified to be significantly up-regulated in MCAO mice in GSE93376. The evaluation of neurologic function and triphenyltetrazolium chloride staining showed that the MCAO mouse models were successfully constructed. Meanwhile, the expressions of TNF-α, pyroptosis-related proteins, Casp8, Gsdmd and Trem2 in MCAO mice were significantly up-regulated. We selected Trem2 for subsequent functional analysis. OGD treatment of BV2 cell in vitro significantly upregulated the expressions of Trem2. Subsequent downregulation of Trem2 expression inOGD-BV2cells further increased the level of pyroptosis. Therefore, Trem2 is a protective factor regulating pyroptosis, thus influencing the progression of cerebral infarction. Conclusions. Casp8, Gsdmd and Trem2 can regulate pyroptosis, thus affecting cerebral infarction. [ABSTRACT FROM AUTHOR]

Background: Obstructive sleep apnea (OSA) is a complex and multi-gene inherited disease caused by both genetic and environmental factors. However, due to the high cost of diagnosis and complex operation, its clinical application is limited. This study aims to explore potential target genes associated with OSA and establish a corresponding diagnostic model. Methods: This study used microarray datasets from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs) related to OSA and perform functional annotation and pathway analysis. The study employed multi-scale embedded gene co-expression network analysis (MEGENA) combined with least absolute shrinkage and selection operator (LASSO) regression analysis to select hub genes and construct a diagnostic model for OSA. In addition, the study conducted correlation analysis between hub genes and OSA-related genes, immunoinfiltration, gene set enrichment analysis (GSEA), miRNA network analysis, and identified potential transcription factors (TFs) and targeted drugs for hub genes. Finally, the study used chronic intermittent hypoxia (CIH) mouse model to simulate OSA hypoxic conditions and verify the expression of hub genes in CIH mice. Results: In this study, a total of 401 upregulated genes and 275 downregulated genes were identified, and enrichment analysis revealed that these differentially expressed genes may be associated with pathways such as vasculature development, cellular response to cytokine stimulus, and negative regulation of cell population proliferation. Through MEGENA combined with LASSO regression, seven OSA hub genes were identified, including C12orf54, FOS, GPR1, OR9A4, MYO5B, RAB39B, and KLHL4. The diagnostic model constructed based on these genes showed strong stability. The expression levels of hub genes were significantly correlated with the expression levels of OSA-related genes and mainly acted on pathways such as the JAK/STAT signaling pathway and the cytosolic DNA-sensing pathway. Drug-target predictions for hub genes were made using the Connectivity Map (CMap) database and the Drug-Gene Interaction database (Dgidb), which identified targeted therapeutic drugs for the hub genes. In vivo experiments showed that the hub genes were all decreasing in the OSA mouse model. [ABSTRACT FROM AUTHOR]

Background This work explored the characteristics of the WRKY transcription factor family in Rhododendron henanense subsp. lingbaoense (Rhl) and the expression patterns of these genes under abiotic stress by conducting bioinformatics and expression analyses. Methods RhlWRKY genes were identified from a gene library of Rhl. Various aspects of these genes were analyzed, including genetic structures, conserved sequences, physicochemical properties, cis-acting elements, and chromosomal location. RNA-seq was employed to analyze gene expression in five different tissues of Rhl: roots, stems, leaves, flowers, and hypocotyls. Additionally, qRT-PCR was used to detect changes in the expression of five RhlWRKY genes under abiotic stress. Result A total of 65 RhlWRKY genes were identified and categorized into three subfamilies based on their structural characteristics: Groups I, II, and III. Group II was further divided into five subtribes, with shared similar genetic structures and conserved motifs among members of the same subtribe. The physicochemical properties of these proteins varied, but the proteins are generally predicted to be hydrophilic. Most proteins are predicted to be in the cell nucleus, and distributed across 12 chromosomes. A total of 84 cis-acting elements were discovered, with many related to responses to biotic stress. Among the identified RhlWRKY genes, there were eight tandem duplicates and 97 segmental duplicates. The majority of duplicate gene pairs exhibited Ka/Ks values

Deciphering the targets of microRNAs (miRNAs) in plants is crucial for comprehending their function and the variation in phenotype that they cause. As the highly cell-specific nature of miRNA regulation, recent computational approaches usually utilize expression data to identify the most physiologically relevant targets. Although these methods are effective, they typically require a large sample size and high-depth sequencing to detect potential miRNA-target pairs, thereby limiting their applicability in improving plant breeding. In this study, we propose a novel miRNA-target prediction framework named kmerPMTF (k-mer-based prediction framework for plant miRNA-target). Our framework effectively extracts the latent semantic embeddings of sequences by utilizing k-mer splitting and a deep self-supervised neural network. We construct multiple similarity networks based on k-mer embeddings and employ graph convolutional networks to derive deep representations of miRNAs and targets and calculate the probabilities of potential associations. We evaluated the performance of kmerPMTF on four typical plant datasets: Arabidopsis thaliana, Oryza sativa, Solanum lycopersicum, and Prunus persica. The results demonstrate its ability to achieve AUPRC values of 84.9%, 91.0%, 80.1%, and 82.1% in 5-fold cross-validation, respectively. Compared with several state-of-the-art existing methods, our framework achieves better performance on threshold-independent evaluation metrics. Overall, our study provides an efficient and simplified methodology for identifying plant miRNA-target associations, which will contribute to a deeper comprehension of miRNA regulatory mechanisms in plants.

The loblolly pine (Pinus taeda L.) is one of the most profitable forest species worldwide owing to its quick growth, high wood yields, and strong adaptability. The AP2/ERF gene family plays a widespread role in the physiological processes of plant defense responses and the biosynthesis of metabolites. Nevertheless, there are no reports on this gene family in loblolly pine (P. taeda). In this study, a total of 303 members of the AP2/ERF gene family were identified. Through multiple sequence alignment and phylogenetic analysis, they were classified into four subfamilies, including AP2 (34), RAV (17), ERF (251), and Soloist (1). An analysis of the conservation domains, conserved motifs, and gene structure revealed that every PtAP2/ERF transcription factor (TF) had at least one AP2 domain. While evolutionary conservation was displayed within the same subfamilies, the distribution of conserved domains, conserved motifs, and gene architectures varied between subfamilies. Cis-element analysis revealed abundant light-responsive elements, phytohormone-responsive elements, and stress-responsive elements in the promoter of the PtAP2/ERF genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of potential target genes showed that the AP2/ERF gene family might play a critical role in plant growth and development, the response to environmental stresses, and metabolite biosynthesis. Utilizing quantitative real-time PCR (qRT-PCR), we examined the expression patterns of 10 randomly selected genes from Group IX after 6 h of treatments with mechanical injury, ethephon (Eth), and methyl jasmonate (MeJA). The AP2/ERF gene family in the loblolly pine was systematically analyzed for the first time in this study, offering a theoretical basis for exploring the functions and applications of AP2/ERF genes.

Background Osteosarcoma is the most common primary malignant bone tumor, but its pathogenesis remains unclear. Ubiquitin-specific processing peptidase 22 (USP22) is reported to be highly expressed and associated with tumor malignancy and prognosis in cancers. However, the role and mechanism of USP22 in osteosarcoma is not fully understood. This study aims to investigate the function and potential mechanism of USP22 in osteosarcoma using bioinformatics analysis combined with experimental validation. Methods We first integrated transcriptomic datasets and clinical information of osteosarcoma from GEO and TCGA databases to assess the expression and prognostic value of USP22 in osteosarcoma. Then, differential expression analysis and weighted gene co-expression network analysis (WGCNA) were conducted to identify USP22-related co-expressed genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to explore the biological functions and signaling pathways of USP22 co-expressed genes. To validate the accuracy of bioinformatics analyses, we downregulated USP22 expression in osteosarcoma cell line Sao-2 using siRNA and assessed its effect on cell proliferation, migration, invasion, apoptosis, and regulation of key signaling pathways. Results We found that USP22 was highly expressed in osteosarcoma tissues and correlated with poor prognosis in osteosarcoma patients. USP22 also showed potential as a diagnostic marker for osteosarcoma. In addition, 344 USP22-related co-expressed genes were identified, mainly involved in signaling pathways such as glycolysis, oxidative phosphorylation, spliceosome, thermogenesis, and cell cycle. The in vitro experiments confirmed the accuracy and reliability of bioinformatics analyses. We found that downregulation of USP22 could inhibit Sao-2 cell proliferation, migration, invasion, and induce apoptosis. Furthermore, downregulation of USP22 significantly reduced aerobic glycolysis levels in Sao-2 cells and inhibited the expression of key enzymes and transporters in aerobic glycolysis pathways such as HK2, PKM2, and GLUT1. Conclusions USP22 plays a critical role in the occurrence, development, and prognosis of osteosarcoma. USP22 could influence Sao-2 cell proliferation, apoptosis, migration, and invasion by regulating the glycolysis pathway, thereby promoting osteosarcoma progression. Therefore, USP22 may be a potential therapeutic target for the treatment of osteosarcoma.

The taxonomic characterization of ancient microbiomes is a key step in the rapidly growing field of paleomicrobiology. While PCR amplification of the 16S ribosomal RNA (rRNA) gene is a widely used technique in modern microbiota studies, this method has systematic biases when applied to ancient microbial DNA. Shotgun metagenomic sequencing has proven to be the most effective method in reconstructing taxonomic profiles of ancient dental calculus samples. Nevertheless, shotgun sequencing approaches come with inherent limitations that could be addressed through hybridization enrichment capture. When employed together, shotgun sequencing and hybridization capture have the potential to enhance the characterization of ancient microbial communities. Here, we develop, test, and apply a hybridization enrichment capture technique to selectively target 16S rRNA gene fragments from the libraries of ancient dental calculus samples generated with shotgun techniques. We simulated data sets generated from hybridization enrichment capture, indicating that taxonomic identification of fragmented and damaged 16S rRNA gene sequences was feasible. Applying this enrichment approach to 15 previously published ancient calculus samples, we observed a 334-fold increase of ancient 16S rRNA gene fragments in the enriched samples when compared to unenriched libraries. Our results suggest that 16S hybridization capture is less prone to the effects of background contamination than 16S rRNA amplification, yielding a higher percentage of on-target recovery. While our enrichment technique detected low abundant and rare taxa within a given sample, these assignments may not achieve the same level of specificity as those achieved by unenriched methods.

Background Gastric cancer (GC) is a malignant tumor that originates from the epithelium of the gastric mucosa and has a poor prognosis. Stomach adenocarcinoma (STAD) covers 95% of total gastric cancer. This study aimed to identify the prognostic value of RNA methylation-related genes in gastric cancer. Methods In this study, The Cancer Genome Atlas (TCGA)-STAD and GSE84426 cohorts were downloaded from public databases. Patients were classified by consistent cluster analysis based on prognosis-related differentially expressed RNA methylation genes Prognostic genes were obtained by differential expression, univariate Cox and least absolute shrinkage and selection operator (LASSO) analyses. The prognostic model was established and validated in the training set, test set and validation set respectively. Independent prognostic analysis was implemented. Finally, the expression of prognostic genes was affirmed by reverse transcription quantitative PCR (RT-qPCR). Results In total, four prognostic genes (ACTA2, SAPCD2, PDK4 and APOD) related to RNA methylation were identified and enrolled into the risk signature. The STAD patients were divided into high- and low-risk groups based on the medium value of the risk score, and patients in the high-risk group had a poor prognosis. In addition, the RNA methylation-relevant risk signature was validated in the test and validation sets, and was authenticated as a reliable independent prognostic predictor. The nomogram was constructed based on the independent predictors to predict the 1/3/5-year survival probability of STAD patients. The gene set enrichment analysis (GSEA) result suggested that the poor prognosis in the high-risk subgroup may be related to immune-related pathways. Finally, the experimental results indicated that the expression trends of RNA methylation-relevant prognostic genes in gastric cancer cells were in agreement with the result of bioinformatics. Conclusion Our study established a novel RNA methylation-related risk signature for STAD, which was of considerable significance for improving prognosis of STAD patients and offering theoretical support for clinical therapy.

Background Global prevalence of neurodegenerative diseases such as Alzheimer’s disease and Parkinson’s disease is increasing gradually, whereas approvals of successful therapeutics for central nervous system disorders are inadequate. Accumulating evidence suggests pivotal roles of the receptor-interacting serine/threonine-protein kinase 1 (RIPK1) in modulating neuroinflammation and necroptosis. Discoveries of potent small molecule inhibitors for RIPK1 with favorable pharmacokinetic properties could thus address the unmet medical needs in treating neurodegeneration. Methods In a structure-based virtual screening, we performed site-specific molecular docking of 4,858 flavonoids against the kinase domain of RIPK1 using AutoDock Vina. We predicted physicochemical descriptors of the top ligands using the SwissADME webserver. Binding interactions of the best ligands and the reference ligand L8D were validated using replicated 500-ns Gromacs molecular dynamics simulations and free energy calculations. Results From Vina docking, we shortlisted the top 20 flavonoids with the highest binding affinities, ranging from −11.7 to −10.6 kcal/mol. Pharmacokinetic profiling narrowed down the list to three orally bioavailable and blood-brain-barrier penetrant flavonoids: Nitiducarpin, Pinocembrin 7-O-benzoate, and Paratocarpin J. Next, trajectories of molecular dynamics simulations of the top protein-ligand complexes were analyzed for binding interactions. The root-mean-square deviation (RMSD) was 1.191 Å (±0.498 Å), 1.725 Å (±0.828 Å), 1.923 Å (±0.942 Å), 0.972 Å (±0.155 Å) for Nitiducarpin, Pinocembrin 7-O-benzoate, Paratocarpin J, and L8D, respectively. The radius of gyration (Rg) was 2.034 nm (±0.015 nm), 2.0.39 nm (± 0.025 nm), 2.053 nm (±0.021 nm), 2.037 nm (±0.016 nm) for Nitiducarpin, Pinocembrin 7-O-benzoate, Paratocarpin J, and L8D, respectively. The solvent accessible surface area (SASA) was 159.477 nm2 (±3.021 nm2), 159.661 nm2 (± 3.707 nm2), 160.755 nm2 (±4.252 nm2), 156.630 nm2 (±3.521 nm2), for Nitiducarpin, Pinocembrin 7-O-benzoate, Paratocarpin J, and L8D complexes, respectively. Therefore, lower RMSD, Rg, and SASA values demonstrated that Nitiducarpin formed the most stable complex with the target protein among the best three ligands. Finally, 2D protein-ligand interaction analysis revealed persistent hydrophobic interactions of Nitiducarpin with the critical residues of RIPK1, including the catalytic triads and the activation loop residues, implicated in the kinase activity and ligand binding. Conclusion Our target-based virtual screening identified three flavonoids as strong RIPK1 inhibitors, with Nitiducarpin exhibiting the most potent inhibitory potential. Future in vitro and in vivo studies with these ligands could offer new hope for developing effective therapeutics and improving the quality of life for individuals affected by neurodegeneration.

Background: The rapid emergence of antibiotic-resistant bacteria directly contributes to a wave of untreatable infections. The lack of new drug development is an important driver of this crisis. Most antibiotics today are small molecules that block vital processes in bacteria. To optimize such effects, the three-dimensional structure of targeted bacterial proteins is imperative, although such a task is time-consuming and tedious, impeding the development of antibiotics. The development of RNA-based therapeutics has catalyzed a new platform of antibiotics--antisense oligonucleotides (ASOs). These molecules hybridize with their target mRNAs with high specificity, knocking down or interfering with protein translation. This study aims to develop a bioinformatics pipeline to identify potent ASO targets in essential bacterial genes. Methods: Three bacterial species (P. gingivalis, H. influenzae, and S. aureus) were used to demonstrate the utility of the pipeline. Open reading frames of bacterial essential genes were downloaded from the Database of Essential Genes (DEG). After filtering for specificity and accessibility, ASO candidates were ranked based on their self-hybridization score, predicted melting temperature, and the position on the gene in an operon. Enrichment analysis was conducted on genes associated with putative potent ASOs. Results: A total of 45,628 ASOs were generated from 348 unique essential genes in P. gingivalis. A total of 1,117 of them were considered putative. A total of 27,273 ASOs were generated from 191 unique essential genes in H. influenzae. A total of 847 of them were considered putative. A total of 175,606 ASOs were generated from 346 essential genes in S. aureus. A total of 7,061 of them were considered putative. Critical biological processes associated with these genes include translation, regulation of cell shape, cell division, and peptidoglycan biosynthetic process. Putative ASO targets generated for each bacterial species are publicly available here: https://github.com/EricSHo/AOA. The results demonstrate that our bioinformatics pipeline is useful in identifying unique and accessible ASO targets in bacterial species that post major public health issues. [ABSTRACT FROM AUTHOR]

Durum and bread wheat are well adapted to the Mediterranean Basin. Twenty-three genotypes of each species were grown to evaluate the intra- and inter-genetic diversity based on omega (ω), gamma (γ) and alpha (α)-gliadin profiles. To achieve this purpose, the endosperm storage proteins (both gliadins and glutenins) were extracted from wheat grains and electrophoresed on sodium dodecyl sulfate (SDS)-polyacrylamide gels. The results of SDS-Polyacrylamide Gel Electrophoresis (SDS-PAGE) revealed nine polymorphic loci out of 16 loci with durum wheat genotypes and nine polymorphic loci out of 18 loci with bead wheat genotypes. The polymorphisms revealed by the SDS-PAGE were 56% and 50% in durum and bread wheat genotypes, respectively. Using the cluster analysis, the durum wheat genotypes were clustered into five groups, while the bread wheat genotypes were grouped into six clusters using un-weighed pair group mean analyses based on ω, γ, and α-gliadins profiles. The 46 durum and bread wheat genotypes were grouped into seven clusters based on the combined ω, γ, and α-gliadins profiles revealed by the SDS-PAGE. The in silico analysis determined the intra-genetic diversity between bread and durum wheat based on the sequences of ω, γ, and α-gliadins. The alignment of ω-gliadin revealed the highest polymorphism (52.1%) between bread and durum wheat, meanwhile, the alignment of γ and α- gliadins revealed very low polymorphism 6.6% and 15.4%, respectively. According to computational studies, all gliadins contain a lot of glutamine and proline residues. The analysis revealed that the bread wheat possessed ω and γ -gliadins with a lower content of proline and a higher content of glutamine than durum wheat. In contrast, durum wheat possessed α-gliadin with a lower content of proline and a higher content of glutamine than bread wheat. In conclusion, the SDS-PAGE, in silico and computational analyses are effective tools to determine the intra- and inter-genetic diversity in tetraploid and hexaploid wheat genotypes based on ω, γ, and α-gliadins profiles. [ABSTRACT FROM AUTHOR]

Non-small cell lung cancer (NSCLC) has long been the deadliest malignancy worldwide, with adenocarcinoma (AD) being the most common pathological subtype. Here we focused on the value of LASTR in LUAD. Using expression analysis, enrichment analysis, immune cell infraction analysis, we found that the expression level of LASTR was significantly increased in LUAD tissue. Meanwhile, LASTR was significantly associated with differential infiltration of various immune cells. Kaplan-Meier survival analysis showed that LUAD related with a poor prognosis in terms of OS, PFI, and DSS compared with high-expression LASTR. The enrichment analysis showed that LASTR is related to the pathays like PI3K-AKT signaling pathway. Thus, the present findings could be helpful in a better understand of LASTR in LUAD. RT-PCR was used to verify the high expression of LASTR in LUAD tissues, and the apoptosis of LUAD cell lines was promoted by CCK8 and Transwell experiments to verify the ability of LASTR to promote the migration and invasion of lung cancer cells in vitro. [ABSTRACT FROM AUTHOR]

Influenza viruses pose a significant and ongoing threat to human health. Many host factors have been identified to be associated with influenza virus infection. However, there is currently a lack of an integrated resource for these host factors. This study integrated human genes and proteins associated with influenza virus infections for 14 subtypes of influenza A viruses, as well as influenza B and C viruses, and built a database named H2Flu to store and organize these genes or proteins. The database includes 28,639 differentially expressed genes (DEGs), 1,850 differentially expressed proteins, and 442 proteins with differential posttranslational modifications after influenza virus infection, as well as 3,040 human proteins that interact with influenza virus proteins and 57 human susceptibility genes. Further analysis showed that the dynamic response of human cells to virus infection, cell type and strain specificity contribute significantly to the diversity of DEGs. Additionally, large heterogeneity was also observed in protein- protein interactions between humans and different types or subtypes of influenza viruses. Overall, the study deepens our understanding of the diversity and complexity of interactions between influenza viruses and humans, and provides a valuable resource for further studies on such interactions. [ABSTRACT FROM AUTHOR]

Background. Foam cells play crucial roles in all phases of atherosclerosis. However, un- til now, the specific mechanisms by which these foam cells contribute to atherosclerosis remain unclear. We aimed to identify novel foam cell biomarkers and interventional targets for atherosclerosis, characterizing their potential mechanisms in the progression of atherosclerosis. Methods. Microarray data of atherosclerosis and foam cells were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expression genes (DEGs) were screened using the "LIMMA'' package in R software. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and Gene Ontology (GO) annotation were both carried out. Hub genes were found in Cytoscape after a protein-protein interaction (PPI) enrichment analysis was carried out. Validation of important genes in the GSE41571 dataset, cellular assays, and tissue samples. Results. A total of 407 DEGs in atherosclerosis and 219 DEGs in foam cells were identified, and the DEGs in atherosclerosis were mainly involved in cell proliferation and differentiation. CSF1R and PLAUR were identified as common hub genes and validated in GSE41571. In addition, we also found that the expression of CSF1R and PLAUR gradually increased with the accumulation of lipids and disease progression in cell and tissue experiments. Conclusion. CSF1R and PLAUR are key hub genes of foam cells and may play an important role in the biological process of atherosclerosis. These results advance our understanding of the mechanism behind atherosclerosis and potential therapeutic targets for future development. [ABSTRACT FROM AUTHOR]

Background: Recent efforts to repurpose existing drugs to different indications have been accompanied by a number of computational methods, which incorporate protein-protein interaction networks and signaling pathways, to aid with prioritizing existing targets and/or drugs. However, many of these existing methods are focused on integrating additional data that are only available for a small subset of diseases or conditions. Methods: We have designed and implemented a new R-based open-source target prioritization and repurposing method that integrates both canonical intracellular signaling information from five public pathway databases and target information from public sources including OpenTargets.org. The Pathway2Targets algorithm takes a list of significant pathways as input, then retrieves and integrates public data for all targets within those pathways for a given condition. It also incorporates a weighting scheme that is customizable by the user to support a variety of use cases including target prioritization, drug repurposing, and identifying novel targets that are biologically relevant for a different indication. Results: As a proof of concept, we applied this algorithm to a public colorectal cancer RNA-sequencing dataset with 144 case and control samples. Our analysis identified 430 targets and ~700 unique drugs based on differential gene expression and signaling pathway enrichment. We found that our highest-ranked predicted targets were significantly enriched in targets with FDA-approved therapeutics for colorectal cancer (p-value < 0.025) that included EGFR, VEGFA, and PTGS2. Interestingly, there was no statistically significant enrichment of targets for other cancers in this same list suggesting high specificity of the results. We also adjusted the weighting scheme to prioritize more novel targets for CRC. This second analysis revealed epidermal growth factor receptor (EGFR), phosphoinositide-3-kinase (PI3K), and two mitogen-activated protein kinases (MAPK14 and MAPK3). These observations suggest that our open-source method with a customizable weighting scheme can accurately prioritize targets that are specific and relevant to the disease or condition of interest, as well as targets that are at earlier stages of development. We anticipate that this method will complement other approaches to repurpose drugs for a variety of indications, which can contribute to the improvement of the quality of life and overall health of such patients. [ABSTRACT FROM AUTHOR]

Background. Genetic variants may potentially play a contributing factor in the development of diseases. Several genetic disease databases are used in medical research and diagnosis but the web applications used to search these databases for disease-associated variants have limitations. The application may not be able to search for large-scale genetic variants, the results of searches may be difficult to interpret and variants mapped from the latest reference genome (GRCH38/hg38) may not be supported. Methods. In this study, we developed a novel R library called "DisVar" to identify disease-associated genetic variants in large-scale individual genomic data. This R library is compatible with variants from the latest reference genome version. DisVar uses five databases of disease-associated variants. Over 100 million variants can be simultaneously searched for specific associated diseases. Results. The package was evaluated using 24 Variant Call Format (VCF) files (215,054 to 11,346,899 sites) from the 1000 Genomes Project. Disease-associated variants were detected in 298,227 hits across all the VCF files, taking a total of 63.58 m to complete. The package was also tested on ClinVar's VCF file (2,120,558 variants), where 20,657 hits associated with diseases were identified with an estimated elapsed time of 45.98 s. Conclusions. DisVar can overcome the limitations of existing tools and is a fast and effective diagnostic and preventive tool that identifies disease-associated variations from large-scale genetic variants against the latest reference genome. [ABSTRACT FROM AUTHOR]

Background: Yersinia pestis, a Gram-negative bacterium, is the causative agent of plague. Y. pestis is a zoonotic pathogen that occasionally infects humans and became endemic in the western United States after spreading from California in 1899. Methods: To better understand evolutionary patterns in Y. pestis from the southwestern United States, we sequenced and analyzed 22 novel genomes from New Mexico. Analytical methods included, assembly, multiple sequences alignment, phylogenetic tree reconstruction, genotype-phenotype correlation, and selection pressure. Results: We identified four genes, including Yscp and locus tag YPO3944, which contained codons undergoing negative selection. We also observed 42 nucleotide sites displaying a statistically significant skew in the observed residue distribution based on the year of isolation. Overall, the three genes with the most statistically significant variations that associated with metadata for these isolates were sapA, fliC, and argD. Phylogenetic analyses point to a single introduction of Y. pestis into the United States with two subsequent, independent movements into New Mexico. Taken together, these analyses shed light on the evolutionary history of this pathogen in the southwestern US over a focused time range and confirm a single origin and introduction into North America. [ABSTRACT FROM AUTHOR]

Background The rapid emergence of antibiotic-resistant bacteria directly contributes to a wave of untreatable infections. The lack of new drug development is an important driver of this crisis. Most antibiotics today are small molecules that block vital processes in bacteria. To optimize such effects, the three-dimensional structure of targeted bacterial proteins is imperative, although such a task is time-consuming and tedious, impeding the development of antibiotics. The development of RNA-based therapeutics has catalyzed a new platform of antibiotics—antisense oligonucleotides (ASOs). These molecules hybridize with their target mRNAs with high specificity, knocking down or interfering with protein translation. This study aims to develop a bioinformatics pipeline to identify potent ASO targets in essential bacterial genes. Methods Three bacterial species (P. gingivalis, H. influenzae, and S. aureus) were used to demonstrate the utility of the pipeline. Open reading frames of bacterial essential genes were downloaded from the Database of Essential Genes (DEG). After filtering for specificity and accessibility, ASO candidates were ranked based on their self-hybridization score, predicted melting temperature, and the position on the gene in an operon. Enrichment analysis was conducted on genes associated with putative potent ASOs. Results A total of 45,628 ASOs were generated from 348 unique essential genes in P. gingivalis. A total of 1,117 of them were considered putative. A total of 27,273 ASOs were generated from 191 unique essential genes in H. influenzae. A total of 847 of them were considered putative. A total of 175,606 ASOs were generated from 346 essential genes in S. aureus. A total of 7,061 of them were considered putative. Critical biological processes associated with these genes include translation, regulation of cell shape, cell division, and peptidoglycan biosynthetic process. Putative ASO targets generated for each bacterial species are publicly available here: https://github.com/EricSHo/AOA. The results demonstrate that our bioinformatics pipeline is useful in identifying unique and accessible ASO targets in bacterial species that post major public health issues.

Durum and bread wheat are well adapted to the Mediterranean Basin. Twenty-three genotypes of each species were grown to evaluate the intra- and inter-genetic diversity based on omega (ω), gamma (γ) and alpha (α)-gliadin profiles. To achieve this purpose, the endosperm storage proteins (both gliadins and glutenins) were extracted from wheat grains and electrophoresed on sodium dodecyl sulfate (SDS)–polyacrylamide gels. The results of SDS-Polyacrylamide Gel Electrophoresis (SDS-PAGE) revealed nine polymorphic loci out of 16 loci with durum wheat genotypes and nine polymorphic loci out of 18 loci with bead wheat genotypes. The polymorphisms revealed by the SDS-PAGE were 56% and 50% in durum and bread wheat genotypes, respectively. Using the cluster analysis, the durum wheat genotypes were clustered into five groups, while the bread wheat genotypes were grouped into six clusters using un-weighed pair group mean analyses based on ω, γ, and α-gliadins profiles. The 46 durum and bread wheat genotypes were grouped into seven clusters based on the combined ω, γ, and α-gliadins profiles revealed by the SDS-PAGE. The in silico analysis determined the intra-genetic diversity between bread and durum wheat based on the sequences of ω, γ, and α-gliadins. The alignment of ω-gliadin revealed the highest polymorphism (52.1%) between bread and durum wheat, meanwhile, the alignment of γ and α-gliadins revealed very low polymorphism 6.6% and 15.4%, respectively. According to computational studies, all gliadins contain a lot of glutamine and proline residues. The analysis revealed that the bread wheat possessed ω and γ -gliadins with a lower content of proline and a higher content of glutamine than durum wheat. In contrast, durum wheat possessed α-gliadin with a lower content of proline and a higher content of glutamine than bread wheat. In conclusion, the SDS-PAGE, in silico and computational analyses are effective tools to determine the intra- and inter-genetic diversity in tetraploid and hexaploid wheat genotypes based on ω, γ, and α-gliadins profiles.

Background Genetic variants may potentially play a contributing factor in the development of diseases. Several genetic disease databases are used in medical research and diagnosis but the web applications used to search these databases for disease-associated variants have limitations. The application may not be able to search for large-scale genetic variants, the results of searches may be difficult to interpret and variants mapped from the latest reference genome (GRCH38/hg38) may not be supported. Methods In this study, we developed a novel R library called “DisVar” to identify disease-associated genetic variants in large-scale individual genomic data. This R library is compatible with variants from the latest reference genome version. DisVar uses five databases of disease-associated variants. Over 100 million variants can be simultaneously searched for specific associated diseases. Results The package was evaluated using 24 Variant Call Format (VCF) files (215,054 to 11,346,899 sites) from the 1000 Genomes Project. Disease-associated variants were detected in 298,227 hits across all the VCF files, taking a total of 63.58 m to complete. The package was also tested on ClinVar’s VCF file (2,120,558 variants), where 20,657 hits associated with diseases were identified with an estimated elapsed time of 45.98 s. Conclusions DisVar can overcome the limitations of existing tools and is a fast and effective diagnostic and preventive tool that identifies disease-associated variations from large-scale genetic variants against the latest reference genome.

Background Recent efforts to repurpose existing drugs to different indications have been accompanied by a number of computational methods, which incorporate protein-protein interaction networks and signaling pathways, to aid with prioritizing existing targets and/or drugs. However, many of these existing methods are focused on integrating additional data that are only available for a small subset of diseases or conditions. Methods We have designed and implemented a new R-based open-source target prioritization and repurposing method that integrates both canonical intracellular signaling information from five public pathway databases and target information from public sources including OpenTargets.org. The Pathway2Targets algorithm takes a list of significant pathways as input, then retrieves and integrates public data for all targets within those pathways for a given condition. It also incorporates a weighting scheme that is customizable by the user to support a variety of use cases including target prioritization, drug repurposing, and identifying novel targets that are biologically relevant for a different indication. Results As a proof of concept, we applied this algorithm to a public colorectal cancer RNA-sequencing dataset with 144 case and control samples. Our analysis identified 430 targets and ~700 unique drugs based on differential gene expression and signaling pathway enrichment. We found that our highest-ranked predicted targets were significantly enriched in targets with FDA-approved therapeutics for colorectal cancer (p-value < 0.025) that included EGFR, VEGFA, and PTGS2. Interestingly, there was no statistically significant enrichment of targets for other cancers in this same list suggesting high specificity of the results. We also adjusted the weighting scheme to prioritize more novel targets for CRC. This second analysis revealed epidermal growth factor receptor (EGFR), phosphoinositide-3-kinase (PI3K), and two mitogen-activated protein kinases (MAPK14 and MAPK3). These observations suggest that our open-source method with a customizable weighting scheme can accurately prioritize targets that are specific and relevant to the disease or condition of interest, as well as targets that are at earlier stages of development. We anticipate that this method will complement other approaches to repurpose drugs for a variety of indications, which can contribute to the improvement of the quality of life and overall health of such patients.

Background Yersinia pestis, a Gram-negative bacterium, is the causative agent of plague. Y. pestis is a zoonotic pathogen that occasionally infects humans and became endemic in the western United States after spreading from California in 1899. Methods To better understand evolutionary patterns in Y. pestis from the southwestern United States, we sequenced and analyzed 22 novel genomes from New Mexico. Analytical methods included, assembly, multiple sequences alignment, phylogenetic tree reconstruction, genotype-phenotype correlation, and selection pressure. Results We identified four genes, including Yscp and locus tag YPO3944, which contained codons undergoing negative selection. We also observed 42 nucleotide sites displaying a statistically significant skew in the observed residue distribution based on the year of isolation. Overall, the three genes with the most statistically significant variations that associated with metadata for these isolates were sapA, fliC, and argD. Phylogenetic analyses point to a single introduction of Y. pestis into the United States with two subsequent, independent movements into New Mexico. Taken together, these analyses shed light on the evolutionary history of this pathogen in the southwestern US over a focused time range and confirm a single origin and introduction into North America.

Background: Psoriasis is an autoimmune skin disease characterized by immunocyte activation, excessive proliferation, and abnormal differentiation of keratinocytes. Signal transducers and activators of transcription 3 (STAT3) play a crucial role in linking activated keratinocytes and immunocytes during psoriasis development. T helper (Th) 17 cells and secreted interleukin (IL)-17A contribute to its pathogenesis. IL-17A treated STAT3 overexpressing mouse model might serve as an animal model for psoriasis. Methods: In this study, we established a mouse model of psoriasiform dermatitis by intradermal IL-17A injection in STAT3 overexpressing mice. Transcriptome analyses were performed on the skin of wild type (WT), STAT3, and IL-17A treated STAT3 mice. Bioinformatics-based functional enrichment analysis was conducted to predict biological pathways. Meanwhile, the morphological and pathological features of skin lesions were observed, and the DEGs were verified by qPCR. Results: IL-17A treated STAT3 mice skin lesions displayed the pathological features of hyperkeratosis and parakeratosis. The DEGs between IL-17A treated STAT3 mice and WT mice were highly consistent with those observed in psoriasis patients, including S100A8, S100A9, Sprr2, and LCE. Gene ontology (GO) analysis of the core DEGs revealed a robust immune response, chemotaxis, and cornified envelope, et al. The major KEGG enrichment pathways included IL-17 and Toll-like receptor signaling pathways. Conclusion: IL-17A exacerbates psoriasis dermatitis in a STAT3 overexpressing mouse. [ABSTRACT FROM AUTHOR]

Background. Phosphatidylinositol binding clathrin assembly protein interacting mitotic regulator (PIMREG) expression is upregulated in a variety of cancers. However, its potential role in breast cancer (BC) remains uncertain. Methods. The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were used to gather relevant information. The expression of PIMREG and its clinical implication in BC were assessed by using Wilcoxon rank-sum test. The prognostic value of PIMREG in BC was evaluated through the Cox regression model and nomogram, and visualized by Kaplan-Meier survival curves. Genes/proteins that interact with PIMREG in BC were also identified through GeneMANIA and MaxLink. Gene set enrichment analysis (GSEA) was then performed. The correlations of the immune cell infiltration and immune checkpoints with the expression of PIMREG in BC were explored via TIMER, TISIDB, and GEPIA. Potential drugs that interact with PIMREG in BC were explored via Q-omic. The siRNA transfection, CCK-8, and transwell migration assay were conducted to explore the function of PIMREG in cell proliferation and migration. Results. PIMREG expression was significantly higher in infiltrating ductal carcinoma, estrogen receptor negative BC, and progestin receptor negative BC. High expression of PIMREG was associated with poor overall survival, disease-specific survival, and progression-free interval. A nomogram based on PIMREG was developed with a satisfactory prognostic value. PIMREG also had a high diagnostic ability, with an area under the curve of 0.940. Its correlations with several immunomodulators were also observed. Immune checkpoint CTLA-4 was significantly positively associated with PIMREG. HDAC2 was found as a potentially critical link between PIMREG and BRCA1/2. In addition, PIMREG knockdown could inhibit cell proliferation and migration in BC. Conclusions. The high expression of PIMREG is associated with poor prognosis and immune checkpoints in BC. HDAC2 may be a critical link between PIMREG and BRCA1/2, potentially a therapeutic target. [ABSTRACT FROM AUTHOR]

Background: Colorectal cancer (CRC) is the second leading cause of cancer-related deaths, and its development is associated with the gains and/or losses of genetic material, which leads to the emergence of main driver genes with higher mutational frequency. In addition, there are other genes with mutations that have weak tumor-promoting effects, known as mini-drivers, which could aggravate the development of oncogenesis when they occur together. The aim of our work was to use computer analysis to explore the survival impact, frequency, and incidence of mutations of possible mini-driver genes to be used for the prognosis of CRC. Methods: We retrieved data from three sources of CRC samples using the cBioPortal platform and analyzed the mutational frequency to exclude genes with driver features and those mutated in less than 5% of the original cohort. We also observed that the mutational profile of these mini-driver candidates is associated with variations in the expression levels. The candidate genes obtained were subjected to Kaplan-Meier curve analysis, making a comparison between mutated and wild-type samples for each gene using a p-value threshold of 0.01. Results: After gene filtering by mutational frequency, we obtained 159 genes of which 60 were associated with a high accumulation of total somatic mutations with Log2 (fold change) > 2 and p values < 10-5. In addition, these genes were enriched to oncogenic pathways such as epithelium-mesenchymal transition, hsa-miR-218-5p downregulation, and extracellular matrix organization. Our analysis identified five genes with possible implications as mini-drivers: DOCK3, FN1, PAPPA2, DNAH11, and FBN2. Furthermore, we evaluated a combined classification where CRC patients with at least one mutation in any of these genes were separated from the main cohort obtaining a p-value < 0.001 in the evaluation of CRC prognosis. Conclusion: Our study suggests that the identification and incorporation of mini-driver genes in addition to known driver genes could enhance the accuracy of prognostic biomarkers for CRC. [ABSTRACT FROM AUTHOR]

Background. The aim of this study was to identify key immune-related genes in acute liver failure (ALF) by constructing an ALF mouse model for transcriptome sequencing. Methods. The C57BL/6 mouse with ALF model was induced by lipopolysaccharide (LPS)/ D-galactosamine (D-GalN). After successful modelling, the liver tissues of all mice were obtained for transcriptome sequencing. The key immune-related genes in mice with ALF were identified by differential expression analysis, immune infiltration analysis, weighted gene co-expression network analysis (WGCNA), enrichment analysis, and protein-protein interaction (PPI) analysis. Results. An LPS/D-GalN-induced ALF mouse model was successfully constructed, and transcriptome sequencing was performed. Significant differences in the proportions of monocytes, macrophages M0, macrophages M1 and neutrophils were shown by immune infiltration analysis, and 5255 genes highly associated with these four immune cells were identified by WGCNA. These immune genes were found to be significantly enriched in the TNF signalling pathway by enrichment analysis. Finally, PPI analysis was performed on genes enriched in this pathway and three key genes (CXCL1, CXCL10 and IL1B) were screened out and revealed to be significantly upregulated in ALF. Conclusions. Key immune-related genes in ALF were identified in this study, which may provide not only potential therapeutic targets for treating ALF and improving its prognosis, but also a reliable scientific basis for the immunotherapy of the disease. [ABSTRACT FROM AUTHOR]

Background. The sirtuins (SIRTs) family is a nicotinamide adenine dinucleotide (NAD+) family of dependent deacetylases, which includes SIRT1-7. This family is related to the development and progression of various tumors. However, a comprehensive analysis of the role of SIRTs in clear cell renal cell carcinoma (ccRCC) is still lacking, and there are few reports on the inhibitory role of SIRT5 in ccRCC. Methods. We used immunohistochemical analysis, and several bioinformatic databases to perform an integrated analysis of the expression and prognostic value of SIRT5 and other SIRT family members in ccRCC along with the associated immune cell infiltration. These databases include TIMER, THPA, cell culture, UALCAN, cBioPortal, WebGestalt, Metascape, DiseaseMeth, STRING database, and Cytoscape. Results. The protein expression of SIRT1, 2, 3, 6, and 7 were upregulated in ccRCC for the Human Protein Atlas database, whereas the expression of SIRT4 and SIRT5 was decreased. The expression based on tumor stage, and grade followed a similar trend. Kaplan-Meier analysis showed that high SIRT4 and SIRT5 expression was positively related to better overall survival (OS), whereas SIRT6 and SIRT7 expression was positively related to worse OS. Further, high SIRT3 expression was related to worse relapse-free survival (RFS), whereas high SIRT5 expression was related to better RFS. To explore the mechanism underlying the function of SIRTs in ccRCC, we also used several databases to perform the functional enrichment analysis and explore the relationship between infiltrating immune cells and seven SIRT family members in ccRCC. The results showed that several SIRT family members, and particularly SIRT5, are correlated with the infiltration of some important immune cells. The protein expression of SIRT5 was significantly lower in tumor tissue compared to normal tissue and was negatively related to the age of the patient ccRCC individual tumor stages, and grades. In human ccRCC samples, strong IHC staining expression of SIRT5 was displayed in adjacent normal tissue than in tumor tissues. Conclusion. SIRT5 may be a prognostic marker and a novel strategy for the treatment of ccRCC. [ABSTRACT FROM AUTHOR]

Background Weight loss effectively reduces cardiometabolic health risks among people with overweight and obesity, but inter-individual variability in weight loss maintenance is large. Here we studied whether baseline gene expression in subcutaneous adipose tissue predicts diet-induced weight loss success. Methods Within the 8-month multicenter dietary intervention study DiOGenes, we classified a low weight-losers (low-WL) group and a high-WL group based on median weight loss percentage (9.9%) from 281 individuals. Using RNA sequencing, we identified the significantly differentially expressed genes between high-WL and low-WL at baseline and their enriched pathways. We used this information together with support vector machines with linear kernel to build classifier models that predict the weight loss classes. Results Prediction models based on a selection of genes that are associated with the discovered pathways ‘lipid metabolism’ (max AUC = 0.74, 95% CI [0.62–0.86]) and ‘response to virus’ (max AUC = 0.72, 95% CI [0.61–0.83]) predicted the weight-loss classes high-WL/low-WL significantly better than models based on randomly selected genes (P < 0.01). The performance of the models based on ‘response to virus’ genes is highly dependent on those genes that are also associated with lipid metabolism. Incorporation of baseline clinical factors into these models did not noticeably enhance the model performance in most of the runs. This study demonstrates that baseline adipose tissue gene expression data, together with supervised machine learning, facilitates the characterization of the determinants of successful weight loss. [ABSTRACT FROM AUTHOR]