Genes & genomics最新文献

Background: High-throughput sequencing, particularly RNA-sequencing (RNA-seq), has advanced differential gene expression analysis, revealing pathways involved in various biological conditions. Traditional pathway-based methods generally consider pathways independently, overlooking the correlations among them and ignoring quite a few overlapping biomarkers between pathways. In addition, most pathway-based approaches assume that biomarkers have linear effects on the phenotype of interest.

Objective: This study aims to develop the HisCoM-KernelS model to identify survival phenotype-related pathways by accommodating complex, nonlinear relationships between genes and survival outcomes, while accounting for inter-pathway correlations.

Methods: We applied HisCoM-KernelS model to the TCGA pancreatic ductal adenocarcinoma (PDAC) RNA-seq dataset, comprising 4,498 protein-coding genes mapped to 186 KEGG pathways from 148 PDAC samples. Kernel machine regression was used to model pathway effects on survival outcomes, incorporating hierarchical gene-pathway structures. Model parameters were estimated using the alternating least squares algorithm, and the significance of pathways was assessed through a permutation test.

Results: HisCoM-KernelS identified several pathways significantly associated with pancreatic cancer survival, including those corroborated by previous studies. HisCoM-KernelS, especially with the Gaussian kernel, showed a better balance of detection rate and number of significant pathways compared to four other existing pathway-based methods: HisCoM-PAGE, Global Test, GSEA, and CoxKM.

Conclusion: HisCoM-KernelS successfully extends pathway-based analysis to survival outcomes, capturing complex nonlinear gene effects and inter-pathway correlations. Its application to the TCGA PDAC dataset emphasizes its utility in identifying biologically relevant pathways, offering a robust tool for survival phenotype research in high-throughput sequencing data.

Background: Granzyme activity can affect the processing and stability of miRNAs within target cells. They also could induce changes in miRNA expression that impact apoptotic signaling. Granzyme-induced apoptosis might result in changes to the miRNA profile, which can further influence the apoptosis and inflammation processes.

Objective: The aim of this study was to bioinformatically analyze which miRNAs and transcription factors bind to the CDS and UTR regions of the granzyme family to regulate gene expression in relation to granzyme evolution and their association with human cancer diseases.

Methods: The expression patterns of granzyme genes were analyzed in various human tissues. MiRNAs binding to the CDS and UTR of the granzyme family were examined, and the transcription factors binding to these miRNAs binding sites were also analyzed. Cytoscape program was used to visualize and analyze the networks of interactions between granzyme mRNA and miRNAs. Additionally, the evolutionary patterns of the granzyme family in relation to miRNAs and transcription factors binding were investigated.

Results: Analysis of the expression patterns of the granzyme family in various human tissues shows that GZMA and GZMK are strongly expressed in lymph nodes. GZMB exhibits strong expression in the bone marrow, while GZMA is prominently expressed in the spleen. Twenty-two miRNAs bind to both GZMK and GZMB mRNA, while six miRNAs bind to both GZMK and GZMM mRNA. The only miRNA that binds to GZMK, GZMB, GZMM, and GZMA mRNA is hsa-miR-146a-5p. Transcription factors JUND, FOS, and JUN are distinctly interconnected with has-miR-5696 and GZMK. Association data between the granzyme family and cancers showed that various miRNAs were consistently implicated and exhibited either upregulation or downregulation.

Conclusion: Although the granzyme family possesses distinct genetic information, it shows relatively high expression levels in the lymph node, spleen, and bone marrow. Many miRNAs specifically regulate granzyme gene expression, and various transcription factors are involved. Analyzing the granzyme genes-miRNAs-transcription factors-related network will provide crucial insights into the mechanisms of cancer development and suppression.

Background: Potato (Solanum tuberosum L.) is one of the most economically significant crops globally. Nevertheless, potato cultivation is becoming increasingly susceptible to a multitude of diseases, including bacterial wilt, which is caused by Ralstonia solanacearum.

Objective: To identify the GRF gene family in potatoes and to examine their expression profiles in response to hormones and R. solanacearum infection.

Methods: A comprehensive genome-wide analysis was conducted to identify the GRF gene family in the potato genome.

Results: A total of 13 GRF genes were identified from the latest potato genome, including five StGRFs belonging to the ɛ group and eight of the non-ɛ group. The transcriptional responses of the StGRFs to two biotic stress-related phytohormones (SA and MeJA) were defined, as well as the response to infection with R. solanacearum in a bacterial wilt-sensitive cultivar, S. tuberosum 'Qingshu 9'. Many StGRF genes exhibited high induction levels in response to R. solanacearum infection and SA treatment while displaying a marked decline in expression in the presence of MeJA. Furthermore, protein interaction network analysis revealed that the StGRF proteins interact with several candidate target proteins, indicating that GRF proteins are ubiquitous regulators in potatoes. However, the associations between two type III effectors (T3Es) RipAC/RipH2 from R. solanacearum isolates and StGRF7 were not detectable in a yeast two-hybrid assay.

Conclusion: This study provides comprehensive information on the GRF gene family and lays a foundation for further research on the molecular mechanism of potato biotic stress adaptation.

Background: In the rapidly advancing field of genomics, many tools have been developed to interpret genetic variants using next-generation sequencing (NGS) data. However, these tools often produce annotated variant files in different formats, which require specific software or programming skills to filter and analyze.

Objective: To provide a filtering tool that can be used with diverse variant annotation tools without requiring specific software or programming skills.

Methods: We developed Germline Variant Annotation and Filtering (GVAF), a command-line software tool that can handle annotated variant files in any table-shaped format. GVAF offers powerful filtering operations without the need for additional software or programming expertise.

Results: Built on the Java framework and bash scripts, it provides extensive features, including flexible filtering rules, recognition of genotype-related fields from variant call format (VCF) files, and customizable result generation. GVAF also integrates easily into existing data analysis pipelines. Compared to other tools, GVAF offers a broader range of functionalities, making it more flexible and intuitive for managing annotated variant files.

Conclusion: This GVAF software and online manual is publicly available at https://www.sysbiolab.org/gvaf for academic users and is designed to streamline the variant interpretation process, aiding researchers in producing meaningful results.

Background: Ginseng (Panax ginseng) is an herb with a long history and a wide range of applications. Ginsenoside is one of the most representative and active ginseng compounds, with various pharmacological effects. Therefore, the development of bioreactors using methyl jasmonate (MeJA) as an inducer for targeted ginsenoside production is of great commercial value. Combined with transcriptomic research tools, screenings to obtain candidate genes involved in ginsenoside biosynthesis are crucial for future discoveries about the molecular mechanism of MeJA-regulated ginsenoside biosynthesis.

Objective and methods: In our study, the ginsenoside content of ginseng adventitious roots treated with MeJA at different times was analyzed. Transcriptome analysis was performed to investigate the effects of MeJA on changes in ginsenoside content in ginseng adventitious roots.

Results: The MeJA could significantly increase changes in the content of pro-ginsenodiol ginsenosides as well as pro-triol ginsenosides Rg3, Re, and Rf in ginseng adventitious roots. Differential gene expression analysis showed that a total of 14,009 differentially expressed genes were obtained from the screening of the present study. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis showed that differentially expressed genes were mainly enriched under GO terms in response to stimuli, metabolic processes, and the regulation of biological processes, with significant annotation to the metabolic terms of terpenoids and polyketides. Two expression modules of genes highly related to ginsenoside biosynthesis were obtained via WGCNA.

Conclusions: Our study provides a reference system for the targeted ginsenoside production using MeJA as an inducer, and also provides genetic and gene resources for subsequently validating genes related to the regulation of ginsenoside biosynthesis using weighted gene co-expression network analysis (WGCNA).

Background: Oplopanax elatus is a plant of therapeutic significance in oriental medicine; however, its mass cultivation is limited owing to the difficulties in propagating it from seeds.

Methods: In this study, we investigated the transcriptome profiles and transcriptional regulatory factors expressed during plantlet regeneration from root tissues of the endangered O. elatus.

Results: The RNA-seq results for the control and regenerated plants cultured in liquid medium for 8 weeks showed that the clean length of the control group was 11,901,667,912 and that of the 8-week sample was 10,115,155,171, indicating a clean value of 97% for both samples. The number of mapped paired-end reads was 63,922,480 for the control group and 54,146,902 for the 8-week sample. The number of genes for which at least one clean data point was mapped was 43,177 in the control group and 42,970 in the 8-week sample. The results of the differentially expressed gene analysis indicate that the number of upregulated genes in the 8-week sample was 158, and the number of downregulated genes was 424. Gene Ontology (GO) analysis of the upregulated genes revealed that GO terms were classified into 14 categories, and genes expressed in the biological process category occurred most frequently. GO terms of the downregulated genes were evenly distributed into two categories: biological process and molecular function. From the upregulated genes, eight reference genes with significant differences in expression were selected and analyzed using real-time PCR. The Oe38836 gene (late embryogenesis abundant protein M17-like isoform X1) showed the highest expression rate that was more than tenfold that of the control. Oe40610 (auxin-responsive protein SAUR21-like) and Oe07114 (glucose-1-phosphate adenyl transferase-like protein) genes showed expression levels that were increased eightfold relative to the control.

Conclusions: The RNA sequencing (RNA-seq) results from the plants regenerated through liquid culture of O. elatus root tissue were confirmed using real-time PCR, indicating their reliability.

Background: Transposable elements (TEs) are known to be inserted into genome to create transcript isoforms or to generate long non-coding RNA (lncRNA) sequences. The insertion of TEs generates a gene protein sequence within the genome, but also provides a microRNA (miRNA) regulatory region.

Objective: To determine the effect of gene sequence changes caused by TE insertion on miRNA binding and to investigate the formation of an overlapping lncRNA that represses it.

Methods: The distribution of overlapping regions between exons and TE regions with lncRNA was examined using the Bedtools. miRNAs that can bind to those overlapping regions were identified through the miRDB web program. For TE-lncRNA overlapping genes, bioinformatic analysis was conducted using DAVID web database. Differential expression analysis was conducted using data from the GEO dataset and TCGA.

Results: Most TEs were distributed more frequently in untranslated regions than open reading frames. There were 30 annotated TE-lncRNA overlapping genes with same strand that could bind to the same miRNA. As a result of identifying the association between these 30 genes and diseases, TGFB2, FCGR2A, DCTN5, and IFI6 were associated with breast cancer, and HMGCS1, FRMD4A, EDNRB, and SNCA were associated with Alzheimer's disease. Analysis of the GEO and TCGA data showed that the relevant expression of miR-891a and miR-28, which bind to the TE overlapping region of DCTN5 and HMGCS1, decreased.

Conclusion: This study indicates that the interaction between TE-lncRNA overlapping genes and miRNAs can affect disease progression.

Background: Transposable elements (TEs) contribute to approximately half of the human genome, and along with many other functions, they have been known to play a role in gene regulation in the genome. With TEs' active/repressed states varying across tissue and cell types, they have the potential to regulate gene expression in a tissue-specific manner.

Objective and methods: To provide a systematic analysis of TEs' contribution in tissue-specific gene regulation, we examined the regulatory elements and genes in association with TE-derived regulatory sequences in 14 human cell lines belonging to 10 different tissue types using the functional genomics data from the ENCODE project. Specifically, we separately analyzed regulatory regions identified by three different approaches (DNase hypersensitive sites (DHS), histone active sites (HA), and histone repressive sites (HR)).

Results: These regulatory regions showed to be distinct from each other by sharing less than 2.5% among all three types and more than 95% showed to be cell line-specific. Despite a lower total TE content overall than the genome average, each regulatory sequence type showed enrichment for one or two specific TE type(s): DHS for long terminal repeats (LTRs) and DNA transposons, HA for short interspersed nucleotide elements (SINEs), and HR for LTRs. In contrast, SINE was shown to be overrepresented in all three types of regulatory sequences located in gene-neighboring regions. TE-regulated genes were mostly shown to have cell line specific pattern, and tissue-specific genes (TSGs) showed higher usage of TE regulatory sequences in the tissue of their expression. While TEs in the regulatory sequences showed to be older than their genome-wide counterparts, younger TEs were shown to be more likely used in cell line specific regulatory sequences.

Conclusions: Collectively, our study provided further evidence enforcing an important contribution of TEs to tissue-specific gene regulation in humans.

Background: Programmed cell death 6 (PDCD6) is known to be involved in apoptosis and tumorigenesis. Given the reported association with urinary cancer susceptibility through SNP analysis, we further analyzed the entire genomic structure of PDCD6.

Methods: Three VNTR regions (MS1-MS3) were identified through the analysis of the genomic structure of PDCD6. To investigate the association between these VNTR regions and urinary cancer susceptibility, genomic DNA was extracted from 413 cancer-free male controls, 267 bladder cancer patients, and 331 prostate cancer patients. Polymerase chain reaction (PCR) was performed to analyze the PDCD6-MS regions. Statistical analysis was performed to determine the association between specific genotypes and cancer risk. In addition, the effect of specific VNTRs on PDCD6 expression was also confirmed using a reporter vector.

Results: Among the three VNTR regions, MS1 and MS2 exhibited monomorphism, while the MS3 region represented polymorphism, with its transmission to subsequent generations through meiosis substantiating its utility as a DNA typing marker. In a case-control study, the presence of rare alleles within PDCD6-MS3 exhibited significant associations with both bladder cancer (OR = 2.37, 95% CI: 1.33-4.95, P = 0.019) and prostate cancer (OR = 2.11, 95% CI: 1.03-4.36, P = 0.038). Furthermore, through luciferase assays, we validated the impact of the MS3 region on modulating PDCD6 expression.

Conclusions: This study suggests that the PDCD6-MS3 region could serve as a prognostic marker for urinary cancers, specifically bladder cancer and prostate cancer. Moreover, the subdued influence exerted by PDCD6-MS3 on the expression of PDCD6 offers another insight concerning the progression of urinary cancer.