Functional & Integrative Genomics最新文献

Parsley is a commonly cultivated Apiaceae species of culinary and medicinal importance. Parsley has several recognized health benefits and the species has been utilized in traditional medicine since ancient times. Although parsley is among the most commonly cultivated members of Apiaceae, no systematic genomic research has been conducted on parsley. In the present work, parsley genome was sequenced using the long-read HiFi (high fidelity) sequencing technology and a draft contig assembly of 1.57 Gb that represents 80.9% of the estimated genome size was produced. The assembly was highly repeat-rich with a repetitive DNA content of 81%. The assembly was phased into a primary and alternate assembly in order to minimize redundant contigs. Scaffolds were constructed with the primary assembly contigs, which were used for the identification of AMP (antimicrobial peptide) genes. Characteristic AMP domains and 3D structures were used to detect and verify antimicrobial peptides. As a result, 23 genes (PcAMP1-23) representing defensin, snakin, thionin, lipid transfer protein and vicilin-like AMP classes were identified. Bioinformatic analyses for the characterization of peptide physicochemical properties indicated that parsley AMPs are extracellular peptides, therefore, plausibly exert their antimicrobial effects through the most commonly described AMP action mechanism of membrane attack. AMPs are attracting increasing attention since they display their fast antimicrobial effects in small doses on both plant and animal pathogens with a significantly reduced risk of resistance development. Therefore, identification and characterization of AMPs is important for their incorporation into plant disease management protocols as well as medicinal research for the treatment of multi-drug resistant infections.

Acute pancreatitis (AP) is an inflammatory disease of the pancreas and the main cause of hospital admissions for gastrointestinal diseases. Here, the work studied the circular RNA DTNB/microRNA-485-5p/MCL1 axis in AP and hoped to unravel the related mechanism. Caerulein exposure replicated an AP model in AR42J cells, and caerulein-mediated expression of circDTNB, miR-485-5p, and MCL1 was recorded. After exposure, cells were intervened with transfection plasmids and tested for LDH release, apoptosis, and inflammation. To determine the interwork of circDTNB, miR-485-5p, and MCL1, prediction results and verification experiments were conducted. Caerulein exposure reduced circDTNB and MCL1, while elevated miR-485-5p levels in AR42J cells. Upregulating circDTNB protected AR42J cells from caerulein-induced LDH cytotoxicity, apoptosis, and inflammation, but circDTNB upregulation-induced protections could be muffled by inhibiting MCL1. On the contrary, downregulating circDTNB further damaged AR42J cells under caerulein exposure, however, this phenomenon could be partially rescued after silencing miR-485-5p. miR-485-5p was mechanistically verified to be a target of circDTNB to mediate MCL1. Overall, the circDTNB/miR-485-5p/MCL1 axis protects inflammatory response and apoptosis in caerulein-exposed AR42J cells, promisingly identifying circDTNB as a novel molecule for AP treatment.

Rice root system plays a crucial role in plant adaptation under adverse conditions, particularly drought stress. However, the regulatory gene networks that govern rice root development during stress exposure remain largely unexplored. In this study, we applied a QTL sequencing method to identify QTL/gene controlling the crown root development under Jasmonic acid simulation using the Bulk-segregant analysis. Two rice cultivars with contrasting phenotypes from the Vietnamese traditional rice collection were used as parent pairs for crossing. The single-seed descent method was employed to generate an F2 population of progenies. This F2/3 population was further segregated based on root count under JA stress. Pooled DNA from the two extreme groups in this population was sequenced, and SNP indexes across all loci in these pools were calculated. We detected a significant genomic region on chromosome 10, spanned from 20.39–20.50 Mb, where two rice RLKs were located, OsPUB54 and OsPUB58. Receptor-like kinases (RLKs) are pivotal in regulating various aspects of root development in plants, and the U-box E3 ubiquitination ligase class was generally known for its degradation of some protein complexes. Notably, OsPUB54 was strongly induced by JA treatment, suggesting its involvement in the degradation of the Aux/IAA protein complex, thereby influencing crown root initiation. Besides, the Eukaryotic translation initiation of factor 3 subunit L (eIF3l) and the Mitogen-activated protein kinase kinase kinase 37 (MAPKKK 37) proteins identified from SNPs with high score index which suggests their significant roles in the translation initiation process and cellular signaling pathways, respectively. This information suggests several clues of how these candidates are involved in modifying the rice root system under stress conditions.

Recent advancements in biomedical technologies and the proliferation of high-dimensional Next Generation Sequencing (NGS) datasets have led to significant growth in the bulk and density of data. The NGS high-dimensional data, characterized by a large number of genomics, transcriptomics, proteomics, and metagenomics features relative to the number of biological samples, presents significant challenges for reducing feature dimensionality. The high dimensionality of NGS data poses significant challenges for data analysis, including increased computational burden, potential overfitting, and difficulty in interpreting results. Feature selection and feature extraction are two pivotal techniques employed to address these challenges by reducing the dimensionality of the data, thereby enhancing model performance, interpretability, and computational efficiency. Feature selection and feature extraction can be categorized into statistical and machine learning methods. The present study conducts a comprehensive and comparative review of various statistical, machine learning, and deep learning-based feature selection and extraction techniques specifically tailored for NGS and microarray data interpretation of humankind. A thorough literature search was performed to gather information on these techniques, focusing on array-based and NGS data analysis. Various techniques, including deep learning architectures, machine learning algorithms, and statistical methods, have been explored for microarray, bulk RNA-Seq, and single-cell, single-cell RNA-Seq (scRNA-Seq) technology-based datasets surveyed here. The study provides an overview of these techniques, highlighting their applications, advantages, and limitations in the context of high-dimensional NGS data. This review provides better insights for readers to apply feature selection and feature extraction techniques to enhance the performance of predictive models, uncover underlying biological patterns, and gain deeper insights into massive and complex NGS and microarray data.

Artificial intelligence (AI) platforms have emerged as pivotal tools in genetics and molecular medicine, as in many other fields. The growth in patient data, identification of new diseases and phenotypes, discovery of new intracellular pathways, availability of greater sets of omics data, and the need to continuously analyse them have led to the development of new AI platforms. AI continues to weave its way into the fabric of genetics with the potential to unlock new discoveries and enhance patient care. This technology is setting the stage for breakthroughs across various domains, including dysmorphology, rare hereditary diseases, cancers, clinical microbiomics, the investigation of zoonotic diseases, omics studies in all medical disciplines. AI’s role in facilitating a deeper understanding of these areas heralds a new era of personalised medicine, where treatments and diagnoses are tailored to the individual’s molecular features, offering a more precise approach to combating genetic or acquired disorders. The significance of these AI platforms is growing as they assist healthcare professionals in the diagnostic and treatment processes, marking a pivotal shift towards more informed, efficient, and effective medical practice. In this review, we will explore the range of AI tools available and show how they have become vital in various sectors of genomic research supporting clinical decisions.

We aimed to explore the aberrant expression status of hsa-miR-141-3p and dual-specificity protein phosphatase 1 (DUSP1) and their relative mechanisms in uterine cervical carcinoma (UCC).Quantitative reverse transcription-polymerase chain reaction (RT-qPCR) was conducted to detect the expression of hsa-miR-141-3p. Immunohistochemical (IHC) staining was performed to examine the expression of DUSP1 in UCC. Gene chips and RNA-seq datasets were also obtained to assess the expression level. Integrated standardized mean difference (SMD) was calculated to evaluate the expression status of hsa-miR-141-3p in UCC tissues comprehensively. DUSP1-overexpression and hsa-miR-141-3p-inhibition HeLa cells were established, and CCK-8, transwell, wound healing, cell cycle, and apoptosis assays were implemented. The targets of hsa-miR-141-3p were obtained with online tools, and the combination of hsa-miR-141-3p and DUSP1 was validated via dual-luciferase reporter assay. Single-cell RNA-seq data were analyzed to explore hsa-miR-141-3p and DUSP1 in different cells. An integrated SMD of 1.41 (95% CI[0.45, 2.38], p = 0.0041) with 558 samples revealed the overexpression of hsa-miR-141-3p in UCC tissues. And the pooled SMD of -1.06 (95% CI[-1.45, -0.66], p < 0.0001) with 1,268 samples indicated the downregulation of DUSP1. Inhibition of hsa-miR-141-3p could upregulate DUSP1 expression and suppress invasiveness and metastasis of HeLa cells. Overexpression of DUSP1 could hamper proliferation, invasion, and migration and boost apoptosis and distribution of G1 phase. The dual-luciferase reporter assay validated the combination of hsa-miR-141-3p and DUSP1. Moreover, the targets of hsa-miR-141-3p were mainly enriched in the MAPK signaling pathway and activated in fibroblasts and endothelial cells. The current study illustrated the upregulation of hsa-miR-141-3p and the downregulation of DUSP1 in UCC tissues. Hsa-miR-141-3p could promote UCC progression by targeting DUSP1.

Protein disulfide isomerase A3 (PDIA3) is an endoplasmic reticulum (ER) protein. It has different functions including glycoprotein folding in the ER. The unfavorable prognosis of cancer patients was related to the abnormal PDIA3 expression level. However, it is unclear how PDIA3 correlates with the malignant characteristics of different tumors and its impact on tumor immunity. Pan-cancer data were downloaded from several databases for large-scale bioinformatics analysis. The immunological functions of PDIA3 were systematically explored at the single-cell sequencing level, including cell communication, cell metabolism, cell evolution and epigenetic modification. We performed immunofluorescence staining to visualize PDIA3 expression and infiltration of macrophages in pan-cancer samples. Further, we performed a loss-of-function assay of PDIA3 in vitro. The CCK8 assay, clone formation assay, and transwell assay were performed. M2 macrophages were co-cultured with different cell lines before the transwell assay was performed. The immunofluorescence staining of pan-cancer samples presented a higher expression of PDIA3 than those of the paired normal tissues. According to single-cell sequencing analysis, expression of PDIA3 was closely associated with cell communication, cell metabolism, cell evolution and epigenetic modification. The knockdown of PDIA3 in tumor cells inhibited cell proliferation and invasion, and restrained cocultured M2 macrophage migration. Furthermore, PDIA3 displayed predictive value in immunotherapy response in human cancer cohorts, indicating a potential therapeutic target. Our study showed that PDIA3 was associated with tumor malignant characteristics and could mediate the migration of M2 macrophages in various tumor types. PDIA3 could be a promising target to achieve tumor control and improve the immune response on a pan-cancer scale.

Graphical Abstract

Gene co-expression networks may encode hitherto inadequately recognized vulnerabilities for adult gliomas. By identifying evolutionally conserved gene co-expression modules around EGFR (EM) or PDGFRA (PM), we recently proposed an EM/PM classification scheme, which assigns IDH-wildtype glioblastomas (GBM) into the EM subtype committed in neural stem cell compartment, IDH-mutant astrocytomas and oligodendrogliomas into the PM subtype committed in early oligodendrocyte lineage. Here, we report the identification of EM/PM subtype-specific gene co-expression networks and the characterization of hub gene polypyrimidine tract-binding protein 1 (PTBP1) as a genomic alteration-independent vulnerability in IDH-wildtype GBM. Supervised by the EM/PM classification scheme, we applied weighted gene co-expression network analysis to identify subtype-specific global gene co-expression modules. These gene co-expression modules were characterized for their clinical relevance, cellular origin and conserved expression pattern during brain development. Using lentiviral vector-mediated constitutive or inducible knockdown, we characterized the effects of PTBP1 on the survival of IDH-wildtype GBM cells, which was complemented with the analysis of PTBP1-depedent splicing pattern and overexpression of splicing target neuron-specific CDC42 (CDC42-N) isoform.  Transcriptomes of adult gliomas can be robustly assigned into 4 large gene co-expression modules that are prognostically relevant and are derived from either malignant cells of the EM/PM subtypes or tumor microenvironment. The EM subtype is associated with a malignant cell-intrinsic gene module involved in pre-mRNA splicing, DNA replication and damage response, and chromosome segregation, and a microenvironment-derived gene module predominantly involved in extracellular matrix organization and infiltrating immune cells. The PM subtype is associated with two malignant cell-intrinsic gene modules predominantly involved in transcriptional regulation and mRNA translation, respectively. Expression levels of these gene modules are independent prognostic factors and malignant cell-intrinsic gene modules are conserved during brain development. Focusing on the EM subtype, we identified PTBP1 as the most significant hub for the malignant cell-intrinsic gene module. PTBP1 is not altered in most glioma genomes. PTBP1 represses the conserved splicing of CDC42-N. PTBP1 knockdown or CDC42-N overexpression disrupts actin cytoskeleton dynamics, causing accumulation of reactive oxygen species and cell apoptosis. PTBP1-mediated repression of CDC42-N splicing represents a potential genomic alteration-independent, developmentally conserved vulnerability in IDH-wildtype GBM.

Distal hereditary motor neuropathy (dHMN) is a progressive neurological disease characterized by distal limb muscle weakness and amyotrophy. Sigma 1 receptor (σ1R), a gene product of SIGMAR1, mutations have been reported to induce dHMN, but its mechanism remains unknown. This study aims to explore the effect of C238T and 31_50del mutations in σ1R on neuronal SH-SY5Y cell functions. The SH-SY5Y cells that overexpressed σ1R, C238T mutant σ1R (σ1R^C238T) or 31_50del mutant σ1R (σ1R^31_50del) were constructed by pEGFPN1 vectors. We used Western blot (WB) and immunofluorescence (IF) staining to detect the expression of σ1R and green fluorescent proteins (GFP). Then, we evaluated the impact of σ1R mutation on apoptosis, autophagy, endoplasmic reticulum stress, and the involvement of the unfolded protein response (UPR) pathway in SH-SY5Y cells. We found that σ1R^C238T and σ1R^31_50del downregulated σ1R and promoted the apoptosis of SH-SY5Y cells. σ1R^C238T and σ1R^31_50del increased p-PERK, p-eIF2α, p-JNK, BIP, ATF4, CHOP, ATF6, XBP1, Caspase3, Caspase12 expressions and Ca²⁺ concentration, whereas decreased ATP content in SH-SY5Y cells. Besides, the expressions of LC3B, Lamp1, ATG7, Beclin-1 and phosphorylation of AMPK and ULK1 were increased, while the p62 level decreased after C238T or 31_50del mutation of σ1R. Additionally, AMPK knockdown abolished the apoptosis mediated by σ1R^C238T or σ1R^31_50del in SH-SY5Y cells. Our results indicated that C238T or 31_50del mutation in σ1R promoted motor neuron apoptosis through the AMPK/ULK1 pathway in dHMN. This study shed light on a better understanding of the neurons pathological mechanisms mediated by σ1R C238T and σ1R 31-50del in dHMN.

Clustered miRNAs consist of two or more miRNAs transcribed together and may coordinately regulate gene expression. Differential expression of clustered miRNAs is found to be controlled by crosstalk of genetic or epigenetic mechanisms. It has been demonstrated that clustered miRNA expression patterns greatly impact cancer cell progression. With the CmirC initiative, we initially developed a comprehensive database to identify copy number variation (CNV) driven clustered miRNAs in cancer. Now, we extended the analysis and identified three miRNAs, mir-96, mir-183, and mir-21, were found to be significantly upregulated in 17 cancer types. Further, CmirC is now upgraded to determine the impact of changes in the DNA methylation status at clustered miRNAs by utilizing The Cancer Genomic Atlas (TCGA) cancer datasets. We examined specific methylation datasets from 9,639 samples, pinpointing 215,435 methylation sites and 27,949 CpG islands with miRNA cluster information. The integrated analysis identified 34 clusters exhibiting differentially methylated CpG sites across 14 cancer types. Furthermore, we determined that CpG islands in the promoter region of 20 miRNA clusters could play a regulatory role. Along with ensuring a straightforward and convenient user experience, CmirC has been updated with improved data browsing and analysis functionalities, as well as enabled hyperlinks to literature and miR-cancer databases. The enhanced version of CmirC is anticipated to play an important role in providing information on the regulation of clustered miRNA expression, and their targeted oncogenes and tumor suppressors. The newly updated version of CmirC is available at https://slsdb.manipal.edu/cmirclust/.