Genomics, proteomics & bioinformatics最新文献

Methamphetamine (METH) is a highly addictive psychostimulant that causes physical and psychological damage and immune system disorder, especially in the liver that contains a significant number of immune cells. Dopamine, a key neurotransmitter in METH addiction and immune regulation, plays a crucial role in this process. Here, we developed a chronic METH administration model and conducted single-cell RNA sequencing (scRNA-seq) to investigate the effect of METH on liver immune cells and involvement of dopamine receptor D1 (DRD1). Our findings reveal that chronic exposure to METH induces immune cell identity shifts from Ifitm3+Macrophage (Mac) and Ccl5+Mac to Cd14+Mac, and from Fyn+CD4+T effector (Teff), CD8+T, and natural killer T cells (NKT) to Fos+CD4+T and Rora+ group 2 innate lymphoid cells (ILC2), along with suppression of multiple functional immune pathways. DRD1 is implicated in regulating certain pathways and identity shifts among the hepatic immune cells. Our results provide valuable insights into development of targeted therapies to mitigate METH-induced immune impairment.

Growing evidence supports the transcription of enhancer RNAs (eRNAs) and their important roles in gene regulation. However, their interactions with other biomolecules and their corresponding functionality remain poorly understood. In an attempt to facilitate mechanistic research, this study presents eRNA-IDO, the first integrative computational platform for the identification, interactome discovery, and functional annotation of human eRNAs. eRNA-IDO comprises two modules: eRNA-ID and eRNA-Anno. Functionally, eRNA-ID can identify eRNAs from de novo assembled transcriptomes. eRNA-ID includes 8 kinds of enhancer makers, enabling users to customize enhancer regions flexibly and conveniently. In addition, eRNA-Anno provides cell-specific/tissue-specific functional annotation for both new and known eRNAs by analyzing the eRNA interactome from prebuilt or user-defined networks between eRNA and coding gene. The prebuilt networks include the Genotype-Tissue Expression (GTEx)-based co-expression networks in normal tissues, The Cancer Genome Atlas (TCGA)-based co-expression networks in cancer tissues, and omics-based eRNA-centric regulatory networks. eRNA-IDO can facilitate research on the biogenesis and functions of eRNAs. The eRNA-IDO server is freely available at http://bioinfo.szbl.ac.cn/eRNA_IDO/.

Cancer progression involves the gradual loss of a differentiated phenotype and the acquisition of progenitor and stem-cell-like features, which are potential culprits of immunotherapy resistance. Although the state-of-art predictive computational methods have facilitated the prediction of cancer stemness, currently there is no efficient resource that can meet various usage requirements. Here, we present the Cancer Stemness Online, an integrated resource for efficiently scoring cancer stemness potential at the bulk and single-cell levels. The resource integrates 8 robust predictive algorithms as well as 27 signature gene sets associated with cancer stemness for predicting stemness scores. Downstream analyses were performed from five different aspects, including identifying the signature genes of cancer stemness, exploring the associations with cancer hallmarks, cellular states, the immune response, and communication with immune cells; investigating the contributions to patient survival; and performing a robustness analysis of cancer stemness among different methods. Moreover, the pre-calculated cancer stemness atlas for more than 40 cancer types can be accessed by users. Both the tables and diverse visualizations of the analytical results are available for download. Together, Cancer Stemness Online is a powerful resource for scoring cancer stemness and expanding the downstream functional interpretation, including immune response as well as cancer hallmarks. Cancer Stemness Online is freely accessible at http://bio-bigdata.hrbmu.edu.cn/CancerStemnessOnline.

Spatial transcriptomics technology has been an essential and powerful method for delineating tissue architecture at the molecular level. However, due to the limitations of the current spatial techniques, the cellular information cannot be directly measured but instead spatial spots typically varying from a diameter of 0.2 to 100 µm are characterized. Therefore, it is vital to apply computational strategies for inferring the cellular composition within each spatial spot. The main objective of this review is to summarize the most recent progresses to estimate the exact cellular proportions for each spatial spot, and to prospect the future directions of this field.

Cancer stem cells (CSCs) constitute a pivotal element within the tumor microenvironment (TME), driving the initiation and progression of cancer. However, the identification of CSCs and their underlying molecular mechanisms in laryngeal squamous cell carcinoma (LSCC) remains a formidable challenge. We employed single-cell RNA sequencing of matched primary tumor tissues, paracancerous tissues, and local lymph nodes from three LSCC patients. Two distinct clusters of stem cells originating from epithelial populations were delineated and verified as CSCs and normal stem cells (NSCs), respectively. CSCs were abundant in the paracancerous tissues compared to the tumor tissues. CSCs showed high expression of stem cell marker genes such as PROM1, ALDH1A1, and SOX4, and increased the activity of tumor-related hypoxia, Wnt/β-catenin, and Notch signaling pathways. We then explored the intricate crosstalk between CSCs and the TME cells and identified targets within the TME that related with CSCs. We also found eight marker genes of CSCs that correlated significantly with the prognosis of LSCC patients. Furthermore, bioinformatics analyses showed that drugs such as erlotinib, OSI-027, and ibrutinib selectively targeted the CSC-specifically expressed genes. In conclusion, our results represent the first comprehensive characterization of CSCs properties in LSCC at the single-cell level.

Magnesium (Mg) deficiency is associated with increased risk and malignancy in colorectal cancer (CRC), yet the underlying mechanisms remain elusive. Here, we used genomic, proteomic, and phosphoproteomic data to elucidate the impact of Mg deficiency on CRC. Genomic analysis identified 160 genes with higher mutation frequencies in Low-Mg tumors, including key driver genes such as KMT2C and ERBB3. Unexpectedly, initiation driver genes of CRC, such as TP53 and APC, displayed higher mutation frequencies in High-Mg tumors. Additionally, proteomic and phosphoproteomic data indicated that low Mg content in tumors may activate epithelial-mesenchymal transition (EMT) by modulating inflammation or remodeling the phosphoproteome of cancer cells. Notably, we observed a negative correlation between the phosphorylation of DBN1 at S142 (DBN1S142p) and Mg content. A mutation in S142 to D (DBN1S142D) mimicking DBN1S142p upregulated MMP2 and enhanced cell migration, while treatment with MgCl2 reduced DBN1S142p, thereby reversing this phenotype. Mechanistically, Mg2+ attenuated the DBN1-ACTN4 interaction by decreasing DBN1S142p, which in turn enhanced the binding of ACTN4 to F-actin and promoted F-actin polymerization, ultimately reducing MMP2 expression. These findings shed new light on the crucial role of Mg deficiency in CRC progression and suggest that Mg supplementation may be a promising preventive and therapeutic strategy for CRC.

Intergeneric hybridization greatly reshapes regulatory interactions among allelic and non-allelic genes. However, their effects on growth diversity remain poorly understood in animals. In this study, we conducted whole-genome sequencing and RNA sequencing (RNA-seq) analyses in diverse hybrid varieties resulting from the intergeneric hybridization of goldfish (Carassius auratus red var.) and common carp (Cyprinus carpio). These hybrid individuals were characterized by distinct mitochondrial genomes and copy number variations. Through a weighted gene correlation network analysis, we identified 3693 genes as candidate growth-regulated genes. Among them, the expression of 3672 genes in subgenome R (originating from goldfish) displayed negative correlations with growth rate, whereas 20 genes in subgenome C (originating from common carp) exhibited positive correlations. Notably, we observed intriguing patterns in the expression of slc2a12 in subgenome C, showing opposite correlations with body weight that changed with water temperatures, suggesting differential interactions between feeding activity and weight gain in response to seasonal changes for hybrid animals. In 40.31% of alleles, we observed dominant trans-regulatory effects in the regulatory interaction between distinct alleles from subgenomes R and C. Integrating analyses of allelic-specific expression and DNA methylation data revealed that the influence of DNA methylation on both subgenomes shapes the relative contribution of allelic expression to the growth rate. These findings provide novel insights into the interaction of distinct subgenomes that underlie heterosis in growth traits and contribute to a better understanding of multiple allele traits in animals.

As the most abundant messenger RNA (mRNA) modification in mRNA, N  6-methyladenosine (m6A) plays a crucial role in RNA fate, impacting cellular and physiological processes in various tumor types. However, our understanding of the function and role of the m6A methylome in tumor heterogeneity remains limited. Herein, we collected and analyzed m6A methylomes across nine human tissues from 97 m6A sequencing (m6A-seq) and RNA sequencing samples. Our findings demonstrate that m6A exhibits different heterogeneity in most tumor tissues compared to normal tissues, which contributes to the diverse clinical outcomes in different cancer types. We also found that the cancer type-specific m6A level regulated the expression of different cancer-related genes in distinct cancer types. Utilizing a novel and reliable method called "m6A-express", we predicted m6A-regulated genes and revealed that cancer type-specific m6A-regulated genes contributed to the prognosis, tumor origin, and infiltration level of immune cells in diverse patient populations. Furthermore, we identified cell-specific m6A regulators that regulate cancer-specific m6A and constructed a regulatory network. Experimental validation was performed, confirming that the cell-specific m6A regulator CAPRIN1 controls the m6A level of TP53. Overall, our work reveals the clinical relevance of m6A in various tumor tissues and explains how such heterogeneity is established. These results further suggest the potential of m6A for cancer precision medicine for patients with different cancer types.

Plants possess diverse cell types and intricate regulatory mechanisms to adapt to the ever-changing environment of nature. Various strategies have been employed to study cell types and their developmental progressions, including single-cell sequencing methods which provide high-dimensional catalogs to address biological concerns. In recent years, single-cell sequencing technologies in transcriptomics, epigenomics, proteomics, metabolomics, and spatial transcriptomics have been increasingly used in plant science to reveal intricate biological relationships at the single-cell level. However, the application of single-cell technologies to plants is more limited due to the challenges posed by cell structure. This review outlines the advancements in single-cell omics technologies, their implications in plant systems, future research applications, and the challenges of single-cell omics in plant systems.