Physiological and molecular bases of the nickel toxicity responses in tomato

Nickel (Ni), a component of urease, is a micronutrient essential for plant growth and development, but excess Ni is toxic to plants. Tomato (Solanum lycopersicum L.) is one of the important vegetables worldwide. Excessive use of fertilizers and pesticides led to Ni contamination in agricultural soils, thus reducing yield and quality of tomatoes. However, the molecular regulatory mechanisms of Ni toxicity responses in tomato plants have largely not been elucidated. Here, we investigated the molecular mechanisms underlying the Ni toxicity response in tomato plants by physio-biochemical, transcriptomic and molecular regulatory network analyses. Ni toxicity repressed photosynthesis, induced the formation of brush-like lateral roots and interfered with micronutrient accumulation in tomato seedlings. Ni toxicity also induced reactive oxygen species accumulation and oxidative stress responses in plants. Furthermore, Ni toxicity reduced the phytohormone concentrations, including auxin, cytokinin and gibberellic acid, thereby retarding plant growth. Transcriptome analysis revealed that Ni toxicity altered the expression of genes involved in carbon/nitrogen metabolism pathways. Taken together, these results provide a theoretical basis for identifying key genes that could reduce excess Ni accumulation in tomato plants and are helpful for ensuring food safety and sustainable agricultural development.

Investigating transcriptome-wide sex dimorphism by multi-level analysis of single-cell RNA sequencing data in ten mouse cell types

BACKGROUND

It is a long established fact that sex is an important factor that influences the transcriptional regulatory processes of an organism. However, understanding sex-based differences in gene expression has been limited because existing studies typically sequence and analyze bulk tissue from female or male individuals. Such analyses average cell-specific gene expression levels where cell-to-cell variation can easily be concealed. We therefore sought to utilize data generated by the rapidly developing single cell RNA sequencing (scRNA-seq) technology to explore sex dimorphism and its functional consequences at the single cell level.

METHODS

Our study included scRNA-seq data of ten well-defined cell types from the brain and heart of female and male young adult mice in the publicly available tissue atlas dataset, Tabula Muris. We combined standard differential expression analysis with the identification of differential distributions in single cell transcriptomes to test for sex-based gene expression differences in each cell type. The marker genes that had sex-specific inter-cellular changes in gene expression formed the basis for further characterization of the cellular functions that were differentially regulated between the female and male cells. We also inferred activities of transcription factor-driven gene regulatory networks by leveraging knowledge of multidimensional protein-to-genome and protein-to-protein interactions and analyzed pathways that were potential modulators of sex differentiation and dimorphism.

RESULTS

For each cell type in this study, we identified marker genes with significantly different mean expression levels or inter-cellular distribution characteristics between female and male cells. These marker genes were enriched in pathways that were closely related to the biological functions of each cell type. We also identified sub-cell types that possibly carry out distinct biological functions that displayed discrepancies between female and male cells. Additionally, we found that while genes under differential transcriptional regulation exhibited strong cell type specificity, six core transcription factor families responsible for most sex-dimorphic transcriptional regulation activities were conserved across the cell types, including ASCL2, EGR, GABPA, KLF/SP, RXRα, and ZF.

CONCLUSIONS

We explored novel gene expression-based biomarkers, functional cell group compositions, and transcriptional regulatory networks associated with sex dimorphism with a novel computational pipeline. Our findings indicated that sex dimorphism might be widespread across the transcriptomes of cell types, cell type-specific, and impactful for regulating cellular activities.

Landscape of transcriptional deregulation in lung cancer

BACKGROUND

Lung cancer is a very heterogeneous disease that can be pathologically classified into different subtypes including small-cell lung carcinoma (SCLC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC) and large-cell carcinoma (LCC). Although much progress has been made towards the oncogenic mechanism of each subtype, transcriptional circuits mediating the upstream signaling pathways and downstream functional consequences remain to be systematically studied.

RESULTS

Here we trained a one-class support vector machine (OC-SVM) model to establish a general transcription factor (TF) regulatory network containing 325 TFs and 18724 target genes. We then applied this network to lung cancer subtypes and identified those deregulated TFs and downstream targets. We found that the TP63/SOX2/DMRT3 module was specific to LUSC, corresponding to squamous epithelial differentiation and/or survival. Moreover, the LEF1/MSC module was specifically activated in LUAD and likely to confer epithelial-to-mesenchymal transition, known important for cancer malignant progression and metastasis. The proneural factor, ASCL1, was specifically up-regulated in SCLC which is known to have a neuroendocrine phenotype. Also, ID2 was differentially regulated between SCLC and LUSC, with its up-regulation in SCLC linking to energy supply for fast mitosis and its down-regulation in LUSC linking to the attenuation of immune response. We further described the landscape of TF regulation among the three major subtypes of lung cancer, highlighting their functional commonalities and specificities.

CONCLUSIONS

Our approach uncovered the landscape of transcriptional deregulation in lung cancer, and provided a useful resource of TF regulatory network for future studies.

TFAP2A is a component of the ZEB1/2 network that regulates TGFB1-induced epithelial to mesenchymal transition

Background

The transition between epithelial and mesenchymal phenotypes (EMT) occurs in a variety of contexts. It is critical for mammalian development and it is also involved in tumor initiation and progression. Master transcription factor (TF) regulators of this process are conserved between mouse and human.

Methods

From a computational analysis of a variety of high-throughput sequencing data sets we initially inferred that TFAP2A is connected to the core EMT network in both species. We then analysed publicly available human breast cancer data for TFAP2A expression and also studied the expression (by mRNA sequencing), activity (by monitoring the expression of its predicted targets), and binding (by electrophoretic mobility shift assay and chromatin immunoprecipitation) of this factor in a mouse mammary gland EMT model system (NMuMG) cell line.

Results

We found that upon induction of EMT, the activity of TFAP2A, reflected in the expression level of its predicted targets, is up-regulated in a variety of systems, both murine and human, while TFAP2A’s expression is increased in more “stem-like” cancers. We provide strong evidence for the direct interaction between the TFAP2A TF and the ZEB2 promoter and we demonstrate that this interaction affects ZEB2 expression. Overexpression of TFAP2A from an exogenous construct perturbs EMT, however, in a manner similar to the downregulation of endogenous TFAP2A that takes place during EMT.

Conclusions

Our study reveals that TFAP2A is a conserved component of the core network that regulates EMT, acting as a repressor of many genes, including ZEB2.

Reviewers

This article has been reviewed by Dr. Martijn Huynen and Dr. Nicola Aceto.

Electronic supplementary material

The online version of this article (doi:10.1186/s13062-017-0180-7) contains supplementary material, which is available to authorized users.

Bioinformatics Analyses of Differentially Expressed Genes Associated with Acute Myocardial Infarction

BACKGROUND

We aimed to predict key genes associated with acute myocardial infarction (AMI) by bioinformatics analysis.

METHODS

The microarray data of GSE48060, including peripheral blood samples from 31 first-time AMI patients within 48-h post-MI and 21 normal controls, were obtained from Gene Expression Omnibus database. The differentially expressed genes (DEGs) in AMI samples compared with normal controls were identified. Functional enrichment analysis was then performed, followed by analysis of protein-protein interaction (PPI) network and transcription regulatory network (TRN).

RESULTS

A total of 385 up- and 504 down-regulated DEGs were identified. They were mainly enriched in five pathways, such as natural killer (NK) cell-mediated cytotoxicity and chemokine signaling pathway. Chemokine (C-C motif) ligand 5 (CCL5) was hub protein in PPI network. Besides, four transcription factors (TFs), including nuclear receptor subfamily 2, group C, member 2 (NR2C2), MYC-associated factor X (MAX), general transcription factor IIIC, polypeptide 2, beta 110 kDa (GTF3C2), and B-cell CLL/lymphoma 3 (BCL3), were identified. Notably, nuclear receptor coactivator 7 (NCOA7) interacted with GTF3C2 and MAX directly.

CONCLUSIONS

CCL5, BCL3, NR2C2, MAX, GTF3C2, and NCOA7 might play important roles in AMI development.