Crosstalk between Receptor and Non-receptor Mediated Chemical Modes of Action in Rat Livers Converges through a Dysregulated Gene Expression Network at Tumor Suppressor Tp53

Widespread Dysregulation of Long Noncoding Genes Associated With Fatty Acid Metabolism, Cell Division, and Immune Response Gene Networks in Xenobiotic-exposed Rat Liver

Xenobiotic exposure dysregulates hundreds of protein-coding genes in mammalian liver, impacting many physiological processes and inducing diverse toxicological responses. Little is known about xenobiotic effects on long noncoding RNAs (lncRNAs), many of which have important regulatory functions. Here, we present a computational framework to discover liver-expressed, xenobiotic-responsive lncRNAs (xeno-lncs) with strong functional, gene regulatory potential and elucidate the impact of xenobiotic exposure on their gene regulatory networks. We assembled the long noncoding transcriptome of xenobiotic-exposed rat liver using RNA-seq datasets from male rats treated with 27 individual chemicals, representing 7 mechanisms of action (MOAs). Ortholog analysis was combined with coexpression data and causal inference methods to infer lncRNA function and deduce gene regulatory networks, including causal effects of lncRNAs on protein-coding gene expression and biological pathways. We discovered > 1400 liver-expressed xeno-lncs, many with human and/or mouse orthologs. Xenobiotics representing different MOAs often regulated common xeno-lnc targets: 123 xeno-lncs were dysregulated by ≥ 10 chemicals, and 5 xeno-lncs responded to ≥ 20 of the 27 chemicals investigated; 81 other xeno-lncs served as MOA-selective markers of xenobiotic exposure. Xeno-lnc-protein-coding gene coexpression regulatory network analysis identified xeno-lncs closely associated with exposure-induced perturbations of hepatic fatty acid metabolism, cell division, or immune response pathways, and with apoptosis or cirrhosis. We also identified hub and bottleneck lncRNAs, which are expected to be key regulators of gene expression. This work elucidates extensive networks of xeno-lnc-protein-coding gene interactions and provides a framework for understanding the widespread transcriptome-altering actions of foreign chemicals in a key-responsive mammalian tissue.

Compound Danshen Dripping Pill Inhibits Retina Cell Apoptosis in Diabetic Rats

Scope: Diabetic retinopathy (DR) is a severe microvascular complication of diabetes. Previous clinical trials have shown that Compound Danshen Dripping Pill (CDDP) improves DR symptoms. However, the mechanism involved remains unclear.

Procedures: Rats fed a high-fat diet and injected with streptozotocin (STZ) were used as an experimental type 2 diabetes rodent model. CDDP was administered to two groups of diabetic rats at 0.2 and 0.4 g/kg/day via gastric gavage for 12 weeks. After the 12 weeks of treatment, retinal function was evaluated by electroretinography (ERG). Histological staining and TdT-mediated dUTP nick-end labeling (TUNEL) assays were also performed. Retinal genome expression was determined by gene array.

Results: We found that CDDP moderated ERG and histological abnormalities in diabetic rats, independent of blood glucose level. A gene array showed that CDDP changed 262 genes significantly in the diabetic retina. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis indicated that differentially expressed genes in the CDDP-treated groups were involved mainly in the apoptosis pathway. Moreover, CDDP reduced the number of TUNEL-positive cells in the diabetic retinas. CDDP prevented the reduction in Bcl-2 expression and the increase in BCL-2 associated X (Bax) and caspase-3 (Casp3) expression in diabetic rats.

Conclusion: Our results suggest that CDDP exerts its neuroprotective functions by inhibiting cell apoptosis in diabetic rats.

A Leveraged Signal-to-Noise Ratio (LSTNR) Method to Extract Differentially Expressed Genes and Multivariate Patterns of Expression From Noisy and Low-Replication RNAseq Data

To life scientists, one important feature offered by RNAseq, a next-generation sequencing tool used to estimate changes in gene expression levels, lies in its unprecedented resolution. It can score countable differences in transcript numbers among thousands of genes and between experimental groups, all at once. However, its high cost limits experimental designs to very small sample sizes, usually N = 3, which often results in statistically underpowered analysis and poor reproducibility. All these issues are compounded by the presence of experimental noise, which is harder to distinguish from instrumental error when sample sizes are limiting (e.g., small-budget pilot tests), experimental populations exhibit biologically heterogeneous or diffuse expression phenotypes (e.g., patient samples), or when discriminating among transcriptional signatures of closely related experimental conditions (e.g., toxicological modes of action, or MOAs). Here, we present a leveraged signal-to-noise ratio (LSTNR) thresholding method, founded on generalized linear modeling (GLM) of aligned read detection limits to extract differentially expressed genes (DEGs) from noisy low-replication RNAseq data. The LSTNR method uses an agnostic independent filtering strategy to define the dynamic range of detected aggregate read counts per gene, and assigns statistical weights that prioritize genes with better sequencing resolution in differential expression analyses. To assess its performance, we implemented the LSTNR method to analyze three separate datasets: first, using a systematically noisy in silico dataset, we demonstrated that LSTNR can extract pre-designed patterns of expression and discriminate between "noise" and "true" differentially expressed pseudogenes at a 100% success rate; then, we illustrated how the LSTNR method can assign patient-derived breast cancer specimens correctly to one out of their four reported molecular subtypes (luminal A, luminal B, Her2-enriched and basal-like); and last, we showed the ability to retrieve five different modes of action (MOA) elicited in livers of rats exposed to three toxicants under three nutritional routes by using the LSTNR method. By combining differential measurements with resolving power to detect DEGs, the LSTNR method offers an alternative approach to interrogate noisy and low-replication RNAseq datasets, which handles multiple biological conditions at once, and defines benchmarks to validate RNAseq experiments with standard benchtop assays.

miR-1269 promotes cell survival and proliferation by targeting tp53 and caspase-9 in lung cancer

Background and aim

Lung cancer is the leading cause of cancer death worldwide. In this study, we aim to elucidate the role of miR-1269 in the pathogenesis of lung cancer.

Methods and results

From the results of analyses using The Cancer Genome Atlas (TCGA) database, we noted the expression of miR-1269 was increased in lung cancer tissue. miR-1269 expression was detected in both the normal adjacent lung tissue and in the tumorous lung tissue of lung cancer patients, and miR-1269 was more highly expressed in the tumors. High expression of miR-1269 correlated with patients’ tumor stage and lymph node metastasis. A Cell Counting Kit-8 (CCK8) analysis and a cloning formation assay showed that overexpression of miR-1269 significantly promoted the growth of A549 cells, and that a lower expression of miR-1269 significantly increased cell apoptosis. We used the TargetScan 6.2 Database to predict the potential targets of miR-1269, and a luciferase activity assay was used to determine the direct interaction between miR-1269, tumor protein p53 (TP53), and caspase-9. Results from Western blots and real-time PCR showed that overexpression of miR-1269 significantly inhibited TP53 and caspase-9 expression. In addition, caspase-3 activity was found to decrease in a miR-1269 mimic group. The results showed that gene silencing of TP53 and caspase-9 significantly inhibited A549 cell growth and promoted cell apoptosis. The results also showed that the inhibition of miR-1269 and caspase-9 expression inhibited cell apoptosis. Immunohistochemistry (IHC) results demonstrated that TP53 and caspase-9 were expressed in low levels in tumor tissues, and that an inverse correlation exists between miR-1269 expression levels and TP53 or caspase-9 expression levels.

Conclusion

These results demonstrate that miR-1269 promotes cell survival and proliferation by targeting TP53 and caspase-9 in lung cancer.