Project description:(I am not the first author of the paper who contributed to the experimental data, I did the modeling) Bistable switches and oscillators have long been considered key mechanisms underlying cell fate decisions and pattern formation in biology. Previous studies of these dynamical behaviors focused on regulatory networks with intuitive feedback loops. It was therefore unclear whether other common biochemical reactions can act as bistable switches or oscillators crucial for cellular and physiological dynamics. In this work, we used mass-action-based models to show that elementary production, degradation and binding reactions involving as few as two RNA species (e.g.an mRNA and a microRNA) can generate bistability and oscillation. We showed that both bistability and oscillation depend on cooperativity of two microRNA binding sites on the mRNA. We therefore termed our model the two-site mRNA-microRNA (MMI2) model. Remarkably, the network structure of the MMI2 model does not have any explicit feedback loop. We estimated that this simple reaction network is applicable to nearly half of human protein-coding genes. Using in vitro and in vivo experiments, we showed the function of a newly proposed MMI2-based switch in governing motor neuron lineage segregation in the spinal cord of mammalian embryos. Our findings reveal a previously underappreciated post-transcriptional mechanism that may have widespread functions in cell fate decisions, oscillatory cell dynamics and tissue patterning. Furthermore, our results challenge the long-standing idea of using intuitive feedback loops to explain bistability and oscillation. In addition to its significance in biology, the MMI2 model enables nontrivial mathematical analysis due to its simplicity. Using algebraic geometry and chemical reaction network theory, we obtained key conditions for bistability of the MMI2 model. These conditions include an inequality that reveals to a hidden feedback loop arising from regulated degradation. For these reasons, we expect that our model will not only provide useful insights into a wide range of problems in cell and developmental biology, but also enable new analytical approaches in systems biology and mathematical biology.

Project description:Background: MicroRNAs are potent regulators of biologic systems that are critical to tissue homeostasis. Individual microRNAs have been identified in airway samples. However, a systems analysis of the microRNA-mRNA networks present in the sputum that contribute to airway inflammation in asthma has not been published. Methods: We conducted a genome-wide analysis of microRNA and messenger RNA (mRNA) in the sputum from patients with asthma and correlated expression with clinical phenotypes. Weighted gene correlation network analysis (WGCNA) was implemented to identify microRNA networks (modules) that significantly correlate with clinical features of asthma and mRNA expression networks. MicroRNA expression in peripheral blood neutrophils and lymphocytes, and in situ hybridization of the sputum were used to identify the cellular sources of microRNAs. MicroRNA expression obtained before and after ozone exposure was also used to identify changes associated with neutrophil counts in the airway. Results: Six microRNA modules were associated with clinical features of asthma. A single module (nely) was associated with a history of hospitalizations, lung function impairment, and numbers of neutrophils and lymphocytes in the sputum. Of the 12 microRNAs in the nely module, hsa-miR-223-3p was the highest expressed microRNA in neutrophils and was associated with increased neutrophil counts in the sputum in response to ozone exposure. Multiple microRNAs in the nely module correlated with two mRNA modules enriched for toll-like receptor (TLR) and Th17 signaling, and endoplasmic reticulum stress. Hsa-miR-223-3p was a key regulator of the TLR and Th17 pathways in the sputum of subjects with asthma. Conclusions: This study of sputum microRNA and mRNA expression from patients with asthma demonstrates the existence of microRNA networks and genes that are associated with features of asthma severity. Among these, hsa-miR-223-3p, a neutrophil-derived microRNA, regulates TLR/Th17 signaling and endoplasmic reticulum stress.

Project description:Transcription signatures have been used to stratify breast cancer patients into clinically distinct subgroups. However, transcription alone does not determine protein expression. Of potentially equal importance for determining the tumor phenotype is the rate at which transcripts are translated to form protein. Protein translation is controlled to a major degree by miRNA, and cancer cells may deregulate the expression of key genes by altering the activity of relevant miRNAs. The importance of miRNA deregulation and the extent to which multiple miRNAs coordinately deregulate key proteins in breast cancer is only partly understood. To gain such insight, we analyzed genome-wide miRNA expression and mRNA/protein expression for a panel of 105 selected cancer related genes in breast carcinomas from 283 patients. The miRNA-mRNA-protein interactome for the selected genes was constructed by modeling protein expression as a joint function of mRNA and miRNA expression, considering the effect of both one miRNA at a time, and all studied miRNAs simultaneously. The interactome represents a map of the global effects of miRNAs on protein expression, capturing direct as well as indirect effects. The results reveal extensive association between miRNA and protein expression as well as coordinated effects of multiple miRNAs on individual proteins. Applying the model onto two other independent primary breast cancer cohorts confirmed the generalizability of key aspects of the interactome map. The mRNA expression profiling of 283 breast cancer samples was performed using the SurePrint G3 Human GE 8x60K one-color microarrays from Agilent (Agilent Technologies, Santa Clara, CA, USA) according to the manufacturer’s protocol (One-Color Microarray-Based Gene Expression Analysis, Low Input Quick Amp Labeling, v.6.5, May 2010). For each sample, 100 ng of RNA was amplified and hybridized on the array. Scanning was performed with Agilent Scanner G2565A, using AgilentG3_GX_1Color as profile. Signals were extracted using FE v.10.7.3.1 and protocol GE1_107_Sep09 (Agilent Technologies). Arrays were log2-transformed, quantile normalized and hospital-adjusted by subtracting from each probe value the mean probe value among samples from the same hospital. The Oslo Breast Cancer Consortium (OSBREAC)

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