Lung retinol storing cells synthesize and secrete retinoic acid,an inducer of alveolus formation

Retinoids play a key role in the formation of pulmonary alveoli. Lipid interstitial cells (LICs) of the alveolar wall store retinol and are concentrated at sites of alveolus formation, suggesting they are an endogenous source of retinoids for alveolus formation. We show in cultured rat lung cells that LICs synthesize and secrete all-trans retinoic acid (ATRA); its secretion is halved by dexamethasone, an inhibitor of alveolus formation. In a second alveolar wall cell, the pulmonary microvascular endothelial cell (PMVC), ATRA increases expression of the mRNA of cellular retinol binding protein-I (CRBP-I), a protein involved in ATRA synthesis. Serum-free, exogenous ATRA-free medium conditioned by LICs rich in retinol storage granules caused a 10-fold greater increase of CRBP-I mRNA in PMVCs than media conditioned by LICs with few retinol storage granules. This action of medium conditioned by retinol storage granule-rich LICs is decreased by a retinoic acid receptor pan-antagonist and by a retinoid X receptor pan-antagonist, suggesting the responsible molecule(s) is a retinoid and that retinoid signaling occurs in a paracrine fashion.     

Inferring proteolytic processes from mass spectrometry time series data using degradation graphs

Background

Proteases play an essential part in a variety of biological processes. Besides their importance under healthy conditions they are also known to have a crucial role in complex diseases like cancer. In recent years, it has been shown that not only the fragments produced by proteases but also their dynamics, especially ex vivo, can serve as biomarkers. But so far, only a few approaches were taken to explicitly model the dynamics of proteolysis in the context of mass spectrometry.

Results

We introduce a new concept to model proteolytic processes, the degradation graph. The degradation graph is an extension of the cleavage graph, a data structure to reconstruct and visualize the proteolytic process. In contrast to previous approaches we extended the model to incorporate endoproteolytic processes and present a method to construct a degradation graph from mass spectrometry time series data. Based on a degradation graph and the intensities extracted from the mass spectra it is possible to estimate reaction rates of the underlying processes. We further suggest a score to rate different degradation graphs in their ability to explain the observed data. This score is used in an iterative heuristic to improve the structure of the initially constructed degradation graph.

Conclusion

We show that the proposed method is able to recover all degraded and generated peptides, the underlying reactions, and the reaction rates of proteolytic processes based on mass spectrometry time series data. We use simulated and real data to demonstrate that a given process can be reconstructed even in the presence of extensive noise, isobaric signals and false identifications. While the model is currently only validated on peptide data it is also applicable to proteins, as long as the necessary time series data can be produced.     

MSSimulator: Simulation of mass spectrometry data

Mass spectrometry coupled to liquid chromatography (LC-MS and LC-MS/MS) is commonly used to analyze the protein content of biological samples in large scale studies, enabling quantitation and identification of proteins and peptides using a wide range of experimental protocols, algorithms, and statistical models to analyze the data. Currently it is difficult to compare the plethora of algorithms for these tasks. So far, curated benchmark data exists for peptide identification algorithms but data that represents a ground truth for the evaluation of LC-MS data is limited. Hence there have been attempts to simulate such data in a controlled fashion to evaluate and compare algorithms. We present MSSimulator, a simulation software for LC-MS and LC-MS/MS experiments. Starting from a list of proteins from a FASTA file, the simulation will perform in-silico digestion, retention time prediction, ionization filtering, and raw signal simulation (including MS/MS), while providing many options to change the properties of the resulting data like elution profile shape, resolution and sampling rate. Several protocols for SILAC, iTRAQ or MS(E) are available, in addition to the usual label-free approach, making MSSimulator the most comprehensive simulator for LC-MS and LC-MS/MS data.     

Mitotic Arrest and Apoptosis in Breast Cancer Cells Induced by Origanum majorana Extract: Upregulation of TNF-α and Downregulation of Survivin and Mutant p53

Background

In the present study, we investigated the effect of Origanum majorana ethanolic extract on the survival of the highly proliferative and invasive triple-negative p53 mutant breast cancer cell line MDA-MB-231.

Results

We found that O. majorana extract (OME) was able to inhibit the viability of the MDA-MB-231 cells in a time- and concentration-dependent manner. The effect of OME on cellular viability was further confirmed by the inhibition of colony growth. We showed, depending on the concentration used, that OME elicited different effects on the MDA-MB 231 cells. Concentrations of 150 and 300 µg/mL induced an accumulation of apoptotic–resistant population of cells arrested in mitotis and overexpressing the cyclin-dependent kinase inhibitor, p21 and the inhibitor of apoptosis, survivin. On the other hand, higher concentrations of OME (450 and 600 µg/mL) triggered a massive apoptosis through the extrinsic pathway, including the activation of tumor necrosis factor-α (TNF-α), caspase 8, caspase 3, and cleavage of PARP, downregulation of survivin as well as depletion of the mutant p53 in MDA-MB-231 cells. Furthermore, OME induced an upregulation of γ-H2AX, a marker of double strand DNA breaks and an overall histone H3 and H4 hyperacetylation.

Conclusion

Our findings provide strong evidence that O. majorana may be a promising chemopreventive and therapeutic candidate against cancer especially for highly invasive triple negative p53 mutant breast cancer; thus validating its complementary and alternative medicinal use.