Assessment and Imaging of Intracellular Magnesium in SaOS-2 Osteosarcoma Cells and Its Role in Proliferation.

Magnesium is an essential nutrient involved in many important processes in living organisms, including protein synthesis, cellular energy production and storage, cell growth and nucleic acid synthesis. In this study, we analysed the effect of magnesium deficiency on the proliferation of SaOS-2 osteosarcoma cells. When quiescent magnesium-starved cells were induced to proliferate by serum addition, the magnesium content was 2-3 times lower in cells maintained in a medium without magnesium compared with cells growing in the presence of the ion. Magnesium depletion inhibited cell cycle progression and caused the inhibition of cell proliferation, which was associated with mTOR hypophosphorylation at Serine 2448. In order to map the intracellular magnesium distribution, an analytical approach using synchrotron-based X-ray techniques was applied. When cell growth was stimulated, magnesium was mainly localized near the plasma membrane in cells maintained in a medium without magnesium. In non-proliferating cells growing in the presence of the ion, high concentration areas inside the cell were observed. These results support the role of magnesium in the control of cell proliferation, suggesting that mTOR may represent an important target for the antiproliferative effect of magnesium. Selective control of magnesium availability could be a useful strategy for inhibiting osteosarcoma cell growth.

Tissue-Specific Subcellular Localization Prediction Using Multi-Label Markov Random Fields.

The understanding of subcellular localization (SCL) of proteins and proteome variation in the different tissues and organs of the human body are two crucial aspects for increasing our knowledge of the dynamic rules of proteins, the cell biology, and the mechanism of diseases. Although there have been tremendous contributions to these two fields independently, the lack of knowledge of the variation of spatial distribution of proteins in the different tissues still exists. Here, we proposed an approach that allows predicting protein SCL on tissue specificity through the use of tissue-specific functional associations and physical protein-protein interactions (PPIs). We applied our previously developed Bayesian collective Markov random fields (BCMRFs) on tissue-specific protein-protein interaction network (PPI network) for nine types of tissues focusing on eight high-level SCL. The evaluated results demonstrate the strength of our approach in predicting tissue-specific SCL. We identified 1,314 proteins that their SCL were previously proven cell line dependent. We predicted 549 novel tissue-specific localized candidate proteins while some of them were validated via text-mining.

Rigidity of a spherical capsule switches the localization of encapsulated particles between inner and peripheral regions under crowding condition: simple model on cellular architecture.

We have investigated the inhomogeneous interior of confined spherical cavities as capsules containing encapsulated binary hard sphere mixtures for different compositions and cavity wall rigidity. Such a greatly simplified model manifests the effects of macromolecular crowding arising from excluded volume interactions in a tiny cell or a cellular nucleus. By fixing the number of large particles, the level of crowding is adjusted by changing the amount of small hard spheres in the cavity. For a rigid cavity, large spheres tend to pack in liquid-like order apart from the surface to the center of the cavity as the crowding level is increased. Whereas, for a soft cavity, larger spheres tend to blend with small spheres in the peripheral region at near the boundary of the cavity, and are susceptible to be depleted from the interior of the cavity as the cavity becomes more crowded. These results may help future elucidation of the thermodynamic pathways to stabilize the inhomogeneous structure of mixtures confined in cavities, such as the derepression of genome materials around the interior rim of the nucleus in a cancerous cell.

Assessment of intracellular mucin content in vivo.

Airway mucus presents a first line of defense against inhaled materials. It also, however, is a significant pathological contributor to chronic lung diseases, such as asthma, cystic fibrosis, and chronic obstructive pulmonary disease. Thus, gaining a better understanding of the mechanisms of mucus production and secretion is an important goal for improving respiratory health. Mucins, the chief glycoprotein components of airway mucus, are very large polymeric glycoproteins, and measuring their production and secretion in experimental animals presents significant technical challenges. Over the past several years, we have developed assays for accurately quantifying mucin production and secretion using histological and biochemical assays. These methods are described here.

Putative copper- and zinc-binding motifs in Streptococcus pneumoniae identified by immobilized metal affinity chromatography and mass spectrometry.

The aim of metalloproteomics is to identify and characterize putative metal-binding proteins and metal-binding motifs. In this study, we performed a systematical metalloproteomic analysis on Streptococcus pneumoniae through the combined use of efficient immobilized metal affinity chromatography enrichment and high-accuracy linear ion trap-Orbitrap MS to identify metal-binding proteins and metal-binding peptides. In total, 232 and 166 putative metal-binding proteins were respectively isolated by Cu- and Zn-immobilized metal affinity chromatography columns, in which 133 proteins were present in both preparations. The putative metalloproteins are mainly involved in protein, nucleotide and carbon metabolisms, oxidation and cell cycle regulation. Based on the sequence of the putative Cu- and Zn-binding peptides, putative Cu-binding motifs were identified: H(X)mH (m=0-11), C(X)(2) C, C(X)nH (n=2-4, 6, 9), H(X)iM (i=0-10) and M(X)tM (t=8 or 12), while putative Zn-binding motifs were identified as follows: H(X)mH (m=1-12), H(X)iM (i=0-12), M(X)tM (t=0, 3 and 4), C(X)nH (n=1, 2, 7, 10 and 11). Equilibrium dialysis and inductively coupled plasma-MS experiments confirmed that the artificially synthesized peptides harboring differential identified metal-binding motifs interacted directly with the metal ions. The metalloproteomic study presented here suggests that the comparably large size and diverse functions of the S. pneumoniae metalloproteome may play important roles in various biological processes and thus contribute to the bacterial pathologies.

A bioluminescent microbial biosensor for in vitro pretreatment assessment of cytarabine efficacy in leukemia.

BACKGROUND: The nucleoside analog cytarabine (Ara-C [cytosine arabinoside]) is the key agent for treating acute myeloid leukemia (AML); however, up to 30% of patients fail to respond to treatment. Screening of patient blood samples to determine drug response before commencement of treatment is needed. This project aimed to construct and evaluate a self-bioluminescent reporter strain of Escherichia coli for use as an Ara-C biosensor and to design an in vitro assay to predict Ara-C response in clinical samples. METHODS: We used transposition mutagenesis to create a cytidine deaminase (cdd)-deficient mutant of E. coli MG1655 that responded to Ara-C. The strain was transformed with the luxCDABE operon and used as a whole-cell biosensor for development an 8-h assay to determine Ara-C uptake and phosphorylation by leukemic cells. RESULTS: Intracellular concentrations of 0.025 µmol/L phosphorylated Ara-C were detected by significantly increased light output (P < 0.05) from the bacterial biosensor. Results using AML cell lines with known response to Ara-C showed close correlation between the 8-h assay and a 3-day cytotoxicity test for Ara-C cell killing. In retrospective tests with 24 clinical samples of bone marrow or peripheral blood, the biosensor-based assay predicted leukemic cell response to Ara-C within 8 h. CONCLUSIONS: The biosensor-based assay may offer a predictor for evaluating the sensitivity of leukemic cells to Ara-C before patients undergo chemotherapy and allow customized treatment of drug-sensitive patients with reduced Ara-C dose levels. The 8-h assay monitors intracellular Ara-CTP (cytosine arabinoside triphosphate) levels and, if fully validated, may be suitable for use in clinical settings.

Boosting accuracy of automated classification of fluorescence microscope images for location proteomics.

BACKGROUND: Detailed knowledge of the subcellular location of each expressed protein is critical to a full understanding of its function. Fluorescence microscopy, in combination with methods for fluorescent tagging, is the most suitable current method for proteome-wide determination of subcellular location. Previous work has shown that neural network classifiers can distinguish all major protein subcellular location patterns in both 2D and 3D fluorescence microscope images. Building on these results, we evaluate here new classifiers and features to improve the recognition of protein subcellular location patterns in both 2D and 3D fluorescence microscope images. RESULTS: We report here a thorough comparison of the performance on this problem of eight different state-of-the-art classification methods, including neural networks, support vector machines with linear, polynomial, radial basis, and exponential radial basis kernel functions, and ensemble methods such as AdaBoost, Bagging, and Mixtures-of-Experts. Ten-fold cross validation was used to evaluate each classifier with various parameters on different Subcellular Location Feature sets representing both 2D and 3D fluorescence microscope images, including new feature sets incorporating features derived from Gabor and Daubechies wavelet transforms. After optimal parameters were chosen for each of the eight classifiers, optimal majority-voting ensemble classifiers were formed for each feature set. Comparison of results for each image for all eight classifiers permits estimation of the lower bound classification error rate for each subcellular pattern, which we interpret to reflect the fraction of cells whose patterns are distorted by mitosis, cell death or acquisition errors. Overall, we obtained statistically significant improvements in classification accuracy over the best previously published results, with the overall error rate being reduced by one-third to one-half and with the average accuracy for single 2D images being higher than 90% for the first time. In particular, the classification accuracy for the easily confused endomembrane compartments (endoplasmic reticulum, Golgi, endosomes, lysosomes) was improved by 5-15%. We achieved further improvements when classification was conducted on image sets rather than on individual cell images. CONCLUSIONS: The availability of accurate, fast, automated classification systems for protein location patterns in conjunction with high throughput fluorescence microscope imaging techniques enables a new subfield of proteomics, location proteomics. The accuracy and sensitivity of this approach represents an important alternative to low-resolution assignments by curation or sequence-based prediction.