Recovery of Aging-Related Size Increase of Skin Epithelial Cells: In vivo Mouse and In vitro Human Study

The size increase of skin epithelial cells during aging is well-known. Here we demonstrate that treatment of aging cells with cytochalasin B substantially decreases cell size. This decrease was demonstrated on a mouse model and on human skin cells in vitro. Six nude mice were treated by topical application of cytochalasin B on skin of the dorsal left midsection for 140 days (the right side served as control for placebo treatment). An average decrease in cell size of 56±16% resulted. A reduction of cell size was also observed on primary human skin epithelial cells of different in vitro age (passages from 1 to 8). A cell strain obtained from a pool of 6 human subjects was treated with cytochalasin B in vitro for 12 hours. We observed a decrease in cell size that became statistically significant and reached 20–40% for cells of older passage (6–8 passages) whereas no substantial change was observed for younger cells. These results may be important for understanding the aging processes, and for cosmetic treatment of aging skin.     

Experimental-numerical analysis of minipig's multi-rooted teeth

The paper pertains to the analysis of the biomechanical behaviour of the periodontal ligament (PDL) by using a combined experimental and numerical approach. Experimental analysis provides information about a two-rooted pig premolar tooth in its socket with regard to morphological configuration and deformational response. The numerical analysis developed for the present investigation adopts a specific anisotropic hyperelastic formulation, accounting for tissue structural arrangement. The parameters to be adopted for the PDL constitutive model are evaluated with reference to data deducted from experimental in vitro tests on different specimens taken from literature. According to morphometric data relieved, solid models are provided as basis for the development of numerical models that adopt the constitutive formulation proposed. A reciprocal validation of experimental and numerical data allows for the evaluation of reliability of results obtained. The work is intended as preliminary investigation to study the correlation between mechanical status of PDL and induction to cellular activity in orthodontic treatments.     

Impaired myocardial metabolic reserve and substrate selection flexibility during stress in patients with idiopathic dilated cardiomyopathy

Under resting conditions, the failing heart shifts fuel use toward greater glucose and lower free fatty acid (FFA) oxidation. We hypothesized that chronic metabolic abnormalities in patients with dilated cardiomyopathy (DCM) are associated with the absence of the normal increase in myocardial glucose uptake and maintenance of cardiac mechanical efficiency in response to pacing stress. In 10 DCM patients and 6 control subjects, we measured coronary flow by intravascular ultrasonometry and sampled arterial and coronary sinus blood. Myocardial metabolism was determined at baseline, during atrial pacing at 130 beats/min, and at 15 min of recovery by infusion of [(3)H]oleate and [(13)C]lactate and measurement of transmyocardial arteriovenous differences of oxygen and metabolites. At baseline, DCM patients showed depressed coronary flow, reduced uptake and oxidation of FFA, and preferential utilization of carbohydrates. During pacing, glucose uptake increased by 106% in control subjects but did not change from baseline in DCM patients. Lactate release increased by 122% in DCM patients but not in control subjects. Cardiac mechanical efficiency in DCM patients was not different compared with control subjects at baseline but was 34% lower during stress. Fatty acid uptake and oxidation did not change with pacing in either group. Our results show that in DCM there is preferential utilization of carbohydrates, which is associated with reduced flow and oxygen consumption at rest and an impaired ability to increase glucose uptake during stress. These metabolic abnormalities might contribute to progressive cardiac deterioration and represent a target for therapeutic strategies aimed at modulating cardiac substrate utilization.     

LTR retrotransposon dynamics in the evolution of the olive (Olea europaea) genome

Improved knowledge of genome composition, especially of its repetitive component, generates important information for both theoretical and applied research. The olive repetitive component is made up of two main classes of sequences: tandem repeats and retrotransposons (REs). In this study, we provide characterization of a sample of 254 unique full-length long terminal repeat (LTR) REs. In the sample, Ty1-Copia elements were more numerous than Ty3-Gypsy elements. Mapping a large set of Illumina whole-genome shotgun reads onto the identified retroelement set revealed that Gypsy elements are more redundant than Copia elements. The insertion time of intact retroelements was estimated based on sister LTR’s divergence. Although some elements inserted relatively recently, the mean insertion age of the isolated retroelements is around 18 million yrs. Gypsy and Copia retroelements showed different waves of transposition, with Gypsy elements especially active between 10 and 25 million yrs ago and nearly inactive in the last 7 million yrs. The occurrence of numerous solo-LTRs related to isolated full-length retroelements was ascertained for two Gypsy elements and one Copia element. Overall, the results reported in this study show that RE activity (both retrotransposition and DNA loss) has impacted the olive genome structure in more ancient times than in other angiosperms.     

Ligand-dependent differences in estrogen receptor beta-interacting proteins identified in lung adenocarcinoma cells corresponds to estrogenic responses

Background

A recent epidemiological study demonstrated a reduced risk of lung cancer mortality in breast cancer patients using antiestrogens. These and other data implicate a role for estrogens in lung cancer, particularly nonsmall cell lung cancer (NSCLC). Approximately 61% of human NSCLC tumors express nuclear estrogen receptor β (ERβ); however, the role of ERβ and estrogens in NSCLC is likely to be multifactorial. Here we tested the hypothesis that proteins interacting with ERβ in human lung adenocarcinoma cells that respond proliferatively to estradiol (E₂) are distinct from those in non-E₂-responsive cells.

Methods

FLAG affinity purification of FLAG-ERβ-interacting proteins was used to isolate ERβ-interacting proteins in whole cell extracts from E₂ proliferative H1793 and non-E₂-proliferative A549 lung adenocarcinoma cell lines. Following trypsin digestion, proteins were identified using liquid chromatography electrospray ionization tandem mass spectrometry (LC-MS/MS). Proteomic data were analyzed using Ingenuity Pathway Analysis. Select results were confirmed by coimmunoprecipitation.

Results

LC-MS/MS identified 27 non-redundant ERβ-interacting proteins. ERβ-interacting proteins included hsp70, hsp60, vimentin, histones and calmodulin. Ingenuity Pathway Analysis of the ERβ-interacting proteins revealed differences in molecular and functional networks between H1793 and A549 lung adenocarcinoma cells. Coimmunoprecipitation experiments in these and other lung adenocarcinoma cells confirmed that ERβ and EGFR interact in a gender-dependent manner and in response to E₂ or EGF. BRCA1 interacted with ERβ in A549 cell lines and in human lung adenocarcinoma tumors, but not normal lung tissue.

Conclusion

Our results identify specific differences in ERβ-interacting proteins in lung adenocarcinoma cells corresponding to ligand-dependent differences in estrogenic responses.

     

Enriched sera protein profiling for detection of non-small cell lung cancer biomarkers

Background

Non Small Cell Lung Cancer (NSCLC) is the major cause of cancer related-death. Many patients receive diagnosis at advanced stage leading to a poor prognosis. At present, no satisfactory screening tests are available in clinical practice and the discovery and validation of new biomarkers is mandatory. Surface Enhanced Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (SELDI-ToF-MS) is a recent high-throughput technique used to detect new tumour markers. In this study we performed SELDI-ToF-MS analysis on serum samples treated with the ProteoMiner? kit, a combinatorial library of hexapeptide ligands coupled to beads, to reduce the wide dynamic range of protein concentration in the sample. Serum from 44 NSCLC patients and 19 healthy controls were analyzed with IMAC30-Cu and H50 ProteinChip Arrays.

Results

Comparing SELDI-ToF-MS protein profiles of NSCLC patients and healthy controls, 28 protein peaks were found significantly different (p < 0.05), and were used as predictors to build decision classification trees. This statistical analysis selected 10 protein peaks in the low-mass range (2-24 kDa) and 6 in the high-mass range (40-80 kDa). The classification models for the low-mass range had a sensitivity and specificity of 70.45% (31/44) and 68.42% (13/19) for IMAC30-Cu, and 72.73% (32/44) and 73.68% (14/19) for H50 ProteinChip Arrays.

Conclusions

These preliminary results suggest that SELDI-ToF-MS protein profiling of serum samples pretreated with ProteoMiner? can improve the discovery of protein peaks differentially expressed between NSCLC patients and healthy subjects, useful to build classification algorithms with high sensitivity and specificity. However, identification of the significantly different protein peaks needs further study in order to provide a better understanding of the biological nature of these potential biomarkers and their role in the underlying disease process.     

Design and initial characterization of the SC-200 proteomics standard mixture

High-throughput (HTP) proteomics studies generate large amounts of data. Interpretation of these data requires effective approaches to distinguish noise from biological signal, particularly as instrument and computational capacity increase and studies become more complex. Resolving this issue requires validated and reproducible methods and models, which in turn requires complex experimental and computational standards. The absence of appropriate standards and data sets for validating experimental and computational workflows hinders the development of HTP proteomics methods. Most protein standards are simple mixtures of proteins or peptides, or undercharacterized reference standards in which the identity and concentration of the constituent proteins is unknown. The Seattle Children's 200 (SC-200) proposed proteomics standard mixture is the next step toward developing realistic, fully characterized HTP proteomics standards. The SC-200 exhibits a unique modular design to extend its functionality, and consists of 200 proteins of known identities and molar concentrations from 6 microbial genomes, distributed into 10 molar concentration tiers spanning a 1,000-fold range. We describe the SC-200's design, potential uses, and initial characterization. We identified 84% of SC-200 proteins with an LTQ-Orbitrap and 65% with an LTQ-Velos (false discovery rate?=?1% for both). There were obvious trends in success rate, sequence coverage, and spectral counts with protein concentration; however, protein identification, sequence coverage, and spectral counts vary greatly within concentration levels.     

Classifying proteins into functional groups based on all-versus-all BLAST of 10 million proteins

To address the monumental challenge of assigning function to millions of sequenced proteins, we completed the first of a kind all-versus-all sequence alignments using BLAST for 9.9 million proteins in the UniRef100 database. Microsoft Windows Azure produced over 3 billion filtered records in 6 days using 475 eight-core virtual machines. Protein classification into functional groups was then performed using Hive and custom jars implemented on top of Apache Hadoop utilizing the MapReduce paradigm. First, using the Clusters of Orthologous Genes (COG) database, a length normalized bit score (LNBS) was determined to be the best similarity measure for classification of proteins. LNBS achieved sensitivity and specificity of 98% each. Second, out of 5.1 million bacterial proteins, about two-thirds were assigned to significantly extended COG groups, encompassing 30 times more assigned proteins. Third, the remaining proteins were classified into protein functional groups using an innovative implementation of a single-linkage algorithm on an in-house Hadoop compute cluster. This implementation significantly reduces the run time for nonindexed queries and optimizes efficient clustering on a large scale. The performance was also verified on Amazon Elastic MapReduce. This clustering assigned nearly 2 million proteins to approximately half a million different functional groups. A similar approach was applied to classify 2.8 million eukaryotic sequences resulting in over 1 million proteins being assign to existing KOG groups and the remainder clustered into 100,000 functional groups.