Multi-technique characterization of pictorial organic binders on XV century polychrome sculptures by combining microand non-invasive sampling approaches

A stony sculptural composition of the Nativity Scene is preserved in Altamura’s Cathedral (Apulia, Italy). This commonly called Apulian “presepe”, attributed to an unknown stonemason, is composed of polychrome carbonate white stone sculptures. While earlier stratigraphic tests have unveiled a complex superimposition of painting layers—meaning that several editions of the sculptures succeeded from the 16th to 20th century—a chemical investigation intended to identify the organic binding media used in painting layers was undertaken. Drawing on current literature, two strategies were exploited: a non-invasive in situ digestion analysis and an approach based on microremoval of painting film followed by the Bligh and Dyer extraction protocol. Both peptide and lipid mixtures were analyzed by matrix-assisted laser desorption/ionization-mass spectrometry (MALDIMS) and reversed-phase liquid chromatography coupled to mass spectrometry by electrospray ionization (RPLC-ESI-MS). Attenuated total reflectance Fourier-transform infrared spectroscopy (ATR-FTIR) examinations were also performed on micro-samples of painting films before lipids and proteins extraction. While human keratins were found to be common contaminants of the artwork’s surfaces, traces of animal collagen, siccative oils, and egg white proteins were evidenced in different sampling zones of the sculptures, thus suggesting the use of non-homogeneous painting techniques in the colored layers

Development of Animal Models to Test the Fundamental Basis of Gene–Environment Interactions

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/93738/1/oby.2008.513.pd

Mapping gene associations in human mitochondria using clinical disease phenotypes

Nuclear genes encode most mitochondrial proteins, and their mutations cause diverse and debilitating clinical disorders. To date, 1,200 of these mitochondrial genes have been recorded, while no standardized catalog exists of the associated clinical phenotypes. Such a catalog would be useful to develop methods to analyze human phenotypic data, to determine genotype-phenotype relations among many genes and diseases, and to support the clinical diagnosis of mitochondrial disorders. Here we establish a clinical phenotype catalog of 174 mitochondrial disease genes and study associations of diseases and genes. Phenotypic features such as clinical signs and symptoms were manually annotated from full-text medical articles and classified based on the hierarchical MeSH ontology. This classification of phenotypic features of each gene allowed for the comparison of diseases between different genes. In turn, we were then able to measure the phenotypic associations of disease genes for which we calculated a quantitative value that is based on their shared phenotypic features. The results showed that genes sharing more similar phenotypes have a stronger tendency for functional interactions, proving the usefulness of phenotype similarity values in disease gene network analysis. We then constructed a functional network of mitochondrial genes and discovered a higher connectivity for non-disease than for disease genes, and a tendency of disease genes to interact with each other. Utilizing these differences, we propose 168 candidate genes that resemble the characteristic interaction patterns of mitochondrial disease genes. Through their network associations, the candidates are further prioritized for the study of specific disorders such as optic neuropathies and Parkinson disease. Most mitochondrial disease phenotypes involve several clinical categories including neurologic, metabolic, and gastrointestinal disorders, which might indicate the effects of gene defects within the mitochondrial system. The accompanying knowledgebase (http://www.mitophenome.org/) supports the study of clinical diseases and associated genes

Aerobic metabolism underlies complexity and capacity

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/66397/1/jphysiol.2007.144709.pd

A genomic storm in critically injured humans

Critical injury in humans induces a genomic storm with simultaneous changes in expression of innate and adaptive immunity genes

Gene Network Analysis of Bone Marrow Mononuclear Cells Reveals Activation of Multiple Kinase Pathways in Human Systemic Lupus Erythematosus

Background: Gene profiling studies provide important information for key molecules relevant to a disease but are less informative of protein-protein interactions, post-translational modifications and regulation by targeted subcellular localization. Integration of genomic data and construction of functional gene networks may provide additional insights into complex diseases such as systemic lupus erythematosus (SLE). Methodology/Principal Findings: We analyzed gene expression microarray data of bone marrow mononuclear cells (BMMCs) from 20 SLE patients (11 with active disease) and 10 controls. Gene networks were constructed using the bioinformatic tool Ingenuity Gene Network Analysis. In SLE patients, comparative analysis of BMMCs genes revealed a network with 19 central nodes as major gene regulators including ERK, JNK, and p38 MAP kinases, insulin, Ca2+ and STAT3. Comparison between active versus inactive SLE identified 30 central nodes associated with immune response, protein synthesis, and post-transcriptional modification. A high degree of identity between networks in active SLE and non-Hodgkin's lymphoma (NHL) patients was found, with overlapping central nodes including kinases (MAPK, ERK, JNK, PKC), transcription factors (NF-kappaB, STAT3), and insulin. In validation studies, western blot analysis in splenic B cells from 5-month-old NZB/NZW F1 lupus mice showed activation of STAT3, ITGB2, HSPB1, ERK, JNK, p38, and p32 kinases, and downregulation of FOXO3 and VDR compared to normal C57Bl/6 mice. Conclusions/Significance: Gene network analysis of lupus BMMCs identified central gene regulators implicated in disease pathogenesis which could represent targets of novel therapies in human SLE. The high similarity between active SLE and NHL networks provides a molecular basis for the reported association of the former with lymphoid malignancies

Gene Expression Profiles Characterize Inflammation Stages in the Acute Lung Injury in Mice

Acute Lung Injury (ALI) carries about 50 percent mortality and is frequently associated with an infection (sepsis). Life-support treatment with mechanical ventilation rescues many patients, although superimposed infection or multiple organ failure can result in death. The outcome of a patient developing sepsis depends on two factors: the infection and the pre-existing inflammation. In this study, we described each stage of the inflammation process using a transcriptional approach and an animal model. Female C57BL6/J mice received an intravenous oleic acid injection to induce an acute lung injury (ALI). Lung expression patterns were analyzed using a 9900 cDNA mouse microarray (MUSV29K). Our gene-expression analysis revealed marked changes in the immune and inflammatory response metabolic pathways, notably lipid metabolism and transcription. The early stage (1 hour–1.5 hours) is characterized by a pro-inflammatory immune response. Later (3 hours–4 hours), the immune cells migrate into inflamed tissues through interaction with vascular endothelial cells. Finally, at late stages of lung inflammation (18 hours–24 hours), metabolism is deeply disturbed. Highly expressed pro-inflammatory cytokines activate transcription of many genes and lipid metabolism. In this study, we described a global overview of critical events occurring during lung inflammation which is essential to understand infectious pathologies such as sepsis where inflammation and infection are intertwined. Based on these data, it becomes possible to isolate the impact of a pathogen at the transcriptional level from the global gene expression modifications resulting from the infection associated with the inflammation

Global gene expression patterns in the post-pneumonectomy lung of adult mice

<p>Abstract</p> <p>Background</p> <p>Adult mice have a remarkable capacity to regenerate functional alveoli following either lung resection or injury that exceeds the regenerative capacity observed in larger adult mammals. The molecular basis for this unique capability in mice is largely unknown. We examined the transcriptomic responses to single lung pneumonectomy in adult mice in order to elucidate prospective molecular signaling mechanisms used in this species during lung regeneration.</p> <p>Methods</p> <p>Unilateral left pneumonectomy or sham thoracotomy was performed under general anesthesia (n = 8 mice per group for each of the four time points). Total RNA was isolated from the remaining lung tissue at four time points post-surgery (6 hours, 1 day, 3 days, 7 days) and analyzed using microarray technology.</p> <p>Results</p> <p>The observed transcriptomic patterns revealed mesenchymal cell signaling, including up-regulation of genes previously associated with activated fibroblasts (Tnfrsf12a, Tnc, Eln, Col3A1), as well as modulation of Igf1-mediated signaling. The data set also revealed early down-regulation of pro-inflammatory cytokine transcripts and up-regulation of genes involved in T cell development/function, but few similarities to transcriptomic patterns observed during embryonic or post-natal lung development. Immunohistochemical analysis suggests that early fibroblast but not myofibroblast proliferation is important during lung regeneration and may explain the preponderance of mesenchymal-associated genes that are over-expressed in this model. This again appears to differ from embryonic alveologenesis.</p> <p>Conclusion</p> <p>These data suggest that modulation of mesenchymal cell transcriptome patterns and proliferation of S100A4 positive mesenchymal cells, as well as modulation of pro-inflammatory transcriptome patterns, are important during post-pneumonectomy lung regeneration in adult mice.</p

Use of Data-Biased Random Walks on Graphs for the Retrieval of Context-Specific Networks from Genomic Data

Extracting network-based functional relationships within genomic datasets is an important challenge in the computational analysis of large-scale data. Although many methods, both public and commercial, have been developed, the problem of identifying networks of interactions that are most relevant to the given input data still remains an open issue. Here, we have leveraged the method of random walks on graphs as a powerful platform for scoring network components based on simultaneous assessment of the experimental data as well as local network connectivity. Using this method, NetWalk, we can calculate distribution of Edge Flux values associated with each interaction in the network, which reflects the relevance of interactions based on the experimental data. We show that network-based analyses of genomic data are simpler and more accurate using NetWalk than with some of the currently employed methods. We also present NetWalk analysis of microarray gene expression data from MCF7 cells exposed to different doses of doxorubicin, which reveals a switch-like pattern in the p53 regulated network in cell cycle arrest and apoptosis. Our analyses demonstrate the use of NetWalk as a valuable tool in generating high-confidence hypotheses from high-content genomic data

The investigation of WNT6 and WNT10A single nucleotide polymorphisms as potential biomarkers for dental pulp calcification in orthodontic patients

The aim of this study is to evaluate if single nucleotide polymorphisms (SNPs) in WNT6 and WNT10A are associated with the risk of dental pulp calcification in orthodontic patients. This cross-sectional study followed the “Strengthening the Reporting of Genetic Association Studies” (STREGA) guidelines. Panoramic radiographs (pre- and post-orthodontic treatment) and genomic DNA from 132 orthodontic patients were studied. Dental pulp calcification (pulp stones and/or pulp space narrowing) was recorded in upper and lower first molars. The SNPs in WNT6 and WNT10A (rs7349332, rs3806557, rs10177996, and rs6754599) were assessed through genotyping analysis using DNA extracted from buccal epithelial cells. The association between pulp calcification and SNPs were analyzed using allelic and genotypic distributions and haplotype frequencies (p<0.05). Prevalence of dental pulp calcification was 42.4% in the 490 studied molars. In the genotypic analysis, the SNPs in WNT10A showed a statistically significant value for molar calcification (p = 0.027 for rs1017799), upper molar calcification (p = 0.040 for rs1017799) (recessive model), and molar calcification (p = 0.046 for rs3806557) (recessive model). In the allelic distribution, the allele C of the SNP rs10177996 in WNT10A was associated with molar calcifications (p = 0.042) and with upper first molar calcification (p = 0.035). Nine combinations of haplotypes showed statistically significant value (p<0.05). The findings of this study indicates that SNPs in WNT10A and WNT6 are associated with dental pulp calcification in molars after orthodontic treatment and may be considered as biomarkers for dental pulp calcification