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

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

Simultaneous Analysis of Proteome, Phospho- and Glycoproteome of Rat Kidney Tissue with Electrostatic Repulsion Hydrophilic Interaction Chromatography

Protein post-translational modifications (PTMs) are regulated separately from protein expression levels. Thus, simultaneous characterization of the proteome and its PTMs is pivotal to an understanding of protein regulation, function and activity. However, concurrent analysis of the proteome and its PTMs by mass spectrometry is a challenging task because the peptides bearing PTMs are present in sub-stoichiometric amounts and their ionization is often suppressed by unmodified peptides of high abundance. We describe here a method for concurrent analysis of phosphopeptides, glycopeptides and unmodified peptides in a tryptic digest of rat kidney tissue with a sequence of ERLIC and RP-LC-MS/MS in a single experimental run, thereby avoiding inter-experimental variation. Optimization of loading solvents and elution gradients permitted ERLIC to be performed with totally volatile solvents. Two SCX and four ERLIC gradients were compared in details, and one ERLIC gradient was found to perform the best, which identified 2929 proteins, 583 phosphorylation sites in 338 phosphoproteins and 722 N-glycosylation sites in 387 glycoproteins from rat kidney tissue. Two hundred low-abundance proteins with important functions were identified only from the glyco- or phospho-subproteomes, reflecting the importance of the enrichment and separation of modified peptides by ERLIC. In addition, this strategy enables identification of unmodified and corresponding modified peptides (partial phosphorylation and N-glycosylation) from the same protein. Interestingly, partially modified proteins tend to occur on proteins involved in transport. Moreover, some membrane or extracellular proteins, such as versican core protein and fibronectin, were found to have both phosphorylation and N-glycosylation, which may permit an assessment of the potential for cross talk between these two vital PTMs and their roles in regulation

Identification of pediatric septic shock subclasses based on genome-wide expression profiling

<p>Abstract</p> <p>Background</p> <p>Septic shock is a heterogeneous syndrome within which probably exist several biological subclasses. Discovery and identification of septic shock subclasses could provide the foundation for the design of more specifically targeted therapies. Herein we tested the hypothesis that pediatric septic shock subclasses can be discovered through genome-wide expression profiling.</p> <p>Methods</p> <p>Genome-wide expression profiling was conducted using whole blood-derived RNA from 98 children with septic shock, followed by a series of bioinformatic approaches targeted at subclass discovery and characterization.</p> <p>Results</p> <p>Three putative subclasses (subclasses A, B, and C) were initially identified based on an empiric, discovery-oriented expression filter and unsupervised hierarchical clustering. Statistical comparison of the three putative subclasses (analysis of variance, Bonferonni correction, <it>P </it>< 0.05) identified 6,934 differentially regulated genes. K-means clustering of these 6,934 genes generated 10 coordinately regulated gene clusters corresponding to multiple signaling and metabolic pathways, all of which were differentially regulated across the three subclasses. Leave one out cross-validation procedures indentified 100 genes having the strongest predictive values for subclass identification. Forty-four of these 100 genes corresponded to signaling pathways relevant to the adaptive immune system and glucocorticoid receptor signaling, the majority of which were repressed in subclass A patients. Subclass A patients were also characterized by repression of genes corresponding to zinc-related biology. Phenotypic analyses revealed that subclass A patients were younger, had a higher illness severity, and a higher mortality rate than patients in subclasses B and C.</p> <p>Conclusion</p> <p>Genome-wide expression profiling can identify pediatric septic shock subclasses having clinically relevant phenotypes.</p

Computational Identification of Transcriptional Regulators in Human Endotoxemia

One of the great challenges in the post-genomic era is to decipher the underlying principles governing the dynamics of biological responses. As modulating gene expression levels is among the key regulatory responses of an organism to changes in its environment, identifying biologically relevant transcriptional regulators and their putative regulatory interactions with target genes is an essential step towards studying the complex dynamics of transcriptional regulation. We present an analysis that integrates various computational and biological aspects to explore the transcriptional regulation of systemic inflammatory responses through a human endotoxemia model. Given a high-dimensional transcriptional profiling dataset from human blood leukocytes, an elementary set of temporal dynamic responses which capture the essence of a pro-inflammatory phase, a counter-regulatory response and a dysregulation in leukocyte bioenergetics has been extracted. Upon identification of these expression patterns, fourteen inflammation-specific gene batteries that represent groups of hypothetically ‘coregulated’ genes are proposed. Subsequently, statistically significant cis-regulatory modules (CRMs) are identified and decomposed into a list of critical transcription factors (34) that are validated largely on primary literature. Finally, our analysis further allows for the construction of a dynamic representation of the temporal transcriptional regulatory program across the host, deciphering possible combinatorial interactions among factors under which they might be active. Although much remains to be explored, this study has computationally identified key transcription factors and proposed a putative time-dependent transcriptional regulatory program associated with critical transcriptional inflammatory responses. These results provide a solid foundation for future investigations to elucidate the underlying transcriptional regulatory mechanisms under the host inflammatory response. Also, the assumption that coexpressed genes that are functionally relevant are more likely to share some common transcriptional regulatory mechanism seems to be promising, making the proposed framework become essential in unravelling context-specific transcriptional regulatory interactions underlying diverse mammalian biological processes

Proteomic analysis of food allergens by MALDI TOF/TOF mass spectrometry

Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry (MS) is largely recognized as an important tool in the analysis of many biomolecules such as proteins and peptides. The MS analysis of digested peptides to identify a protein or some of its modifications is a key step in proteomics. MALDI-MS is well suited for the peptide mass fingerprinting (PMF) technique, as well as selected fragmentation of various precursors using collisional-induced dissociation (CID) or post-source decay (PSD). In the last few years, MALDI-MS has played a significant role in food chemistry, especially in the detection of food adulterations, characterization of food allergens, and investigation of protein structural modifications induced by various industrial processes that could be an issue in terms of food quality and safety. Here, we present simple extraction protocols of allergenic proteins in food commodities such as milk, egg, hazelnut, and lupin seeds. Classic bottom-up approaches based on Sodium Dodecyl Sulphate&nbsp;(SDS) gel electrophoresis separation followed by in-gel digestion or direct in-solution digestion of whole samples are described. MALDI-MS and MS/MS analyses are discussed along with a comparison of data obtained by using the most widespread matrices for proteomic studies, namely, α-cyano-4-hydroxy-cinnamic acid (CHCA) and α-cyano-4-chloro-cinnamic acid (CClCA). The choice of the most suitable MALDI matrix is fundamental for high-throughput screening of putative food allergens

1,8-Bis(dimethylamino)naphthalene/9-aminoacridine: A new binary matrix for lipid fingerprinting of intact bacteria by matrix assisted laser desorption ionization mass spectrometry

The effectiveness of a novel binary matrix composed of 1,8-bis(dimethylamino)naphthalene (DMAN;proton sponge) and 9-aminoacridine (9AA) for the direct lipid analysis of whole bacterial cells by matrixassisted laser desorption ionization mass spectrometry (MALDI MS) is demonstrated. Deprotonated ana-lyte signals nearly free of matrix-related ions were observed in negative ion mode. The effect of themost important factors (laser energy, pulse voltage, DMAN/9AA ratio, analyte/matrix ratio) was investi-gated using a Box–Behnken response surface design followed by multi-response optimization in order tosimultaneously maximize signal-to-noise (S/N) ratio and resolution. The chemical surface compositionof single or mixed matrices was explored by X-ray photoelectron spectroscopy (XPS). Moreover, XPSimaging was used to map the spatial distribution of a model phospholipid in single or binary matrices.The DMAN/9AA binary matrix was then successfully applied to the analysis of intact Gram positive(Lactobacillus sanfranciscensis) or Gram negative (Escherichia coli) microorganisms. About fifty majormembrane components (free fatty acids, mono-, di- and tri-glycerides, phospholipids, glycolipids andcardiolipins) were quickly and easily detected over a mass range spanning from ca. 200 to ca. 1600 m/z.Moreover, mass spectra with improved S/N ratio (compared to single matrices), reduced chemical noiseand no formation of matrix-clusters were invariably obtained demonstrating the potential of this binarymatrix to improve sensitivity