Principal components analysis based methodology to identify differentially expressed genes in time-course microarray data

<p>Abstract</p> <p>Background</p> <p>Time-course microarray experiments are being increasingly used to characterize dynamic biological processes. In these experiments, the goal is to identify genes differentially expressed in time-course data, measured between different biological conditions. These differentially expressed genes can reveal the changes in biological process due to the change in condition which is essential to understand differences in dynamics.</p> <p>Results</p> <p>In this paper, we propose a novel method for finding differentially expressed genes in time-course data and across biological conditions (say <it>C</it>₁and <it>C</it>₂). We model the expression at <it>C</it>₁using Principal Component Analysis and represent the expression profile of each gene as a linear combination of the dominant Principal Components (PCs). Then the expression data from <it>C</it>₂is projected on the developed PCA model and scores are extracted. The difference between the scores is evaluated using a hypothesis test to quantify the significance of differential expression. We evaluate the proposed method to understand differences in two case studies (1) the heat shock response of wild-type and HSF1 knockout mice, and (2) cell-cycle between wild-type and Fkh1/Fkh2 knockout Yeast strains.</p> <p>Conclusion</p> <p>In both cases, the proposed method identified biologically significant genes.</p

Sarcoidosis activates diverse transcriptional programs in bronchoalveolar lavage cells

Abstract Background Sarcoidosis is a multisystem immuno-inflammatory disorder of unknown etiology that most commonly involves the lungs. We hypothesized that an unbiased approach to identify pathways activated in bronchoalveolar lavage (BAL) cells can shed light on the pathogenesis of this complex disease. Methods We recruited 15 patients with various stages of sarcoidosis and 12 healthy controls. All subjects underwent bronchoscopy with lavage. For each subject, total RNA was extracted from BAL cells and hybridized to an Affymetrix U133A microarray. Rigorous statistical methods were applied to identify differential gene expression between subjects with sarcoidosis vs. controls. To better elucidate pathways differentially activated between these groups, we integrated network and gene set enrichment analyses of BAL cell transcriptional profiles. Results Sarcoidosis patients were either non-smokers or former smokers, all had lung involvement and only two were on systemic prednisone. Healthy controls were all non-smokers. Comparison of BAL cell gene expression between sarcoidosis and healthy subjects revealed over 1500 differentially expressed genes. Several previously described immune mediators, such as interferon gamma, were upregulated in the sarcoidosis subjects. Using an integrative computational approach we constructed a modular network of over 80 gene sets that were highly enriched in patients with sarcoidosis. Many of these pathways mapped to inflammatory and immune-related processes including adaptive immunity, T-cell signaling, graft vs. host disease, interleukin 12, 23 and 17 signaling. Additionally, we uncovered a close association between the proteasome machinery and adaptive immunity, highlighting a potentially important and targetable relationship in the pathobiology of sarcoidosis. Conclusions BAL cells in sarcoidosis are characterized by enrichment of distinct transcriptional programs involved in immunity and proteasomal processes. Our findings add to the growing evidence implicating alveolar resident immune effector cells in the pathogenesis of sarcoidosis and identify specific pathways whose activation may modulate disease progression

Agent-Based Modeling of Endotoxin-Induced Acute Inflammatory Response in Human Blood Leukocytes

Inflammation is a highly complex biological response evoked by many stimuli. A persistent challenge in modeling this dynamic process has been the (nonlinear) nature of the response that precludes the single-variable assumption. Systems-based approaches offer a promising possibility for understanding inflammation in its homeostatic context. In order to study the underlying complexity of the acute inflammatory response, an agent-based framework is developed that models the emerging host response as the outcome of orchestrated interactions associated with intricate signaling cascades and intercellular immune system interactions.An agent-based modeling (ABM) framework is proposed to study the nonlinear dynamics of acute human inflammation. The model is implemented using NetLogo software. Interacting agents involve either inflammation-specific molecules or cells essential for the propagation of the inflammatory reaction across the system. Spatial orientation of molecule interactions involved in signaling cascades coupled with the cellular heterogeneity are further taken into account. The proposed in silico model is evaluated through its ability to successfully reproduce a self-limited inflammatory response as well as a series of scenarios indicative of the nonlinear dynamics of the response. Such scenarios involve either a persistent (non)infectious response or innate immune tolerance and potentiation effects followed by perturbations in intracellular signaling molecules and cascades.The ABM framework developed in this study provides insight on the stochastic interactions of the mediators involved in the propagation of endotoxin signaling at the cellular response level. The simulation results are in accordance with our prior research effort associated with the development of deterministic human inflammation models that include transcriptional dynamics, signaling, and physiological components. The hypothetical scenarios explored in this study would potentially improve our understanding of how manipulating the behavior of the molecular species could manifest into emergent behavior of the overall system

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

Bladder inflammatory transcriptome in response to tachykinins: Neurokinin 1 receptor-dependent genes and transcription regulatory elements

Background Tachykinins (TK), such as substance P, and their neurokinin receptors which are ubiquitously expressed in the human urinary tract, represent an endogenous system regulating bladder inflammatory, immune responses, and visceral hypersensitivity. Increasing evidence correlates alterations in the TK system with urinary tract diseases such as neurogenic bladders, outflow obstruction, idiopathic detrusor instability, and interstitial cystitis. However, despite promising effects in animal models, there seems to be no published clinical study showing that NK-receptor antagonists are an effective treatment of pain in general or urinary tract disorders, such as detrusor overactivity. In order to search for therapeutic targets that could block the tachykinin system, we set forth to determine the regulatory network downstream of NK1 receptor activation. First, NK1R-dependent transcripts were determined and used to query known databases for their respective transcription regulatory elements (TREs). Methods: An expression analysis was performed using urinary bladders isolated from sensitized wild type (WT) and NK1R-/- mice that were stimulated with saline, LPS, or antigen to provoke inflammation. Based on cDNA array results, NK1R-dependent genes were selected. PAINT software was used to query TRANSFAC database and to retrieve upstream TREs that were confirmed by electrophoretic mobility shift assays. Results: The regulatory network of TREs driving NK1R-dependent genes presented cRel in a central position driving 22% of all genes, followed by AP-1, NF-kappaB, v-Myb, CRE-BP1/c-Jun, USF, Pax-6, Efr-1, Egr-3, and AREB6. A comparison between NK1R-dependent and NK1R-independent genes revealed Nkx-2.5 as a unique discriminator. In the presence of NK1R, Nkx2-5 _01 was significantly correlated with 36 transcripts which included several candidates for mediating bladder development (FGF) and inflammation (PAR-3, IL-1R, IL-6, α-NGF, TSP2). In the absence of NK1R, the matrix Nkx2-5_02 had a predominant participation driving 8 transcripts, which includes those involved in cancer (EYA1, Trail, HSF1, and ELK-1), smooth-to-skeletal muscle trans-differentiation, and Z01, a tight-junction protein, expression. Electrophoretic mobility shift assays confirmed that, in the mouse urinary bladder, activation of NK1R by substance P (SP) induces both NKx-2.5 and NF-kappaB translocations. Conclusion: This is the first report describing a role for Nkx2.5 in the urinary tract. As Nkx2.5 is the unique discriminator of NK1R-modulated inflammation, it can be imagined that in the near future, new based therapies selective for controlling Nkx2.5 activity in the urinary tract may be used in the treatment in a number of bladder disorders

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