Gene regulation and systems biology最新文献

Background: The premature fusion of one cranial suture, also referred to as non-syndromic craniosynostosis, most commonly involves premature fusion of the sagittal, coronal, or metopic sutures, in that order. Population-based epidemiological studies have found that the birth prevalence of single-suture craniosynostosis is both suture- and sex-dependent.

Methods: Transcriptomic data from 199 individuals with isolated sagittal (n = 100), unilateral coronal (n = 50), and metopic (n = 49) synostosis were compared against a control population (n = 50) to identify transcripts accounting for the different sex-based frequencies observed in this disease.

Results: Differential sex-based gene expression was classified as either gained (divergent) or lost (convergent) in affected individuals to identify transcripts related to disease predilection. Divergent expression was dependent on synostosis sub-type, and was extensive in metopic craniosynostosis specifically. Convergent microarray-based expression was independent of synostosis sub-type, with convergent expression of FBN2, IGF2BP3, PDE1C and TINAGL1 being the most robust across all synostosis sub-types.

Conclusions: Analysis of sex-based gene expression followed by validation by qRT-PCR identified that concurrent upregulation of FBN2 and IGF2BP3, and downregulation of TINAGL1 in craniosynostosis cases were all associated with increased RUNX2 expression and may represent a transcriptomic signature that can be used to characterize a subset of single-suture craniosynostosis cases.

Physarum polycephalum is a unicellular eukaryote belonging to the amoebozoa group of organisms. The complex life cycle involves various cell types that differ in morphology, function, and biochemical composition. Sporulation, one step in the life cycle, is a stimulus-controlled differentiation response of macroscopic plasmodial cells that develop into fruiting bodies. Well-established Mendelian genetics and the occurrence of macroscopic cells with a naturally synchronous population of nuclei as source of homogeneous cell material for biochemical analyses make Physarum an attractive model organism for studying the regulatory control of cell differentiation. Here, we develop an approach using RNA-sequencing (RNA-seq), without needing to rely on a genome sequence as a reference, for studying the transcriptomic changes during stimulus-triggered commitment to sporulation in individual plasmodial cells. The approach is validated through the obtained expression patterns and annotations, and particularly the results from up- and downregulated genes, which correlate well with previous studies.

Bacterial gene regulation involves transcription factors (TF) that bind to DNA recognition sequences in operon promoters. These recognition sequences, many of which are palindromic, are known as regulatory elements or transcription factor binding sites (TFBS). Some TFs are global regulators that can modulate the expression of hundreds of genes. In this study we examine global regulator half-sites, where a half-site, which we shall call a binding motif (BM), is one half of a palindromic TFBS. We explore the hypothesis that the number of BMs plays an important role in transcriptional regulation, examining empirical data from transcriptional profiling of the CRP and ArcA regulons. We compare the power of BM counts and of full TFBS characteristics to predict induced transcriptional activity. We find that CRP BM counts have a nonlinear effect on CRP-dependent transcriptional activity and predict this activity better than full TFBS quality or location.

Bone responds with increased bone formation to mechanical loading, and the time course of bone formation after initiating mechanical loading is well characterized. However, the regulatory activities governing the loading-dependent changes in gene expression are not well understood. The goal of this study was to identify the time-dependent regulatory mechanisms that governed mechanical loading-induced gene expression in bone using a predictive bioinformatics algorithm. A standard model for bone loading in rodents was employed in which the right forelimb was loaded axially for three minutes per day, while the left forearm served as a non-loaded, contralateral control. Animals were subjected to loading sessions every day, with 24 hours between sessions. Ulnas were sampled at 11 time points, from 4 hours to 32 days after beginning loading. Using a predictive bioinformatics algorithm, we created a linear model of gene expression and identified 44 transcription factor binding motifs and 29 microRNA binding sites that were predicted to regulate gene expression across the time course. Known and novel transcription factor binding motifs were identified throughout the time course, as were several novel microRNA binding sites. These time-dependent regulatory mechanisms may be important in controlling the loading-induced bone formation process.

L-selectin plays important roles in lymphocyte homing and leukocyte rolling. Mounting evidence shows that it is involved in many disease entities including diabetes, ischemia/reperfusion injuries, inflammatory diseases, and tumor metastasis. Regulation of L-selectin at protein level has been well characterized. However, the regulation of human L-selectin transcription remains largely unknown. To address transcriptional regulation of L-selectin, we cloned 1088 bp 5' of the start codon ATG. Luciferase analysis of the serial 5' deletion mutants located the core promoter region at -288/-1. A major transcription initiation site was mapped at -115 by 5'RACE. Transcription factors Sp1, Ets1, Mzf1, Klf2, and Irf1 bind to and transactivate the L-selectin promoter. Significantly, FOXO1 binds to a FOXO1 motif, CCCTTTGG, at -87/-80, and transactivates the L-selectin promoter in a dose-dependent manner. Over-expression of a constitutive-active FOXO1 increased the endogenous L-selectin expression in Jurkat cells. We conclude that FOXO1 regulates L-selectin expression through targeting its promoter.

To further investigate the potential role of α-tocopherol in maintaining immuno-homeostasis in bovine cells (Madin-Darby bovine kidney epithelial cell line), we undertook in vitro experiments using recombinant TNF-α as an immuno-stimulant to simulate inflammation response in cells with or without α-tocopherol pre-treatment. Using microarray global-profiling and IPA (Ingenuity Pathways Analysis, Ingenuity(®) Systems, http://www.ingenuity.com) data analysis on TNF-α-induced gene perturbation in those cells, we focused on determining whether α-tocopherol treatment of normal bovine cells in a standard cell culture condition can modify cell's immune response induced by TNF-α challenge. When three datasets were filtered and compared using IPA, there were a total of 1750 genes in all three datasets for comparison, 97 genes were common in all three sets; 615 genes were common in at least two datasets; there were 261 genes unique in TNF-α challenge, 399 genes were unique in α-tocopherol treatment, and 378 genes were unique in the α-tocopherol plus TNF-α treatment. TNF-α challenge induced significant change in gene expression. Many of those genes induced by TNF-α are related to the cells immune and inflammatory responses. The results of IPA data analysis showed that α-tocopherol-pretreatment of cells modulated cell's response to TNF-α challenge. In most of the canonical pathways, α-tocopherol pretreatment showed the antagonistic effect against the TNF-α-induced pro-inflammatory responses. We concluded that α-tocopherol pre-treatment has a significant antagonistic effect that modulates the cell's response to the TNF-α challenge by altering the gene expression activities of some important signaling molecules.

This paper includes a conceptual framework for cell cycle modeling into which the experimenter can map observed data and evaluate mechanisms of cell cycle control. The basic model exhibits qualitative stability, meaning that regardless of magnitudes of system parameters its instances are guaranteed to be stable in the sense that all feasible trajectories converge to a certain trajectory. Qualitative stability can also be described by the signs of real parts of eigenvalues of the system matrix. On the biological side, the resulting model can be tuned to approximate experimental data pertaining to human fibroblast cell lines treated with ionizing radiation, with or without disabled DNA damage checkpoints. Together these properties validate a fundamental, first order systems view of cell dynamics. Classification Codes: 15A68.

Effects of high fat diet (HFD) on obesity and, subsequently, on diabetes are highly variable and modulated by genetics in both humans and rodents. In this report, we characterized the response of Goto-Kakizaki (GK) rats, a spontaneous polygenic model for lean diabetes and healthy Wistar-Kyoto (WKY) controls, to high fat feeding from weaning to 20 weeks of age. Animals fed either normal diet or HFD were sacrificed at 4, 8, 12, 16 and 20 weeks of age and a wide array of physiological measurements were made along with gene expression profiling using Affymetrix gene array chips. Mining of the microarray data identified differentially regulated genes (involved in inflammation, metabolism, transcription regulation, and signaling) in diabetic animals, as well as the response of both strains to HFD. Functional annotation suggested that HFD increased inflammatory differences between the two strains. Chronic inflammation driven by heightened innate immune response was identified to be present in GK animals regardless of diet. In addition, compensatory mechanisms by which WKY animals on HFD resisted the development of diabetes were identified, thus illustrating the complexity of diabetes disease progression.

High-throughput 'omics' data analysis via bioinformatics is one key component of the systems biology approach. The systems approach is particularly well-suited for the study of the interactions between nutrition and physiological state with tissue metabolism and functions during key life stages of organisms such as the transition from pregnancy to lactation in mammals, ie, the peripartal period. In modern dairy cows with an unprecedented genetic potential for milk synthesis, the nature of the physiologic and metabolic adaptations during the peripartal period is multifaceted and involves key tissues such as liver, adipose, and mammary. In order to understand such adaptation, we have reviewed several works performed in our and other labs. In addition, we have used a novel bioinformatics approach, Dynamic Impact Approach (DIA), in combination with partly previously published data to help interpret longitudinal biological adaptations of bovine liver, adipose, and mammary tissue to lactation using transcriptomics datasets. Use of DIA with transcriptomic data from those tissues during normal physiological adaptations and in animals fed different levels of energy prepartum allowed visualization and integration of most-impacted metabolic pathways around the time of parturition. The DIA is a suitable tool for applying the integrative systems biology approach. The ultimate goal is to visualize the complexity of the systems at study and uncover key molecular players involved in the tissue's adaptations to physiological state or nutrition.

In this paper, a new model of self-organized criticality is introduced. This model, called the gene expression paradigm, is motivated by the problem of gene expression initiation in the newly-born daughter cells after mitosis. The model is fundamentally different in dynamics and properties from the well known sand-pile paradigm. Simulation experiments demonstrate that a critical total number of proteins exists below which transcription is impossible. Above this critical threshold, the system enters the regime of self-sustained oscillations with standard deviations and periods proportional to the genes' complexities with probability one. The borderline between these two regimes is very sharp. Importantly, such a self-organization emerges without any deterministic feedback loops or external supervision, and is a result of completely random redistribution of proteins between inactive genes. Given the size of the genome, the domain of self-organized oscillatory motion is also limited by the genes' maximal complexities. Below the critical complexity, all the regimes of self-organized oscillations are self-similar and largely independent of the genes' complexities. Above the level of critical complexity, the whole-genome transcription is impossible. Again, the borderline between the domains of oscillations and quiescence is very sharp. The gene expression paradigm is an example of cellular automata with the domain of application potentially far beyond its biological context. The model seems to be simple enough for staging an experiment for verification of its remarkable properties.