Hierarchical structure and modules in the Escherichia coli transcriptional regulatory network revealed by a new top-down approach

BACKGROUND: Cellular functions are coordinately carried out by groups of genes forming functional modules. Identifying such modules in the transcriptional regulatory network (TRN) of organisms is important for understanding the structure and function of these fundamental cellular networks and essential for the emerging modular biology. So far, the global connectivity structure of TRN has not been well studied and consequently not applied for the identification of functional modules. Moreover, network motifs such as feed forward loop are recently proposed to be basic building blocks of TRN. However, their relationship to functional modules is not clear. RESULTS: In this work we proposed a top-down approach to identify modules in the TRN of E. coli. By studying the global connectivity structure of the regulatory network, we first revealed a five-layer hierarchical structure in which all the regulatory relationships are downward. Based on this regulatory hierarchy, we developed a new method to decompose the regulatory network into functional modules and to identify global regulators governing multiple modules. As a result, 10 global regulators and 39 modules were identified and shown to have well defined functions. We then investigated the distribution and composition of the two basic network motifs (feed forward loop and bi-fan motif) in the hierarchical structure of TRN. We found that most of these network motifs include global regulators, indicating that these motifs are not basic building blocks of modules since modules should not contain global regulators. CONCLUSION: The transcriptional regulatory network of E. coli possesses a multi-layer hierarchical modular structure without feedback regulation at transcription level. This hierarchical structure builds the basis for a new and simple decomposition method which is suitable for the identification of functional modules and global regulators in the transcriptional regulatory network of E. coli. Analysis of the distribution of feed forward loops and bi-fan motifs in the hierarchical structure suggests that these network motifs are not elementary building blocks of functional modules in the transcriptional regulatory network of E. coli

Exploring the Cosmic Reionization Epoch in Frequency Space: An Improved Approach to Remove the Foreground in 21 cm Tomography

Aiming to correctly restore the redshifted 21 cm signals emitted by the neutral hydrogen during the cosmic reionization processes, we re-examine the separation approaches based on the quadratic polynomial fitting technique in frequency space to investigate whether they works satisfactorily with complex foreground, by quantitatively evaluate the quality of restored 21 cm signals in terms of sample statistics. We construct the foreground model to characterize both spatial and spectral substructures of the real sky, and use it to simulate the observed radio spectra. By comparing between different separation approaches through statistical analysis of restored 21 cm spectra and corresponding power spectra, as well as their constraints on the mean halo bias $b$ and average ionization fraction $x_e$ of the reionization processes, at $z=8$ and the noise level of 60 mK we find that, although the complex foreground can be well approximated with quadratic polynomial expansion, a significant part of Mpc-scale components of the 21 cm signals (75% for $\gtrsim 6h^{-1}$ Mpc scales and 34% for $\gtrsim 1h^{-1}$ Mpc scales) is lost because it tends to be mis-identified as part of the foreground when single-narrow-segment separation approach is applied. The best restoration of the 21 cm signals and the tightest determination of $b$ and $x_e$ can be obtained with the three-narrow-segment fitting technique as proposed in this paper. Similar results can be obtained at other redshifts.Comment: 33 pages, 14 figures. Accepted for publication in Ap

Natural characteristics analysis of aircraft wing box based on finite element method and measured data

Compared with other mechanical products, aircraft structures have more rigorous requirements on flying performance, safety, reliability and service life. Based on the finite element method (FEM), the key component of the wing box model is explored in this paper, which provides a reference for the structure design and manufacture of aircraft wing box. The three-dimensional point cloud data of components are obtained by optical measurement systems, the deviation analysis between the point cloud model and the nominal model is carried out as a prerequisite, and then the natural characteristics of the model is analyzed. The results show that 99.15 % of the measured points have deviations within 0.38 mm, which verifies the accuracy of the nominal model. The first six modes are all bending modal shape, and the larger amplitude region mainly occurs in the wing ribs, which means its bending strength should be improved for structure design. Besides, the sixth-mode simultaneously result in front spar, stringer and rib bending vibration

Endothelial Differentiation Gene-1, a New Downstream Gene Is Involved in RTEF-1 Induced Angiogenesis in Endothelial Cells

Related Transcriptional Enhancer Factor-1 (RTEF-1) has been suggested to induce angiogenesis through regulating target genes. Whether RTEF-1 has a direct role in angiogenesis and what specific genes are involved in RTEF-1 driven angiogenisis have not been elucidated. We found that over-expressing RTEF-1 in Human dermal microvascular endothelial cells-1 (HMEC-1) significantly increased endothelial cell aggregation, growth and migration while the processes were inhibited by siRNA of RTEF-1. In addition, we observed that Endothelial differentiation gene-1 (Edg-1) expression was up-regulated by RTEF-1 at the transcriptional level. RTEF-1 could bind to Edg-1 promoter and subsequently induce its activity. Edg-1 siRNA significantly blocked RTEF-1-driven increases in endothelial cell aggregation in a Matrigel assay and retarded RTEF-1-induced endothelial cell growth and migration. Pertussis Toxin (PTX), a Gi/Go protein sensitive inhibitor, was found to inhibit RTEF-1 driven endothelial cell aggregation and migration. Our data demonstrates that Edg-1 is a potential target gene of RTEF-1 and is involved in RTEF-1-induced angiogenesis in endothelial cells. Gi/Go protein coupled receptor pathway plays a role in RTEF-1 driven angiogenesis in endothelial cells

Prevalence of smoking in patients with bipolar disorder, major depressive disorder and schizophrenia and their relationships with quality of life

Few studies have compared the prevalence of smoking between patients with bipolar disorder, major depressive disorder (MDD) and schizophrenia. This study examined the prevalence of smoking and its relationships with demographic and clinical characteristics, and quality of life (QOL) in patients with these psychiatric disorders. A total of 1,102 inpatients were consecutively screened. Psychopathology and QOL were measured with standardized instruments. The prevalence of current smoking in the whole sample was 16.7%; 17.5% in bipolar disorder, 10.6% in MDD and 18.5% in schizophrenia. The rates of smoking in bipolar disorder (p = 0.004, OR = 2.5, 95%CI: 1.3–4.7) and schizophrenia (p = 0.03, OR = 2.0, 95%CI: 1.06–3.8) were significantly higher than in MDD, while no difference was found between bipolar disorder and schizophrenia. Smokers had a higher mental QOL than non-smokers (p = 0.007) in MDD, but no difference was found in the other two groups. Male gender, living alone, higher personal income, older age of onset, health insurance coverage, and first episode was significantly associated with smoking in one or more diagnostic groups. Smoking appears more common in bipolar disorder and schizophrenia than in MDD in China. The figures in all disorders were lower than that reported in most of other countries

GenEpi: gene-based epistasis discovery using machine learning.

BackgroundGenome-wide association studies (GWAS) provide a powerful means to identify associations between genetic variants and phenotypes. However, GWAS techniques for detecting epistasis, the interactions between genetic variants associated with phenotypes, are still limited. We believe that developing an efficient and effective GWAS method to detect epistasis will be a key for discovering sophisticated pathogenesis, which is especially important for complex diseases such as Alzheimer's disease (AD).ResultsIn this regard, this study presents GenEpi, a computational package to uncover epistasis associated with phenotypes by the proposed machine learning approach. GenEpi identifies both within-gene and cross-gene epistasis through a two-stage modeling workflow. In both stages, GenEpi adopts two-element combinatorial encoding when producing features and constructs the prediction models by L1-regularized regression with stability selection. The simulated data showed that GenEpi outperforms other widely-used methods on detecting the ground-truth epistasis. As real data is concerned, this study uses AD as an example to reveal the capability of GenEpi in finding disease-related variants and variant interactions that show both biological meanings and predictive power.ConclusionsThe results on simulation data and AD demonstrated that GenEpi has the ability to detect the epistasis associated with phenotypes effectively and efficiently. The released package can be generalized to largely facilitate the studies of many complex diseases in the near future

Diethyl 2,6-(2,4-dichloro­phen­yl)-4,8-dioxo-2,3,6,7-tetra­hydro-1H,5H-2,3a,4a,6,7a,8a-hexa­azacyclo­penta­[def]fluorene-8b,8c-dicarboxyl­ate

The title mol­ecule, C28H28Cl4N6O6, is built up from four fused rings, viz. two nearly planar imidazole five-membered rings which adopt envelope conformations with the C=O groups at the flap position, and two triazine six-membered rings which adopt chair conformations. Each six-membered ring has a 2,4-dichloro­benzyl substituent attached to an N atom. In the mol­ecule, the two ethyl groups are each disordered between two orientations in 0.784 (16)/0.216 (16) and 0.631 (10)/0.37 (10) ratios. Weak inter­molecular C—H⋯O hydrogen bonds help to stabilize the crystal packing

Left ventricular mass and hemodynamic overload in normotensive hemodialysis patients

Left ventricular mass and hemodynamic overload in normotensive hemodialysis patients.BackgroundIt remains uncertain whether the hemodynamic parameters are important determinants of left ventricular mass (LVM) in normotensive chronic hemodialysis (NTHD) patients, as has been found in their hypertensive counterparts.MethodsForty NTHD patients (mean age, 53.7 ± 14.4 years; male/female, 18/22) without the requirement of antihypertensive drugs for at least six months were studied. Controls were 41 hypertensive hemodialysis patients (HTHD) and 46 normotensive subjects with normal renal function (NTNR). The influence of anthropometrics, cardiovascular structure and function, and volume status on LVM (by two-dimensional echocardiography) was analyzed by steps of multiple linear regression.ResultsAs compared with the NTNR and NTHD group, the HTHD group had obvious pressure and volume/flow overload, and greater LV wall thickness, chamber size and mass. In contrast, NTHD subjects had similar blood pressure, large artery function, LV chamber size and stroke volume as the NTNR subjects. However, the NTHD patients still had greater wall thickness and LVM, along with greater cardiac output, lower total peripheral resistance and lower end-systolic meridional stress to volume ratio (ESSV) than the NTNR group. LVM in the NTHD group was significantly positively related to averaged systolic blood pressure (SBPavg), body surface area, extracellular fluid (ECF), carotid intima-media thickness (IMT), aortic pulse wave velocity (PWV), and negatively related to ESSV and Kt/V. The independent significant noncardiac structural determinants of LVM in NTHD subjects were ESSV, SBPavg, PWV and SV (model r2 = 0.617, P < 0.001).ConclusionsThe NTHD patients, without significant pressure and volume overload, still had increased LVM that was partially explained by the persistent flow overload and subclinical LV dysfunction