Identification of self‐regulatory network motifs in reverse engineering gene regulatory networks using microarray gene expression data

Gene Regulatory Networks (GRNs) are reconstructed from the microarray gene expression data through diversified computational approaches. This process ensues in symmetric and diagonal interaction of gene pairs that cannot be modelled as direct activation, inhibition, and self‐regulatory interactions. The values of gene co‐expressions could help in identifying co‐regulations among them. The proposed approach aims at computing the differences in variances of co‐expressed genes rather than computing differences in values of mean expressions across experimental conditions. It adopts multivariate co‐variances using principal component analysis (PCA) to predict an asymmetric and non‐diagonal gene interaction matrix, to select only those gene pair interactions that exhibit the maximum variances in gene regulatory expressions. The asymmetric gene regulatory interactions help in identifying the controlling regulatory agents, thus lowering the false positive rate by minimizing the connections between previously unlinked network components. The experimental results on real as well as in silico datasets including time‐series RTX therapy, Arabidopsis thaliana, DREAM‐3, and DREAM‐8 datasets, in comparison with existing state‐of‐the‐art approaches demonstrated the enhanced performance of the proposed approach for predicting positive and negative feedback loops and self‐regulatory interactions. The generated GRNs hold the potential in determining the real nature of gene pair regulatory interactions.Inspec keywords: molecular biophysics, principal component analysis, genetics, biology computing, reverse engineeringOther keywords: controlling regulatory agents, interacting genes, unlinked network components, self‐regulatory interactions, gene pair regulatory interactions, self‐regulatory network motifs, reverse engineering gene regulatory networks, microarray gene expression data, diversified computational approaches, symmetric interaction, diagonal interaction, gene pairs, gene co‐expressions, co‐expressed genes, mean expressions, gene regulatory expressions, asymmetric gene regulatory interactions     

Identification and analysis of circRNA–miRNA–mRNA regulatory network in hepatocellular carcinoma

This study was to identify important circRNA–miRNA–mRNA (ceRNAs) regulatory mechanisms in hepatocellular carcinoma (HCC). The circRNA dataset {"type":"entrez-geo","attrs":{"text":"GSE97332","term_id":"97332"}}GSE97332 and miRNA dataset {"type":"entrez-geo","attrs":{"text":"GSE57555","term_id":"57555"}}GSE57555 were used for analyses. Functional enrichment analysis for miRNA and target gene was conducted using cluster Profiler. Survival analysis was conducted through R package Survival. The ceRNAs and drug–gene interaction networks were constructed. The ceRNAs network contained five miRNAs including hsa‐miR‐25‐3p, hsa‐miR‐3692‐5p, hsa‐miR‐4270, hsa‐miR‐331‐3p, and hsa‐miR‐125a‐3p. Among the network, hsa‐miR‐25‐3p targeted the most genes, hsa‐miR‐3692‐5p and hsa‐miR‐4270 were targeted by more circRNAs than other miRNAs, hsa‐circ‐0034326 and hsa‐circ‐0011950 interacted with three miRNAs. Furthermore, target genes, including NRAS, ITGA5, SLC7A1, SEC14L2, SLC12A5, and SMAD2 were obtained in drug–gene interaction network. Survival analysis showed NRAS, ITGA5, SLC7A1, SEC14L2, SLC12A5, and SMAD2 were significantly associated with prognosis of HCC. NRAS, ITGA5, and SMAD2 were significantly enriched in proteoglycans in cancer. Moreover, hsa‐circ‐0034326 and hsa‐circ‐0011950 might function as ceRNAs to play key roles in HCC. Furthermore, miR‐25‐3p, miR‐3692‐5p, and miR‐4270 might be significant for HCC development. NRAS, ITGA5, SEC14L2, SLC12A5, and SMAD2 might be prognostic factors for HCC patients via proteoglycans in cancer pathway. Taken together, the findings will provide novel insight into pathogenesis, selection of therapeutic targets and prognostic factors for HCC.Inspec keywords: cancer, cellular biophysics, patient diagnosis, bioinformatics, tumours, biochemistry, molecular biophysics, genetics, drugs, RNAOther keywords: ITGA5, SMAD2, hsa‐circ‐0034326, SEC14L2, SLC12A5, target gene, survival analysis, drug–gene interaction network, miRNAs, hsa‐miR‐25‐3p, hsa‐miR‐3692‐5p, hsa‐miR‐4270, hsa‐miR‐331‐3p, hsa‐miR‐125a‐3p, hsa‐circ‐0011950, SLC7A1, pathogenesis, therapeutic targets, prognostic factors, circRNA‐miRNA‐mRNA regulatory network, current 125.0 A     

Regulatory network motifs and hotspots of cancer genes in a mammalian cellular signalling network

Mutations or overexpression of signalling genes can result in cancer development and metastasis. In this study, we manually assembled a human cellular signalling network and developed a robust bioinformatics strategy for extracting cancer-associated single nucleotide polymorphisms (SNPs) using expressed sequence tags (ESTs). We then investigated the relationships of cancer-associated genes [cancer-associated SNP genes, known as cancer genes (CG) and cell mobility genes (CMGs)] in a signalling network context. Through a graph-theory-based analysis, we found that CGs are significantly enriched in network hub proteins and cancer-associated genes are significantly enriched or depleted in some particular network motif types. Furthermore, we identified a substantial number of hotspots, the three- and four-node network motifs in which all nodes are either CGs or CMGs. More importantly, we uncovered that CGs are enriched in the convergent target nodes of most network motifs, although CMGs are enriched in the source nodes of most motifs. These results have implications for the foundations of the regulatory mechanisms of cancer development and metastasis.     

DDA提高结核杆菌组合DNA疫苗免疫效果的研究

将编码结核杆菌的三种抗原Ag85B,MPT64,MPT83的基因片段分别插入到真核表达载体中,混合后作为组合疫苗免疫小鼠,DDA作为佐剂提高了三价疫苗的免疫原性和免疫保护效果.添加DDA后Ag85B,MPT64,MPT83抗原特异的IFN-γ的含量分别为(265.37±79.2)U/ml,(185.31±58.3)U/ml,(108.13±54.4)U/ml,分别比非佐剂组高16U/ml,45U/ml,2 U/ml.IL-4的含量在各组中相差不多,仅为纳克级.攻毒后细菌计数结果显示,肺脏和脾脏的载菌量在添加佐剂的三价组中降低了三个数量级,都达到了未加佐剂组相应脏器的一半菌量.病理切片显示结果与载菌量一致,添加佐剂组肺部淋巴细胞相对较少,巨噬细胞增多.因此,DDA作为佐剂提高了组合疫苗的免疫效果.     

基于局部网络拓扑特征的鲁棒节点定位算法

针对无线传感器网络的较大测距误差严重影响定位算法精度和鲁棒性的问题,利用节点均匀部署网络的拓扑特征,提出了一种基于局部网络拓扑特征的鲁棒节点定位算法(LFLS算法).该算法通过构建节点测距高估粗差阈值参数和测距低估粗差阈值参数,在对未知节点1跳测距数据集进行粗差识别及剔除等预处理滤波的基础上,使用高斯加权最小二乘定位算法实现节点定位.仿真结果表明,基于局部网络拓扑特征的鲁棒节点定位算法的定位精度明显优于未采用局部网络拓扑特征进行粗差预处理的加权最小二乘定位算法,其中粗差测距直接相关节点的定位精度改进尤为明显.     

Identification of static load characteristics based on measurements in medium-voltage distribution network

The authors deal with static load (SL) characteristics (P-V and Q-V characteristics) of a medium-voltage distribution network derived from field measurements. The experiments have been performed by changing the tap position of an on-load tap-changing transformer in different seasons, different week days and day periods. P-V and Q-V load characteristics are divided into two classes, transient and steady-state characteristics. The differences between them are discussed and quantified. Both classes are mathematically described by polynomial, linear and exponential SL models. Model parameters are obtained by curve fitting using a least square method. The adequacy of developed load models is critically assessed and discussed. All the static characteristics are grouped according to seasons and day periods and are mutually compared. Finally, the parameters of the SL models for residential load without electrical heating are suggested for different seasons.     

Direct voltammetry and catalysis with Mycobacterium tuberculosis catalase-peroxidase, peroxidases, and catalase in lipid films.

Stable films of dimyristoylphosphatidylcholine and M. tuberculosis catalase-peroxidase (KatG), several peroxidases, myoglobin, and catalase showed reversible FeIII/FeII voltammetry on pyrolytic graphite electrodes and catalytic current for hydrogen peroxide and oxygen. Amperometric responses for these films to H2O2 at 0 V are likely to contain significant contributions from catalytic reduction of oxygen produced during the catalytic cycles. Relative apparent turnover rates at pH 6 based on steady-state currents at 0 V versus SCE in the presence of H2O2 were in the order horseradish peroxidase > cytochrome c peroxidase (CcP) > soybean peroxidase > myoglobin > KatG > catalase. Lower currents for the very efficient peroxide scavengers KatG and catalase may be related to the instability of their compounds I in the presence of H2O2. KatG catalyzed the electrochemical reduction of oxygen more efficiently than catalase and CcP but less efficiently than the other peroxidases. DMPC films incorporating glucose oxidase and peroxidases gave good analytical responses to glucose, demonstrating the feasibility of dual enzyme-lipid films for biosensor fabrication.     

Therapeutic aerosol bioengineering of targeted, inhalable microparticle formulations to treat Mycobacterium tuberculosis (MTb)

Therapeutic aerosol bioengineering (TAB) of Mycobacterium tuberculosis (MTb) therapies using inhalable microparticles offers a unique opportunity to target drugs to the site of infection in the alveolar macrophages, thereby increasing dosing in the lungs and limiting systemic exposure to often toxic drugs. Previous work by us used sophisticated, high content analysis to design the optimal poly(lactide-co-glycolic) acid (PLGA) microparticle for delivery of drugs to alveolar macrophages. Herein, we applied this technology to three different anti-MTb drugs. These formulations were then tested for encapsulation efficiency, drug-release, in vitro killing against MTb and aerosol performance. Methods for encapsulating each of the drugs in the PLGA microparticles were successfully developed and found to be capable of controlling the release of the drug for up to 4 days. The efficacy of each of the encapsulated anti-MTb drugs was maintained and in some cases enhanced post-encapsulation. A method of processing these drug-loaded microparticles for inhalation using standard dry powder inhaler devices was successfully developed that enabled a very high respirable dose of the drug to be delivered from a simple dry powder inhaler device. Overall, TAB offers unique opportunities to more effectively treat MTb with many potential clinical and economic benefits resulting.     

Deciphering the expression dynamics of ANGPTL8 associated regulatory network in insulin resistance using formal modelling approaches

ANGPTL8 is a recently identified novel hormone which regulates both glucose and lipid metabolism. The increase in ANGPTL8 during compensatory insulin resistance has been recently reported to improve glucose tolerance and a part of cytoprotective metabolic circuit. However, the exact signalling entities and dynamics involved in this process have remained elusive. Therefore, the current study was conducted with a specific aim to model the regulation of ANGPTL8 with emphasis on its role in improving glucose tolerance during insulin resistance. The main contribution of this study is the construction of a discrete model (based on kinetic logic of René Thomas) and its equivalent Stochastic Petri Net model of ANGPTL8 associated Biological Regulatory Network (BRN) which can predict its dynamic behaviours. The predicted results of these models are in‐line with the previous experimental observations and provide comprehensive insights into the signalling dynamics of ANGPTL8 associated BRN. The authors’ results support the hypothesis that ANGPTL8 plays an important role in supplementing the insulin signalling pathway during insulin resistance and its loss can aggravate the pathogenic process by quickly leading towards Diabetes Mellitus. The results of this study have potential therapeutic implications for treatment of Diabetes Mellitus and are suggestive of its potential as a glucose‐lowering agent.Inspec keywords: molecular biophysics, biomembranes, diseases, stochastic processes, biochemistry, patient treatment, Petri nets, genetics, sugar, cellular biophysics, biology computingOther keywords: ANGPTL8 associated regulatory network, formal modelling approaches, lipid metabolism, compensatory insulin resistance, glucose tolerance, equivalent Stochastic Petri Net model, ANGPTL8 associated BRN     

Novel phage amplified multichannel series piezoelectric quartz crystal sensor for rapid and sensitive detection of Mycobacterium tuberculosis

The key factors that control the spread and mortality rate of tuberculosis (TB) are rapid detection and diagnosis. However, the current detection of Mycobacterium tuberculosis (M. tuberculosis) cannot meet the recommended requirements for clinical diagnosis in turnaround time. In this paper, the feature of phage D29 that infects M. tuberculosis and Mycobacterium smegmatis (M. smegmatis) was combined with the sensitivity of multichannel series piezoelectric quartz crystal sensor (MSPQC) to detect M. tuberculosis. The phage D29 played a role of inhibiting the growth of M. tuberculosis and M. smegmatis. M. tuberculosis is used to protect phage D29 from being killed by ferrous ammonium sulfate (FAS) and carries phage D29 into the detection medium containing M. smegmatis. The action of M. smegmatis indicated the existence state of phage D29 in the detection medium. The growth curve of M. smegmatis obtained by MSPQC indicated the state of the growth of M. tuberculosis. Therefore, M. tuberculosis in the sample could be rapidly detected by evaluating the extent of inhibiting the growth of M. smegmatis compared with the normal growth of M. smegmatis. The detection of M. tuberculosis was transformed into the detection of M. smegmatis, which is more rapid and sensitive than that of M. tuberculosis. For 10(2) cfu/mL of M. tuberculosis in clinical sample, the turnaround time was less than 30 h. Although statistical analysis showed that no significant difference existed between the results of the proposed method here and the BACTEC960 MGIT method in clinical M. tuberculosis detection, the phage amplified MSPQC (PA MSPQC) method presented here was faster and more economical.     

Exogenous re-infection and the dynamics of tuberculosis epidemics: local effects in a network model of transmission

Infection with Mycobacterium tuberculosis leads to tuberculosis (TB) disease by one of the three possible routes: primary progression after a recent infection; re-activation of a latent infection; or exogenous re-infection of a previously infected individual. Recent studies show that optimal TB control strategies may vary depending on the predominant route to disease in a specific population. It is therefore important for public health policy makers to understand the relative frequency of each type of TB within specific epidemiological scenarios. Although molecular epidemiologic tools have been used to estimate the relative contribution of recent transmission and re-activation to the burden of TB disease, it is not possible to use these techniques to distinguish between primary disease and re-infection on a population level. Current estimates of the contribution of re-infection therefore rely on mathematical models which identify the parameters most consistent with epidemiological data; these studies find that exogenous re-infection is important only when TB incidence is high. A basic assumption of these models is that people in a population are all equally likely to come into contact with an infectious case. However, theoretical studies demonstrate that the social and spatial structure can strongly influence the dynamics of infectious disease transmission. Here, we use a network model of TB transmission to evaluate the impact of non-homogeneous mixing on the relative contribution of re-infection over realistic epidemic trajectories. In contrast to the findings of previous models, our results suggest that re-infection may be important in communities where the average disease incidence is moderate or low as the force of infection can be unevenly distributed in the population. These results have important implications for the development of TB control strategies.     

Protein dynamics in iron-starved Mycobacterium tuberculosis revealed by turnover and abundance measurement using hybrid-linear ion trap-Fourier transform mass spectrometry

To study the proteome response of Mycobacterium tuberculosis H37Rv to a change in iron level, iron-starved late-log-phase cells were diluted in fresh low- and high-iron media containing [ (15)N]-labeled asparagine as the sole nitrogen source for labeling the proteins synthesized upon dilution. We determined the relative protein abundance and protein turnover in M. tuberculosis H37Rv under these two conditions. For measurements, we used a high-resolution hybrid-linear ion trap-Fourier transform mass spectrometer coupled with nanoliquid chromatography separation. While relative protein abundance analysis shows that only 5 proteins were upregulated by high iron, 24 proteins had elevated protein turnover for the cells in the high-iron medium. This suggests that protein turnover is a sensitive parameter to assess the proteome dynamics. Cluster analysis was used to explore the interconnection of protein abundance and turnover, revealing coordination of the cellular processes of protein synthesis, degradation, and secretion that determine the abundance and allocation of a protein in the cytosol and the extracellular matrix of the cells. Further potential utility of the approach is discussed.     

Identification model of multi-layered neural network parameters and its applications in the petroleum production

This paper creates a LM (Levenberg-Marquardt) algorithm model which is appropriate to solve the problem about weights value of feedforward neural network. On the base of this model, we provide two applications in the oilfield production. Firstly, we simulated the functional relationships between the petrophysical and electrical properties of the rock by neural networks model, and studied oil saturation. Under the precision of data is confirmed, this method can reduce the number of experiments. Secondly, we simulated the relationships between investment and income by the neural networks model, and studied invest saturation point and income growth rate. It is very significant to guide the investment decision. The research result shows that the model is suitable for the modeling and identification of nonlinear systems due to the great fit characteristic of neural network and very fast convergence speed of LM algorithm.     

Hilbert transform‐based time‐series analysis of the circadian gene regulatory network

In this work, the authors propose the Hilbert transform (HT)‐based numerical method to analyse the time series of the circadian rhythms. They demonstrate the application of HT by taking both deterministic and stochastic time series that they get from the simulation of the fruit fly model Drosophila melanogaster and show how to extract the period, construct phase response curves, determine period sensitivity of the parameters to perturbations and build Arnold tongues to identify the regions of entrainment. They also derive a phase model that they numerically simulate to capture whether the circadian time series entrains to the forcing period completely (phase locking) or only partially (phase slips) or neither. They validate the phase model, and numerics with the experimental time series forced under different temperature cycles. Application of HT to the circadian time series appears to be a promising tool to extract the characteristic information about circadian rhythms.Inspec keywords: time series, genetics, Hilbert transforms, stochastic processes, circadian rhythms, signal processing, medical signal processingOther keywords: phase model, experimental time series, circadian time series, circadian rhythms, circadian gene regulatory network, deterministic time series, stochastic time series, fruit fly model, phase response curves, period sensitivity, phase locking, phase slips, Hilbert transform, time‐series analysis, signal processing