Integrative analysis of SF-1 transcription factor dosage impact on genome-wide binding and gene expression regulation

Steroidogenic Factor-1 (SF-1) is a nuclear receptor that has a pivotal role in the development of adrenal glands and gonads and in the control of steroid hormone production, being also implicated in the pathogenesis of adrenocortical tumors. We have analyzed the mechanisms how SF-1 controls gene expression in adrenocortical cells and showed that it regulates different categories of genes according to its dosage. Significant correlations exist between the localization of SF-1-binding sites in chromatin under different dosage conditions and dosage-dependent regulation of gene expression. Our study revealed unexpected functional interactions between SF-1 and Neuron-Restrictive Silencer Factor/RE1-Silencing Transcription Factor (NRSF/REST), which was first characterized as a repressor of neuronal gene expression in non-neuronal tissues, in the regulation of gene expression in steroidogenic cells. When overexpressed, SF-1 reshapes the repertoire of NRSF/REST—regulated genes, relieving repression of key steroidogenic genes. These data show that NRSF/REST has a novel function in regulating gene expression in steroidogenic cells and suggest that it may have a broad role in regulating tissue-specific gene expression programs

ER-Mitochondria contact sites : a new regulator of cellular calcium flux comes into play

Endoplasmic reticulum (ER)-mitochondria membrane contacts are hotspots for calcium signaling. In this issue, Raturi et al. (2016. J. Cell Biol. http://dx.doi.org/10.1083/jcb.201512077) show that the thioredoxin TMX1 inhibits the calcium pump SERCA2b at ER-mitochondria contact sites, thereby affecting ER-mitochondrial calcium transfer and mitochondrial bioenergetics

Surveillance of the spread of avian influenza virus type A in live bird markets in Tripoli, Libya, and determination of the associated risk factors

Background and Aim: Studies on avian influenza virus (AIV) in Libya are few and limited. This study aimed to determine the presence of AIV in live bird markets (LBMs) in Tripoli and determine the risk factors associated with AIV spread. Materials and Methods: In total, 269 cloacal swabs were randomly collected from different bird species in 9 LBMs located in Tripoli and its surrounding regions. The target species were ducks, geese, local chickens, Australian chickens, Brahma chickens, turkeys, pigeons, quails, peacock broiler chicks, and pet birds. Total RNA was extracted from the swab samples and used for real-time polymerase chain reaction to detect AIV type A. Results: Of the 269 samples, 28 (10.41% of total samples) were positive for AIV type A. The LBMs with positive samples were Souq Aljumaa, Souq Alkhamees, Souq Althulatha, and Souq Tajoura. The highest percentage (35.71%) of AIV was recorded in Souq Aljumaa. Positive results for AIV type A were obtained primarily in three species of birds: Ducks (14/65; highest percentage: 21.5%), local chickens (12/98; 12.24%), and geese (2/28; 7.14%). Furthermore, the following three risk factors associated with the spread of AIV type A were identified: Time spent by breeders/vendors at the market (odds ratio [OR] = 11.181; 95% confidence interval [CI] = 3.827–32.669), methods used for disposing dead birds (OR = 2.356; 95% CI = 1.005–5.521), and last visited LBM (OR = 0.740; 95% CI = 0.580–0.944). Restricting the movement of poultry vendors from one market to another may protect against AIV spread. Conclusion: The findings of this study indicate the high risk of AIV spread in LBMs and highlight the need for continuous surveillance of LBMs across the country

Consensus-Based Data Management within Fog Computing For the Internet of Things

The Internet of Things (IoT) infrastructure forms a gigantic network of interconnected and interacting devices. This infrastructure involves a new generation of service delivery models, more advanced data management and policy schemes, sophisticated data analytics tools, and effective decision making applications. IoT technology brings automation to a new level wherein nodes can communicate and make autonomous decisions in the absence of human interventions. IoT enabled solutions generate and process enormous volumes of heterogeneous data exchanged among billions of nodes. This results in Big Data congestion, data management, storage issues and various inefficiencies. Fog Computing aims at solving the issues with data management as it includes intelligent computational components and storage closer to the data sources. Often, an IoT-enabled infrastructure is shared among many users with various requirements. Sharing resources, sharing operational costs and collective decision making (consensus) among many stakeholders is frequently neglected. This research addresses an essential requirement for adaptive, autonomous and consensus-based Fog computational solutions which are able to support distributed and in-network schemes and policies. These network schemes and policies need to meet the requirements of many users. In this work, innovative consensus-based computational solutions are investigated. These proposed solutions aim to correlate and organise data for effective management and decision making in Fog. Instead of individual decision making, the algorithms aim to aggregate several decisions into a consensus decision representing a collective agreement, benefiting from the individuals variant knowledge and meeting multiple stakeholders requirements. In order to validate the proposed solutions, hybrid research methodology is involved that includes the design of a test-bed and the execution of several experiments. In order to investigate the effectiveness of the paradigm, three experiments were designed and validated. Real-life sensor data and synthetic statistical data was collected, processed and analysed. Bayesian Machine Learning models and Analytics were used to consolidate the design and evaluate the performance of the algorithms. In the Fog environment, the first scenario tests the Aggregation by Distribution algorithm. The solution contribute in achieving a notable efficiency of data delivery obtained with a minimal loss in precision. The second scenario validates the merits of the approach in predicting the activities of high mobility IoT applications. The third scenario tests the applications related to smart home IoT. All proposed Consensus algorithms use statistical analysis to support effective decision making in Fog and enable data aggregation for optimal storage, data transmission, processing and analytics. The final results of all experiments showed that all the implemented consensus approaches surpass the individual ones in different performance terms. Formal results also showed that the paradigm is a good fit in many IoT environments and can be suitable for different scenarios when applying data analysis to correlate data with the design. Finally, the design demonstrates that Fog Computing can compete with Cloud Computing in terms of accuracy with an added preference of locality

Piles in chalk under axial loading

Chalk exhibits a viscous behavior depending on time and it presents a noticeable creep under constant load. Consequently, piles founded in chalk media may have their design greatly affected. However, the different standards of the design of deep foundations don’t take into account this viscous behavior and its effect on the pile settlement in the long term. This paper deals with the case of piles under monotonic axial loads in chalk. Two methods of predicting pile settlement are developed: the transfer curves method tz, taking into account the viscosity, and the macroelement technique

Knockdown of SF-1 and RNF31 Affects Components of Steroidogenesis, TGFβ, and Wnt/β-catenin Signaling in Adrenocortical Carcinoma Cells

The orphan nuclear receptor Steroidogenic Factor-1 (SF-1, NR5A1) is a critical regulator of development and homeostasis of the adrenal cortex and gonads. We recently showed that a complex containing E3 ubiquitin ligase RNF31 and the known SF-1 corepressor DAX-1 (NR0B1) interacts with SF-1 on target promoters and represses transcription of steroidogenic acute regulatory protein (StAR) and aromatase (CYP19) genes. To further evaluate the role of SF-1 in the adrenal cortex and the involvement of RNF31 in SF-1-dependent pathways, we performed genome-wide gene-expression analysis of adrenocortical NCI-H295R cells where SF-1 or RNF31 had been knocked down using RNA interference. We find RNF31 to be deeply connected to cholesterol metabolism and steroid hormone synthesis, strengthening its role as an SF-1 coregulator. We also find intriguing evidence of negative crosstalk between SF-1 and both transforming growth factor (TGF) β and Wnt/β-catenin signaling. This crosstalk could be of importance for adrenogonadal development, maintenance of adrenocortical progenitor cells and the development of adrenocortical carcinoma. Finally, the SF-1 gene profile can be used to distinguish malignant from benign adrenocortical tumors, a finding that implicates SF-1 in the development of malignant adrenocortical carcinoma

Mammalian Comparative Sequence Analysis of the Agrp Locus

Agouti-related protein encodes a neuropeptide that stimulates food intake. Agrp expression in the brain is restricted to neurons in the arcuate nucleus of the hypothalamus and is elevated by states of negative energy balance. The molecular mechanisms underlying Agrp regulation, however, remain poorly defined. Using a combination of transgenic and comparative sequence analysis, we have previously identified a 760 bp conserved region upstream of Agrp which contains STAT binding elements that participate in Agrp transcriptional regulation. In this study, we attempt to improve the specificity for detecting conserved elements in this region by comparing genomic sequences from 10 mammalian species. Our analysis reveals a symmetrical organization of conserved sequences upstream of Agrp, which cluster into two inverted repeat elements. Conserved sequences within these elements suggest a role for homeodomain proteins in the regulation of Agrp and provide additional targets for functional evaluation