TRANSWESD: inferring cellular networks with transitive reduction

Motivation: Distinguishing direct from indirect influences is a central issue in reverse engineering of biological networks because it facilitates detection and removal of false positive edges. Transitive reduction is one approach for eliminating edges reflecting indirect effects but its use in reconstructing cyclic interaction graphs with true redundant structures is problematic

Trans-cleaving hammerhead ribozymes with tertiary stabilizing motifs: in vitro and in vivo activity against a structured viroid RNA

Trans-cleaving hammerheads with discontinuous or extended stem I and with tertiary stabilizing motifs (TSMs) have been tested previously against short RNA substrates in vitro at low Mg2+ concentration. However, the potential of these ribozymes for targeting longer and structured RNAs in vitro and in vivo has not been examined. Here, we report the in vitro cleavage of short RNAs and of a 464-nt highly structured RNA from potato spindle tuber viroid (PSTVd) by hammerheads with discontinuous and extended formats at submillimolar Mg2+. Under these conditions, hammerheads derived from eggplant latent viroid and peach latent mosaic viroid (PLMVd) with discontinuous and extended formats, respectively, where the most active. Furthermore, a PLMVd-derived hammerhead with natural TSMs showed activity in vivo against the same long substrate and interfered with systemic PSTVd infection, thus reinforcing the idea that this class of ribozymes has potential to control pathogenic RNA replicons

The C-Terminal Domain of the Novel Essential Protein Gcp Is Critical for Interaction with Another Essential Protein YeaZ of Staphylococcus aureus

Previous studies have demonstrated that the novel protein Gcp is essential for the viability of various bacterial species including Staphylococcus aureus; however, the reason why it is required for bacterial growth remains unclear. In order to explore the potential mechanisms of this essentiality, we performed RT-PCR analysis and revealed that the gcp gene (sa1854) was co-transcribed with sa1855, yeaZ (sa1856) and sa1857 genes, indicating these genes are located in the same operon. Furthermore, we demonstrated that Gcp interacts with YeaZ using a yeast two-hybrid (Y2H) system and in vitro pull down assays. To characterize the Gcp-YeaZ interaction, we performed alanine scanning mutagenesis on the residues of C-terminal segment of Gcp. We found that the mutations of the C-terminal Y317-F322 region abolished the interaction of Gcp and YeaZ, and the mutations of the D324-N329 and S332-Y336 regions alleviated Gcp binding to YeaZ. More importantly, we demonstrated that these key regions of Gcp are also necessary for the bacterial survival since these mutated Gcp could not complement the depletion of endogenous Gcp. Taken together, our data suggest that the interaction of Gcp and YeaZ may contribute to the essentiality of Gcp for S. aureus survival. Our findings provide new insights into the potential mechanisms and biological functions of this novel essential protein

Impact of work arrangements during the COVID-19 pandemic on mental health in France

• Symptoms of anxiety/depression were found in 28.8% of the participants at least once. • Unemployment and financial difficulties were associated with anxiety/depression. • Targeted mental health support could lessen mental health impact

Petri Nets with Fuzzy Logic (PNFL): Reverse Engineering and Parametrization

Background: The recent DREAM4 blind assessment provided a particularly realistic and challenging setting for network reverse engineering methods. The in silico part of DREAM4 solicited the inference of cycle-rich gene regulatory networks from heterogeneous, noisy expression data including time courses as well as knockout, knockdown and multifactorial perturbations. Methodology and Principal Findings: We inferred and parametrized simulation models based on Petri Nets with Fuzzy Logic (PNFL). This completely automated approach correctly reconstructed networks with cycles as well as oscillating network motifs. PNFL was evaluated as the best performer on DREAM4 in silico networks of size 10 with an area under the precision-recall curve (AUPR) of 81%. Besides topology, we inferred a range of additional mechanistic details with good reliability, e.g. distinguishing activation from inhibition as well as dependent from independent regulation. Our models also performed well on new experimental conditions such as double knockout mutations that were not included in the provided datasets. Conclusions: The inference of biological networks substantially benefits from methods that are expressive enough to deal with diverse datasets in a unified way. At the same time, overly complex approaches could generate multiple different models that explain the data equally well. PNFL appears to strike the balance between expressive power and complexity. This also applies to the intuitive representation of PNFL models combining a straightforward graphical notation with colloquial fuzzy parameters

Transcriptome changes in age-related macular degeneration

Age-related macular degeneration (AMD) is a debilitating, common cause of visual impairment. While the last decade has seen great progress in understanding the pathophysiology of AMD, the molecular changes that occur in eyes with AMD are still poorly understood. In the current issue of Genome Medicine, Newman and colleagues present the first systematic transcriptional profile analysis of AMD-affected tissues, providing a comprehensive set of expression data for different regions (macula versus periphery), tissues (retina versus retinal pigment epithelium (RPE)/choroid), and disease state (control versus early or advanced AMD). Their findings will serve as a foundation for additional systems-level research into the pathogenesis of this blinding disease

Integrative inference of gene-regulatory networks in Escherichia coli using information theoretic concepts and sequence analysis

<p>Abstract</p> <p>Background</p> <p>Although <it>Escherichia coli </it>is one of the best studied model organisms, a comprehensive understanding of its gene regulation is not yet achieved. There exist many approaches to reconstruct regulatory interaction networks from gene expression experiments. Mutual information based approaches are most useful for large-scale network inference.</p> <p>Results</p> <p>We used a three-step approach in which we combined gene regulatory network inference based on directed information (DTI) and sequence analysis. DTI values were calculated on a set of gene expression profiles from 19 time course experiments extracted from the Many Microbes Microarray Database. Focusing on influences between pairs of genes in which one partner encodes a transcription factor (TF) we derived a network which contains 878 TF - gene interactions of which 166 are known according to RegulonDB. Afterward, we selected a subset of 109 interactions that could be confirmed by the presence of a phylogenetically conserved binding site of the respective regulator. By this second step, the fraction of known interactions increased from 19% to 60%. In the last step, we checked the 44 of the 109 interactions not yet included in RegulonDB for functional relationships between the regulator and the target and, thus, obtained ten TF - target gene interactions. Five of them concern the regulator LexA and have already been reported in the literature. The remaining five influences describe regulations by Fis (with two novel targets), PhdR, PhoP, and KdgR. For the validation of our approach, one of them, the regulation of lipoate synthase (LipA) by the pyruvate-sensing pyruvate dehydrogenate repressor (PdhR), was experimentally checked and confirmed.</p> <p>Conclusions</p> <p>We predicted a set of five novel TF - target gene interactions in <it>E. coli</it>. One of them, the regulation of <it>lipA </it>by the transcriptional regulator PdhR was validated experimentally. Furthermore, we developed DTInfer, a new R-package for the inference of gene-regulatory networks from microarrays using directed information.</p

Strategic Learning for Active, Adaptive, and Autonomous Cyber Defense

The increasing instances of advanced attacks call for a new defense paradigm that is active, autonomous, and adaptive, named as the \texttt{`3A'} defense paradigm. This chapter introduces three defense schemes that actively interact with attackers to increase the attack cost and gather threat information, i.e., defensive deception for detection and counter-deception, feedback-driven Moving Target Defense (MTD), and adaptive honeypot engagement. Due to the cyber deception, external noise, and the absent knowledge of the other players' behaviors and goals, these schemes possess three progressive levels of information restrictions, i.e., from the parameter uncertainty, the payoff uncertainty, to the environmental uncertainty. To estimate the unknown and reduce uncertainty, we adopt three different strategic learning schemes that fit the associated information restrictions. All three learning schemes share the same feedback structure of sensation, estimation, and actions so that the most rewarding policies get reinforced and converge to the optimal ones in autonomous and adaptive fashions. This work aims to shed lights on proactive defense strategies, lay a solid foundation for strategic learning under incomplete information, and quantify the tradeoff between the security and costs.Comment: arXiv admin note: text overlap with arXiv:1906.1218

Rhodium Nanoparticle Shape Dependence in the Reduction of NO by CO

The shape dependence of the catalytic reduction of nitric oxide by carbon monoxide on rhodium nanopolyhedra and nanocubes was studied from 230 to 270 degrees C. The nanocubes are found to exhibit higher turnover frequency and lower activation energy than the nanopolyhedra. These trends are compared to previous studies on Rh single crystals.Chemistry, PhysicalSCI(E)EI21ARTICLE3-4317-32213

A Common Complement C3 Variant Is Associated with Protection against Wet Age-Related Macular Degeneration in a Japanese Population

博士（医学）神戸大