CoryneRegNet 6.0—Updated database content, new analysis methods and novel features focusing on community demands

Post-genomic analysis techniques such as next-generation sequencing have produced vast amounts of data about micro organisms including genetic sequences, their functional annotations and gene regulatory interactions. The latter are genetic mechanisms that control a cell's characteristics, for instance, pathogenicity as well as survival and reproduction strategies. CoryneRegNet is the reference database and analysis platform for corynebacterial gene regulatory networks. In this article we introduce the updated version 6.0 of CoryneRegNet and describe the updated database content which includes, 6352 corynebacterial regulatory interactions compared with 4928 interactions in release 5.0 and 3235 regulations in release 4.0, respectively. We also demonstrate how we support the community by integrating analysis and visualization features for transiently imported custom data, such as gene regulatory interactions. Furthermore, with release 6.0, we provide easy-to-use functions that allow the user to submit data for persistent storage with the CoryneRegNet database. Thus, it offers important options to its users in terms of community demands. CoryneRegNet is publicly available at http://www.coryneregnet.de

Modeling the Impact of Future Climate on Drainage Infrastructures

Research has shown a potential 20% increase in future heavy and extreme precipitation events over the Midwestern States. Drainage infrastructures designed using current design conditions may not be able to convey projected runoffs resulting in flooding and damage to infrastructure. The objective of this paper is to determine the effects of future climate variability on culvert selections in a southwest South Dakota watershed. The scope of the study was defined through a comprehensive literature review. Future climate events were based on a 20% increase in current annual precipitation over the Upper White River Subbasin Watershed. A portion of the White River was modeled to obtain simulated current and future peak discharges for a 10, 25, 50, and 100 year return period using ArcGIS and HEC-HMS. A previously washed out 12 foot CMP culvert on BIA-route 32 was analyzed under each specified return period, using HY-8 and Hydraflow Express, to verify culvert performance. This was compared to the capacity of the current 12 foot x 12 foot – side by side – box culvert following the same procedure. Results indicated the 12 foot CMP culvert was underdesigned for the current 25 year return period; intuitively was also not able to convey the future 25 year return period. The 25 year return period was the main focus of the study because BIA-Route 32 is classified as local and street road (ADT \u3e 100) with a minimum design return period of 25 year precipitation event (SDDOT, 2013). Compared to the 12 foot x 12 foot –side by side xix –box culvert which was able to convey the current 25 and 50 year return periods, but was unable to convey the projected future 25 year return period. The 12 foot x 12 foot – side by side – box culvert being able to convey the current but not the future peak discharges was an indication of future climate having a possible effect on culvert design

Reliable transfer of transcriptional gene regulatory networks between taxonomically related organisms

Baumbach J, Rahmann S, Tauch A. Reliable transfer of transcriptional gene regulatory networks between taxonomically related organisms. BMC Systems Biology. 2009;3(1):8.Background: Transcriptional regulation of gene activity is essential for any living organism. Transcription factors therefore recognize specific binding sites within the DNA to regulate the expression of particular target genes. The genome-scale reconstruction of the emerging regulatory networks is important for biotechnology and human medicine but cost-intensive, time-consuming, and impossible to perform for any species separately. By using bioinformatics methods one can partially transfer networks from well-studied model organisms to closely related species. However, the prediction quality is limited by the low level of evolutionary conservation of the transcription factor binding sites, even within organisms of the same genus. Results: Here we present an integrated bioinformatics workflow that assures the reliability of transferred gene regulatory networks. Our approach combines three methods that can be applied on a large-scale: re-assessment of annotated binding sites, subsequent binding site prediction, and homology detection. A gene regulatory interaction is considered to be conserved if (1) the transcription factor, (2) the adjusted binding site, and (3) the target gene are conserved. The power of the approach is demonstrated by transferring gene regulations from the model organism Corynebacterium glutamicum to the human pathogens C. diphtheriae, C. jeikeium, and the biotechnologically relevant C. efficiens. For these three organisms we identified reliable transcriptional regulations for similar to 40% of the common transcription factors, compared to similar to 5% for which knowledge was available before. Conclusion: Our results suggest that trustworthy genome-scale transfer of gene regulatory networks between organisms is feasible in general but still limited by the level of evolutionary conservation

Gαq, Gγ1 and Plc21C control Drosophila body fat storage.

Adaptive mobilization of body fat is essential for energy homeostasis in animals. In insects, the adipokinetic hormone (Akh) systemically controls body fat mobilization. Biochemical evidence supports that Akh signals via a G protein-coupled receptor (GPCR) called Akh receptor (AkhR) using cyclic-AMP (cAMP) and Ca2+ second messengers to induce storage lipid release from fat body cells. Recently, we provided genetic evidence that the intracellular calcium (iCa2+) level in fat storage cells controls adiposity in the fruit fly Drosophila melanogaster. However, little is known about the genes, which mediate Akh signalling downstream of the AkhR to regulate changes in iCa2+. Here, we used thermogenetics to provide in vivo evidence that the GPCR signal transducers G protein α q subunit (Gαq), G protein γ1 (Gγ1) and Phospholipase C at 21C (Plc21C) control cellular and organismal fat storage in Drosophila. Transgenic modulation of Gαq, Gγ1 and Plc21C affected the iCa2+ of fat body cells and the expression profile of the lipid metabolism effector genes midway and brummer, which results in severely obese or lean flies. Moreover, functional impairment of Gαq, Gγ1 and Plc21C antagonised Akh-induced fat depletion. This study characterizes Gαq, Gγ1 and Plc21C as anti-obesity genes and supports the model that Akh employs the Gαq/Gγ1/Plc21C module of iCa2+ control to regulate lipid mobilization in adult Drosophila

One-Component Order Parameter in URu2_2Si2_2 Uncovered by Resonant Ultrasound Spectroscopy and Machine Learning

The unusual correlated state that emerges in URu$_2$Si$_2$ below T$_{HO}$ = 17.5 K is known as "hidden order" because even basic characteristics of the order parameter, such as its dimensionality (whether it has one component or two), are "hidden". We use resonant ultrasound spectroscopy to measure the symmetry-resolved elastic anomalies across T$_{HO}$. We observe no anomalies in the shear elastic moduli, providing strong thermodynamic evidence for a one-component order parameter. We develop a machine learning framework that reaches this conclusion directly from the raw data, even in a crystal that is too small for traditional resonant ultrasound. Our result rules out a broad class of theories of hidden order based on two-component order parameters, and constrains the nature of the fluctuations from which unconventional superconductivity emerges at lower temperature. Our machine learning framework is a powerful new tool for classifying the ubiquitous competing orders in correlated electron systems