Standard for Synthesis of Customized Peptides by Non-Ribosomal Peptide Synthetases

The purpose of this RFC is to introduce a standardized framework for the engineering of customizable non-ribosomal peptide synthetases (NRPS) and their application for in vivo and in vitro synthesis of short non-ribosomal peptides (NRPs) of user-defined sequence and structure

HiCT: High Throughput Protocols For CPE Cloning And Transformation

The purpose of this RFC is to provide instructions for a rapid and cost efficient cloning and transformation method which allows for the manufacturing of multi-fragment plasmid constructs in a parallelized manner: High Throughput Circular Extension Cloning and Transformation (HiCT). Description of construct libraries generated by the HiCT method can be found at http://2013.igem.org/Team:Heidelberg/Indigoidine. This RFC also points out further optimization strategies with regard to construct stability, reduction of transformation background and the generation of competent cells

Smoothing and slope calculation.

<p>The oxygen levels in the media of MCF-7 cells exposed to different Cisplatin concentrations (as depicted in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0131233#pone.0131233.g002" target="_blank">Fig 2G</a>) between day 2.2 and 5 (A) are smoothed (C) by replacing each time point by the average of an 11 time point-neighbourhood. The smoothing can be seen more precisely on a bigger scale as it is the case for 75–80% a.s. and day 3–3.5 by comparing the raw data (B) to the smoothed data (D). The slope of each time point (E) is then obtained by performing a linear fit of each point and 15 points on either side of it. The residual error of the fit is displayed as a grey shadow around each curve (very small in this case). The legend in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0131233#pone.0131233.g003" target="_blank">Fig 3E</a> is valid for all the subfigures, microM stands for micromolar.</p

Medium oxygen level of MCF-7 cells when exposed to different concentrations of Cisplatin over time.

<p>All the sensors are measured empty for sensor calibration between minutes 0 and 136 (A), then a sensor calibration is performed (B), followed by normalisation to the wells containing only medium (C). The whole time frame of the experiment is visible in the raw data (D): MCF-7 cell seeding at 136 min, medium change at day 1.09 and Cisplatin addition at different concentrations at day 2.1. The sensor correction and the normalisation to the conditions containing only medium are applied to all the data (E, F respectively). The time points used for sensor correction/normalisation (A, B, C) are depicted as black rectangles (D, E, F respectively). The triplicates of the sensor corrected and normalised data are averaged (G) and their standard deviation is displayed as a grey shadow around each curve. The legend at the bottom of the figure is valid for all the subfigures, microM stands for micromolar. These graphs are displayed as they are produced by the TReCCA Analyser.</p

Time-resolved IC₅₀ of MCF-7 cells exposed to Cisplatin.

<p>The oxygen level in the media of MCF-7 cells treated with different Cisplatin concentrations between days 2.2 and 5 (as depicted in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0131233#pone.0131233.g003" target="_blank">Fig 3A</a>) are fitted for each time point, as exemplified for day 2.50 (green), 2.89 (turquoise), 3.28 (blue), 3.66 (pink), 4.05 (red), 4.43 (yellow) (A). MicroM stands for micromolar. The IC_<b>50</b> over time (B) is then determined from the values of all the fits between days 2.5 and 4.7. The residual error of the fit is displayed as a grey shadow around each curve.</p

TReCCA Analyser user interface and analysis steps.

<p>Consecutive tabs are clicked through for data analysis and plotting. These include the “Analysis options” tab (A), where the analyses to be performed on the data are selected and their corresponding parameters entered, and the “Graph output” tab (B), where the graphs are visualised and can be further customised. These tabs can be seen at a higher resolution in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0131233#pone.0131233.s001" target="_blank">S1</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0131233#pone.0131233.s002" target="_blank">S2</a> Figs. All the analysis steps are summarised in the program flow chart (C). The black rectangle encloses a more precise representation of the available analysis options. The black arrows depict the succession of steps that are performed in our example of the analysis of the effect of Cisplatin on MCF-7 cells, the coloured arrows depict alternative analysis flows.</p

Time-Resolved Cell Culture Assay Analyser (TReCCA Analyser) for the Analysis of On-Line Data: Data Integration—Sensor Correction—Time-Resolved IC₅₀ Determination

<div><p>Time-resolved cell culture assays circumvent the need to set arbitrary end-points and reveal the dynamics of quality controlled experiments. However, they lead to the generation of large data sets, which can represent a complexity barrier to their use. We therefore developed the Time-Resolved Cell Culture Assay (TReCCA) Analyser program to perform standard cell assay analyses efficiently and make sophisticated in-depth analyses easily available. The functions of the program include data normalising and averaging, as well as smoothing and slope calculation, pin-pointing exact change time points. A time-resolved IC₅₀/EC₅₀ calculation provides a better understanding of drug toxicity over time and a more accurate drug to drug comparison. Finally the logarithmic sensor recalibration function, for sensors with an exponential calibration curve, homogenises the sensor output and enables the detection of low-scale changes. To illustrate the capabilities of the TReCCA Analyser, we performed on-line monitoring of dissolved oxygen in the culture media of the breast cancer cell line MCF-7 treated with different concentrations of the anti-cancer drug Cisplatin. The TReCCA Analyser is freely available at <a href="http://www.uni-heidelberg.de/fakultaeten/biowissenschaften/ipmb/biologie/woelfl/Research.html" target="_blank">www.uni-heidelberg.de/fakultaeten/biowissenschaften/ipmb/biologie/woelfl/Research.html</a>. By introducing the program, we hope to encourage more systematic use of time-resolved assays and lead researchers to fully exploit their data.</p></div

Spatial centrosome proteome of human neural cells uncovers disease-relevant heterogeneity

The centrosome provides an intracellular anchor for the cytoskeleton, regulating cell division, cell migration, and cilia formation. We used spatial proteomics to elucidate protein interaction networks at the centrosome of human induced pluripotent stem cell-derived neural stem cells (NSCs) and neurons. Centrosome-associated proteins were largely cell type-specific, with protein hubs involved in RNA dynamics. Analysis of neurodevelopmental disease cohorts identified a significant overrepresentation of NSC centrosome proteins with variants in patients with periventricular heterotopia (PH). Expressing the PH-associated mutant pre-mRNA-processing factor 6 (PRPF6) reproduced the periventricular misplacement in the developing mouse brain, highlighting missplicing of transcripts of a microtubule-associated kinase with centrosomal location as essential for the phenotype. Collectively, cell type-specific centrosome interactomes explain how genetic variants in ubiquitous proteins may convey brain-specific phenotypes