Transcriptomic response of yeast cells to ATX1 deletion under different copper levels.

BACKGROUND: Iron and copper homeostatic pathways are tightly linked since copper is required as a cofactor for high affinity iron transport. Atx1p plays an important role in the intracellular copper transport as a copper chaperone transferring copper from the transporters to Ccc2p for its subsequent insertion into Fet3p, which is required for high affinity iron transport. RESULTS: In this study, genome-wide transcriptional landscape of ATX1 deletants grown in media either lacking copper or having excess copper was investigated. ATX1 deletants were allowed to recover full respiratory capacity in the presence of excess copper in growth environment. The present study revealed that iron ion homeostasis was not significantly affected by the absence of ATX1 either at the transcriptional or metabolic levels, suggesting other possible roles for Atx1p in addition to its function as a chaperone in copper-dependent iron absorption. The analysis of the transcriptomic response of atx1∆/atx1∆ and its integration with the genetic interaction network highlighted for the first time, the possible role of ATX1 in cell cycle regulation, likewise its mammalian counterpart ATOX1, which was reported to play an important role in the copper-stimulated proliferation of non-small lung cancer cells. CONCLUSIONS: The present finding revealed the dispensability of Atx1p for the transfer of copper ions to Ccc2p and highlighted its possible role in the cell cycle regulation. The results also showed the potential of Saccharomyces cerevisiae as a model organism in studying the capacity of ATOX1 as a therapeutic target for lung cancer therapy.The authors greatly acknowledge the Turkish State Planning Organization DPT09K120520, Bogazici University Research Fund through Project No 5562 and TUBITAK through Project No 110 M692 for the financial support provided for this research.This is the final version of the article. It first appeared from BioMed Central via http://dx.doi.org/10.1186/s12864-016-2771-

Online Data Condensation for Digitalised Biopharmaceutical Processes

Efficient control of a bioprocess relies on the ability to systematically capture and represent the process dynamics of critical process parameters. Multivariate monitoring techniques in biopharmaceuticals has resulted in the generation of large amounts of data comprising real-time measurements of critical quality and performance attributes. If exploited efficiently, these can provide an opportunity for developing better control action. For this, it is important to have a comprehensive view of the critical process parameter landscape, which can only be achieved by integrating both online and offline data into a single data matrix that can then be subjected to standard data analysis protocols. However, owing to the difference in the number of readings available for variables recorded online and offline, there is a need for new methods to achieve condensation capability. This paper introduces a novel methodology for condensing online data into an offline data matrix, which performed better when compared to traditionally employed averaging and helped increase the number of variables available for representing the design space of the process. The method was also used to understand how error propagates through online data, so as to identify an interval of tolerance in online monitoring of bioprocesses

A Novel Strategy for Selection and Validation of Reference Genes in Dynamic Multidimensional Experimental Design in Yeast

<div><h3>Background</h3><p>Understanding the dynamic mechanism behind the transcriptional organization of genes in response to varying environmental conditions requires time-dependent data. The dynamic transcriptional response obtained by real-time RT-qPCR experiments could only be correctly interpreted if suitable reference genes are used in the analysis. The lack of available studies on the identification of candidate reference genes in dynamic gene expression studies necessitates the identification and the verification of a suitable gene set for the analysis of transient gene expression response.</p> <h3>Principal Findings</h3><p>In this study, a candidate reference gene set for RT-qPCR analysis of dynamic transcriptional changes in <em>Saccharomyces cerevisiae</em> was determined using 31 different publicly available time series transcriptome datasets. Ten of the twelve candidates (<em>TPI1</em>, <em>FBA1</em>, <em>CCW12</em>, <em>CDC19</em>, <em>ADH1</em>, <em>PGK1</em>, <em>GCN4</em>, <em>PDC1</em>, <em>RPS26A</em> and <em>ARF1</em>) we identified were not previously reported as potential reference genes. Our method also identified the commonly used reference genes <em>ACT1</em> and <em>TDH3</em>. The most stable reference genes from this pool were determined as <em>TPI1</em>, <em>FBA1</em>, <em>CDC19</em> and <em>ACT1</em> in response to a perturbation in the amount of available glucose and as <em>FBA1</em>, <em>TDH3</em>, <em>CCW12</em> and <em>ACT1</em> in response to a perturbation in the amount of available ammonium. The use of these newly proposed gene sets outperformed the use of common reference genes in the determination of dynamic transcriptional response of the target genes, <em>HAP4</em> and <em>MEP2</em>, in response to relaxation from glucose and ammonium limitations, respectively.</p> <h3>Conclusions</h3><p>A candidate reference gene set to be used in dynamic real-time RT-qPCR expression profiling in yeast was proposed for the first time in the present study. Suitable pools of stable reference genes to be used under different experimental conditions could be selected from this candidate set in order to successfully determine the expression profiles for the genes of interest.</p> </div

CLUSTERnGO: a user-defined modelling platform for two-stage clustering of time-series data.

MOTIVATION: Simple bioinformatic tools are frequently used to analyse time-series datasets regardless of their ability to deal with transient phenomena, limiting the meaningful information that may be extracted from them. This situation requires the development and exploitation of tailor-made, easy-to-use and flexible tools designed specifically for the analysis of time-series datasets. RESULTS: We present a novel statistical application called CLUSTERnGO, which uses a model-based clustering algorithm that fulfils this need. This algorithm involves two components of operation. Component 1 constructs a Bayesian non-parametric model (Infinite Mixture of Piecewise Linear Sequences) and Component 2, which applies a novel clustering methodology (Two-Stage Clustering). The software can also assign biological meaning to the identified clusters using an appropriate ontology. It applies multiple hypothesis testing to report the significance of these enrichments. The algorithm has a four-phase pipeline. The application can be executed using either command-line tools or a user-friendly Graphical User Interface. The latter has been developed to address the needs of both specialist and non-specialist users. We use three diverse test cases to demonstrate the flexibility of the proposed strategy. In all cases, CLUSTERnGO not only outperformed existing algorithms in assigning unique GO term enrichments to the identified clusters, but also revealed novel insights regarding the biological systems examined, which were not uncovered in the original publications. AVAILABILITY AND IMPLEMENTATION: The C++ and QT source codes, the GUI applications for Windows, OS X and Linux operating systems and user manual are freely available for download under the GNU GPL v3 license at http://www.cmpe.boun.edu.tr/content/CnG. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.This work was supported by the Turkish State Planning Organization [DPT09K120520 to B.K.]; the Bogazici University Research Fund [10A05D4 to B.K., 08A506 to B.K., 6882-12A01D5 to A.T.C.]; TUBITAK [106M444 to B.K., 110E292 to A.T.C.], Biotechnology and Biological Sciences Research Council [BRIC2.2 grant BB/K011138/1 to S.G.O.]; and EU 7th Framework Programme [BIOLEDGE Contract No: 289126 to S.G.O.].This is the final version of the article. It first appeared from Oxford University Press via http://dx.doi.org/10.1093/bioinformatics/btv53

CamOptimus: a tool for exploiting complex adaptive evolution to optimize experiments and processes in biotechnology

Multiple interacting factors affect the performance of engineered biological systems in synthetic biology projects. The complexity of these biological systems means that experimental design should often be treated as a multiparametric optimization problem. However, the available methodologies are either impractical, due to a combinatorial explosion in the number of experiments to be performed, or are inaccessible to most experimentalists due to the lack of publicly available, user-friendly software. Although evolutionary algorithms may be employed as alternative approaches to optimize experimental design, the lack of simple-to-use software again restricts their use to specialist practitioners. In addition, the lack of subsidiary approaches to further investigate critical factors and their interactions prevents the full analysis and exploitation of the biotechnological system. We have addressed these problems and, here, provide a simple-to-use and freely available graphical user interface to empower a broad range of experimental biologists to employ complex evolutionary algorithms to optimize their experimental designs. Our approach exploits a Genetic Algorithm to discover the subspace containing the optimal combination of parameters, and Symbolic Regression to construct a model to evaluate the sensitivity of the experiment to each parameter under investigation. We demonstrate the utility of this method using an example in which the culture conditions for the microbial production of a bioactive human protein are optimized. CamOptimus is available through: (https://doi.org/10.17863/CAM.10257).EU 7th Framework Programme (BIOLEDGE Contract No: 289126 to S. G. O and J. R), BBSRC (BRIC2.2 to S. G. O. and N. K. H. S.), Synthetic Biology Research Initiative Cambridge (SynBioFund to D. D., A. C. C. and J. M. L. D.

Kp1.0 : Genome-scale metabolic model for Komagataella phaffii

Kp.1.0 can also be accessible through BIOMODELS database (MODEL1703150000)

Additional file 6: of Transcriptomic response of yeast cells to ATX1 deletion under different copper levels

Intracellular and extracellular iron levels. This figure represents the intracellular (blue) and extracellular (red) iron levels in the reference and ATX1 deleted cells under copper deficient and high copper conditions. Error bars show the standard deviation. (TIF 59 kb

Online Data Condensation for Digitalised Biopharmaceutical Processes

Efficient control of a bioprocess relies on the ability to systematically capture and represent the process dynamics of critical process parameters. Multivariate monitoring techniques in biopharmaceuticals has resulted in the generation of large amounts of data comprising real-time measurements of critical quality and performance attributes. If exploited efficiently, these can provide an opportunity for developing better control action. For this, it is important to have a comprehensive view of the critical process parameter landscape, which can only be achieved by integrating both online and offline data into a single data matrix that can then be subjected to standard data analysis protocols. However, owing to the difference in the number of readings available for variables recorded online and offline, there is a need for new methods to achieve condensation capability. This paper introduces a novel methodology for condensing online data into an offline data matrix, which performed better when compared to traditionally employed averaging and helped increase the number of variables available for representing the design space of the process. The method was also used to understand how error propagates through online data, so as to identify an interval of tolerance in online monitoring of bioprocesses.the University of Cambridge – AstraZeneca Beacon Collaborative project and by the Biotechnology and Biological Sciences Research Council, Grant number: BB/L013770/1