The complexity of silk under the spotlight of synthetic biology.

Biochem Soc Trans. 2016 Aug 15;44(4):1151-7

Authors: Vollrath F

Abstract
For centuries silkworm filaments have been the focus of R&D innovation centred on textile manufacture with high added value. Most recently, silk research has focused on more fundamental issues concerning bio-polymer structure-property-function relationships. This essay outlines the complexity and fundamentals of silk spinning, and presents arguments for establishing this substance as an interesting and important subject at the interface of systems biology (discovery) and synthetic biology (translation). It is argued that silk is a generic class of materials where each type of silk presents a different embodiment of emergent properties that combine genetically determined (anticipatory) and environmentally responsive components. In spiders' webs the various silks have evolved to form the interactive components of an intricate fabric that provides an extended phenotype to the spider's body morphology.

PMID: 27528763 [PubMed - in process]

The gain and loss of long noncoding RNA associated-competing endogenous RNAs in prostate cancer.

Oncotarget. 2016 Aug 9;

Authors: Liu D, Yu X, Wang S, Dai E, Jiang L, Wang J, Yang Q, Yang F, Zhou S, Jiang W

Abstract
Prostate cancer (PC) is one of the most common solid tumors in men. However, the molecular mechanism of PC remains unclear. Numerous studies have demonstrated that long noncoding RNA (lncRNA) can act as microRNA (miRNA) sponge, one type of competing endogenous RNAs (ceRNAs), which offers a novel viewpoint to elucidate the mechanisms of PC. Here, we proposed an integrative systems biology approach to infer the gain and loss of ceRNAs in PC. First, we re-annotated exon microarray data to obtain lncRNA expression profiles of PC. Second, by integrating mRNA and miRNA expression, as well as miRNA targets, we constructed lncRNA-miRNA-mRNA ceRNA networks in cancer and normal samples. The lncRNAs in these two ceRNA networks tended to have a longer transcript length and cover more exons than the lncRNAs not involved in ceRNA networks. Next, we further extracted the gain and loss ceRNA networks in PC. We found that the gain ceRNAs in PC participated in cell cycle, and the loss ceRNAs in PC were associated with metabolism. We also identified potential prognostic ceRNA pairs such as MALAT1-EGR2 and MEG3-AQP3. Finally, we inferred a novel mechanism of known drugs, such as cisplatin, for the treatment of PC through gain and loss ceRNA networks. The potential drugs such as 1,2,6-tri-O-galloyl-beta-D-glucopyranose (TGGP) could modulate lncRNA-mRNA competing relationships, which may uncover new strategy for treating PC. In summary, we systematically investigated the gain and loss of ceRNAs in PC, which may prove useful for identifying potential biomarkers and therapeutics for PC.

PMID: 27528026 [PubMed - as supplied by publisher]

Validation of a network-based strategy for the optimization of combinatorial target selection in breast cancer therapy: siRNA knockdown of network targets in MDA-MB-231 cells as an in vitro model for inhibition of tumor development.

Oncotarget. 2016 Aug 4;

Authors: Tilli TM, Carels N, Tuszynski JA, Pasdar M

Abstract
Network-based strategies provided by systems biology are attractive tools for cancer therapy. Modulation of cancer networks by anticancer drugs may alter the response of malignant cells and/or drive network re-organization into the inhibition of cancer progression. Previously, using systems biology approach and cancer signaling networks, we identified top-5 highly expressed and connected proteins (HSP90AB1, CSNK2B, TK1, YWHAB and VIM) in the invasive MDA-MB-231 breast cancer cell line. Here, we have knocked down the expression of these proteins, individually or together using siRNAs. The transfected cell lines were assessed for in vitro cell growth, colony formation, migration and invasion relative to control transfected MDA-MB-231, the non-invasive MCF-7 breast carcinoma cell line and the non-tumoral mammary epithelial cell line MCF-10A. The knockdown of the top-5 upregulated connectivity hubs successfully inhibited the in vitro proliferation, colony formation, anchorage independence, migration and invasion in MDA-MB-231 cells; with minimal effects in the control transfected MDA-MB-231 cells or MCF-7 and MCF-10A cells. The in vitro validation of bioinformatics predictions regarding optimized multi-target selection for therapy suggests that protein expression levels together with protein-protein interaction network analysis may provide an optimized combinatorial target selection for a highly effective anti-metastatic precision therapy in triple-negative breast cancer. This approach increases the ability to identify not only druggable hubs as essential targets for cancer survival, but also interactions most susceptible to synergistic drug action. The data provided in this report constitute a preliminary step toward the personalized clinical application of our strategy to optimize the therapeutic use of anti-cancer drugs.

PMID: 27527857 [PubMed - as supplied by publisher]

AID/APOBEC-network reconstruction identifies pathways associated with survival in ovarian cancer.

BMC Genomics. 2016;17(1):643

Authors: Svoboda M, Meshcheryakova A, Heinze G, Jaritz M, Pils D, Castillo-Tong DC, Hager G, Thalhammer T, Jensen-Jarolim E, Birner P, Braicu I, Sehouli J, Lambrechts S, Vergote I, Mahner S, Zimmermann P, Zeillinger R, Mechtcheriakova D

Abstract
BACKGROUND: Building up of pathway-/disease-relevant signatures provides a persuasive tool for understanding the functional relevance of gene alterations and gene network associations in multifactorial human diseases. Ovarian cancer is a highly complex heterogeneous malignancy in respect of tumor anatomy, tumor microenvironment including pro-/antitumor immunity and inflammation; still, it is generally treated as single disease. Thus, further approaches to investigate novel aspects of ovarian cancer pathogenesis aiming to provide a personalized strategy to clinical decision making are of high priority. Herein we assessed the contribution of the AID/APOBEC family and their associated genes given the remarkable ability of AID and APOBECs to edit DNA/RNA, and as such, providing tools for genetic and epigenetic alterations potentially leading to reprogramming of tumor cells, stroma and immune cells.
RESULTS: We structured the study by three consecutive analytical modules, which include the multigene-based expression profiling in a cohort of patients with primary serous ovarian cancer using a self-created AID/APOBEC-associated gene signature, building up of multivariable survival models with high predictive accuracy and nomination of top-ranked candidate/target genes according to their prognostic impact, and systems biology-based reconstruction of the AID/APOBEC-driven disease-relevant mechanisms using transcriptomics data from ovarian cancer samples. We demonstrated that inclusion of the AID/APOBEC signature-based variables significantly improves the clinicopathological variables-based survival prognostication allowing significant patient stratification. Furthermore, several of the profiling-derived variables such as ID3, PTPRC/CD45, AID, APOBEC3G, and ID2 exceed the prognostic impact of some clinicopathological variables. We next extended the signature-/modeling-based knowledge by extracting top genes co-regulated with target molecules in ovarian cancer tissues and dissected potential networks/pathways/regulators contributing to pathomechanisms. We thereby revealed that the AID/APOBEC-related network in ovarian cancer is particularly associated with remodeling/fibrotic pathways, altered immune response, and autoimmune disorders with inflammatory background.
CONCLUSIONS: The herein study is, to our knowledge, the first one linking expression of entire AID/APOBECs and interacting genes with clinical outcome with respect to survival of cancer patients. Overall, data propose a novel AID/APOBEC-derived survival model for patient risk assessment and reconstitute mapping to molecular pathways. The established study algorithm can be applied further for any biologically relevant signature and any type of diseased tissue.

PMID: 27527602 [PubMed - as supplied by publisher]

The T helper type 17/regulatory T cell paradigm in pregnancy.

Immunology. 2016 May;148(1):13-21

Authors: Figueiredo AS, Schumacher A

Abstract
T helper type 17 (Th17) and regulatory T (Treg) cells are active players in the establishment of tolerance and defence. These attributes of the immune system enmesh to guarantee the right level of protection. The healthy immune system, on the one hand, recognizes and eliminates dangerous non-self pathogens and, on the other hand, protects the healthy self. However, there are circumstances where this fine balance is disrupted. In fact, in situations such as in pregnancy, the foreign fetal antigens challenge the maternal immune system and Treg cells will dominate Th17 cells to guarantee fetal survival. In other situations such as autoimmunity, where the Th17 responses are often overwhelming, the immune system shifts towards an inflammatory profile and attacks the healthy tissue from the self. Interestingly, autoimmune patients have meliorating symptoms during pregnancy. This connects with the antagonist role of Th17 and Treg cells, and their specific profiles during these two immune challenging situations. In this review, we put into perspective the Th17/Treg ratio during pregnancy and autoimmunity, as well as in pregnant women with autoimmune conditions. We further review existing systems biology approaches that study specific mechanisms of these immune cells using mathematical modelling and we point out possible future directions of investigation. Understanding what maintains or disrupts the balance between these two opponent yet reciprocal cells in healthy physiological settings, sheds light into the development of innovative pharmacological approaches to fight pregnancy loss and autoimmunity.

PMID: 26855005 [PubMed - indexed for MEDLINE]

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Systems biology insights into the meaning of the platelet's dual-receptor thrombin signaling.

J Thromb Haemost. 2016 Aug 11;

Authors: Sveshnikova AN, Balatskiy AV, Demianova AS, Shepelyuk TO, Shakhidzhanov SS, Balatskaya MN, Pichugin AV, Ataullakhanov FI, Panteleev MA

Abstract
BACKGROUND: Activation of human platelets with thrombin proceeds via two protease-activated receptors (PARs), PAR1 and PAR4, that have identical main intracellular signaling responses. Although there is evidence that they have different cleavage/inactivation kinetics (and some secondary variations in signaling), the reason for such redundancy is not clear.
METHODS: We developed a multicompartmental stochastic computational systems biology model of dual-receptor thrombin signaling in platelets to gain insight into the mechanisms and roles of PAR1 and PAR4 functioning. Experiments employing continuous flow cytometry of washed human platelets were used to validate the model and test its predictions. Activity of PAR receptors in donors was evaluated by mRNA measurement and by polymorphism sequencing.
RESULTS: While PAR1 activation produced rapid and short-lived response, signaling via PAR4 developed slowly and propagated in time. Response of the dual-receptor system was both rapid and prolonged in time. Inclusion of PAR1/PAR4 heterodimer formation promoted PAR4 signaling in the medium range of thrombin concentration (about 10 nM), with little contribution at high and low thrombin. Different dynamics and dose-dependence of procoagulant platelet formation in healthy donors was associated with individual variations in PAR1 and PAR4 activities and particularly by the Ala120Thr polymorphism in the F2RL3 gene encoding PAR4.
CONCLUSIONS: The dual-receptor combination is critical to produce a response combining three critical advantages: sensitivity to thrombin concentration, rapid onset and steady propagation; specific features of the protease-activated receptors do not allow combination of all three in a single receptor. This article is protected by copyright. All rights reserved.

PMID: 27513817 [PubMed - as supplied by publisher]

Systems metabolic engineering of Escherichia coli for the heterologous production of high value molecules-a veteran at new shores.

Curr Opin Biotechnol. 2016 Aug 8;42:178-188

Authors: Becker J, Wittmann C

Abstract
For more than fifty years, Escherichia coli has represented a remarkable success story in industrial biotechnology. Traditionally known as a producer of l-amino acids, E. coli has also entered the precious market of high-value molecules and is becoming a flexible, efficient production platform for various therapeutics, pre-biotics, nutraceuticals and pigments. This tremendous progress is enabled by systems metabolic engineering concepts that integrate systems biology and synthetic biology into the design and engineering of powerful E. coli cell factories.

PMID: 27513555 [PubMed - as supplied by publisher]

Integrative approach for inference of gene regulatory networks using lasso-based random featuring and application to psychiatric disorders.

BMC Med Genomics. 2016;9 Suppl 2:50

Authors: Kim D, Kang M, Biswas A, Liu C, Gao J

Abstract
BACKGROUND: Inferring gene regulatory networks is one of the most interesting research areas in the systems biology. Many inference methods have been developed by using a variety of computational models and approaches. However, there are two issues to solve. First, depending on the structural or computational model of inference method, the results tend to be inconsistent due to innately different advantages and limitations of the methods. Therefore the combination of dissimilar approaches is demanded as an alternative way in order to overcome the limitations of standalone methods through complementary integration. Second, sparse linear regression that is penalized by the regularization parameter (lasso) and bootstrapping-based sparse linear regression methods were suggested in state of the art methods for network inference but they are not effective for a small sample size data and also a true regulator could be missed if the target gene is strongly affected by an indirect regulator with high correlation or another true regulator.
RESULTS: We present two novel network inference methods based on the integration of three different criteria, (i) z-score to measure the variation of gene expression from knockout data, (ii) mutual information for the dependency between two genes, and (iii) linear regression-based feature selection. Based on these criterion, we propose a lasso-based random feature selection algorithm (LARF) to achieve better performance overcoming the limitations of bootstrapping as mentioned above.
CONCLUSIONS: In this work, there are three main contributions. First, our z score-based method to measure gene expression variations from knockout data is more effective than similar criteria of related works. Second, we confirmed that the true regulator selection can be effectively improved by LARF. Lastly, we verified that an integrative approach can clearly outperform a single method when two different methods are effectively jointed. In the experiments, our methods were validated by outperforming the state of the art methods on DREAM challenge data, and then LARF was applied to inferences of gene regulatory network associated with psychiatric disorders.

PMID: 27510319 [PubMed - in process]

Integrative analysis of human omics data using biomolecular networks.

Mol Biosyst. 2016 Aug 11;

Authors: Robinson JL, Nielsen J

Abstract
High-throughput '-omics' technologies have given rise to an increasing abundance of genome-scale data detailing human biology at the molecular level. Although these datasets have already made substantial contributions to a more comprehensive understanding of human physiology and diseases, their interpretation becomes increasingly cryptic and nontrivial as they continue to expand in size and complexity. Systems biology networks offer a scaffold upon which omics data can be integrated, facilitating the extraction of new and physiologically relevant information from the data. Two of the most prevalent networks that have been used for such integrative analyses of omics data are genome-scale metabolic models (GEMs) and protein-protein interaction (PPI) networks, both of which have demonstrated success among many different omics and sample types. This integrative approach seeks to unite 'top-down' omics data with 'bottom-up' biological networks in a synergistic fashion that draws on the strengths of both strategies. As the volume and resolution of high-throughput omics data continue to grow, integrative network-based analyses are expected to play an increasingly important role in their interpretation.

PMID: 27510223 [PubMed - as supplied by publisher]

An integrated global chemomics and system biology approach to analyze the mechanisms of the traditional Chinese medicinal preparation Eriobotrya japonica - Fritillaria usuriensis dropping pills for pulmonary diseases.

BMC Complement Altern Med. 2016;16:4

Authors: Tao J, Hou Y, Ma X, Liu D, Tong Y, Zhou H, Gao J, Bai G

Abstract
BACKGROUND: Traditional Chinese medicine (TCM) herbal formulae provide valuable therapeutic strategies. However, the active ingredients and mechanisms of action remain unclear for most of these formulae. Therefore, the identification of complex mechanisms is a major challenge in TCM research.
METHODS: This study used a network pharmacology approach to clarify the anti-inflammatory and cough suppressing mechanisms of the Chinese medicinal preparation Eriobotrya japonica - Fritillaria usuriensis dropping pills (ChuanbeiPipa dropping pills, CBPP). The chemical constituents of CBPP were identified by high-quality ultra-performance liquid chromatography/quadrupole time-of-flight mass spectrometry (UPLC/Q-TOF-MS), and anti-inflammatory ingredients were selected and analyzed using the PharmMapper and Kyoto Encyclopedia of Genes and Genomes (KEGG) bioinformatics websites to predict the target proteins and related pathways, respectively. Then, an RNA-sequencing (RNA-Seq) analysis was carried out to investigate the different expression of genes in the lung tissue of rats with chronic bronchitis.
RESULTS: Six main constituents affected 19 predicted pathways, including ursolic acid and oleanolic acid from Eriobotrya japonica (Thunb.) Lindl. (Eri), peiminine from Fritillaria usuriensis Maxim. (Fri), platycodigenin and polygalacic acid from Platycodon grandiflorum (Jacq.) A. DC. (Pla) and guanosine from Pinellia ternata (Thunb.) Makino. (Pin). Expression of 34 genes was significantly decreased after CBPP treatment, affecting four therapeutic functions: immunoregulation, anti-inflammation, collagen formation and muscle contraction.
CONCLUSION: The active components acted on the mitogen activated protein kinase (MAPK) pathway, transforming growth factor (TGF)-beta pathway, focal adhesion, tight junctions and the action cytoskeleton to exert anti-inflammatory effects, resolve phlegm, and relieve cough. This novel approach of global chemomics-integrated systems biology represents an effective and accurate strategy for the study of TCM with multiple components and multiple target mechanisms.

PMID: 26742634 [PubMed - indexed for MEDLINE]

PLS-Based and Regularization-Based Methods for the Selection of Relevant Variables in Non-targeted Metabolomics Data.

Front Mol Biosci. 2016;3:35

Authors: Bujak R, Daghir-Wojtkowiak E, Kaliszan R, Markuszewski MJ

Abstract
Non-targeted metabolomics constitutes a part of the systems biology and aims at determining numerous metabolites in complex biological samples. Datasets obtained in the non-targeted metabolomics studies are high-dimensional due to sensitivity of mass spectrometry-based detection methods as well as complexity of biological matrices. Therefore, a proper selection of variables which contribute into group classification is a crucial step, especially in metabolomics studies which are focused on searching for disease biomarker candidates. In the present study, three different statistical approaches were tested using two metabolomics datasets (RH and PH study). The orthogonal projections to latent structures-discriminant analysis (OPLS-DA) without and with multiple testing correction as well as the least absolute shrinkage and selection operator (LASSO) with bootstrapping, were tested and compared. For the RH study, OPLS-DA model built without multiple testing correction selected 46 and 218 variables based on the VIP criteria using Pareto and UV scaling, respectively. For the PH study, 217 and 320 variables were selected based on the VIP criteria using Pareto and UV scaling, respectively. In the RH study, OPLS-DA model built after correcting for multiple testing, selected 4 and 19 variables as in terms of Pareto and UV scaling, respectively. For the PH study, 14 and 18 variables were selected based on the VIP criteria in terms of Pareto and UV scaling, respectively. In the RH and PH study, the LASSO selected 14 and 4 variables with reproducibility between 99.3 and 100%, respectively. In the light of PLS-based models, the larger the search space the higher the probability of developing models that fit the training data well with simultaneous poor predictive performance on the validation set. The LASSO offers potential improvements over standard linear regression due to the presence of the constrain, which promotes sparse solutions. This paper is the first one to date utilizing the LASSO penalized logistic regression in untargeted metabolomics studies.

PMID: 27508208 [PubMed]

Predicting overlapping protein complexes from weighted protein interaction graphs by gradually expanding dense neighborhoods.

Artif Intell Med. 2016 Jul;71:62-9

Authors: Dimitrakopoulos C, Theofilatos K, Pegkas A, Likothanassis S, Mavroudi S

Abstract
OBJECTIVE: Proteins are vital biological molecules driving many fundamental cellular processes. They rarely act alone, but form interacting groups called protein complexes. The study of protein complexes is a key goal in systems biology. Recently, large protein-protein interaction (PPI) datasets have been published and a plethora of computational methods that provide new ideas for the prediction of protein complexes have been implemented. However, most of the methods suffer from two major limitations: First, they do not account for proteins participating in multiple functions and second, they are unable to handle weighted PPI graphs. Moreover, the problem remains open as existing algorithms and tools are insufficient in terms of predictive metrics.
METHOD: In the present paper, we propose gradually expanding neighborhoods with adjustment (GENA), a new algorithm that gradually expands neighborhoods in a graph starting from highly informative "seed" nodes. GENA considers proteins as multifunctional molecules allowing them to participate in more than one protein complex. In addition, GENA accepts weighted PPI graphs by using a weighted evaluation function for each cluster.
RESULTS: In experiments with datasets from Saccharomyces cerevisiae and human, GENA outperformed Markov clustering, restricted neighborhood search and clustering with overlapping neighborhood expansion, three state-of-the-art methods for computationally predicting protein complexes. Seven PPI networks and seven evaluation datasets were used in total. GENA outperformed existing methods in 16 out of 18 experiments achieving an average improvement of 5.5% when the maximum matching ratio metric was used. Our method was able to discover functionally homogeneous protein clusters and uncover important network modules in a Parkinson expression dataset. When used on the human networks, around 47% of the detected clusters were enriched in gene ontology (GO) terms with depth higher than five in the GO hierarchy.
CONCLUSIONS: In the present manuscript, we introduce a new method for the computational prediction of protein complexes by making the realistic assumption that proteins participate in multiple protein complexes and cellular functions. Our method can detect accurate and functionally homogeneous clusters.

PMID: 27506132 [PubMed - in process]

Targeting glycolysis in the malaria parasite Plasmodium falciparum.

FEBS J. 2016 Feb;283(4):634-46

Authors: van Niekerk DD, Penkler GP, du Toit F, Snoep JL

Abstract
UNLABELLED: Glycolysis is the main pathway for ATP production in the malaria parasite Plasmodium falciparum and essential for its survival. Following a sensitivity analysis of a detailed kinetic model for glycolysis in the parasite, the glucose transport reaction was identified as the step whose activity needed to be inhibited to the least extent to result in a 50% reduction in glycolytic flux. In a subsequent inhibitor titration with cytochalasin B, we confirmed the model analysis experimentally and measured a flux control coefficient of 0.3 for the glucose transporter. In addition to the glucose transporter, the glucokinase and phosphofructokinase had high flux control coefficients, while for the ATPase a small negative flux control coefficient was predicted. In a broader comparative analysis of glycolytic models, we identified a weakness in the P. falciparum pathway design with respect to stability towards perturbations in the ATP demand.
DATABASE: The mathematical model described here has been submitted to the JWS Online Cellular Systems Modelling Database and can be accessed at http://jjj.bio.vu.nl/database/vanniekerk1. The SEEK-study including the experimental data set is available at DOI 10.15490/seek.1.
INVESTIGATION: 56 (http://dx.doi.org/10.15490/seek.1.
INVESTIGATION: 56).

PMID: 26648082 [PubMed - indexed for MEDLINE]

In silico modeling for tumor growth visualization.

BMC Syst Biol. 2016;10(1):59

Authors: Jeanquartier F, Jean-Quartier C, Cemernek D, Holzinger A

Abstract
BACKGROUND: Cancer is a complex disease. Fundamental cellular based studies as well as modeling provides insight into cancer biology and strategies to treatment of the disease. In silico models complement in vivo models. Research on tumor growth involves a plethora of models each emphasizing isolated aspects of benign and malignant neoplasms. Biologists and clinical scientists are often overwhelmed by the mathematical background knowledge necessary to grasp and to apply a model to their own research.
RESULTS: We aim to provide a comprehensive and expandable simulation tool to visualizing tumor growth. This novel Web-based application offers the advantage of a user-friendly graphical interface with several manipulable input variables to correlate different aspects of tumor growth. By refining model parameters we highlight the significance of heterogeneous intercellular interactions on tumor progression. Within this paper we present the implementation of the Cellular Potts Model graphically presented through Cytoscape.js within a Web application. The tool is available under the MIT license at https://github.com/davcem/cpm-cytoscape and http://styx.cgv.tugraz.at:8080/cpm-cytoscape/ .
CONCLUSION: In-silico methods overcome the lack of wet experimental possibilities and as dry method succeed in terms of reduction, refinement and replacement of animal experimentation, also known as the 3R principles. Our visualization approach to simulation allows for more flexible usage and easy extension to facilitate understanding and gain novel insight. We believe that biomedical research in general and research on tumor growth in particular will benefit from the systems biology perspective.

PMID: 27503052 [PubMed - in process]

Databases and tools for constructing signal transduction networks in cancer.

BMB Rep. 2016 Aug 8;

Authors: Nam S

Abstract
Traditionally, biologists have devoted their careers to studying individual biological entities of their own interest, partly due to lack of available data regarding that entity. Now, in the field of cancer, tremendously large, high-throughput data, too complex for conventional processing methods (i.e., "big data"), has accumulated and made freely available in public data repositories. Such challenges urge biologists to inspect their biological entities of interest using novel approaches, firstly including repository data retrieval. Most of all, these revolutionary changes demand new interpretations of huge datasets, at a systems-level, by so called "systems biology." One of representative applications of systems biology is to generate a biological network from high-throughput big data, providing a global map of molecular events associated with specific phenotype changes. In this review, we introduce the repositories of cancer big data and cutting-edge systems biology tools for network generation and improved identification of therapeutic targets.

PMID: 27502015 [PubMed - as supplied by publisher]

Genotypes, Networks, Phenotypes: Moving Toward Plant Systems Genetics.

Annu Rev Cell Dev Biol. 2016 Aug 3;

Authors: Ogura T, Busch W

Abstract
One of the central goals in biology is to understand how and how much of the phenotype of an organism is encoded in its genome. Although many genes that are crucial for organismal processes have been identified, much less is known about the genetic bases underlying quantitative phenotypic differences in natural populations. We discuss the fundamental gap between the large body of knowledge generated over the past decades by experimental genetics in the laboratory and what is needed to understand the genotypeto- phenotype problem on a broader scale. We argue that systems genetics, a combination of systems biology and the study of natural variation using quantitative genetics, will help to address this problem. We present major advances in these two mostly disconnected areas that have increased our understanding of the developmental processes of flowering time control and root growth. We conclude by illustrating and discussing the efforts that have been made toward systems genetics specifically in plants. Expected final online publication date for the Annual Review of Cell and Developmental Biology Volume 32 is October 06, 2016. Please see http://www.annualreviews.org/catalog/pubdates.aspx for revised estimates.

PMID: 27501448 [PubMed - as supplied by publisher]

Natural search algorithms as a bridge between organisms, evolution, and ecology.

Proc Natl Acad Sci U S A. 2016 Aug 5;

Authors: Hein AM, Carrara F, Brumley DR, Stocker R, Levin SA

Abstract
The ability to navigate is a hallmark of living systems, from single cells to higher animals. Searching for targets, such as food or mates in particular, is one of the fundamental navigational tasks many organisms must execute to survive and reproduce. Here, we argue that a recent surge of studies of the proximate mechanisms that underlie search behavior offers a new opportunity to integrate the biophysics and neuroscience of sensory systems with ecological and evolutionary processes, closing a feedback loop that promises exciting new avenues of scientific exploration at the frontier of systems biology.

PMID: 27496324 [PubMed - as supplied by publisher]

Predicting the cell death responsiveness and sensitization of glioma cells to TRAIL and temozolomide.

Oncotarget. 2016 Aug 1;

Authors: Weyhenmeyer BC, Noonan J, Würstle ML, Lincoln FA, Johnston G, Rehm M, Murphy BM

Abstract
Genotoxic chemotherapy with temozolomide (TMZ) is a mainstay of treatment for glioblastoma (GBM); however, at best, TMZ provides only modest survival benefit to a subset of patients. Recent insight into the heterogeneous nature of GBM suggests a more personalized approach to treatment may be necessary to overcome cancer drug resistance and improve patient care. These include novel therapies that can be used both alone and with TMZ to selectively reactivate apoptosis within malignant cells. For this approach to work, reliable molecular signatures that can accurately predict treatment responsiveness need to be identified first. Here, we describe the first proof-of-principle study that merges quantitative protein-based analysis of apoptosis signaling networks with data- and knowledge-driven mathematical systems modeling to predict treatment responsiveness of GBM cell lines to various apoptosis-inducing stimuli. These include monotherapies with TMZ and TRAIL, which activate the intrinsic and extrinsic apoptosis pathways, respectively, as well as combination therapies of TMZ+TRAIL. We also successfully employed this approach to predict whether individual GBM cell lines could be sensitized to TMZ or TRAIL via the selective targeting of Bcl-2/Bcl-xL proteins with ABT-737. Our findings suggest that systems biology-based approaches could assist in personalizing treatment decisions in GBM to optimize cell death induction.

PMID: 27494880 [PubMed - as supplied by publisher]

Tetanus toxin production is triggered by the transition from amino acid consumption to peptides.

Anaerobe. 2016 Aug 1;

Authors: Licona-Cassani C, Steen JA, Zaragoza NE, Moonen G, Moutafis G, Hodson MP, Power J, Nielsen LK, Marcellin E

Abstract
Bacteria produce some of the most potent molecules known, of which many cause serious diseases such as tetanus. For prevention, billions of people and countless animals are immunised with the highly effective vaccine, industrially produced by large-scale fermentation. However, toxin production is often hampered by low yields and batch-to-batch variability. Improved productivity has been constrained by a lack of understanding of the molecular mechanisms controlling toxin production. Here we have developed a reproducible experimental framework for screening phenotypic determinants in Clostridium tetani under a process that mimics an industrial setting. We show that amino acid depletion induces production of the toxin. Using time-course transcriptomics and extracellular metabolomics to generate a 'fermentation atlas' that ascribe growth behaviour, nutrient consumption and gene expression to the fermentation phases, we found a subset of preferred amino acids. Exponential growth is characterised by the consumption of those amino acids followed by a slower exponential growth phase where peptides are consumed, and toxin is produced. The results aim at assisting in fermentation medium design towards the improvement of vaccine production yields and reproducibility. In conclusion, our work not only provides deep fermentation dynamics but represents the foundation for bioprocess design based on C. tetani physiological behaviour under industrial settings.

PMID: 27492724 [PubMed - as supplied by publisher]