Brain Radiation Information Data Exchange (BRIDE): integration of experimental data from low-dose ionising radiation research for pathway discovery.

BMC Bioinformatics. 2016;17(1):212

Authors: Karapiperis C, Kempf SJ, Quintens R, Azimzadeh O, Vidal VL, Pazzaglia S, Bazyka D, Mastroberardino PG, Scouras ZG, Tapio S, Benotmane MA, Ouzounis CA

Abstract
BACKGROUND: The underlying molecular processes representing stress responses to low-dose ionising radiation (LDIR) in mammals are just beginning to be understood. In particular, LDIR effects on the brain and their possible association with neurodegenerative disease are currently being explored using omics technologies.
RESULTS: We describe a light-weight approach for the storage, analysis and distribution of relevant LDIR omics datasets. The data integration platform, called BRIDE, contains information from the literature as well as experimental information from transcriptomics and proteomics studies. It deploys a hybrid, distributed solution using both local storage and cloud technology.
CONCLUSIONS: BRIDE can act as a knowledge broker for LDIR researchers, to facilitate molecular research on the systems biology of LDIR response in mammals. Its flexible design can capture a range of experimental information for genomics, epigenomics, transcriptomics, and proteomics. The data collection is available at: <bride.azurewebsites.net>.

PMID: 27170263 [PubMed - in process]

Molecular stripping in the NF-κB/IκB/DNA genetic regulatory network.

Proc Natl Acad Sci U S A. 2016 Jan 5;113(1):110-5

Authors: Potoyan DA, Zheng W, Komives EA, Wolynes PG

Abstract
Genetic switches based on the [Formula: see text] system are master regulators of an array of cellular responses. Recent kinetic experiments have shown that [Formula: see text] can actively remove NF-κB bound to its genetic sites via a process called "molecular stripping." This allows the [Formula: see text] switch to function under kinetic control rather than the thermodynamic control contemplated in the traditional models of gene switches. Using molecular dynamics simulations of coarse-grained predictive energy landscape models for the constituent proteins by themselves and interacting with the DNA we explore the functional motions of the transcription factor [Formula: see text] and its various binary and ternary complexes with DNA and the inhibitor IκB. These studies show that the function of the [Formula: see text] genetic switch is realized via an allosteric mechanism. Molecular stripping occurs through the activation of a domain twist mode by the binding of [Formula: see text] that occurs through conformational selection. Free energy calculations for DNA binding show that the binding of [Formula: see text] not only results in a significant decrease of the affinity of the transcription factor for the DNA but also kinetically speeds DNA release. Projections of the free energy onto various reaction coordinates reveal the structural details of the stripping pathways.

PMID: 26699500 [PubMed - indexed for MEDLINE]

Generating Systems Biology Markup Language Models from the Synthetic Biology Open Language.

ACS Synth Biol. 2015 Aug 21;4(8):873-9

Authors: Roehner N, Zhang Z, Nguyen T, Myers CJ

Abstract
In the context of synthetic biology, model generation is the automated process of constructing biochemical models based on genetic designs. This paper discusses the use cases for model generation in genetic design automation (GDA) software tools and introduces the foundational concepts of standards and model annotation that make this process useful. Finally, this paper presents an implementation of model generation in the GDA software tool iBioSim and provides an example of generating a Systems Biology Markup Language (SBML) model from a design of a 4-input AND sensor written in the Synthetic Biology Open Language (SBOL).

PMID: 25822671 [PubMed - indexed for MEDLINE]

Quo vadis? The challenges of recombinant protein folding and secretion in Pichia pastoris.

Appl Microbiol Biotechnol. 2015 Apr;99(7):2925-38

Authors: Puxbaum V, Mattanovich D, Gasser B

Abstract
The development of Pichia pastoris as a production platform for recombinant proteins has been a remarkable success story over the last three decades. Stable cheap production processes and the good protein secretion abilities were pacemakers of this development. However, limitations of protein folding, glycosylation or secretion have been identified quite early on. With the availability of genome sequences and the development of systems biology characterization in the last 5 years, remarkable success in strain improvement was achieved. Here, we focus on recent developments of characterization and improvement of P. pastoris production strains regarding protein folding, intracellular trafficking, glycosylation and proteolytic degradation.

PMID: 25722021 [PubMed - indexed for MEDLINE]

The proteomic response of cheliped myofibril tissue in the eurythermal porcelain crab Petrolisthes cinctipes to heat shock following acclimation to daily temperature fluctuations.

J Exp Biol. 2015 Feb 1;218(Pt 3):388-403

Authors: Garland MA, Stillman JH, Tomanek L

Abstract
The porcelain crab Petrolisthes cinctipes lives under rocks and in mussel beds in the mid-intertidal zone where it experiences immersion during high tide and saturating humid conditions in air during low tide, which can increase habitat temperature by up to 20°C. To identify the biochemical changes affected by increasing temperature fluctuations and subsequent heat shock, we acclimated P. cinctipes for 30 days to one of three temperature regimes: (1) constant 10°C, (2) daily temperature fluctuations between 10 and 20°C (5 h up-ramp to 20°C, 1 h down-ramp to 10°C) and (3) 10-30°C (up-ramp to 30°C). After acclimation, animals were exposed to either 10°C or a 30°C heat shock to analyze the proteomic changes in claw muscle tissue. Following acclimation to 10-30°C (measured at 10°C), enolase and ATP synthase increased in abundance. Following heat shock, isoforms of arginine kinase and glycolytic enzymes such as aldolase, triose phosphate isomerase and glyceraldehyde 3-phosphate dehydrogenase increased across all acclimation regimes. Full-length isoforms of hemocyanin increased abundance following acclimation to 10-30°C, but hemocyanin fragments increased after heat shock following constant 10°C and fluctuating 10-20°C, possibly playing a role as antimicrobial peptides. Following constant 10°C and fluctuating 10-20°C, paramyosin and myosin heavy chain type-B increased in abundance, respectively, whereas myosin light and heavy chain decreased with heat shock. Actin-binding proteins, which stabilize actin filaments (filamin and tropomyosin), increased during heat shock following 10-30°C; however, actin severing and depolymerization proteins (gelsolin and cofilin) increased during heat shock following 10-20°C, possibly promoting muscle fiber restructuring. RAF kinase inhibitor protein and prostaglandin reductase increased during heat shock following constant 10°C and fluctuating 10-20°C, possibly inhibiting an immune response during heat shock. The results suggest that ATP supply, muscle fiber restructuring and immune responses are all affected by temperature fluctuations and subsequent acute heat shock in muscle tissue. Furthermore, although heat shock after acclimation to constant 10°C and fluctuating 10-30°C showed the greatest effects on the proteome, moderately fluctuating temperatures (10-20°C) broadened the temperature range over which claw muscle was able to respond to an acute heat shock with limited changes in the muscle proteome.

PMID: 25653421 [PubMed - indexed for MEDLINE]

The microbiome-immune-host defense barrier complex (microimmunosome) and developmental programming of noncommunicable diseases.

Reprod Toxicol. 2016 May 7;

Authors: R Dietert R

Abstract
Through its role as gatekeeper and filter to the external world, the microbiome affects developmental programming of physiological systems including the immune system. In turn, the immune system must tolerate, personalize, and prune the microbiome. Immune and host barrier status in early life significantly effects everything from embryo viability and pregnancy duration to the likelihood of misregulated inflammation, and risk of noncommunicable diseases (NCDs). Since the programming of and interactions among the microbiome, the host defense barrier, and the immune system can affect inflammation-driven health risks across the lifespan, a systems biology-type understanding of these three biological components may be useful. Here, I consider the potential utility of focusing on programming of a newly-defined systems biology unit termed the "microimmunosome."

PMID: 27167696 [PubMed - as supplied by publisher]

Sparse factor model for co-expression networks with an application using prior biological knowledge.

Stat Appl Genet Mol Biol. 2016 May 11;

Authors: Blum Y, Houée-Bigot M, Causeur D

Abstract
Inference on gene regulatory networks from high-throughput expression data turns out to be one of the main current challenges in systems biology. Such networks can be very insightful for the deep understanding of interactions between genes. Because genes-gene interactions is often viewed as joint contributions to known biological mechanisms, inference on the dependence among gene expressions is expected to be consistent to some extent with the functional characterization of genes which can be derived from ontologies (GO, KEGG, …). The present paper introduces a sparse factor model as a general framework either to account for a prior knowledge on joint contributions of modules of genes to latent biological processes or to infer on the corresponding co-expression network. We propose an ℓ1 - regularized EM algorithm to fit a sparse factor model for correlation. We demonstrate how it helps extracting modules of genes and more generally improves the gene clustering performance. The method is compared to alternative estimation procedures for sparse factor models of relevance networks in a simulation study. The integration of a biological knowledge based on the gene ontology (GO) is also illustrated on a liver expression data generated to understand adiposity variability in chicken.

PMID: 27166726 [PubMed - as supplied by publisher]

Many-molecule encapsulation by an icosahedral shell.

Elife. 2016 May 11;5

Authors: Perlmutter JD, Mohajerani F, Hagan MF

Abstract
We computationally study how an icosahedral shell assembles around hundreds of molecules. Such a process occurs during the formation of the carboxysome, a bacterial microcompartment that assembles around many copies of the enzymes ribulose 1,5-bisphosphate carboxylase/oxygenase and carbonic anhydrase to facilitate carbon fixation in cyanobacteria. Our simulations identify two classes of assembly pathways leading to encapsulation of many-molecule cargoes. In one, shell assembly proceeds concomitantly with cargo condensation. In the other, the cargo first forms a dense globule; then, shell proteins assemble around and bud from the condensed cargo complex. Although the model is simplified, the simulations predict intermediates and closure mechanisms not accessible in experiments, and show how assembly can be tuned between these two pathways by modulating protein interactions. In addition to elucidating assembly pathways and critical control parameters for microcompartment assembly, our results may guide the reengineering of viruses as nanoreactors that self-assemble around their reactants.

PMID: 27166515 [PubMed - as supplied by publisher]

Computational modeling approaches in gonadotropin signaling.

Theriogenology. 2016 Apr 20;

Authors: Ayoub MA, Yvinec R, Crépieux P, Poupon A

Abstract
Follicle-stimulating hormone and LH play essential roles in animal reproduction. They exert their function through binding to their cognate receptors, which belong to the large family of G protein-coupled receptors. This recognition at the plasma membrane triggers a plethora of cellular events, whose processing and integration ultimately lead to an adapted biological response. Understanding the nature and the kinetics of these events is essential for innovative approaches in drug discovery. The study and manipulation of such complex systems requires the use of computational modeling approaches combined with robust in vitro functional assays for calibration and validation. Modeling brings a detailed understanding of the system and can also be used to understand why existing drugs do not work as well as expected, and how to design more efficient ones.

PMID: 27165991 [PubMed - as supplied by publisher]

Punctuated evolution and transitional hybrid network in an ancestral cell cycle of fungi.

Elife. 2016;5

Authors: Medina EM, Turner JJ, Gordân R, Skotheim JM, Buchler NE

Abstract
Although cell cycle control is an ancient, conserved, and essential process, some core animal and fungal cell cycle regulators share no more sequence identity than non-homologous proteins. Here, we show that evolution along the fungal lineage was punctuated by the early acquisition and entrainment of the SBF transcription factor through horizontal gene transfer. Cell cycle evolution in the fungal ancestor then proceeded through a hybrid network containing both SBF and its ancestral animal counterpart E2F, which is still maintained in many basal fungi. We hypothesize that a virally-derived SBF may have initially hijacked cell cycle control by activating transcription via the cis-regulatory elements targeted by the ancestral cell cycle regulator E2F, much like extant viral oncogenes. Consistent with this hypothesis, we show that SBF can regulate promoters with E2F binding sites in budding yeast.

PMID: 27162172 [PubMed - in process]

Global de novo protein-protein interactome elucidates interactions of drought responsive proteins in horsegram (Macrotyloma uniflorum).

J Proteome Res. 2016 May 10;

Authors: Bhardwaj J, Gangwar I, Panzade GP, Shankar R, Yadav SK

Abstract
Inspired by the availability of de novo transcriptome of horsegram (Macrotyloma uniflorum) and recent developments in systems biology studies, first ever global protein-protein interactome (PPI) map was constructed for this highly drought tolerant legume. Large-scale studies of PPIs and the constructed database would provide rationale behind the interplay at cascading translational levels for drought stress adaptive mechanisms in horsegram. Using a bidirectional approach (interolog and domain-based), a high confidence interactome map and database for horsegram was constructed. Available transcriptomic information for shoot and root tissues of a sensitive genotype (M-191; genotype 1) and a drought tolerant (M-249; genotype 2) of horsegram was utilized to draw comparative PPI sub-networks under drought stress. High confidence 6804 interactions were predicted among 1812 proteins covering about one-fourth of the horsegram proteome. Highest number of interactions (33.86%) in horsegram interactome matched with Arabidopsis PPI data. Top five hub nodes mostly included ubiquitin and heat shock related proteins. Higher numbers of PPIs were found to be responsive in shoot tissue (416) and root tissue (2228) of genotype 2 compared to shoot tissue (136) and root tissue (579) of genotype 1. Characterization of PPIs using gene ontology analysis revealed that kinase and transferase activities involved in signal transduction, cellular processes, nucleocytoplasmic transport, protein ubiquitination and localization of molecules were most responsive to drought stress. Hence, these could be framed in stress adaptive mechanisms of horsegram. Being the first legume global PPI map, it would provide new insights in gene and protein regulatory networks for drought stress tolerance mechanisms in horsegram. Information compiled in form of database (MauPIR) will provide the much needed high confidence systems biology information for horsegram genes, proteins and involved processes. This information would ease the effort and increase the efficacy for similar studies on other legumes. Public access is available at http://14.139.59.221/MauPIR/.

PMID: 27161830 [PubMed - as supplied by publisher]

Proteome-wide alterations on adipose tissue from obese patients as age-, diabetes- and gender-specific hallmarks.

Sci Rep. 2016;6:25756

Authors: Gómez-Serrano M, Camafeita E, García-Santos E, López JA, Rubio MA, Sánchez-Pernaute A, Torres A, Vázquez J, Peral B

Abstract
Obesity is a main global health issue and an outstanding cause of morbidity and mortality predisposing to type 2 diabetes (T2DM) and cardiovascular diseases. Huge research efforts focused on gene expression, cellular signalling and metabolism in obesity have improved our understanding of these disorders; nevertheless, to bridge the gap between the regulation of gene expression and changes in signalling/metabolism, protein levels must be assessed. We have extensively analysed visceral adipose tissue from age-, T2DM- and gender-matched obese patients using high-throughput proteomics and systems biology methods to identify new biomarkers for the onset of T2DM in obesity, as well as to gain insight into the influence of aging and gender in these disorders. About 250 proteins showed significant abundance differences in the age, T2DM and gender comparisons. In diabetic patients, remarkable gender-specific hallmarks were discovered regarding redox status, immune response and adipose tissue accumulation. Both aging and T2DM processes were associated with mitochondrial remodelling, albeit through well-differentiated proteome changes. Systems biology analysis highlighted mitochondrial proteins that could play a key role in the age-dependent pathophysiology of T2DM. Our findings could serve as a framework for future research in Translational Medicine directed at improving the quality of life of obese patients.

PMID: 27160966 [PubMed - in process]

Understanding the Metabolic Consequences of Human Arylsulfatase A Deficiency through a Computational Systems Biology Study.

Cent Nerv Syst Agents Med Chem. 2016 May 10;

Authors: Echeverri Olga Y, Salazar Diego A, Rodriguez-Lopez A, Janneth G, Almeciga-Diaz Carlos J, Barrera Luis A

Abstract
The nervous system is responsible for the communication between the organism and its environment. This task is possible by the presence of the myelin sheath, which is a double membrane formed by about 75% lipids and 25% proteins. The sulfatide represents one of the main lipids of the myelin band; its degradation is catabolized by the enzyme Arylsulfatase A (ARSA), to generated galactosylceramide. Mutations affecting ARSA function lead to the neurodegenerative disease Metachromatic Leukodystrophy. This disease is characterized by accumulation of sulfatide within the band of myelin affecting its functionality. The biochemical consequences of ARSA deficiency are not well understood yet. In this paper, we used an in-silico systems-biology approach to model the biochemical consequences of ARSA deficiency within a general human metabolic network (Recon2) and a glia cellular model. We expected that ARSA deficiency mainly affected the glycosphingolipid pathways. However, the results suggest that mitochondrial metabolism and amino acid transport were the main reactions affected within both cellular models. In the glia cell model, it was highlighted the high number of affected reactions of neurotransmitters metabolism, while only a reduced effect was observed in reactions involved in glycosphingolipids metabolism. We hypothesize that ARSA deficiency might lead to metabolic consequences that not only compromise the myelin band or the glycosphingolipids metabolism but also the overall metabolic function of the nervous system. Furthermore, these results offer the bases for the design of in-vitro and in-vivo experiments that allow generating new knowledge of MLD pathophysiology and other neurodegenerative diseases.

PMID: 27160716 [PubMed - as supplied by publisher]

Perturbation biology nominates upstream-downstream drug combinations in RAF inhibitor resistant melanoma cells.

Elife. 2015;4

Authors: Korkut A, Wang W, Demir E, Aksoy BA, Jing X, Molinelli EJ, Babur Ö, Bemis DL, Onur Sumer S, Solit DB, Pratilas CA, Sander C

Abstract
Resistance to targeted cancer therapies is an important clinical problem. The discovery of anti-resistance drug combinations is challenging as resistance can arise by diverse escape mechanisms. To address this challenge, we improved and applied the experimental-computational perturbation biology method. Using statistical inference, we build network models from high-throughput measurements of molecular and phenotypic responses to combinatorial targeted perturbations. The models are computationally executed to predict the effects of thousands of untested perturbations. In RAF-inhibitor resistant melanoma cells, we measured 143 proteomic/phenotypic entities under 89 perturbation conditions and predicted c-Myc as an effective therapeutic co-target with BRAF or MEK. Experiments using the BET bromodomain inhibitor JQ1 affecting the level of c-Myc protein and protein kinase inhibitors targeting the ERK pathway confirmed the prediction. In conclusion, we propose an anti-cancer strategy of co-targeting a specific upstream alteration and a general downstream point of vulnerability to prevent or overcome resistance to targeted drugs.

PMID: 26284497 [PubMed - indexed for MEDLINE]

Recent advances in large-scale protein interactome mapping.

F1000Res. 2016;5

Authors: Mehta V, Trinkle-Mulcahy L

Abstract
Protein-protein interactions (PPIs) underlie most, if not all, cellular functions. The comprehensive mapping of these complex networks of stable and transient associations thus remains a key goal, both for systems biology-based initiatives (where it can be combined with other 'omics' data to gain a better understanding of functional pathways and networks) and for focused biological studies. Despite the significant challenges of such an undertaking, major strides have been made over the past few years. They include improvements in the computation prediction of PPIs and the literature curation of low-throughput studies of specific protein complexes, but also an increase in the deposition of high-quality data from non-biased high-throughput experimental PPI mapping strategies into publicly available databases.

PMID: 27158474 [PubMed]

Putting the Genome in Context: Gene-Environment Interactions in Type 2 Diabetes.

Curr Diab Rep. 2016 Jul;16(7):57

Authors: Franks PW, Paré G

Abstract
The genome is often the conduit through which environmental exposures convey their effects on health and disease. Whilst not all diseases act by directly perturbing the genome, the phenotypic responses are often genetically determined. Hence, whilst diseases are often defined has having differing degrees of genetic determination, genetic and environmental factors are, with few exceptions, inseparable features of most diseases, not least type 2 diabetes. It follows that to optimize diabetes, prevention and treatment will require that the etiological roles of genetic and environmental risk factors be jointly considered. As we discuss here, studies focused on quantifying gene-environment and gene-treatment interactions are gathering momentum and may eventually yield data that helps guide health-related choices and medical interventions for type 2 diabetes and other complex diseases.

PMID: 27155607 [PubMed - as supplied by publisher]

Identification of line-specific strategies for improving carotenoid production in synthetic maize through data-driven mathematical modelling.

Plant J. 2016 May 7;

Authors: Comas J, Benfeitas R, Vilaprinyo E, Sorribas A, Solsona F, Farré G, Berman J, Zorrilla U, Capell T, Sandmann G, Zhu C, Christou P, Alves R

Abstract
Plant Synthetic Biology is still in its infancy. However, Synthetic Biology approaches have been used to manipulate and improve the nutritional and health value of staple food crops, such as rice, potato, or maize. With current technologies, production yields of the synthetic nutrients are a result of trial and error, and systematic rational strategies to optimize those yields are still lacking. Here, we present a workflow that combines gene expression and quantitative metabolomics with mathematical modeling to identify strategies for increasing production yields of nutritionally important carotenoids in the seed endosperm synthesized through alternative biosynthetic pathways in synthetic lines of white maize, which is normally devoid of carotenoids. Quantitative metabolomics and gene expression data are used to create and fit parameters of mathematical models that are specific for four independent maize lines. Sensitivity analysis and simulation of each model is used to predict which gene activities should be further engineered in order to increase production yields for carotenoid accumulation in each line. Some of these predictions (e.g. increasing Zmlycb/Gllycb will increase accumulated β-carotenes) are valid across the four maize lines and consistent with experimental observations in other systems. Other predictions are line-specific. The workflow is adaptable to any other biological system for which appropriate quantitative information is available. Furthermore, we validate some of the predictions using experimental data from additional synthetic maize lines for which no models were developed. This article is protected by copyright. All rights reserved.

PMID: 27155093 [PubMed - as supplied by publisher]

Modelling tumour cell proliferation from vascular structure using tissue decomposition into avascular elements.

J Theor Biol. 2016 May 4;

Authors: Besenhard MO, Jarzabek M, O'Farrell AC, Callanan JJ, Prehn JH, Byrne AT, Huber HJ

Abstract
Computer models allow the mechanistically detailed study of tumour proliferation and its dependency on nutrients. However, the computational study of large vascular tumours requires detailed information on the 3-dimensional vessel network and rather high computation times due to complex geometries. This study puts forward the idea of partitioning vascularised tissue into connected avascular elements that can exchange cells and nutrients between each other. Our method is able to rapidly calculate the evolution of proliferating as well as dead and quiescent cells, and hence a proliferative index, from a given amount and distribution of vascularisation of arbitrary complexity. Applying our model, we found that a heterogeneous vessel distribution provoked a higher proliferative index, suggesting increased malignancy, and increased the amount of dead cells compared to a more static tumour environment when a homogenous vessel distribution was assumed. We subsequently demonstrated that under certain amounts of vascularisation, cell proliferation may even increase when vessel density decreases, followed by a subsequent decrease of proliferation. This effect was due to a trade-off between an increase in compensatory proliferation for replacing dead cells and a decrease of cell population due to lack of oxygen supply in lowly vascularised tumours. Findings were illustrated by an ectopic colorectal cancer mouse xenograft model. Our presented approach can be in the future applied to study the effect of cytostatic, cytotoxic and anti-angiogenic chemotherapy and is ideally suited for translational systems biology, where rapid interaction between theory and experiment is essential.

PMID: 27155046 [PubMed - as supplied by publisher]

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Complex media and enzymatic kinetics.

Anal Chem. 2016 May 5;

Authors: Bakalis EO, Soldà A, Kosmas MK, Rapino S, Zerbetto F

Abstract
Enzymatic reactions in complex environments often take place with concentrations of enzyme comparable to that of substrate molecules. Two such cases occur when an enzyme is used to detect low concentrations of substrate/analyte or inside a living cell. Such concentrations do not agree with standard in-vitro conditions, aimed at satisfying one of the founding hypothesis of the Michaelis-Menten reaction scheme, MM. It would be desirable to generalize the classical approach, and show its applicability to complex systems. A permeable micrometrically-structured hydrogel matrix was fabricated by protein cross-linking. Glucose oxidase enzyme (GOx) was embedded in the matrix and used as a prototypical system. The concentration of H2O2 was monitored in time and fitted by an accurate solution of the enzymatic kinetic scheme, which is expressed in terms of simple functions. The approach can also find applications in digital micro-fluidics and in systems biology where the kinetics response in the linear regimes often employed must be replaced.

PMID: 27149003 [PubMed - as supplied by publisher]