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]

Understanding the Causes and Implications of Endothelial Metabolic Variation in Cardiovascular Disease through Genome-Scale Metabolic Modeling.

Front Cardiovasc Med. 2016;3:10

Authors: McGarrity S, Halldórsson H, Palsson S, Johansson PI, Rolfsson Ó

Abstract
High-throughput biochemical profiling has led to a requirement for advanced data interpretation techniques capable of integrating the analysis of gene, protein, and metabolic profiles to shed light on genotype-phenotype relationships. Herein, we consider the current state of knowledge of endothelial cell (EC) metabolism and its connections to cardiovascular disease (CVD) and explore the use of genome-scale metabolic models (GEMs) for integrating metabolic and genomic data. GEMs combine gene expression and metabolic data acting as frameworks for their analysis and, ultimately, afford mechanistic understanding of how genetic variation impacts metabolism. We demonstrate how GEMs can be used to investigate CVD-related genetic variation, drug resistance mechanisms, and novel metabolic pathways in ECs. The application of GEMs in personalized medicine is also highlighted. Particularly, we focus on the potential of GEMs to identify metabolic biomarkers of endothelial dysfunction and to discover methods of stratifying treatments for CVDs based on individual genetic markers. Recent advances in systems biology methodology, and how these methodologies can be applied to understand EC metabolism in both health and disease, are thus highlighted.

PMID: 27148541 [PubMed]

Corrigendum: Synechocystis: not just a plug-bug for CO2, but a green E. coli.

Front Bioeng Biotechnol. 2016;4:32

Authors: Branco Dos Santos F, Du W, Hellingwerf KJ

Abstract
[This corrects the article on p. 36 in vol. 2, PMID: 25279375.].

PMID: 27148526 [PubMed - as supplied by publisher]

Data-based Reconstruction of Gene Regulatory Networks of Fungal Pathogens.

Front Microbiol. 2016;7:570

Authors: Guthke R, Gerber S, Conrad T, Vlaic S, Durmuş S, Çakır T, Sevilgen FE, Shelest E, Linde J

Abstract
In the emerging field of systems biology of fungal infection, one of the central roles belongs to the modeling of gene regulatory networks (GRNs). Utilizing omics-data, GRNs can be predicted by mathematical modeling. Here, we review current advances of data-based reconstruction of both small-scale and large-scale GRNs for human pathogenic fungi. The advantage of large-scale genome-wide modeling is the possibility to predict central (hub) genes and thereby indicate potential biomarkers and drug targets. In contrast, small-scale GRN models provide hypotheses on the mode of gene regulatory interactions, which have to be validated experimentally. Due to the lack of sufficient quantity and quality of both experimental data and prior knowledge about regulator-target gene relations, the genome-wide modeling still remains problematic for fungal pathogens. While a first genome-wide GRN model has already been published for Candida albicans, the feasibility of such modeling for Aspergillus fumigatus is evaluated in the present article. Based on this evaluation, opinions are drawn on future directions of GRN modeling of fungal pathogens. The crucial point of genome-wide GRN modeling is the experimental evidence, both used for inferring the networks (omics 'first-hand' data as well as literature data used as prior knowledge) and for validation and evaluation of the inferred network models.

PMID: 27148247 [PubMed]

Research Infrastructure for Collaborative Team Science: Challenges in Technology-Supported Workflows in and Across Laboratories, Institutions, and Geographies.

Semin Nephrol. 2015 May;35(3):291-302

Authors: Mirel B, Luo A, Harris M

Abstract
Collaborative research has many challenges. One under-researched challenge is how to align collaborators' research practices and evolving analytical reasoning with technologies and configurations of technologies that best support them. The goal of such alignment is to enhance collaborative problem solving capabilities in research. Toward this end, we draw on our own research and a synthesis of the literature to characterize the workflow of collaborating scientists in systems-level renal disease research. We describe the various phases of a hypothetical workflow among diverse collaborators within and across laboratories, extending from their primary analysis through secondary analysis. For each phase, we highlight required technology supports, and. At time, complementary organizational supports. This survey of supports matching collaborators' analysis practices and needs in research projects to technological support is preliminary, aimed ultimately at developing a research capability framework that can help scientists and technologists mutually understand workflows and technologies that can help enable and enhance them.

PMID: 26215866 [PubMed - indexed for MEDLINE]

APOL1 Kidney Disease Risk Variants: An Evolving Landscape.

Semin Nephrol. 2015 May;35(3):222-36

Authors: Dummer PD, Limou S, Rosenberg AZ, Heymann J, Nelson G, Winkler CA, Kopp JB

Abstract
Apolipoprotein L1 (APOL1) genetic variants account for much of the excess risk of chronic and end-stage kidney disease, which results in a significant global health disparity for persons of African ancestry. We estimate the lifetime risk of kidney disease in APOL1 dual-risk allele individuals to be at least 15%. Experimental evidence suggests a direct role of APOL1 in pore formation, cellular injury, and programmed cell death in renal injury. The APOL1 BH3 motif, often associated with cell death, is unlikely to play a role in APOL1-induced cytotoxicity because it is not conserved within the APOL family and is dispensable for cell death in vitro. We discuss two models for APOL1 trypanolytic activity: one involving lysosome permeabilization and another involving colloid-osmotic swelling of the cell body, as well as their relevance to human pathophysiology. Experimental evidence from human cell culture models suggests that both mechanisms may be operative. A systems biology approach whereby APOL1-associated perturbations in gene and protein expression in affected individuals are correlated with molecular pathways may be productive to elucidate APOL1 function in vivo.

PMID: 26215860 [PubMed - indexed for MEDLINE]

Identification of miR-34a-target interactions by a combined network based and experimental approach.

Oncotarget. 2016 Apr 29;

Authors: Hart M, Rheinheimer S, Leidinger P, Backes C, Menegatti J, Fehlmann T, Grässer F, Keller A, Meese E

Abstract
Circulating miRNAs have been associated with numerous human diseases. The lack of understanding the functional roles of blood-born miRNAs limits, however, largely their value as disease marker. In a systems biology analysis we identified miR-34a as strongly associated with pathogenesis. Genome-wide analysis of miRNAs in blood cell fractions highlighted miR-34a as most significantly up-regulated in CD3+ cells of lung cancer patients. By our in silico analysis members of the protein kinase C family (PKC) were indicated as miR-34a target genes. Using a luciferase assay, we confirmed binding of miR-34a-5p to target sequences within the 3'UTRs of five PKC family members. To verify the biological effect, we transfected HEK 293T and Jurkat cells with miR-34a-5p causing reduced endogenous protein levels of PKC isozymes. By combining bioinformatics approaches with experimental validation, we demonstrate that one of the most relevant disease associated miRNAs has the ability to control the expression of a gene family.

PMID: 27144431 [PubMed - as supplied by publisher]

Spatial Cross-Talk Between Oxidative Stress and DNA Replication in Human Fibroblasts.

J Proteome Res. 2016 May 4;

Authors: Radulovic M, Baqader NO, Stoeber K, Godovac-Zimmermann J

Abstract
MS-based proteomics has been applied to a differential network analysis of the nuclear-cytoplasmic subcellular distribution of proteins between cell cycle arrest: (a) at the origin activation checkpoint for DNA replication, or (b) in response to oxidative stress. Significant changes were identified for 401 proteins. Cellular response combines changes in trafficking and in total abundance to vary the local compartmental abundances that are the basis of cellular response. Appreciable changes for both perturbations were observed for 245 proteins, but cross-talk between oxidative stress and DNA replication is dominated by 49 proteins that show strong changes for both. Many nuclear processes are influenced by a spatial switch involving the proteins {KPNA2, KPNB1, PCNA, PTMA, SET} and heme/iron proteins HMOX1 and FTH1. Dynamic spatial distribution data is presented for proteins involved in caveolae, extracellular matrix remodelling, TGFβ signalling, IGF pathways, emerin complexes, mitochondrial protein import complexes, spliceosomes, proteasomes, etc. The data indicates that for spatially heterogeneous cells, cross-compartmental communication is integral to their systems biology, that coordinated spatial redistribution for crucial protein networks underlies many functional changes, and that information on dynamic spatial redistribution of proteins is essential to obtain comprehensive pictures of cellular function. We describe how spatial data of the type presented here can provide priorities for further investigation of crucial features of high-level spatial coordination across cells. We suggest that the present data is related to increasing indications that much of subcellular protein transport is constitutive and that perturbation of these constitutive transport processes may be related to cancer and other diseases. A quantitative, spatially resolved nucleus-cytoplasm interaction network is provided for further investigations.

PMID: 27142241 [PubMed - as supplied by publisher]