EGF-dependent re-routing of vesicular recycling switches spontaneous phosphorylation suppression to EGFR signaling.

Elife. 2015 Nov 26;4:

Authors: Baumdick M, Brüggemann Y, Schmick M, Xouri G, Sabet O, Davis L, Chin JW, Bastiaens PI

Abstract
Autocatalytic activation of epidermal growth factor receptor (EGFR) coupled to dephosphorylating activity of protein tyrosine phosphatases (PTPs) ensures robust yet diverse responses to extracellular stimuli. The inevitable tradeoff of this plasticity is spontaneous receptor activation and spurious signaling. We show that a ligand-mediated switch in EGFR trafficking enables suppression of spontaneous activation while maintaining EGFR's capacity to transduce extracellular signals. Autocatalytic phosphorylation of tyrosine 845 on unliganded EGFR monomers is suppressed by vesicular recycling through perinuclear areas with high PTP1B activity. Ligand-binding results in phosphorylation of the c-Cbl docking tyrosine and ubiquitination of the receptor. This secondary signal relies on EGF-induced EGFR self-association and switches suppressive recycling to directional trafficking. The re-routing regulates EGFR signaling response by the transit-time to late endosomes where it is switched-off by high PTP1B activity. This ubiquitin-mediated switch in EGFR trafficking is a uniquely suited solution to suppress spontaneous activation while maintaining responsiveness to EGF.

PMID: 26609808 [PubMed - indexed for MEDLINE]

Endocrine resistance in breast cancer--An overview and update.

Mol Cell Endocrinol. 2015 Dec 15;418 Pt 3:220-34

Authors: Clarke R, Tyson JJ, Dixon JM

Abstract
Tumors that express detectable levels of the product of the ESR1 gene (estrogen receptor-α; ERα) represent the single largest molecular subtype of breast cancer. More women eventually die from ERα+ breast cancer than from either HER2+ disease (almost half of which also express ERα) and/or from triple negative breast cancer (ERα-negative, progesterone receptor-negative, and HER2-negative). Antiestrogens and aromatase inhibitors are largely indistinguishable from each other in their abilities to improve overall survival and almost 50% of ERα+ breast cancers will eventually fail one or more of these endocrine interventions. The precise reasons why these therapies fail in ERα+ breast cancer remain largely unknown. Pharmacogenetic explanations for Tamoxifen resistance are controversial. The role of ERα mutations in endocrine resistance remains unclear. Targeting the growth factors and oncogenes most strongly correlated with endocrine resistance has proven mostly disappointing in their abilities to improve overall survival substantially, particularly in the metastatic setting. Nonetheless, there are new concepts in endocrine resistance that integrate molecular signaling, cellular metabolism, and stress responses including endoplasmic reticulum stress and the unfolded protein response (UPR) that provide novel insights and suggest innovative therapeutic targets. Encouraging evidence that drug combinations with CDK4/CDK6 inhibitors can extend recurrence free survival may yet translate to improvements in overall survival. Whether the improvements seen with immunotherapy in other cancers can be achieved in breast cancer remains to be determined, particularly for ERα+ breast cancers. This review explores the basic mechanisms of resistance to endocrine therapies, concluding with some new insights from systems biology approaches further implicating autophagy and the UPR in detail, and a brief discussion of exciting new avenues and future prospects.

PMID: 26455641 [PubMed - indexed for MEDLINE]

Immunological Signatures after Bordetella pertussis Infection Demonstrate Importance of Pulmonary Innate Immune Cells.

PLoS One. 2016;11(10):e0164027

Authors: Raeven RH, Brummelman J, van der Maas L, Tilstra W, Pennings JL, Han WG, van Els CA, van Riet E, Kersten GF, Metz B

Abstract
Effective immunity against Bordetella pertussis is currently under discussion following the stacking evidence of pertussis resurgence in the vaccinated population. Natural immunity is more effective than vaccine-induced immunity indicating that knowledge on infection-induced responses may contribute to improve vaccination strategies. We applied a systems biology approach comprising microarray, flow cytometry and multiplex immunoassays to unravel the molecular and cellular signatures in unprotected mice and protected mice with infection-induced immunity, around a B. pertussis challenge. Pre-existing systemic memory Th1/Th17 cells, memory B-cells, and mucosal IgA specific for Ptx, Vag8, Fim2/3 were detected in the protected mice 56 days after an experimental infection. In addition, pre-existing high activity and reactivation of pulmonary innate cells such as alveolar macrophages, M-cells and goblet cells was detected. The pro-inflammatory responses in the lungs and serum, and neutrophil recruitment in the spleen upon an infectious challenge of unprotected mice were absent in protected mice. Instead, fast pulmonary immune responses in protected mice led to efficient bacterial clearance and harbored potential new gene markers that contribute to immunity against B. pertussis. These responses comprised of innate makers, such as Clca3, Retlna, Glycam1, Gp2, and Umod, next to adaptive markers, such as CCR6+ B-cells, CCR6+ Th17 cells and CXCR6+ T-cells as demonstrated by transcriptome analysis. In conclusion, besides effective Th1/Th17 and mucosal IgA responses, the primary infection-induced immunity benefits from activation of pulmonary resident innate immune cells, achieved by local pathogen-recognition. These molecular signatures of primary infection-induced immunity provided potential markers to improve vaccine-induced immunity against B. pertussis.

PMID: 27711188 [PubMed - in process]

Time Hierarchies and Model Reduction in Canonical Non-linear Models.

Front Genet. 2016;7:166

Authors: Löwe H, Kremling A, Marin-Sanguino A

Abstract
The time-scale hierarchies of a very general class of models in differential equations is analyzed. Classical methods for model reduction and time-scale analysis have been adapted to this formalism and a complementary method is proposed. A unified theoretical treatment shows how the structure of the system can be much better understood by inspection of two sets of singular values: one related to the stoichiometric structure of the system and another to its kinetics. The methods are exemplified first through a toy model, then a large synthetic network and finally with numeric simulations of three classical benchmark models of real biological systems.

PMID: 27708665 [PubMed - in process]

Plant sulfur and Big Data.

Plant Sci. 2015 Dec;241:1-10

Authors: Kopriva S, Calderwood A, Weckopp SC, Koprivova A

Abstract
Sulfur is an essential mineral nutrient for plants, therefore, the pathways of its uptake and assimilation have been extensively studied. Great progress has been made in elucidation of the individual genes and enzymes and their regulation. Sulfur assimilation has been intensively investigated by -omics technologies and has been target of several genome wide genetic approaches. This brought a significant step in our understanding of the regulation of the pathway and its integration in cellular metabolism. However, the large amount of information derived from other experiments not directly targeting sulfur has also brought new and exciting insights into processes affecting sulfur homeostasis. In this review we will integrate the findings of the targeted experiments with those that brought unintentional progress in sulfur research, and will discuss how to synthesize the large amount of information available in various repositories into a meaningful dissection of the regulation of a specific metabolic pathway. We then speculate how this might be used to further advance knowledge on control of sulfur metabolism and what are the main questions to be answered.

PMID: 26706053 [PubMed - indexed for MEDLINE]

The evolution of standards and data management practices in systems biology.

Mol Syst Biol. 2015 Dec 23;11(12):851

Authors: Stanford NJ, Wolstencroft K, Golebiewski M, Kania R, Juty N, Tomlinson C, Owen S, Butcher S, Hermjakob H, Le Novère N, Mueller W, Snoep J, Goble C

PMID: 26700851 [PubMed - indexed for MEDLINE]

The clinical impact of recent advances in LC-MS for cancer biomarker discovery and verification.

Expert Rev Proteomics. 2016;13(1):99-114

Authors: Wang H, Shi T, Qian WJ, Liu T, Kagan J, Srivastava S, Smith RD, Rodland KD, Camp DG

Abstract
Mass spectrometry (MS) -based proteomics has become an indispensable tool with broad applications in systems biology and biomedical research. With recent advances in liquid chromatography (LC) and MS instrumentation, LC-MS is making increasingly significant contributions to clinical applications, especially in the area of cancer biomarker discovery and verification. To overcome challenges associated with analyses of clinical samples (for example, a wide dynamic range of protein concentrations in bodily fluids and the need to perform high throughput and accurate quantification of candidate biomarker proteins), significant efforts have been devoted to improve the overall performance of LC-MS-based clinical proteomics platforms. Reviewed here are the recent advances in LC-MS and its applications in cancer biomarker discovery and quantification, along with the potentials, limitations and future perspectives.

PMID: 26581546 [PubMed - indexed for MEDLINE]

Quantification of oxidative stress phenotypes based on high-throughput growth profiling of protein kinase and phosphatase knockouts.

FEMS Yeast Res. 2016 Feb;16(1):fov101

Authors: Altıntaş A, Martini J, Mortensen UH, Workman CT

Abstract
Cellular responses to oxidative stress are important for restoring redox balance and ensuring cell survival. Genetic defects in response factors can lead to impaired response to oxidative damage and contribute to disease and aging. In single cell organisms, such as yeasts, the integrity of the oxidative stress response can be observed through its influences on growth characteristics. In this study, we investigated the time-dependent batch growth effects as a function of oxidative stress levels in protein kinase and phosphatase deletion backgrounds of Saccharomyces cerevisiae. In total, 41 different protein kinases and phosphatase mutants were selected for their known activities in oxidative stress or other stress response pathways and were investigated for their dosage-dependent response to hydrogen peroxide. Detailed growth profiles were analyzed after the induction of stress for growth rate, lag time duration and growth efficiency, and by a novel method to identify stress-induced diauxic shift delay. This approach extracts more phenotypic information than traditional plate-based methods due to the assessment of time dynamics in the time scale of minutes. With this approach, we were able to identify surprisingly diverse sensitivity and resistance patterns as a function of gene knockout.

PMID: 26564984 [PubMed - indexed for MEDLINE]

Proteomic landscape in Central and Eastern Europe: the 9th Central and Eastern European Proteomic Conference, Poznań, Poland.

Expert Rev Proteomics. 2016;13(1):5-7

Authors: Gadher SJ, Marczak Ł, Łuczak M, Stobiecki M, Widlak P, Kovarova H

Abstract
Every year since 2007, the Central and Eastern European Proteomic Conference (CEEPC) has excelled in representing state-of-the-art proteomics in and around Central and Eastern Europe, and linking it to international institutions worldwide. Its mission remains to contribute to all approaches of proteomics including traditional and often-revisited methodologies as well as the latest technological achievements in clinical, quantitative and structural proteomics with a view to systems biology of a variety of processes. The 9th CEEPC was held from June 15th to 18th, 2015, at the Institute of Bioorganic Chemistry, Polish Academy of Sciences in Poznań, Poland. The scientific program stimulated exchange of proteomic knowledge whilst the spectacular venue of the conference allowed participants to enjoy the cobblestoned historical city of Poznań.

PMID: 26558656 [PubMed - indexed for MEDLINE]

Linking Microbiota to Human Diseases: A Systems Biology Perspective.

Trends Endocrinol Metab. 2015 Dec;26(12):758-70

Authors: Wu H, Tremaroli V, Bäckhed F

Abstract
The human gut microbiota encompasses a densely populated ecosystem that provides essential functions for host development, immune maturation, and metabolism. Alterations to the gut microbiota have been observed in numerous diseases, including human metabolic diseases such as obesity, type 2 diabetes (T2D), and irritable bowel syndrome, and some animal experiments have suggested causality. However, few studies have validated causality in humans and the underlying mechanisms remain largely to be elucidated. We discuss how systems biology approaches combined with new experimental technologies may disentangle some of the mechanistic details in the complex interactions of diet, microbiota, and host metabolism and may provide testable hypotheses for advancing our current understanding of human-microbiota interaction.

PMID: 26555600 [PubMed - indexed for MEDLINE]

PPIXpress: construction of condition-specific protein interaction networks based on transcript expression.

Bioinformatics. 2016 Feb 15;32(4):571-8

Authors: Will T, Helms V

Abstract
UNLABELLED: Protein-protein interaction networks are an important component of modern systems biology. Yet, comparatively few efforts have been made to tailor their topology to the actual cellular condition being studied. Here, we present a network construction method that exploits expression data at the transcript-level and thus reveals alterations in protein connectivity not only caused by differential gene expression but also by alternative splicing. We achieved this by establishing a direct correspondence between individual protein interactions and underlying domain interactions in a complete but condition-unspecific protein interaction network. This knowledge was then used to infer the condition-specific presence of interactions from the dominant protein isoforms. When we compared contextualized interaction networks of matched normal and tumor samples in breast cancer, our transcript-based construction identified more significant alterations that affected proteins associated with cancerogenesis than a method that only uses gene expression data. The approach is provided as the user-friendly tool PPIXpress.
AVAILABILITY AND IMPLEMENTATION: PPIXpress is available at https://sourceforge.net/projects/ppixpress/.

PMID: 26508756 [PubMed - indexed for MEDLINE]

Analytical Lipidomics in Metabolic and Clinical Research.

Trends Endocrinol Metab. 2015 Dec;26(12):671-3

Authors: Hyötyläinen T, Orešič M

Abstract
Lipidomic analysis, which enables comprehensive characterization of molecular lipids in biological systems, is rapidly becoming an essential tool in biomedical research. While lipidomics already have contributed to several conceptual advances in metabolic research and led to new, validated disease biomarkers, its translation into the clinic remains a challenge.

PMID: 26439978 [PubMed - indexed for MEDLINE]

Architecture of a minimal signaling pathway explains the T-cell response to a 1 million-fold variation in antigen affinity and dose.

Proc Natl Acad Sci U S A. 2016 Oct 4;:

Authors: Lever M, Lim HS, Kruger P, Nguyen J, Trendel N, Abu-Shah E, Maini PK, van der Merwe PA, Dushek O

Abstract
T cells must respond differently to antigens of varying affinity presented at different doses. Previous attempts to map peptide MHC (pMHC) affinity onto T-cell responses have produced inconsistent patterns of responses, preventing formulations of canonical models of T-cell signaling. Here, a systematic analysis of T-cell responses to 1 million-fold variations in both pMHC affinity and dose produced bell-shaped dose-response curves and different optimal pMHC affinities at different pMHC doses. Using sequential model rejection/identification algorithms, we identified a unique, minimal model of cellular signaling incorporating kinetic proofreading with limited signaling coupled to an incoherent feed-forward loop (KPL-IFF) that reproduces these observations. We show that the KPL-IFF model correctly predicts the T-cell response to antigen copresentation. Our work offers a general approach for studying cellular signaling that does not require full details of biochemical pathways.

PMID: 27702900 [PubMed - as supplied by publisher]

Time is ripe: maturation of metabolomics in chronobiology.

Curr Opin Biotechnol. 2016 Oct 1;43:70-76

Authors: Rhoades SD, Sengupta A, Weljie AM

Abstract
Sleep and circadian rhythms studies have recently benefited from metabolomics analyses, uncovering new connections between chronobiology and metabolism. From untargeted mass spectrometry to quantitative nuclear magnetic resonance spectroscopy, a diversity of analytical approaches has been applied for biomarker discovery in the field. In this review we consider advances in the application of metabolomics technologies which have uncovered significant effects of sleep and circadian cycles on several metabolites, namely phosphatidylcholine species, medium-chain carnitines, and aromatic amino acids. Study design and data processing measures essential for detecting rhythmicity in metabolomics data are also discussed. Future developments in these technologies are anticipated vis-à-vis validating early findings, given metabolomics has only recently entered the ring with other systems biology assessments in chronometabolism studies.

PMID: 27701007 [PubMed - as supplied by publisher]

Stochasticity in the miR-9/Hes1 oscillatory network can account for clonal heterogeneity in the timing of differentiation.

Elife. 2016 Oct 04;5:

Authors: Phillips NE, Manning CS, Pettini T, Biga V, Marinopoulou E, Stanley P, Boyd J, Bagnall J, Paszek P, Spiller DG, White MR, Goodfellow M, Galla T, Rattray M, Papalopulu N

Abstract
Recent studies suggest that cells make stochastic choices with respect to differentiation or division. However, the molecular mechanism underlying such stochasticity is unknown. We previously proposed that the timing of vertebrate neuronal differentiation is regulated by molecular oscillations of a transcriptional repressor, HES1, tuned by a post-transcriptional repressor, miR-9. Here, we computationally model the effects of intrinsic noise on the Hes1/miR-9 oscillator as a consequence of low molecular numbers of interacting species, determined experimentally. We report that increased stochasticity spreads the timing of differentiation in a population, such that initially equivalent cells differentiate over a period of time. Surprisingly, inherent stochasticity also increases the robustness of the progenitor state and lessens the impact of unequal, random distribution of molecules at cell division on the temporal spread of differentiation at the population level. This advantageous use of biological noise contrasts with the view that noise needs to be counteracted.

PMID: 27700985 [PubMed - in process]

Gene pathway development in human epicardial adipose tissue during early life.

JCI Insight. 2016 Aug 18;1(13):e87460

Authors: Ojha S, Fainberg HP, Wilson V, Pelella G, Castellanos M, May ST, Lotto AA, Sacks H, Symonds ME, Budge H

Abstract
Studies in rodents and newborn humans demonstrate the influence of brown adipose tissue (BAT) in temperature control and energy balance and a critical role in the regulation of body weight. Here, we obtained samples of epicardial adipose tissue (EAT) from neonates, infants, and children in order to evaluate changes in their transcriptional landscape by applying a systems biology approach. Surprisingly, these analyses revealed that the transition to infancy is a critical stage for changes in the morphology of EAT and is reflected in unique gene expression patterns of a substantial proportion of thermogenic gene transcripts (~10%). Our results also indicated that the pattern of gene expression represents a distinct developmental stage, even after the rebound in abundance of thermogenic genes in later childhood. Using weighted gene coexpression network analyses, we found precise anthropometric-specific correlations with changes in gene expression and the decline of thermogenic capacity within EAT. In addition, these results indicate a sequential order of transcriptional events affecting cellular pathways, which could potentially explain the variation in the amount, or activity, of BAT in adulthood. Together, these results provide a resource to elucidate gene regulatory mechanisms underlying the progressive development of BAT during early life.

PMID: 27699231 [PubMed - in process]

Data on translatome analysis of Mycoplasma gallisepticum.

Data Brief. 2016 Dec;9:422-424

Authors: Fisunov GY, Evsyutina DV, Govorun VM

Abstract
Mycoplasma gallisepticum is a bacterium of class Mollicutes which encompasses wall-less bacteria with significantly reduced genomes. Due to their overall reduction and simplicity mycoplasmas serve as a model of minimal cell and are used for systems biology studies. Here we present raw data on translatome (ribosome-bound mRNA) analysis of Mycoplasma gallisepticum under logarithm growth and heat stress. The data supports the publication of "Ribosomal profiling of Mycoplasma gallisepticum" (G. Y. Fisunov, D. V Evsyutina, A. A. Arzamasov, I. O. Butenko, V. M. Govorun, 2015) [1].

PMID: 27699194 [PubMed - in process]

A perspective on bridging scales and design of models using low-dimensional manifolds and data-driven model inference.

Philos Trans A Math Phys Eng Sci. 2016 Nov 13;374(2080):

Authors: Tegnér J, Zenil H, Kiani NA, Ball G, Gomez-Cabrero D

Abstract
Systems in nature capable of collective behaviour are nonlinear, operating across several scales. Yet our ability to account for their collective dynamics differs in physics, chemistry and biology. Here, we briefly review the similarities and differences between mathematical modelling of adaptive living systems versus physico-chemical systems. We find that physics-based chemistry modelling and computational neuroscience have a shared interest in developing techniques for model reductions aiming at the identification of a reduced subsystem or slow manifold, capturing the effective dynamics. By contrast, as relations and kinetics between biological molecules are less characterized, current quantitative analysis under the umbrella of bioinformatics focuses on signal extraction, correlation, regression and machine-learning analysis. We argue that model reduction analysis and the ensuing identification of manifolds bridges physics and biology. Furthermore, modelling living systems presents deep challenges as how to reconcile rich molecular data with inherent modelling uncertainties (formalism, variables selection and model parameters). We anticipate a new generative data-driven modelling paradigm constrained by identified governing principles extracted from low-dimensional manifold analysis. The rise of a new generation of models will ultimately connect biology to quantitative mechanistic descriptions, thereby setting the stage for investigating the character of the model language and principles driving living systems.This article is part of the themed issue 'Multiscale modelling at the physics-chemistry-biology interface'.

PMID: 27698038 [PubMed - in process]

Systems biology: impressions from a newcomer graduate student in 2016.

Adv Physiol Educ. 2016 Dec;40(4):443-445

Authors: Simpson MR

Abstract
As a newcomer, the philosophical basis of systems biology seems intuitive and appealing, the underlying philosophy being that the whole of a living system cannot be completely understood by the study of its individual parts. Yet answers to the questions "What is systems biology?" and "What constitutes a systems biology approach in 2016?" are somewhat more elusive. This seems to be due largely to the diversity of disciplines involved and the varying emphasis placed on the computational modeling and experimental aspects of systems biology. As such, the education of systems biology would benefit from multidisciplinary collaboration with both instructors and students from a range of disciplines within the same course. This essay is the personal reflection of a graduate student trying to get an introductory overview of the field of systems biology and some thoughts about effective education of systems biology.

PMID: 27697957 [PubMed - in process]

The metabolome 18 years on: a concept comes of age.

Metabolomics. 2016;12(9):148

Authors: Kell DB, Oliver SG

Abstract
BACKGROUND: The term 'metabolome' was introduced to the scientific literature in September 1998.
AIM AND KEY SCIENTIFIC CONCEPTS OF THE REVIEW: To mark its 18-year-old 'coming of age', two of the co-authors of that paper review the genesis of metabolomics, whence it has come and where it may be going.

PMID: 27695392 [PubMed - in process]