AMIGO2, a toolbox for dynamic modeling, optimization and control in systems biology.

Bioinformatics. 2016 Jul 4;

Authors: Balsa-Canto E, Henriques D, Gabor A, Banga JR

Abstract
MOTIVATION: Many problems of interest in dynamic modeling and control of biological systems can be posed as non-linear optimization problems subject to algebraic and dynamic constraints. In the context of modeling, this is the case of e.g. parameter estimation, optimal experimental design and dynamic flux balance analysis. In the context of control, model-based metabolic engineering or drug dose optimization problems can be formulated as (multi-objective) optimal control problems. Finding a solution to those problems is a very challenging task which requires advanced numerical methods.
RESULTS: This work presents the AMIGO2 toolbox: the first multiplatform software tool that automatizes the solution of all those problems, offering a suite of state-of-the-art (multi-objective) global optimizers and advanced simulation approaches.
AVAILABILITY: The toolbox and its documentation are available at: sites.google.com/site/amigo2toolbox CONTACT: ebalsa@iim.csic.es.

PMID: 27378288 [PubMed - as supplied by publisher]

Comparative genomic analysis of novel Acinetobacter symbionts: A combined systems biology and genomics approach.

Sci Rep. 2016;6:29043

Authors: Gupta V, Haider S, Sood U, Gilbert JA, Ramjee M, Forbes K, Singh Y, Lopes BS, Lal R

Abstract
The increasing trend of antibiotic resistance in Acinetobacter drastically limits the range of therapeutic agents required to treat multidrug resistant (MDR) infections. This study focused on analysis of novel Acinetobacter strains using a genomics and systems biology approach. Here we used a network theory method for pathogenic and non-pathogenic Acinetobacter spp. to identify the key regulatory proteins (hubs) in each strain. We identified nine key regulatory proteins, guaA, guaB, rpsB, rpsI, rpsL, rpsE, rpsC, rplM and trmD, which have functional roles as hubs in a hierarchical scale-free fractal protein-protein interaction network. Two key hubs (guaA and guaB) were important for insect-associated strains, and comparative analysis identified guaA as more important than guaB due to its role in effective module regulation. rpsI played a significant role in all the novel strains, while rplM was unique to sheep-associated strains. rpsM, rpsB and rpsI were involved in the regulation of overall network topology across all Acinetobacter strains analyzed in this study. Future analysis will investigate whether these hubs are useful as drug targets for treating Acinetobacter infections.

PMID: 27378055 [PubMed - as supplied by publisher]

CO2-evoked release of PGE2 modulates sighs and inspiration as demonstrated in brainstem organotypic culture.

Elife. 2016 Jul 5;5

Authors: Forsberg D, Horn Z, Tserga E, Smedler E, Silberberg G, Shvarev Y, Kaila K, Uhlén P, Herlenius E

Abstract
Inflammation-induced release of prostaglandin E2 (PGE2) changes breathing patterns and the response to CO2 levels. This may have fatal consequences in newborn babies and result in sudden infant death. To elucidate the underlying mechanisms, we present a novel breathing brainstem organotypic culture that generates rhythmic neural network and motor activity for 3 weeks. We show that increased CO2 elicits a gap junction-dependent release of PGE2. This alters neural network activity in the preBötzinger rhythm-generating complex and in the chemosensitive brainstem respiratory regions, thereby increasing sigh frequency and the depth of inspiration. We used mice lacking eicosanoid prostanoid 3 receptors (EP3R), breathing brainstem organotypic slices and optogenetic inhibition of EP3R(+/+)cells to demonstrate that the EP3R is important for the ventilatory response to hypercapnia. Our study identifies a novel pathway linking the inflammatory and respiratory systems, with implications for inspiration and sighs throughout life, and the ability to autoresuscitate when breathing fails.

PMID: 27377173 [PubMed - as supplied by publisher]

Piecewise linear approximations to model the dynamics of adaptation to osmotic stress by food-borne pathogens.

Int J Food Microbiol. 2016 Jun 22;

Authors: Métris A, George SM, Ropers D

Abstract
Addition of salt to food is one of the most ancient and most common methods of food preservation. However, little is known of how bacterial cells adapt to such conditions. We propose to use piecewise linear approximations to model the regulatory adaptation of Escherichiacoli to osmotic stress. We apply the method to eight selected genes representing the functions known to be at play during osmotic adaptation. The network is centred on the general stress response factor, sigma S, and also includes a module representing the catabolic repressor CRP-cAMP. Glutamate, potassium and supercoiling are combined to represent the intracellular regulatory signal during osmotic stress induced by salt. The output is a module where growth is represented by the concentration of stable RNAs and the transcription of the osmotic gene osmY. The time course of gene expression of transport of osmoprotectant represented by the symporter proP and of the osmY is successfully reproduced by the network. The behaviour of the rpoS mutant predicted by the model is in agreement with experimental data. We discuss the application of the model to food-borne pathogens such as Salmonella; although the genes considered have orthologs, it seems that supercoiling is not regulated in the same way. The model is limited to a few selected genes, but the regulatory interactions are numerous and span different time scales. In addition, they seem to be condition specific: the links that are important during the transition from exponential to stationary phase are not all needed during osmotic stress. This model is one of the first steps towards modelling adaptation to stress in food safety and has scope to be extended to other genes and pathways, other stresses relevant to the food industry, and food-borne pathogens. The method offers a good compromise between systems of ordinary differential equations, which would be unmanageable because of the size of the system and for which insufficient data are available, and the more abstract Boolean methods.

PMID: 27377009 [PubMed - as supplied by publisher]

Affinity and dose of TCR engagement yield proportional enhancer and gene activity in CD4+ T cells.

Elife. 2016;5

Authors: Allison KA, Sajti E, Collier JG, Gosselin D, Troutman TD, Stone EL, Hedrick SM, Glass CK

Abstract
Affinity and dose of T cell receptor (TCR) interaction with antigens govern the magnitude of CD4+ T cell responses, but questions remain regarding the quantitative translation of TCR engagement into downstream signals. We find that while the response of mouse CD4+ T cells to antigenic stimulation is bimodal, activated cells exhibit analog responses proportional to signal strength. Gene expression output reflects TCR signal strength, providing a signature of T cell activation. Expression changes rely on a pre-established enhancer landscape and quantitative acetylation at AP-1 binding sites. Finally, we show that graded expression of activation genes depends on ERK pathway activation, suggesting that an ERK-AP-1 axis plays an important role in translating TCR signal strength into proportional activation of enhancers and genes essential for T cell function.

PMID: 27376549 [PubMed - as supplied by publisher]

Using systems biology to evaluate targets and mechanism of action of drugs for diabetes comorbidities.

Diabetologia. 2016 Jul 4;

Authors: Mayer B

Abstract
Medications approved for diabetes-associated renal and cardiovascular morbidities and candidate drugs currently in development are subject to substantial variability in drug response. Heterogeneity on a molecular phenotype level is not apparent at clinical presentation, which means that inter-individual differences in drug effect at the molecular level are masked. These findings identify the need for optimising patient phenotyping via use of molecular biomarkers for a personalised therapy approach. Molecular diversity may, on the one hand, result from the effect of genetic polymorphisms on drug transport, metabolism and effective target modulation. Equally relevant, differences may be due to molecular pathologies. The presence of distinct molecular phenotypes is suggested by classifiers aimed at modelling progressive disease. Such functions for prognosis incorporate a complex set of clinical variables or a multitude of molecular markers reflecting a diverse set of molecular disease mechanisms. This information on disease pathology and the mechanism of action of the drug needs to be systematically integrated with data on molecular biomarkers to develop an experimental tool for personalising medicine. The large amount of molecular data available for characterising diabetes-associated morbidities allows for elucidation of molecular process model representations of disease pathologies. Selecting biomarker candidates on such grounds and, in turn identifying their association with progressive disease allows for the identification of molecular processes associated with disease progression. The molecular effect of a drug can also be modelled at a molecular process level, and the integration of disease pathology and drug effect molecular models reveals candidate biomarkers for assessing drug response. Such tools serve as enrichment strategies aimed at adding precision to drug development and use.

PMID: 27376542 [PubMed - as supplied by publisher]

DNA methyltransferase inhibitors: an updated patent review (2012-2015).

Expert Opin Ther Pat. 2016 Jul 4;

Authors: Xu P, Hu G, Luo C, Liang Z

Abstract
INTRODUCTION: DNA methyltransferases (DNMTs), important enzymes involved in epigenetic regulation of gene expression, represent promising targets in cancer therapy. DNMT inhibitors (DNMTi), which can modulate the aberrant DNA methylation pattern in a reversible way via inhibiting DNMT activity, have attracted significant attention in recent years.
AREAS COVERED: This review outlines the newly patented inhibitors targeting DNMTs, mainly incorporating small molecular inhibitors and oligonucleotide derivatives. The chemical structures, biological activity, and the encouraging clinical research in progress are delineated in detail.
EXPERT OPINION: Two drugs, azacitidine and decitabine, have evidently shown efficacy in hematologic malignancies, yet do not work well on solid tumors, have low specificity, substantial toxicity, and poor bioavailability. With the rapid advancement in systems biology, drug combinations, such as DNMTi, in conjugation with histone deacetylase inhibitors (HDACi) or immunotherapy, probably serve as an efficient way of implementing epigenetic therapy. Meanwhile, the resolved autoinhibitory structures of DNMTs afford a novel strategy for targeting the protein-protein interface involved in the autoinhibitory interactions. The molecular mechanism underlying the conformational transitions would also shed new light on the design of allosteric inhibitors. Both strategies would produce inhibitors with more selectivity compared to nucleotide derivatives.

PMID: 27376512 [PubMed - as supplied by publisher]

Dynamic and modular gene regulatory networks drive the development of gametogenesis.

Brief Bioinform. 2016 Jul 3;

Authors: Che D, Wang Y, Bai W, Li L, Liu G, Zhang L, Zuo Y, Tao S, Hua J, Liao M

Abstract
Gametogenesis is a complex process, which includes mitosis and meiosis and results in the production of ovum and sperm. The development of gametogenesis is dynamic and needs many different genes to work synergistically, but it is lack of global perspective research about this process. In this study, we detected the dynamic process of gametogenesis from the perspective of systems biology based on protein-protein interaction networks (PPINs) and functional analysis. Results showed that gametogenesis genes have strong synergistic effects in PPINs within and between different phases during the development. Addition to the synergistic effects on molecular networks, gametogenesis genes showed functional consistency within and between different phases, which provides the further evidence about the dynamic process during the development of gametogenesis. At last, we detected and provided the core molecular modules of different phases about gametogenesis. The gametogenesis genes and related modules can be obtained from our Web site Gametogenesis Molecule Online (GMO, http://gametsonline.nwsuaflmz.com/index.php), which is freely accessible. GMO may be helpful for the reference and application of these genes and modules in the future identification of key genes about gametogenesis. Summary, this work provided a computational perspective and frame to the analysis of the gametogenesis dynamics and modularity in both human and mouse.

PMID: 27373733 [PubMed - as supplied by publisher]

[Using (1)H-nuclear magnetic resonance metabolomics and gene ontology to establish pathological staging model for esophageal cancer patients].

Zhonghua Wai Ke Za Zhi. 2016 Jul 1;54(7):540-545

Authors: Chen X, Wang K, Chen W, Jiang H, Deng PC, Li ZJ, Peng J, Zhou ZY, Yang H, Huang GX, Zeng J

Abstract
Objectives: By combining the metabolomics and computational biology, to explore the relationship between metabolic phenotype and pathological stage in esophageal cancer patients, to find the mechanism of metabolic network disturbance and develop a new method for fast preoperative clinical staging. Methods: A prospective cohort study (from April 2013 to January 2016) was conducted. The preoperative patients from Sichuan Provincial People's Hospital, who were diagnosed with esophageal cancer from May 2013 to April 2014 were included, and their serum samples were collected to detect (1)H-nuclear magnetic resonance (NMR) metabolomics for the purpose of drawing the metabolic fingerprinting in different stages of patients with esophageal cancer. The data were processed with these methods-principal components analysis: partial least squares regression and support vector machine, for the exploration of the enzyme-gene network regulatory mechanism in abnormal esophageal cancer metabolic network regulation and to build the quantitative prediction model of esophageal cancer staging in the end. All data were processed on high-performance computing platforms Matalab. The comparison of data had used Wilcoxon test, variance analysis, χ(2) test and Fisher exact test. Results: Twenty patients with different stages of esophageal cancer were included; and their serum metabolic fingerprinting could differentiate different tumor stages. There were no difference among the five teams in the age (F=1.086, P>0.05), the body mass index (F=1.035, P>0.05), the distance from the incisors to tumor (F=1.078, P>0.05). Among the patients with different TNM stages, there was a significant difference in plasma metabolome. Compared to ⅡB, ⅢA, Ⅳstage patients, increased levels of butanone, ethanol amine, homocysteine, hydroxy acids and estriol, together with decreased levels of glycoprotein, creatine, choline, isobutyricacid, alanine, leucine, valine, were observed inⅠB, ⅡA stage patients. Four metabolic markers (ethanol amine, hydroxy-propionic acid, homocysteine and estriol) were eventually selected. gene ontology analysis showed that 54 enzymes and genes regulated the 4 key metabolic markers. The quantitative prediction model of esophageal cancer staging based on esophageal cancer NMR spectrum were established. Cross-validation results showed that the predicted effect was good (root mean square error=5.3, R(2)=0.47, P=0.036). Conclusions: The systems biology approaches based on metabolomics and enzyme-gene regulatory network analysis can be used to quantify the metabolic network disturbance of patients with advanced esophageal cancer, and to predict preoperative clinical staging of esophageal cancer patients by plasma NMR metabolomics.

PMID: 27373482 [PubMed - as supplied by publisher]

Mineral and metabolic profiles in tea leaves and flowers during flower development.

Plant Physiol Biochem. 2016 Jun 14;106:316-326

Authors: Jia S, Wang Y, Hu J, Ding Z, Liang Q, Zhang Y, Wang H

Abstract
Tea [Camellia sinensis (L.) O. Kuntze] is one of the most popular non-alcoholic beverage crops in the world, and the physiological processes and gene regulations involved in development in tea plants have been well characterized. However, relatively little is known about the metabolic changes combined with mineral distributions that occur during flower development. Here we detected the contents of 11 elements in tea leaves and flowers and found that, some of them, especially phosphorus, sulfur and copper, showed significant changes during tea flowering. We also detected 122 metabolites in tea leaves and flowers and found that, 72 of them showed significant differences between flowers and leaves, of which sugars, organic acids, and flavonoids dominated. The sugars, such as trehalose and galactose, all accumulated in tea flowers, and the organic acids, such as malic acid, citric acid and fumaric acid involved in TCA cycle. The flavonoids, like epicatechin, catechin gallate and epigallocatechin, were more abundant in leaves. Furthermore, we found that the contents of 33 metabolites changed during the development of flowers. Especially, citric acid, phenylalanine and most flavonoids decreased while fructose and galactose increased during flowering stages in flowers. We also analyzed the correlations between the ions and metabolites and found that, some mineral nutrients including phosphorus, sulfur, manganese and zinc had close relations to organic acids, flavonoids, sugars and several amino acids during flowering. We mapped the metabolic pathway according to the KEGG database. This work will serve as the foundation for a systems biology approach to the understanding of mineral metabolism.

PMID: 27372442 [PubMed - as supplied by publisher]

Proteomic screening and lasso regression reveal differential signaling in insulin and insulin-like growth factor I pathways.

Mol Cell Proteomics. 2016 Jun 30;

Authors: Erdem C, Nagle AM, Casa AJ, Litzenburger BC, Wang YF, Taylor DL, Lee AV, Lezon TR

Abstract
Insulin and insulin-like growth factor I (IGF1) influence cancer risk and progression through poorly understood mechanisms. To better understand the roles of insulin and IGF1 signaling in breast cancer, we combined proteomic screening with computational network inference to uncover differences in IGF1 and insulin induced signaling. Using reverse phase protein array, we measured the levels of 134 proteins in 21 breast cancer cell lines stimulated with IGF1 or insulin for up to 48 hours. We then constructed directed protein expression networks using three separate methods: (i) lasso regression, (ii) conventional matrix inversion, and (iii) entropy maximization. These networks, named here as the time translation models, were analyzed and the inferred interactions were ranked by differential magnitude to identify pathway differences. The two top candidates, chosen for experimental validation, were shown to regulate IGF1/insulin induced phosphorylation events. First, acetyl-CoA carboxylase (ACC) knock-down was shown to increase the level of MAPK phosphorylation. Second, stable knock-down of E-Cadherin increased the phospho-Akt protein levels. Both of the knock-down perturbations incurred phosphorylation responses stronger in IGF1 stimulated cells compared to insulin. Overall, the time-translation modeling coupled to wet-lab experiments has proven to be powerful in inferring differential interactions downstream of IGF1 and insulin signaling, in vitro.

PMID: 27364358 [PubMed - as supplied by publisher]

Digital signaling decouples activation probability and population heterogeneity.

Elife. 2015;4:e08931

Authors: Kellogg RA, Tian C, Lipniacki T, Quake SR, Tay S

Abstract
Digital signaling enhances robustness of cellular decisions in noisy environments, but it is unclear how digital systems transmit temporal information about a stimulus. To understand how temporal input information is encoded and decoded by the NF-κB system, we studied transcription factor dynamics and gene regulation under dose- and duration-modulated inflammatory inputs. Mathematical modeling predicted and microfluidic single-cell experiments confirmed that integral of the stimulus (or area, concentration × duration) controls the fraction of cells that activate NF-κB in the population. However, stimulus temporal profile determined NF-κB dynamics, cell-to-cell variability, and gene expression phenotype. A sustained, weak stimulation lead to heterogeneous activation and delayed timing that is transmitted to gene expression. In contrast, a transient, strong stimulus with the same area caused rapid and uniform dynamics. These results show that digital NF-κB signaling enables multidimensional control of cellular phenotype via input profile, allowing parallel and independent control of single-cell activation probability and population heterogeneity.

PMID: 26488364 [PubMed - indexed for MEDLINE]

Calibration and analysis of genome-based models for microbial ecology.

Elife. 2015;4:e08208

Authors: Louca S, Doebeli M

Abstract
Microbial ecosystem modeling is complicated by the large number of unknown parameters and the lack of appropriate calibration tools. Here we present a novel computational framework for modeling microbial ecosystems, which combines genome-based model construction with statistical analysis and calibration to experimental data. Using this framework, we examined the dynamics of a community of Escherichia coli strains that emerged in laboratory evolution experiments, during which an ancestral strain diversified into two coexisting ecotypes. We constructed a microbial community model comprising the ancestral and the evolved strains, which we calibrated using separate monoculture experiments. Simulations reproduced the successional dynamics in the evolution experiments, and pathway activation patterns observed in microarray transcript profiles. Our approach yielded detailed insights into the metabolic processes that drove bacterial diversification, involving acetate cross-feeding and competition for organic carbon and oxygen. Our framework provides a missing link towards a data-driven mechanistic microbial ecology.

PMID: 26473972 [PubMed - indexed for MEDLINE]

Codon-level information improves predictions of inter-residue contacts in proteins by correlated mutation analysis.

Elife. 2015;4:e08932

Authors: Jacob E, Unger R, Horovitz A

Abstract
Methods for analysing correlated mutations in proteins are becoming an increasingly powerful tool for predicting contacts within and between proteins. Nevertheless, limitations remain due to the requirement for large multiple sequence alignments (MSA) and the fact that, in general, only the relatively small number of top-ranking predictions are reliable. To date, methods for analysing correlated mutations have relied exclusively on amino acid MSAs as inputs. Here, we describe a new approach for analysing correlated mutations that is based on combined analysis of amino acid and codon MSAs. We show that a direct contact is more likely to be present when the correlation between the positions is strong at the amino acid level but weak at the codon level. The performance of different methods for analysing correlated mutations in predicting contacts is shown to be enhanced significantly when amino acid and codon data are combined.

PMID: 26371555 [PubMed - indexed for MEDLINE]

Advanced continuous cultivation methods for systems microbiology.

Microbiology. 2015 Sep;161(9):1707-19

Authors: Adamberg K, Valgepea K, Vilu R

Abstract
Increasing the throughput of systems biology-based experimental characterization of in silico-designed strains has great potential for accelerating the development of cell factories. For this, analysis of metabolism in the steady state is essential as only this enables the unequivocal definition of the physiological state of cells, which is needed for the complete description and in silico reconstruction of their phenotypes. In this review, we show that for a systems microbiology approach, high-resolution characterization of metabolism in the steady state--growth space analysis (GSA)--can be achieved by using advanced continuous cultivation methods termed changestats. In changestats, an environmental parameter is continuously changed at a constant rate within one experiment whilst maintaining cells in the physiological steady state similar to chemostats. This increases the resolution and throughput of GSA compared with chemostats, and, moreover, enables following of the dynamics of metabolism and detection of metabolic switch-points and optimal growth conditions. We also describe the concept, challenge and necessary criteria of the systematic analysis of steady-state metabolism. Finally, we propose that such systematic characterization of the steady-state growth space of cells using changestats has value not only for fundamental studies of metabolism, but also for systems biology-based metabolic engineering of cell factories.

PMID: 26220303 [PubMed - indexed for MEDLINE]

Sex-differential heterologous (non-specific) effects of vaccines: an emerging public health issue that needs to be understood and exploited.

Expert Rev Vaccines. 2016 Jun 30;:1-9

Authors: Flanagan KL, Plebanski M

Abstract
INTRODUCTION: Vaccines have heterologous effects on the immune system, leading to altered susceptibility to a range of pathogens, and possibly allergy and autoimmunity. Effects are often sex-differential. This review discusses the evidence, mechanisms and public health implications of the non-specific effects of vaccines (NSEs).
AREAS COVERED: This article firstly discusses the World Health Organization systematic review of the evidence for sex-differential heterologous effects of vaccines, and further PubMed indexed studies on NSEs on susceptibility to infectious diseases, allergy, autoimmunity and malignancy in animals and humans. Potential immunological mechanisms are evaluated, including sex-differential effects. Finally it describes how advances in systems biology might be applied to study such effects. Expert commentary: This section points out the need to understand immune mechanisms in order to exploit beneficial vaccine effects, and diminish deleterious ones. It suggests analysis of vaccine effects by sex is important, and discusses the future for personalised vaccines that take these effects into account.

PMID: 27362915 [PubMed - as supplied by publisher]

Recon 2.2: from reconstruction to model of human metabolism.

Metabolomics. 2016;12:109

Authors: Swainston N, Smallbone K, Hefzi H, Dobson PD, Brewer J, Hanscho M, Zielinski DC, Ang KS, Gardiner NJ, Gutierrez JM, Kyriakopoulos S, Lakshmanan M, Li S, Liu JK, Martínez VS, Orellana CA, Quek LE, Thomas A, Zanghellini J, Borth N, Lee DY, Nielsen LK, Kell DB, Lewis NE, Mendes P

Abstract
INTRODUCTION: The human genome-scale metabolic reconstruction details all known metabolic reactions occurring in humans, and thereby holds substantial promise for studying complex diseases and phenotypes. Capturing the whole human metabolic reconstruction is an on-going task and since the last community effort generated a consensus reconstruction, several updates have been developed.
OBJECTIVES: We report a new consensus version, Recon 2.2, which integrates various alternative versions with significant additional updates. In addition to re-establishing a consensus reconstruction, further key objectives included providing more comprehensive annotation of metabolites and genes, ensuring full mass and charge balance in all reactions, and developing a model that correctly predicts ATP production on a range of carbon sources.
METHODS: Recon 2.2 has been developed through a combination of manual curation and automated error checking. Specific and significant manual updates include a respecification of fatty acid metabolism, oxidative phosphorylation and a coupling of the electron transport chain to ATP synthase activity. All metabolites have definitive chemical formulae and charges specified, and these are used to ensure full mass and charge reaction balancing through an automated linear programming approach. Additionally, improved integration with transcriptomics and proteomics data has been facilitated with the updated curation of relationships between genes, proteins and reactions.
RESULTS: Recon 2.2 now represents the most predictive model of human metabolism to date as demonstrated here. Extensive manual curation has increased the reconstruction size to 5324 metabolites, 7785 reactions and 1675 associated genes, which now are mapped to a single standard. The focus upon mass and charge balancing of all reactions, along with better representation of energy generation, has produced a flux model that correctly predicts ATP yield on different carbon sources.
CONCLUSION: Through these updates we have achieved the most complete and best annotated consensus human metabolic reconstruction available, thereby increasing the ability of this resource to provide novel insights into normal and disease states in human. The model is freely available from the Biomodels database (http://identifiers.org/biomodels.db/MODEL1603150001).

PMID: 27358602 [PubMed - as supplied by publisher]

Mapping transcription factor interactome networks using HaloTag protein arrays.

Proc Natl Acad Sci U S A. 2016 Jun 29;

Authors: Yazaki J, Galli M, Kim AY, Nito K, Aleman F, Chang KN, Carvunis AR, Quan R, Nguyen H, Song L, Alvarez JM, Huang SC, Chen H, Ramachandran N, Altmann S, Gutiérrez RA, Hill DE, Schroeder JI, Chory J, LaBaer J, Vidal M, Braun P, Ecker JR

Abstract
Protein microarrays enable investigation of diverse biochemical properties for thousands of proteins in a single experiment, an unparalleled capacity. Using a high-density system called HaloTag nucleic acid programmable protein array (HaloTag-NAPPA), we created high-density protein arrays comprising 12,000 Arabidopsis ORFs. We used these arrays to query protein-protein interactions for a set of 38 transcription factors and transcriptional regulators (TFs) that function in diverse plant hormone regulatory pathways. The resulting transcription factor interactome network, TF-NAPPA, contains thousands of novel interactions. Validation in a benchmarked in vitro pull-down assay revealed that a random subset of TF-NAPPA validated at the same rate of 64% as a positive reference set of literature-curated interactions. Moreover, using a bimolecular fluorescence complementation (BiFC) assay, we confirmed in planta several interactions of biological interest and determined the interaction localizations for seven pairs. The application of HaloTag-NAPPA technology to plant hormone signaling pathways allowed the identification of many novel transcription factor-protein interactions and led to the development of a proteome-wide plant hormone TF interactome network.

PMID: 27357687 [PubMed - as supplied by publisher]

Quantitative deep-mapping of the cultured podocyte proteome uncovers shifts in proteostatic mechanisms during differentiation.

Am J Physiol Cell Physiol. 2016 Jun 29;:ajpcell.00121.2016

Authors: Rinschen MM, Schroeter CB, Koehler S, Ising C, Schermer B, Kann M, Benzing T, Brinkkoetter PT

Abstract
The renal filtration barrier is maintained by the renal podocyte, an epithelial postmitotic cell. Immortalized mouse podocyte cell lines - both in the differentiated and undifferentiated state - are widely utilized tools to estimate podocyte injury and cytoskeletal rearrangement processes in vitro. Here, we mapped the cultured podocyte proteome at a depth of more than 8800 proteins and quantified 7240 proteins. Copy numbers of proteins mutated in forms of hereditary nephrotic syndrome or focal segmental glomerulosclerosis (FSGS) were assessed. We found that cultured podocytes express abundant copy numbers of endogenous receptors such as tyrosine kinase membrane receptors, the G-protein coupled receptor (GPCR), NPR3 (ANP receptor) and several poorly characterized GPCRs. The dataset was correlated with deep mapping mRNA sequencing ("mRNAseq") data from the native mouse podocyte, the native mouse podocyte proteome and staining intensities from the human protein atlas. The generated dataset was similar to these previously published resources, but several native and high-abundant podocyte-specific proteins were not identified in the dataset. Notably, this dataset detected general perturbations in proteostatic mechanisms as a dominant alteration during podocyte differentiation, with high proteasome activity in the undifferentiated state and markedly increased expression of lysosomal proteins in the differentiated state. Phosphoproteomics analysis of mouse podocytes at a resolution of more than 3000 sites suggested a preference of phosphorylation of actin-filament associated proteins in the differentiated state. The dataset obtained here provides a resource and provides the means for deep mapping of the native podocyte proteome and phosphoproteome in a similar manner.

PMID: 27357545 [PubMed - as supplied by publisher]

Unified pre- and postsynaptic long-term plasticity enables reliable and flexible learning.

Elife. 2015;4

Authors: Costa RP, Froemke RC, Sjöström PJ, van Rossum MC

Abstract
Although it is well known that long-term synaptic plasticity can be expressed both pre- and postsynaptically, the functional consequences of this arrangement have remained elusive. We show that spike-timing-dependent plasticity with both pre- and postsynaptic expression develops receptive fields with reduced variability and improved discriminability compared to postsynaptic plasticity alone. These long-term modifications in receptive field statistics match recent sensory perception experiments. Moreover, learning with this form of plasticity leaves a hidden postsynaptic memory trace that enables fast relearning of previously stored information, providing a cellular substrate for memory savings. Our results reveal essential roles for presynaptic plasticity that are missed when only postsynaptic expression of long-term plasticity is considered, and suggest an experience-dependent distribution of pre- and postsynaptic strength changes.

PMID: 26308579 [PubMed - indexed for MEDLINE]