Comparative assessment of strategies to identify similar ligand-binding pockets in proteins

Background

Detecting similar ligand-binding sites in globally unrelated proteins has a wide range of applications in modern drug discovery, including drug repurposing, the prediction of side effects, and drug-target interactions. Although a number of techniques to compare binding pockets have been developed, this problem still poses significant challenges.

Results

We evaluate the performance of three algorithms to calculate similarities between ligand-binding sites, APoc, SiteEngine, and G-LoSA. Our assessment considers not only the capabilities to identify similar pockets and to construct accurate local alignments, but also the dependence of these alignments on the sequence order. We point out certain drawbacks of previously compiled datasets, such as the inclusion of structurally similar proteins, leading to an overestimated performance. To address these issues, a rigorous procedure to prepare unbiased, high-quality benchmarking sets is proposed. Further, we conduct a comparative assessment of techniques directly aligning binding pockets to indirect strategies employing structure-based virtual screening with AutoDock Vina and rDock.

Conclusions

Thorough benchmarks reveal that G-LoSA offers a fairly robust overall performance, whereas the accuracy of APoc and SiteEngine is satisfactory only against easy datasets. Moreover, combining various algorithms into a meta-predictor improves the performance of existing methods to detect similar binding sites in unrelated proteins by 5–10%. All data reported in this paper are freely available at https://osf.io/6ngbs/.

The Mouse as a Model Organism for Assessing Anesthetic Sensitivity

The mouse has been used in many medical fields as a powerful model to reveal the genetic basis of human physiology and disease. The past two decades have witnessed an enormous wealth of genetic and informatic resources dedicated to this humble organism. With the ongoing revolution in mapping neural circuitry governing behavior, the mouse is an ideal model organism poised to unravel the mysteries of general anesthetic action. This chapter will describe and provide guidelines for anesthetic phenotyping in the mouse including both motor-dependent and motor-independent assessments.

NAD+ Intermediates: The Biology and Therapeutic Potential of NMN and NR

Research on the biology of NAD+ has been gaining momentum, providing many critical insights into the pathogenesis of age-associated functional decline and diseases. In particular, two key NAD+ intermediates, nicotinamide riboside (NR) and nicotinamide mononucleotide (NMN), have been extensively studied over the past several years. Supplementing these NAD+ intermediates has shown preventive and therapeutic effects, ameliorating age-associated pathophysiologies and disease conditions. Although the pharmacokinetics and metabolic fates of NMN and NR are still under intensive investigation, these NAD+ intermediates can exhibit distinct behavior, and their fates appear to depend on the tissue distribution and expression levels of NAD+ biosynthetic enzymes, nucleotidases, and presumptive transporters for each. A comprehensive concept that connects NAD+ metabolism to the control of aging and longevity in mammals has been proposed, and the stage is now set to test whether these exciting preclinical results can be translated to improve human health.

Recon3D enables a three-dimensional view of gene variation in human metabolism

Genome-scale network reconstructions have helped uncover the molecular basis of metabolism. Here we present Recon3D, a computational resource that includes three-dimensional (3D) metabolite and protein structure data and enables integrated analyses of metabolic functions in humans. We use Recon3D to functionally characterize mutations associated with disease, and identify metabolic response signatures that are caused by exposure to certain drugs. Recon3D represents the most comprehensive human metabolic network model to date, accounting for 3,288 open reading frames (representing 17% of functionally annotated human genes), 13,543 metabolic reactions involving 4,140 unique metabolites, and 12,890 protein structures. These data provide a unique resource for investigating molecular mechanisms of human metabolism. Recon3D is available at http://vmh.life.

Metabolomic elucidation of the effects of media and carbon sources on fatty acid production by Yarrowia lipolytica

Lipid production by oleaginous Yarrowia lipolytica depends highly on culture environments, such as carbon sources, carbon/nitrogen (C/N) ratios, types of media, and cellular growth phases. In this study, the effects of media and carbon sources on lipid and metabolite production were investigated by profiling fatty acids and intracellular metabolites of Y. lipolytica grown in various media. The highest total fatty acid yield 114.04 ± 6.23 mg/g dry cell weight was achieved by Y. lipolytica grown in minimal medium with glycerol (SCG) in the exponential phase. The high lipid production by Y. lipolytica in SCG was presumed to be due to the higher C/N ratio in SCG than in the complex media. Moreover, glycerol promoted lipid production better than glucose in both complex and minimal media because glycerol can easily incorporate into the core of triglycerides. Metabolite profiling revealed that levels of long-chain fatty acids, such as stearic acid, palmitic acid, and arachidic acid, increased in SCG medium. Meanwhile, in complex media supplemented with either glucose or glycerol, levels of amino acids, such as cysteine, methionine, and glycine, highly increased. This metabolomic approach could be applied to modulate the global metabolic network of Y. lipolytica for producing lipids and other valuable products.

Datasets2Tools, repository and search engine for bioinformatics datasets, tools and canned analyses

Biomedical data repositories such as the Gene Expression Omnibus (GEO) enable the search and discovery of relevant biomedical digital data objects. Similarly, resources such as OMICtools, index bioinformatics tools that can extract knowledge from these digital data objects. However, systematic access to pre-generated 'canned' analyses applied by bioinformatics tools to biomedical digital data objects is currently not available. Datasets2Tools is a repository indexing 31,473 canned bioinformatics analyses applied to 6,431 datasets. The Datasets2Tools repository also contains the indexing of 4,901 published bioinformatics software tools, and all the analyzed datasets. Datasets2Tools enables users to rapidly find datasets, tools, and canned analyses through an intuitive web interface, a Google Chrome extension, and an API. Furthermore, Datasets2Tools provides a platform for contributing canned analyses, datasets, and tools, as well as evaluating these digital objects according to their compliance with the findable, accessible, interoperable, and reusable (FAIR) principles. By incorporating community engagement, Datasets2Tools promotes sharing of digital resources to stimulate the extraction of knowledge from biomedical research data. Datasets2Tools is freely available from: http://amp.pharm.mssm.edu/datasets2tools.

Integration of genome-scale metabolic networks into whole-body PBPK models shows phenotype-specific cases of drug-induced metabolic perturbation

Drug-induced perturbations of the endogenous metabolic network are a potential root cause of cellular toxicity. A mechanistic understanding of such unwanted side effects during drug therapy is therefore vital for patient safety. The comprehensive assessment of such drug-induced injuries requires the simultaneous consideration of both drug exposure at the whole-body and resulting biochemical responses at the cellular level. We here present a computational multi-scale workflow that combines whole-body physiologically based pharmacokinetic (PBPK) models and organ-specific genome-scale metabolic network (GSMN) models through shared reactions of the xenobiotic metabolism. The applicability of the proposed workflow is illustrated for isoniazid, a first-line antibacterial agent against Mycobacterium tuberculosis, which is known to cause idiosyncratic drug-induced liver injuries (DILI). We combined GSMN models of a human liver with N-acetyl transferase 2 (NAT2)-phenotype-specific PBPK models of isoniazid. The combined PBPK-GSMN models quantitatively describe isoniazid pharmacokinetics, as well as intracellular responses, and changes in the exometabolome in a human liver following isoniazid administration. Notably, intracellular and extracellular responses identified with the PBPK-GSMN models are in line with experimental and clinical findings. Moreover, the drug-induced metabolic perturbations are distributed and attenuated in the metabolic network in a phenotype-dependent manner. Our simulation results show that a simultaneous consideration of both drug pharmacokinetics at the whole-body and metabolism at the cellular level is mandatory to explain drug-induced injuries at the patient level. The proposed workflow extends our mechanistic understanding of the biochemistry underlying adverse events and may be used to prevent drug-induced injuries in the future.

The genotype of a patient determines the extent of drug-induced metabolic perturbations on the endogenous cellular network of the liver. A team around Lars Kuepfer at Germany’s RWTH Aachen University developed a computational workflow that links drug pharmacokinetics at the whole-body level with a cellular network of the liver. The authors used the competitive cofactor and energy demands in endogenous and drug metabolism to establish a multi-scale model for the antibiotic isoniazid. Their model quantitatively describes how isoniazid pharmacokinetics alter the intracellular liver biochemistry and the utilization of extracellular metabolites in different patient genotypes. The study outlines how a mechanistic understanding of genotype-dependent drug-induced metabolic perturbations may help to explain diverging incidence rates of toxic events in different patient subgroups. This could reduce the occurrence of toxic side effects during drug treatments in the future.

NetLand: quantitative modeling and visualization of Waddington's epigenetic landscape using probabilistic potential

Summary

Waddington’s epigenetic landscape is a powerful metaphor for cellular dynamics driven by gene regulatory networks (GRNs). Its quantitative modeling and visualization, however, remains a challenge, especially when there are more than two genes in the network. A software tool for Waddington’s landscape has not been available in the literature. We present NetLand, an open-source software tool for modeling and simulating the kinetic dynamics of GRNs, and visualizing the corresponding Waddington’s epigenetic landscape in three dimensions without restriction on the number of genes in a GRN. With an interactive and graphical user interface, NetLand can facilitate the knowledge discovery and experimental design in the study of cell fate regulation (e.g. stem cell differentiation and reprogramming).

Availability and Implementation

NetLand can run under operating systems including Windows, Linux and OS X. The executive files and source code of NetLand as well as a user manual, example models etc. can be downloaded from http://netland-ntu.github.io/NetLand/.

Supplementary information

Supplementary data are available at Bioinformatics online.

Apixaban and Rosuvas--tatin Pharmacokinetics in Nonalcoholic Fatty Liver Disease

There is little known about the impact of nonalcoholic fatty liver disease (NAFLD) on drug metabolism and transport. We examined the pharmacokinetics of oral apixaban (2.5 mg) and rosuvastatin (5 mg) when administered simultaneously in subjects with magnetic resonance imaging-confirmed NAFLD (N = 22) and healthy control subjects (N = 12). The area under the concentration-time curve to the last sampling time (AUC_0-12) values for apixaban were not different between control and NAFLD subjects (671 and 545 ng/ml × hour, respectively; P = 0.15). Similarly, the AUC_0-12 values for rosuvastatin did not differ between the control and NAFLD groups (25.4 and 20.1 ng/ml × hour, respectively; P = 0.28). Furthermore, hepatic fibrosis in NAFLD subjects was not associated with differences in apixaban or rosuvastatin pharmacokinetics. Decreased systemic exposures for both apixaban and rosuvastatin were associated with increased body weight (P < 0.001 and P < 0.05, respectively). In multivariable linear regression analyses, only participant weight but not NAFLD, age, or SLCO1B1/ABCG2/CYP3A5 genotypes, was associated with apixaban and rosuvastatin AUC_0-12 (P < 0.001 and P = 0.06, respectively). NAFLD does not appear to affect the pharmacokinetics of apixaban or rosuvastatin.

White and beige adipocytes: are they metabolically distinct?

The white adipose tissue (WAT) exhibits great plasticity and can undergo "browning" and acquire features of the brown adipose tissue (BAT), which takes place following cold exposure, chronic endurance exercise or β3-adrenergic stimulation. WAT that underwent browning is characterized by the presence of "beige" adipocytes, which are morphologically similar to brown adipocytes, express uncoupling protein 1 (UCP1) and are considered thermogenically competent. Thus, inducing a BAT-like phenotype in the WAT could promote energy dissipation within this depot, reducing the availability of substrate that would otherwise be stored in the WAT. Importantly, BAT in humans only represents a small proportion of total body mass, which limits the thermogenic capacity of this tissue. Therefore, browning of the WAT could significantly expand the energy-dissipating capacity of the organism and be of therapeutic value in the treatment of metabolic diseases. However, the question remains as to whether WAT indeed changes its metabolic profile from an essentially fat storage/release compartment to an energy dissipating compartment that functions much like BAT. Here, we discuss the differences with respect to thermogenic capacity and metabolic characteristics between white and beige adipocytes to determine whether the latter cells indeed significantly enhance their capacity to dissipate energy through UCP1-mediated mitochondrial uncoupling or by the activation of alternative UCP1-independent futile cycles.

Perfect Adaptation and Optimal Equilibrium Productivity in a Simple Microbial Biofuel Metabolic Pathway Using Dynamic Integral Control

The production of complex biomolecules by genetically engineered organisms is one of the most promising applications of metabolic engineering and synthetic biology. To obtain processes with high productivity, it is therefore crucial to design and implement efficient dynamic in vivo regulation strategies. We consider here the microbial biofuel production model of Dunlop et al. (2010) for which we demonstrate that an antithetic dynamic integral control strategy can achieve robust perfect adaptation for the intracellular biofuel concentration in the presence of poorly known network parameters and implementation errors in certain rate parameters of the controller. We also show that the maximum equilibrium extracellular biofuel productivity is fully defined by some of the network parameters and, in this respect, it can only be achieved when all the corresponding parameters are perfectly known. Since this optimum is a network property, it cannot be improved by the use of any controller that measures the intracellular biofuel concentration and acts on the production of pump proteins. Additional intrinsic fundamental properties for the process are also unveiled, the most important ones being the existence of a conservation relation between the productivity and the toxicity, a low sensitivity of the optimal productivity with respect to a poor implementation of the set-point for the intracellular biofuel, and a strong intrinsic robustness property of the optimal productivity with respect to poorly known parameters. Taken together, these results demonstrate that a high and robust equilibrium rate of production for the extracellular biofuel can be achieved when the parameters of the model are poorly known and those of the controllers are poorly implemented. Finally, several advantages of the proposed dynamic strategy over a static one are also emphasized.

Muscle molecular adaptations to endurance exercise training are conditioned by glycogen availability: a proteomics-based analysis in the McArdle mouse model

KEY POINTS

Although they are unable to utilize muscle glycogen, McArdle mice adapt favourably to an individualized moderate-intensity endurance exercise training regime. Yet, they fail to reach the performance capacity of healthy mice with normal glycogen availability. There is a remarkable difference in the protein networks involved in muscle tissue adaptations to endurance exercise training in mice with and without glycogen availability. Indeed, endurance exercise training promoted the expression of only three proteins common to both McArdle and wild-type mice: LIMCH1, PARP1 and TIGD4. In turn, trained McArdle mice presented strong expression of mitogen-activated protein kinase 12 (MAPK12).

ABSTRACT

McArdle's disease is an inborn disorder of skeletal muscle glycogen metabolism that results in blockade of glycogen breakdown due to mutations in the myophosphorylase gene. We recently developed a mouse model carrying the homozygous p.R50X common human mutation (McArdle mouse), facilitating the study of how glycogen availability affects muscle molecular adaptations to endurance exercise training. Using quantitative differential analysis by liquid chromatography with tandem mass spectrometry, we analysed the quadriceps muscle proteome of 16-week-old McArdle (n = 5) and wild-type (WT) (n = 4) mice previously subjected to 8 weeks' moderate-intensity treadmill training or to an equivalent control (no training) period. Protein networks enriched within the differentially expressed proteins with training in WT and McArdle mice were assessed by hypergeometric enrichment analysis. Whereas endurance exercise training improved the estimated maximal aerobic capacity of both WT and McArdle mice as compared with controls, it was ∼50% lower than normal in McArdle mice before and after training. We found a remarkable difference in the protein networks involved in muscle tissue adaptations induced by endurance exercise training with and without glycogen availability, and training induced the expression of only three proteins common to McArdle and WT mice: LIM and calponin homology domains-containing protein 1 (LIMCH1), poly (ADP-ribose) polymerase 1 (PARP1 - although the training effect was more marked in McArdle mice), and tigger transposable element derived 4 (TIGD4). Trained McArdle mice presented strong expression of mitogen-activated protein kinase 12 (MAPK12). Through an in-depth proteomic analysis, we provide mechanistic insight into how glycogen availability affects muscle protein signalling adaptations to endurance exercise training.

Insights into the neurochemical signature of the Innervation of Beige Fat

Objective

The potential for brown adipose tissue (BAT) to be targeted as a therapeutic option to combat obesity has been heightened by the discovery of a brown–like form of inducible “beige” adipose tissue in white fat which has overlapping structural and functional properties to “classical” BAT. The likelihood that both beige and brown fat are recruited functionally by neural mechanisms, taken together with the lack of a detailed understanding of the nature of changes in the nervous system when white adipose tissue (WAT) is transformed to brown, provides the impetus for this study. Here, we aim to identify whether there is a shift in the gene expression profile in neurons directly innervating inguinal white adipose tissue (iWAT) that has undergone “beiging” to a signature that is more similar to neurons projecting to BAT.

Methods

Two groups of rats, one housed at thermoneutrality (27 °C) and the other exposed to cold (8 °C) for 7 days, were killed, and their T13/L1 ganglia, stellate ganglion (T1/T2), or superior cervical ganglion (SCG, C2/3) removed. This approach yielded ganglia containing neurons that innervate either beiged white fat (8 °C for 7 days), inguinal WAT (27 °C for 7 days), BAT (both 27 °C and 8 °C for 7 days) or non-WAT (8 °C for 7 days), the latter included to isolate changes in gene expression that were more aligned with a response to cold exposure than the transformation of white to beige adipocytes. Bioinformatics analyses of RNA sequencing data was performed followed by Ingenuity Pathway Analysis (IPA) to determine differential gene expression and recruitment of biosynthetic pathways.

Results

When iWAT is “beiged” there is a significant shift in the gene expression profile of neurons in sympathetic ganglia (T13/L1) innervating this depot toward a gene neurochemical signature that is similar to the stellate ganglion projecting to BAT. Bioinformatics analyses of “beiging” related genes revealed upregulation of genes encoding neuropeptides proopiomelanocortin (POMC) and calcitonin-gene related peptide (CGRP) within ganglionic neurons. Treatment of differentiated 3T3L1 adipocytes with αMSH, one of the products cleaved from POMC, results in an elevation in lipolysis and the beiging of these cells as indicated by changes in gene expression markers of browning (Ucp1 and Ppargc1a).

Conclusion

These data indicate that, coincident with beiging, there is a shift toward a “brown-like” neurochemical signature of postganglionic neurons projecting to inguinal white fat, an increased expression of POMC, and, consistent with a causative role for this prohormone in beiging, an αMSH-mediated increase in beige gene markers in isolated adipocytes.

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RNA Seq showed shifts in neuronal gene expression following browning of white fat.

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Gene expression in ganglia projecting to white fat became brown-like with beiging.

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Bioinformatics analyses revealed neuronal gene candidates associated with beiging.

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Prominent gene candidates associated with beiging included POMC and CGRP.

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POMC cleavage product α-MSH caused beiging of cultured fat cells.

A roadmap towards personalized immunology

Big data generation and computational processing will enable medicine to evolve from a "one-size-fits-all" approach to precise patient stratification and treatment. Significant achievements using "Omics" data have been made especially in personalized oncology. However, immune cells relative to tumor cells show a much higher degree of complexity in heterogeneity, dynamics, memory-capability, plasticity and "social" interactions. There is still a long way ahead on translating our capability to identify potentially targetable personalized biomarkers into effective personalized therapy in immune-centralized diseases. Here, we discuss the recent advances and successful applications in "Omics" data utilization and network analysis on patients' samples of clinical trials and studies, as well as the major challenges and strategies towards personalized stratification and treatment for infectious or non-communicable inflammatory diseases such as autoimmune diseases or allergies. We provide a roadmap and highlight experimental, clinical, computational analysis, data management, ethical and regulatory issues to accelerate the implementation of personalized immunology.

Primitive Endoderm Differentiation: From Specification to Epithelialization

At the time of implantation, the mouse blastocyst has developed three cell lineages: the epiblast (Epi), the primitive endoderm (PrE), and the trophectoderm (TE). The PrE and TE are extraembryonic tissues but their interactions with the Epi are critical to sustain embryonic growth, as well as to pattern the embryo. We review here the cellular and molecular events that lead to the production of PrE and Epi lineages and discuss the different hypotheses that are proposed for the induction of these cell types. In the second part, we report the current knowledge about the epithelialization of the PrE.

A stochastic and dynamical view of pluripotency in mouse embryonic stem cells

Pluripotent embryonic stem cells are of paramount importance for biomedical sciences because of their innate ability for self-renewal and differentiation into all major cell lines. The fateful decision to exit or remain in the pluripotent state is regulated by complex genetic regulatory networks. The rapid growth of single-cell sequencing data has greatly stimulated applications of statistical and machine learning methods for inferring topologies of pluripotency regulating genetic networks. The inferred network topologies, however, often only encode Boolean information while remaining silent about the roles of dynamics and molecular stochasticity inherent in gene expression. Herein we develop a framework for systematically extending Boolean-level network topologies into higher resolution models of networks which explicitly account for the promoter architectures and gene state switching dynamics. We show the framework to be useful for disentangling the various contributions that gene switching, external signaling, and network topology make to the global heterogeneity and dynamics of transcription factor populations. We find the pluripotent state of the network to be a steady state which is robust to global variations of gene switching rates which we argue are a good proxy for epigenetic states of individual promoters. The temporal dynamics of exiting the pluripotent state, on the other hand, is significantly influenced by the rates of genetic switching which makes cells more responsive to changes in extracellular signals.

Boolean network modeling in systems pharmacology

Quantitative systems pharmacology (QSP) is an emerging discipline that aims to discover how drugs modulate the dynamics of biological components in molecular and cellular networks and the impact of those perturbations on human pathophysiology. The integration of systems-based experimental and computational approaches is required to facilitate the advancement of this field. QSP models typically consist of a series of ordinary differential equations (ODE). However, this mathematical framework requires extensive knowledge of parameters pertaining to biological processes, which is often unavailable. An alternative framework that does not require knowledge of system-specific parameters, such as Boolean network modeling, could serve as an initial foundation prior to the development of an ODE-based model. Boolean network models have been shown to efficiently describe, in a qualitative manner, the complex behavior of signal transduction and gene/protein regulatory processes. In addition to providing a starting point prior to quantitative modeling, Boolean network models can also be utilized to discover novel therapeutic targets and combinatorial treatment strategies. Identifying drug targets using a network-based approach could supplement current drug discovery methodologies and help to fill the innovation gap across the pharmaceutical industry. In this review, we discuss the process of developing Boolean network models and the various analyses that can be performed to identify novel drug targets and combinatorial approaches. An example for each of these analyses is provided using a previously developed Boolean network of signaling pathways in multiple myeloma. Selected examples of Boolean network models of human (patho-)physiological systems are also reviewed in brief.

Approximate Bayesian computation reveals the importance of repeated measurements for parameterising cell-based models of growing tissues

The growth and dynamics of epithelial tissues govern many morphogenetic processes in embryonic development. A recent quantitative transition in data acquisition, facilitated by advances in genetic and live-imaging techniques, is paving the way for new insights to these processes. Computational models can help us understand and interpret observations, and then make predictions for future experiments that can distinguish between hypothesised mechanisms. Increasingly, cell-based modelling approaches such as vertex models are being used to help understand the mechanics underlying epithelial morphogenesis. These models typically seek to reproduce qualitative phenomena, such as cell sorting or tissue buckling. However, it remains unclear to what extent quantitative data can be used to constrain these models so that they can then be used to make quantitative, experimentally testable predictions. To address this issue, we perform an in silico study to investigate whether vertex model parameters can be inferred from imaging data, and explore methods to quantify the uncertainty of such estimates. Our approach requires the use of summary statistics to estimate parameters. Here, we focus on summary statistics of cellular packing and of laser ablation experiments, as are commonly reported from imaging studies. We find that including data from repeated experiments is necessary to generate reliable parameter estimates that can facilitate quantitative model predictions.

Neuroprotective Drug for Nerve Trauma Revealed Using Artificial Intelligence

Here we used a systems biology approach and artificial intelligence to identify a neuroprotective agent for the treatment of peripheral nerve root avulsion. Based on accumulated knowledge of the neurodegenerative and neuroprotective processes that occur in motoneurons after root avulsion, we built up protein networks and converted them into mathematical models. Unbiased proteomic data from our preclinical models were used for machine learning algorithms and for restrictions to be imposed on mathematical solutions. Solutions allowed us to identify combinations of repurposed drugs as potential neuroprotective agents and we validated them in our preclinical models. The best one, NeuroHeal, neuroprotected motoneurons, exerted anti-inflammatory properties and promoted functional locomotor recovery. NeuroHeal endorsed the activation of Sirtuin 1, which was essential for its neuroprotective effect. These results support the value of network-centric approaches for drug discovery and demonstrate the efficacy of NeuroHeal as adjuvant treatment with surgical repair for nervous system trauma.

The use of infrared thermography in the measurement and characterization of brown adipose tissue activation

Interest in brown adipose tissue has increased in recent years as a potential target for novel obesity, diabetes and metabolic disease treatments. One of the significant limitations to rapid progress has been the difficulty in measuring brown adipose tissue activity, especially in humans. Infrared thermography (IRT) is being increasingly recognized as a valid and complementary method to standard imaging modalities, such as positron emission tomography-computed tomography (PET/CT). In contrast to PET/CT, it is non-invasive, cheap and quick, allowing, for the first time, the possibility of large studies of brown adipose tissue (BAT) on healthy populations and children. Variations in study protocols and analysis methods currently limit direct comparison between studies but IRT following appropriate BAT stimulation consistently shows a change in supraclavicular skin temperature and a close association with results from BAT measurements from other methods.

The Library of Integrated Network-Based Cellular Signatures NIH Program: System-Level Cataloging of Human Cells Response to Perturbations

The Library of Integrated Network-Based Cellular Signatures (LINCS) is an NIH Common Fund program that catalogs how human cells globally respond to chemical, genetic, and disease perturbations. Resources generated by LINCS include experimental and computational methods, visualization tools, molecular and imaging data, and signatures. By assembling an integrated picture of the range of responses of human cells exposed to many perturbations, the LINCS program aims to better understand human disease and to advance the development of new therapies. Perturbations under study include drugs, genetic perturbations, tissue micro-environments, antibodies, and disease-causing mutations. Responses to perturbations are measured by transcript profiling, mass spectrometry, cell imaging, and biochemical methods, among other assays. The LINCS program focuses on cellular physiology shared among tissues and cell types relevant to an array of diseases, including cancer, heart disease, and neurodegenerative disorders. This Perspective describes LINCS technologies, datasets, tools, and approaches to data accessibility and reusability.

NAD(H) and NADP(H) Redox Couples and Cellular Energy Metabolism

SIGNIFICANCE

The nicotinamide adenine dinucleotide (NAD⁺)/reduced NAD⁺ (NADH) and NADP⁺/reduced NADP⁺ (NADPH) redox couples are essential for maintaining cellular redox homeostasis and for modulating numerous biological events, including cellular metabolism. Deficiency or imbalance of these two redox couples has been associated with many pathological disorders. Recent Advances: Newly identified biosynthetic enzymes and newly developed genetically encoded biosensors enable us to understand better how cells maintain compartmentalized NAD(H) and NADP(H) pools. The concept of redox stress (oxidative and reductive stress) reflected by changes in NAD(H)/NADP(H) has increasingly gained attention. The emerging roles of NAD⁺-consuming proteins in regulating cellular redox and metabolic homeostasis are active research topics.

CRITICAL ISSUES

The biosynthesis and distribution of cellular NAD(H) and NADP(H) are highly compartmentalized. It is critical to understand how cells maintain the steady levels of these redox couple pools to ensure their normal functions and simultaneously avoid inducing redox stress. In addition, it is essential to understand how NAD(H)- and NADP(H)-utilizing enzymes interact with other signaling pathways, such as those regulated by hypoxia-inducible factor, to maintain cellular redox homeostasis and energy metabolism.

FUTURE DIRECTIONS

Additional studies are needed to investigate the inter-relationships among compartmentalized NAD(H)/NADP(H) pools and how these two dinucleotide redox couples collaboratively regulate cellular redox states and cellular metabolism under normal and pathological conditions. Furthermore, recent studies suggest the utility of using pharmacological interventions or nutrient-based bioactive NAD⁺ precursors as therapeutic interventions for metabolic diseases. Thus, a better understanding of the cellular functions of NAD(H) and NADP(H) may facilitate efforts to address a host of pathological disorders effectively. Antioxid. Redox Signal. 28, 251-272.

A Reduced Transcriptome Approach to Assess Environmental Toxicants Using Zebrafish Embryo Test

Omics approaches can monitor responses and alterations of biological pathways at genome-scale, which are useful to predict potential adverse effects by environmental toxicants. However, high throughput application of transcriptomics in chemical assessment is limited due to the high cost and lack of "standardized" toxicogenomic methods. Here, a reduced zebrafish transcriptome (RZT) approach was developed to represent the whole transcriptome and to profile bioactivity of chemical and environmental mixtures in zebrafish embryo. RZT gene set of 1637 zebrafish Entrez genes was designed to cover a wide range of biological processes, and to faithfully capture gene-level and pathway-level changes by toxicants compared with the whole transcriptome. Concentration-response modeling was used to calculate the effect concentrations (ECs) of DEGs and corresponding molecular pathways. To validate the RZT approach, quantitative analysis of gene expression by RNA-ampliseq technology was used to identify differentially expressed genes (DEGs) at 32 hpf following exposure to seven serial dilutions of reference chemical BPA (10-10E^-5μM) or each of four water samples ranging from wastewater to drinking water (relative enrichment factors 10-6.4 × 10^-4). The RZT-ampliseq-embryo approach was both sensitive and able to identify a wide spectrum of biological activities associated with BPA exposure. Water quality was benchmarked based on the sensitivity distribution curve of biological pathways detected using RZT-ampliseq-embryo. Finally, the most sensitive biological pathways were identified, including those linked with adverse reproductive outcomes, genotoxicity and development outcomes. RZT-ampliseq-embryo approach provides an efficient and cost-effective tool to prioritize toxicants based on responsiveness of biological pathways.

ModuleDiscoverer: Identification of regulatory modules in protein-protein interaction networks

The identification of disease-associated modules based on protein-protein interaction networks (PPINs) and gene expression data has provided new insights into the mechanistic nature of diverse diseases. However, their identification is hampered by the detection of protein communities within large-scale, whole-genome PPINs. A presented successful strategy detects a PPIN's community structure based on the maximal clique enumeration problem (MCE), which is a non-deterministic polynomial time-hard problem. This renders the approach computationally challenging for large PPINs implying the need for new strategies. We present ModuleDiscoverer, a novel approach for the identification of regulatory modules from PPINs and gene expression data. Following the MCE-based approach, ModuleDiscoverer uses a randomization heuristic-based approximation of the community structure. Given a PPIN of Rattus norvegicus and public gene expression data, we identify the regulatory module underlying a rodent model of non-alcoholic steatohepatitis (NASH), a severe form of non-alcoholic fatty liver disease (NAFLD). The module is validated using single-nucleotide polymorphism (SNP) data from independent genome-wide association studies and gene enrichment tests. Based on gene enrichment tests, we find that ModuleDiscoverer performs comparably to three existing module-detecting algorithms. However, only our NASH-module is significantly enriched with genes linked to NAFLD-associated SNPs. ModuleDiscoverer is available at http://www.hki-jena.de/index.php/0/2/490 (Others/ModuleDiscoverer).

Constraining Genome-Scale Models to Represent the Bow Tie Structure of Metabolism for 13C Metabolic Flux Analysis

Determination of internal metabolic fluxes is crucial for fundamental and applied biology because they map how carbon and electrons flow through metabolism to enable cell function. 13C Metabolic Flux Analysis (13C MFA) and Two-Scale 13C Metabolic Flux Analysis (2S-13C MFA) are two techniques used to determine such fluxes. Both operate on the simplifying approximation that metabolic flux from peripheral metabolism into central “core” carbon metabolism is minimal, and can be omitted when modeling isotopic labeling in core metabolism. The validity of this “two-scale” or “bow tie” approximation is supported both by the ability to accurately model experimental isotopic labeling data, and by experimentally verified metabolic engineering predictions using these methods. However, the boundaries of core metabolism that satisfy this approximation can vary across species, and across cell culture conditions. Here, we present a set of algorithms that (1) systematically calculate flux bounds for any specified “core” of a genome-scale model so as to satisfy the bow tie approximation and (2) automatically identify an updated set of core reactions that can satisfy this approximation more efficiently. First, we leverage linear programming to simultaneously identify the lowest fluxes from peripheral metabolism into core metabolism compatible with the observed growth rate and extracellular metabolite exchange fluxes. Second, we use Simulated Annealing to identify an updated set of core reactions that allow for a minimum of fluxes into core metabolism to satisfy these experimental constraints. Together, these methods accelerate and automate the identification of a biologically reasonable set of core reactions for use with 13C MFA or 2S-13C MFA, as well as provide for a substantially lower set of flux bounds for fluxes into the core as compared with previous methods. We provide an open source Python implementation of these algorithms at https://github.com/JBEI/limitfluxtocore.

Integration of pan-cancer transcriptomics with RPPA proteomics reveals mechanisms of epithelial-mesenchymal transition

Integrating data from multiple regulatory layers across cancer types could elucidate additional mechanisms of oncogenesis. Using antibody-based protein profiling of 736 cancer cell lines, along with matching transcriptomic data, we show that pan-cancer bimodality in the amounts of mRNA, protein, and protein phosphorylation reveals mechanisms related to the epithelial-mesenchymal transition (EMT). Based on the bimodal expression of E-cadherin, we define an EMT signature consisting of 239 genes, many of which were not previously associated with EMT. By querying gene expression signatures collected from cancer cell lines after small-molecule perturbations, we identify enrichment for histone deacetylase (HDAC) inhibitors as inducers of EMT, and kinase inhibitors as mesenchymal-to-epithelial transition (MET) promoters. Causal modeling of protein-based signaling identifies putative drivers of EMT. In conclusion, integrative analysis of pan-cancer proteomic and transcriptomic data reveals key regulatory mechanisms of oncogenic transformation.

Profiling molecular and phenotypic characteristics of large collections of cancer cell lines can be used to identify distinct and common oncogenic pathways across cancer types. So far, most large-scale data obtained from cancer cell lines have been at the genomic, transcriptomic, and phenotypic levels. Recently, high-quality data at the level of cell signaling through protein abundances and phosphorylation sites has become available. By integrating this newly generated protein data with prior transcriptomic data, and by visualizing all cancer cell lines using dimensionality reduction techniques, pan-cancer cell lines are strikingly shown to organize into a gradient of epithelial to mesenchymal types. Interestingly, many of the measured proteins and transcripts display bimodality; the expression of genes, proteins, and protein phosphorylations is either high or low, strongly suggesting that they act as molecular switches. Focusing on further characterizing molecular switches of epithelial-mesenchymal transitions, we identify candidate regulators and small molecules that can induce or reverse such transition, as well as potential causal relationships between proteins. Since the mesenchymal state of tumors is known to be associated with metastasis and later-stage cancer development, better understanding the regulatory mechanisms of epithelial-to-mesenchymal transition can lead to improved targeted therapeutics.

Cell-specific prediction and application of drug-induced gene expression profiles

Gene expression profiling of in vitro drug perturbations is useful for many biomedical discovery applications including drug repurposing and elucidation of drug mechanisms. However, limited data availability across cell types has hindered our capacity to leverage or explore the cell-specificity of these perturbations. While recent efforts have generated a large number of drug perturbation profiles across a variety of human cell types, many gaps remain in this combinatorial drug-cell space. Hence, we asked whether it is possible to fill these gaps by predicting cell-specific drug perturbation profiles using available expression data from related conditions--i.e. from other drugs and cell types. We developed a computational framework that first arranges existing profiles into a three-dimensional array (or tensor) indexed by drugs, genes, and cell types, and then uses either local (nearest-neighbors) or global (tensor completion) information to predict unmeasured profiles. We evaluate prediction accuracy using a variety of metrics, and find that the two methods have complementary performance, each superior in different regions in the drug-cell space. Predictions achieve correlations of 0.68 with true values, and maintain accurate differentially expressed genes (AUC 0.81). Finally, we demonstrate that the predicted profiles add value for making downstream associations with drug targets and therapeutic classes.

Hybrid phenolic-inducible promoters towards construction of self-inducible systems for microbial lignin valorization

BACKGROUND

Engineering strategies to create promoters that are both higher strength and tunable in the presence of inexpensive compounds are of high importance to develop metabolic engineering technologies that can be commercialized. Lignocellulosic biomass stands out as the most abundant renewable feedstock for the production of biofuels and chemicals. However, lignin a major polymeric component of the biomass is made up of aromatic units and remains as an untapped resource. Novel synthetic biology tools for the expression of heterologous proteins are critical for the effective engineering of a microbe to valorize lignin. This study demonstrates the first successful attempt in the creation of engineered promoters that can be induced by aromatics present in lignocellulosic hydrolysates to increase heterologous protein production.

RESULTS

A hybrid promoter engineering approach was utilized for the construction of phenolic-inducible promoters of higher strength. The hybrid promoters were constructed by replacing the spacer region of an endogenous promoter, P_emrR present in E. coli that was naturally inducible by phenolics. In the presence of vanillin, the engineered promoters P_vtac, P_vtrc, and P_vtic increased protein expression by 4.6-, 3.0-, and 1.5-fold, respectively, in comparison with a native promoter, P_emrR. In the presence of vanillic acid, P_vtac, P_vtrc, and P_vtic improved protein expression by 9.5-, 6.8-, and 2.1-fold, respectively, in comparison with P_emrR. Among the cells induced with vanillin, the emergence of a sub-population constituting the healthy and dividing cells using flow cytometry was observed. The analysis also revealed this smaller sub-population to be the primary contributor for the increased expression that was observed with the engineered promoters.

CONCLUSIONS

This study demonstrates the first successful attempt in the creation of engineered promoters that can be induced by aromatics to increase heterologous protein production. Employing promoters inducible by phenolics will provide the following advantages: (1) develop substrate inducible systems; (2) lower operating costs by replacing expensive IPTG currently used for induction; (3) develop dynamic regulatory systems; and (4) provide flexibility in operating conditions. The flow cytometry findings strongly suggest the need for novel approaches to maintain a healthy cell population in the presence of phenolics to achieve increased heterologous protein expression and, thereby, valorize lignin efficiently.

The importance of thermodynamics for molecular systems, and the importance of molecular systems for thermodynamics

Improved understanding of molecular systems has only emphasised the sophistication of networks within the cell. Simultaneously, the advance of nucleic acid nanotechnology, a platform within which reactions can be exquisitely controlled, has made the development of artificial architectures and devices possible. Vital to this progress has been a solid foundation in the thermodynamics of molecular systems. In this pedagogical review and perspective, we discuss how thermodynamics determines both the overall potential of molecular networks, and the minute details of design. We then argue that, in turn, the need to understand molecular systems is helping to drive the development of theories of thermodynamics at the microscopic scale.

BioDataome: a collection of uniformly preprocessed and automatically annotated datasets for data-driven biology

Biotechnology revolution generates a plethora of omics data with an exponential growth pace. Therefore, biological data mining demands automatic, 'high quality' curation efforts to organize biomedical knowledge into online databases. BioDataome is a database of uniformly preprocessed and disease-annotated omics data with the aim to promote and accelerate the reuse of public data. We followed the same preprocessing pipeline for each biological mart (microarray gene expression, RNA-Seq gene expression and DNA methylation) to produce ready for downstream analysis datasets and automatically annotated them with disease-ontology terms. We also designate datasets that share common samples and automatically discover control samples in case-control studies. Currently, BioDataome includes ∼5600 datasets, ∼260 000 samples spanning ∼500 diseases and can be easily used in large-scale massive experiments and meta-analysis. All datasets are publicly available for querying and downloading via BioDataome web application. We demonstrate BioDataome's utility by presenting exploratory data analysis examples. We have also developed BioDataome R package found in: https://github.com/mensxmachina/BioDataome/.Database URL: http://dataome.mensxmachina.org/.

Assessing Carbon Source-Dependent Phenotypic Variability in Pseudomonas putida

The soil bacterium Pseudomonas putida is rapidly becoming a platform of choice for applications that require a microbial host highly resistant to different types of stresses and elevated rates of reducing power regeneration. P. putida is capable of growing in a wide variety of carbon sources that range from simple sugars to complex substrates such as aromatic compounds. Interestingly, the growth of the reference strain KT2440 on glycerol as the sole carbon source is characterized by a prolonged lag phase, not observed with other carbon substrates. This macroscopic phenomenon has been shown to be connected with the stochastic expression of the glp genes, which encode the enzymes needed for glycerol processing. In this protocol, we propose a general procedure to examine bacterial growth in small-scale cultures while monitoring the metabolic activity of individual cells. Assessing the metabolic capacity of single bacteria by means of fluorescence microscopy and flow cytometry, in combination with the analysis of the temporal takeoff of growth in single-cell cultures, is a simple and easy-to-implement approach. It can help to understand the link between macroscopic phenotypes (e.g., microbial growth in batch cultures) and stochastic phenomena at the genetic level. The implementation of these methodologies revealed that the adoption of a glycerol-metabolizing regime by P. putida KT2440 is not the result of a gradual change in the whole population, but it rather reflects a time-dependent bimodal switch between metabolically inactive (i.e., not growing) to fully active (i.e., growing) bacteria.

Computational Systems Biology of Metabolism in Infection

A systems approach to elucidate the effect of infection on cell metabolism provides several opportunities from a better understanding of molecular mechanisms to the identification of potential biomarkers and drug targets. This is obvious from the fact that we have witnessed the accelerated use of computational systems biology in the last five years to study metabolic changes in pathogen and/or host cells in response to infection. In this chapter, we aim to present a comprehensive review of the recent research by focusing on genome-scale metabolic network models of pathogen-host systems and genome-wide metabolomics and fluxomics analysis of infected cells.

A Network-Based Integrative Workflow to Unravel Mechanisms Underlying Disease Progression

Unraveling mechanisms underlying diseases has motivated the development of systems biology approaches. The key challenges for the development of mathematical models and computational tool are (1) the size of molecular networks, (2) the nonlinear nature of spatio-temporal interactions, and (3) feedback loops in the structure of interaction networks. We here propose an integrative workflow that combines structural analyses of networks, high-throughput data, and mechanistic modeling. As an illustration of the workflow, we use prostate cancer as a case study with the aim of identifying key functional components associated with primary to metastasis transitions. Analysis carried out by the workflow revealed that HOXD10, BCL2, and PGR are the most important factors affected in primary prostate samples, whereas, in the metastatic state, STAT3, JUN, and JUNB are playing a central role. The identified key elements of each network are validated using patient survival analysis. The workflow presented here allows experimentalists to use heterogeneous data sources for the identification of diagnostic and prognostic signatures.

Mechanistic modeling quantifies the influence of tumor growth kinetics on the response to anti-angiogenic treatment

Tumors exploit angiogenesis, the formation of new blood vessels from pre-existing vasculature, in order to obtain nutrients required for continued growth and proliferation. Targeting factors that regulate angiogenesis, including the potent promoter vascular endothelial growth factor (VEGF), is therefore an attractive strategy for inhibiting tumor growth. Computational modeling can be used to identify tumor-specific properties that influence the response to anti-angiogenic strategies. Here, we build on our previous systems biology model of VEGF transport and kinetics in tumor-bearing mice to include a tumor compartment whose volume depends on the “angiogenic signal” produced when VEGF binds to its receptors on tumor endothelial cells. We trained and validated the model using published in vivo measurements of xenograft tumor volume, producing a model that accurately predicts the tumor’s response to anti-angiogenic treatment. We applied the model to investigate how tumor growth kinetics influence the response to anti-angiogenic treatment targeting VEGF. Based on multivariate regression analysis, we found that certain intrinsic kinetic parameters that characterize the growth of tumors could successfully predict response to anti-VEGF treatment, the reduction in tumor volume. Lastly, we use the trained model to predict the response to anti-VEGF therapy for tumors expressing different levels of VEGF receptors. The model predicts that certain tumors are more sensitive to treatment than others, and the response to treatment shows a nonlinear dependence on the VEGF receptor expression. Overall, this model is a useful tool for predicting how tumors will respond to anti-VEGF treatment, and it complements pre-clinical in vivo mouse studies.

One hallmark of cancer is angiogenesis, the formation of new blood capillaries from pre-existing vessels. Angiogenesis promotes tumor growth by enabling the tumor to obtain oxygen and nutrients from the surrounding microenvironment. Cancer drugs that inhibit angiogenesis ("anti-angiogenic therapies") have focused on inhibiting proteins that promote the growth of new blood vessels. The response to anti-angiogenic therapy is highly variable, and some tumors do not respond at all. Therefore, identifying a biomarker that predicts how specific tumors will respond would be extremely valuable. This work uses a computational model of tumor-bearing mice to investigate the response to anti-angiogenic treatment that targets the potent promoter of angiogenesis, vascular endothelial growth factor (VEGF), and how the response is influenced by tumor growth kinetics. We show that certain properties of tumor growth can be used to predict how much the tumor volume will be reduced upon administration of an anti-VEGF drug. This work identifies tumor growth parameters that may be reliable biomarkers for predicting how tumors will respond to anti-VEGF therapy. Our computational model generates novel, testable hypotheses and nicely complements pre-clinical studies of anti-angiogenic therapeutics.

Predictive model identifies strategies to enhance TSP1-mediated apoptosis signaling

BACKGROUND

Thrombospondin-1 (TSP1) is a matricellular protein that functions to inhibit angiogenesis. An important pathway that contributes to this inhibitory effect is triggered by TSP1 binding to the CD36 receptor, inducing endothelial cell apoptosis. However, therapies that mimic this function have not demonstrated clear clinical efficacy. This study explores strategies to enhance TSP1-induced apoptosis in endothelial cells. In particular, we focus on establishing a computational model to describe the signaling pathway, and using this model to investigate the effects of several approaches to perturb the TSP1-CD36 signaling network.

METHODS

We constructed a molecularly-detailed mathematical model of TSP1-mediated intracellular signaling via the CD36 receptor based on literature evidence. We employed systems biology tools to train and validate the model and further expanded the model by accounting for the heterogeneity within the cell population. The initial concentrations of signaling species or kinetic rates were altered to simulate the effects of perturbations to the signaling network.

RESULTS

Model simulations predict the population-based response to strategies to enhance TSP1-mediated apoptosis, such as downregulating the apoptosis inhibitor XIAP and inhibiting phosphatase activity. The model also postulates a new mechanism of low dosage doxorubicin treatment in combination with TSP1 stimulation. Using computational analysis, we predict which cells will undergo apoptosis, based on the initial intracellular concentrations of particular signaling species.

CONCLUSIONS

This new mathematical model recapitulates the intracellular dynamics of the TSP1-induced apoptosis signaling pathway. Overall, the modeling framework predicts molecular strategies that increase TSP1-mediated apoptosis, which is useful in many disease settings.

Linking physiologically-based pharmacokinetic and genome-scale metabolic networks to understand estradiol biology

Background

Estrogen is a vital hormone that regulates many biological functions within the body. These include roles in the development of the secondary sexual organs in both sexes, plus uterine angiogenesis and proliferation during the menstrual cycle and pregnancy in women. The varied biological roles of estrogens in human health also make them a therapeutic target for contraception, mitigation of the adverse effects of the menopause, and treatment of estrogen-responsive tumours. In addition, endogenous (e.g. genetic variation) and external (e.g. exposure to estrogen-like chemicals) factors are known to impact estrogen biology. To understand how these multiple factors interact to determine an individual’s response to therapy is complex, and may be best approached through a systems approach.

Methods

We present a physiologically-based pharmacokinetic model (PBPK) of estradiol, and validate it against plasma kinetics in humans following intravenous and oral exposure. We extend this model by replacing the intrinsic clearance term with: a detailed kinetic model of estrogen metabolism in the liver; or, a genome-scale model of liver metabolism. Both models were validated by their ability to reproduce clinical data on estradiol exposure. We hypothesise that the enhanced mechanistic information contained within these models will lead to more robust predictions of the biological phenotype that emerges from the complex interactions between estrogens and the body.

Results

To demonstrate the utility of these models we examine the known drug-drug interactions between phenytoin and oral estradiol. We are able to reproduce the approximate 50% reduction in area under the concentration-time curve for estradiol associated with this interaction. Importantly, the inclusion of a genome-scale metabolic model allows the prediction of this interaction without directly specifying it within the model. In addition, we predict that PXR activation by drugs results in an enhanced ability of the liver to excrete glucose. This has important implications for the relationship between drug treatment and metabolic syndrome.

Conclusions

We demonstrate how the novel coupling of PBPK models with genome-scale metabolic networks has the potential to aid prediction of drug action, including both drug-drug interactions and changes to the metabolic landscape that may predispose an individual to disease development.

Electronic supplementary material

The online version of this article (10.1186/s12918-017-0520-3) contains supplementary material, which is available to authorized users.

The Experiment Data Depot: A Web-Based Software Tool for Biological Experimental Data Storage, Sharing, and Visualization

Although recent advances in synthetic biology allow us to produce biological designs more efficiently than ever, our ability to predict the end result of these designs is still nascent. Predictive models require large amounts of high-quality data to be parametrized and tested, which are not generally available. Here, we present the Experiment Data Depot (EDD), an online tool designed as a repository of experimental data and metadata. EDD provides a convenient way to upload a variety of data types, visualize these data, and export them in a standardized fashion for use with predictive algorithms. In this paper, we describe EDD and showcase its utility for three different use cases: storage of characterized synthetic biology parts, leveraging proteomics data to improve biofuel yield, and the use of extracellular metabolite concentrations to predict intracellular metabolic fluxes.

Stability of the hybrid epithelial/mesenchymal phenotype

Epithelial-to-Mesenchymal Transition (EMT) and its reverse – Mesenchymal to Epithelial Transition (MET) – are hallmarks of cellular plasticity during embryonic development and cancer metastasis. During EMT, epithelial cells lose cell-cell adhesion and gain migratory and invasive traits either partially or completely, leading to a hybrid epithelial/mesenchymal (hybrid E/M) or a mesenchymal phenotype respectively. Mesenchymal cells move individually, but hybrid E/M cells migrate collectively as observed during gastrulation, wound healing, and the formation of tumor clusters detected as Circulating Tumor Cells (CTCs). Typically, the hybrid E/M phenotype has largely been tacitly assumed to be transient and ‘metastable’. Here, we identify certain ‘phenotypic stability factors’ (PSFs) such as GRHL2 that couple to the core EMT decision-making circuit (miR-200/ZEB) and stabilize hybrid E/M phenotype. Further, we show that H1975 lung cancer cells can display a stable hybrid E/M phenotype and migrate collectively, a behavior that is impaired by knockdown of GRHL2 and another previously identified PSF - OVOL. In addition, our computational model predicts that GRHL2 can also associate hybrid E/M phenotype with high tumor-initiating potential, a prediction strengthened by the observation that the higher levels of these PSFs may be predictive of poor patient outcome. Finally, based on these specific examples, we deduce certain network motifs that can stabilize the hybrid E/M phenotype. Our results suggest that partial EMT, i.e. a hybrid E/M phenotype, need not be ‘metastable’, and strengthen the emerging notion that partial EMT, but not necessarily a complete EMT, is associated with aggressive tumor progression.

Identification of a gene signature for discriminating metastatic from primary melanoma using a molecular interaction network approach

Understanding the biological factors that are characteristic of metastasis in melanoma remains a key approach to improving treatment. In this study, we seek to identify a gene signature of metastatic melanoma. We configured a new network-based computational pipeline, combined with a machine learning method, to mine publicly available transcriptomic data from melanoma patient samples. Our method is unbiased and scans a genome-wide protein-protein interaction network using a novel formulation for network scoring. Using this, we identify the most influential, differentially expressed nodes in metastatic as compared to primary melanoma. We evaluated the shortlisted genes by a machine learning method to rank them by their discriminatory capacities. From this, we identified a panel of 6 genes, ALDH1A1, HSP90AB1, KIT, KRT16, SPRR3 and TMEM45B whose expression values discriminated metastatic from primary melanoma (87% classification accuracy). In an independent transcriptomic data set derived from 703 primary melanomas, we showed that all six genes were significant in predicting melanoma specific survival (MSS) in a univariate analysis, which was also consistent with AJCC staging. Further, 3 of these genes, HSP90AB1, SPRR3 and KRT16 remained significant predictors of MSS in a joint analysis (HR = 2.3, P = 0.03) although, HSP90AB1 (HR = 1.9, P = 2 × 10-4) alone remained predictive after adjusting for clinical predictors.

Understanding interactions between populations: Individual based modelling and quantification using pair correlation functions

Understanding the underlying mechanisms that produce the huge variety of swarming and aggregation patterns in animals and cells is fundamental in ecology, developmental biology, and regenerative medicine, to name but a few examples. Depending upon the nature of the interactions between individuals (cells or animals), a variety of different large-scale spatial patterns can be observed in their distribution; examples include cell aggregates, stripes of different coloured skin cells, etc. For the case where all individuals are of the same type (i.e., all interactions are alike), a considerable literature already exists on how the collective organisation depends on the inter-individual interactions. Here, we focus on the less studied case where there are two different types of individuals present. Whilst a number of continuum models of this scenario exist, it can be difficult to compare these models to experimental data, since real cells and animals are discrete. In order to overcome this problem, we develop an agent-based model to simulate some archetypal mechanisms involving attraction and repulsion. However, with this approach (as with experiments), each realisation of the model is different, due to stochastic effects. In order to make useful comparisons between simulations and experimental data, we need to identify the robust features of the spatial distributions of the two species which persist over many realisations of the model (for example, the size of aggregates, degree of segregation or intermixing of the two species). In some cases, it is possible to do this by simple visual inspection. In others, the features of the pattern are not so clear to the unaided eye. In this paper, we introduce a pair correlation function (PCF), which allows us to analyse multi-species spatial distributions quantitatively. We show how the differing strengths of inter-individual attraction and repulsion between species give rise to different spatial patterns, and how the PCF can be used to quantify these differences, even when it might be impossible to recognise them visually.

The emerging role of systems biology for engineering protein production in CHO cells

To meet the ever-growing demand for effective, safe, and affordable protein therapeutics, decades of intense efforts have aimed to maximize the quantity and quality of recombinant proteins produced in CHO cells. Bioprocessing innovations and cell engineering efforts have improved product titer; however, uncharacterized cellular processes and gene regulatory mechanisms still hinder cell growth, specific productivity, and protein quality. Herein, we summarize recent advances in systems biology and data-driven approaches aiming to unravel how molecular pathways, cellular processes, and extrinsic factors (e.g. media supplementation) influence recombinant protein production. In particular, as the available omics data for CHO cells continue to grow, predictive models and screens will be increasingly used to unravel the biological drivers of protein production, which can be used with emerging genome editing technologies to rationally engineer cells to further control the quantity, quality and affordability of many biologic drugs.

When Do Two-Stage Processes Outperform One-Stage Processes?

Apart from product yield and titer, volumetric productivity is a key performance indicator for many biotechnological processes. Due to the inherent trade-off between the production of biomass as catalyst and of the actual target product, yield and volumetric productivity cannot be optimized simultaneously. Therefore, in combination with genetic techniques for dynamic regulation of metabolic fluxes, two-stage fermentations (TSFs) with separated growth and production phase have recently gained much interest because of their potential to improve the productivity of bioprocesses while still allowing high product yields. However, despite some successful case studies, so far it has not been discussed and analyzed systematically whether or under which conditions a TSF guarantees superior productivity compared to one-stage fermentation (OSF). In this study, we use mathematical models to demonstrate that the volumetric productivity of a TSF is not automatically better than of a corresponding OSF. Our analysis reveals that the sharp decrease of the specific substrate uptake rate usually observed in (non-growth) production phases severely impacts the volumetric productivity and thus raises a big challenge for designing competitive TSF processes. We discuss possible approaches such as enforced ATP wasting to improve substrate utilization rates in the production phase by which TSF processes can become superior to OSF. We also analyze additional factors influencing the relative performance of OSF and TSF and show that OSF processes can be more appropriate if a high product yield is an economic constraint. In conclusion, a careful assessment of the trade-offs between substrate uptake rates, yields, and productivity is necessary when deciding for OSF vs. TSF processes.

Computational Systems Biology Approach for the Study of Rheumatoid Arthritis: From a Molecular Map to a Dynamical Model

In this work we present a systematic effort to summarize current biological pathway knowledge concerning Rheumatoid Arthritis (RA). We are constructing a detailed molecular map based on exhaustive literature scanning, strict curation criteria, re-evaluation of previously published attempts and most importantly experts' advice. The RA map will be web-published in the coming months in the form of an interactive map, using the MINERVA platform, allowing for easy access, navigation and search of all molecular pathways implicated in RA, serving thus, as an on line knowledgebase for the disease. Moreover the map could be used as a template for Omics data visualization offering a first insight about the pathways affected in different experimental datasets. The second goal of the project is a dynamical study focused on synovial fibroblasts' behavior under different initial conditions specific to RA, as recent studies have shown that synovial fibroblasts play a crucial role in driving the persistent, destructive characteristics of the disease. Leaning on the RA knowledgebase and using the web platform Cell Collective, we are currently building a Boolean large scale dynamical model for the study of RA fibroblasts' activation.