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.

Systems biology of lactic acid bacteria: For food and thought

Lactic acid bacteria (LAB) ferment plants, fish, meats and milk and turn them into tasty food products with increased shelf life; other LAB help digesting food and create a healthy environment in the intestine. The economic and societal importance of these relatively simple and small bacteria is immense. In this review we hope to show that their adaptations to nutrient-rich environments provides fascinating and often puzzling behaviours that give rise to many fundamental evolutionary biological questions in need of a systems biology approach. We will provide examples of such questions, compare the (metabolic) behaviour of LAB to that of other model organisms, and provide the latest insights, if available.

Evaluation of RNAi and CRISPR technologies by large-scale gene expression profiling in the Connectivity Map

The application of RNA interference (RNAi) to mammalian cells has provided the means to perform phenotypic screens to determine the functions of genes. Although RNAi has revolutionized loss-of-function genetic experiments, it has been difficult to systematically assess the prevalence and consequences of off-target effects. The Connectivity Map (CMAP) represents an unprecedented resource to study the gene expression consequences of expressing short hairpin RNAs (shRNAs). Analysis of signatures for over 13,000 shRNAs applied in 9 cell lines revealed that microRNA (miRNA)-like off-target effects of RNAi are far stronger and more pervasive than generally appreciated. We show that mitigating off-target effects is feasible in these datasets via computational methodologies to produce a consensus gene signature (CGS). In addition, we compared RNAi technology to clustered regularly interspaced short palindromic repeat (CRISPR)-based knockout by analysis of 373 single guide RNAs (sgRNAs) in 6 cells lines and show that the on-target efficacies are comparable, but CRISPR technology is far less susceptible to systematic off-target effects. These results will help guide the proper use and analysis of loss-of-function reagents for the determination of gene function.

Process optimization for enhancing production of cis-4-hydroxy-L-proline by engineered Escherichia coli

Background

Understanding the bioprocess limitations is critical for the efficient design of biocatalysts to facilitate process feasibility and improve process economics. In this study, a proline hydroxylation process with recombinant Escherichia coli expressing l-proline cis-4-hydroxylase (SmP4H) was investigated. The factors that influencing the metabolism of microbial hosts and process economics were focused on for the optimization of cis-4-hydroxy-l-proline (CHOP) production.

Results

In recombinant E. coli, SmP4H synthesis limitation was observed. After the optimization of expression system, CHOP production was improved in accordance with the enhanced SmP4H synthesis. Furthermore, the effects of the regulation of proline uptake and metabolism on whole-cell catalytic activity were investigated. The improved CHOP production by repressing putA gene responsible for l-proline degradation or overexpressing l-proline transporter putP on CHOP production suggested the important role of substrate uptake and metabolism on the whole-cell biocatalyst efficiency. Through genetically modifying these factors, the biocatalyst activity was significantly improved, and CHOP production was increased by twofold. Meanwhile, to further improve process economics, a two-strain coupling whole-cell system was established to supply co-substrate (α-ketoglutarate, α-KG) with a cheaper chemical l-glutamate as a starting material, and 13.5 g/L of CHOP was successfully produced.

Conclusions

In this study, SmP4H expression, and l-proline uptake and degradation, were uncovered as the hurdles for microbial production of CHOP. Accordingly, the whole-cell biocatalysts were metabolically engineered for enhancing CHOP production. Meanwhile, a two-strain biotransformation system for CHOP biosynthesis was developed aiming at supplying α-KG more economically. Our work provided valuable insights into the design of recombinant microorganism to improve the biotransformation efficiency that catalyzed by Fe(II)/α-KG-dependent dioxygenase.

Electronic supplementary material

The online version of this article (10.1186/s12934-017-0821-7) contains supplementary material, which is available to authorized users.

A harmonized immunoassay with liquid chromatography-mass spectrometry analysis in egg allergen determination

Food allergy is a serious health issue worldwide. Implementing allergen labeling regulations is extremely challenging for regulators, food manufacturers, and analytical kit manufacturers. Here we have developed an "amino acid sequence immunoassay" approach to ELISA. The new ELISA comprises of a monoclonal antibody generated via an analyte specific peptide antigen and sodium lauryl sulfate/sulfite solution. This combination enables the antibody to access the epitope site in unfolded analyte protein. The newly developed ELISA recovered 87.1%-106.4% ovalbumin from ovalbumin-incurred model processed foods, thereby demonstrating its applicability as practical egg allergen determination. Furthermore, the comparison of LC-MS/MS and the new ELISA, which targets the amino acid sequence conforming to the LC-MS/MS detection peptide, showed a good agreement. Consequently the harmonization of two methods was demonstrated. The complementary use of the new ELISA and LC-MS analysis can offer a wide range of practical benefits in terms of easiness, cost, accuracy, and efficiency in food allergen analysis. In addition, the new assay is attractive in respect to its easy antigen preparation and predetermined specificity. Graphical abstract The ELISA composing of the monoclonal antibody targeting the amino acid sequence conformed to LC-MS detection peptide, and the protein conformation unfolding reagent was developed. In ovalbumin determination, the developed ELISA showed a good agreement with LC-MS analysis. Consequently the harmonization of immunoassay with LC-MS analysis by using common target amino acid sequence was demonstrated.

Effects of angiotensin-neprilysin inhibition compared to angiotensin inhibition on ventricular arrhythmias in reduced ejection fraction patients under continuous remote monitoring of implantable defibrillator devices

BACKGROUND

Angiotensin-neprilysin inhibition compared to angiotensin inhibition decreased sudden cardiac death in patients with reduced ejection fraction heart failure (rEFHF). The precise mechanism remains unclear.

OBJECTIVE

The purpose of this study was to explore the effect of angiotensin-neprilysin inhibition on ventricular arrhythmias compared to angiotensin inhibition in rEFHF patients with an implantable cardioverter-defibrillator (ICD) and remote monitoring.

METHODS

We prospectively included 120 patients with ICD and (1) New York Heart Association functional class ≥II; (2) left ventricular ejection fraction ≤40%; and (3) remote monitoring. For 9 months, patients received 100% angiotensin inhibition with angiotensin-converting enzyme inhibitor (ACEi) or angiotensin receptor blocker (ARB), beta-blockers, and mineraloid antagonist. Subsequently, ACEi or ARB was changed to sacubitril-valsartan in all patients, who were followed for 9 months. Appropriate shocks, nonsustained ventricular tachycardia (NSVT), premature ventricular contraction (PVC) burden, and biventricular pacing percentage were analyzed.

RESULTS

Patients were an average age of 69 ± 8 years and had mean left ventricular ejection fraction of 30.4% ± 4% (82% ischemic). Use of beta-blockers (98%), mineraloid antagonist (97%) and antiarrhythmic drugs was similar before and after sacubitril-valsartan. Sacubitril-valsartan significantly decreased NSVT episodes (5.4 ± 0.5 vs 15 ± 1.7 in angiotensin inhibition; P <.002), sustained ventricular tachycardia, and appropriate ICD shocks (0.8% vs 6.7% in angiotensin inhibition; P <.02). PVCs per hour decreased after sacubitril-valsartan (33 ± 12 vs 78 ± 15 in angiotensin inhibition; P <.0003) and was associated with increased biventricular pacing percentage (from 95% ± 6% to 98.8% ± 1.3%; P <.02).

CONCLUSION

Angiotensin-neprilysin inhibition decreased ventricular arrhythmias and appropriate ICD shocks in rEFHF patients under home monitoring compared to angiotensin inhibition.

The Many-Faced Program of Epithelial-Mesenchymal Transition: A System Biology-Based View

System biology uses a range of experimental and statistical methods to dissect complex processes that results from alterations in biological models. Given the complexity of the epithelial–mesenchymal transition (EMT) program, system biology represents a promising approach to understanding its fine molecular regulation by the interpretation of high-throughput datasets. Herein, we review recent contributions of system biology applied to the field of EMT physiology and illustrate the importance of these approaches to model biological networks that are perturbed during the transition. Together, these results allowed the definition of an EMT signature across different tumor types, the identification of dysregulated processes and new modules of regulation, making possible to reveal the EMT molecular visage underneath.

Combination of type II fatty acid biosynthesis enzymes and thiolases supports a functional β-oxidation reversal

An engineered reversal of the β-oxidation cycle (r-BOX) and the fatty acid biosynthesis (FAB) pathway are promising biological platforms for advanced fuel and chemical production in part due to their iterative nature supporting the synthesis of various chain length products. While diverging in their carbon-carbon elongation reaction mechanism, iterative operation of each pathway relies on common chemical conversions (reduction, dehydration, and reduction) differing only in the attached moiety (acyl carrier protein (ACP) in FAB vs Coenzyme A in r-BOX). Given this similarity, we sought to determine whether FAB enzymes can be used in the context of r-BOX as a means of expanding available r-BOX components with a ubiquitous set of well characterized enzymes. Using enzymes from the type II FAB pathway (FabG, FabZ, and FabI) in conjunction with a thiolase catalyzing a non-decarboxylative condensation, we demonstrate that FAB enzymes support a functional r-BOX. Pathway operation with FAB enzymes was improved through computationally directed protein design to develop FabZ variants with amino acid substitutions designed to disrupt hydrogen bonding at the FabZ-ACP interface and introduce steric and electrostatic repulsion between the FabZ and ACP. FabZ with R126W and R121E substitutions resulted in improved carboxylic acid and alcohol production from one- and multiple-turn r-BOX compared to the wild-type enzyme. Furthermore, the ability for FAB enzymes to operate on functionalized intermediates was exploited to produce branched chain carboxylic acids through an r-BOX with functionalized priming. These results not only provide an expanded set of enzymes within the modular r-BOX pathway, but can also potentially expand the scope of products targeted through this pathway by operating with CoA intermediates containing various functional groups.

The potential role of leptin in tumor invasion and metastasis

The adipocyte-released hormone-like cytokine/adipokine leptin behaves differently in obesity compared to its functions in the normal healthy state. In obese individuals, elevated leptin levels act as a pro-inflammatory adipokine and are associated with certain types of cancers. Further, a growing body of evidence suggests that higher circulating leptin concentrations and/or elevated expression of leptin receptors (Ob-R) in tumors may be poor prognostic factors. Although the underlying pathological mechanisms of leptin's association with poor prognosis are not clear, leptin can impact the tumor microenvironment in several ways. For example, leptin is associated with a number of biological components that could lead to tumor cell invasion and distant metastasis. This includes interactions with carcinoma-associated fibroblasts, tumor promoting effects of infiltrating macrophages, activation of matrix metalloproteinases, transforming growth factor-β signaling, etc. Recent studies also have shown that leptin plays a role in the epithelial-mesenchymal transition, an important phenomenon for cancer cell migration and/or metastasis. Furthermore, leptin's potentiating effects on insulin-like growth factor-I, epidermal growth factor receptor and HER2/neu have been reported. Regarding unfavorable prognosis, leptin has been shown to influence both adenocarcinomas and squamous cell carcinomas. Features of poor prognosis such as tumor invasion, lymph node involvement and distant metastasis have been recorded in several cancer types with higher levels of leptin and/or Ob-R. This review will describe the current scenario in a precise manner. In general, obesity indicates poor prognosis in cancer patients.

Bioconversion of distillers' grains hydrolysates to advanced biofuels by an Escherichia coli co-culture

BACKGROUND

First generation bioethanol production utilizes the starch fraction of maize, which accounts for approximately 60% of the ash-free dry weight of the grain. Scale-up of this technology for fuels applications has resulted in a massive supply of distillers' grains with solubles (DGS) coproduct, which is rich in cellulosic polysaccharides and protein. It was surmised that DGS would be rapidly adopted for animal feed applications, however, this has not been observed based on inconsistency of the product stream and other logistics-related risks, especially toxigenic contaminants. Therefore, efficient valorization of DGS for production of petroleum displacing products will significantly improve the techno-economic feasibility and net energy return of the established starch bioethanol process. In this study, we demonstrate 'one-pot' bioconversion of the protein and carbohydrate fractions of a DGS hydrolysate into C4 and C5 fusel alcohols through development of a microbial consortium incorporating two engineered Escherichia coli biocatalyst strains.

RESULTS

The carbohydrate conversion strain E. coli BLF2 was constructed from the wild type E. coli strain B and showed improved capability to produce fusel alcohols from hexose and pentose sugars. Up to 12 g/L fusel alcohols was produced from glucose or xylose synthetic medium by E. coli BLF2. The second strain, E. coli AY3, was dedicated for utilization of proteins in the hydrolysates to produce mixed C4 and C5 alcohols. To maximize conversion yield by the co-culture, the inoculation ratio between the two strains was optimized. The co-culture with an inoculation ratio of 1:1.5 of E. coli BLF2 and AY3 achieved the highest total fusel alcohol titer of up to 10.3 g/L from DGS hydrolysates. The engineered E. coli co-culture system was shown to be similarly applicable for biofuel production from other biomass sources, including algae hydrolysates. Furthermore, the co-culture population dynamics revealed by quantitative PCR analysis indicated that despite the growth rate difference between the two strains, co-culturing didn't compromise the growth of each strain. The q-PCR analysis also demonstrated that fermentation with an appropriate initial inoculation ratio of the two strains was important to achieve a balanced co-culture population which resulted in higher total fuel titer.

CONCLUSIONS

The efficient conversion of DGS hydrolysates into fusel alcohols will significantly improve the feasibility of the first generation bioethanol process. The integrated carbohydrate and protein conversion platform developed here is applicable for the bioconversion of a variety of biomass feedstocks rich in sugars and proteins.

Exploration of serum biomarkers for predicting the response to Inchinkoto (ICKT), a Japanese traditional herbal medicine

INTRODUCTION

In patients with obstructive jaundice, biliary drainage sometimes fails to result in improvement. A pharmaceutical-grade choleretic herbal medicine, Inchinkoto (ICKT), has been proposed to exert auxiliary effects on biliary drainage; however, its effects are variable among patients.

OBJECTIVES

The aim of this study is to explore serum biomarkers that are associated with pharmaceutical efficacy of ICKT.

METHODS

Obstructive jaundice patients who underwent external biliary decompression were enrolled (n = 37). ICKT was given orally 3 times a day at daily dose of 7.5 g. Serum and bile samples were collected before, 3 h after, and 24 h after ICKT administration. The concentrations of total bilirubin, direct bilirubin, and total bile acid in bile specimens were measured. Metabolites in serum samples were comprehensively profiled using LC-MS/MS and GC-MS/MS. Pharmacokinetic analysis of major ICKT components was also performed.

RESULTS

ICKT administration significantly decreased serum ALT and increased bile volume after 24 h. The serum concentrations of ICKT components were not well correlated with the efficacy of ICKT. However, the ratio of 2-hydroxyisobutyric acid to arachidonic acid and the ratio of glutaric acid to niacinamide, exhibited good performance as biomarkers for the efficacy of ICKT on bile flow and ALT, respectively. Additionally, comprehensive correlation analysis revealed that serum glucuronic acid was highly correlated with serum total bilirubin, suggesting that this metabolite may be deeply involved in the pathogenesis of jaundice.

CONCLUSIONS

The present study indicates that ICKT is efficacious and provides candidates for predicting ICKT efficacy. Further validation studies are warranted.

Metabolic Engineering of Oleaginous Yeasts for Production of Fuels and Chemicals

Oleaginous yeasts have been increasingly explored for production of chemicals and fuels via metabolic engineering. Particularly, there is a growing interest in using oleaginous yeasts for the synthesis of lipid-related products due to their high lipogenesis capability, robustness, and ability to utilize a variety of substrates. Most of the metabolic engineering studies in oleaginous yeasts focused on Yarrowia that already has plenty of genetic engineering tools. However, recent advances in systems biology and synthetic biology have provided new strategies and tools to engineer those oleaginous yeasts that have naturally high lipid accumulation but lack genetic tools, such as Rhodosporidium, Trichosporon, and Lipomyces. This review highlights recent accomplishments in metabolic engineering of oleaginous yeasts and recent advances in the development of genetic engineering tools in oleaginous yeasts within the last 3 years.

A synthetic biology approach to transform Yarrowia lipolytica into a competitive biotechnological producer of β-carotene

The increasing market demands of β-carotene as colorant, antioxidant and vitamin precursor, requires novel biotechnological production platforms. Yarrowia lipolytica, is an industrial organism unable to naturally synthesize carotenoids but with the ability to produce high amounts of the precursor Acetyl-CoA. We first found that a lipid overproducer strain was capable of producing more β-carotene than a wild type after expressing the heterologous pathway. Thereafter, we developed a combinatorial synthetic biology approach base on Golden Gate DNA assembly to screen the optimum promoter-gene pairs for each transcriptional unit expressed. The best strain reached a production titer of 1.5 g/L and a maximum yield of 0.048 g/g of glucose in flask. β-carotene production was further increased in controlled conditions using a fed-batch fermentation. A total production of β-carotene of 6.5 g/L and 90 mg/g DCW with a concomitant production of 42.6 g/L of lipids was achieved. Such high titers suggest that engineered Y. lipolytica is a competitive producer organism of β-carotene.

Elucidation of complexity and prediction of interactions in microbial communities

Microorganisms engage in complex interactions with other members of the microbial community, higher organisms as well as their environment. However, determining the exact nature of these interactions can be challenging due to the large number of members in these communities and the manifold of interactions they can engage in. Various omic data, such as 16S rRNA gene sequencing, shotgun metagenomics, metatranscriptomics, metaproteomics and metabolomics, have been deployed to unravel the community structure, interactions and resulting community dynamics in situ. Interpretation of these multi-omic data often requires advanced computational methods. Modelling approaches are powerful tools to integrate, contextualize and interpret experimental data, thus shedding light on the underlying processes shaping the microbiome. Here, we review current methods and approaches, both experimental and computational, to elucidate interactions in microbial communities and to predict their responses to perturbations.

Corn bran bioprocessing: Development of an integrated process for microbial lipids production

This study investigated the potential of corn bran as a feedstock for microbial lipid production using oleaginous yeast, Trichosporon oleaginosus ATCC20509. Different conditions (solid loading of biomass, acid loading, and pretreatment duration) were applied to optimize pretreatment processes using the Box-Behnken design. The highest sugar yield of 0.53g/g was obtained from corn bran hydrolysates at a pretreatment condition of 5% solid loading and 1% acid loading for 30min. Compared with synthetic media, up to 50% higher lipid accumulations in T. oleaginosus were achieved using corn bran hydrolysates during fermentation. Also, the direct effect of pretreatment condition on the lipid accumulation of T. oleaginosus was investigated using response surface methodology (RSM). Solid loading of biomass during the pretreatment process significantly affected the fermentation process for lipid accumulation of T. oleaginosus. The RSM model can provide useful information to design an integrated bioconversion platform.

Integration of Genome Scale Metabolic Networks and Gene Regulation of Metabolic Enzymes With Physiologically Based Pharmacokinetics

The scope of physiologically based pharmacokinetic (PBPK) modeling can be expanded by assimilation of the mechanistic models of intracellular processes from systems biology field. The genome scale metabolic networks (GSMNs) represent a whole set of metabolic enzymes expressed in human tissues. Dynamic models of the gene regulation of key drug metabolism enzymes are available. Here, we introduce GSMNs and review ongoing work on integration of PBPK, GSMNs, and metabolic gene regulation. We demonstrate example models.

A Computational Method of Defining Potential Biomarkers based on Differential Sub-Networks

Analyzing omics data from a network-based perspective can facilitate biomarker discovery. To improve disease diagnosis and identify prospective information indicating the onset of complex disease, a computational method for identifying potential biomarkers based on differential sub-networks (PB-DSN) is developed. In PB-DSN, Pearson correlation coefficient (PCC) is used to measure the relationship between feature ratios and to infer potential networks. A differential sub-network is extracted to identify crucial information for discriminating different groups and indicating the emergence of complex diseases. Subsequently, PB-DSN defines potential biomarkers based on the topological analysis of these differential sub-networks. In this study, PB-DSN is applied to handle a static genomics dataset of small, round blue cell tumors and a time-series metabolomics dataset of hepatocellular carcinoma. PB-DSN is compared with support vector machine-recursive feature elimination, multivariate empirical Bayes statistics, analyzing time-series data based on dynamic networks, molecular networks based on PCC, PinnacleZ, graph-based iterative group analysis, KeyPathwayMiner and BioNet. The better performance of PB-DSN not only demonstrates its effectiveness for the identification of discriminative features that facilitate disease classification, but also shows its potential for the identification of warning signals.

Common and cell-type specific responses to anti-cancer drugs revealed by high throughput transcript profiling

More effective use of targeted anti-cancer drugs depends on elucidating the connection between the molecular states induced by drug treatment and the cellular phenotypes controlled by these states, such as cytostasis and death. This is particularly true when mutation of a single gene is inadequate as a predictor of drug response. The current paper describes a data set of ~600 drug cell line pairs collected as part of the NIH LINCS Program ( http://www.lincsproject.org/ ) in which molecular data (reduced dimensionality transcript L1000 profiles) were recorded across dose and time in parallel with phenotypic data on cellular cytostasis and cytotoxicity. We report that transcriptional and phenotypic responses correlate with each other in general, but whereas inhibitors of chaperones and cell cycle kinases induce similar transcriptional changes across cell lines, changes induced by drugs that inhibit intra-cellular signaling kinases are cell-type specific. In some drug/cell line pairs significant changes in transcription are observed without a change in cell growth or survival; analysis of such pairs identifies drug equivalence classes and, in one case, synergistic drug interactions. In this case, synergy involves cell-type specific suppression of an adaptive drug response.

De novo pathway-based biomarker identification

Gene expression profiles have been extensively discussed as an aid to guide the therapy by predicting disease outcome for the patients suffering from complex diseases, such as cancer. However, prediction models built upon single-gene (SG) features show poor stability and performance on independent datasets. Attempts to mitigate these drawbacks have led to the development of network-based approaches that integrate pathway information to produce meta-gene (MG) features. Also, MG approaches have only dealt with the two-class problem of good versus poor outcome prediction. Stratifying patients based on their molecular subtypes can provide a detailed view of the disease and lead to more personalized therapies. We propose and discuss a novel MG approach based on de novo pathways, which for the first time have been used as features in a multi-class setting to predict cancer subtypes. Comprehensive evaluation in a large cohort of breast cancer samples from The Cancer Genome Atlas (TCGA) revealed that MGs are considerably more stable than SG models, while also providing valuable insight into the cancer hallmarks that drive them. In addition, when tested on an independent benchmark non-TCGA dataset, MG features consistently outperformed SG models. We provide an easy-to-use web service at http://pathclass.compbio.sdu.dk where users can upload their own gene expression datasets from breast cancer studies and obtain the subtype predictions from all the classifiers.

A protein phosphatase network controls the temporal and spatial dynamics of differentiation commitment in human epidermis

Epidermal homeostasis depends on a balance between stem cell renewal and terminal differentiation. The transition between the two cell states, termed commitment, is poorly understood. Here, we characterise commitment by integrating transcriptomic and proteomic data from disaggregated primary human keratinocytes held in suspension to induce differentiation. Cell detachment induces several protein phosphatases, five of which - DUSP6, PPTC7, PTPN1, PTPN13 and PPP3CA – promote differentiation by negatively regulating ERK MAPK and positively regulating AP1 transcription factors. Conversely, DUSP10 expression antagonises commitment. The phosphatases form a dynamic network of transient positive and negative interactions that change over time, with DUSP6 predominating at commitment. Boolean network modelling identifies a mandatory switch between two stable states (stem and differentiated) via an unstable (committed) state. Phosphatase expression is also spatially regulated in vivo and in vitro. We conclude that an auto-regulatory phosphatase network maintains epidermal homeostasis by controlling the onset and duration of commitment.

Reconciling conflicting models for global control of cell-cycle transcription

Models for the control of global cell-cycle transcription have advanced from a CDK-APC/C oscillator, a transcription factor (TF) network, to coupled CDK-APC/C and TF networks. Nonetheless, current models were challenged by a recent study that concluded that the cell-cycle transcriptional program is primarily controlled by a CDK-APC/C oscillator in budding yeast. Here we report an analysis of the transcriptome dynamics in cyclin mutant cells that were not queried in the previous study. We find that B-cyclin oscillation is not essential for control of phase-specific transcription. Using a mathematical model, we demonstrate that the function of network TFs can be retained in the face of significant reductions in transcript levels. Finally, we show that cells arrested at mitotic exit with non-oscillating levels of B-cyclins continue to cycle transcriptionally. Taken together, these findings support a critical role of a TF network and a requirement for CDK activities that need not be periodic.

paraGSEA: a scalable approach for large-scale gene expression profiling

More studies have been conducted using gene expression similarity to identify functional connections among genes, diseases and drugs. Gene Set Enrichment Analysis (GSEA) is a powerful analytical method for interpreting gene expression data. However, due to its enormous computational overhead in the estimation of significance level step and multiple hypothesis testing step, the computation scalability and efficiency are poor on large-scale datasets. We proposed paraGSEA for efficient large-scale transcriptome data analysis. By optimization, the overall time complexity of paraGSEA is reduced from O(mn) to O(m+n), where m is the length of the gene sets and n is the length of the gene expression profiles, which contributes more than 100-fold increase in performance compared with other popular GSEA implementations such as GSEA-P, SAM-GS and GSEA2. By further parallelization, a near-linear speed-up is gained on both workstations and clusters in an efficient manner with high scalability and performance on large-scale datasets. The analysis time of whole LINCS phase I dataset (GSE92742) was reduced to nearly half hour on a 1000 node cluster on Tianhe-2, or within 120 hours on a 96-core workstation. The source code of paraGSEA is licensed under the GPLv3 and available at http://github.com/ysycloud/paraGSEA.

Clustergrammer, a web-based heatmap visualization and analysis tool for high-dimensional biological data

Most tools developed to visualize hierarchically clustered heatmaps generate static images. Clustergrammer is a web-based visualization tool with interactive features such as: zooming, panning, filtering, reordering, sharing, performing enrichment analysis, and providing dynamic gene annotations. Clustergrammer can be used to generate shareable interactive visualizations by uploading a data table to a web-site, or by embedding Clustergrammer in Jupyter Notebooks. The Clustergrammer core libraries can also be used as a toolkit by developers to generate visualizations within their own applications. Clustergrammer is demonstrated using gene expression data from the cancer cell line encyclopedia (CCLE), original post-translational modification data collected from lung cancer cells lines by a mass spectrometry approach, and original cytometry by time of flight (CyTOF) single-cell proteomics data from blood. Clustergrammer enables producing interactive web based visualizations for the analysis of diverse biological data.

Metabolomic analysis of insulin resistance across different mouse strains and diets

Insulin resistance is a major risk factor for many diseases. However, its underlying mechanism remains unclear in part because it is triggered by a complex relationship between multiple factors, including genes and the environment. Here, we used metabolomics combined with computational methods to identify factors that classified insulin resistance across individual mice derived from three different mouse strains fed two different diets. Three inbred ILSXISS strains were fed high-fat or chow diets and subjected to metabolic phenotyping and metabolomics analysis of skeletal muscle. There was significant metabolic heterogeneity between strains, diets, and individual animals. Distinct metabolites were changed with insulin resistance, diet, and between strains. Computational analysis revealed 113 metabolites that were correlated with metabolic phenotypes. Using these 113 metabolites, combined with machine learning to segregate mice based on insulin sensitivity, we identified C22:1-CoA, C2-carnitine, and C16-ceramide as the best classifiers. Strikingly, when these three metabolites were combined into one signature, they classified mice based on insulin sensitivity more accurately than each metabolite on its own or other published metabolic signatures. Furthermore, C22:1-CoA was 2.3-fold higher in insulin-resistant mice and correlated significantly with insulin resistance. We have identified a metabolomic signature composed of three functionally unrelated metabolites that accurately predicts whole-body insulin sensitivity across three mouse strains. These data indicate the power of simultaneous analysis of individual, genetic, and environmental variance in mice for identifying novel factors that accurately predict metabolic phenotypes like whole-body insulin sensitivity.

Recent advances in the synthesis of functional materials by engineered and recombinant living cells

At the interface of materials science and synthetic biology, several concepts were recently developed for the production of functional materials by living cells. Selected recent strategies for this are highlighted here with a focus on bioactive, electronic and fluorescent materials.