Unbiased Identification of Blood-based Biomarkers for Pulmonary Tuberculosis by Modeling and Mining Molecular Interaction Networks

Efficient diagnosis of tuberculosis (TB) is met with multiple challenges, calling for a shift of focus from pathogen-centric diagnostics towards identification of host-based multi-marker signatures. Transcriptomics offer a list of differentially expressed genes, but cannot by itself identify the most influential contributors to the disease phenotype. Here, we describe a computational pipeline that adopts an unbiased approach to identify a biomarker signature. Data from RNA sequencing from whole blood samples of TB patients were integrated with a curated genome-wide molecular interaction network, from which we obtain a comprehensive perspective of variations that occur in the host due to TB. We then implement a sensitive network mining method to shortlist gene candidates that are most central to the disease alterations. We then apply a series of filters that include applicability to multiple publicly available datasets as well as additional validation on independent patient samples, and identify a signature comprising 10 genes - FCGR1A, HK3, RAB13, RBBP8, IFI44L, TIMM10, BCL6, SMARCD3, CYP4F3 and SLPI, that can discriminate between TB and healthy controls as well as distinguish TB from latent tuberculosis and HIV in most cases. The signature has the potential to serve as a diagnostic marker of TB.

Computational proteome-wide screening predicts neurotoxic drug-protein interactome for the investigational analgesic BIA 10-2474

The investigational compound BIA 10-2474, designed as a long-acting and reversible inhibitor of fatty acid amide hydrolase for the treatment of neuropathic pain, led to the death of one participant and hospitalization of five others due to intracranial hemorrhage in a Phase I clinical trial. Putative off-target activities of BIA 10-2474 have been suggested to be major contributing factors to the observed neurotoxicity in humans, motivating our study's proteome-wide screening approach to investigate its polypharmacology. Accordingly, we performed an in silico screen against 80,923 protein structures reported in the Protein Data Bank. The resulting list of 284 unique human interactors was further refined using target-disease association analyses to a subset of proteins previously linked to neurological, intracranial, inflammatory, hemorrhagic or clotting processes and/or diseases. Eleven proteins were identified as potential targets of BIA 10-2474, and the two highest-scoring proteins, Factor VII and thrombin, both essential blood-clotting factors, were predicted to be inhibited by BIA 10-2474 and suggest a plausible mechanism of toxicity. Once this small molecule becomes commercially available, future studies will be conducted to evaluate the predicted inhibitory effect of BIA 10-2474 on blood clot formation specifically in the brain.

Advances and challenges in logical modeling of cell cycle regulation: perspective for multi-scale, integrative yeast cell models

The eukaryotic cell cycle is robustly designed, with interacting molecules organized within a definite topology that ensures temporal precision of its phase transitions. Its underlying dynamics are regulated by molecular switches, for which remarkable insights have been provided by genetic and molecular biology efforts. In a number of cases, this information has been made predictive, through computational models. These models have allowed for the identification of novel molecular mechanisms, later validated experimentally. Logical modeling represents one of the youngest approaches to address cell cycle regulation. We summarize the advances that this type of modeling has achieved to reproduce and predict cell cycle dynamics. Furthermore, we present the challenge that this type of modeling is now ready to tackle: its integration with intracellular networks, and its formalisms, to understand crosstalks underlying systems level properties, ultimate aim of multi-scale models. Specifically, we discuss and illustrate how such an integration may be realized, by integrating a minimal logical model of the cell cycle with a metabolic network.

NetDiff - Bayesian model selection for differential gene regulatory network inference

Differential networks allow us to better understand the changes in cellular processes that are exhibited in conditions of interest, identifying variations in gene regulation or protein interaction between, for example, cases and controls, or in response to external stimuli. Here we present a novel methodology for the inference of differential gene regulatory networks from gene expression microarray data. Specifically we apply a Bayesian model selection approach to compare models of conserved and varying network structure, and use Gaussian graphical models to represent the network structures. We apply a variational inference approach to the learning of Gaussian graphical models of gene regulatory networks, that enables us to perform Bayesian model selection that is significantly more computationally efficient than Markov Chain Monte Carlo approaches. Our method is demonstrated to be more robust than independent analysis of data from multiple conditions when applied to synthetic network data, generating fewer false positive predictions of differential edges. We demonstrate the utility of our approach on real world gene expression microarray data by applying it to existing data from amyotrophic lateral sclerosis cases with and without mutations in C9orf72, and controls, where we are able to identify differential network interactions for further investigation.

Literature mining supports a next-generation modeling approach to predict cellular byproduct secretion

The metabolic byproducts secreted by growing cells can be easily measured and provide a window into the state of a cell; they have been essential to the development of microbiology, cancer biology, and biotechnology. Progress in computational modeling of cells has made it possible to predict metabolic byproduct secretion with bottom-up reconstructions of metabolic networks. However, owing to a lack of data, it has not been possible to validate these predictions across a wide range of strains and conditions. Through literature mining, we were able to generate a database of Escherichia coli strains and their experimentally measured byproduct secretions. We simulated these strains in six historical genome-scale models of E. coli, and we report that the predictive power of the models has increased as they have expanded in size and scope. The latest genome-scale model of metabolism correctly predicts byproduct secretion for 35/89 (39%) of designs. The next-generation genome-scale model of metabolism and gene expression (ME-model) correctly predicts byproduct secretion for 40/89 (45%) of designs, and we show that ME-model predictions could be further improved through kinetic parameterization. We analyze the failure modes of these simulations and discuss opportunities to improve prediction of byproduct secretion.

A Stochastic Model of the Yeast Cell Cycle Reveals Roles for Feedback Regulation in Limiting Cellular Variability

The cell division cycle of eukaryotes is governed by a complex network of cyclin-dependent protein kinases (CDKs) and auxiliary proteins that govern CDK activities. The control system must function reliably in the context of molecular noise that is inevitable in tiny yeast cells, because mistakes in sequencing cell cycle events are detrimental or fatal to the cell or its progeny. To assess the effects of noise on cell cycle progression requires not only extensive, quantitative, experimental measurements of cellular heterogeneity but also comprehensive, accurate, mathematical models of stochastic fluctuations in the CDK control system. In this paper we provide a stochastic model of the budding yeast cell cycle that accurately accounts for the variable phenotypes of wild-type cells and more than 20 mutant yeast strains simulated in different growth conditions. We specifically tested the role of feedback regulations mediated by G1- and SG2M-phase cyclins to minimize the noise in cell cycle progression. Details of the model are informed and tested by quantitative measurements (by fluorescence in situ hybridization) of the joint distributions of mRNA populations in yeast cells. We use the model to predict the phenotypes of ~30 mutant yeast strains that have not yet been characterized experimentally.

The cell division cycle—the process by which a living cell makes a new replica of itself—is fundamental to all aspects of biological growth, development and reproduction. If cells make mistakes in cell cycle progression, they may die or give birth to aberrant progeny. Such mistakes are the root cause of serious human diseases such as cancer. Hence, we would like to understand how cells control cell cycle events and correct mistakes before they do serious damage. Yeast cells are especially suited to studying cell cycle progression because so much is known about the underlying molecular control system, and because yeast cells—being so small—are especially vulnerable to random fluctuations in molecular regulators of the cell cycle. Experimental studies have identified feedback signals in the regulatory network that appear to keep these fluctuations within manageable limits. To place these proposals in a rigorous theoretical framework, we present a stochastic model of the major feedback controls in the yeast cell cycle. Our model accounts accurately for a range of observations about cell cycle variability in wild-type and mutant cells, and makes a host of verifiable predictions about mutant strains that are seriously compromised in cell cycle progression.

Mechanism of Arachidonic Acid Accumulation during Aging in Mortierella alpina: A Large-Scale Label-Free Comparative Proteomics Study

Arachidonic acid (ARA) is an important polyunsaturated fatty acid having various beneficial physiological effects on the human body. The aging of Mortierella alpina has long been known to significantly improve ARA yield, but the exact mechanism is still elusive. Herein, multiple approaches including large-scale label-free comparative proteomics were employed to systematically investigate the mechanism mentioned above. Upon ultrastructural observation, abnormal mitochondria were found to aggregate around shrunken lipid droplets. Proteomics analysis revealed a total of 171 proteins with significant alterations of expression during aging. Pathway analysis suggested that reactive oxygen species (ROS) were accumulated and stimulated the activation of the malate/pyruvate cycle and isocitrate dehydrogenase, which might provide additional NADPH for ARA synthesis. EC 4.2.1.17-hydratase might be a key player in ARA accumulation during aging. These findings provide a valuable resource for efforts to further improve the ARA content in the oil produced by aging M. alpina.

MetaMIS: a metagenomic microbial interaction simulator based on microbial community profiles

Background

The complexity and dynamics of microbial communities are major factors in the ecology of a system. With the NGS technique, metagenomics data provides a new way to explore microbial interactions. Lotka-Volterra models, which have been widely used to infer animal interactions in dynamic systems, have recently been applied to the analysis of metagenomic data.

Results

In this paper, we present the Lotka-Volterra model based tool, the Metagenomic Microbial Interacticon Simulator (MetaMIS), which is designed to analyze the time series data of microbial community profiles. MetaMIS first infers underlying microbial interactions from abundance tables for operational taxonomic units (OTUs) and then interprets interaction networks using the Lotka-Volterra model. We also embed a Bray-Curtis dissimilarity method in MetaMIS in order to evaluate the similarity to biological reality. MetaMIS is designed to tolerate a high level of missing data, and can estimate interaction information without the influence of rare microbes. For each interaction network, MetaMIS systematically examines interaction patterns (such as mutualism or competition) and refines the biotic role within microbes. As a case study, we collect a human male fecal microbiome and show that Micrococcaceae, a relatively low abundance OTU, is highly connected with 13 dominant OTUs and seems to play a critical role. MetaMIS is able to organize multiple interaction networks into a consensus network for comparative studies; thus we as a case study have also identified a consensus interaction network between female and male fecal microbiomes.

Conclusions

MetaMIS provides an efficient and user-friendly platform that may reveal new insights into metagenomics data. MetaMIS is freely available at: https://sourceforge.net/projects/metamis/.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1359-0) contains supplementary material, which is available to authorized users.

GEN3VA: aggregation and analysis of gene expression signatures from related studies

BACKGROUND

Genome-wide gene expression profiling of mammalian cells is becoming a staple of many published biomedical and biological research studies. Such data is deposited into data repositories such as the Gene Expression Omnibus (GEO) for potential reuse. However, these repositories currently do not provide simple interfaces to systematically analyze collections of related studies.

RESULTS

Here we present GENE Expression and Enrichment Vector Analyzer (GEN3VA), a web-based system that enables the integrative analysis of aggregated collections of tagged gene expression signatures identified and extracted from GEO. Each tagged collection of signatures is presented in a report that consists of heatmaps of the differentially expressed genes; principal component analysis of all signatures; enrichment analysis with several gene set libraries across all signatures, which we term enrichment vector analysis; and global mapping of small molecules that are predicted to reverse or mimic each signature in the aggregate. We demonstrate how GEN3VA can be used to identify common molecular mechanisms of aging by analyzing tagged signatures from 244 studies that compared young vs. old tissues in mammalian systems. In a second case study, we collected 86 signatures from treatment of human cells with dexamethasone, a glucocorticoid receptor (GR) agonist. Our analysis confirms consensus GR target genes and predicts potential drug mimickers.

CONCLUSIONS

GEN3VA can be used to identify, aggregate, and analyze themed collections of gene expression signatures from diverse but related studies. Such integrative analyses can be used to address concerns about data reproducibility, confirm results across labs, and discover new collective knowledge by data reuse. GEN3VA is an open-source web-based system that is freely available at: http://amp.pharm.mssm.edu/gen3va .

Quantitative stem cell biology: the threat and the glory

Major technological innovations over the past decade have transformed our ability to extract quantitative data from biological systems at an unprecedented scale and resolution. These quantitative methods and associated large datasets should lead to an exciting new phase of discovery across many areas of biology. However, there is a clear threat: will we drown in these rivers of data? On 18th July 2016, stem cell biologists gathered in Cambridge for the 5th annual Cambridge Stem Cell Symposium to discuss 'Quantitative stem cell biology: from molecules to models'. This Meeting Review provides a summary of the data presented by each speaker, with a focus on quantitative techniques and the new biological insights that are emerging.

Epigenetic Treatment of Persistent Viral Infections

Preclinical Research Approximately 2,500 years ago, Hippocrates used the word herpes as a medical term to describe lesions that appeared to creep or crawl on the skin, advocating heat as a possible treatment. During the last 50 years, pharmaceutical research has made great strides, and therapeutic options have expanded to include small molecule antiviral agents, protease inhibitors, preventive vaccines for a handful of the papillomaviruses, and even cures for hepatitis C virus infections. However, effective treatments for persistent and recurrent viral infections, particularly the highly prevalent herpesviruses, continue to represent a significant unmet medical need, affecting the majority of the world's population. Exploring the population diversity of the human microbiome and the effects its compositional variances have on the immune system, health, and disease are the subjects of intense investigational research and study. Among the collection of viruses, bacteria, fungi, and single-cell eukaryotes that comprise the human microbiome, the virome has been grossly understudied relative to the influence it exerts on human pathophysiology, much as mitochondria have until recently failed to receive the attention they deserve, given their critical biomedical importance. Fortunately, cellular epigenetic machinery offers a wealth of druggable targets for therapeutic intervention in numerous disease indications, including those outlined above. With advances in synthetic biology, engineering our body's commensal microorganisms to seek out and destroy pathogenic species is clearly on the horizon. This is especially the case given recent breakthroughs in genetic manipulation with tools such as the CRISPR/Cas (clustered regularly interspaced short palindromic repeats/CRISPR-associated) gene-editing platforms. Tying these concepts together with our previous work on the microbiome and neurodegenerative and neuropsychiatric diseases, we suggest that, because mammalian cells respond to a viral infection by triggering a cascade of antiviral innate immune responses governed substantially by the cell's mitochondria, small molecule carnitinoids represent a new class of therapeutics with potential widespread utility against many infectious insults. Drug Dev Res 78 : 24-36, 2017.   © 2016 Wiley Periodicals, Inc.

Time Hierarchies and Model Reduction in Canonical Non-linear Models

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

Sequence Assembly of Yarrowia lipolytica Strain W29/CLIB89 Shows Transposable Element Diversity

Yarrowia lipolytica, an oleaginous yeast, is capable of accumulating significant cellular mass in lipid making it an important source of biosustainable hydrocarbon-based chemicals. In spite of a similar number of protein-coding genes to that in other Hemiascomycetes, the Y. lipolytica genome is almost double that of model yeasts. Despite its economic importance and several distinct strains in common use, an independent genome assembly exists for only one strain. We report here a de novo annotated assembly of the chromosomal genome of an industrially-relevant strain, W29/CLIB89, determined by hybrid next-generation sequencing. For the first time, each Y. lipolytica chromosome is represented by a single contig. The telomeric rDNA repeats were localized by Irys long-range genome mapping and one complete copy of the rDNA sequence is reported. Two large structural variants and retroelement differences with reference strain CLIB122 including a full-length, novel Ty3/Gypsy long terminal repeat (LTR) retrotransposon and multiple LTR-like sequences are described. Strikingly, several of these are adjacent to RNA polymerase III-transcribed genes, which are almost double in number in Y. lipolytica compared to other Hemiascomycetes. In addition to previously-reported dimeric RNA polymerase III-transcribed genes, tRNA pseudogenes were identified. Multiple full-length and truncated LINE elements are also present. Therefore, although identified transposons do not constitute a significant fraction of the Y. lipolytica genome, they could have played an active role in its evolution. Differences between the sequence of this strain and of the existing reference strain underscore the utility of an additional independent genome assembly for this economically important organism.

Long-term response to sunitinib: everolimus treatment in metastatic clear cell renal cell carcinoma

AIM

The study aim was to evaluate progression-free survival (PFS) and overall survival (OS) in patients with metastatic clear cell renal cell carcinoma on sunitinib (SU) and SU-everolimus treatment.

PATIENTS & METHODS

After 7 years of enrollment and 9 years of follow-up, 193 consecutively presenting patients (151 men and 42 women) were treated.

RESULTS

A total of 157 patients (81.3%) died and 36 patients (18.7%) survived. Median PFS in 193 SU-treated patients was 14.7 months and OS was 28.8 months. Median PFS was 13.98 months and median OS was 26.67 months in 175 patients treated with SU only or on SU-everolimus.

CONCLUSION

The development of SU-induced hypothyroidism, hypertension, neutropenia and edema was a significant predictive and prognostic factor.

Rapid Discrimination Among Putative Mechanistic Models of Biochemical Systems

An overarching goal in molecular biology is to gain an understanding of the mechanistic basis underlying biochemical systems. Success is critical if we are to predict effectively the outcome of drug treatments and the development of abnormal phenotypes. However, data from most experimental studies is typically noisy and sparse. This allows multiple potential mechanisms to account for experimental observations, and often devising experiments to test each is not feasible. Here, we introduce a novel strategy that discriminates among putative models based on their repertoire of qualitatively distinct phenotypes, without relying on knowledge of specific values for rate constants and binding constants. As an illustration, we apply this strategy to two synthetic gene circuits exhibiting anomalous behaviors. Our results show that the conventional models, based on their well-characterized components, cannot account for the experimental observations. We examine a total of 40 alternative hypotheses and show that only 5 have the potential to reproduce the experimental data, and one can do so with biologically relevant parameter values.

Bioconversion of glycerol into lipids by Rhodosporidium toruloides in a two-stage process and characterization of lipid properties

Rhodosporidium toruloides AS 2.1389 has been considered a promising oleaginous strain due to its flexible substrate adaptability, high lipid content, and coproduction of some pigments. In previous work, R. toruloides has shown good potential to directly convert crude glycerol into intracellular lipids. However, the difference in nutritional demand between cell growth and lipid accumulation was found to be a dilemma. Therefore, a glycerol-based two-stage process was proposed in the present work to separately meet the nutritional demand of both the cell proliferation phase and lipid accumulation phase. Factors that affect microbial conversion of glycerol into lipid were investigated, statistically analyzed, and optimized. As a result, 26.5 g L^-1 biomass with 10 g L^-1 lipid was obtained in the two-stage process. Lipid yield (0.20 g g^-1) and productivity (0.083 g L^-1 h^-1) achieved were significantly higher than the previously optimized batch culture. In R. toruloides lipids, the dominant fatty acid compositions are palmitic acid (28.5%), stearic acid (12.9%), oleic acid (41.3%), and linoleic acid (12.8%). Phospholipids accounts for 0.63% in total lipid. Lipase-catalyzed methanolysis could achieve up to 95% biodiesel yield. The characterization of R. toruloides lipid suggests its great application potential for biodiesel and specialty-type lipid products.

Modeling heterogeneity in the pluripotent state: A promising strategy for improving the efficiency and fidelity of stem cell differentiation

Pluripotency can be considered a functional characteristic of pluripotent stem cells (PSCs) populations and their niches, rather than a property of individual cells. In this view, individual cells within the population independently adopt a variety of different expression states, maintained by different signaling, transcriptional, and epigenetics regulatory networks. In this review, we propose that generation of integrative network models from single cell data will be essential for getting a better understanding of the regulation of self-renewal and differentiation. In particular, we suggest that the identification of network stability determinants in these integrative models will provide important insights into the mechanisms mediating the transduction of signals from the niche, and how these signals can trigger differentiation. In this regard, the differential use of these stability determinants in subpopulation-specific regulatory networks would mediate differentiation into different cell fates. We suggest that this approach could offer a promising avenue for the development of novel strategies for increasing the efficiency and fidelity of differentiation, which could have a strong impact on regenerative medicine.

Automated Synthesis and Analysis of Switching Gene Regulatory Networks

Studying the gene regulatory networks (GRNs) that govern how cells change into specific cell types with unique roles throughout development is an active area of experimental research. The fate specification process can be viewed as a biological program prescribing the system dynamics, governed by a network of genetic interactions. To investigate the possibility that GRNs are not fixed but rather change their topology, for example as cells progress through commitment, we introduce the concept of Switching Gene Regulatory Networks (SGRNs) to enable the modelling and analysis of network reconfiguration. We define the synthesis problem of constructing SGRNs that are guaranteed to satisfy a set of constraints representing experimental observations of cell behaviour. We propose a solution to this problem that employs methods based upon Satisfiability Modulo Theories (SMT) solvers, and evaluate the feasibility and scalability of our approach by considering a set of synthetic benchmarks exhibiting possible biological behaviour of cell development. We outline how our approach is applied to a more realistic biological system, by considering a simplified network involved in the processes of neuron maturation and fate specification in the mammalian cortex.

Design Space Toolbox V2: Automated Software Enabling a Novel Phenotype-Centric Modeling Strategy for Natural and Synthetic Biological Systems

Mathematical models of biochemical systems provide a means to elucidate the link between the genotype, environment, and phenotype. A subclass of mathematical models, known as mechanistic models, quantitatively describe the complex non-linear mechanisms that capture the intricate interactions between biochemical components. However, the study of mechanistic models is challenging because most are analytically intractable and involve large numbers of system parameters. Conventional methods to analyze them rely on local analyses about a nominal parameter set and they do not reveal the vast majority of potential phenotypes possible for a given system design. We have recently developed a new modeling approach that does not require estimated values for the parameters initially and inverts the typical steps of the conventional modeling strategy. Instead, this approach relies on architectural features of the model to identify the phenotypic repertoire and then predict values for the parameters that yield specific instances of the system that realize desired phenotypic characteristics. Here, we present a collection of software tools, the Design Space Toolbox V2 based on the System Design Space method, that automates (1) enumeration of the repertoire of model phenotypes, (2) prediction of values for the parameters for any model phenotype, and (3) analysis of model phenotypes through analytical and numerical methods. The result is an enabling technology that facilitates this radically new, phenotype-centric, modeling approach. We illustrate the power of these new tools by applying them to a synthetic gene circuit that can exhibit multi-stability. We then predict values for the system parameters such that the design exhibits 2, 3, and 4 stable steady states. In one example, inspection of the basins of attraction reveals that the circuit can count between three stable states by transient stimulation through one of two input channels: a positive channel that increases the count, and a negative channel that decreases the count. This example shows the power of these new automated methods to rapidly identify behaviors of interest and efficiently predict parameter values for their realization. These tools may be applied to understand complex natural circuitry and to aid in the rational design of synthetic circuits.

An open RNA-Seq data analysis pipeline tutorial with an example of reprocessing data from a recent Zika virus study

RNA-seq analysis is becoming a standard method for global gene expression profiling. However, open and standard pipelines to perform RNA-seq analysis by non-experts remain challenging due to the large size of the raw data files and the hardware requirements for running the alignment step. Here we introduce a reproducible open source RNA-seq pipeline delivered as an IPython notebook and a Docker image. The pipeline uses state-of-the-art tools and can run on various platforms with minimal configuration overhead. The pipeline enables the extraction of knowledge from typical RNA-seq studies by generating interactive principal component analysis (PCA) and hierarchical clustering (HC) plots, performing enrichment analyses against over 90 gene set libraries, and obtaining lists of small molecules that are predicted to either mimic or reverse the observed changes in mRNA expression. We apply the pipeline to a recently published RNA-seq dataset collected from human neuronal progenitors infected with the Zika virus (ZIKV). In addition to confirming the presence of cell cycle genes among the genes that are downregulated by ZIKV, our analysis uncovers significant overlap with upregulated genes that when knocked out in mice induce defects in brain morphology. This result potentially points to the molecular processes associated with the microcephaly phenotype observed in newborns from pregnant mothers infected with the virus. In addition, our analysis predicts small molecules that can either mimic or reverse the expression changes induced by ZIKV. The IPython notebook and Docker image are freely available at:  http://nbviewer.jupyter.org/github/maayanlab/Zika-RNAseq-Pipeline/blob/master/Zika.ipynb and  https://hub.docker.com/r/maayanlab/zika/.

Concerted changes in transcriptional regulation of genes involved in DNA methylation, demethylation, and folate-mediated one-carbon metabolism pathways in the NCI-60 cancer cell line panel in response to cancer drug treatment

BACKGROUND

Aberrant patterns of DNA methylation are abundant in cancer, and epigenetic pathways are increasingly being targeted in cancer drug treatment. Genetic components of the folate-mediated one-carbon metabolism pathway can affect DNA methylation and other vital cell functions, including DNA synthesis, amino acid biosynthesis, and cell growth.

RESULTS

We used a bioinformatics tool, the Transcriptional Pharmacology Workbench, to analyze temporal changes in gene expression among epigenetic regulators of DNA methylation and demethylation, and one-carbon metabolism genes in response to cancer drug treatment. We analyzed gene expression information from the NCI-60 cancer cell line panel after treatment with five antitumor agents, 5-azacytidine, doxorubicin, vorinostat, paclitaxel, and cisplatin. Each antitumor agent elicited concerted changes in gene expression of multiple pathway components across the cell lines. Expression changes of FOLR2, SMUG1, GART, GADD45A, MBD1, MTR, MTHFD1, and CTH were significantly correlated with chemosensitivity to some of the agents. Among many genes with concerted expression response to individual antitumor agents were genes encoding DNA methyltransferases DNMT1, DNMT3A, and DNMT3B, epigenetic and DNA repair factors MGMT, GADD45A, and MBD1, and one-carbon metabolism pathway members MTHFD1, TYMS, DHFR, MTR, MAT2A, SLC19A1, ATIC, and GART.

CONCLUSIONS

These transcriptional changes are likely to influence vital cellular functions of DNA methylation and demethylation, cellular growth, DNA biosynthesis, and DNA repair, and some of them may contribute to cytotoxic and apoptotic action of the drugs. This concerted molecular response was observed in a time-dependent manner, which may provide future guidelines for temporal selection of genetic drug targets for combination drug therapy treatment regimens.

Durable response of glioblastoma to adjuvant therapy consisting of temozolomide and a weekly dose of AMD3100 (plerixafor), a CXCR4 inhibitor, together with lapatinib, metformin and niacinamide

Glioblastoma multiforme (GBM) is a CNS (central nervous system) malignancy with a low cure rate. Median time to progression after standard treatment is 7 months and median overall survival is 15 months [1]. Post-treatment vasculogenesis promoted by recruitment of bone marrow derived cells (BMDCs, CD11b+ myelomonocytes) is one of main mechanisms of GBM resistance to initial chemoradiotherapy treatment [2]. Local secretion of SDF-1, cognate ligand of BMDCs CXCR4 receptors attracts BMDCs to the post-radiation tumor site.[3]. This SDF-1 hypoxia-dependent effect can be blocked by AMD3100 (plerixafor) [4]. We report a GBM case treated after chemo- radiotherapy with plerixafor and a combination of an mTOR, a Sirt1 and an EGFRvIII inhibitor. After one year temozolomide and the EGFRvIII inhibitor were stopped. Plerixafor, and the MTOR and Sirt-1 inhibitors were continued. He is in clinical and radiologic remission 30 months from the initiation of his adjuvant treatment. To our knowledge, this is the first report of a patient treated for over two years with a CXCR4 inhibitor (plerixafor), as part of his adjuvant treatment. We believe there is sufficient experimental evidence to consider AMD3100 (plerixafor) part of the adjuvant treatment of GBM.

Integration and Visualization of Translational Medicine Data for Better Understanding of Human Diseases

Translational medicine is a domain turning results of basic life science research into new tools and methods in a clinical environment, for example, as new diagnostics or therapies. Nowadays, the process of translation is supported by large amounts of heterogeneous data ranging from medical data to a whole range of -omics data. It is not only a great opportunity but also a great challenge, as translational medicine big data is difficult to integrate and analyze, and requires the involvement of biomedical experts for the data processing. We show here that visualization and interoperable workflows, combining multiple complex steps, can address at least parts of the challenge. In this article, we present an integrated workflow for exploring, analysis, and interpretation of translational medicine data in the context of human health. Three Web services-tranSMART, a Galaxy Server, and a MINERVA platform-are combined into one big data pipeline. Native visualization capabilities enable the biomedical experts to get a comprehensive overview and control over separate steps of the workflow. The capabilities of tranSMART enable a flexible filtering of multidimensional integrated data sets to create subsets suitable for downstream processing. A Galaxy Server offers visually aided construction of analytical pipelines, with the use of existing or custom components. A MINERVA platform supports the exploration of health and disease-related mechanisms in a contextualized analytical visualization system. We demonstrate the utility of our workflow by illustrating its subsequent steps using an existing data set, for which we propose a filtering scheme, an analytical pipeline, and a corresponding visualization of analytical results. The workflow is available as a sandbox environment, where readers can work with the described setup themselves. Overall, our work shows how visualization and interfacing of big data processing services facilitate exploration, analysis, and interpretation of translational medicine data.

From dirt to industrial applications: Pseudomonas putida as a Synthetic Biology chassis for hosting harsh biochemical reactions

The soil bacterium Pseudomonas putida is endowed with a central carbon metabolic network capable of fulfilling high demands of reducing power. This situation arises from a unique metabolic architecture that encompasses the partial recycling of triose phosphates to hexose phosphates-the so-called EDEMP cycle. In this article, the value of P. putida as a bacterial chassis of choice for contemporary, industrially-oriented metabolic engineering is addressed. The biochemical properties that make this bacterium adequate for hosting biotransformations involving redox reactions as well as toxic compounds and intermediates are discussed. Finally, novel developments and open questions in the continuous quest for an optimal microbial cell factory are presented at the light of current and future needs in the area of biocatalysis.

Tumor Budding: The Name is EMT. Partial EMT

Tumor budding is a histological phenomenon encountered in various cancers, whereby individual malignant cells and/or small clusters of malignant cells are seen in the tumor stroma. Postulated to be mirror epithelial-mesenchymal transition, tumor budding has been associated with poor cancer outcomes. However, the vast heterogeneity in its exact definition, methodology of assessment, and patient stratification need to be resolved before it can be routinely used as a standardized prognostic feature. Here, we discuss the heterogeneity in defining and assessing tumor budding, its clinical significance across multiple cancer types, and its prospective implementation in clinical practice. Next, we review the emerging evidence about partial, rather than complete, epithelial-mesenchymal phenotype at the tumor bud level, and its connection with tumor proliferation, quiescence, and stemness. Finally, based on recent literature, indicating a co-expression of epithelial and mesenchymal markers in many tumor buds, we posit tumor budding to be a manifestation of this hybrid epithelial/mesenchymal phenotype displaying collective cell migration.

Node-independent elementary signaling modes: A measure of redundancy in Boolean signaling transduction networks

The redundancy of a system denotes the amount of duplicate components or mechanisms in it. For a network, especially one in which mass or information is being transferred from an origin to a destination, redundancy is related to the robustness of the system. Existing network measures of redundancy rely on local connectivity (e.g. clustering coefficients) or the existence of multiple paths. As in many systems there are functional dependencies between components and paths, a measure that not only characterizes the topology of a network, but also takes into account these functional dependencies, becomes most desirable.

We propose a network redundancy measure in a prototypical model that contains functionally dependent directed paths: a Boolean model of a signal transduction network. The functional dependencies are made explicit by using an expanded network and the concept of elementary signaling modes (ESMs). We define the redundancy of a Boolean signal transduction network as the maximum number of node-independent ESMs and develop a methodology for identifying all maximal node-independent ESM combinations. We apply our measure to a number of signal transduction network models and show that it successfully distills known properties of the systems and offers new functional insights. The concept can be easily extended to similar related forms, e.g. edge-independent ESMs.

Signal Transduction Pathways of EMT Induced by TGF-β, SHH, and WNT and Their Crosstalks

Epithelial-to-mesenchymal transition (EMT) is a key step in development, wound healing, and cancer development. It involves cooperation of signaling pathways, such as transformation growth factor-β (TGF-β), Sonic Hedgehog (SHH), and WNT pathways. These signaling pathways crosstalk to each other and converge to key transcription factors (e.g., SNAIL1) to initialize and maintain the process of EMT. The functional roles of multi-signaling pathway crosstalks in EMT are sophisticated and, thus, remain to be explored. In this review, we focused on three major signal transduction pathways that promote or regulate EMT in carcinoma. We discussed the network structures, and provided a brief overview of the current therapy strategies and drug development targeted to these three signal transduction pathways. Finally, we highlighted systems biology approaches that can accelerate the process of deconstructing complex networks and drug discovery.

Experimental testing of a new integrated model of the budding yeast Start transition

Mathematical modeling of the cell cycle has unveiled recurrent features and emergent behaviors of cellular networks. Constructing new mutants and performing experimental tests during development of a new model of the budding yeast cell cycle yields a more efficient modeling process and results in several testable hypotheses.

The cell cycle is composed of bistable molecular switches that govern the transitions between gap phases (G1 and G2) and the phases in which DNA is replicated (S) and partitioned between daughter cells (M). Many molecular details of the budding yeast G1–S transition (Start) have been elucidated in recent years, especially with regard to its switch-like behavior due to positive feedback mechanisms. These results led us to reevaluate and expand a previous mathematical model of the yeast cell cycle. The new model incorporates Whi3 inhibition of Cln3 activity, Whi5 inhibition of SBF and MBF transcription factors, and feedback inhibition of Whi5 by G1–S cyclins. We tested the accuracy of the model by simulating various mutants not described in the literature. We then constructed these novel mutant strains and compared their observed phenotypes to the model’s simulations. The experimental results reported here led to further changes of the model, which will be fully described in a later article. Our study demonstrates the advantages of combining model design, simulation, and testing in a coordinated effort to better understand a complex biological network.