Molecular interaction map of the mammalian cell cycle control and DNA repair systems

TOPAS-Tissue: A Framework for the Simulation of the Biological Response to Ionizing Radiation at the Multi-Cellular Level

This work aims to develop and validate a framework for the multiscale simulation of the biological response to ionizing radiation in a population of cells forming a tissue. We present TOPAS-Tissue, a framework to allow coupling two Monte Carlo (MC) codes: TOPAS with the TOPAS-nBio extension, capable of handling the track-structure simulation and subsequent chemistry, and CompuCell3D, an agent-based model simulator for biological and environmental behavior of a population of cells. We verified the implementation by simulating the experimental conditions for a clonogenic survival assay of a 2-D PC-3 cell culture model (10 cells in 10,000 µm2) irradiated by MV X-rays at several absorbed dose values from 0-8 Gy. The simulation considered cell growth and division, irradiation, DSB induction, DNA repair, and cellular response. The survival was obtained by counting the number of colonies, defined as a surviving primary (or seeded) cell with progeny, at 2.7 simulated days after irradiation. DNA repair was simulated with an MC implementation of the two-lesion kinetic model and the cell response with a p53 protein-pulse model. The simulated survival curve followed the theoretical linear-quadratic response with dose. The fitted coefficients α = 0.280 ± 0.025/Gy and β = 0.042 ± 0.006/Gy2 agreed with published experimental data within two standard deviations. TOPAS-Tissue extends previous works by simulating in an end-to-end way the effects of radiation in a cell population, from irradiation and DNA damage leading to the cell fate. In conclusion, TOPAS-Tissue offers an extensible all-in-one simulation framework that successfully couples Compucell3D and TOPAS for multiscale simulation of the biological response to radiation.

Comprehensive molecular interaction map of TGFβ induced epithelial to mesenchymal transition in breast cancer

Breast cancer is one of the prevailing cancers globally, with a high mortality rate. Metastatic breast cancer (MBC) is an advanced stage of cancer, characterised by a highly nonlinear, heterogeneous process involving numerous singling pathways and regulatory interactions. Epithelial-mesenchymal transition (EMT) emerges as a key mechanism exploited by cancer cells. Transforming Growth Factor-β (TGFβ)-dependent signalling is attributed to promote EMT in advanced stages of breast cancer. A comprehensive regulatory map of TGFβ induced EMT was developed through an extensive literature survey. The network assembled comprises of 312 distinct species (proteins, genes, RNAs, complexes), and 426 reactions (state transitions, nuclear translocations, complex associations, and dissociations). The map was developed by following Systems Biology Graphical Notation (SBGN) using Cell Designer and made publicly available using MINERVA ( http://35.174.227.105:8080/minerva/?id=Metastatic_Breast_Cancer_1 ). While the complete molecular mechanism of MBC is still not known, the map captures the elaborate signalling interplay of TGFβ induced EMT-promoting MBC. Subsequently, the disease map assembled was translated into a Boolean model utilising CaSQ and analysed using Cell Collective. Simulations of these have captured the known experimental outcomes of TGFβ induced EMT in MBC. Hub regulators of the assembled map were identified, and their transcriptome-based analysis confirmed their role in cancer metastasis. Elaborate analysis of this map may help in gaining additional insights into the development and progression of metastatic breast cancer.

The oscillation of mitotic kinase governs cell cycle latches in mammalian cells

The mammalian cell cycle alternates between two phases - S-G2-M with high levels of A- and B-type cyclins (CycA and CycB, respectively) bound to cyclin-dependent kinases (CDKs), and G1 with persistent degradation of CycA and CycB by an activated anaphase promoting complex/cyclosome (APC/C) bound to Cdh1 (also known as FZR1 in mammals; denoted APC/C:Cdh1). Because CDKs phosphorylate and inactivate Cdh1, these two phases are mutually exclusive. This 'toggle switch' is flipped from G1 to S by cyclin-E bound to a CDK (CycE:CDK), which is not degraded by APC/C:Cdh1, and from M to G1 by Cdc20-bound APC/C (APC/C:Cdc20), which is not inactivated by CycA:CDK or CycB:CDK. After flipping the switch, cyclin E is degraded and APC/C:Cdc20 is inactivated. Combining mathematical modelling with single-cell timelapse imaging, we show that dysregulation of CycB:CDK disrupts strict alternation of the G1-S and M-G1 switches. Inhibition of CycB:CDK results in Cdc20-independent Cdh1 'endocycles', and sustained activity of CycB:CDK drives Cdh1-independent Cdc20 endocycles. Our model provides a mechanistic explanation for how whole-genome doubling can arise, a common event in tumorigenesis that can drive tumour evolution.

Protein kinase C epsilon activation regulates proliferation, migration, and epithelial to mesenchymal-like transition in rat Schwann cells

Introduction

Protein kinase type C-ε (PKCε) plays an important role in the sensitization of primary afferent nociceptors, promoting mechanical hyperalgesia. In accordance, we showed that PKCε is present in sensory neurons of the peripheral nervous system (PNS), participating in the control of pain onset and chronification. Recently, it was found that PKCε is also implicated in the control of cell proliferation, promoting mitogenesis and metastatic invasion in some types of cancer. However, its role in the main glial cell of the PNS, the Schwann cells (SCs), was still not investigated.

Methods

Rat primary SCs culture were treated with different pharmacologic approaches, including the PKCε agonist dicyclopropyl-linoleic acid (DCP-LA) 500 nM, the human recombinant brain derived neurotrophic factor (BDNF) 1 nM and the TrkB receptor antagonist cyclotraxin B 10 nM. The proliferation (by cell count), the migration (by scratch test and Boyden assay) as well as some markers of SCs differentiation and epithelial-mesenchymal transition (EMT) process (by qRT-PCR and western blot) were analyzed.

Results

Overall, we found that PKCε is constitutively expressed in SCs, where it is likely involved in the switch from the proliferative toward the differentiated state. Indeed, we demonstrated that PKCε activation regulates SCs proliferation, increases their migration, and the expression of some markers (e.g., glycoprotein P0 and the transcription factor Krox20) of SCs differentiation. Through an autocrine mechanism, BDNF activates TrkB receptor, and controls SCs proliferation via PKCε. Importantly, PKCε activation likely promoted a partial EMT process in SCs.

Discussion

PKCε mediates relevant actions in the neuronal and glial compartment of the PNS. In particular, we posit a novel function for PKCε in the transformation of SCs, assuming a role in the mechanisms controlling SCs' fate and plasticity.

Topological origins of yielding in short-ranged weakly attractive colloidal gels

Yielding of the particulate network in colloidal gels under applied deformation is accompanied by various microstructural changes, including rearrangement, bond rupture, anisotropy, and reformation of secondary structures. While much work has been done to understand the physical underpinnings of yielding in colloidal gels, its topological origins remain poorly understood. Here, employing a series of tools from network science, we characterize the bonds using their orientation and network centrality. We find that bonds with higher centralities in the network are ruptured the most at all applied deformation rates. This suggests that a network analysis of the particulate structure can be used to predict the failure points in colloidal gels a priori.

Quantifying Cellular Repair, Misrepair and Apoptosis Induced by Boron Ions, Gamma Rays and PRIMA-1 Using the RHR Formulation

The recent interaction cross-section-based formulation for radiation-induced direct cellular inactivation, mild and severe sublethal damage, DNA-repair and cell survival have been developed to accurately describe cellular repair, misrepair and apoptosis in TP53 wild-type and mutant cells. The principal idea of this new non-homologous repairable-homologous repairable (RHR) damage formulation is to separately describe the mild damage that can be rapidly handled by the most basic repair processes including the non-homologous end joining (NHEJ), and more complex damage requiring longer repair times and high-fidelity homologous recombination (HR) repair. Taking the interaction between these two key mammalian DNA repair processes more accurately into account has significantly improved the method as indicated in the original publication. Based on the principal mechanisms of 7 repair and 8 misrepair processes presently derived, it has been possible to quite accurately describe the probability that some of these repair processes when unsuccessful can induce cellular apoptosis with increasing doses of γrays, boron ions and PRIMA-1. Interestingly, for all LETs studied (≈0.3-160 eV/nm) the increase in apoptosis saturates when the cell survival reaches about 10% and the fraction of un-hit cells is well below the 1% level. It is shown that most of the early cell kill for low-to-medium LETs are due to apoptosis since the cell survival as well as the non-apoptotic cells agree very well at low doses and other death processes dominate beyond D > 1 Gy. The low-dose apoptosis is due to the fact that the full activation of the checkpoint kinases ATM and Chk2 requires >8 and >18 DSBs per cell to phosphorylate p53 at serine 15 and 20. Therefore, DNA repair is not fully activated until well after 1/2 Gy, and the cellular response may be apoptotic by default before the low-dose hyper sensitivity (LDHS) is replaced by an increased radiation tolerance as the DNA repair processes get maximal efficiency. In effect, simultaneously explaining the LDHS and inverse dose rate phenomena. The partial contributions by the eight newly derived misrepair processes was determined so they together accurately described the experimental apoptosis induction data for γ rays and boron ions. Through these partial misrepair contributions it was possible to predict the apoptotic response based solely on carefully analyzed cell survival data, demonstrating the usefulness of an accurate DNA repair-based cell survival approach. The peak relative biological effectiveness (RBE) of the boron ions was 3.5 at 160 eV/nm whereas the analogous peak relative apoptotic effectiveness (RAE) was 3.4 but at 40 eV/nm indicating the clinical value of the lower LET light ion (15 \le {\rm{LET}} \le 55{\rm{\ eV}}/{\rm{nm}},{\rm{\ }}2 \le Z \le 5) in therapeutic applications to maximize tumor apoptosis and senescence. The new survival expressions were also applied on mouse embryonic fibroblasts with key knocked-out repair genes, showing a good agreement between the principal non-homologous and homologous repair terms and also a reasonable prediction of the associated apoptotic induction. Finally, the formulation was used to estimate the increase in DNA repair and apoptotic response in combination with the mutant p53 reactivating compound PRIMA-1 and γ rays, indicating a 10-2 times increase in apoptosis with 5 μM of the compound reaching apoptosis levels not far from peak apoptosis boron ions in a TP53 mutant cell line. To utilize PRIMA-1 induced apoptosis and cellular sensitization for reactive oxygen species (ROS), concomitant biologically optimized radiation therapy is proposed to maximize the complication free tumor cure for the multitude of TP53 mutant tumors seen in the clinic. The experimental data also indicated the clinically very important high-absorbed dose ROS effect of PRIMA-1.

Synchronization in asymmetrically coupled networks with homogeneous oscillators

Synchronization among coupled oscillators is a common feature of symmetrically coupled networks with homogeneous, i.e., identical, oscillators. Recently, it was reported [T. Nishikawa and A. Motter, Phys. Rev. Lett. 117, 114101 (2016)PRLTAO0031-900710.1103/PhysRevLett.117.114101 and Y. Zhang, T. Nishikawa, and A. E. Motter, Phys. Rev. E 95, 062215 (2017)2470-004510.1103/PhysRevE.95.062215], however, that in networks with asymmetrically coupled oscillators, synchronization can only be found to be stable when the oscillators are heterogenous or nonidentical. In this manuscript, it is proven, mathematically, that the conclusions in those works are incorrect, and that stable synchronization states can, and do, exist in asymmetrically coupled homogeneous oscillators. Theoretical results are confirmed with numerical simulations.

Hepatocellular carcinoma (HepG2/C3A) cell-based 3D model for genotoxicity testing of chemicals

The major weakness of the current in vitro genotoxicity test systems is the inability of the indicator cells to express metabolic enzymes needed for the activation and detoxification of genotoxic compounds, which consequently can lead to misleading results. Thus, there is a significant emphasis on developing hepatic cell models, including advanced in vitro three-dimensional (3D) cell-based systems, which better imitate in vivo cell behaviour and offer more accurate and predictive data for human exposures. In this study, we developed an approach for genotoxicity testing with 21-day old spheroids formed from human hepatocellular carcinoma cells (HepG2/C3A) using the dynamic clinostat bioreactor system (CelVivo BAM/bioreactor) under controlled conditions. The spheroids were exposed to indirect-acting genotoxic compounds, polycyclic aromatic hydrocarbon [PAH; benzo(a) pyrene B(a)P], and heterocyclic aromatic amine [PhIP]) at non-cytotoxic concentrations for 24 and 96 h. The results showed that both environmental pollutants B(a)P and PhIP significantly increased the level of DNA strand breaks assessed by the comet assay. Further, the mRNA level of selected genes encoding metabolic enzymes from phase I and II, and DNA damage responsive genes was determined (qPCR). The 21-day old spheroids showed higher basal expression of genes encoding metabolic enzymes compared to monolayer culture. In spheroids, B(a)P or PhIP induced compound-specific up-regulation of genes implicated in their metabolism, and deregulation of genes implicated in DNA damage and immediate-early response. The study demonstrated that this model utilizing HepG2/C3A spheroids grown under dynamic clinostat conditions represents a very sensitive and promising in vitro model for genotoxicity and environmental studies and can thus significantly contribute to a more reliable assessment of genotoxic activities of pure chemicals, and complex environmental samples even at very low for environmental exposure relevant concentrations.

Pathway information extracted from 25 years of pathway figures

Thousands of pathway diagrams are published each year as static figures inaccessible to computational queries and analyses. Using a combination of machine learning, optical character recognition, and manual curation, we identified 64,643 pathway figures published between 1995 and 2019 and extracted 1,112,551 instances of human genes, comprising 13,464 unique NCBI genes, participating in a wide variety of biological processes. This collection represents an order of magnitude more genes than found in the text of the same papers, and thousands of genes missing from other pathway databases, thus presenting new opportunities for discovery and research.

The Atlas of Inflammation Resolution (AIR)

Acute inflammation is a protective reaction by the immune system in response to invading pathogens or tissue damage. Ideally, the response should be localized, self-limited, and returning to homeostasis. If not resolved, acute inflammation can result in organ pathologies leading to chronic inflammatory phenotypes. Acute inflammation and inflammation resolution are complex coordinated processes, involving a number of cell types, interacting in space and time. The biomolecular complexity and the fact that several biomedical fields are involved, make a multi- and interdisciplinary approach necessary. The Atlas of Inflammation Resolution (AIR) is a web-based resource capturing an essential part of the state-of-the-art in acute inflammation and inflammation resolution research. The AIR provides an interface for users to search thousands of interactions, arranged in inter-connected multi-layers of process diagrams, covering a wide range of clinically relevant phenotypes. By mapping experimental data onto the AIR, it can be used to elucidate drug action as well as molecular mechanisms underlying different disease phenotypes. For the visualization and exploration of information, the AIR uses the Minerva platform, which is a well-established tool for the presentation of disease maps. The molecular details of the AIR are encoded using international standards. The AIR was created as a freely accessible resource, supporting research and education in the fields of acute inflammation and inflammation resolution. The AIR connects research communities, facilitates clinical decision making, and supports research scientists in the formulation and validation of hypotheses. The AIR is accessible through https://air.bio.informatik.uni-rostock.de.

WITHDRAWN: The Atlas of Inflammation Resolution (AIR)

The Publisher regrets that this article is an accidental duplication of an article that has already been published, https://doi.org/10.1016/j.mam.2020.100894. The duplicate article has therefore been withdrawn. The full Elsevier Policy on Article Withdrawal can be found at https://www.elsevier.com/about/our-business/policies/article-withdrawal.

DNA damage accumulates and responses are engaged in human ALS brain and spinal motor neurons and DNA repair is activatable in iPSC-derived motor neurons with SOD1 mutations

DNA damage is implicated in the pathogenesis of amyotrophic lateral sclerosis (ALS). However, relationships between DNA damage accumulation, DNA damage response (DDR), and upper and lower motor neuron vulnerability in human ALS are unclear; furthermore, it is unknown whether epigenetic silencing of DNA repair pathways contributes to ALS pathogenesis. We tested the hypotheses that DNA damage accumulates in ALS motor neurons along with diminished DDR, and that DNA repair genes undergo hypermethylation. Human postmortem CNS tissue was obtained from ALS cases (N = 34) and age-matched controls without neurologic disease (N = 15). Compared to age-matched controls, abasic sites accumulated in genomic DNA of ALS motor cortex and laser capture microdissection-acquired spinal motor neurons but not in motor neuron mitochondrial DNA. By immunohistochemistry, DNA damage accumulated significantly in upper and lower motor neurons in ALS cases as single-stranded DNA and 8-hydroxy-deoxyguanosine (OHdG) compared to age-matched controls. Significant DDR was engaged in ALS motor neurons as evidenced by accumulation of c-Abl, nuclear BRCA1, and ATM activation. DNA damage and DDR were present in motor neurons at pre-attritional stages and throughout the somatodendritic attritional stages of neurodegeneration. Motor neurons with DNA damage were also positive for activated p53 and cleaved caspase-3. Gene-specific promoter DNA methylation pyrosequencing identified the DNA repair genes Ogg1, Apex1, Pnkp and Aptx as hypomethylated in ALS. In human induced-pluripotent stem cell (iPSC)-derived motor neurons with familial ALS SOD1 mutations, DNA repair capacity was similar to isogenic control motor neurons. Our results show that vulnerable neurons in human ALS accumulate DNA damage, and contrary to our hypothesis, strongly activate and mobilize response effectors and DNA repair genes. This DDR in ALS motor neurons involves recruitment of c-Abl and BRCA1 to the nucleus in vivo, and repair of DNA double-strand breaks in human ALS motor neurons with SOD1 mutations in cell culture.

Prader-Willi syndrome and Angelman syndrome: Visualisation of the molecular pathways for two chromosomal disorders

Objectives: Prader-Willi syndrome (PWS) and Angelman syndrome (AS) are two syndromes that are caused by the same chromosomal deletion on 15q11.2-q13. Due to methylation patterns, different genes are responsible for the two distinct phenotypes resulting in the disorders. Patients of both disorders exhibit hypotonia in neonatal stage, delay in development and hypopigmentation. Typical features for PWS include hyperphagia, which leads to obesity, the major cause of mortality, and hypogonadism. In AS, patients suffer from a more severe developmental delay, they have a distinctive behaviour that is often described as unnaturally happy, and a tendency for epileptic seizures. For both syndromes, we identified and visualised molecular downstream pathways of the deleted genes that could give insight on the development of the clinical features.Methods: This was done by consulting literature, genome browsers and pathway databases to identify molecular interactions and to construct downstream pathways.Results: A pathway visualisation was created and uploaded to the open pathway database WikiPathways covering all molecular pathways that were found.Conclusions: The visualisation of the downstream pathways of PWS- and AS-deleted genes shows that some of the typical symptoms are caused by multiple genes and reveals critical gaps in the current knowledge.

ndexr-an R package to interface with the network data exchange

Motivation

Seamless exchange of biological network data enables bioinformatic algorithms to integrate networks as prior knowledge input as well as to document resulting network output. However, the interoperability between pathway databases and various methods and platforms for analysis is currently lacking. The Network Data Exchange (NDEx) is an open-source data commons that facilitates the user-centered sharing and publication of networks of many types and formats.

Results

Here, we present a software package that allows users to programmatically connect to and interface with NDEx servers from within R. The network repository can be searched and networks can be retrieved and converted into igraph-compatible objects. These networks can be modified and extended within R and uploaded back to the NDEx servers.

Availability and implementation

ndexr is a free and open-source R package, available via GitHub (https://github.com/frankkramer-lab/ndexr) and Bioconductor (http://bioconductor.org/packages/ndexr/).

Contact

florian.auer@med.uni-goettingen.de.

Supplementary information

Supplementary data are available at Bioinformatics online.

Optimal treatment allocations in space and time for on-line control of an emerging infectious disease

A key component in controlling the spread of an epidemic is deciding where, when and to whom to apply an intervention. We develop a framework for using data to inform these decisions in realtime. We formalize a treatment allocation strategy as a sequence of functions, one per treatment period, that map up-to-date information on the spread of an infectious disease to a subset of locations where treatment should be allocated. An optimal allocation strategy optimizes some cumulative outcome, e.g. the number of uninfected locations, the geographic footprint of the disease or the cost of the epidemic. Estimation of an optimal allocation strategy for an emerging infectious disease is challenging because spatial proximity induces interference between locations, the number of possible allocations is exponential in the number of locations, and because disease dynamics and intervention effectiveness are unknown at out-break. We derive a Bayesian on-line estimator of the optimal allocation strategy that combines simulation-optimization with Thompson sampling. The estimator proposed performs favourably in simulation experiments. This work is motivated by and illustrated using data on the spread of white nose syndrome, which is a highly fatal infectious disease devastating bat populations in North America.

Systems medicine disease maps: community-driven comprehensive representation of disease mechanisms

The development of computational approaches in systems biology has reached a state of maturity that allows their transition to systems medicine. Despite this progress, intuitive visualisation and context-dependent knowledge representation still present a major bottleneck. In this paper, we describe the Disease Maps Project, an effort towards a community-driven computationally readable comprehensive representation of disease mechanisms. We outline the key principles and the framework required for the success of this initiative, including use of best practices, standards and protocols. We apply a modular approach to ensure efficient sharing and reuse of resources for projects dedicated to specific diseases. Community-wide use of disease maps will accelerate the conduct of biomedical research and lead to new disease ontologies defined from mechanism-based disease endotypes rather than phenotypes.

Multifactorial Modes of Action of Arsenic Trioxide in Cancer Cells as Analyzed by Classical and Network Pharmacology

Arsenic trioxide is a traditional remedy in Chinese Medicine since ages. Nowadays, it is clinically used to treat acute promyelocytic leukemia (APL) by targeting PML/RARA. However, the drug's activity is broader and the mechanisms of action in other tumor types remain unclear. In this study, we investigated molecular modes of action by classical and network pharmacological approaches. CEM/ADR5000 resistance leukemic cells were similar sensitive to As2O3 as their wild-type counterpart CCRF-CEM (resistance ratio: 1.88). Drug-resistant U87.MG ΔEGFR glioblastoma cells harboring mutated epidermal growth factor receptor were even more sensitive (collateral sensitive) than wild-type U87.MG cells (resistance ratio: 0.33). HCT-116 colon carcinoma p53-/- knockout cells were 7.16-fold resistant toward As2O3 compared to wild-type cells. Forty genes determining cellular responsiveness to As2O3 were identified by microarray and COMPARE analyses in 58 cell lines of the NCI panel. Hierarchical cluster analysis-based heat mapping revealed significant differences between As2O3 sensitive cell lines and resistant cell lines with p-value: 1.86 × 10-5. The genes were subjected to Galaxy Cistrome gene promoter transcription factor analysis to predict the binding of transcription factors. We have exemplarily chosen NF-kB and AP-1, and indeed As2O3 dose-dependently inhibited the promoter activity of these two transcription factors in reporter cell lines. Furthermore, the genes identified here and those published in the literature were assembled and subjected to Ingenuity Pathway Analysis for comprehensive network pharmacological approaches that included all known factors of resistance of tumor cells to As2O3. In addition to pathways related to the anticancer effects of As2O3, several neurological pathways were identified. As arsenic is well-known to exert neurotoxicity, these pathways might account for neurological side effects. In conclusion, the activity of As2O3 is not restricted to acute promyelocytic leukemia. In addition to PML/RARA, numerous other genes belonging to diverse functional classes may also contribute to its cytotoxicity. Network pharmacology is suited to unravel the multifactorial modes of action of As2O3.

Signaling Protein Networks as Targets of New Antineoplastic Drugs

In-depth analysis of molecular regulatory networks in cancer holds the promise of improved knowledge of the pathophysiology of tumor cells so that it will become possible to design a detailed molecular tumor taxonomy. This knowledge will also offer new opportunities for the identification and validation of key molecular tumor targets to be exploited for novel therapeutic approaches. Some signaling proteins have already been identified as such, e.g. c-Myc, Cyclin D1, Bcl-XL, kinases and some nuclear receptors. This has led to the successful development of a few function-modulatory drugs (Glivec, SERM, Iressa), providing proof-of-principle of the validity of this approach. Further developments are likely to derive from “-omic” approaches, aimed at the understanding of signaling networks and of the mechanism of action of newfound lead molecules. High-throughput screening of small drug-like molecules from combinatorial chemical libraries or from microbial extracts will identify novel, “intelligent” drug candidates. An additional medicinal chemistry strategy (via 40–50 unit rosary-bead chains) has the potential to be much more effective than small molecules in interfering with protein-protein interactions. This may lead to considerably higher selectivity and effectiveness compared with historical approaches in drug discovery.

BEL Commons: an environment for exploration and analysis of networks encoded in Biological Expression Language

The rapid accumulation of knowledge in the field of systems and networks biology during recent years requires complex, but user-friendly and accessible web applications that allow from visualization to complex algorithmic analysis. While several web applications exist with various focuses on creation, revision, curation, storage, integration, collaboration, exploration, visualization and analysis, many of these services remain disjoint and have yet to be packaged into a cohesive environment.

Here, we present BEL Commons: an integrative knowledge discovery environment for networks encoded in the Biological Expression Language (BEL). Users can upload files in BEL to be parsed, validated, compiled and stored with fine granular permissions. After, users can summarize, explore and optionally shared their networks with the scientific community. We have implemented a query builder wizard to help users find the relevant portions of increasingly large and complex networks and a visualization interface that allows them to explore their resulting networks. Finally, we have included a dedicated analytical service for performing data-driven analysis of knowledge networks to support hypothesis generation.

Secretion modification region-derived peptide blocks exosome release and mediates cell cycle arrest in breast cancer cells

PURPOSE

Discovery and development of a novel anticancer PEG-SMR-Clu peptide to prevent breast cancer metastasis. How breast cancer cells and primary mammary epithelial cells interact and communicate with each other to promote tumorigenesis and how to prevent tumor metastasis has long been a concern of researchers. Cancer cells secrete exosomes containing proteins and RNA. These factors can influence tumor development by directly targeting cancer cells and tumor stroma. In this study, we determined the effects of a peptide as an inhibitor of exosome secretion on breast tumors. We developed a peptide derived from the Secretion Modification Region (SMR) of HIV-1 Nef protein that was modified with PEG on the N-terminus and with a Clusterin (Clu)-binding peptide on the C-terminus. Attachment of PEG to the SMR peptide, termed PEGylation, offers improved water solubility and stability as well as reduced clearance through the kidneys, leading to a longer circulation time. The 12-mer Clu-binding peptide plays multiple roles in tumor development and metastasis. The Clu peptide can be detected by antibody in vivo, thus it has the potential to be used to monitor tumor status and treatment efficacy in animal studies and eventually in cancer patients.

RESULTS

PEG-SMRwt-Clu and PEG-SMRwt peptides inhibited the growth of both of MCF-7 (estrogen responsive, ER+) and MDA-MD-231 (estrogen non-responsive, ER-) human breast cancer cells in a dose and time-dependent manner, without inducing cytotoxic effects. The SMRwt peptide, combined with paclitaxel, induced G2/M phase cell cycle arrest on MCF-7 and MDA-MB-231 cells but did not promote apoptosis. PEG-SMRwt-Clu peptide treatment blocked exosome release from both MCF-7 and MDA-MB-231 cells. This effect was blocked by knockdown of the chaperone protein mortalin by either antibody or siRNA.

MATERIALS AND METHODS

MCF-7 and MDA-MB-231 breast tumor cells were treated with PEG-SMR-Clu peptide alone and in combination with paclitaxel and cisplatin. Cell proliferation and viabilty were determined via cell cycle analysis using Cellometer imaging cytometry, Annexin V and MTT assays. The effects of the PEG-SMR-Clu peptide on tumor exosome release were determined by testing isolated exosome fractions, for (i) expression of CD63 and Alix proteins by Western blotting, (ii) NanoSight nanoparticle tracking analysis (NTA 10) to measure exosomes size and concentration, and (iii) measurement of acetylcholinesterase (AchE) for exosome specific enzyme activity.

CONCLUSIONS

PEG-SMRwt-CLU peptides inhibited the growth of human breast cancer cells and blocked tumor exosome release in vitro. The peptide alone did not cause increased cytotoxicity or apoptosis induction, but did cause cell cycle G2/M phase arrest in both estrogen responsive and non-responsive breast cancer cells. These data suggest a potential therapeutic value of SMR to prevent breast cancer metastasis and as an adjuvant for the chemotherapeutic treatment of human breast cancer.

Logical model specification aided by model-checking techniques: application to the mammalian cell cycle regulation

MOTIVATION

Understanding the temporal behaviour of biological regulatory networks requires the integration of molecular information into a formal model. However, the analysis of model dynamics faces a combinatorial explosion as the number of regulatory components and interactions increases.

RESULTS

We use model-checking techniques to verify sophisticated dynamical properties resulting from the model regulatory structure in the absence of kinetic assumption. We demonstrate the power of this approach by analysing a logical model of the molecular network controlling mammalian cell cycle. This approach enables a systematic analysis of model properties, the delineation of model limitations, and the assessment of various refinements and extensions based on recent experimental observations. The resulting logical model accounts for the main irreversible transitions between cell cycle phases, the sequential activation of cyclins, and the inhibitory role of Skp2, and further emphasizes the multifunctional role for the cell cycle inhibitor Rb.

AVAILABILITY AND IMPLEMENTATION

The original and revised mammalian cell cycle models are available in the model repository associated with the public modelling software GINsim (http://ginsim.org/node/189).

CONTACT

thieffry@ens.fr

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

An Observed Effect of p53 Status on the Bystander Response to Radiation-Induced Cellular Photon Emission

In this study, we investigated the potential influence of p53 on ultraviolet (UV) signal generation and response of bystander cells to the UV signals generated by beta-irradiated cells. Five cell lines of various p53 status (HaCaT, mutated; SW48, wild-type; HT29, mutated; HCT116^+/+, wild-type; HCT116^-/-, null) were irradiated with beta particles from tritium. Signal generation (photon emission at 340 ± 5 nm) was quantified from irradiated cells using a photomultiplier tube. Bystander response (clonogenic survival) was assessed by placing reporter cell flasks directly superior to irradiated signal-emitting cells. All cell lines emitted significant quantities of UV after tritium exposure. The magnitudes of HaCaT and HT29 photon emission at 340 nm were similar to each other while they were significantly different from the stronger signals emitted from SW48, HCT116^+/+ and HCT116^-/- cells. In regard to the bystander responses, HaCaT, HCT116^+/+ and SW48 cells demonstrated significant reductions in survival as a result of exposure to emission signals. HCT116^-/- and HT29 cells did not exhibit any changes in survival and thus were considered to be lacking the mechanisms or functions required to elicit a response. The survival response was found not to correlate with the observed signal strength for all experimental permutations; this may be attributed to varying emission spectra from cell line to cell line or differences in response sensitivity. Overall, these results suggest that the UV-mediated bystander response is influenced by the p53 status of the cell line. Wild-type p53 cells (HCT116^+/+ and SW48) demonstrated significant responses to UV signals whereas the p53-null cell line (HCT116^-/-) lacked any response. The two mutated p53 cell lines exhibited contrasting responses, which may be explained by unique modulation of functions by different point mutations. The reduced response (cell death) exhibited by p53-mutated cells compared to p53 wild-type cells suggests a possible role of the assessed p53 mutations in radiation-induced cancer susceptibility and reduced efficacy of radiation-directed therapy.

DNA-binding protects p53 from interactions with cofactors involved in transcription-independent functions

Binding-induced conformational changes of a protein at regions distant from the binding site may play crucial roles in protein function and regulation. The p53 tumour suppressor is an example of such an allosterically regulated protein. Little is known, however, about how DNA binding can affect distal sites for transcription factors. Furthermore, the molecular details of how a local perturbation is transmitted through a protein structure are generally elusive and occur on timescales hard to explore by simulations. Thus, we employed state-of-the-art enhanced sampling atomistic simulations to unveil DNA-induced effects on p53 structure and dynamics that modulate the recruitment of cofactors and the impact of phosphorylation at Ser215. We show that DNA interaction promotes a conformational change in a region 3 nm away from the DNA binding site. Specifically, binding to DNA increases the population of an occluded minor state at this distal site by more than 4-fold, whereas phosphorylation traps the protein in its major state. In the minor conformation, the interface of p53 that binds biological partners related to p53 transcription-independent functions is not accessible. Significantly, our study reveals a mechanism of DNA-mediated protection of p53 from interactions with partners involved in the p53 transcription-independent signalling. This also suggests that conformational dynamics is tightly related to p53 signalling.

Novel phase I study combining G1 phase, S phase, and G2/M phase cell cycle inhibitors in patients with advanced malignancies

PURPOSE

Cancer is a manifestation of aberrant cellular proliferation, and the cell cycle is one of the most successfully drugged targets in oncology. No prior study has been reported that simultaneously targets the 3 principal cell cycle phases populated by proliferating cells--G1, S, and G2/M.

METHODS

Temsirolimus (G1 inhibitor), topotecan (S inhibitor), and bortezomib (G2/M inhibitor) were administered in combination to patients with advanced malignancies using a 3+3 dose escalation schedule to assess the safety and establish the maximum tolerated dose (primary endpoints) of this cell cycle targeting approach. An in silico pharmacodynamic model using established effects of each of these agents on the cell cycle was used to validate the regimen and to guide the dosing regimen.

RESULTS

Sixty-two subjects were enrolled. The most common adverse events and dose-limiting toxicities were cytopenias, consistent with the cell cycle targeting approach employed. All cytopenias resolved to baseline values upon holding study drug administration. The maximum tolerated dose was temsirolimus 15 mg/kg IV D1, 8, 15; topotecan 2.8 mg/m(2) IV D1, 8; and bortezomib 0.6 mg/m2 IV D1, 4, 8, 11 [DOSAGE ERROR CORRECTED] of a 21-day cycle. In silico modeling suggests the regimen induces cell population shifts from G2/M and S phases to G1 phase and the quiescent G0 phase. Eighteen percent of subjects (11/62) achieved partial response (n = 2, serous ovarian and papillary thyroid) or stable disease for > 6 months (n = 9).

CONCLUSION

Combining drugs with inhibitory activity of G1 phase, S phase, and G2/M phase is safe and warrants further evaluation.

Single-nucleus RNA-seq of differentiating human myoblasts reveals the extent of fate heterogeneity

Myoblasts are precursor skeletal muscle cells that differentiate into fused, multinucleated myotubes. Current single-cell microfluidic methods are not optimized for capturing very large, multinucleated cells such as myotubes. To circumvent the problem, we performed single-nucleus transcriptome analysis. Using immortalized human myoblasts, we performed RNA-seq analysis of single cells (scRNA-seq) and single nuclei (snRNA-seq) and found them comparable, with a distinct enrichment for long non-coding RNAs (lncRNAs) in snRNA-seq. We then compared snRNA-seq of myoblasts before and after differentiation. We observed the presence of mononucleated cells (MNCs) that remained unfused and analyzed separately from multi-nucleated myotubes. We found that while the transcriptome profiles of myoblast and myotube nuclei are relatively homogeneous, MNC nuclei exhibited significant heterogeneity, with the majority of them adopting a distinct mesenchymal state. Primary transcripts for microRNAs (miRNAs) that participate in skeletal muscle differentiation were among the most differentially expressed lncRNAs, which we validated using NanoString. Our study demonstrates that snRNA-seq provides reliable transcriptome quantification for cells that are otherwise not amenable to current single-cell platforms. Our results further indicate that snRNA-seq has unique advantage in capturing nucleus-enriched lncRNAs and miRNA precursors that are useful in mapping and monitoring differential miRNA expression during cellular differentiation.

In vivo Signatures of Genotoxic and Non-genotoxic Chemicals

This chapter reviews the findings from a broad array of in vivo genomic studies with the goal of identifying a general signature of genotoxicity (GSG) that is indicative of exposure to genotoxic agents (i.e. agents that are active in either the bacterial mutagenesis and/or the in vivo micronucleus test). While the GSG has largely emerged from systematic studies of rat and mouse liver, its response is evident across a broad collection of genotoxic treatments that cover a variety of tissues and species. Pathway-based characterization of the GSG indicates that it is enriched with genes that are regulated by p53. In addition to the GSG, another pan-tissue signature related to bone marrow suppression (a common effect of genotoxic agent exposure) is reviewed. Overall, these signatures are quite effective in identifying genotoxic agents; however, there are situations where false positive findings can occur, for example when necrotizing doses of non-genotoxic soft electrophiles (e.g. thioacetamide) are used. For this reason specific suggestions for best practices for generating for use in the creation and application of in vivo genomic signatures are reviewed.

Systems medicine in colorectal cancer: from a mathematical model toward a new type of clinical trial

Current colorectal cancer (CRC) treatment guidelines are primarily based on clinical features, such as cancer stage and grade. However, outcomes may be improved using molecular treatment guidelines. Potentially useful biomarkers include driver mutations and somatically inherited alterations, signaling proteins (their expression levels and (post) translational modifications), mRNAs, micro-RNAs and long noncoding RNAs. Moving to an integrated system is potentially very relevant. To implement such an integrated system: we focus on an important region of the signaling network, immediately above the G1-S restriction point, and discuss the reconstruction of a Molecular Interaction Map and interrogating it with a dynamic mathematical model. Extensive model pretraining achieved satisfactory, validated, performance. The model helps to propose future target combination priorities, and restricts drastically the number of drugs to be finally tested at a cellular, in vivo, and clinical-trial level. Our model allows for the inclusion of the unique molecular profiles of each individual patient's tumor. While existing clinical guidelines are well established, dynamic modeling may be used for future targeted combination therapies, which may progressively become part of clinical practice within the near future. WIREs Syst Biol Med 2016, 8:314-336. doi: 10.1002/wsbm.1342 For further resources related to this article, please visit the WIREs website.

Structure-Based Analysis Reveals Cancer Missense Mutations Target Protein Interaction Interfaces

Recently it has been shown that cancer mutations selectively target protein-protein interactions. We hypothesized that mutations affecting distinct protein interactions involving established cancer genes could contribute to tumor heterogeneity, and that novel mechanistic insights might be gained into tumorigenesis by investigating protein interactions under positive selection in cancer. To identify protein interactions under positive selection in cancer, we mapped over 1.2 million nonsynonymous somatic cancer mutations onto 4,896 experimentally determined protein structures and analyzed their spatial distribution. In total, 20% of mutations on the surface of known cancer genes perturbed protein-protein interactions (PPIs), and this enrichment for PPI interfaces was observed for both tumor suppressors (Odds Ratio 1.28, P-value < 10⁻⁴) and oncogenes (Odds Ratio 1.17, P-value < 10⁻³). To study this further, we constructed a bipartite network representing structurally resolved PPIs from all available human complexes in the Protein Data Bank (2,864 proteins, 3,072 PPIs). Analysis of frequently mutated cancer genes within this network revealed that tumor-suppressors, but not oncogenes, are significantly enriched with functional mutations in homo-oligomerization regions (Odds Ratio 3.68, P-Value < 10⁻⁸). We present two important examples, TP53 and beta-2-microglobulin, for which the patterns of somatic mutations at interfaces provide insights into specifically perturbed biological circuits. In patients with TP53 mutations, patient survival correlated with the specific interactions that were perturbed. Moreover, we investigated mutations at the interface of protein-nucleotide interactions and observed an unexpected number of missense mutations but not silent mutations occurring within DNA and RNA binding sites. Finally, we provide a resource of 3,072 PPI interfaces ranked according to their mutation rates. Analysis of this list highlights 282 novel candidate cancer genes that encode proteins participating in interactions that are perturbed recurrently across tumors. In summary, mutation of specific protein interactions is an important contributor to tumor heterogeneity and may have important implications for clinical outcomes.

Comprehensive Map of Molecules Implicated in Obesity

Obesity is a global epidemic affecting over 1.5 billion people and is one of the risk factors for several diseases such as type 2 diabetes mellitus and hypertension. We have constructed a comprehensive map of the molecules reported to be implicated in obesity. A deep curation strategy was complemented by a novel semi-automated text mining system in order to screen 1,000 full-length research articles and over 90,000 abstracts that are relevant to obesity. We obtain a scale free network of 804 nodes and 971 edges, composed of 510 proteins, 115 genes, 62 complexes, 23 RNA molecules, 83 simple molecules, 3 phenotype and 3 drugs in "bow-tie" architecture. We classify this network into 5 modules and identify new links between the recently discovered fat mass and obesity associated FTO gene with well studied examples such as insulin and leptin. We further built an automated docking pipeline to dock orlistat as well as other drugs against the 24,000 proteins in the human structural proteome to explain the therapeutics and side effects at a network level. Based upon our experiments, we propose that therapeutic effect comes through the binding of one drug with several molecules in target network, and the binding propensity is both statistically significant and different in comparison with any other part of human structural proteome.

A Dynamical Framework for the All-or-None G1/S Transition

The transition from G1 into DNA replication (S phase) is an emergent behavior resulting from dynamic and complex interactions between cyclin-dependent kinases (Cdks), Cdk inhibitors (CKIs), and the anaphase-promoting complex/cyclosome (APC/C). Understanding the cellular decision to commit to S phase requires a quantitative description of these interactions. We apply quantitative imaging of single human cells to track the expression of G1/S regulators and use these data to parametrize a stochastic mathematical model of the G1/S transition. We show that a rapid, proteolytic, double-negative feedback loop between Cdk2:Cyclin and the Cdk inhibitor p27(Kip1) drives a switch-like entry into S phase. Furthermore, our model predicts that increasing Emi1 levels throughout S phase are critical in maintaining irreversibility of the G1/S transition, which we validate using Emi1 knockdown and live imaging of G1/S reporters. This work provides insight into the general design principles of the signaling networks governing the temporally abrupt transitions between cell-cycle phases.

Systems Medicine in Oncology: Signaling Network Modeling and New-Generation Decision-Support Systems

Two different perspectives are the main focus of this book chapter: (1) A perspective that looks to the future, with the goal of devising rational associations of targeted inhibitors against distinct altered signaling-network pathways. This goal implies a sufficiently in-depth molecular diagnosis of the personal cancer of a given patient. A sufficiently robust and extended dynamic modeling will suggest rational combinations of the abovementioned oncoprotein inhibitors. The work toward new selective drugs, in the field of medicinal chemistry, is very intensive. Rational associations of selective drug inhibitors will become progressively a more realistic goal within the next 3-5 years. Toward the possibility of an implementation in standard oncologic structures of technologically sufficiently advanced countries, new (legal) rules probably will have to be established through a consensus process, at the level of both diagnostic and therapeutic behaviors.(2) The cancer patient of today is not the patient of 5-10 years from now. How to support the choice of the most convenient (and already clinically allowed) treatment for an individual cancer patient, as of today? We will consider the present level of artificial intelligence (AI) sophistication and the continuous feeding, updating, and integration of cancer-related new data, in AI systems. We will also report briefly about one of the most important projects in this field: IBM Watson US Cancer Centers. Allowing for a temporal shift, in the long term the two perspectives should move in the same direction, with a necessary time lag between them.

A hybrid model of cell cycle in mammals

Time plays an essential role in many biological systems, especially in cell cycle. Many models of biological systems rely on differential equations, but parameter identification is an obstacle to use differential frameworks. In this paper, we present a new hybrid modeling framework that extends René Thomas' discrete modeling. The core idea is to associate with each qualitative state "celerities" allowing us to compute the time spent in each state. This hybrid framework is illustrated by building a 5-variable model of the mammalian cell cycle. Its parameters are determined by applying formal methods on the underlying discrete model and by constraining parameters using timing observations on the cell cycle. This first hybrid model presents the most important known behaviors of the cell cycle, including quiescent phase and endoreplication.

Network analysis reveals stage-specific changes in zebrafish embryo development using time course whole transcriptome profiling and prior biological knowledge

BACKGROUND

Molecular networks act as the backbone of molecular activities within cells, offering a unique opportunity to better understand the mechanism of diseases. While network data usually constitute only static network maps, integrating them with time course gene expression information can provide clues to the dynamic features of these networks and unravel the mechanistic driver genes characterizing cellular responses. Time course gene expression data allow us to broadly "watch" the dynamics of the system. However, one challenge in the analysis of such data is to establish and characterize the interplay among genes that are altered at different time points in the context of a biological process or functional category. Integrative analysis of these data sources will lead us a more complete understanding of how biological entities (e.g., genes and proteins) coordinately perform their biological functions in biological systems.

RESULTS

In this paper, we introduced a novel network-based approach to extract functional knowledge from time-dependent biological processes at a system level using time course mRNA sequencing data in zebrafish embryo development. The proposed method was applied to investigate 1α, 25(OH)2D3-altered mechanisms in zebrafish embryo development. We applied the proposed method to a public zebrafish time course mRNA-Seq dataset, containing two different treatments along four time points. We constructed networks between gene ontology biological process categories, which were enriched in differential expressed genes between consecutive time points and different conditions. The temporal propagation of 1α, 25-Dihydroxyvitamin D3-altered transcriptional changes started from a few genes that were altered initially at earlier stage, to large groups of biological coherent genes at later stages. The most notable biological processes included neuronal and retinal development and generalized stress response. In addition, we also investigated the relationship among biological processes enriched in co-expressed genes under different conditions. The enriched biological processes include translation elongation, nucleosome assembly, and retina development. These network dynamics provide new insights into the impact of 1α, 25-Dihydroxyvitamin D3 treatment in bone and cartilage development.

CONCLUSION

We developed a network-based approach to analyzing the DEGs at different time points by integrating molecular interactions and gene ontology information. These results demonstrate that the proposed approach can provide insight on the molecular mechanisms taking place in vertebrate embryo development upon treatment with 1α, 25(OH)2D3. Our approach enables the monitoring of biological processes that can serve as a basis for generating new testable hypotheses. Such network-based integration approach can be easily extended to any temporal- or condition-dependent genomic data analyses.

Preclinical Childhood Sarcoma Models: Drug Efficacy Biomarker Identification and Validation

Over the past 35 years, cure rates for pediatric cancers have increased dramatically. However, it is clear that further dose intensification using cytotoxic agents or radiation therapy is not possible without enhancing morbidity and long-term effects. Consequently, novel, less genotoxic, agents are being sought to complement existing treatments. Here, we discuss preclinical human tumor xenograft models of pediatric cancers that may be used practically to identify novel agents for soft tissue and bone sarcomas, and "omics" approaches to identifying biomarkers that may identify sensitive and resistant tumors to these agents.

Fast transformation from time series to visibility graphs

The visibility graph method is used to transform time series into complex networks. In this letter, a fast transform algorithm is proposed for obtaining a visibility graph. Based on the strategy of "divide & conquer," the time complexity of the proposed algorithm is raised to O(n log n), which is more efficient than the previous basic algorithm whose time complexity is O(n(2)).

Advances in dynamic modeling of colorectal cancer signaling-network regions, a path toward targeted therapies

The interconnected network of pathways downstream of the TGFβ, WNT and EGF-families of receptor ligands play an important role in colorectal cancer pathogenesis.We studied and implemented dynamic simulations of multiple downstream pathways and described the section of the signaling network considered as a Molecular Interaction Map (MIM). Our simulations used Ordinary Differential Equations (ODEs), which involved 447 reactants and their interactions.Starting from an initial "physiologic condition", the model can be adapted to simulate individual pathologic cancer conditions implementing alterations/mutations in relevant onco-proteins. We verified some salient model predictions using the mutated colorectal cancer lines HCT116 and HT29. We measured the amount of MYC and CCND1 mRNAs and AKT and ERK phosphorylated proteins, in response to individual or combination onco-protein inhibitor treatments. Experimental and simulation results were well correlated. Recent independently published results were also predicted by our model.Even in the presence of an approximate and incomplete signaling network information, a predictive dynamic modeling seems already possible. An important long term road seems to be open and can be pursued further, by incremental steps, toward even larger and better parameterized MIMs. Personalized treatment strategies with rational associations of signaling-proteins inhibitors, could become a realistic goal.

Quantitative and logic modelling of molecular and gene networks

Behaviours of complex biomolecular systems are often irreducible to the elementary properties of their individual components. Explanatory and predictive mathematical models are therefore useful for fully understanding and precisely engineering cellular functions. The development and analyses of these models require their adaptation to the problems that need to be solved and the type and amount of available genetic or molecular data. Quantitative and logic modelling are among the main methods currently used to model molecular and gene networks. Each approach comes with inherent advantages and weaknesses. Recent developments show that hybrid approaches will become essential for further progress in synthetic biology and in the development of virtual organisms.

Fluorescein hydrazones as novel nonintercalative topoisomerase catalytic inhibitors with low DNA toxicity

Fluorescein hydrazones (3a-3l) were synthesized in three steps with 86-91% overall yields. Topo I- and IIα-mediated relaxation and cell viability assay were evaluated. 3d inhibited 47% Topo I (camptothecin, 34%) and 20% Topo II (etoposide 24%) at 20 μM. 3l inhibited 61% Topo II (etoposide 24%) at 20 μM. 3d and 3l were further evaluated to determine their mode of action with diverse methods of kDNA decatenation, DNA-Topo cleavage complex, comet, DNA intercalating/unwinding, and Topo IIα-mediated ATP hydrolysis assays. 3d functioned as a nonintercalative dual inhibitor against the catalytic activities of Topo I and Topo IIα. 3l acted as a Topo IIα specific nonintercalative catalytic inhibitor. 3d activated apoptotic proteins as it increased the level of cleaved capase-3 and cleaved PARP in a dose- and time-dependent manner. The dose- and time-dependent increase of G1 phase population was observed by treatment of 3d along with the increase of p27(kip1) and the decrease of cyclin D1 expression.

Stability, complexity and robustness in population dynamics

The problem of stability in population dynamics concerns many domains of application in demography, biology, mechanics and mathematics. The problem is highly generic and independent of the population considered (human, animals, molecules,…). We give in this paper some examples of population dynamics concerning nucleic acids interacting through direct nucleic binding with small or cyclic RNAs acting on mRNAs or tRNAs as translation factors or through protein complexes expressed by genes and linked to DNA as transcription factors. The networks made of these interactions between nucleic acids (considered respectively as edges and nodes of their interaction graph) are complex, but exhibit simple emergent asymptotic behaviours, when time tends to infinity, called attractors. We show that the quantity called attractor entropy plays a crucial role in the study of the stability and robustness of such genetic networks.

A unique four-hub protein cluster associates to glioblastoma progression

Gliomas are the most frequent brain tumors. Among them, glioblastomas are malignant and largely resistant to available treatments. Histopathology is the gold standard for classification and grading of brain tumors. However, brain tumor heterogeneity is remarkable and histopathology procedures for glioma classification remain unsatisfactory for predicting disease course as well as response to treatment. Proteins that tightly associate with cancer differentiation and progression, can bear important prognostic information. Here, we describe the identification of protein clusters differentially expressed in high-grade versus low-grade gliomas. Tissue samples from 25 high-grade tumors, 10 low-grade tumors and 5 normal brain cortices were analyzed by 2D-PAGE and proteomic profiling by mass spectrometry. This led to identify 48 differentially expressed protein markers between tumors and normal samples. Protein clustering by multivariate analyses (PCA and PLS-DA) provided discrimination between pathological samples to an unprecedented extent, and revealed a unique network of deranged proteins. We discovered a novel glioblastoma control module centered on four major network hubs: Huntingtin, HNF4α, c-Myc and 14-3-3ζ. Immunohistochemistry, western blotting and unbiased proteome-wide meta-analysis revealed altered expression of this glioblastoma control module in human glioma samples as compared with normal controls. Moreover, the four-hub network was found to cross-talk with both p53 and EGFR pathways. In summary, the findings of this study indicate the existence of a unifying signaling module controlling glioblastoma pathogenesis and malignant progression, and suggest novel targets for development of diagnostic and therapeutic procedures.

Mathematical model of dynamic protein interactions regulating p53 protein stability for tumor suppression

In the field of cancer biology, numerous genes or proteins form extremely complex regulatory network, which determines cancer cell fate and cancer cell survival. p53 is a major tumor suppressor that is lost in more than 50% of human cancers. It has been well known that a variety of proteins regulate its protein stability, which is essential for its tumor suppressive function. It remains elusive how we could understand and target p53 stabilization process through network analysis. In this paper we discuss the use of random walk and stationary distribution to measure the compound effect of a network of genes or proteins. This method is applied to the network of nine proteins that influence the protein stability of p53 via regulating the interaction between p53 and its regulator MDM2. Our study identifies that some proteins such as HDAC1 in the network of p53 regulators may have more profound effects on p53 stability, agreeing with the established findings on HDAC1. This work shows the importance of using mathematical analysis to dissect the complexity of biology networks in cancer.