Dynamics of the mammalian cell cycle in physiological and pathological conditions

Adipose-Derived exosome from Diet-Induced-Obese mouse attenuates LPS-Induced acute lung injury by inhibiting inflammation and Apoptosis: In vivo and in silico insight

BACKGROUND

Acute lung injury (ALI) is a severe clinical condition in the intensive care units, and obesity is a high risk of ALI. Paradoxically, obese ALI patients had better prognosis than non-obese patients, and the mechanism remains largely unknown.

METHODS

Mouse models of ALI and diet-induced-obesity (DIO) were used to investigate the effect of exosomes derived from adipose tissue. The adipose-derived exosomes (ADEs) were isolated by ultracentrifugation, and the role of exosomal miRNAs in the ALI was studied.

RESULTS

Compared with ADEs of control mice (C-Exo), ADEs of DIO mice (D-Exo) increased survival rate and mitigated pulmonary lesions of ALI mice. GO and KEGG analyses showed that the target genes of 40 differentially expressed miRNAs between D-Exo and C-Exo were mainly involved with inflammation, apoptosis and cell cycle. Furthermore, the D-Exo treatment significantly decreased Ly6G+ cell infiltration, down-regulated levels of pro-inflammatory cytokines (IL-6, IL-12, TNF-α, MCP-1) and chemokines (IL-8 and MIP-2), reduced pulmonary apoptosis and arrest at G0G1 phase (P < 0.01). And the protective effects of D-Exo were better than those of C-Exo (P < 0.05). Compared with the C-Exo mice, the levels of miR-16-5p and miR-335-3p in the D-Exo mice were significantly up-regulated (P < 0.05), and the expressions of IKBKB and TNFSF10, respective target of miR-16-5p and miR-335-3p by bioinformatic analysis, were significantly down-regulated in the D-Exo mice (P < 0.05).

CONCLUSIONS

Exosomes derived from adipose tissue of DIO mice are potent to attenuate LPS-induced ALI, which could be contributed by exosome-carried miRNAs. Our data shed light on the interaction between obesity and ALI.

Pharmacokinetic/pharmacodynamic model of a methionine starvation based anti-cancer drug

A new therapeutic approach against cancer is developed by the firm Erytech. This approach is based on starved cancer cells of an amino acid essential to their growth (the L-methionine). The depletion of plasma methionine level can be induced by an enzyme, the methionine-γ-lyase. The new therapeutic formulation is a suspension of erythrocytes encapsulating the activated enzyme. Our work reproduces a preclinical trial of a new anti-cancer drug with a mathematical model and numerical simulations in order to replace animal experiments and to have a deeper insight on the underlying processes. With a combination of a pharmacokinetic/pharmacodynamic model for the enzyme, substrate, and co-factor with a hybrid model for tumor, we develop a "global model" that can be calibrated to simulate different human cancer cell lines. The hybrid model includes a system of ordinary differential equations for the intracellular concentrations, partial differential equations for the concentrations of nutrients and drugs in the extracellular matrix, and individual based model for cancer cells. This model describes cell motion, division, differentiation, and death determined by the intracellular concentrations. The models are developed on the basis of experiments in mice carried out by Erytech. Parameters of the pharmacokinetics model were determined by fitting a part of experimental data on the concentration of methionine in blood. Remaining experimental protocols effectuated by Erytech were used to validate the model. The validated PK model allowed the investigation of pharmacodynamics of cell populations. Numerical simulations with the global model show cell synchronization and proliferation arrest due to treatment similar to the available experiments. Thus, computer modeling confirms a possible effect of treatment based on the decrease of methionine concentration. The main goal of the study is the development of an integrated pharmacokinetic/pharmacodynamic model for encapsulated methioninase and of a mathematical model of tumor growth/regression in order to determine the kinetics of L-methionine depletion after co-administration of Erymet product and Pyridoxine.

SNS-032 attenuates liver fibrosis by anti-active hepatic stellate cells via inhibition of cyclin dependent kinase 9

Liver fibrosis is a common pathological process of all chronic liver diseases. Hepatic stellate cells (HSCs) play a central role in the development of liver fibrosis. Cyclin-dependent kinase 9 (CDK9) is a cell cycle kinase that regulates mRNA transcription and elongation. A CDK9 inhibitor SNS-032 has been reported to have good effects in anti-tumor. However, the role of SNS-032 in the development of liver fibrosis is unclear. In this study, SNS-032 was found to alleviate hepatic fibrosis by inhibiting the activation and inducing the apoptosis of active HSCs in carbon tetrachloride-induced model mice. In vitro, SNS-032 inhibited the activation and proliferation of active HSCs and induced the apoptosis of active HSCs by downregulating the expression of CDK9 and its downstream signal transductors, such phosphorylated RNA polymerase II and Bcl-2. CDK9 short hairpin RNA was transfected into active HSCs to further elucidate the mechanism of the above effects. Similar results were observed in active HSCs after CDK9 knockdown. In active HSCs with CDK9 knockdown, the expression levels of CDK9, phosphorylated RNA polymerase II, XIAP, Bcl-2, Mcl-1, and ɑ-SMA significantly decreased, whereas those of cleaved-PARP1 and Bax decreased prominently. These results indicated that SNS-032 is a potential drug and CDK9 might be a new prospective target for the treatment of liver fibrosis.

Network mechanisms and dysfunction within an integrated computational model of progression through mitosis in the human cell cycle

The cellular protein-protein interaction network that governs cellular proliferation (cell cycle) is highly complex. Here, we have developed a novel computational model of human mitotic cell cycle, integrating diverse cellular mechanisms, for the purpose of generating new hypotheses and predicting new experiments designed to help understand complex diseases. The pathogenic state investigated is infection by a human herpesvirus. The model starts at mitotic entry initiated by the activities of Cyclin-dependent kinase 1 (CDK1) and Polo-like kinase 1 (PLK1), transitions through Anaphase-promoting complex (APC/C) bound to Cell division cycle protein 20 (CDC20), and ends upon mitotic exit mediated by APC/C bound to CDC20 homolog 1 (CDH1). It includes syntheses and multiple mechanisms of degradations of the mitotic proteins. Prior to this work, no such comprehensive model of the human mitotic cell cycle existed. The new model is based on a hybrid framework combining Michaelis-Menten and mass action kinetics for the mitotic interacting reactions. It simulates temporal changes in 12 different mitotic proteins and associated protein complexes in multiple states using 15 interacting reactions and 26 ordinary differential equations. We have defined model parameter values using both quantitative and qualitative data and using parameter values from relevant published models, and we have tested the model to reproduce the cardinal features of human mitosis determined experimentally by numerous laboratories. Like cancer, viruses create dysfunction to support infection. By simulating infection of the human herpesvirus, cytomegalovirus, we hypothesize that virus-mediated disruption of APC/C is necessary to establish a unique mitotic collapse with sustained CDK1 activity, consistent with known mechanisms of virus egress. With the rapid discovery of cellular protein-protein interaction networks and regulatory mechanisms, we anticipate that this model will be highly valuable in helping us to understand the network dynamics and identify potential points of therapeutic interventions.

Periostin plays a critical role in the cell cycle in lung fibroblasts

BACKGROUND

Idiopathic pulmonary fibrosis (IPF) is a devastating disease with a median survival of only three to 5 years. Fibroblast proliferation is a hallmark of IPF as is secretion of extracellular matrix proteins from fibroblasts. However, it is still uncertain how IPF fibroblasts acquire the ability to progressively proliferate. Periostin is a matricellular protein highly expressed in the lung tissues of IPF patients, playing a critical role in the pathogenesis of pulmonary fibrosis. However, it remains undetermined whether periostin affects lung fibroblast proliferation.

METHODS

In this study, we first aimed at identifying periostin-dependently expressed genes in lung fibroblasts using DNA microarrays. We then examined whether expression of cyclins and CDKs controlling cell cycle progression occur in a periostin-dependent manner. We next examined whether downregulation of cell proliferation-promoting genes by knockdown of periostin or integrin, a periostin receptor, using siRNA, is reflected in the cell proliferation of lung fibroblasts. We then looked at whether lung fibroblasts derived from IPF patients also require periostin for maximum proliferation. We finally investigated whether CP4715, a potent inhibitor against integrin α_Vβ₃ (a periostin receptor), which we have recently found blocks TGF-β signaling, followed by reduced BLM-induced pulmonary fibrosis in mice, can block proliferation of lung fibroblasts derived from IPF patients.

RESULTS

Many cell-cycle-related genes are involved in the upregulated or downregulated genes by periostin knockdown. We confirmed that in lung fibroblasts, periostin silencing downregulates expression of several cell-cycle-related molecules, including the cyclin, CDK, and, E2F families, as well as transcription factors such as B-MYB and FOXM1. Periostin or integrin silencing slowed proliferation of lung fibroblasts and periostin silencing increased the distribution of the G0/G1 phase, whereas the distribution of the G2/M phase was decreased. Lung fibroblasts derived from IPF patients also required periostin for maximum proliferation. Moreover, CP4715 downregulated proliferation along with expression of cell-cycle-related genes in IPF lung fibroblasts as well as in normal lung fibroblasts.

CONCLUSIONS

Periostin plays a critical role in the proliferation of lung fibroblasts and the present results provide us a solid basis for considering inhibitors of the periostin/integrin α_Vβ₃ interaction for the treatment of IPF patients.

Molecular targets in cancer prevention by 4-(methylthio)butyl isothiocyanate - A comprehensive review

The consumption of cruciferous vegetables rich in isothiocyanates has long been associated with a reduced risk of various types of cancer. 4-(methylthio)butyl isothiocyanate also called erucin is an isothiocyanate present in appreciable quantity in the seeds of Eruca sativa Mill. plant. Although the literature has revealed its protective effects via inducing phase II enzymes and inhibiting carcinogen activating phase I enzymes, recent studies also suggest that, it inhibits the proliferation of cancer cells by altering the telomerase activity, dynamics of microtubules, expression of histone deacetylases, and other molecular pathways. With this in mind, the emphasis has been made to review the molecular targets involved in cancer prevention by 4-(methylthio)butyl isothiocyanate.

P21 and P27 promote tumorigenesis and progression via cell cycle acceleration in seminal vesicles of TRAMP mice

Transgenic adenocarcinoma mouse prostate (TRAMP) model is established to mimic human prostate cancer progression, where seminal vesicle lesions often occur and has been described as phyllodes-like epithelial-stromal tumors. However, the molecular mechanism regulating tumorigenesis and progression in seminal vesicles of TRAMP mice remains largely unknown. In this study, C57BL/6 TRAMP mice were found to have a significantly shorter lifespan than wild-type (WT) mice and all of the seminal vesicles were markedly increased in size and weight with age from 24 weeks exhibiting a clearly papillary-phyllode pattern, though no obvious difference was observed in multiple organs including heart, liver, spleen, lungs, kidneys, testicles and bone between TRAMP and WT mice, and less than 10% of TRAMP mice developed prostate tumors. Western blotting showed Cyclin (CCN) B1 and CCND1 were remarkably overexpressed in seminal vesicle tumors of TRAMP mice at 24 weeks of age and increased with age till the end of trial, which was confirmed by Immunohistochemistry (IHC). P21 and P27 were also significantly augmented, whereas P53 and phosphorylated P53 (p-P53) were constantly expressed in normal controls and P53 did not appear to be mutated. Not only cyclin-dependent kinase (CDK) 1 and phosphorylated forkhead box protein (FOX) O1 but also CDK4, CDK6 and phosphorylated retinoblastoma-associated protein (RB) had similar increase trends, so did epidermal growth factor receptor (EGFR), AKT serine/threonine kinase (AKT), and their respective phosphorylation levels. Signal transducer and activator of transcription (STAT) 3, p-STAT3, enhancer of zeste homolog 2 (EZH2) and EZH2 mediated trimethylation of histone H3 lysine 27 (H3K27me3) were considerably elevated, too. Taken together, this finding suggests P21 and P27 promote carcinogenesis and development in seminal vesicles of TRAMP mice via accelerating cell cycle progression, in which oncogenic transformation of P21 and P27 might be through regulation of EGFR-AKT signaling.

Pan-Cancer analysis of the expression and regulation of matrisome genes across 32 tumor types

The microenvironment plays a central role in cancer, and neoplastic cells actively shape it to their needs by complex arrays of extracellular matrix (ECM) proteins, enzymes, cytokines and growth factors collectively referred to as the matrisome. Studies on the cancer matrisome have been performed for single or few neoplasms, but a more systematic analysis is still missing. Here we present a Pan-Cancer study of matrisome gene expression in 10,487 patients across 32 tumor types, supplemented with transcription factors (TFs) and driver genes/pathways regulating each tumor's matrisome. We report on 919 TF-target pairs, either used specifically or shared across tumor types, and their prognostic significance, 40 master regulators, 31 overarching regulatory pathways and the potential for druggability with FDA-approved cancer drugs. These results provide a comprehensive transcriptional architecture of the cancer matrisome and suggest the need for development of specific matrisome-targeting approaches for future therapies.

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In-depth characterization of matrisome gene expression and regulation in 10,487 patients across 32 human tumor types.

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Identification of transcription factor (TF) and “master regulators” governing each cancer’s matrisome.

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Analysis unveils therapeutic possibilities and suggests new treatments by repurposing of FDA-approved cancer drugs.

Personalization of Logical Models With Multi-Omics Data Allows Clinical Stratification of Patients

Logical models of cancer pathways are typically built by mining the literature for relevant experimental observations. They are usually generic as they apply for large cohorts of individuals. As a consequence, they generally do not capture the heterogeneity of patient tumors and their therapeutic responses. We present here a novel framework, referred to as PROFILE, to tailor logical models to a particular biological sample such as a patient tumor. This methodology permits to compare the model simulations to individual clinical data, i.e., survival time. Our approach focuses on integrating mutation data, copy number alterations (CNA), and expression data (transcriptomics or proteomics) to logical models. These data need first to be either binarized or set between 0 and 1, and can then be incorporated in the logical model by modifying the activity of the node, the initial conditions or the state transition rates. The use of MaBoSS, a tool based on Monte-Carlo kinetic algorithm to perform stochastic simulations on logical models results in model state probabilities, and allows for a semi-quantitative study of the model phenotypes and perturbations. As a proof of concept, we use a published generic model of cancer signaling pathways and molecular data from METABRIC breast cancer patients. For this example, we test several combinations of data incorporation and discuss that, with these data, the most comprehensive patient-specific cancer models are obtained by modifying the nodes' activity of the model with mutations, in combination or not with CNA data, and altering the transition rates with RNA expression. We conclude that these model simulations show good correlation with clinical data such as patients' Nottingham prognostic index (NPI) subgrouping and survival time. We observe that two highly relevant cancer phenotypes derived from personalized models, Proliferation and Apoptosis, are biologically consistent prognostic factors: patients with both high proliferation and low apoptosis have the worst survival rate, and conversely. Our approach aims to combine the mechanistic insights of logical modeling with multi-omics data integration to provide patient-relevant models. This work leads to the use of logical modeling for precision medicine and will eventually facilitate the choice of patient-specific drug treatments by physicians.

A drift-diffusion checkpoint model predicts a highly variable and growth-factor-sensitive portion of the cell cycle G1 phase

Even among isogenic cells, the time to progress through the cell cycle, or the intermitotic time (IMT), is highly variable. This variability has been a topic of research for several decades and numerous mathematical models have been proposed to explain it. Previously, we developed a top-down, stochastic drift-diffusion+threshold (DDT) model of a cell cycle checkpoint and showed that it can accurately describe experimentally-derived IMT distributions [Leander R, Allen EJ, Garbett SP, Tyson DR, Quaranta V. Derivation and experimental comparison of cell-division probability densities. J. Theor. Biol. 2014;358:129-135]. Here, we use the DDT modeling approach for both descriptive and predictive data analysis. We develop a custom numerical method for the reliable maximum likelihood estimation of model parameters in the absence of a priori knowledge about the number of detectable checkpoints. We employ this method to fit different variants of the DDT model (with one, two, and three checkpoints) to IMT data from multiple cell lines under different growth conditions and drug treatments. We find that a two-checkpoint model best describes the data, consistent with the notion that the cell cycle can be broadly separated into two steps: the commitment to divide and the process of cell division. The model predicts one part of the cell cycle to be highly variable and growth factor sensitive while the other is less variable and relatively refractory to growth factor signaling. Using experimental data that separates IMT into G1 vs. S, G2, and M phases, we show that the model-predicted growth-factor-sensitive part of the cell cycle corresponds to a portion of G1, consistent with previous studies suggesting that the commitment step is the primary source of IMT variability. These results demonstrate that a simple stochastic model, with just a handful of parameters, can provide fundamental insights into the biological underpinnings of cell cycle progression.

Selection for female traits of high fertility affects male reproductive performance and alters the testicular transcriptional profile

Background

Many genes important for reproductive performance are shared by both sexes. However, fecundity indices are primarily based on female parameters such as litter size. We examined a fertility mouse line (FL2), which has a considerably increased number of offspring and a total litter weight of 180% compared to a randomly bred control line (Ctrl) after more than 170 generations of breeding. In the present study, we investigated whether there might be a parallel evolution in males after more than 40 years of breeding in this outbred mouse model.

Results

Males of the fertility mouse line FL2 showed reduced sperm motility performance in a 5 h thermal stress experiment and reduced birth rate in the outbred mouse line. Transcriptional analysis of the FL2 testis showed the differential expression of genes associated with steroid metabolic processes (Cyp1b1, Cyp19a1, Hsd3b6, and Cyp21a1) and female fecundity (Gdf9), accompanied by 150% elevated serum progesterone levels in the FL2 males. Cluster analysis revealed the downregulation of genes of the kallikrein-related peptidases (KLK) cluster located on chromosome 7 in addition to alterations in gene expression with serine peptidase activity, e.g., angiotensinogen (Agt), of the renin-angiotensin system essential for ovulation. Although a majority of functional annotations map to female reproduction and ovulation, these genes are differentially expressed in FL2 testis.

Conclusions

These data indicate that selection for primary female traits of increased litter size not only affects sperm characteristics but also manifests as transcriptional alterations of the male side likely with direct long-term consequences for the reproductive performance of the mouse line.

Electronic supplementary material

The online version of this article (10.1186/s12864-017-4288-z) contains supplementary material, which is available to authorized users.

Dissipative structures and biological rhythms

Sustained oscillations abound in biological systems. They occur at all levels of biological organization over a wide range of periods, from a fraction of a second to years, and with a variety of underlying mechanisms. They control major physiological functions, and their dysfunction is associated with a variety of physiological disorders. The goal of this review is (i) to give an overview of the main rhythms observed at the cellular and supracellular levels, (ii) to briefly describe how the study of biological rhythms unfolded in the course of time, in parallel with studies on chemical oscillations, (iii) to present the major roles of biological rhythms in the control of physiological functions, and (iv) the pathologies associated with the alteration, disappearance, or spurious occurrence of biological rhythms. Two tables present the main examples of cellular and supracellular rhythms ordered according to their period, and their role in physiology and pathophysiology. Among the rhythms discussed are neural and cardiac rhythms, metabolic oscillations such as those occurring in glycolysis in yeast, intracellular Ca⁺⁺ oscillations, cyclic AMP oscillations in Dictyostelium amoebae, the segmentation clock that controls somitogenesis, pulsatile hormone secretion, circadian rhythms which occur in all eukaryotes and some bacteria with a period close to 24 h, the oscillatory dynamics of the enzymatic network driving the cell cycle, and oscillations in transcription factors such as NF-ΚB and tumor suppressors such as p53. Ilya Prigogine's concept of dissipative structures applies to temporal oscillations and allows us to unify within a common framework the various rhythms observed at different levels of biological organization, regardless of their period and underlying mechanism.

Spectral imaging to visualize higher-order genomic organization

A concern in the field of genomics is the proper interpretation of large, high-throughput sequencing datasets. The use of DNA FISH followed by high-content microscopy is a valuable tool for validation and contextualization of frequently occurring gene pairing events at the single-cell level identified by deep sequencing. However, these techniques possess certain limitations. Firstly, they do not permit the study of colocalization of many gene loci simultaneously. Secondly, the direct assessment of the relative position of many clustered gene loci within their respective chromosome territories is impossible. Thus, methods are required to advance the study of higher-order nuclear and cellular organization. Here, we describe a multiplexed DNA FISH technique combined with indirect immunofluorescence to study the relative position of 6 distinct genomic or cellular structures. This can be achieved in a single hybridization step using spectral imaging during image acquisition and linear unmixing. Here, we detail the use of this method to quantify gene pairing between highly expressed spliceosomal genes and compare these data to randomly positioned in silico simulated gene clusters. This is a potentially universally applicable approach for the validation of 3C-based technologies, deep imaging of spatial organization within the nucleus and global cellular organization.