On the accuracy of the epigenetic copy machine: comprehensive specificity analysis of the DNMT1 DNA methyltransferase.

The specificity of DNMT1 for hemimethylated DNA is a central feature for the inheritance of DNA methylation. We investigated this property in competitive methylation kinetics using hemimethylated (HM), hemihydroxymethylated (OH) and unmethylated (UM) substrates with single CpG sites in a randomized sequence context. DNMT1 shows a strong flanking sequence dependent HM/UM specificity of 80-fold on average, which is slightly enhanced on long hemimethylated DNA substrates. To explain this strong effect of a single methyl group, we propose a novel model in which the presence of the 5mC methyl group changes the conformation of the DNMT1-DNA complex into an active conformation by steric repulsion. The HM/OH preference is flanking sequence dependent and on average only 13-fold, indicating that passive DNA demethylation by 5hmC generation is not efficient in many flanking contexts. The CXXC domain of DNMT1 has a moderate flanking sequence dependent contribution to HM/UM specificity during DNA association to DNMT1, but not if DNMT1 methylates long DNA molecules in processive methylation mode. Comparison of genomic methylation patterns from mouse ES cell lines with various deletions of DNMTs and TETs with our data revealed that the UM specificity profile is most related to cellular methylation patterns, indicating that de novo methylation activity of DNMT1 shapes the DNA methylome in these cells.

Inference of finite mixture models and the effect of binning.

Finite mixture models are widely used in the life sciences for data analysis. Yet, the calibration of these models to data is still challenging as the optimization problems are often ill-posed. This holds for censored and uncensored data, and is caused by symmetries and other types of non-identifiabilities. Here, we discuss the problem of parameter estimation and model selection for finite mixture models from a theoretical perspective. We provide a review of the existing literature and illustrate the ill-posedness of the calibration problem for mixtures of uniform distributions and mixtures of normal distributions. Furthermore, we assess the effect of interval censoring on this estimation problem. Interestingly, we find that a proper treatment of censoring can facilitate the estimation of the number of mixture components compared to inference from uncensored data, which is an at first glance surprising result. The aim of the manuscript is to raise awareness of challenges in the calibration of finite mixture models and to provide an overview about available techniques.

The tumor suppressor protein DLC1 maintains protein kinase D activity and Golgi secretory function.

Many newly synthesized cellular proteins pass through the Golgi complex from where secretory transport carriers sort them to the plasma membrane and the extracellular environment. The formation of these secretory carriers at the trans-Golgi network is promoted by the protein kinase D (PKD) family of serine/threonine kinases. Here, using mathematical modeling and experimental validation of the PKD activation and substrate phosphorylation kinetics, we reveal that the expression level of the PKD substrate deleted in liver cancer 1 (DLC1), a Rho GTPase-activating protein that is inhibited by PKD-mediated phosphorylation, determines PKD activity at the Golgi membranes. RNAi-mediated depletion of DLC1 reduced PKD activity in a Rho-Rho-associated protein kinase (ROCK)-dependent manner, impaired the exocytosis of the cargo protein horseradish peroxidase, and was associated with the accumulation of the small GTPase RAB6 on Golgi membranes, indicating a protein-trafficking defect. In summary, our findings reveal that DLC1 maintains basal activation of PKD at the Golgi and Golgi secretory activity, in part by down-regulating Rho-ROCK signaling. We propose that PKD senses cytoskeletal changes downstream of DLC1 to coordinate Rho signaling with Golgi secretory function.

MEMO: multi-experiment mixture model analysis of censored data.

MOTIVATION: The statistical analysis of single-cell data is a challenge in cell biological studies. Tailored statistical models and computational methods are required to resolve the subpopulation structure, i.e. to correctly identify and characterize subpopulations. These approaches also support the unraveling of sources of cell-to-cell variability. Finite mixture models have shown promise, but the available approaches are ill suited to the simultaneous consideration of data from multiple experimental conditions and to censored data. The prevalence and relevance of single-cell data and the lack of suitable computational analytics make automated methods, that are able to deal with the requirements posed by these data, necessary. RESULTS: We present MEMO, a flexible mixture modeling framework that enables the simultaneous, automated analysis of censored and uncensored data acquired under multiple experimental conditions. MEMO is based on maximum-likelihood inference and allows for testing competing hypotheses. MEMO can be applied to a variety of different single-cell data types. We demonstrate the advantages of MEMO by analyzing right and interval censored single-cell microscopy data. Our results show that an examination of censoring and the simultaneous consideration of different experimental conditions are necessary to reveal biologically meaningful subpopulation structures. MEMO allows for a stringent analysis of single-cell data and enables researchers to avoid misinterpretation of censored data. Therefore, MEMO is a valuable asset for all fields that infer the characteristics of populations by looking at single individuals such as cell biology and medicine. AVAILABILITY AND IMPLEMENTATION: MEMO is implemented in MATLAB and freely available via github (https://github.com/MEMO-toolbox/MEMO). CONTACTS: eva-maria.geissen@ist.uni-stuttgart.de or nicole.radde@ist.uni-stuttgart.de SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A sphingolipid rheostat controls apoptosis versus apical cell extrusion as alternative tumour-suppressive mechanisms.

Evasion of cell death is a hallmark of cancer, and consequently the induction of cell death is a common strategy in cancer treatment. However, the molecular mechanisms regulating different types of cell death are poorly understood. We have formerly shown that in the epidermis of hypomorphic zebrafish hai1a mutant embryos, pre-neoplastic transformations of keratinocytes caused by unrestrained activity of the type II transmembrane serine protease Matriptase-1 heal spontaneously. This healing is driven by Matriptase-dependent increased sphingosine kinase (SphK) activity and sphingosine-1-phosphate (S1P)-mediated keratinocyte loss via apical cell extrusion. In contrast, amorphic hai1afr26 mutants with even higher Matriptase-1 and SphK activity die within a few days. Here we show that this lethality is not due to epidermal carcinogenesis, but to aberrant tp53-independent apoptosis of keratinocytes caused by increased levels of pro-apoptotic C16 ceramides, sphingolipid counterparts to S1P within the sphingolipid rheostat, which severely compromises the epidermal barrier. Mathematical modelling of sphingolipid rheostat homeostasis, combined with in vivo manipulations of components of the rheostat or the ceramide de novo synthesis pathway, indicate that this unexpected overproduction of ceramides is caused by a negative feedback loop sensing ceramide levels and controlling ceramide replenishment via de novo synthesis. Therefore, despite their initial decrease due to increased conversion to S1P, ceramides eventually reach cell death-inducing levels, making transformed pre-neoplastic keratinocytes die even before they are extruded, thereby abrogating the normally barrier-preserving mode of apical live cell extrusion. Our results offer an in vivo perspective of the dynamics of sphingolipid homeostasis and its relevance for epithelial cell survival versus cell death, linking apical cell extrusion and apoptosis. Implications for human carcinomas and their treatments are discussed.

The effect of normalisation and error model choice on the distribution of the maximum likelihood estimator for a biochemical reaction.

Sparse and noisy measurements make parameter estimation for biochemical reaction networks difficult and might lead to ill-posed optimisation problems. This is potentiated if the data has to be normalised, and only fold changes rather than absolute amounts are available. Here, the authors consider the propagation of measurement noise to the distribution of the maximum likelihood (ML) estimator in an in silico study. Therefore, a model of a reversible reaction is considered, for which reaction rate constants using fold changes is estimated. Noise propagation is analysed for different normalisation strategies and different error models. In particular, accuracy, precision, and asymptotic properties of the ML estimator is investigated. Results show that normalisation by the mean of a time series outperforms normalisation by a single time point in the example provided by the authors. Moreover, the error model with a heavy-tail distribution is slightly more robust to large measurement noise, but, beyond this, the choice of the error model did not have a significant impact on the estimation results provided by the authors.

Bayesian estimation reveals that reproducible models in Systems Biology get more citations.

The Systems Biology community has taken numerous actions to develop data and modeling standards towards FAIR data and model handling. Nevertheless, the debate about incentives and rewards for individual researchers to make their results reproducible is ongoing. Here, we pose the specific question of whether reproducible models have a higher impact in terms of citations. Therefore, we statistically analyze 328 published models recently classified by Tiwari et al. based on their reproducibility. For hypothesis testing, we use a flexible Bayesian approach that provides complete distributional information for all quantities of interest and can handle outliers. The results show that in the period from 2013, i.e., 10 years after the introduction of SBML, to 2020, the group of reproducible models is significantly more cited than the non-reproducible group. We show that differences in journal impact factors do not explain this effect and that this effect increases with additional standardization of data and error model integration via PEtab. Overall, our statistical analysis demonstrates the long-term merits of reproducible modeling for the individual researcher in terms of citations. Moreover, it provides evidence for the increased use of reproducible models in the scientific community.

Reversible switching and stability of the epigenetic memory system in bacteria.

In previous work, we have developed a DNA methylation-based epigenetic memory system that operates in Escherichia coli to detect environmental signals, trigger a phenotypic switch of the cells and store the information in DNA methylation. The system is based on the CcrM DNA methyltransferase and a synthetic zinc finger (ZnF4), which binds DNA in a CcrM methylation-dependent manner and functions as a repressor for a ccrM gene expressed together with an egfp reporter gene. Here, we developed a reversible reset for this memory system by adding an increased concentration of ZnSO4 to the bacterial cultivation medium and demonstrate that one bacterial culture could be reversibly switched ON and OFF in several cycles. We show that a previously developed differential equation model of the memory system can also describe the new data. Then, we studied the long-term stability of the ON-state of the system over approximately 100 cell divisions showing a gradual loss of ON-state signal starting after 4 days of cultivation that is caused by individual cells switching from an ON- into the OFF-state. Over time, the methylation of the ZnF4-binding sites is not fully maintained leading to an increased OFF switching probability of cells, because stronger binding of ZnF4 to partially demethylated operator sites leads to further reductions in the cellular concentrations of CcrM. These data will support future design to further stabilize the ON-state and enforce the binary switching behaviour of the system. Together with the development of a reversible OFF switch, our new findings strongly increase the capabilities of bacterial epigenetic biosensors.

Model-Based Design of a Synthetic Oscillator Based on an Epigenetic Methylation Memory System.

Oscillations are an important component in biological systems; grasping their mechanisms and regulation, however, is difficult. Here, we use the theory of dynamical systems to support the design of oscillatory systems based on epigenetic control elements. Specifically, we use results that extend the Poincaré-Bendixson theorem for monotone control systems that are coupled to a negative feedback circuit. The methodology is applied to a synthetic epigenetic memory system based on DNA methylation that serves as a monotone control system, which is coupled to a negative feedback. This system is generally able to show sustained oscillations according to its structure; however, a first experimental implementation showed that fine-tuning of several parameters is required. We provide design support by exploring the experimental design space using systems-theoretic analysis of a computational model.

Flanking sequences influence the activity of TET1 and TET2 methylcytosine dioxygenases and affect genomic 5hmC patterns.

TET dioxygenases convert 5-methylcytosine (5mC) preferentially in a CpG context into 5-hydroxymethylcytosine (5hmC) and higher oxidized forms, thereby initiating DNA demethylation, but details regarding the effects of the DNA sequences flanking the target 5mC site on TET activity are unknown. We investigated oxidation of libraries of DNA substrates containing one 5mC or 5hmC residue in randomized sequence context using single molecule readout of oxidation activity and sequence and show pronounced 20 and 70-fold flanking sequence effects on the catalytic activities of TET1 and TET2, respectively. Flanking sequence preferences were similar for TET1 and TET2 and also for 5mC and 5hmC substrates. Enhanced flanking sequence preferences were observed at non-CpG sites together with profound effects of flanking sequences on the specificity of TET2. TET flanking sequence preferences are reflected in genome-wide and local patterns of 5hmC and DNA demethylation in human and mouse cells indicating that they influence genomic DNA modification patterns in combination with locus specific targeting of TET enzymes.

Model-based robustness and bistability analysis for methylation-based, epigenetic memory systems.

In recent years, epigenetic memory systems have been developed based on DNA methylation and positive feedback systems. Achieving a robust design for these systems is generally a challenging and multifactorial task. We developed and validated a novel mathematical model to describe methylation-based epigenetic memory systems that capture switching dynamics of methylation levels and methyltransferase amounts induced by different inputs. A bifurcation analysis shows that the system operates in the bistable range, but in its current setup is not robust to changes in parameters. An expansion of the model captures heterogeneity of cell populations by accounting for distributed cell division rates. Simulations predict that the system is highly sensitive to variations in temperature, which affects cell division and the efficiency of the zinc finger repressor. A moderate decrease in temperature leads to a highly heterogeneous response to input signals and bistability on a single-cell level. The predictions of our model were confirmed by flow cytometry experiments conducted in this study. Overall, the results of our study give insights into the functional mechanisms of methylation-based memory systems and demonstrate that the switching dynamics can be highly sensitive to experimental conditions.

DNA sequence-dependent activity and base flipping mechanisms of DNMT1 regulate genome-wide DNA methylation.

DNA methylation maintenance by DNMT1 is an essential process in mammals but molecular mechanisms connecting DNA methylation patterns and enzyme activity remain elusive. Here, we systematically analyzed the specificity of DNMT1, revealing a pronounced influence of the DNA sequences flanking the target CpG site on DNMT1 activity. We determined DNMT1 structures in complex with preferred DNA substrates revealing that DNMT1 employs flanking sequence-dependent base flipping mechanisms, with large structural rearrangements of the DNA correlating with low catalytic activity. Moreover, flanking sequences influence the conformational dynamics of the active site and cofactor binding pocket. Importantly, we show that the flanking sequence preferences of DNMT1 highly correlate with genomic methylation in human and mouse cells, and 5-azacytidine triggered DNA demethylation is more pronounced at CpG sites with flanks disfavored by DNMT1. Overall, our findings uncover the intricate interplay between CpG-flanking sequence, DNMT1-mediated base flipping and the dynamic landscape of DNA methylation.

Impact of measurement noise, experimental design, and estimation methods on Modular Response Analysis based network reconstruction.

Modular Response Analysis (MRA) is a method to reconstruct signalling networks from steady-state perturbation data which has frequently been used in different settings. Since these data are usually noisy due to multi-step measurement procedures and biological variability, it is important to investigate the effect of this noise onto network reconstruction. Here we present a systematic study to investigate propagation of noise from concentration measurements to network structures. Therefore, we design an in silico study of the MAPK and the p53 signalling pathways with realistic noise settings. We make use of statistical concepts and measures to evaluate accuracy and precision of individual inferred interactions and resulting network structures. Our results allow to derive clear recommendations to optimize the performance of MRA based network reconstruction: First, large perturbations are favorable in terms of accuracy even for models with non-linear steady-state response curves. Second, a single control measurement for different perturbation experiments seems to be sufficient for network reconstruction, and third, we recommend to execute the MRA workflow with the mean of different replicates for concentration measurements rather than using computationally more involved regression strategies.

Sampling-based Bayesian approaches reveal the importance of quasi-bistable behavior in cellular decision processes on the example of the MAPK signaling pathway in PC-12 cell lines.

BACKGROUND: Positive and negative feedback loops are ubiquitous motifs in biochemical signaling pathways. The mitogen-activated protein kinase (MAPK) pathway module is part of many distinct signaling networks and comprises several of these motifs, whose functioning depends on the cell line at hand and on the particular context. The maintainance of specificity of the response of the MAPK module to distinct stimuli has become a key paradigm especially in PC-12 cells, where the same module leads to different cell fates, depending on the stimulating growth factor. This cell fate is regulated by differences in the ERK (MAPK) activation profile, which shows a transient response upon stimulation with EGF, while the response is sustained in case of NGF. This behavior was explained by different effective network topologies. It is widely believed that this sustained response requires a bistable system. RESULTS: In this study we present a sampling-based Bayesian model analysis on a dataset, in which PC-12 cells have been stimulated with different growth factors. This is combined with novel analysis methods to investigate the role of feedback interconnections to shape ERK response. Results strongly suggest that, besides bistability, an additional effect called quasi-bistability can contribute to explain the observed responses of the system to different stimuli. Quasi-bistability is the ability of a monostable system to maintain two distinct states over a long time period upon a transient signal, which is also related to positive feedback, but cannot be detected by standard steady state analysis methods. CONCLUSIONS: Although applied on a specific example, our framework is generic enough to be also relevant for other regulatory network modeling studies that comprise positive feedback to explain cellular decision making processes. Overall, this study advices to focus not only on steady states, but also to take transient behavior into account in the analysis.

A computational model of PKD and CERT interactions at the trans-Golgi network of mammalian cells.

BACKGROUND: In mammalian cells protein-lipid interactions at the trans-Golgi network (TGN) determine the formation of vesicles, which transfer secretory proteins to the cellular membrane. This process is regulated by a complex molecular network including protein kinase D (PKD), which is directly involved in the fission of transport vesicles, and its interaction with the ceramide transfer protein CERT that transports ceramide from the endoplasmic reticulum to the TGN. RESULTS: Here we present a novel quantitative kinetic model for the interactions of the key players PKD, phosphatidylinositol 4-kinase III beta (PI4KIII ß) and CERT at the TGN membranes. We use sampling-based Bayesian analysis and perturbation experiments for model calibration and validation. CONCLUSIONS: Our quantitative predictions of absolute molecular concentrations and reaction fluxes have major biological implications: Model comparison provides evidence that PKD and CERT interact in a cooperative manner to regulate ceramide transfer. Furthermore, we identify active PKD to be the dominant regulator of the network, especially of CERT-mediated ceramide transfer.