New insights into the early mechanisms of epileptogenesis in a zebrafish model of Dravet syndrome.

OBJECTIVE: To pinpoint the earliest cellular defects underlying seizure onset (epileptogenic period) during perinatal brain development in a new zebrafish model of Dravet syndrome (DS) and to investigate potential disease-modifying activity of the 5HT2 receptor agonist fenfluramine. METHODS: We used CRISPR/Cas9 mutagenesis to introduce a missense mutation, designed to perturb ion transport function in all channel isoforms, into scn1lab, the zebrafish orthologue of SCN1A (encoding voltage-gated sodium channel alpha subunit 1). We performed behavioral analysis and electroencephalographic recordings to measure convulsions and epileptiform discharges, followed by single-cell RNA-Seq, morphometric analysis of transgenic reporter-labeled γ-aminobutyric acidergic (GABAergic) neurons, and pharmacological profiling of mutant larvae. RESULTS: Homozygous mutant (scn1labmut/mut ) larvae displayed spontaneous seizures with interictal, preictal, and ictal discharges (mean = 7.5 per 20-minute recording; P < .0001; one-way analysis of variance). Drop-Seq analysis revealed a 2:1 shift in the ratio of glutamatergic to GABAergic neurons in scn1labmut/mut larval brains versus wild type (WT), with dynamic changes in neuronal, glial, and progenitor cell populations. To explore disease pathophysiology further, we quantified dendritic arborization in GABAergic neurons and observed a 40% reduction in arbor number compared to WT (P < .001; n = 15 mutant, n = 16 WT). We postulate that the significant reduction in inhibitory arbors causes an inhibitory to excitatory neurotransmitter imbalance that contributes to seizures and enhanced electrical brain activity in scn1labmut/mut larvae (high-frequency range), with subsequent GABAergic neuronal loss and astrogliosis. Chronic fenfluramine administration completely restored dendritic arbor numbers to normal in scn1labmut/mut larvae, whereas similar treatment with the benzodiazepine diazepam attenuated seizures, but was ineffective in restoring neuronal cytoarchitecture. BrdU labeling revealed cell overproliferation in scn1labmut/mut larval brains that were rescued by fenfluramine but not diazepam. SIGNIFICANCE: Our findings provide novel insights into early mechanisms of DS pathogenesis, describe dynamic cell population changes in the scn1labmut/mut brain, and present first-time evidence for potential disease modification by fenfluramine.

Critical Points in Tumorigenesis: A Carcinogen-Initiated Phase Transition Analyzed via Single-Cell Proteomics.

A kinetic, single-cell proteomic study of chemically induced carcinogenesis is interpreted by treating the single-cell data as fluctuations of an open system transitioning between different steady states. In analogy to a first-order transition, phase coexistence and the loss of degrees of freedom are observed. The transition is detected well before the appearance of the traditional biomarker of the carcinogenic transformation.

Is mitochondrial DNA turnover slower than commonly assumed?

Mutations arise during DNA replication due to oxidative lesions and intrinsic polymerase errors. Mitochondrial DNA (mtDNA) mutation rate is therefore closely linked to the mitochondrial DNA turnover process, especially in post mitotic cells. This makes the mitochondrial DNA turnover rate critical for understanding the origin and dynamics of mtDNA mutagenesis in post mitotic cells. Experimental mitochondrial turnover quantification has been based on different mitochondrial macromolecules, such as mitochondrial proteins, lipids and DNA, and the experimental data suggested highly divergent turnover rates, ranging from over 2 days to about 1 year. In this article we argue that mtDNA turnover rate cannot be as fast as is often envisaged. Using a stochastic model based on the chemical master equation, we show that a turnover rate corresponding to mtDNA half-life in the order of months is the most consistent with published mtDNA mutation levels.

Maximizing signal-to-noise ratio in the random mutation capture assay.

The 'Random Mutation Capture' assay allows for the sensitive quantitation of DNA mutations at extremely low mutation frequencies. This method is based on PCR detection of mutations that render the mutated target sequence resistant to restriction enzyme digestion. The original protocol prescribes an end-point dilution to about 0.1 mutant DNA molecules per PCR well, such that the mutation burden can be simply calculated by counting the number of amplified PCR wells. However, the statistical aspects associated with the single molecular nature of this protocol and several other molecular approaches relying on binary (on/off) output can significantly affect the quantification accuracy, and this issue has so far been ignored. The present work proposes a design of experiment (DoE) using statistical modeling and Monte Carlo simulations to obtain a statistically optimal sampling protocol, one that minimizes the coefficient of variance in the measurement estimates. Here, the DoE prescribed a dilution factor at about 1.6 mutant molecules per well. Theoretical results and experimental validation revealed an up to 10-fold improvement in the information obtained per PCR well, i.e. the optimal protocol achieves the same coefficient of variation using one-tenth the number of wells used in the original assay. Additionally, this optimization equally applies to any method that relies on binary detection of a small number of templates.

Global parameter estimation methods for stochastic biochemical systems.

BACKGROUND: The importance of stochasticity in cellular processes having low number of molecules has resulted in the development of stochastic models such as chemical master equation. As in other modelling frameworks, the accompanying rate constants are important for the end-applications like analyzing system properties (e.g. robustness) or predicting the effects of genetic perturbations. Prior knowledge of kinetic constants is usually limited and the model identification routine typically includes parameter estimation from experimental data. Although the subject of parameter estimation is well-established for deterministic models, it is not yet routine for the chemical master equation. In addition, recent advances in measurement technology have made the quantification of genetic substrates possible to single molecular levels. Thus, the purpose of this work is to develop practical and effective methods for estimating kinetic model parameters in the chemical master equation and other stochastic models from single cell and cell population experimental data. RESULTS: Three parameter estimation methods are proposed based on the maximum likelihood and density function distance, including probability and cumulative density functions. Since stochastic models such as chemical master equations are typically solved using a Monte Carlo approach in which only a finite number of Monte Carlo realizations are computationally practical, specific considerations are given to account for the effect of finite sampling in the histogram binning of the state density functions. Applications to three practical case studies showed that while maximum likelihood method can effectively handle low replicate measurements, the density function distance methods, particularly the cumulative density function distance estimation, are more robust in estimating the parameters with consistently higher accuracy, even for systems showing multimodality. CONCLUSIONS: The parameter estimation methodologies described in this work have provided an effective and practical approach in the estimation of kinetic parameters of stochastic systems from either sparse or dense cell population data. Nevertheless, similar to kinetic parameter estimation in other modelling frameworks, not all parameters can be estimated accurately, which is a common problem arising from the lack of complete parameter identifiability from the available data.

Caenorhabditis elegans life span studies: the challenge of maintaining synchronous cohorts.

Due to its many advantages, the nematode worm Caenorhabditis elegans (C. elegans) is commonly employed as a convenient model for aging studies as well as for testing life span effects of chemical compounds. However, some challenges exist in the context of such life span studies, particularly in relation to generation and maintenance of synchronized cohorts, and these challenges are not always fully appreciated. Here we discuss the impact of incomplete control of nematode proliferation on life span studies and suggest some solutions to minimize these artefacts.

Stochastic drift in mitochondrial DNA point mutations: a novel perspective ex silico.

The mitochondrial free radical theory of aging (mFRTA) implicates Reactive Oxygen Species (ROS)-induced mutations of mitochondrial DNA (mtDNA) as a major cause of aging. However, fifty years after its inception, several of its premises are intensely debated. Much of this uncertainty is due to the large range of values in the reported experimental data, for example on oxidative damage and mutational burden in mtDNA. This is in part due to limitations with available measurement technologies. Here we show that sample preparations in some assays necessitating high dilution of DNA (single molecule level) may introduce significant statistical variability. Adding to this complexity is the intrinsically stochastic nature of cellular processes, which manifests in cells from the same tissue harboring varying mutation load. In conjunction, these random elements make the determination of the underlying mutation dynamics extremely challenging. Our in silico stochastic study reveals the effect of coupling the experimental variability and the intrinsic stochasticity of aging process in some of the reported experimental data. We also show that the stochastic nature of a de novo point mutation generated during embryonic development is a major contributor of different mutation burdens in the individuals of mouse population. Analysis of simulation results leads to several new insights on the relevance of mutation stochasticity in the context of dividing tissues and the plausibility of ROS "vicious cycle" hypothesis.

Deceptively simple but simply deceptive--Caenorhabditis elegans lifespan studies: considerations for aging and antioxidant effects.

The nematode worm Caenorhabditis elegans (C. elegans) is increasingly popular as a model organism for aging studies as well as for testing antioxidants and other compounds for effects on longevity. However, results in the literature are sometimes confusing and contradictory. This review introduces C. elegans as a model organism, discusses aspects that make it attractive for aging and antioxidant research, and addresses some problems and potential artifacts.