SYSTEMS BIOLOGY OF AGING: MODELING & ANALYSIS OF MITOCHONDRIAL GENOME INTEGRITY

Ph.DDOCTOR OF PHILOSOPH

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 2days to about 1year. 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 level

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 template

The γ-secretase substrate proteome and its role in cell signaling regulation

γ-Secretases mediate the regulated intramembrane proteolysis (RIP) of more than 150 integral membrane proteins. We developed an unbiased γ-secretase substrate identification (G-SECSI) method to study to what extent these proteins are processed in parallel. We demonstrate here parallel processing of at least 85 membrane proteins in human microglia in steady-state cell culture conditions. Pharmacological inhibition of γ-secretase caused substantial changes of human microglial transcriptomes, including the expression of genes related to the disease-associated microglia (DAM) response described in Alzheimer disease (AD). While the overall effects of γ-secretase deficiency on transcriptomic cell states remained limited in control conditions, exposure of mouse microglia to AD-inducing amyloid plaques strongly blocked their capacity to mount this putatively protective DAM cell state. We conclude that γ-secretase serves as a critical signaling hub integrating the effects of multiple extracellular stimuli into the overall transcriptome of the cell.</p

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

Targeting EGLN2/PHD1 protects motor neurons and normalizes the astrocytic interferon response

Neuroinflammation and dysregulated energy metabolism are linked to motor neuron degeneration in amyotrophic lateral sclerosis (ALS). The egl-9 family hypoxia-inducible factor (EGLN) enzymes, also known as prolyl hydroxylase domain (PHD) enzymes, are metabolic sensors regulating cellular inflammation and metabolism. Using an oligonucleotide-based and a genetic approach, we showed that the downregulation of Egln2 protected motor neurons and mitigated the ALS phenotype in two zebrafish models and a mouse model of ALS. Single-nucleus RNA sequencing of the murine spinal cord revealed that the loss of EGLN2 induced an astrocyte-specific downregulation of interferon-stimulated genes, mediated via the stimulator of interferon genes (STING) protein. In addition, we found that the genetic deletion of EGLN2 restored this interferon response in patient induced pluripotent stem cell (iPSC)-derived astrocytes, confirming the link between EGLN2 and astrocytic interferon signaling. In conclusion, we identified EGLN2 as a motor neuron protective target normalizing the astrocytic interferon-dependent inflammatory axis in vivo, as well as in patient-derived cells

Stem cell-associated heterogeneity in Glioblastoma results from intrinsic tumor plasticity shaped by the microenvironment

The identity and unique capacity of cancer stem cells (CSC) to drive tumor growth and resistance have been challenged in brain tumors. Here we report that cells expressing CSC-associated cell membrane markers in Glioblastoma (GBM) do not represent a clonal entity defined by distinct functional properties and transcriptomic profiles, but rather a plastic state that most cancer cells can adopt. We show that phenotypic heterogeneity arises from non-hierarchical, reversible state transitions, instructed by the microenvironment and is predictable by mathematical modeling. Although functional stem cell properties were similar in vitro, accelerated reconstitution of heterogeneity provides a growth advantage in vivo, suggesting that tumorigenic potential is linked to intrinsic plasticity rather than CSC multipotency. The capacity of any given cancer cell to reconstitute tumor heterogeneity cautions against therapies targeting CSC-associated membrane epitopes. Instead inherent cancer cell plasticity emerges as a novel relevant target for treatment.publishedVersio

Early alterations in the MCH system link aberrant neuronal activity and sleep disturbances in a mouse model of Alzheimer's disease.

Early Alzheimer's disease (AD) is associated with hippocampal hyperactivity and decreased sleep quality. Here we show that homeostatic mechanisms transiently counteract the increased excitatory drive to CA1 neurons in AppNL-G-F mice, but that this mechanism fails in older mice. Spatial transcriptomics analysis identifies Pmch as part of the adaptive response in AppNL-G-F mice. Pmch encodes melanin-concentrating hormone (MCH), which is produced in sleep-active lateral hypothalamic neurons that project to CA1 and modulate memory. We show that MCH downregulates synaptic transmission, modulates firing rate homeostasis in hippocampal neurons and reverses the increased excitatory drive to CA1 neurons in AppNL-G-F mice. AppNL-G-F mice spend less time in rapid eye movement (REM) sleep. AppNL-G-F mice and individuals with AD show progressive changes in morphology of CA1-projecting MCH axons. Our findings identify the MCH system as vulnerable in early AD and suggest that impaired MCH-system function contributes to aberrant excitatory drive and sleep defects, which can compromise hippocampus-dependent functions

Global parameter estimation methods for stochastic biochemical systems

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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.</p> <p>Conclusions</p> <p>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.</p