Impact of the NAD+ salvage pathway on the motor system and bioenergetics

Nicotinamide adenine dinucleotide (NAD+) is a metabolite pivotal to numerous cellular functions, including energy metabolism, DNA repair, and protein modification. The majority of NAD+ is generated by the NAD+ salvage pathway, which is rate limited by nicotinamide phosphoribosyltransferase (NAMPT). NAMPT and the NAD+ salvage pathway are critical to most cell types, but are especially important to neuronal health and function. Additionally, the NAD+ salvage pathway is increasingly being investigated for being involved in neurodegenerative diseases. Recently, the importance of NAMPT to motor and neuromuscular junction (NMJ) activity and survival has been reported. However, how the NAD+ salvage pathway affects NMJ function and what metabolic pathway are dysregulated when the salvage pathway becomes impaired warrants further investigation. In Chapter one, I review intracellular NAD+ homeostasis, NAD+ salvage pathway enzymes, and the importance of NAD+ homeostasis in health and neurodegenerative diseases, specifically ALS. In Chapter two and Chapter three, I used inducible and conditional projection neuron-specific NAMPT knockout mouse, which previously was found to experience profound motor dysfunction and neuron loss. I investigated NMJ structure and function following NAMPT deletion and what benefits NMN administration produces. I also studied how the metabolic and transcriptional profiles in the motor cortex of these knockout mice are altered after NAMPT loss. In Chapter Four, based on the similar phenotypes between the NAMPT knockout mice and ALS mice and NAD+ reduction in ALS mouse model, I investigated how dietary NMN supplementation affects motor behavior and NMJ function in SOD1G93A ALS mice. Overall, the results from these studies provide novel insights into how the NAD+ salvage path is critical for NMJs and what metabolic stress and signaling pathways are responsible for the neuronal death after NAMPT deletionIncludes bibliographical references

First Steps Towards a Runtime Analysis of Neuroevolution

We consider a simple setting in neuroevolution where an evolutionary algorithm optimizes the weights and activation functions of a simple artificial neural network. We then define simple example functions to be learned by the network and conduct rigorous runtime analyses for networks with a single neuron and for a more advanced structure with several neurons and two layers. Our results show that the proposed algorithm is generally efficient on two example problems designed for one neuron and efficient with at least constant probability on the example problem for a two-layer network. In particular, the so-called harmonic mutation operator choosing steps of size $j$ with probability proportional to $1/j$ turns out as a good choice for the underlying search space. However, for the case of one neuron, we also identify situations with hard-to-overcome local optima. Experimental investigations of our neuroevolutionary algorithm and a state-of-the-art CMA-ES support the theoretical findings.Comment: 27 pages; full version of paper published at FOGA 2023 and available at AC