Amyotrophic Lateral Sclerosis (ALS) is a fatal neurodegenerative disease characterized by the progressive loss of motor function. Pathological mechanisms and clinical measures vary extensively from patient to patient, creating a spectrum of disease phenotypes with a poorly understood influence on individual outcomes like disease duration. The inability to ascertain patient phenotype has hindered clinical trial design and the development of more personalized and effective therapeutics. Wholistic analytical methods (‘-omics’) have provided unprecedented molecular resolution into cellular and system level disease processes and offer a foundation to better understand ALS disease variability. Building off initiatives by the New York Genome Center ALS Consortium and Target ALS groups, the goal of this work was to stratify a large patient cohort utilizing a range of bioinformatic strategies and bulk tissue gene expression (transcriptomes) from the brain and spinal cord. Central Hypothesis: Variability in the onset and progression of ALS is partially captured by molecular subgroups (subtypes) with distinct gene expression profiles and implicated pathologies. Work presented in this dissertation addresses the following: (Chapter 2): The use of unsupervised clustering and gene enrichment methods for the identification and characterization of patient subtypes in the postmortem cortex and spinal cord. Results obtained from this Chapter establish three ALS subtypes, identify uniquely dysregulated pathways, and examine intra-patient concordance between regions of the central nervous system. (Chapter 3): Patient subtypes from Chapter 2 are considered in the context of clinical outcomes, leveraging multiple survival models and gene co-expression analyses. Results from this Chapter establish a weak association between ALS subtype and clinical outcomes including disease duration and age at symptom onset. (Chapter 4): Utilizing differential expression analysis, ‘marker’ genes are defined and leveraged with supervised classification (“machine learning”) methods to develop a suite of classifiers design to stratify patients by subtype. Results from this Chapter provide postmortem marker genes for two of the three ALS subtypes and offer a foundation for clinical stratification. Significance: Knowledge gained from this research provides a foundation to stratify patients in the clinic and prior to enrollment in clinical trials, offering a path toward improved therapies.

The current gold standard treatment for Parkinson’s Disease is levodopa, which is an orally ingested central nervous system agent that gains therapeutic efficacy after being converted into dopamine in the brain. While current methods exist to evaluate treatment efficacy and prescribe targeted therapies to prevent its premature metabolism, they do not consider the presence of drug-metabolizing enzymes encoded by bacteria in our microbiome. An interspecies bacterial pathway has recently been identified that prematurely converts L-dopa to dopamine in the gut and reduces the available concentration to carry out the target effect. In this work, an untargeted, metabolomic approach was used to detect and quantify volatile metabolites produced during levodopa metabolism in E. faecalis OG1RF cultures. The compounds produced during this process serve as the direct products of bacterial drug modifications by E. faecalis that solely occur in the presence of levodopa. By employing GC-MS techniques to quantify these products, potential confirmative biomarkers can be identified that evaluate treatment efficacy across patients. The unique metabolites identified in this study hold the potential to eventually serve as biomarkers for Parkinson’s treatment efficacy and provide insight to the functional characteristics of E. faecalis levodopa metabolism across the 10 million patients of Parkinson’s Disease. In future efforts, the identity of these metabolites will be verified along with their significant association to L-dopa metabolism.