Lactate Produced by Glycogenolysis in Astrocytes Regulates Memory Processing

When administered either systemically or centrally, glucose is a potent enhancer of memory processes. Measures of glucose levels in extracellular fluid in the rat hippocampus during memory tests reveal that these levels are dynamic, decreasing in response to memory tasks and loads; exogenous glucose blocks these decreases and enhances memory. The present experiments test the hypothesis that glucose enhancement of memory is mediated by glycogen storage and then metabolism to lactate in astrocytes, which provide lactate to neurons as an energy substrate. Sensitive bioprobes were used to measure brain glucose and lactate levels in 1-sec samples. Extracellular glucose decreased and lactate increased while rats performed a spatial working memory task. Intrahippocampal infusions of lactate enhanced memory in this task. In addition, pharmacological inhibition of astrocytic glycogenolysis impaired memory and this impairment was reversed by administration of lactate or glucose, both of which can provide lactate to neurons in the absence of glycogenolysis. Pharmacological block of the monocarboxylate transporter responsible for lactate uptake into neurons also impaired memory and this impairment was not reversed by either glucose or lactate. These findings support the view that astrocytes regulate memory formation by controlling the provision of lactate to support neuronal functions

Machine Learning Applications For Neurological Diseases

Neurological conditions affect the brain and other parts of the nervous system. This includes neurodegenerative diseases like Huntington’s Disease, psychiatric conditions like schizophrenia, and brain cancers like glioblastoma. These conditions are particularly challenging to study because they affect such a vital and complex organ system, making it difficult to understand disease etiology and to develop high-quality model systems. Because of these challenges, experiments studying neurological diseases typically either contain very few patient samples or are collected from imperfect model systems. Machine learning approaches have proven helpful for processing these types of datasets and identifying relevant biological signal. In this thesis, I detail five examples of the utility of machine learning methods for analyzing neurological disease data. Some chapters focus primarily on the development of novel machine learning methods, while others discuss the implementation of established algorithms leading to significant advancements in our understanding of the given disease. Chapter 2 details a novel gene set scoring algorithm that significantly improves upon existing methods. This new approach is particularly useful for analyzing single-cell transcriptomics assays, which are becoming increasingly common in neurological disease studies. In Chapter 3, I describe how multi-omic integration of ATAC-Seq, ChIP-Seq, and RNA-seq data revealed a novel population of cycling cells relevant to Huntington’s Disease models. In Chapter 4, I discuss an improved multi-commodity flow algorithm for omics data integration and highlight its utility for understanding drug effects in glioblastoma. Chapter 5 highlights how clustering and the Prize-Collecting Steiner Forest algorithm led to a better understanding of proteomic subtypes in medulloblastoma tumors. Lastly, Chapter 6 expands upon the work in Chapter 5, and details how I used computational approaches to figure out that some medulloblastoma tumors contain cells recapitulating cerebellar granule neuron development. In summary, this thesis showcases the value machine learning techniques for analyzing the small, complicated datasets typically found in neurological disease experiments. Throughout this work, I emphasize the importance of collecting and integrating multiple types of biological data to get a more complete understanding of these conditions.Ph.D

Shallow Sparsely-Connected Autoencoders for Gene Set Projection

When analyzing biological data, it can be helpful to consider gene sets, or predefined groups of biologically related genes. Methods exist for identifying gene sets that are differential between conditions, but large public datasets from consortium projects and single-cell RNA-Sequencing have opened the door for gene set analysis using more sophisticated machine learning techniques, such as autoencoders and variational autoencoders. We present shallow sparsely-connected autoencoders (SSCAs) and variational autoencoders (SSCVAs) as tools for projecting gene-level data onto gene sets. We tested these approaches on single-cell RNA-Sequencing data from blood cells and on RNA-Sequencing data from breast cancer patients. Both SSCA and SSCVA can recover known biological features from these datasets and the SSCVA method often outperforms SSCA (and six existing gene set scoring algorithms) on classification and prediction tasks.National Institutes of Health (U.S.) (Grant R01NS089076)National Institutes of Health (U.S.) (Grant 1U01CA18498

Proteomics, Post-translational Modifications, and Integrative Analyses Reveal Molecular Heterogeneity within Medulloblastoma Subgroups

There is a pressing need to identify therapeutic targets in tumors with low mutation rates such as the malignant pediatric brain tumor medulloblastoma. To address this challenge, we quantitatively profiled global proteomes and phospho-proteomes of 45 medulloblastoma samples. Integrated analyses revealed that tumors with similar RNA expression vary extensively at the post-transcriptional and post-translational levels. We identified distinct pathways associated with two subsets of SHH tumors, and found post-translational modifications of MYC that are associated with poor outcomes in group 3 tumors. We found kinases associated with subtypes and showed that inhibiting PRKDC sensitizes MYC-driven cells to radiation. Our study shows that proteomics enables a more comprehensive, functional readout, providing a foundation for future therapeutic strategies

Ventricular dyssynchrony late after the Fontan operation is associated with decreased survival

Abstract Background Ventricular dyssynchrony and its relationship to clinical outcomes is not well characterized in patients following Fontan palliation. Methods Single-center retrospective analysis of cardiac magnetic resonance (CMR) imaging of patients with a Fontan circulation and an age-matched healthy comparison cohort as controls. Feature tracking was performed on all slices of a ventricular short-axis cine stack. Circumferential and radial strain, strain rate, and displacement were measured; and multiple dyssynchrony metrics were calculated based on timing of these measurements (including standard deviation of time-to-peak, maximum opposing wall delay, and maximum base-to-apex delay). Primary endpoint was a composite measure including time to death, heart transplant or heart transplant listing (D/HTx). Results A total of 503 cases (15 y; IQR 10, 21) and 42 controls (16 y; IQR 11, 20) were analyzed. Compared to controls, Fontan patients had increased dyssynchrony metrics, longer QRS duration, larger ventricular volumes, and worse systolic function. Dyssynchrony metrics were higher in patients with right ventricular (RV) or mixed morphology compared to those with LV morphology. At median follow-up of 4.3 years, 11% had D/HTx. Multiple risk factors for D/HTx were identified, including RV morphology, ventricular dilation, dysfunction, QRS prolongation, and dyssynchrony. Ventricular dilation and RV morphology were independently associated with D/HTx. Conclusions Compared to control LVs, single right and mixed morphology ventricles in the Fontan circulation exhibit a higher degree of mechanical dyssynchrony as evaluated by CMR-FT. Dyssynchrony indices correlate with ventricular size and function and are associated with death or need for heart transplantation. These data add to the growing understanding regarding factors that can be used to risk-stratify patients with the Fontan circulation