Alzheimer’s disease (AD) is one of the most prevalent neurodegenerative diseases worldwide, with no effective treatments or preventions. Evidence suggests that environmental factors, including dietary nutrients, contribute to the etiology of AD. Choline is an essential nutrient found in many common foods. Choline is produced endogenously, but not at levels sufficient for healthy metabolic function and thus requires dietary supplementation. Literature shows that ~90% of Americans do not meet the adequate intake threshold for dietary choline consumption and therefore are dietary choline-deficient. While dietary choline supplementation throughout life has been shown to have significant health benefits, such as reducing AD pathology and improving cognition in a mouse model of AD, the impacts of dietary choline deficiency are unknown. Experiments were designed to understand the effects of dietary choline deficiency in healthy, non-transgenic mice (NonTg) and in the 3xTg-AD mouse model of AD. From 3 to 12 months of age, mice received either adequate choline (ChN) in the diet or were put on a choline-deficient (Ch-) diet. A Ch- diet leads to significant weight gain throughout life in both the NonTg and 3xTg-AD mice, with AD mice showing a greater increase. Additionally, impaired glucose metabolism, which is a risk factor for AD, was induced in both NonTg Ch- and 3xTg-AD Ch- mice. Interestingly, Ch- induced cardiomegaly in 3xTg-AD mice and elevated markers of cardiac dysfunction in NonTg mice to similar levels in 3xTg-AD mice. Finally, Ch- exacerbated amyloid-β plaque pathology and tau hyperphosphorylation in the hippocampus and cortex of 3xTg-AD mice. Proteomic analyses revealed Ch- induced changes in hippocampal proteins associated with postsynaptic receptor regulation, microtubule stabilization, and neuronal development, as well as well-known AD-associated proteins (MAPT, BACE1, MECP2, CREBBP). Proteomic analyses also revealed Ch- induced changes of plasma proteins associated with secondary pathologies of AD including inflammation, immune response insulin metabolism, and mitochondrial dysfunction (SAA1, SAA2, IDE, HSPD1, VDAC-1, VDACE-2). Taken together, these data suggest that dietary choline deficiency induces system-wide cellular and molecular dysfunction associated with AD across several pathogenic axes, through proteomic changes not only in the hippocampus but also in the plasma.

In recent years, biological research and clinical healthcare has been disrupted by the ability to retrieve vast amounts of information pertaining to an organism’s health and biological systems. From increasingly accessible wearables collecting realtime biometric data to cutting-edge high throughput biological sequencing methodologies providing snapshots of an organism’s molecular profile, biological data is rapidly increasing in its prevalence. As more biological data continues to be harvested, artificial intelligence and machine learning are well positioned to aid in leveraging this big data for breakthrough scientific outcomes and revolutionized medical care. <br/><br/>The coming decade’s intersection between biology and computational science will be ripe with opportunities to utilize biological big data to advance human health and mitigate disease. Standardization, aggregation and centralization of this biological data will be critical to drawing novel scientific insights that will lead to a more robust understanding of disease etiology and therapeutic avenues. Future development of cheaper, more accessible molecular sensing technology, in conjunction with the emergence of more precise wearables, will pave the road to a truly personalized and preventative healthcare system. However, with these vast opportunities come significant threats. As biological big data advances, privacy and security concerns may hinder society's adoption of these technologies and subsequently dampen the positive impacts this information can have on society. Moreover, the openness of biological data serves as a national security threat given that this data can be used to identify medical vulnerabilities in a population, highlighting the dual-use implications of biological big data. <br/><br/>Additional factors to be considered by academia, private industry, and defense include the ongoing relationship between science and society at-large, as well as the political and social dimensions surrounding the public’s trust in science. Organizations that seek to contribute to the future of biological big data must also remain vigilant to equity, representation and bias in their data sets and data processing techniques. Finally, the positive impacts of biological big data lie on the foundation of responsible innovation, as these emerging technologies do not operate in standalone fashion but rather form a complex ecosystem.