Tracing Alzheimer's Genetic Footprints: A Pioneering Longitudinal Study Using Artificial Intelligence to Unravel Mutation-Driven Risks and Progression in Virtual Patients; Part 2 – The APP, PSEN1 and PSEN2 mutations

Abstract

Alzheimer’s Disease (AD), the most prevalent neurodegenerative disorder, presents complex challenges in early diagnosis and management. This study, leveraging the innovative aiHumanoid platform, explores the genetic underpinnings of AD through a longitudinal analysis of mutations in APP, PSEN1, and PSEN2 genes. Using virtual patient simulations that integrate extensive medical and genetic literature, we examined the influence of these mutations on disease progression and biomarker development from early childhood through advanced age. Our findings reveal distinct progression patterns associated with these genetic variants, highlighting their potential in identifying early biomarkers, and refining diagnostic strategies. This part of our research broadens our understanding by including a wider array of genetic factors, thereby enhancing the potential for personalized treatment approaches. Ethical considerations regarding the use of AI in research were carefully addressed to ensure responsible technology integration into healthcare settings. The insights gained emphasize the transformative potential of AI in advancing AD research, laying a groundwork for future innovations in diagnostic and therapeutic methodologies.

aiCRISPRL: An Artificial Intelligence Platform for Stem Cell and Organoid Simulation with Extensive Gene Editing Capabilities

“…Our previous successful gene editing applications of the DeepNEU platform are summarized in Table 1 above [5-7,10-21]. In the current project, we have extended those results by simulating HeLa cells and then introducing variable LOF mutations into a specific DNA damage repair gene (MSH2) in the aiHeLa cells.…”

“…The DeepNEU platform is an advanced literature-validated unsupervised machine learning platform [5][6][7][10][11][12][13][14][15][16][17][18][19][20][21]. Here we are presenting the updated database of DeepNEU (v8.4).…”

“…These four factors were given a value of +1 indicating that they were turned on for the first iteration and then allowed to evolve over successive iterations until a new system wide steady state is achieved. The detailed methodology for generating aiPSC simulations has been reported in detail previously [5][6][7][10][11][12][13][14][15][16][17][18][19][20][21].…”

“…Previous versions of the DeepNEU platform have successfully simulated a broad range of aiPSC derived cell types including neural stem cells, cardiac myocytes, skeletal muscle cells, lung cells, brain cells, ovarian cancer cells, lung adenocarcinoma cells, and natural killer cells (NK) [5][6][7][10][11][12][13][14][15][16][17][18][19][20][21]. Using a similar approach and a consensus gene mutational profile from multiple sources [22][23][24][25][26], we used the gene-editing capabilities of aiCRISPRL to create simulations (aiHeLa) that are most like original HeLa cells.…”

“…On review of our previous body of work [5][6][7][10][11][12][13][14][15][16][17][18][19][20][21], the aiCRISPRL functions of the DeepNEU platform successfully used gene editing to create single-gene mutations, multiple mutations and even edit the entire genome of the SARS-CoV-2 and NIPAH viruses. These edits created both loss of function (LOF) and gain of function (GOF) mutations.…”