Alzheimer’s disease (AD) is a complex neurodegenerative disease with genetic and environmental origins. Women experience cognitive decline and brain atrophy or degeneration earlier than men, but men have a higher mortality rate.
Researchers at Baylor College of Medicine and the Jan Duncan Neurological Institute (Duncan NRI) at Texas Children’s Hospital used a new method they developed to identify sex-specific genes and molecular pathways that contribute to the development and progression of this condition. discovered. Evolutionary Action Machine Learning (EAML). their research Nature Communications.
“EAML allows us to identify genetic factors that influence Alzheimer’s disease risk separately in men and women,” said Cullen, Baylor University Professor of Biochemistry, Molecular Biology, Molecular Genetics and Human Genetics. Dr. Olivier Lichtage, Professor of Pharmacology and Chemical Biology. He is also a member of the Dun L Duncan Comprehensive Cancer Center in Baylor. “This approach has allowed us to efficiently leverage large amounts of evolutionary data, allowing us to more precisely interrogate small cohorts and identify genes involved in sex-specific differences in Alzheimer’s disease. ”
EAML is a computational approach involving nine machine learning algorithms for analyzing the impact of DNA mutations on the resulting protein structure and function and using evolutionary action scores to estimate adverse effects on biological processes.
Relationship between human genetic mutations and Alzheimer’s disease
Lichtarge et al. used EAML to analyze genetic mutations in 2,729 Alzheimer’s disease patients and 2,441 non-Alzheimer’s disease patients and identified 98 genes associated with Alzheimer’s disease. These include several genes known to play key roles in the biology of Alzheimer’s disease and combine machine learning approaches with phylogenetic evolutionary information to develop complex diseases such as Alzheimer’s disease. It supports the general value of identifying relevant genes and pathways.
They also found that these genes were aberrantly expressed in AD brains, showing that they are functionally related.
Researchers have identified specific pathways in neuroinflammation, microglial and astrocyte biology. This is consistent with their possible involvement in altered processes associated with Alzheimer’s disease.
Drosophila reveals the impact of genetic mutations in neurodegeneration
Next, they worked with Dr. Ismael Al-Ramahi, Dr. Juan Botas, and their team at Baylor University. Alzheimer’s and Neurodegenerative Disease Center and Duncan NRI tested a Drosophila AD model representing 98 AD-associated genes.
For this reason, They used a state-of-the-art robot-assisted behavioral testing platformThis enables high-throughput screening of live Drosophila. They found 36 genes that regulate tau-induced degeneration and 29 genes that regulate Aβ42-induced neurodegeneration. These included nine genes that could ameliorate neurodegeneration caused by both tau and Aβ42, two proteins known to accumulate in AD patients.
These findings strongly validate the functional involvement of the identified genes in mediating neurodegeneration in living animals and highlight potential therapeutic avenues.
difference between men and women
The team then applied EAML analysis separately to men and women within this cohort. They found 157 AD-related genes in males and 127 AD-related genes in females. These genes were more closely related to known AD genes identified in genome-wide association studies than those identified in studies that combined rather than segregated males and females. These findings suggest that gender analysis increases the sensitivity of identification of Alzheimer’s disease-associated genes and improves the predictability of risk.
Furthermore, they found that certain biological pathways may have a more significant impact on the development of Alzheimer’s disease in one sex than the other. For example, the female-specific EAML gene was found to be involved in cell division control and DNA quality control.
“We were thrilled to discover a group of genes related to BRCA1, a gene that protects the brain in women and is associated with breast cancer. “This suggests a potential biological link between the two diseases seen,” said the Jan Duncan Duncan Institute for Neurology, Baylor, Assistant Professor of Molecular and Human Genetics. said Al-Ramahi, a member of “These findings may have important implications for the development of therapeutic strategies and the design of gender-stratified clinical trials for Alzheimer’s disease.”
Analyzing small samples using EAML produces accurate and reliable predictions
Moreover, EAML maintained consistent and robust target-driven predictive capabilities even when analyzing smaller sample sizes. With just 700 samples, EAML was able to retrieve over 50% of the AD-related genes found in the entire dataset. This is significantly better than the forecasting algorithms currently in use. The authors conclude that this markedly improved capability will enable researchers to reach accurate and reliable predictions using smaller datasets where known methods do not yield reliable results. We believe this will pave the way for incorporating gender-specific analyzes into potential disease-gene association studies.
“The success of using EAML to discover new targets for Alzheimer’s disease not only provides new perspectives on the genetic factors that influence this disease, but also explores disease-gene associations. It also underscores the importance of systematically applying sex-specific analysis when conducting research,” said Professor Bottas. Molecular and Human Genetics at Baylor University. “This revolutionary approach has the potential to revolutionize our understanding of complex diseases such as Alzheimer’s disease and drive the development of personalized therapies tailored to each individual’s genetic makeup.”
Other participants involved in the study include Thomas Bulkar, Kwanhyuk Lee, Min Pham, Dillon Shapiro, Yashwanth Ragissetti, Shirin Soleimani, Samantha Mota, and Kevin Wilhelm. Mr. Mariam Saminasab, Mr. Yongwon Kim, Mr. Unna Hu, Mr. Jennifer Asmussen and Mr. Panagiotis Katssonis. The author is affiliated with Baylor College of Medicine, the Jan and Dan Duncan Neurological Institute at Texas Children’s Hospital, and he is affiliated with one or more of his institutions at UTHealth McGovern Medical School.
This study was funded by a grant from the National Institutes of Health.
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