Functional variants identify sex-specific genes and pathways in Alzheimer's Disease

An integrated approach to identifying sex-specific genes, transcription factors, and pathways relevant to Alzheimer's disease

BACKGROUND

Age represents a significant risk factor for the development of Alzheimer's disease (AD); however, recent research has documented an influencing role of sex in several features of AD. Understanding the impact of sex on specific molecular mechanisms associated with AD remains a critical challenge to creating tailored therapeutic interventions.

METHODS

The exploration of the sex-based differential impact on disease (SDID) in AD used a systematic review to first select transcriptomic studies of AD with data regarding sex in the period covering 2002 to 2021 with a focus on the primary brain regions affected by AD - the cortex (CT) and the hippocampus (HP). A differential expression analysis for each study and two tissue-specific meta-analyses were then performed. Focusing on the CT due to the presence of significant SDID-related alterations, a comprehensive functional characterization was conducted: protein-protein network interaction and over-representation analyses to explore biological processes and pathways and a VIPER analysis to estimate transcription factor activity.

RESULTS

We selected 8 CT and 5 HP studies from the Gene Expression Omnibus (GEO) repository for tissue-specific meta-analyses. We detected 389 significantly altered genes in the SDID comparison in the CT. Generally, female AD patients displayed more affected genes than males; we grouped said genes into six subsets according to their expression profile in female and male AD patients. Only subset I (repressed genes in female AD patients) displayed significant results during functional profiling. Female AD patients demonstrated more significant impairments in biological processes related to the regulation and organization of synapsis and pathways linked to neurotransmitters (glutamate and GABA) and protein folding, Aβ aggregation, and accumulation compared to male AD patients. These findings could partly explain why we observe more pronounced cognitive decline in female AD patients. Finally, we detected 23 transcription factors with different activation patterns according to sex, with some associated with AD for the first time. All results generated during this study are readily available through an open web resource Metafun-AD (https://bioinfo.cipf.es/metafun-ad/).

CONCLUSION

Our meta-analyses indicate the existence of differences in AD-related mechanisms in female and male patients. These sex-based differences will represent the basis for new hypotheses and could significantly impact precision medicine and improve diagnosis and clinical outcomes in AD patients.

SPIN: sex-specific and pathway-based interpretable neural network for sexual dimorphism analysis

Sexual dimorphism in prevalence, severity and genetic susceptibility exists for most common diseases. However, most genetic and clinical outcome studies are designed in sex-combined framework considering sex as a covariate. Few sex-specific studies have analyzed males and females separately, which failed to identify gene-by-sex interaction. Here, we propose a novel unified biologically interpretable deep learning-based framework (named SPIN) for sexual dimorphism analysis. We demonstrate that SPIN significantly improved the C-index up to 23.6% in TCGA cancer datasets, and it was further validated using asthma datasets. In addition, SPIN identifies sex-specific and -shared risk loci that are often missed in previous sex-combined/-separate analysis. We also show that SPIN is interpretable for explaining how biological pathways contribute to sexual dimorphism and improve risk prediction in an individual level, which can result in the development of precision medicine tailored to a specific individual's characteristics.

Circulating microRNA miR-425-5p Associated with Brain White Matter Lesions and Inflammatory Processes

White matter lesions (WML) emerge as a consequence of vascular injuries in the brain. While they are commonly observed in aging, associations have been established with neurodegenerative and neurological disorders such as dementia or stroke. Despite substantial research efforts, biological mechanisms are incomplete and biomarkers indicating WMLs are lacking. Utilizing data from the population-based Study of Health in Pomerania (SHIP), our objective was to identify plasma-circulating micro-RNAs (miRNAs) associated with WMLs, thus providing a foundation for a comprehensive biological model and further research. In linear regression models, direct association and moderating factors were analyzed. In 648 individuals, we identified hsa-miR-425-5p as directly associated with WMLs. In subsequent analyses, hsa-miR-425-5p was found to regulate various genes associated with WMLs with particular emphasis on the SH3PXD2A gene. Furthermore, miR-425-5p was found to be involved in immunological processes. In addition, noteworthy miRNAs associated with WMLs were identified, primarily moderated by the factors of sex or smoking status. All identified miRNAs exhibited a strong over-representation in neurodegenerative and neurological diseases. We introduced hsa-miR-425-5p as a promising candidate in WML research probably involved in immunological processes. Mir-425-5p holds the potential as a biomarker of WMLs, shedding light on potential mechanisms and pathways in vascular dementia.

Long-term social isolation stress exacerbates sex-specific neurodegeneration markers in a natural model of Alzheimer's disease

Social interactions have a significant impact on health in humans and animal models. Social isolation initiates a cascade of stress-related physiological disorders and stands as a significant risk factor for a wide spectrum of morbidity and mortality. Indeed, social isolation stress (SIS) is indicative of cognitive decline and risk to neurodegenerative conditions, including Alzheimer's disease (AD). This study aimed to evaluate the impact of chronic, long-term SIS on the propensity to develop hallmarks of AD in young degus (Octodon degus), a long-lived animal model that mimics sporadic AD naturally. We examined inflammatory factors, bioenergetic status, reactive oxygen species (ROS), oxidative stress, antioxidants, abnormal proteins, tau protein, and amyloid-β (Aβ) levels in the hippocampus of female and male degus that were socially isolated from post-natal and post-weaning until adulthood. Additionally, we explored the effect of re-socialization following chronic isolation on these protein profiles. Our results showed that SIS promotes a pro-inflammatory scenario more severe in males, a response that was partially mitigated by a period of re-socialization. In addition, ATP levels, ROS, and markers of oxidative stress are severely affected in female degus, where a period of re-socialization fails to restore them as it does in males. In females, these effects might be linked to antioxidant enzymes like catalase, which experience a decline across all SIS treatments without recovery during re-socialization. Although in males, a previous enzyme in antioxidant pathway diminishes in all treatments, catalase rebounds during re-socialization. Notably, males have less mature neurons after chronic isolation, whereas phosphorylated tau and all detectable forms of Aβ increased in both sexes, persisting even post re-socialization. Collectively, these findings suggest that long-term SIS may render males more susceptible to inflammatory states, while females are predisposed to oxidative states. In both scenarios, the accumulation of tau and Aβ proteins increase the individual susceptibility to early-onset neurodegenerative conditions such as AD.

Evolutionary Action-Machine Learning Model Identifies Candidate Genes Associated With Early-Onset Coronary Artery Disease

Background Coronary artery disease is a primary cause of death around the world, with both genetic and environmental risk factors. Although genome-wide association studies have linked >100 unique loci to its genetic basis, these only explain a fraction of disease heritability. Methods and Results To find additional gene drivers of coronary artery disease, we applied machine learning to quantitative evolutionary information on the impact of coding variants in whole exomes from the Myocardial Infarction Genetics Consortium. Using ensemble-based supervised learning, the Evolutionary Action-Machine Learning framework ranked each gene's ability to classify case and control samples and identified 79 significant associations. These were connected to known risk loci; enriched in cardiovascular processes like lipid metabolism, blood clotting, and inflammation; and enriched for cardiovascular phenotypes in knockout mouse models. Among them, INPP5F and MST1R are examples of potentially novel coronary artery disease risk genes that modulate immune signaling in response to cardiac stress. Conclusions We concluded that machine learning on the functional impact of coding variants, based on a massive amount of evolutionary information, has the power to suggest novel coronary artery disease risk genes for mechanistic and therapeutic discoveries in cardiovascular biology, and should also apply in other complex polygenic diseases.