Citations

Abstract Identification of gene expression changes in post-mortem brain tissue of Alzheimer’s disease donors compared to controls have implicated numerous biological pathways for Alzheimer’s disease pathophysiology. Nonetheless, there is still limited understanding of how gene expression dysregulation underpins specific proteinopathies core to Alzheimer’s disease. Here we investigate brain transcriptomic changes in a well characterized cohort of Alzheimer’s disease donors to identify genes and networks that associate with Alzheimer’s disease endophenotypes including neuropathology measures (Braak stage, Thal phase and cerebral amyloid angiopathy score) and Alzheimer’s disease-related brain protein levels (Apolipoprotein E, Amyloid-β 40, Amyloid-β 42, tau, and phospho-Tau). Bulk transcriptome measures were collected from the temporal cortex tissue of 477 Alzheimer’s disease donors. Following quality control, transcriptome-wide association studies were performed for each endophenotype. We used weighted gene co-expression network analysis to build co-expression networks and integrated transcriptome with epigenetic and genetic data from the same donors. We detected a total of 5,740 Bonferroni significant temporal cortex gene associations with Alzheimer’s disease endophenotypes, most of which were with brain tau levels. We discovered tau-associated co-expression modules enriched in known and novel Alzheimer’s disease pathways. We found that a beneficial (or neutral) brain biochemical state of higher total tau and lower phospho-Tau are associated with increased levels of synaptic, DNA damage/repair, nucleic acid metabolism and myelin processes. In contrast, in a detrimental state of lower total and higher phospho-Tau, there is upregulation of vascular and immune, and downregulation of mitochondrial and myelin pathways. There are brain gene expression perturbations that are associated with Alzheimer’s disease endophenotypes. While some of these associations are common across multiple endophenotypes, many are distinct for different Alzheimer’s disease-related proteins. Based on these findings, we propose a hypothetical model of dynamic brain gene expression changes that track with progressive Alzheimer’s disease proteostasis. These expression changes hold potential to serve as dynamic, precision biomarkers of brain Alzheimer’s disease progression. This study demonstrates the potential of integrative multiomics and deep Alzheimer’s disease endophenotype analyses in well-characterized brain tissues to uncover with precision the complex biology of Alzheimer’s disease.

Abstract Introduction Alzheimer’s disease (AD) is characterized by pathologies including amyloid, tau, neurodegeneration reflected in established blood biomarkers and track with clinical changes. However, the association between peripheral cell-specific signatures and AD-related phenotypes remain poorly characterized. Methods We analyzed bulk blood transcriptome data from the Mayo Clinic Study of Aging (MCSA) and Emory University Vascular study. We used BayesPrism, CIBERSORTx, and an in-house pipeline CNNreg to deconvolute these data and obtain peripheral cell proportions. Cell type specific transcripts were estimated with BayesPrism. We compared cell proportions between cases (AD/MCI) and controls. Association analysis was performed between cell type specific transcripts and AD-related phenotypes including diagnosis and cognition. Meta-analysis of transcripts associations and gene ontology analysis were conducted to assess the enriched pathways for significant genes. Results In MCSA, B and CD4+ T cells proportions are significantly lower while that of myeloid cells higher in cases. The Emory cohort had similar trends. We identified transcripts associated with AD-related phenotypes in CD4+ T and CD8+ T cells. In CD4+ T cells, transcripts downregulated in case are enriched in response to stimulus pathway, whereas those upregulated in negative regulation of immune response. Downregulation of CD4+ T genes enriched in extracellular matrix disassembly and epigenetic regulation and upregulation of those in protein localization associate with better cognition. In CD8+T cells, genes pertinent to vascular development were downregulated while those to metabolic processes were upregulated in cases. Downregulation of CD8+T genes involved in lipid transportation and upregulation of those in mitochondria associate with better cognition. Conclusion We identified peripheral cellular transcriptional changes associated with AD/MCI phenotypes and involved in important biological pathways, revealing potential disease mechanisms in AD. Funding Source RF1 AG051504 Topic Categories Neuroimmunology (NEUR)

Background Suicidal behaviour shows notable sex differences, and understanding whether genetic factors contribute to these differences is critical for identifying at-risk individuals and prevention. Objective We aim to investigate the genetic contribution to suicide attempts and examine whether genetics account for sex differences in incidence. Methods This population-based cohort study includes 3.1 million individuals born 1963–1998 and followed through Swedish National Registers, including hospitals and specialist outpatient diagnoses and cause of death data. Suicide attempts were identified using ICD codes, indicating intentional self-harm, self-harm using lethal methods or leading to hospitalisation, or resulting in death. Familial aggregation, coaggregation, pedigree heritability and genetic correlations were estimated using genealogical data. For sex-specific analyses, we examined mother–daughter, female sibling, father–son and male sibling pairs, separately. Findings Suicide attempts were more common among females than males (3.3% vs 2.6%). In both sexes, risk aggregated within families (ORs ranged 1.6–3.4 across relative types) and was higher in first-degree than second-degree relatives. Familial aggregation was stronger in females than in males, and in same-sex first degree relatives compared with cross-sex pairs. Pedigree heritability was 41.9% (95% CI 36.0 to 48.4%) and did not differ significantly by sex (female 51.4% (95% CI 40.1% to 58.6%), male 45.1% (95% CI 32.3% to 52.5%), Bootstrap p value 0.40). Suicide attempt showed moderate to high pedigree genetic correlations with psychiatric disorders, strongest with substance use disorders (SUD, r g =0.85 (95% CI 0.83 to 0.96)), with no significant sex differences. The genetic correlation between female and male suicide attempts was high (0.85 (95% CI 0.80 to 0.99)), suggesting a substantial genetic overlap. Conclusions Suicide attempt has a moderate heritable component that largely overlaps between females and males and with other psychiatric disorders, particularly SUD. Stronger familial aggregation in females and in same-sex pairs highlights the potential role of sex-specific environmental or social factors. Future research should focus on non-genetic contributors and their potential interaction with genetic factors to better understand and address sex disparities in suicidal behaviour Clinical implications Genetic risk for suicide attempt is substantial but does not fully explain sex differences in incidence. Clinicians should, therefore, consider non-genetic, including sex-specific environmental and social factors, alongside family history and psychiatric comorbidity when assessing suicidal risk.

Abstract BACKGROUND: Anthracycline chemotherapy-related cardiomyopathy (CCM) is a serious adverse event that can occur several years after completion of breast cancer (BC) therapy. Demographic and clinical risk factors have failed to predict which patients will experience CCM. Genetic variants can account for a significant proportion of inter-individual variation. Knowledge of genetic risk variants prior to chemotherapy will be informative for risk stratification and early intervention. The goal of our study is to identify genetic variants that predispose patients to CCM. Methods: We developed a cardiotoxicity registry at Mayo Clinic, Florida that enrolls patients who are to undergo standard of care chemotherapy in the early BC setting. Patients were (and continue to be) enrolled and consented for chart review and DNA sequencing. We sequenced whole exomes of the first 136 patients (anthracycline N=55, anti-HER2 (no anthracycline) N=71, other chemotherapy, N=10), primarily focusing on Titin (TTN) truncating variants, known to be present in ∼25% of patients with primary dilated cardiomyopathy, and previously reported in 7.5% of patients with CCM, followed by exploration of increased burden of rare non-synonymous variants in 60 established genes for primary cardiomyopathy. Filtering of rare non-synonymous variants included minor allele frequency <0.5% (in GnomAD database European, African and Asian ancestries) and CADDv1.7 PHRED score ≥20. Results: 18/55 (33%) patients treated with anthracycline experienced CCM. TTN truncating variants in were identified in 2/18 (11%) CCM patients and absent in 37 patients who did not experience CCM. Both patients with TTN variants had severe HF and required cardiac transplant. We identified a pathogenic variant in LMNA (p.Arg190Gln) in 1/18 (5.5%) patients and the same rare (p.Glu1127Gly) variant in RYR2 occurred in 2/18 (11%) of patients (Table 1). We noted enrichment of rare missense variants in anthracycline CCM patients compared to HER2 treated without anthracycline, CCM, p=0.0001. Conclusions: Pathogenic variants in TTN, LMNA, are enriched in BC patients (17%) who develop anthracycline-related cardiomyopathy. Early genetic screening may improve risk stratification and guide personalized cardiac monitoring to prevent CCM. Further research is needed to clarify the role of additional rare variants like RYR2 and to develop a CCM risk score based in idiopathic CM genes. Incorporating genetic risk assessments into treatment planning has the potential to enhance oncologic outcomes as well as long-term survivorship by preserving cardiac health. Citation Format: P. P. Advani, A. McPherson, J. Reddy, J. Schneider, S. Baheti, T. Nguyen, J. C. Ray, N. Norton. Genomic Insights into Anthracycline Cardiotoxicity: Enhancing Survivorship Care through Early Risk Identification [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2025; 2025 Dec 9-12; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2026;32(4 Suppl):Abstract nr PS1-01-22.

Abstract Background African Americans (AA) are twice as likely to develop dementia than non‐Hispanic Whites but remain underrepresented in Alzheimer's Disease (AD) research. Recent studies have identified AA specific risk factors for AD. Therefore, targeted biomarkers and/or therapies may be needed to effectively treat AD in AA. In this study, we aimed to determine if genetic variants that show association with AD‐risk, plasma transcript or protein levels in AA, may serve as accurate and accessible AD biomarkers. Method In 189 clinically diagnosed AA AD cases and 183 AA cognitively unimpaired (CU) controls, we performed targeted DNA sequencing of 10 AD‐associated loci. Genetic variants at these loci were tested for association with corresponding plasma transcripts and total tau protein levels previously measured in this cohort using a custom nanoString panel and Simoa assays, respectively. Utilizing phased haplotypes (SHAPEIT4) from target sequencing, we inferred local ancestry (RFMix v2) using five superpopulations from the 1000 Genomes Project as anchors. Subsequently, we tested the association of each ancestry specific allelic dosages with endophenotypes using Tractor. In receiver operating characteristic (ROC) analyses, the most significant e/pQTLs were added sequentially to a base model that included age and sex to identify e/pQTLs that improved the accuracy to correctly classify AD cases and CU controls. Result Of the 5,112 variants that were tested, 70 were nominally associated with AD‐risk and with plasma transcript or protein levels. Seven of these 70 variants also showed nominally significant associations of AD‐risk and transcript levels with either African or East Asian local ancestry. ROC analysis of age, sex, 33 e/pQTLs in APOE , SORL1 , TLR4 , EPHA1 , ABCA7 , CLU , and CR1 , and plasma levels of APP, ABCA7, AKAP9, CD14, CLU, and APOE ‐ɛ4 dosage achieved 88.2% area under the curve to discriminate AD vs. CU, a 30.4% improvement over the base model that only included age and sex. Conclusion Plasma transcript levels and e/pQTLs are promising diagnostic biomarkers for AD that may improve accessibility and reduce costs for a more accurate diagnosis of AD.

Abstract Background Most existing biomarkers for Alzheimer's disease (AD) inform on core AD pathologies in the central nervous system, namely amyloid, tau and neurodegeneration (ATN). However, molecular disease mechanisms underlying AD are complex and heterogeneous, highlighting the need for cost‐effective blood‐based biomarkers that reflect brain molecular changes in AD beyond ATN. Method Using peripheral blood transcriptome data from the Mayo Clinic Study of Aging(MCSA) and the Alzheimer's Disease Neuroimaging Initiative, we identified genes associated with AD or mild cognitive impairment (MCI) diagnosis, cognitive, and harmonized neuroimaging measures in two cohorts, followed by meta‐analysis. We identified consensus gene co‐expression modules in MCSA and ADNI using WGCNA and assessed the association between module eigengenes and aforementioned measures. Using seven bulk brain RNAseq cohorts, we confirmed the preservation of coexpression modules across blood and brain. Enrichment analyses of Gene Ontology (GO) terms and cell‐types were performed using significant and/or hub genes. The MCSA transcriptome was further deconvoluted to obtain cell‐type proportions and cell‐type specific expression. Result Blood transcriptome modules M5 and M8 are upregulated in AD/MCI and associate with worse cognition, whereas M1, M10, M13, M16, and M21 are downregulated in AD/MCI and associate with better cognition. M17 associates with more microhemorrhages. Cell‐type enrichment analysis revealed that M5 is enriched in basophils, monocytes, and neutrophils; M8 in monocytes and neutrophils; M13 in natural kills cells; M17 in megakaryocytes; M21 in B cells. GO analysis shows broad consistency with the cell‐type enrichment analysis. M1, 5, and 8 are preserved in the brain network modules. From these modules, we prioritized transcripts significantly associated with at least one AD‐related phenotype in both blood and brain. Cell‐type deconvolution analysis shows that prioritized transcripts in M1 associate with better cognition and higher metabolic activity in B cells. Conclusion We identified blood transcripts and modules associated with AD/MCI, cognition, or neuroimaging, with three preserved in brain networks. These three modules represent centrally linked peripheral molecular signatures that can reveal perturbed brain pathways and cell level transcriptional changes in AD/MCI. We aim to further investigate the prioritized transcripts in additional datasets to establish them as potential novel biomarkers and therapeutic targets.

Abstract Background Progressive supranuclear palsy (PSP) is a devastating neurodegenerative disorder with rapid progression to death. Multiple efforts to develop a disease‐modifying therapy for PSP have yet to succeed. We previously reported the discovery of three potential therapeutic targets for PSP. These three genes, DDR2, KANK2, and STOM, are consistently upregulated in the brains of PSP patients and mouse models of tauopathy. Reducing their expression in a drosophila model significantly rescued the tau‐induced eye degeneration and tau seeding activity. To translate these targets into the clinic, we are developing antisense oligonucleotides (ASO) as potential therapies. Method We screened candidate ASOs in neuroglioma cell line H4. ASO hits were validated for their potency and safety. We also used iPSC‐derived neurons from PSP patients to test the potency and potential toxicity of our ASOs. To investigate target engagement, therapeutic efficacy and preliminary pharmacodynamic properties, we treated iPSC‐derived midbrain organoids with the ASO leads. Lastly, we will assess the off‐target effects of the ASOs by investigating the levels of mRNA transcripts that share similar sequences as our targets using RNAseq. Result Our in vitro screening identified ASO leads that significantly reduce the target gene expressions at mRNA and protein levels in a dose‐dependent manner. The ASO hits also exhibit low cellular toxicity in the H4 cell line. Upon treating iPSC neurons with ASO hits, we were able to validate the dose‐dependent reduction of target mRNAs. Cell viability analysis indicated low toxicity, suggesting that these ASOs are potent and safe. In midbrain organoid model, DDR2‐targeting ASO showed time‐dependent target engagement and were prioritized for further development. Conclusion We developed candidate therapeutics for PSP using ASOs that silence novel therapeutic targets discovered in our systems based cross‐species screening. We showed that our ASOs have low toxicity and confirmed target engagement at both mRNA and protein levels in vitro. The results suggest a favorable preclinical profile of our ASO leads that warrants additional investigations. Importantly, PSP shares congruent pathophysiological changes with other tauopathies, such as Alzheimer’s disease. These common perturbed pathways indicate that our ASOs can be potentially repurposed for multiple neurodegenerative tauopathies.

Abstract Background Plasma biomarkers offer improved accessibility compared to established diagnostic modalities for Alzheimer's disease (AD). However, current plasma biomarker candidates have not been tested comprehensively in diverse populations, and represent only a fraction of pathways dysregulated in AD. This study seeks to address these knowledge gaps through untargeted plasma biomarker discovery in African Americans (AA), an underrepresented group in AD research despite having a higher risk of developing dementia compared to non‐Hispanic Whites (NHW). Method Untargeted proteomics were generated using the SomaScan 7k platform on plasma samples from AA study participants clinically diagnosed as AD dementia ( n = 181) or cognitively unimpaired (CU; n = 142). Machine learning was implemented to identify a set of plasma proteins to distinguish between AD dementia and CU. Predictive models were constructed by elastic net regression optimized with nested cross‐validation on 6,853 proteins and evaluated by receiver operating characteristic analysis. The generalizability of plasma protein predictors was evaluated in an external Alzheimer's Disease Research Center cohort of 369 NHW and AA study participants diagnosed as AD dementia or CU. Result Thirty‐six proteins were selected as predictors. Proteins selected by machine learning were enriched for processes previously associated with AD including synaptic dysfunction, inflammation, vascular dysfunction, and amyloid processing. The 36 plasma proteins alone achieved an area under the curve (AUC) of 0.94 to classify AD dementia in the discovery cohort, representing a 22% improvement over a model including age and sex. The 36 proteins were validated in an external cohort also achieving AUC=0.94, a 35% improvement over age and sex. Conclusion Biomarker discovery combining untargeted proteomics and machine learning identified a set of plasma proteins that could serve as a novel and highly accurate diagnostic plasma biomarker panel for AD. Work is underway to assess the association of the predictive panel with p ‐tau217, medical comorbidities, and social/structural determinants of health.