Publications

BACKGROUND: A significant proportion of individuals maintain cognition despite extensive Alzheimer's disease (AD) pathology, known as cognitive resilience. Understanding the molecular mechanisms that protect these individuals could reveal therapeutic targets for AD.

METHODS: This study defines molecular and cellular signatures of cognitive resilience by integrating bulk RNA and single-cell transcriptomic data with genetics across multiple brain regions. We analyzed data from the Religious Order Study and the Rush Memory and Aging Project (ROSMAP), including bulk RNA sequencing (n = 631 individuals) and multiregional single-nucleus RNA sequencing (n = 48 individuals). Subjects were categorized into AD, resilient, and control based on β-amyloid and tau pathology, and cognitive status. We identified and prioritized protected cell populations using whole-genome sequencing-derived genetic variants, transcriptomic profiling, and cellular composition.

RESULTS: Transcriptomics and polygenic risk analysis position resilience as an intermediate AD state. Only GFAP and KLF4 expression distinguished resilience from controls at tissue level, whereas differential expression of genes involved in nucleic acid metabolism and signaling differentiated AD and resilient brains. At the cellular level, resilience was characterized by broad downregulation of LINGO1 expression and reorganization of chaperone pathways, specifically downregulation of Hsp90 and upregulation of Hsp40, Hsp70, and Hsp110 families in excitatory neurons. MEF2C, ATP8B1, and RELN emerged as key markers of resilient neurons. Excitatory neuronal subtypes in the entorhinal cortex (ATP8B+ and MEF2Chigh) exhibited unique resilience signaling through activation of neurotrophin (BDNF-NTRK2, modulated by LINGO1) and angiopoietin (ANGPT2-TEK) pathways. MEF2C+ inhibitory neurons were over-represented in resilient brains, and the expression of genes associated with rare genetic variants revealed vulnerable somatostatin (SST) cortical interneurons that survive in AD resilience. The maintenance of excitatory-inhibitory balance emerges as a key characteristic of resilience.

CONCLUSIONS: We have defined molecular and cellular hallmarks of cognitive resilience, an intermediate state in the AD continuum. Resilience mechanisms include preserved neuronal function, balanced network activity, and activation of neurotrophic survival signaling. Specific excitatory neuronal populations appear to play a central role in mediating cognitive resilience, while a subset of vulnerable interneurons likely provides compensation against AD-associated hyperexcitability. This study offers a framework to leverage natural protective mechanisms to mitigate neurodegeneration and preserve cognition in AD.

Noncoding genetic variants underlie many complex diseases, yet identifying and interpreting their functional impacts remains challenging. Late-onset Alzheimer's disease (LOAD), a polygenic neurodegenerative disorder, exemplifies this challenge. The disease is strongly associated with noncoding variation, including common variants enriched in microglial enhancers and rare variants that are hypothesized to influence neurodevelopment and synaptic plasticity. These variants often perturb regulatory sequences by disrupting transcription factor (TF) motifs or altering local TF interactions, thereby reshaping gene expression and chromatin accessibility. However, assessing their impact is complicated by the context-dependent functions of regulatory sequences, underscoring the need to systematically examine variant effects across diverse tissues, cell types, and cellular states. Here, we combined in vitro and in vivo massively parallel reporter assays (MPRAs) with interpretable machine-learning models to systematically characterize common and rare variants across myeloid and neural contexts. Parallel profiling of variants in four immune states in vitro and three mouse brain regions in vivo revealed that individual variants can differentially and even oppositely modulate regulatory function depending on cell-type and cell-state contexts. Common variants associated with LOAD tended to exert stronger effects in immune contexts, whereas rare variants showed more pronounced impacts in brain contexts. Interpretable sequence-to-function deep-learning models elucidated how genetic variation leads to cell-type-specific differences in regulatory activity, pinpointing both direct transcription-factor motif disruptions and subtler tuning of motif context. To probe the broader functional consequences of a locus prioritized by our reporter assays and models, we used CRISPR interference to silence an enhancer within the SEC63-OSTM1 locus that harbors four functional rare variants, revealing its gatekeeper role in inflammation and amyloidogenesis. These findings underscore the context-dependent nature of noncoding variant effects in LOAD and provide a generalizable framework for the mechanistic interpretation of risk alleles in complex diseases.

The identification of Alzheimer's disease (AD)-associated genomic variants has provided powerful insight into disease etiology. Genome-wide association studies (GWASs) of AD have successfully identified previously unidentified targets but have almost exclusively used additive genetic models. Here, we performed a family-based GWAS of a recessive inheritance model using whole-genome sequencing from families affected by AD. We found an association between AD risk and the variant rs7161410, which is located in an intron of the PRKCH gene encoding protein kinase C eta (PKCη). In addition, a rare PRKCH missense mutation, K65R, was in linkage disequilibrium with rs7161410 and was present in homozygous carriers of the rs7161410 risk allele. In vitro analysis revealed that the catalytic rate, lipid dependence, and peptide substrate binding of the purified variant were indistinguishable from those of the wild-type kinase. However, cellular studies revealed that the K65R PKCη variant had reduced cytosolic activity and, instead, enhanced localization and signaling at the Golgi. Moreover, the K65R variant had altered interaction networks in transfected cells, particularly with proteins involved in Golgi processes such as vesicle transport. In human brain tissue, the AD-associated recessive genotype of rs7161410 was associated with increased expression of PRKCH, particularly in the amygdala. This association of aberrant PKCη signaling with AD and the insight into how its function is altered may lead to previously unidentified therapeutic targets for prevention and treatment.

UNLABELLED: The identification of Alzheimer's disease (AD)-associated genomic variants has provided powerful insight into disease etiology. Genome-wide association studies (GWAS) for AD have successfully identified new targets but have almost exclusively utilized additive genetic models. Here, we performed a family-based GWAS under a recessive inheritance model using whole genome sequencing from families affected by AD. We found that the variant, rs7161410, located in an intron of the PRKCH gene, encoding protein kinase C eta (PKCη), was associated with AD risk (p-value=1.41 × 10-7). Further analysis revealed a rare PRKCH missense mutation K65R in linkage disequilibrium with rs7161410, which was present in homozygous carriers of the rs7161410 risk allele. We show that this mutation leads to enhanced localization and signaling of PKCη at the Golgi. The novel genetically-validated association of aberrant PKCη signaling with AD opens avenues for new therapeutic targets aimed at prevention and treatment.

ONE SENTENCE SUMMARY: Protein kinase C eta enhances Golgi-localized signaling and is associated with Alzheimer's disease.

BACKGROUND: A significant proportion of individuals maintain healthy cognitive function despite having extensive Alzheimer's disease (AD) pathology, known as cognitive resilience. Understanding the molecular mechanisms that protect these individuals can identify therapeutic targets for AD dementia. This study aims to define molecular and cellular signatures of cognitive resilience, protection and resistance, by integrating genetics, bulk RNA, and single-nucleus RNA sequencing data across multiple brain regions from AD, resilient, and control individuals.

METHODS: We analyzed data from the Religious Order Study and the Rush Memory and Aging Project (ROSMAP), including bulk (n=631) and multi-regional single nucleus (n=48) RNA sequencing. Subjects were categorized into AD, resilient, and control based on β-amyloid and tau pathology, and cognitive status. We identified and prioritized protected cell populations using whole genome sequencing-derived genetic variants, transcriptomic profiling, and cellular composition distribution.

RESULTS: Transcriptomic results, supported by GWAS-derived polygenic risk scores, place cognitive resilience as an intermediate state in the AD continuum. Tissue-level analysis revealed 43 genes enriched in nucleic acid metabolism and signaling that were differentially expressed between AD and resilience. Only GFAP (upregulated) and KLF4 (downregulated) showed differential expression in resilience compared to controls. Cellular resilience involved reorganization of protein folding and degradation pathways, with downregulation of Hsp90 and selective upregulation of Hsp40, Hsp70, and Hsp110 families in excitatory neurons. Excitatory neuronal subpopulations in the entorhinal cortex (ATP8B1+ and MEF2Chigh) exhibited unique resilience signaling through neurotrophin (modulated by LINGO1) and angiopoietin (ANGPT2/TEK) pathways. We identified MEF2C, ATP8B1, and RELN as key markers of resilient excitatory neuronal populations, characterized by selective vulnerability in AD. Protective rare variant enrichment highlighted vulnerable populations, including somatostatin (SST) inhibitory interneurons, validated through immunofluorescence showing co-expression of rare variant associated RBFOX1 and KIF26B in SST+ neurons in the dorsolateral prefrontal cortex. The maintenance of excitatory-inhibitory balance emerges as a key characteristic of resilience.

CONCLUSIONS: We identified molecular and cellular hallmarks of cognitive resilience, an intermediate state in the AD continuum. Resilience mechanisms include preservation of neuronal function, maintenance of excitatory/inhibitory balance, and activation of protective signaling pathways. Specific excitatory neuronal populations appear to play a central role in mediating cognitive resilience, while a subset of vulnerable SST interneurons likely provide compensation against AD-associated dysregulation. This study offers a framework to leverage natural protective mechanisms to mitigate neurodegeneration and preserve cognition in AD.

Alzheimer's disease (AD) presents a complex pathological landscape, posing challenges to current therapeutic strategies that primarily target amyloid-β (Aβ). Using a novel integrative pathway activity analysis (IPAA), we identified 83 dysregulated pathways common between both post-mortem AD brains and three-dimensional AD cellular models showing robust Aβ42 accumulation. p38 mitogen-activated protein kinase (MAPK) was the most upregulated common pathway. Active p38 MAPK levels increased in the cellular models, human brains, and 5XFAD mice and selectively localized to presynaptic dystrophic neurites. Unbiased phosphoproteomics confirmed increased phosphorylation of p38 MAPK substrates. Downstream activation of MAPK-activated protein kinase 2 (MK2) plays a crucial role in Aβ42-p38 MAPK-mediated tau pathology. Therapeutic targeting of the p38 MAPK-MK2 axis with selective inhibitors significantly reduced Aβ42-driven tau pathology and neuronal loss. IPAA prioritizes the best models to derisk target-drug discovery by integrating human tissue gene expression with functional readouts from cellular models, enabling the identification and validation of high-confidence AD therapeutic targets.

In precision medicine, both predicting the disease susceptibility of an individual and forecasting its disease-free survival are areas of key research. Besides the classical epidemiological predictor variables, data from multiple (omic) platforms are increasingly available. To integrate this wealth of information, we propose new methodology to combine both cooperative learning, a recent approach to leverage the predictive power of several datasets, and polygenic hazard score models. Polygenic hazard score models provide a practitioner with a more differentiated view of the predicted disease-free survival than the one given by merely a point estimate, for instance computed with a polygenic risk score. Our aim is to leverage the advantages of cooperative learning for the computation of polygenic hazard score models via Cox's proportional hazard model, thereby improving the prediction of the disease-free survival. In our experimental study, we apply our methodology to forecast the disease-free survival for Alzheimer's disease (AD) using three layers of data. One layer contains epidemiological variables such as sex, APOE (apolipoprotein E, a genetic risk factor for AD) status and 10 leading principal components. Another layer contains selected genomic loci, and the last layer contains methylation data for selected CpG sites. We demonstrate that the survival curves computed via cooperative learning yield an AUC of around $0.7$, above the state-of-the-art performance of its competitors. Importantly, the proposed methodology returns (1) a linear score that can be easily interpreted (in contrast to machine learning approaches), and (2) a weighting of the predictive power of the involved data layers, allowing for an assessment of the importance of each omic (or other) platform. Similarly to polygenic hazard score models, our methodology also allows one to compute individual survival curves for each patient.

Cancer cells edit gene expression to evade immunosurveillance. However, genome-wide studies of gene editing during early tumorigenesis are lacking. Here we used single-cell RNA sequencing in a breast cancer genetically engineered mouse model (GEMM) to identify edited genes without bias. Late tumors repressed antitumor immunity genes, reducing infiltrating immune cells and tumor-immune cell communications. Innate immune genes, especially interferon-stimulated genes, dominated the list of downregulated tumor genes, while genes that regulate cell-intrinsic malignancy were mostly unedited. Naive and activated CD8+ T cells in early tumors were replaced with exhausted or precursor-exhausted cells in late tumors. Repression of immune genes was reversed by inhibiting DNA methylation using low-dose decitabine, which suppressed tumor growth and restored immune control, increasing the number, functionality and memory of tumor-infiltrating lymphocytes and reducing the number of myeloid suppressor cells. Decitabine induced important interferon, pyroptosis and necroptosis genes, inflammatory cell death and immune control in GEMM and implanted breast and melanoma tumors.

Genetic similarity matrices are commonly used to assess population substructure (PS) in genetic studies. Through simulation studies and by the application to whole-genome sequencing (WGS) data, we evaluate the performance of three genetic similarity matrices: the unweighted and weighted Jaccard similarity matrices and the genetic relationship matrix. We describe different scenarios that can create numerical pitfalls and lead to incorrect conclusions in some instances. We consider scenarios in which PS is assessed based on loci that are located across the genome ('globally') and based on loci from a specific genomic region ('locally'). We also compare scenarios in which PS is evaluated based on loci from different minor allele frequency bins: common (>5%), low-frequency (5-0.5%) and rare (<0.5%) single-nucleotide variations (SNVs). Overall, we observe that all approaches provide the best clustering performance when computed based on rare SNVs. The performance of the similarity matrices is very similar for common and low-frequency variants, but for rare variants, the unweighted Jaccard matrix provides preferable clustering features. Based on visual inspection and in terms of standard clustering metrics, its clusters are the densest and the best separated in the principal component analysis of variants with rare SNVs compared with the other methods and different allele frequency cutoffs. In an application, we assessed the role of rare variants on local and global PS, using WGS data from multiethnic Alzheimer's disease data sets and European or East Asian populations from the 1000 Genome Project.

Charting microRNA (miRNA) regulation across pathways is key to characterizing their function. Yet, no method currently exists that can quantify how miRNAs regulate multiple interconnected pathways or prioritize them for their ability to regulate coordinate transcriptional programs. Existing methods primarily infer one-to-one relationships between miRNAs and pathways using differentially expressed genes. We introduce PanomiR, an in silico framework for studying the interplay of miRNAs and disease functions. PanomiR integrates gene expression, mRNA-miRNA interactions and known biological pathways to reveal coordinated multi-pathway targeting by miRNAs. PanomiR utilizes pathway-activity profiling approaches, a pathway co-expression network and network clustering algorithms to prioritize miRNAs that target broad-scale transcriptional disease phenotypes. It directly resolves differential regulation of pathways, irrespective of their differential gene expression, and captures co-activity to establish functional pathway groupings and the miRNAs that may regulate them. PanomiR uses a systems biology approach to provide broad but precise insights into miRNA-regulated functional programs. It is available at https://bioconductor.org/packages/PanomiR.