Faculty Bibliography

Linear regression is one of the most used statistical techniques in neuroscience, including the study of the neuropathology of Alzheimer's disease (AD) dementia. However, the practical utility of this approach is often limited because dependent variables are often highly skewed and fail to meet the assumption of normality. Applying linear regression analyses to highly skewed datasets can generate imprecise results, which lead to erroneous estimates derived from statistical models. Furthermore, the presence of outliers can introduce unwanted bias, which affect estimates derived from linear regression models. Although a variety of data transformations can be utilized to mitigate these problems, these approaches are also associated with various caveats. By contrast, a robust regression approach does not impose distributional assumptions on data allowing for results to be interpreted in a similar manner to that derived using a linear regression analysis. Here, we demonstrate the utility of applying robust regression to the analysis of data derived from studies of human brain neurodegeneration where the error distribution of a dependent variable does not meet the assumption of normality. We show that the application of a robust regression approach to two independent published human clinical neuropathologic data sets provides reliable estimates of associations. We also demonstrate that results from a linear regression analysis can be biased if the dependent variable is significantly skewed, further indicating robust regression as a suitable alternate approach.

Mechanisms of Alzheimer's disease (AD) and its putative prodromal stage, amnestic mild cognitive impairment (aMCI), involve the dysregulation of multiple candidate molecular pathways that drive selective cellular vulnerability in cognitive brain regions. However, the spatiotemporal overlap of markers for pathway dysregulation in different brain regions and cell types presents a challenge for pinpointing causal versus epiphenomenal changes characterizing disease progression. To approach this problem, we performed Weighted Gene Co-expression Network Analysis and STRING interactome analysis of gene expression patterns quantified in frontal cortex samples (Brodmann area 10) from subjects who died with a clinical diagnosis of no cognitive impairment, aMCI, or mild/moderate AD. Frontal cortex was chosen due to the relatively protracted involvement of this region in AD, which might reveal pathways associated with disease onset. A co-expressed network correlating with clinical diagnosis was functionally associated with insulin signaling, with insulin (INS) being the most highly connected gene within the network. Co-expressed networks correlating with neuropathological diagnostic criteria (e.g., NIA-Reagan Likelihood of AD) were associated with platelet-endothelium-leucocyte cell adhesion pathways and hypoxia-oxidative stress. Dysregulation of these functional pathways may represent incipient alterations impacting disease progression and the clinical presentation of aMCI and AD.

BACKGROUND:Research in recent years firmly established that microglial cells play an important role in the pathogenesis of Alzheimer's disease (AD). In parallel, a series of studies showed that, under both homeostatic and pathological conditions, microglia are a heterogeneous cell population. In AD, amyloid-β (Aβ) plaque-associated microglia (PAM) display a clearly distinct phenotype compared to plaque-distant microglia (PCM), suggesting that these two microglia subtypes likely differently contribute to disease progression. So far, molecular characterization of PAM was performed indirectly using single cell RNA sequencing (scRNA-seq) approaches or based on markers that are supposedly up-regulated in this microglia subpopulation. METHODS:In this study based on a well-characterized AD mouse model, we combined cell-specific laser capture microdissection and RNA-seq analysis to i) identify, without preconceived notions of the molecular and/or functional changes that would affect these cells, the genes and gene networks that are dysregulated in PAM or PCM at three critical stages of the disease, and ii) to investigate the potential contribution of both plaque-associated and plaque-distant microglia. RESULTS:First, we established that our approach allows selective isolation of microglia, while preserving spatial information and preventing transcriptome changes induced by classical purification approaches. Then, we identified, in PAM and PCM subpopulations, networks of co-deregulated genes and analyzed their potential functional roles in AD. Finally, we investigated the dynamics of microglia transcriptomic remodeling at early, intermediate and late stages of the disease and validated select findings in postmortem human AD brain. CONCLUSIONS:Our comprehensive study provides useful transcriptomic information regarding the respective contribution of PAM and PCM across the Aβ pathology progression. It highlights specific pathways that would require further study to decipher their roles across disease progression. It demonstrates that the proximity of microglia to Aβ-plaques dramatically alters the microglial transcriptome and reveals that these changes can have both positive and negative impacts on the surrounding cells. These opposing effects may be driven by local microglia heterogeneity also demonstrated by this study. Our approach leads to molecularly define the less well studied plaque-distant microglia. We show that plaque-distant microglia are not bystanders of the disease, although the transcriptomic changes are far less striking compared to what is observed in plaque-associated microglia. In particular, our results suggest they may be involved in Aβ oligomer detection and in Aβ-plaque initiation, with increased contribution as the disease progresses.

Aberrant cortisol and activation of the glucocorticoid receptor (GR) play an essential role in age-related progression of Alzheimer's disease (AD). However, the GR pathways required for influencing the pathobiology of AD dementia remain unknown. To address this, we studied an early phase of AD-like progression in the well-established APP/PS1 mouse model combined with targeted mutations in the BDNF-dependent GR phosphorylation sites (serines 134/267) using molecular, behavioral and neuroimaging approaches. We found that disrupting GR phosphorylation (S134A/S267A) in mice exacerbated the deleterious effects of the APP/PS1 genotype on mortality, neuroplasticity and cognition, without affecting either amyloid-β deposition or vascular pathology. The dynamics, maturation and retention of task-induced new dendritic spines of cortical excitatory neurons required GR phosphorylation at the BDNF-dependent sites that amyloid-β compromised. Parallel studies in postmortem human prefrontal cortex revealed AD subjects had downregulated BDNF signaling and concomitant upregulated cortisol pathway activation, which correlated with cognitive decline. These results provide key evidence that the loss of neurotrophin-mediated GR phosphorylation pathway promotes the detrimental effects of the brain cortisol response that contributes to the onset and/or progression of AD dementia. These findings have important translational implications as they provide a novel approach to treating AD dementia by identifying drugs that increase GR phosphorylation selectively at the neurotrophic sites to improve memory and cognition.

The increasingly appreciated prevalence of complicated stressor-to-phenotype associations in human disease requires a greater understanding of how specific stressors affect systems or interactome properties. Many currently untreatable diseases arise due to variations in, and through a combination of, multiple stressors of genetic, epigenetic, and environmental nature. Unfortunately, how such stressors lead to a specific disease phenotype or inflict a vulnerability to some cells and tissues but not others remains largely unknown and unsatisfactorily addressed. Analysis of cell- and tissue-specific interactome networks may shed light on organization of biological systems and subsequently to disease vulnerabilities. However, deriving human interactomes across different cell and disease contexts remains a challenge. To this end, this opinion article links stressor-induced protein interactome network perturbations to the formation of pathologic scaffolds termed epichaperomes, revealing a viable and reproducible experimental solution to obtaining rigorous context-dependent interactomes. This article presents our views on how a specialized 'omics platform called epichaperomics may complement and enhance the currently available conventional approaches and aid the scientific community in defining, understanding, and ultimately controlling interactome networks of complex diseases such as Alzheimer's disease. Ultimately, this approach may aid the transition from a limited single-alteration perspective in disease to a comprehensive network-based mindset, which we posit will result in precision medicine paradigms for disease diagnosis and treatment.

Background/UNASSIGNED:African American women (AAW) have a high risk of both cardiometabolic (CM) illness and depressive symptoms. Depressive symptoms co-occur in individuals with CM illness at higher rates than the general population, and accelerated aging may explain this. In this secondary analysis, we examined associations between age acceleration; depressive symptoms; and CM traits (hypertension, diabetes mellitus [DM], and obesity) in a cohort of AAW. Methods/UNASSIGNED:Genomic and clinical data from the InterGEN cohort (n = 227) were used. Age acceleration was based on the Horvath method of DNA methylation (DNAm) age estimation. Accordingly, DNAm age acceleration (DNAm AA) was defined as the residuals from a linear regression of DNAm age on chronological age. Spearman's correlations, linear and logistic regression examined associations between DNAm AA, depressive symptoms, and CM traits. Results/UNASSIGNED:DNAm AA did not associate with total depressive symptom scores. DNAm AA correlated with specific symptoms including self-disgust/self-hate (-0.13, 95% CI -0.26, -0.01); difficulty with making decisions (-0.15, 95% CI -0.28, -0.02); and worry over physical health (0.15, 95% CI 0.02, 0.28), but were not statistically significant after multiple comparison correction. DNAm AA associated with obesity (0.08, 95% CI 1.02, 1.16), hypertension (0.08, 95% CI 1.01, 1.17), and DM (0.20, 95% CI 1.09, 1.40), after adjustment for potential confounders. Conclusions/UNASSIGNED:Associations between age acceleration and depressive symptoms may be highly nuanced and dependent on study design contexts. Factors other than age acceleration may explain the connection between depressive symptoms and CM traits. AAW with CM traits may be at increased risk of accelerated aging.

Adaptation to acute and chronic stress and/or persistent stressors is a subject of wide interest in central nervous system disorders. In this context, stress is an effector of change in organismal homeostasis and the response is generated when the brain perceives a potential threat. Herein, we discuss a nuanced and granular view whereby a wide variety of genotoxic and environmental stressors, including aging, genetic risk factors, environmental exposures, and age- and lifestyle-related changes, act as direct insults to cellular, as opposed to organismal, homeostasis. These two concepts of how stressors impact the central nervous system are not mutually exclusive. We discuss how maladaptive stressor-induced changes in protein connectivity through epichaperomes, disease-associated pathologic scaffolds composed of tightly bound chaperones, co-chaperones, and other factors, impact intracellular protein functionality altering phenotypes, that in turn disrupt and remodel brain networks ranging from intercellular to brain connectome levels. We provide an evidence-based view on how these maladaptive changes ranging from stressor to phenotype provide unique precision medicine opportunities for diagnostic and therapeutic development, especially in the context of neurodegenerative disorders including Alzheimer's disease where treatment options are currently limited.

Cancer cell plasticity due to the dynamic architecture of interactome networks provides a vexing outlet for therapy evasion. Here, through chemical biology approaches for systems level exploration of protein connectivity changes applied to pancreatic cancer cell lines, patient biospecimens, and cell- and patient-derived xenografts in mice, we demonstrate interactomes can be re-engineered for vulnerability. By manipulating epichaperomes pharmacologically, we control and anticipate how thousands of proteins interact in real-time within tumours. Further, we can essentially force tumours into interactome hyperconnectivity and maximal protein-protein interaction capacity, a state whereby no rebound pathways can be deployed and where alternative signalling is supressed. This approach therefore primes interactomes to enhance vulnerability and improve treatment efficacy, enabling therapeutics with traditionally poor performance to become highly efficacious. These findings provide proof-of-principle for a paradigm to overcome drug resistance through pharmacologic manipulation of proteome-wide protein-protein interaction networks.