Faculty Bibliography

Individuals with Down syndrome (DS) have a partial or complete trisomy of chromosome 21, resulting in an increased risk for early-onset Alzheimer's disease (AD)-type dementia by early midlife. Despite ongoing clinical trials to treat late-onset AD, individuals with DS are often excluded. Furthermore, timely diagnosis or management is often not available. Of the genetic causes of AD, people with DS represent the largest cohort. Currently, there is a knowledge gap regarding the underlying neurobiological mechanisms of DS-related AD (DS-AD), partly due to limited access to well-characterized brain tissue and biomaterials for research. To address this challenge, we created an international consortium of brain banks focused on collecting and disseminating brain tissue from persons with DS throughout their lifespan, named the Down Syndrome Biobank Consortium (DSBC) consisting of 11 biobanking sites located in Europe, India, and the USA. This perspective describes the DSBC harmonized protocols and tissue dissemination goals.

Down syndrome (DS) is a genetic disorder caused by triplication of human chromosome 21. In addition to intellectual disability, DS is defined by a premature aging phenotype and Alzheimer's disease (AD) neuropathology, including septohippocampal circuit vulnerability and degeneration of basal forebrain cholinergic neurons (BFCNs). The Ts65Dn mouse model recapitulates key aspects of DS/AD pathology, namely age-associated atrophy of BFCNs and cognitive decline in septohippocampal-dependent behavioral tasks. We investigated whether maternal choline supplementation (MCS), a well-tolerated treatment modality, protects vulnerable BFCNs from age- and genotype-associated degeneration in trisomic offspring. We also examined the effect of trisomy, and MCS, on GABAergic basal forebrain parvalbumin neurons (BFPNs), an unexplored neuronal population in this DS model. Unbiased stereological analyses of choline acetyltransferase (ChAT)-immunoreactive BFCNs and parvalbumin-immunoreactive BFPNs were conducted using confocal z-stacks of the medial septal nucleus and the vertical limb of the diagonal band (MSN/VDB) in Ts65Dn mice and disomic (2N) littermates at 3-4 and 10-12 months of age. MCS trisomic offspring displayed significant increases in ChAT-immunoreactive neuron number and density compared to unsupplemented counterparts, as well as increases in the area of the MSN/VDB occupied by ChAT-immunoreactive neuropil. MCS also rescued BFPN number and density in Ts65Dn offspring, a novel rescue of a non-cholinergic cell population. Furthermore, MCS prevented age-associated loss of BFCNs and MSN/VDB regional area in 2N offspring, indicating genotype-independent neuroprotective benefits. These findings demonstrate MCS provides neuroprotection of vulnerable BFCNs and non-cholinergic septohippocampal BFPNs, indicating this modality has translational value as an early life therapy for DS, as well as extending benefits to the aging population at large.

Systems-level assessments of protein-protein interaction (PPI) network dysfunctions are currently out-of-reach because approaches enabling proteome-wide identification, analysis, and modulation of context-specific PPI changes in native (unengineered) cells and tissues are lacking. Herein, we take advantage of chemical binders of maladaptive scaffolding structures termed epichaperomes and develop an epichaperome-based 'omics platform, epichaperomics, to identify PPI alterations in disease. We provide multiple lines of evidence, at both biochemical and functional levels, demonstrating the importance of these probes to identify and study PPI network dysfunctions and provide mechanistically and therapeutically relevant proteome-wide insights. As proof-of-principle, we derive systems-level insight into PPI dysfunctions of cancer cells which enabled the discovery of a context-dependent mechanism by which cancer cells enhance the fitness of mitotic protein networks. Importantly, our systems levels analyses support the use of epichaperome chemical binders as therapeutic strategies aimed at normalizing PPI networks.

Basal forebrain cholinergic neuron (BFCN) degeneration is a hallmark of Down syndrome (DS) and Alzheimer's disease (AD). Current therapeutics in these disorders have been unsuccessful in slowing disease progression, likely due to poorly understood complex pathological interactions and dysregulated pathways. The Ts65Dn trisomic mouse model recapitulates both cognitive and morphological deficits of DS and AD, including BFCN degeneration and has shown lifelong behavioral changes due to maternal choline supplementation (MCS). To test the impact of MCS on trisomic BFCNs, we performed laser capture microdissection to individually isolate choline acetyltransferase-immunopositive neurons in Ts65Dn and disomic littermates, in conjunction with MCS at the onset of BFCN degeneration. We utilized single population RNA sequencing (RNA-seq) to interrogate transcriptomic changes within medial septal nucleus (MSN) BFCNs. Leveraging multiple bioinformatic analysis programs on differentially expressed genes (DEGs) by genotype and diet, we identified key canonical pathways and altered physiological functions within Ts65Dn MSN BFCNs, which were attenuated by MCS in trisomic offspring, including the cholinergic, glutamatergic and GABAergic pathways. We linked differential gene expression bioinformatically to multiple neurological functions, including motor dysfunction/movement disorder, early onset neurological disease, ataxia and cognitive impairment via Ingenuity Pathway Analysis. DEGs within these identified pathways may underlie aberrant behavior in the DS mice, with MCS attenuating the underlying gene expression changes. We propose MCS ameliorates aberrant BFCN gene expression within the septohippocampal circuit of trisomic mice through normalization of principally the cholinergic, glutamatergic, and GABAergic signaling pathways, resulting in attenuation of underlying neurological disease functions.

Single-cell and single-population RNA sequencing (RNA-seq) is a rapidly evolving new field of intense investigation. Recent studies indicate unique transcriptomic profiles are derived based on the spatial localization of neurons within circuits and regions. Individual neuronal subtypes can have vastly different transcriptomic fingerprints, well beyond the basic excitatory neuron and inhibitory neuron designations. To study single-population gene expression profiles of spatially characterized neurons, we have developed a methodology combining laser capture microdissection (LCM), RNA purification of single populations of neurons, and subsequent library preparation for downstream applications, including RNA-seq. LCM provides the benefit of isolating single neurons characterized by morphology or via transmitter-identified and/or receptor immunoreactivity and enables spatial localization within the sample. We utilize unfixed human postmortem and mouse brain tissue that is frozen to preserve RNA quality in order to isolate the desired neurons of interest. Microisolated neurons are then pooled for RNA purification utilizing as few as 250 individual neurons from a tissue section, precluding extraneous nonspecific tissue contaminants. Library preparation is performed from picogram RNA quantities extracted from LCM-captured neurons. Single-population RNA-seq analysis demonstrates that microisolated neurons from both postmortem human and mouse brain tissues are viable for transcriptomic profiling, including differential gene expression assessment and bioinformatic pathway inquiry.

Diseases are manifestations of complex changes in protein-protein interaction (PPI) networks whereby stressors, genetic, environmental, and combinations thereof, alter molecular interactions and perturb the individual from the level of cells and tissues to the entire organism. Targeting stressor-induced dysfunctions in PPI networks has therefore become a promising but technically challenging frontier in therapeutics discovery. This opinion provides a new framework based upon disrupting epichaperomes - pathological entities that enable dysfunctional rewiring of PPI networks - as a mechanism to revert context-specific PPI network dysfunction to a normative state. We speculate on the implications of recent research in this area for a precision medicine approach to detecting and treating complex diseases, including cancer and neurodegenerative disorders.

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.