Citations

Abstract Background Since November 2019, the SARS-CoV-2 pandemic has created challenges for preventing and managing COVID-19 in children and adolescents. Most research to develop new therapeutic interventions or to repurpose existing ones has been undertaken in adults, and although most cases of infection in pediatric populations are mild, there have been many cases of critical and fatal infection. Understanding the risk factors for severe illness and the evidence for safety, efficacy, and effectiveness of therapies for COVID-19 in children is necessary to optimize therapy. Methods A panel of experts in pediatric infectious diseases, pediatric infectious diseases pharmacology, and pediatric intensive care medicine from 21 geographically diverse North American institutions was re-convened. Through a series of teleconferences and web-based surveys and a systematic review with meta-analysis of data for risk factors, a guidance statement comprising a series of recommendations for risk stratification, treatment, and prevention of COVID-19 was developed and refined based on expert consensus. Results There are identifiable clinical characteristics that enable risk stratification for patients at risk for severe COVID-19. These risk factors can be used to guide the treatment of hospitalized and non-hospitalized children and adolescents with COVID-19 and to guide preventative therapy where options remain available.

AbstractBackgroundFrontotemporal Dementia (FTD) patients are geographically dispersed and often have behavioral and/or motor deficits that limit access to in‐person clinical trials and research. To address these participation barriers, we investigated the feasibility of installing in‐home sensor technologies to remotely assess everyday functioning in FTD.MethodWe aimed to recruit 20 FTD participants with a variety of clinical syndromes, including behavioral variant FTD (bvFTD), primary progressive aphasia (PPA), corticobasal syndrome (CBS), and progressive supranuclear palsy (PSP). We also aimed to enroll a study partner for each participant that lived in the same home to serve as a control. The Collaborative Aging Research Using Technology (CART) platform was installed in participants’ homes to collect continuous data for up to two years, using motion sensors, physiological monitoring devices (e.g., scales, sleep sensors), electronic pillboxes, and wearable actigraphs. Computer use and driving patterns were also tracked. Technology was installed either via a study coordinator visit or remotely through video and written instructions, based on schedule and logistics. Participants were asked to complete a weekly Qualtrics survey about time away from home, visitors, and health changes.ResultOf the 159 UCSF participants available for recruitment through ALLFTD and other FTD projects, 28% (N = 45) met inclusion criteria. Of those 45, ten were ineligible due to advanced disease severity or relocation to an assisted living facility. A third (36%, N = 17) declined due to overwhelmed study partners (N = 3), declined any subproject participation (N = 7), sensitivity to participant’s diagnosis (N = 2), or lack of response (N = 5). Two additional participants were recruited through UCLA’s ALLFTD cohort. The platform has been installed in 19 homes (bvFTD: n = 10; PPA: n = 5; CBS/PSP: n = 4). Two homes discontinued due to death or relocation to assisted living facility. Mean data collection is 360 days (max = 527). Of 103 reported technological issues, 92% were resolved without additional home visits, either by phone or email. Participants completed 65% of weekly health questionnaires.ConclusionsAlthough there were some recruitment and retention challenges, these findings generally provide preliminary support for the feasibility and sustainability of conducting comprehensive in‐home passive data research in FTD.

AbstractBackgroundRepetitive head impact (RHI) exposure from the routine play of American football increases the risk of later‐life cognitive impairment and dementia from the neurodegenerative disease, chronic traumatic encephalopathy (CTE). Although neuropathological studies of CTE have found comorbid neurodegenerative and other pathologies, neuritic Aβ plaques are infrequent, particularly in early stages. However, amyloid PET imaging has not been fully characterized in cognitively impaired former American football players.MethodIn this cross‐sectional, observational cohort study from the DIAGNOSE CTE Research Project, florbetapir PET mean cortical standard uptake value ratios (SUVR) and positivity (defined by average SUVR >1.10, indicative of moderate‐to‐frequent neuritic Aβ plaques), were analyzed in 119 former professional (PRO) National Football League players, 60 former college football players (COL), and 58 men unexposed (UE) to RHI who were cognitively asymptomatic. Former players were categorized into one of four diagnostic groups: cognitively normal (CN), subjective memory complaints (SMC), mild cognitive impairment (MCI), and dementia (DEM). All participants were 45‐74 yo. Multivariable linear and logistic regressions were conducted, with race, education, age, and APOE4 genotype included as covariates. A priori power calculations demonstrated adequate power to detect significant findingsResultNo differences between the three exposure groups in average florbetapir SUVR or proportion of elevated florbetapir uptake were found. Pairwise group comparisons resulted in no differences in average SUVR between PRO and COL (‐0.01, 95% CI [‐0.033, 0.025], p = 0.95), PRO and UE (0.02, 95% CI [‐0.010, 0.029], p = 0.41), and COL and UE (0.02, 95% CI [0.0004, 0.039], p = 0.36). Analysis of covariance resulted in no differences among diagnostic groups (F = 0.94, p = 0.43). Neither years of playing football nor measures of cognition and daily functioning showed significant associations with florbetapir SUVR.ConclusionFormer professional and college American football players with cognitive impairment and dementia did not have elevated florbetapir amyloid PET compared to a group of men without RHI exposure. Their rates of florbetapir positivity in the football players were lower than expected for a group of men this age. These findings suggest that cognitive impairment in former elite football players may not be due to Alzheimer’s disease pathology.

AbstractBackgroundA hallmark of Alzheimer’s disease is amyloid‐β (Aβ) deposition in the brain parenchyma as senile plaques which Aß frequently accompanies in the cerebral vasculature as cerebral amyloid angiopathy (CAA). Previous studies demonstrated that the cerebrovascular system is essential for Aβ clearance and interactions exist between cerebrovascular disease and AD. Neuroimaging data, including measures on small vessel disease, amyloid deposits, and microhemorrhages (MCH), can reflect both cerebrovascular disease and AD pathology, thereby serving as intermediate phenotypes of both conditions. We aim to identify genes and co‐expression networks in blood associated with these neuroimaging endophenotypes and the biological pathways enriched in these perturbed transcriptional networks.MethodWe conducted differential gene expression and Weighted Gene Co‐Expression Network analyses (WGCNA) on blood RNA sequencing data of 407 participants from the longitudinally followed Mayo Clinic Study of Aging cohort. The neuroimaging phenotypes include MCH, infarction, and amyloid deposits measured by Pittsburgh compound B positron emission tomography (PiB PET).ResultWGCNA analysis showed a nominally significant association (p<0.05) between several module eigengenes (MEs) and MCH and PiB PET. One module, M42, remained significantly associated with PiB PET after Bonferroni correction for all 43 modules tested. Pathway enrichment analysis of M42 identified two significant pathways (FDR‐adjusted p<0.05) including phosphodiesterase I activity and IgE binding. These pathways contain genes such as MS4A2 encoding the high‐affinity IgE receptor involved in the immune response. Similarly, ENPP2 in the phosphodiesterase I activity pathway represents an additional candidate gene previously implicated in Alzheimer’s disease.ConclusionWe generated co‐expression networks and identified one network module with a statistically significant association between brain amyloid deposits and blood transcriptome data. Further investigations of this network and its enriched pathways are necessary to understand their roles in amyloidosis, CAA, and neurodegeneration. Future studies will include seeking replication of these results in other datasets. Such studies can help elucidate the biological mechanisms of pathogenesis of cerebrovascular damage and amyloid deposits. In addition, in a clinical setting, blood levels of transcripts discovered in this study may serve as predictive or diagnostic biomarkers with mechanistic implications.

AbstractBackgroundInter‐cellular communication within the gliovascular unit (GVU) is critical for cerebral blood flow regulation, and maintenance of the blood‐brain‐barrier (BBB) properties. The breakdown of BBB in Alzheimer’s disease (AD) is well‐established, but precise underlying molecular changes remain unclear. Additionally, whether GVU molecular alterations observed in AD brains are also detected in blood from living patients is unknown. Further, these GVU molecular perturbations require further investigation in different model systems to identify both human brain‐specific and cross‐species conserved alterations. In this study, we investigated prioritized GVU molecules altered in AD brains for their conservation in blood and cross‐species model systems.MethodsWe performed single nucleus RNA sequencing (snRNAseq) of temporal cortex tissue in AD and control brains. We analyzed this data to detect cell‐specific GVU molecular perturbations and their interactions. We investigated molecular interactions between vascular and astrocyte clusters, the major cell types of the GVU of the BBB. To determine whether GVU transcriptional alterations detected in the brain are preserved in the blood, existing blood expression, genetic, and neuroimaging data from two longitudinal antemortem cohorts were analyzed. Using model systems, including mouse, drosophila, and zebrafish, we evaluated the cross‐species conservation of the top GVU alterations detected in AD brains.ResultsBrain snRNAseq revealed transcriptional profiles of 6,541 astrocytes and 2,210 vascular cells. The latter formed three distinct vascular clusters characterized as pericytes, endothelia and perivascular fibroblasts. We identified differentially expressed genes and their enriched pathways within these clusters and observed the highest levels of transcriptional changes within pericytes. Vascular targets that interact with astrocytic ligands have biological functions in cell signaling, angiogenesis, amyloid ß metabolism, and cytoskeletal architecture. We discovered that genetic variants influencing blood expression levels of some of the prioritized GVU genes were associated with neuroimaging burden of cerebrovascular disease in living human cohorts. Our ongoing studies in model systems revealed conservation of some of the top prioritized molecular perturbations across species.ConclusionOur findings prioritized by multiscale, cross‐tissue human data revealed GVU perturbations within interacting pericyte and astrocyte molecules, which are conserved across multiple cross‐species models. These results nominate new molecular targets and mechanistic insights for BBB disruptions in AD.

AbstractBackgroundAlthough we have greatly increased our ability to identify the presence of neurodegenerative pathology during life, additional techniques and targets are still needed to increase diagnostic specificity. Recently, it has become evident that the neuroinflammatory response might be tailored towards distinct diseases resulting in disease specific “neuroinflammatory signatures”. Therefore, a detailed comparison of neuroinflammatory molecules present among distinct neurodegenerative diseases could provide novel information of disease specific therapeutic targets or differential biomarkers.MethodTo better examine potential neuroinflammatory signatures, a 71 immune‐related protein multiplex ELISA panel was utilized to analyze anterior cingulate grey matter from 127 individuals neuropathologically diagnosed with Alzheimer’s disease (AD), but chronic traumatic encephalopathy (CTE), progressive supranuclear palsy (PSP), corticobasal degeneration (CBD), and argyrophilic grain disease (AGD). A partial least square regression analysis was used for unbiased clustering and identifying proteins that were distinctly correlated with each disease correcting for age and gender. Receiver operator characteristic and binary logistic regression analyses were then used to examine the ability of each candidate protein to distinguish diseases. Validation in postmortem cerebrospinal fluid (CSF) from 15 AD and 14 CTE cases was performed to determine if candidate proteins could act as possible biomarkers. Finally, histology was performed using candidate proteins to visualize cell type expression for better understanding of potential mechanistic pathways.ResultFive clusters of neuroimmune proteins were identified and compared to determine if clusters were specific to distinct disease. Each cluster was found to correlate with either CTE, AD, PSP, CBD, or AGD. When examining which proteins were the strongest driver of each cluster, it was observed the most distinctive protein for CTE was CCL21, AD was FLT3L, and PSP was IL13. Individual proteins that were specific to CBD and AGD were not observed. CCL21 was observed to be elevated in CTE CSF compared to AD, further validating the use as possible biomarkers.ConclusionOverall, these results highlight that different neuroinflammatory responses might underlie unique mechanisms in related neurodegenerative pathologies. Additionally, the use of distinct neuroinflammatory signatures could help differentiate between neuropathologies and act as novel biomarker candidate to increase specificity for in‐life diagnoses.

AbstractBackgroundNeurodegenerative disorders affect different brain regions at different time points and to varying degrees, although the underlying reasons for this variability in regional susceptibility is unknown. Brain regions have different proportions of cell types. This cell proportion variability may be further influenced by presence and degree of brain disorders, including neurodegenerative diseases. We postulate that genetic and other factors that influence brain cell type proportions may also provide insights into brain regional susceptibility to neurodegenerative diseases.MethodsUtilizing bulk transcriptomics data from 7 brain regions obtained from 3 cohorts and corresponding whole genome sequencing (WGS) data, genetic variants were evaluated for association with cell type proportions across brain regions. Utilizing cell type specific marker genes for deconvolution provided by McKenzie et.al, we estimated surrogate variables for proportion (SVP) of various cell types using BRETIGEA. We then evaluated relationship between SVP and AD as well as Age, Sex, APOE ε4 and neuropathology measures. Genetic variants from WGS were then tested for association with SVP to identify distinct and conserved patterns across 6 cell types and 7 brain regions.ResultsAnalysis of SVP by diagnosis, sex, age, APOE ε4 and neuropathology measures identified significant differences in cell proportions in various cell types and brain regions. GWAS of SVP identified 134 loci that reached genome‐wide significance (5e‐8). After conservative adjustment for multiple testing (6 cell types and 7 brain regions; 42 tests), variants proximal to SYNPR and VENTXP7 remained significant (p‐value<1.19E‐09). Variants proximal to SYNPR were associated with increased proportion of oligodendrocytes in cerebellum whereas variants near VENTXP7 were associated with reduced proportion of astrocytes in DLPFC. Variants in SYNPR have previously been associated with neurofibrillary tangles and diffuse plaque measurements while variants in VENTXP7 have been associated with measurement of neurofibrillary tangles, cognitive decline, unipolar depression, and bipolar disorder.ConclusionsIt is likely that there are genetic variants associated with cell proportion in brain, may vary by cell type and brain region, while others may act more broadly. Identification of genetic variants associated with cell proportions in different brain regions may provide insights into affected cells and regional vulnerabilities.

AbstractBackgroundTwo major risk factors of Alzheimer’s disease (AD) are advanced age and the APOE‐ε4 risk allele. However, despite having these risk factors, some individuals escape from developing AD or have slower cognitive decline. In this study we hypothesized that “resilient” individuals, defined as those who remain cognitively normal despite being >80 years‐old and carrying at least one copy of the APOE‐ε4 allele, have concerted changes in their blood transcriptomes that are associated with resilience to AD.MethodWe generated blood RNA‐sequence data from 105 APOE‐ε4 carriers from the Mayo Clinic Study of Aging (MCSA) who remained cognitively normal after age 80 (resilient), or who developed mild cognitive impairment (MCI) or AD (non‐resilient). Weighted gene co‐expression network analysis (WGCNA) was performed to evaluate the association of the blood co‐expression network with primary phenotypes of resilience and memory scores. To validate our findings, we constructed a consensus co‐expression network using both MCSA and a replication cohort of blood microarray data from 91 Alzheimer’s Disease Neuroimaging Initiative (ADNI) study participants who had a similar demographic profile. We annotated the consensus modules for their enrichment of cell types, biological processes, genes within previously published genome‐wide significant (GWS) AD‐associated loci, and genes that reside in consensus modules built from seven brain regions across three different autopsy cohorts. To identify genetic variants regulating blood consensus module co‐expression, we performed module quantitative trait locus (modQTL) analysis.ResultWe found co‐expression modules significantly (q<0.05) correlated with the memory phenotype, logical memory delayed recall, across both MCSA and ADNI cohorts. Some of these modules were enriched with blood cell types and biological processes, including telomere maintenance, mitochondrial functions, and immune response. We found 1 module significantly (q<0.05) enriched with genes near GWS AD‐associated loci and 4 modules with genes in brain consensus modules. Lastly, we found 8 modQTLs significantly (Bonferroni‐p<0.05) associated with the co‐expression of 5 different modules.ConclusionWe identified blood co‐expression modules, biological pathways and specific genetic variants associated with cognitive resilience in the presence of AD risk factors. This study provides novel insights into the potential role of peripheral blood in the development of and resilience to AD.

AbstractBackgroundAlzheimer’s disease (AD) is a complex, multicellular disease with immunological and genetic components. Previous studies demonstrated associations between AD and the missense variants ABI3‐rs616338‐T and PLCG2‐rs72824905‐G. The perturbed expression of these genes has been demonstrated to disrupt microglial function. To date, the transcriptomic profiles of variant carriers in the context of microglial subtype and state have not been explored in human brain. This represents a knowledge gap in our understanding of AD risk and resilience. We have developed and optimized a single nucleus RNAseq (snRNAseq) approach to study the impact of ABI3 and PLCG2 variants on microglial enriched nuclei from frozen human brain.MethodDOCK8 was identified as a nuclear microglial marker based on previous brain snRNAseq data. DOCK8 based microglial enrichment using fluorescent activated nuclei sorting (FANS) was validated using RT‐qPCR and snRNAseq in human cerebellum and temporal cortex (TCX), respectively. We are applying this enrichment and snRNAseq approach to TCX tissue from a cohort of 30 donors harboring ABI3 or PLCG2 missense mutations, or neither. Standard snRNAseq alignment and quality control pipelines will be applied, and nuclei annotated based on putative microglial markers. Differential gene expression will be assessed using pseudo bulk and MAST approaches, for association with ABI3 and PLCG2 genotype, diagnosis groups, risk factors, and pathology scores.ResultDOCK8 has several commercially available antibodies, making it an ideal microglia‐selection marker. Using RT‐qPCR in microglia enriched nuclei, we demonstrated an enrichment of microglia marker genes and depletion of other brain cell type markers. This is further supported by snRNA‐seq data from DOCK8‐sorted TCX tissue that yielded a 5x increase in microglia proportion (56% vs. 11% in unselected nuclei), enabling analyses of microglia sub‐populations. Leveraging this approach, we will investigate affected genes and pathways and uncover the resilience and risk mechanisms within microglia sub‐populations in ABI3 and PLCG2 variant‐carriers.ConclusionOur study presents a novel method for enriching microglial nuclei from frozen human brains and enables transcriptomic profiling of microglial subpopulations in ABI3 and PLCG2 missense variant‐carriers. We expect these insights will reveal novel genes and pathways that may play a key role in microglial function in AD.