Citations

Abstract A subset of stem-like cells in glioblastoma (GBM; GSC) underlies tumor propagation, therapeutic resistance, and tumor recurrence. Immune evasion is critical for GSCs to carry out these functions. However, the molecular mechanisms employed by GSCs to escape antitumor immunity remain largely unknown. The reprogramming transcription factors Oct4 and Sox2 function as core multipotency factors and play an essential role in the formation and maintenance of GSCs, but the roles of these transcription factors in GSC immune escape have not been well explored. Here we examine how Oct4/Sox2 coexpression contributes to the immunosuppressive phenotype of GSCs. Combined transcription profiling and functional studies of Oct4/Sox2 coexpressing GSCs and differentiated GBM cells demonstrated that Oct4 and Sox2 cooperatively induce an immunosuppressive transcriptome consisting of multiple immunosuppressive checkpoints (i.e., PD-L1, CD70, A2aR, TDO) and dysregulation of cytokines and chemokines that are associated with an immunosuppressive tumor microenvironment. Mechanistically, induction and function of BRD/H3k27Ac-dependent immunosuppressive genes played a role in the immunosuppressive phenotype of GSCs. Pan-BET bromodomain inhibitors (e.g., JQ1) and shBRD4 constructs significantly inhibited the immunosuppressive transcriptome and immunosuppressive biological responses induced by Oct4/Sox2. Our findings identify targetable mechanisms by which tumor-propagating GSCs contribute to the immunosuppressive microenvironment in GBM. Significance: This report identifies mechanisms by which the reprogramming transcription factors Oct4 and Sox2 function to drive the immunomodulatory transcriptome of GSCs and contribute to the immunosuppressive microenvironment in GBM.

Purpose Individuals with primary progressive apraxia of speech (PPAOS) have apraxia of speech (AOS) in which disruptions in articulation or prosody predominate the speech pattern, referred to, respectively, as phonetic or prosodic subtypes. Many develop aphasia and/or dysarthria. Past research has demonstrated that simple temporal acoustic measures are sensitive to the presence of AOS. The aim of this study was to describe the change in temporal acoustic measures over time and assess if specific patterns of AOS or co-occurring aphasia or dysarthria impact the rate of change over time. Method Durations for multiple productions of the words cat , catnip , catapult , and catastrophe , in an imitative speech task, were recorded for 73 patients, with two to six visits each. A linear mixed-effects model was used to assess the cross-sectional differences and longitudinal influence of AOS subtype and presence of aphasia/dysarthria on speech rate. Pearson correlations were calculated between rate measures and performance on other clinical measures. Results Cross-sectionally, patients with prosodic-predominant PPAOS produced words more slowly than those with phonetic-predominant PPAOS. Patients with either aphasia or dysarthria produced words more slowly than those without. Longitudinally, the speech rate of patients with phonetic-predominant PPAOS had a reduction of 0.5 syllables per second per year. Patients with prosodic-predominant AOS changed less quickly, as did those who developed aphasia. Dysarthria did not impact rate of change. There were strong associations between speech rate measures and other clinical indices of speech and language functioning. Conclusion Simple temporal acoustic measures may reflect the subtype of AOS (phonetic or prosodic predominant), serve as an index of progression of AOS, and inform prognostication relative to the presenting combination of speech and language features. Supplemental Material https://doi.org/10.23641/asha.13564724

Objectives/HypothesisThe purpose of this study is to develop consensus on key points that would support the use of systemic bevacizumab for the treatment of recurrent respiratory papillomatosis (RRP), and to provide preliminary guidance surrounding the use of this treatment modality.Study DesignDelphi method‐based survey series.MethodsA multidisciplinary, multi‐institutional panel of physicians with experience using systemic bevacizumab for the treatment of RRP was established. The Delphi method was used to identify and obtain consensus on characteristics associated with systemic bevacizumab use across five domains: 1) patient characteristics; 2) disease characteristics; 3) treating center characteristics; 4) prior treatment characteristics; and 5) prior work‐up.ResultsThe international panel was composed of 70 experts from 12 countries, representing pediatric and adult otolaryngology, hematology/oncology, infectious diseases, pediatric surgery, family medicine, and epidemiology. A total of 189 items were identified, of which consensus was achieved on Patient Characteristics (9), Disease Characteristics (10), Treatment Center Characteristics (22), and Prior Workup Characteristics (18).ConclusionThis consensus statement provides a useful starting point for clinicians and centers hoping to offer systemic bevacizumab for RRP and may serve as a framework to assess the components of practices and centers currently using this therapy. We hope to provide a strategy to offer the treatment and also to provide a springboard for bevacizumab's use in combination with other RRP treatment protocols. Standardized delivery systems may facilitate research efforts and provide dosing regimens to help shape best‐practice applications of systemic bevacizumab for patients with early‐onset or less‐severe disease phenotypes.Level of Evidence5 Laryngoscope, 131:E1941–E1949, 2021

This work investigates the use of an intelligent and unobstructive sensing technique for maintaining vehicle cabin’s indoor air quality while simultaneously assessing the driver metabolic rate. CO2 accumulation patterns are of great interest because CO2 can have negative cognitive effects at higher concentrations and also since CO2 accumulation rate can potentially be used to determine a person’s metabolic rate. The management of the vehicle’s ventilation system was controlled by periodically alternating the air recirculation mode within the cabin, which was actuated based on the CO2 levels inside the vehicle’s cabin. The CO2 accumulation periods were used to assess the driver’s metabolic rate, using a model that considered the vehicle’s air exchange rate. In the process of the method optimization, it was found that the vehicle’s air exchange rate (λ [h−1]) depends on the vehicle speeds, following the relationship: λ = 0.060 × (speed) − 0.88 when driving faster than 17 MPH. An accuracy level of 95% was found between the new method to assess the driver’s metabolic rate (1620 ± 140 kcal/day) and the reference method of indirect calorimetry (1550 ± 150 kcal/day) for a total of N = 16 metabolic assessments at various vehicle speeds. The new sensing method represents a novel approach for unobstructive assessment of driver metabolic rate while maintaining indoor air quality within the vehicle cabin.

Disrupted nucleocytoplasmic transport (NCT) has been implicated in neurodegenerative disease pathogenesis; however, the mechanisms by which disrupted NCT causes neurodegeneration remain unclear. In a Drosophila screen, we identified ref(2)P/p62 , a key regulator of autophagy, as a potent suppressor of neurodegeneration caused by the GGGGCC hexanucleotide repeat expansion (G4C2 HRE) in C9orf72 that causes amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD). We found that p62 is increased and forms ubiquitinated aggregates due to decreased autophagic cargo degradation. Immunofluorescence and electron microscopy of Drosophila tissues demonstrate an accumulation of lysosome-like organelles that precedes neurodegeneration. These phenotypes are partially caused by cytoplasmic mislocalization of Mitf/TFEB, a key transcriptional regulator of autophagolysosomal function. Additionally, TFEB is mislocalized and downregulated in human cells expressing GGGGCC repeats and in C9-ALS patient motor cortex. Our data suggest that the C9orf72 -HRE impairs Mitf/TFEB nuclear import, thereby disrupting autophagy and exacerbating proteostasis defects in C9-ALS/FTD.

AbstractBackgroundRecent evidence has implicated brain networks in the pathophysiology of Alzheimer’s disease (AD). However, the phenotypic variability in AD, inter‐individual differences in tau PET uptake patterns, and dynamic nature of functional connectivity have been relatively neglected. In this talk, I will discuss our recent work linking measures of dynamic brain organization to spatially independent tau PET patterns defined in a phenotypically diverse cohort.MethodsWe identified all Mayo Clinic participants with concurrent amyloid PET (PiB), tau PET (AV1451) and task‐free fMRI (impaired participants were all Aβ+). We used independent component analysis (ICA) to identify spatially independent patterns of tau PET uptake. For the fMRI data, we used a Hidden‐Markov Model (HMM) design to identify distinct patterns of activity/connectivity (‘states’). The ICA and HMM parameters were used to project/segment an independent cohort (ADNI) and subsequent longitudinal timepoints for the Mayo cohort. Multilevel Bayesian regression models were fit in Stan to explore the association between phenotype, component load and brain state.ResultsThe optimal HMM model identified 10 brain states (Figure 1). The optimal ICA decomposition identified 42 tau PET components, of which 13 were potentially related to AD pathobiology (Figure 1). Component loads and brain state dwell time differed between diagnostic groups in both Mayo and ADNI cohorts. One state was much more likely to occur in AD in both cohorts (State 8). Cross‐sectionally, State 8 dwell time was associated with higher tau loads, while higher baseline dwell‐time was predictive of ∆tau in participants with at least 2 timepoints (Figure 2).ConclusionWe found links between dynamics of functional connectivity, tau deposition, and cognitive impairment across the AD spectrum using two different cohorts and longitudinal data. Specifically: 1) our method for characterizing tau PET uptake was able to capture the magnitude and spatial pattern of binding across diverse phenotypes; 2) Our data supports a role for dynamic brain connectivity in cognitive impairment due to Alzheimer’s disease; 3) Dynamic connectivity appears to be directly related to AD pathobiology as measured by tau PET. Incorporating the time‐varying nature of connectivity will be important in future studies of connectome‐based disease progression in AD.

AbstractBackgroundPast research has demonstrated that clinical EEG is sensitive to dementia associated with Alzheimer's (AD) and Parkinson’s diseases (PD), and nonfluent/agrammatic Primary Progressive Aphasia (agPPA), but not Primary Progressive Apraxia of Speech (PPAOS). However, clinical EEG studies do not quantify interactions of multiple brain areas, or network activity. Graph theory (GT) simplifies analyzing and comparing complex brain networks. Studies have shown changes in EEG GT measures in AD and PD, but have not been studied in agPPA or PPAOS. The goal of this study was to use GT to characterize network‐level changes and associations with speech and language functioning in patients with agPPA and PPAOS.MethodsScalp EEGs of 14 agPPA and 7 PPAOS patients were collected during relaxed wakefulness (10‐20 positions, 256Hz sampling, 1–200Hz bandpass filter). Eight artifact‐free epochs were selected from non‐overlapping 1024‐point epochs. Via Brainwave software, GT weighted connectivity and minimum spanning tree (MST) measures were calculated for theta and alpha frequency bands. Differences in theta MST measures between agPPA and PPAOS were assessed with Wilcoxon rank‐sum tests. Spearman correlations were computed between behavioral and all network measures across all patients.ResultsTheta frequency MST diameter and eccentricity were significantly lower and leaf was significantly higher in agPPA compared to PPAOS patients; betweenness centrality did not differ. This indicates agPPA networks are more integrated (star‐like) than PPAOS. There were significant correlations between several network measures (e.g. kappa, lambda, PLI) and indices of speech‐language functioning.ConclusionsThe results provide EEG evidence of network alteration in agPPA and PPAOS patients. The GT measures and associations with behavioral measures provide support for reduced global efficiency, or network specificity, in these patients. The correlation of language measures with lambda (path length) in the theta frequency band suggests agPPA is associated with an inefficient path length and/or increased integration. The correlation between apraxia of speech (AOS) measures and MST eccentricity in the alpha frequency band suggests that more severe AOS is associated with reduced distance between two nodes. This study demonstrates potential for EEG GT measures to quantify network changes and distinguish network changes between related but different clinical syndromes.

AbstractBackgroundAlzheimer’s disease (AD) is marked by selective disruptions to anatomically‐distinct brain regions and their connectivity which collectively support cognition. Based on a variation of pattern‐recognition analysis of FDG‐PET images, we previously developed a framework, the global functional state space (GFSS), which captures this macroscale architecture by assigning each individual a series of weightings on 10 independent patterns (eigenbrains=EBs). In this study, we applied the GFSS as a quantitative endophenotype to test whether known AD genetic risk factors were associated with variability in large‐scale neurodynamics represented by the GFSS.MethodThe study sample included 410 participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) with evidence of cognitive impairment (CDR‐global>0) and amyloidosis (positive amyloid‐PET). The GFSS was estimated for each individual using the set of 10 EBs to describe global patterns of variation in glucose metabolism. AD genetic risk variants identified in prior large consortium case‐control studies were included for analysis if the variant (or a proxy by linkage disequilibrium r 2>0.7) was assayed on both genotyping platforms used in the sample and if the minor allele frequency exceeded 1%. Genetic associations with the collective set of EBs were tested using multivariate linear regression under a dominant genetic model.ResultSignificant multivariate associations were identified for CD33 (p=7.04x10‐4), MEF2C (p=0.001), and SORL1 (p=0.02). These findings remained significant after covarying for age and sex. The strongest component loadings for CD33 (p=9.09x10‐4; std. β=‐0.16) and MEF2C (p=1.59x10‐4; std. β=‐0.18) were for EB1, with the minor allele in each case associated with a negative axis deviation in the direction of the GFSS area representing episodic memory dysfunction. SORL1 displayed its strongest loading onto EB4 (p=0.03, std. β=‐0.11). The association of APOE ɛ4 with the GFSS was marginally nonsignificant (p=0.09).ConclusionWe identified associations of risk variants in CD33, MEF2C, and SORL1 with differences in patterns of FDG hypometabolism in persons with symptomatic AD. These findings establish the GFSS as a useful endophenotype for genetic analyses, particularly as an avenue toward a mechanistic understanding of how heritable factors may influence inter‐individual variability and syndromic diversity in AD. Further investigation in larger and independent cohorts is warranted.

AbstractBackgroundThe Network Failure Quotient (NFQ) metric summarizes functional MRI network integrity. We wanted to investigate the stability of the NFQ results across time, hardware, software and protocol changes during the last decade in ADNI Resting State (RS‐) fMRI.MethodIn ADNI‐2 (2009‐2016) RS‐fMRI was acquired on Philips scanners. In ADNI‐3 (2017‐present) RS‐fMRI is acquired on GE, Philips, and Siemens systems with “Basic” and “Advanced” variants based on the availability of simultaneous multi‐slice (SMS, multi‐band) imaging. (Table 1.) Resting state images were preprocessed by performing slice timing correction, trimming volumes from the first 10 seconds of acquired data, despiking and within‐series alignment. Simultaneous bandpass and regression was performed using spatial registration parameters and their first temporal derivatives, mean WM signal, mean CSF signal and global signal. Dual regression via GIFT was applied creating regional time courses. The NFQ is calculated as NFQ=(pDMN_to_vDMN + pDMN_to_adDMN) / (pDMN +vDMN) where pDMN_to_vDMN and pDMN_to_adDMN are correlations between time courses in the posterior DMN, ventral DMN and anterior‐dorsal DMN. pDMN and vDMN are the within‐region correlations. NFQ values from 1447 scans of 739 individual study participants (Table 1) were analyzed using linear mixed effects models to account for age, clinical diagnosis, vendor, and time trends.ResultBox plots of NFQ values by diagnosis within ADNI‐phase and protocol are shown in Figure 2 and suggest important differences among clinical diagnoses and between ADNI‐2 and ADNI‐3. The mixed model analysis indicates that a 10‐year increase in age is associated with a 0.05±0.01 (mean±SD) increase in NFQ; individuals with MCI average roughly 0.05±0.01 higher than unimpaired and individuals with dementia average 0.14±0.02 higher. Differences by vendor for ADNI‐2 and ADNI‐3 are summarized in Figures 2 and 3 with effect sizes on par with these clinical factors. Mean NFQ for Phillips ADNI‐2 were highest (0.54±0.02); somewhat lower were Philips ADNI‐3 (0.51±0.02) and Siemens ADNI‐3 advanced (0.49±0.03); Siemens ADNI‐3 basic (0.40±0.02) and GE ADNI‐3 were the lowest (0.38±0.02).ConclusionNFQ values consistently increase with impairment. Vendor‐specific differences are observed and vendor changes over the life of a study introduce variability on the scale of disease‐related changes in NFQ.