Citations

Abstract Eosinophils isolated from normal individuals were cultured in the presence of human rIL-5 (hrIL-5) for up to 14 days, and the effects of this exposure were determined. First, the hrIL-5-cultured eosinophils were activated and degranulated more readily than freshly isolated eosinophils. For example, eosinophils cultured for 7 days with hrIL-5 released 30 and 10% of granule eosinophil-derived neurotoxin (EDN) when exposed to Sepharose 4B beads coupled to secretory IgA and IgG, respectively, whereas freshly isolated eosinophils released only 19 and 4%, respectively, of their EDN in response to the same stimuli. Degranulation of hrIL-5-cultured eosinophils was not augmented by further exposure to hrIL-5, whereas degranulation of freshly isolated cells to secretory IgA and IgG beads was increased by exposure to hrIL-5. Second, eosinophils cultured with hrIL-5 had prolonged viability in vitro. For example, after four days of culture with 50 U/ml of hrIL-5, 86% of eosinophils were viable compared to 12% in medium alone. Third, hrIL-5-cultured eosinophils became hypodense, and electron microscopy showed that they contained granules with core and matrix lucency and with evidence of granule fusion. Fourth, hrIL-5-cultured eosinophils spontaneously lost 30 to 60% of their EDN, eosinophil cationic protein, and eosinophil peroxidase and about 50% of their eosinophil granule major basic protein content compared to freshly isolated eosinophils, and all four of the granule proteins were released into the culture medium. Fifth, detailed studies of eosinophils cultured in hrIL-5 showed that 89 +/- 10% of the starting quantity of EDN could be recovered at 7 days. Whereas 99 +/- 1% of the EDN at day 0 was cell associated, by 7 days 60 +/- 9% was in the cell supernatants. Thus, hrIL-5 activates eosinophils, increases their viability, decreases their density, and their content of granule proteins and causes release of the granule proteins into culture fluids. The striking loss of granule proteins during culture with hrIL-5 may be an important mechanism for deposition of these cationic toxins in various diseases where IL-5 plays a role.

Abstract The NK cell activating receptor NKG2D plays a critical role in the destruction of malignant cells, but many of the cell-signaling mechanisms governing NKG2D-mediated cellular cytotoxicity are unknown. We have identified an NKG2D-mediated signaling pathway that governs both conjugate formation and cytotoxic granule polarization. We demonstrate that an interaction between the regulatory subunit of PI3K, p85, and the adaptor protein CrkL is required for efficient NKG2D-mediated cellular cytotoxicity. We show decreased NK cell - target cell conjugate formation in NK cells treated with PI3K inhibitors or depleted of CrkL. Independent of adhesion, we find that microtubule organization center polarization toward target cells expressing the NKG2D ligand MICA or toward anti-NKG2D coated beads is impaired in the absence of CrkL. Antibody-stimulated granule release is also impaired in NK cells depleted of CrkL. Furthermore, our data indicate that the small Ras-family GTPase Rap1 is required for NK cell killing and is activated downstream of NKG2D engagement in a PI3K- and CrkL-dependent manner. Taken together, our data identify an NKG2D-activated signaling pathway that collectively orchestrates NK cell adhesion, cell polarization and granule release.

Abstract We tested the effects of different cytokines on IgA- and IgG-induced eosinophil degranulation in vitro to determine the potential interaction between eosinophils and mononuclear cells. Purified normodense eosinophils were incubated with cytokines (including rIL-1, rIL-2, rIL-3, rIL-4, rIL-5, rIL-6, IFN-gamma, granulocyte-macrophage CSF stimulating factor (GM-CSF), and TNF) for 1 to 3 h after which Ig-coupled Sepharose 4B beads were added as targets and the mixtures were incubated with the eosinophils at 37 degrees C for 4 h. The Ig used were secretory IgA (sIgA), serum IgA and IgG, and myeloma IgA and IgG. The release of eosinophil-derived neurotoxin (EDN) was measured by RIA as an index of degranulation. rIL-5 was the most potent enhancer of Ig-induced degranulation and increased EDN release by 48% for sIgA and 136% for IgG. The effect of rIL-5 appeared as quickly as 15 min after incubation of eosinophils, sIgA beads and IL-5. GM-CSF and rIL-3 also enhanced Ig-induced EDN release but less potently than rIL-5. GM-CSF and rIL-5 by themselves induced a small but significant release of EDN from eosinophils in the absence of Ig-coated beads; rIL-3 did not. However, IFN-gamma suppressed sIgA-induced EDN release by 23%. The other cytokines did not have any effect on eosinophil degranulation. These results suggest that cytokines which induce eosinophil differentiation and proliferation during hematopoiesis also enhance the effector function of mature eosinophils and that IFN-gamma partially down-regulates eosinophil degranulation.

Abstract Background Glial fibrillary acidic protein (GFAP) is a promising candidate blood‐based biomarker for Alzheimer's disease (AD) diagnosis and prognostication. The timing of its disease‐associated changes, its clinical correlates, and biofluid‐type dependency will influence its clinical utility. Methods We evaluated plasma, serum, and cerebrospinal fluid (CSF) GFAP in families with autosomal dominant AD (ADAD), leveraging the predictable age at symptom onset to determine changes by stage of disease. Results Plasma GFAP elevations appear a decade before expected symptom onset, after amyloid beta (Aβ) accumulation and prior to neurodegeneration and cognitive decline. Plasma GFAP distinguished Aβ‐positive from Aβ‐negative ADAD participants and showed a stronger relationship with Aβ load in asymptomatic than symptomatic ADAD. Higher plasma GFAP was associated with the degree and rate of neurodegeneration and cognitive impairment. Serum GFAP showed similar relationships, but these were less pronounced for CSF GFAP. Conclusion Our findings support a role for plasma GFAP as a clinical biomarker of Aβ‐related astrocyte reactivity that is associated with cognitive decline and neurodegeneration. Highlights Plasma glial fibrillary acidic protein (GFAP) elevations appear a decade before expected symptom onset in autosomal dominant Alzheimer's disease (ADAD). Plasma GFAP was associated to amyloid positivity in asymptomatic ADAD. Plasma GFAP increased with clinical severity and predicted disease progression. Plasma and serum GFAP carried similar information in ADAD, while cerebrospinal fluid GFAP did not.

AbstractBackgroundCyclin E1 (CCNE1) is a potential predictive marker and therapeutic target in tubo‐ovarian high‐grade serous carcinoma (HGSC). Smaller studies have revealed unfavorable associations for CCNE1 amplification and CCNE1 overexpression with survival, but to date no large‐scale, histotype‐specific validation has been performed. The hypothesis was that high‐level amplification of CCNE1 and CCNE1 overexpression, as well as a combination of the two, are linked to shorter overall survival in HGSC.MethodsWithin the Ovarian Tumor Tissue Analysis consortium, amplification status and protein level in 3029 HGSC cases and mRNA expression in 2419 samples were investigated.ResultsHigh‐level amplification (>8 copies by chromogenic in situ hybridization) was found in 8.6% of HGSC and overexpression (>60% with at least 5% demonstrating strong intensity by immunohistochemistry) was found in 22.4%. CCNE1 high‐level amplification and overexpression both were linked to shorter overall survival in multivariate survival analysis adjusted for age and stage, with hazard stratification by study (hazard ratio [HR], 1.26; 95% CI, 1.08‐1.47, p = .034, and HR, 1.18; 95% CI, 1.05‐1.32, p = .015, respectively). This was also true for cases with combined high‐level amplification/overexpression (HR, 1.26; 95% CI, 1.09‐1.47, p = .033). CCNE1 mRNA expression was not associated with overall survival (HR, 1.00 per 1‐SD increase; 95% CI, 0.94‐1.06; p = .58). CCNE1 high‐level amplification is mutually exclusive with the presence of germline BRCA1/2 pathogenic variants and shows an inverse association to RB1 loss.ConclusionThis study provides large‐scale validation that CCNE1 high‐level amplification is associated with shorter survival, supporting its utility as a prognostic biomarker in HGSC.

AbstractBackgroundMonitoring changes in cognitive impairment is an important aspect of care for aging patients. Traditionally, this is done with pen‐and‐paper assessments such as the Mini‐Mental State Exam (MMSE). EEG is a low‐cost and widely available neurophysiology tool that provides a data‐rich measure of brain health, but signal complexity has limited its impact. Advances in machine learning bring new possibilities to decode those complex signals and leverage EEG in patient care. Here, we engineer novel EEG features for assessing brain health with the [Banded Fractal Variability] (BFV) technique. We use [BFV] and other EEG metrics to assess patients’ cognitive function via a continuous score (a [Cognitive Impairment Index], or [CII]). We validate the diagnostic value of the EEG features by comparing our model against reduced models lacking the EEG features.MethodThe patient sample (N=97, mean (sd) age = 69.1 (11.0), 52.6% female) was collected at the Pacific Brain Health Center. Diagnostic categories included subjective cognitive impairment (n=44), mild cognitive impairment (n=26), and dementia (n=27). Mixed disease (AD, DLB, FTD, vascular, diabetes) was common in the impaired sample, and 39.6% were diagnosed with AD. We developed a continuous [CII] model trained on patients’ MMSE scores (range = 11 to 30, mean = 26.6) using a gradient boosting model with [Banded Fractal Variability] and other EEG features computed from clinical wake EEGs. Model development and testing took place within a rigorous nested cross‐validation (NCV) procedure to prevent overfitting. Performance was compared to reduced (no‐EEG features) models using the non‐parametric Wilcoxon sign‐rank test.ResultThe approach showed a Mean Absolute Error of 1.98 (sd = 0.28) across 5 test folds in the NCV outer loops, significantly outperforming both the baseline intercept‐only model (p = 0.031) and the demographics (age and years of education) model (p = 0.031).ConclusionThese preliminary results suggest that our [CII] derived from machine‐learning and quantitative EEG using [BFV] is a viable approach to assess and track patients’ cognitive impairment regardless of disease status across all impairment levels, raising the possibility that a brief EEG scan could shed light on patient health normally accessible only through extensive cognitive and neuropsychological testing.

AbstractBackgroundAlzheimer’s disease (AD) lacks a fast, easy, reliable, and inexpensive method of diagnosis. Currently, diagnosis is based on time‐consuming behavioral tests and the exclusion of other potential causes of impairment. Several biomarkers show good or promising diagnostic performance (e.g. CSF, tau PET, MRI, blood), but are either expensive, invasive, or still in development and while some can detect preclinical disease stages, all appear slow to change relative to the rate of cognitive decline. Here we develop a prototype diagnostic classifier based on novel metrics of brain activity in resting state electroencephalography (EEG) that correlates well with mental status.MethodArchival resting‐state EEG recordings of older adults (N=248) came from a memory clinic and university‐based clinic with a range of clinical diagnoses including subjective cognitive impairment (SCI), mild cognitive impairment (MCI), and dementia, representing AD, vascular dementia, and Lewy body dementia, TBI, and depression. We developed XGBoost classifiers to detect AD using EEG, age, and sex under increasingly challenging conditions. We computed metrics of periodic and aperiodic brain activity using the FOOOF algorithm. Furthermore, we developed a novel technique called [Banded Fractal Variability], which yields a set of features based on fluctuations in the fractal dimension within canonical frequency bands. We trained classifiers using cross‐validation to avoid overfitting during hyperparameter selection.ResultAlong with ROCAUC, we report an optimal sensitivity, specificity, and accuracy for the point on the ROC curve that maximizes Youden’s j. The classification tasks were healthy vs. probable AD (ROCAUC = 98%, sensitivity = 90%, specificity = 99%, accuracy = 96%), SCI vs. mild AD (ROCAUC = 89%, sensitivity = 76%, specificity = 87%, accuracy = 84%), and AD vs. other pathologies (no AD diagnosis) in MCI and dementia patients (ROCAUC = 82%, sensitivity = 72%, specificity = 87%, accuracy = 80%). All ROCAUC values were stronger than would be expected by chance (ps < 0.001).ConclusionThese preliminary results suggest that AD could be diagnosed in the clinic on the basis of machine‐learning classifiers and resting‐state EEG. Furthermore, they demonstrate that [Banded Fractal Variability] carries clinically‐relevant information about AD.

Objective Large vestibular aqueduct (LVA) is the most common inner ear dysplasia identified in patients with hearing loss. Our objective was to systematically quantify LVA morphologies and correlate imaging findings with established audiometric outcomes. Study Design Retrospective review. Setting Tertiary referral center. Patients Patients with large vestibular aqueduct identified radiographically, with or without hearing loss. Intervention(s) Diagnostic only. Main Outcome Measure(s) Vestibular aqueduct (VA) width at midpoint, width at external aperture, and length were measured on cross-sectional imaging. Morphology was classified as type I (borderline), type II (tubular), or type III (funneled). Audiometric endpoints included air/bone conduction, pure tone averages, and air-bone gaps at 250 and 500 Hz. Statistical associations were evaluated using linear regression models, adjusted for age at first audiogram and sex. Results One hundred seventeen patients (197 ears) were included, with mean age at first audiogram of 22.2 years (standard deviation, 21.7 yr). Imaging features associated with poor audiometric outcomes were increasing VA width at midpoint and external aperture, decreasing VA length, dilated extraosseous endolymphatic sac, cochleovestibular malformations, and increasing VA type (III > II > I). Conclusions Quantitative LVA measurements and a standardized morphologic classification system aid in prediction of early audiometric endpoints.