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INTRODUCTION Incorporating blood‐based Alzheimer's disease biomarkers such as tau and amyloid beta (Aβ) into screening algorithms may improve screening efficiency. METHODS Plasma Aβ, phosphorylated tau (p‐tau)181, and p‐tau217 concentration levels from AHEAD 3–45 study participants were measured using mass spectrometry. Tau concentration ratios for each proteoform were calculated to normalize for inter‐individual differences. Receiver operating characteristic (ROC) curve analysis was performed for each biomarker against amyloid positivity, defined by > 20 Centiloids. Mixture of experts analysis assessed the value of including tau concentration ratios into the existing predictive algorithm for amyloid positron emission tomography status. RESULTS The area under the receiver operating curve (AUC) was 0.87 for Aβ42/Aβ40, 0.74 for phosphorylated variant p‐tau181 ratio (p‐tau181/np‐tau181), and 0.92 for phosphorylated variant p‐tau217 ratio (p‐tau217/np‐tau217). The Plasma Predicted Centiloid (PPC), a predictive model including p‐tau217/np‐tau217, Aβ42/Aβ40, age, and apolipoprotein E improved AUC to 0.95. DISCUSSION Including plasma p‐tau217/np‐tau217 along with Aβ42/Aβ40 in predictive algorithms may streamline screening preclinical individuals into anti‐amyloid clinical trials. ClinicalTrials.gov Identifier: NCT04468659 Highlights The addition of plasma phosphorylated variant p‐tau217 ratio (p‐tau217/np‐tau217) significantly improved plasma biomarker algorithms for identifying preclinical amyloid positron emission tomography positivity. Prediction performance at higher NAV Centiloid levels was improved with p‐tau217/np‐tau217. All models generated for this study are incorporated into the Plasma Predicted Centiloid (PPC) app for public use.

Background The development of blood-based biomarker tests that are accurate and robust for Alzheimer’s disease (AD) pathology have the potential to aid clinical diagnosis and facilitate enrollment in AD drug trials. We developed a high-resolution mass spectrometry (MS)-based test that quantifies plasma Aβ42 and Aβ40 concentrations and identifies the ApoE proteotype. We evaluated robustness, clinical performance, and commercial viability of this MS biomarker assay for distinguishing brain amyloid status. Methods We used the novel MS assay to analyze 414 plasma samples that were collected, processed, and stored using site-specific protocols, from six independent US cohorts. We used receiver operating characteristic curve (ROC) analyses to assess assay performance and accuracy for predicting amyloid status (positive, negative, and standard uptake value ratio; SUVR). After plasma analysis, sites shared brain amyloid status, defined using diverse, site-specific methods and cutoff values; amyloid PET imaging using various tracers or CSF Aβ42/40 ratio. Results Plasma Aβ42/40 ratio was significantly ( p  < 0.001) lower in the amyloid positive vs. negative participants in each cohort. The area under the ROC curve (AUC-ROC) was 0.81 (95% CI = 0.77–0.85) and the percent agreement between plasma Aβ42/40 and amyloid positivity was 75% at the optimal (Youden index) cutoff value. The AUC-ROC (0.86; 95% CI = 0.82–0.90) and accuracy (81%) for the plasma Aβ42/40 ratio improved after controlling for cohort heterogeneity. The AUC-ROC (0.90; 95% CI = 0.87–0.93) and accuracy (86%) improved further when Aβ42/40, ApoE4 copy number and participant age were included in the model. Conclusions This mass spectrometry-based plasma biomarker test: has strong diagnostic performance; can accurately distinguish brain amyloid positive from amyloid negative individuals; may aid in the diagnostic evaluation process for Alzheimer’s disease; and may enhance the efficiency of enrolling participants into Alzheimer’s disease drug trials.

Objectives Concentrations of amyloid‐β peptides (Aβ42/Aβ40) and neurofilament light (NfL) can be measured in plasma or cerebrospinal fluid (CSF) and are associated with Alzheimer’s disease brain pathology and cognitive impairment. This study directly compared plasma and CSF measures of Aβ42/Aβ40 and NfL as predictors of cognitive decline. Methods Participants were 65 years or older and cognitively normal at baseline with at least one follow‐up cognitive assessment. Analytes were measured with the following types of assays: plasma Aβ42/Aβ40, immunoprecipitation‐mass spectrometry; plasma NfL, Simoa; CSF Aβ42/Aβ40, automated immunoassay; CSF NfL plate‐based immunoassay. Mixed effects models evaluated the global cognitive composite score over a maximum of 6 years as predicted by the fluid biomarkers. Results Analyses included 371 cognitively normal participants, aged 72.7 ± 5.2 years (mean ± standard deviation) with an average length of follow‐up of 3.9 ± 1.6 years. Standardized concentrations of biomarkers were associated with annualized cognitive change: plasma Aβ42/Aβ40, 0.014 standard deviations (95% confidence intervals 0.002 to 0.026); CSF Aβ42/Aβ40, 0.020 (0.008 to 0.032); plasma Nfl, −0.018 (−0.030 to −0.005); and CSF NfL, −0.024 (−0.036 to −0.012). Power analyses estimated that 266 individuals in each treatment arm would be needed to detect a 50% slowing of decline if identified by abnormal plasma measures versus 229 for CSF measures. Interpretation Both plasma and CSF measures of Aβ42/Aβ40 and NfL predicted cognitive decline. A clinical trial that enrolled individuals based on abnormal plasma Aβ42/Aβ40 and NfL levels would require only a marginally larger cohort than if CSF measures were used.

Receptor kinases play important roles in plant growth and development, but only few of them have been functionally characterized in depth. Over the past decade CRINKLY 4 (CR4)-related research has peaked as a result of a newly discovered role of ARABIDOPSIS CR4 (ACR4) in the root. Here, we comprehensively review the available (A)CR4 literature and describe its role in embryo, seed, shoot, and root development, but we also flag an unexpected role in plant defence. In addition, we discuss ACR4 domains and protein structure, describe known ACR4-interacting proteins and substrates, and elaborate on the transcriptional regulation of ACR4. Finally, we address the missing knowledge in our understanding of ACR4 signalling.

Arabidopsis CRINKLY4 (ACR4) is a receptor-like kinase (RLK) involved in the global development of the plant. The Arabidopsis genome encodes four homologs of ACR4 that contain sequence similarity and analogous architectural elements to ACR4, termed Arabidopsis CRINKLY4 Related (AtCRRs) proteins. Additionally, a signaling module has been previously proposed including a postulated peptide ligand, CLE40, the ACR4 RLK, and the WOX5 transcription factor that engage in a possible feedback mechanism controlling stem cell differentiation. However, little biochemical evidence is available to ascertain the molecular aspects of receptor heterodimerization and the role of phosphorylation in these interactions. Therefore, we have undertaken an investigation of the in vitro interactions between the intracellular domains (ICD) of ACR4, the CRRs and WOX5. We demonstrate that interaction can occur between ACR4 and all four CRRs in the unphosphorylated state. However, phosphorylation dependency is observed for the interaction between ACR4 and CRR3. Furthermore, sequence analysis of the ACR4 gene family has revealed a conserved 'KDSAF' motif that may be involved in protein-protein interactions among the receptor family. We demonstrate that peptides harboring this conserved motif in CRR3 and CRK1are able to bind to the ACR4 kinase domain. Our investigations also indicate that the ACR4 ICD can interact with and phosphorylate the transcription factor WOX5.

Alzheimer’s disease (AD) is a progressive irreversible neurodegenerative disorder that represents a major global public health concern. Traditionally, AD is diagnosed using cerebrospinal fluid biomarker analysis or brain imaging modalities. Recently, less burdensome, more widely available blood biomarker (BBM) assays for amyloid-beta (Aβ42/40) and phosphorylated-tau concentrations have been found to accurately identify the presence/absence of brain amyloid plaques and tau tangles and have helped to streamline AD diagnosis. However, few BBMs have been rigorously analytically validated. Herein, we report the analytical validation of a novel liquid chromatography–tandem mass spectrometry (LC-MS/MS) multiplex method for quantifying plasma phosphorylated-tau217 (p-tau217) and non-phosphorylated-tau217 (np-tau217) peptide concentrations. We combined the p-tau217/np-tau217 concentrations ratio (%p-tau217) and the previously validated LC-MS/MS multiplex assay for plasma Aβ42/40 into a new multianalyte assay with algorithmic analysis (MAAA; PrecivityAD2™ test) that identifies brain amyloid status based on brain amyloid positron emission tomography. We found (a) the %p-tau217 assay is precise, accurate, sensitive, and linear over a wide analytical measurement range, and free from carryover and interference; (b) the pre-analytical specimen collection, processing, storage, and shipping conditions that maintain plasma tau peptide stability; and (c) using the measured analytical imprecision for plasma Aβ42/40 and p-tau217/np-tau217 levels in a worst-case scenario model, the PrecivityAD2 test algorithm for amyloid pathology classification changed for only 3.5% of participants from brain amyloid positive to negative, or from negative to positive. The plasma sample preparation and LC-MS/MS methods underlying the PrecivityAD2 test are suitable for use in the clinical laboratory and valid for the test’s intended purpose: to aid in the diagnostic evaluation of individuals aged 55 and older with signs or symptoms of mild cognitive impairment or dementia.

Background The amyloid probability score (APS) is the model read‐out of the analytically validated mass spectrometry‐based PrecivityAD® blood test that incorporates the plasma Aβ42/40 ratio, ApoE proteotype, and age to identify the likelihood of brain amyloid plaques among cognitively impaired individuals being evaluated for Alzheimer's disease. Purpose This study aimed to provide additional independent evidence that the pre‐established APS algorithm, along with its cutoff values, discriminates between amyloid positive and negative individuals. Methods The diagnostic performance of the PrecivityAD test was analyzed in a cohort of 200 nonrandomly selected Australian Imaging, Biomarker & Lifestyle Flagship Study of Aging (AIBL) study participants, who were either cognitively impaired or healthy controls, and for whom a blood sample and amyloid PET imaging were available. Results In a subset of the dataset aligned with the Intended Use population (patients aged 60 and older with CDR ≥0.5), the pre‐established APS algorithm predicted amyloid PET with a sensitivity of 84.9% (CI: 72.9–92.1%) and specificity of 96% (CI: 80.5–99.3%), exclusive of 13 individuals for whom the test was inconclusive. Interpretation The study shows individuals with a high APS are more likely than those with a low APS to have abnormal amounts of amyloid plaques and be on an amyloid accumulation trajectory, a dynamic and evolving process characteristic of progressive AD pathology. Exploratory data suggest APS retains its diagnostic performance in healthy individuals, supporting further screening studies in the cognitively unimpaired.

BACKGROUND With the availability of disease‐modifying therapies for Alzheimer's disease (AD), it is important for clinicians to have tests to aid in AD diagnosis, especially when the presence of amyloid pathology is a criterion for receiving treatment. METHODS High‐throughput, mass spectrometry‐based assays were used to measure %p‐tau217 and amyloid beta (Aβ)42/40 ratio in blood samples from 583 individuals with suspected AD (53% positron emission tomography [PET] positive by Centiloid > 25). An algorithm (PrecivityAD2 test) was developed using these plasma biomarkers to identify brain amyloidosis by PET. RESULTS The area under the receiver operating characteristic curve (AUC‐ROC) for %p‐tau217 (0.94) was statistically significantly higher than that for p‐tau217 concentration (0.91). The AUC‐ROC for the PrecivityAD2 test output, the Amyloid Probability Score 2, was 0.94, yielding 88% agreement with amyloid PET. Diagnostic performance of the APS2 was similar by ethnicity, sex, age, and apoE4 status. DISCUSSION The PrecivityAD2 blood test showed strong clinical validity, with excellent agreement with brain amyloidosis by PET.

The diagnostic evaluation for Alzheimer disease may be improved by a blood-based diagnostic test identifying presence of brain amyloid plaque pathology. To determine the clinical performance associated with a diagnostic algorithm incorporating plasma amyloid-β (Aβ) 42:40 ratio, patient age, and apoE proteotype to identify brain amyloid status. This cohort study includes analysis from 2 independent cross-sectional cohort studies: the discovery cohort of the Plasma Test for Amyloidosis Risk Screening (PARIS) study, a prospective add-on to the Imaging Dementia-Evidence for Amyloid Scanning study, including 249 patients from 2018 to 2019, and MissionAD, a dataset of 437 biobanked patient samples obtained at screenings during 2016 to 2019. Data were analyzed from May to November 2020. Amyloid detected in blood and by positron emission tomography (PET) imaging. The main outcome was the diagnostic performance of plasma Aβ42:40 ratio, together with apoE proteotype and age, for identifying amyloid PET status, assessed by accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). All 686 participants (mean [SD] age 73.2 [6.3] years; 368 [53.6%] men; 378 participants [55.1%] with amyloid PET findings) had symptoms of mild cognitive impairment or mild dementia. The AUC of plasma Aβ42:40 ratio for PARIS was 0.79 (95% CI, 0.73-0.85) and 0.86 (95% CI, 0.82-0.89) for MissionAD. Ratio cutoffs for Aβ42:40 based on the Youden index were similar between cohorts (PARIS: 0.089; MissionAD: 0.092). A logistic regression model (LRM) incorporating Aβ42:40 ratio, apoE proteotype, and age improved diagnostic performance within each cohort (PARIS: AUC, 0.86 [95% CI, 0.81-0.91]; MissionAD: AUC, 0.89 [95% CI, 0.86-0.92]), and overall accuracy was 78% (95% CI, 72%-83%) for PARIS and 83% (95% CI, 79%-86%) for MissionAD. The model developed on the prospectively collected samples from PARIS performed well on the MissionAD samples (AUC, 0.88 [95% CI, 0.84-0.91]; accuracy, 78% [95% CI, 74%-82%]). Training the LRM on combined cohorts yielded an AUC of 0.88 (95% CI, 0.85-0.91) and accuracy of 81% (95% CI, 78%-84%). The output of this LRM is the Amyloid Probability Score (APS). For clinical use, 2 APS cutoff values were established yielding 3 categories, with low, intermediate, and high likelihood of brain amyloid plaque pathology. These findings suggest that this blood biomarker test could allow for distinguishing individuals with brain amyloid-positive PET findings from individuals with amyloid-negative PET findings and serve as an aid for Alzheimer disease diagnosis.