Validation of Plasma Proteomic Biomarkers Relating to Brain Amyloid Burden in the EMIF-Alzheimer's Disease Multimodal Biomarker Discovery Cohort

Integrating amyloid and tau imaging with proteomics and genomics in Alzheimer's disease

Alzheimer's disease (AD) is the most common neurodegenerative disease and is characterized by the aggregation of β-amyloid (Aβ) and tau in the brain. Breakthroughs in disease-modifying treatments targeting Aβ bring new hope for the management of AD. But to effectively modify and someday even prevent AD, a better understanding is needed of the biological mechanisms that underlie and link Aβ and tau in AD. Developments of high-throughput omics, including genomics, proteomics, and transcriptomics, together with molecular imaging of Aβ and tau with positron emission tomography (PET), allow us to discover and understand the biological pathways that regulate the aggregation and spread of Aβ and tau in living humans. The field of integrated omics and PET studies of Aβ and tau in AD is growing rapidly. We here provide an update of this field, both in terms of biological insights and in terms of future clinical implications of integrated omics-molecular imaging studies.

Blood DNA methylomic signatures associated with CSF biomarkers of Alzheimer's disease in the EMIF-AD study

INTRODUCTION

We investigated blood DNA methylation patterns associated with 15 well-established cerebrospinal fluid (CSF) biomarkers of Alzheimer's disease (AD) pathophysiology, neuroinflammation, and neurodegeneration.

METHODS

We assessed DNA methylation in 885 blood samples from the European Medical Information Framework for Alzheimer's Disease (EMIF-AD) study using the EPIC array.

RESULTS

We identified Bonferroni-significant differential methylation associated with CSF YKL-40 (five loci) and neurofilament light chain (NfL; seven loci) levels, with two of the loci associated with CSF YKL-40 levels correlating with plasma YKL-40 levels. A co-localization analysis showed shared genetic variants underlying YKL-40 DNA methylation and CSF protein levels, with evidence that DNA methylation mediates the association between genotype and protein levels. Weighted gene correlation network analysis identified two modules of co-methylated loci correlated with several amyloid measures and enriched in pathways associated with lipoproteins and development.

DISCUSSION

We conducted the most comprehensive epigenome-wide association study (EWAS) of AD-relevant CSF biomarkers to date. Future work should explore the relationship between YKL-40 genotype, DNA methylation, and protein levels in the brain.

HIGHLIGHTS

Blood DNA methylation was assessed in the EMIF-AD MBD study. Epigenome-wide association studies (EWASs) were performed for 15 Alzheimer's disease (AD)-relevant cerebrospinal fluid (CSF) biomarker measures. Five Bonferroni-significant loci were associated with YKL-40 levels and seven with neurofilament light chain (NfL). DNA methylation in YKL-40 co-localized with previously reported genetic variation. DNA methylation potentially mediates the effect of single-nucleotide polymorphisms (SNPs) in YKL-40 on CSF protein levels.

Plasmodium knowlesi infection is associated with elevated circulating biomarkers of brain injury and endothelial activation

Introduction

Malaria remains a major public health concern with substantial morbidity and mortality worldwide. In Malaysia, the emergence of Plasmodium knowlesi has led to a surge in zoonotic malaria cases and deaths in recent years. Signs of cerebral involvement have been observed in a non-comatose, fatal case of severe knowlesi infection, but the potential impact of this malaria species on the brain remains underexplored. To address this gap, we investigated circulating levels of brain injury, inflammation, and vascular biomarkers in a cohort of knowlesi-infected patients and controls.

Methods

Archived plasma samples from 19 patients with confirmed symptomatic knowlesi infection and 19 healthy, age-matched controls from Peninsular Malaysia were analysed. A total of 52 plasma biomarkers of brain injury, inflammation, and vascular activation were measured using Luminex and SIMOA assays. Wilcoxon tests were used to examine group differences, and biomarker profiles were explored through hierarchical clustering heatmap analysis.

Results

Bonferroni-corrected analyses revealed significantly elevated brain injury biomarker levels in knowlesi-infected patients, including S100B (p<0.0001), Tau (p=0.0007), UCH-L1 (p<0.0001), αSyn (p<0.0001), Park7 (p=0.0006), NRGN (p=0.0022), and TDP-43 (p=0.005). Compared to controls, levels were lower in the infected group for BDNF (p<0.0001), CaBD (p<0.0001), CNTN1 (p<0.0001), NCAM-1 (p<0.0001), GFAP (p=0.0013), and KLK6 (p=0.0126). Hierarchical clustering revealed distinct group profiles for circulating levels of brain injury and vascular activation biomarkers.

Conclusions

Our findings highlight for the first time the impact of Plasmodium knowlesi infection on the brain, with distinct alterations in cerebral injury and endothelial activation biomarker profiles compared to healthy controls. Further studies are warranted to investigate the pathophysiology and clinical significance of these altered surrogate markers, through both neuroimaging and long-term neurocognitive assessments.

Predicting AT(N) pathologies in Alzheimer's disease from blood-based proteomic data using neural networks

BACKGROUND AND OBJECTIVE

Blood-based biomarkers represent a promising approach to help identify early Alzheimer's disease (AD). Previous research has applied traditional machine learning (ML) to analyze plasma omics data and search for potential biomarkers, but the most modern ML methods based on deep learning has however been scarcely explored. In the current study, we aim to harness the power of state-of-the-art deep learning neural networks (NNs) to identify plasma proteins that predict amyloid, tau, and neurodegeneration (AT[N]) pathologies in AD.

METHODS

We measured 3,635 proteins using SOMAscan in 881 participants from the European Medical Information Framework for AD Multimodal Biomarker Discovery study (EMIF-AD MBD). Participants underwent measurements of brain amyloid β (Aβ) burden, phosphorylated tau (p-tau) burden, and total tau (t-tau) burden to determine their AT(N) statuses. We ranked proteins by their association with Aβ, p-tau, t-tau, and AT(N), and fed the top 100 proteins along with age and apolipoprotein E (APOE) status into NN classifiers as input features to predict these four outcomes relevant to AD. We compared NN performance of using proteins, age, and APOE genotype with performance of using age and APOE status alone to identify protein panels that optimally improved the prediction over these main risk factors. Proteins that improved the prediction for each outcome were aggregated and nominated for pathway enrichment and protein-protein interaction enrichment analysis.

RESULTS

Age and APOE alone predicted Aβ, p-tau, t-tau, and AT(N) burden with area under the curve (AUC) scores of 0.748, 0.662, 0.710, and 0.795. The addition of proteins significantly improved AUCs to 0.782, 0.674, 0.734, and 0.831, respectively. The identified proteins were enriched in five clusters of AD-associated pathways including human immunodeficiency virus 1 infection, p53 signaling pathway, and phosphoinositide-3-kinase-protein kinase B/Akt signaling pathway.

CONCLUSION

Combined with age and APOE genotype, the proteins identified have the potential to serve as blood-based biomarkers for AD and await validation in future studies. While the NNs did not achieve better scores than the support vector machine model used in our previous study, their performances were likely limited by small sample size.

A Perspective: Challenges in Dementia Research

Although dementia is a common and devastating disease that has been studied intensely for more than 100 years, no effective disease modifying treatment has been found. At this impasse, new approaches are important. The purpose of this paper is to provide, in the context of current research, one clinician's perspective regarding important challenges in the field in the form of specific challenges. These challenges not only illustrate the scope of the problems inherent in finding treatments for dementia, but can also be specific targets to foster discussion, criticism and new research. One common theme is the need to transform research activities from small projects in individual laboratories/clinics to larger multinational projects, in which each clinician and researcher works as an integral part. This transformation will require collaboration between researchers, large corporations, regulatory/governmental authorities and the general population, as well as significant financial investments. However, the costs of transforming the approach are small in comparison with the cost of dementia.

A new generation of AD biomarkers: 2019 to 2021

Alzheimer's disease (AD) is the most common form of dementia and cases are rising worldwide. The effort to fight this disease is hampered by a lack of disease-modifying treatments and the absence of an early, accurate diagnostic tool. Neuropathology begins years or decades before symptoms occur and, upon onset of symptoms, diagnosis can take a year or more. Such delays postpone treatment and make research into the early stages of the disease difficult. Ideally, clinicians require a minimally invasive test that can detect AD in its early stages, before cognitive symptoms occur. Advances in proteomic technologies have facilitated the study of promising biomarkers of AD. Over the last two years (2019-2021) studies have identified and validated many species which can be measured in cerebrospinal fluid (CSF), plasma, or in both fluids, and which have a high predictive value for AD. We herein discuss proteins which have been highlighted as promising biomarkers of AD in the last two years, and consider implications for future research within the research framework of the amyloid (A), tau (T), neurodegeneration (N) scoring system. We review recently identified species of amyloid and tau which may improve diagnosis when used in combination with current measures such as amyloid-beta-42 (Aβ42), total tau (t-tau) and phosphorylated tau (p-tau). In addition, several proteins have been identified as likely proxies for neurodegeneration, including neurofilament light (NfL), synaptosomal-associated protein 25 (SNAP-25) and neurogranin (NRGN). Finally, proteins originating from diverse processes such as neuroinflammation, lipid transport and mitochondrial dysfunction could aid in both AD diagnosis and patient stratification.

Diagnostic Accuracy of Blood-Based Biomarker Panels: A Systematic Review

Background

Because of high prevalence of Alzheimer's disease (AD) in low- and middle-income countries (LMICs), there is an urgent need for inexpensive and minimally invasive diagnostic tests to detect biomarkers in the earliest and asymptomatic stages of the disease. Blood-based biomarkers are predicted to have the most impact for use as a screening tool and predict the onset of AD, especially in LMICs. Furthermore, it has been suggested that panels of markers may perform better than single protein candidates.

Methods

Medline/Pubmed was searched to identify current relevant studies published from January 2016 to December 2020. We included all full-text articles examining blood-based biomarkers as a set of protein markers or panels to aid in AD's early diagnosis, prognosis, and characterization.

Results

Seventy-six articles met the inclusion criteria for systematic review. Majority of the studies reported plasma and serum as the main source for biomarker determination in blood. Protein-based biomarker panels were reported to aid in AD diagnosis and prognosis with better accuracy than individual biomarkers. Conventional (amyloid-beta and tau) and neuroinflammatory biomarkers, such as amyloid beta-42, amyloid beta-40, total tau, phosphorylated tau-181, and other tau isoforms, were the most represented. We found the combination of amyloid beta-42/amyloid beta-40 ratio and APOEε4 status to be most represented with high accuracy for predicting amyloid beta-positron emission tomography status.

Conclusion

Assessment of Alzheimer's disease biomarkers in blood as a non-invasive and cost-effective alternative will potentially contribute to early diagnosis and improvement of therapeutic interventions. Given the heterogeneous nature of AD, combination of markers seems to perform better in the diagnosis and prognosis of the disease than individual biomarkers.

Blood-Based ATN Biomarkers of Alzheimer's Disease: A Meta-Analysis

BACKGROUND

The Amyloid Tau Neurodegeneration (ATN) framework was proposed to define the biological state underpinning Alzheimer's disease (AD). Blood-based biomarkers offer a scalable alternative to the costly and invasive currently available biomarkers.

OBJECTIVE

In this meta-analysis we sought to assess the diagnostic performance of plasma amyloid (Aβ40, Aβ42, Aβ42/40 ratio), tangle (p-tau181), and neurodegeneration (total tau [t-tau], neurofilament light [NfL]) biomarkers.

METHODS

Electronic databases were screened for studies reporting biomarker concentrations for AD and control cohorts. Biomarker performance was examined by random-effect meta-analyses based on the ratio between biomarker concentrations in patients and controls.

RESULTS

83 studies published between 1996 and 2020 were included in the analyses. Aβ42/40 ratio as well as Aβ42 discriminated AD patients from controls when using novel platforms such as immunomagnetic reduction (IMR). We found significant differences in ptau-181 concentration for studies based on single molecule array (Simoa), but not for studies based on IMR or ELISA. T-tau was significantly different between AD patients and control in IMR and Simoa but not in ELISA-based studies. In contrast, NfL differentiated between groups across platforms. Exosome studies showed strong separation between patients and controls for Aβ42, t-tau, and p-tau181.

CONCLUSION

Currently available assays for sampling plasma ATN biomarkers appear to differentiate between AD patients and controls. Novel assay methodologies have given the field a significant boost for testing these biomarkers, such as IMR for Aβ, Simoa for p-tau181. Enriching samples through extracellular vesicles shows promise but requires further validation.

Plasma Proteomic Biomarkers Relating to Alzheimer's Disease: A Meta-Analysis Based on Our Own Studies

Background and Objective: Plasma biomarkers for the diagnosis and stratification of Alzheimer's disease (AD) are intensively sought. However, no plasma markers are well established so far for AD diagnosis. Our group has identified and validated various blood-based proteomic biomarkers relating to AD pathology in multiple cohorts. The study aims to conduct a meta-analysis based on our own studies to systematically assess the diagnostic performance of our previously identified blood biomarkers. Methods: To do this, we included seven studies that our group has conducted during the last decade. These studies used either Luminex xMAP or ELISA to measure proteomic biomarkers. As proteins measured in these studies differed, we selected protein based on the criteria that it must be measured in at least four studies. We then examined biomarker performance using random-effect meta-analyses based on the mean difference between biomarker concentrations in AD and controls (CTL), AD and mild cognitive impairment (MCI), MCI, and CTL as well as MCI converted to dementia (MCIc) and non-converted (MCInc) individuals. Results: An overall of 2,879 subjects were retrieved for meta-analysis including 1,053 CTL, 895 MCI, 882 AD, and 49 frontotemporal dementia (FTD) patients. Six proteins were measured in at least four studies and were chosen for meta-analyses for AD diagnosis. Of them, three proteins had significant difference between AD and controls, among which alpha-2-macroglobulin (A2M) and ficolin-2 (FCN2) increased in AD while fibrinogen gamma chain (FGG) decreased in AD compared to CTL. Furthermore, FGG significantly increased in FTD compared to AD. None of the proteins passed the significance between AD and MCI, or MCI and CTL, or MCIc and MCInc, although complement component 4 (CC4) tended to increase in MCIc individuals compared to MCInc. Conclusions: The results suggest that A2M, FCN2, and FGG are promising biomarkers to discriminate AD patients from controls, which are worthy of further validation.

The validation status of blood biomarkers of amyloid and phospho-tau assessed with the 5-phase development framework for AD biomarkers

PURPOSE

The development of blood biomarkers that reflect Alzheimer's disease (AD) pathophysiology (phosphorylated tau and amyloid-β) has offered potential as scalable tests for dementia differential diagnosis and early detection. In 2019, the Geneva AD Biomarker Roadmap Initiative included blood biomarkers in the systematic validation of AD biomarkers.

METHODS

A panel of experts convened in November 2019 at a two-day workshop in Geneva. The level of maturity (fully achieved, partly achieved, preliminary evidence, not achieved, unsuccessful) of blood biomarkers was assessed based on the Biomarker Roadmap methodology and discussed fully during the workshop which also evaluated cerebrospinal fluid (CSF) and positron emission tomography (PET) biomarkers.

RESULTS

Plasma p-tau has shown analytical validity (phase 2 primary aim 1) and first evidence of clinical validity (phase 3 primary aim 1), whereas the maturity level for Aβ remains to be partially achieved. Full and partial achievement has been assigned to p-tau and Aβ, respectively, in their associations to ante-mortem measures (phase 2 secondary aim 2). However, only preliminary evidence exists for the influence of covariates, assay comparison and cut-off criteria.

CONCLUSIONS

Despite the relative infancy of blood biomarkers, in comparison to CSF biomarkers, much has already been achieved for phases 1 through 3 - with p-tau having greater success in detecting AD and predicting disease progression. However, sufficient data about the effect of covariates on the biomarker measurement is lacking. No phase 4 (real-world performance) or phase 5 (assessment of impact/cost) aim has been tested, thus not achieved.

The plasma peptides of Alzheimer's disease

Background

A practical strategy to discover proteins specific to Alzheimer’s dementia (AD) may be to compare the plasma peptides and proteins from patients with dementia to normal controls and patients with neurological conditions like multiple sclerosis or other diseases. The aim was a proof of principle for a method to discover proteins and/or peptides of plasma that show greater observation frequency and/or precursor intensity in AD. The endogenous tryptic peptides of Alzheimer’s were compared to normals, multiple sclerosis, ovarian cancer, breast cancer, female normal, sepsis, ICU Control, heart attack, along with their institution-matched controls, and normal samples collected directly onto ice.

Methods

Endogenous tryptic peptides were extracted from blinded, individual AD and control EDTA plasma samples in a step gradient of acetonitrile for random and independent sampling by LC–ESI–MS/MS with a set of robust and sensitive linear quadrupole ion traps. The MS/MS spectra were fit to fully tryptic peptides within proteins identified using the X!TANDEM algorithm. Observation frequency of the identified proteins was counted using SEQUEST algorithm. The proteins with apparently increased observation frequency in AD versus AD Control were revealed graphically and subsequently tested by Chi Square analysis. The proteins specific to AD plasma by Chi Square with FDR correction were analyzed by the STRING algorithm. The average protein or peptide log₁₀ precursor intensity was compared across disease and control treatments by ANOVA in the R statistical system.

Results

Peptides and/or phosphopeptides of common plasma proteins such as complement C2, C7, and C1QBP among others showed increased observation frequency by Chi Square and/or precursor intensity in AD. Cellular gene symbols with large Chi Square values (χ2 ≥ 25, p ≤ 0.001) from tryptic peptides included KIF12, DISC1, OR8B12, ZC3H12A, TNF, TBC1D8B, GALNT3, EME2, CD1B, BAG1, CPSF2, MMP15, DNAJC2, PHACTR4, OR8B3, GCK, EXOSC7, HMGA1 and NT5C3A among others. Similarly, increased frequency of tryptic phosphopeptides were observed from MOK, SMIM19, NXNL1, SLC24A2, Nbla10317, AHRR, C10orf90, MAEA, SRSF8, TBATA, TNIK, UBE2G1, PDE4C, PCGF2, KIR3DP1, TJP2, CPNE8, and NGF amongst others. STRING analysis showed an increase in cytoplasmic proteins and proteins associated with alternate splicing, exocytosis of luminal proteins, and proteins involved in the regulation of the cell cycle, mitochondrial functions or metabolism and apoptosis. Increases in mean precursor intensity of peptides from common plasma proteins such as DISC1, EXOSC5, UBE2G1, SMIM19, NXNL1, PANO, EIF4G1, KIR3DP1, MED25, MGRN1, OR8B3, MGC24039, POLR1A, SYTL4, RNF111, IREB2, ANKMY2, SGKL, SLC25A5, CHMP3 among others were associated with AD. Tryptic peptides from the highly conserved C-terminus of DISC1 within the sequence MPGGGPQGAPAAAGGGGVSHRAGSRDCLPPAACFR and ARQCGLDSR showed a higher frequency and highest intensity in AD compared to all other disease and controls.

Conclusion

Proteins apparently expressed in the brain that were directly related to Alzheimer’s including Nerve Growth Factor (NFG), Sphingomyelin Phosphodiesterase, Disrupted in Schizophrenia 1 (DISC1), the cell death regulator retinitis pigmentosa (NXNl1) that governs the loss of nerve cells in the retina and the cell death regulator ZC3H12A showed much higher observation frequency in AD plasma vs the matched control. There was a striking agreement between the proteins known to be mutated or dis-regulated in the brains of AD patients with the proteins observed in the plasma of AD patients from endogenous peptides including NBN, BAG1, NOX1, PDCD5, SGK3, UBE2G1, SMPD3 neuronal proteins associated with synapse function such as KSYTL4, VTI1B and brain specific proteins such as TBATA.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12014-021-09320-2.

Proteomic Profiling of Plasma and Brain Tissue from Alzheimer's Disease Patients Reveals Candidate Network of Plasma Biomarkers

BACKGROUND

Alzheimer's disease (AD) is the most prevalent form of dementia with two pathological hallmarks of tau-containing neurofibrillary tangles and amyloid-β protein (Aβ)-containing neuritic plaques. Although Aβ and tau have been explored as potential biomarkers, levels of these pathological proteins in blood fail to distinguish AD from healthy control subjects.

OBJECTIVE

We aim to discover potential plasma proteins associated with AD pathology by performing tandem mass tag (TMT)-based quantitative proteomic analysis of proteins from peripheral and central nervous system compartments.

METHODS

We performed comparative proteomic analyses of plasma collected from AD patients and cognitively normal subjects. In addition, proteomic profiles from the inferior frontal cortex, superior frontal cortex, and cerebellum of postmortem brain tissue from five AD patients and five non-AD controls were compared with plasma proteomic profiles to search for common biomarkers. Liquid chromatography-mass spectrometry was used to analyze plasma and brain tissue labeled with isobaric TMT for relative protein quantification.

RESULTS

Our results showed that the proteins in complement coagulation cascade and interleukin-6 signaling were significantly altered in both plasma and brains of AD patients.

CONCLUSION

Our results demonstrate the relevance in immune responses between the peripheral and central nervous systems. Those differentially regulated plasma proteins are explored as candidate biomarker profiles that illustrate chronic neuroinflammation in brains of AD patients.

The search for a convenient procedure to detect one of the earliest signs of Alzheimer's disease: A systematic review of the prediction of brain amyloid status

INTRODUCTION

Convenient, cost-effective tests for amyloid beta (Aβ) are needed to identify those at higher risk for developing Alzheimer's disease (AD). This systematic review evaluates recent models that predict dichotomous Aβ. (PROSPERO: CRD42020144734).

METHODS

We searched Embase and identified 73 studies from 29,581 for review. We assessed study quality using established tools, extracted information, and reported results narratively.

RESULTS

We identified few high-quality studies due to concerns about Aβ determination and analytical issues. The most promising convenient, inexpensive classifiers consist of age, apolipoprotein E genotype, cognitive measures, and/or plasma Aβ. Plasma Aβ may be sufficient if pre-analytical variables are standardized and scalable assays developed. Some models lowered costs associated with clinical trial recruitment or clinical screening.

DISCUSSION

Conclusions about models are difficult due to study heterogeneity and quality. Promising prediction models used demographic, cognitive/neuropsychological, imaging, and plasma Aβ measures. Further studies using standardized Aβ determination, and improved model validation are required.