Fluid Biomarkers in Alzheimer’s Disease and Other Neurodegenerative Disorders: Toward Integrative Diagnostic Frameworks and Tailored Treatments

Insights into AlphaFold's breakthrough in neurodegenerative diseases

Neurodegenerative diseases (ND) are disorders of the central nervous system (CNS) characterized by impairment in neurons' functions, and complete loss, leading to memory loss, and difficulty in learning, language, and movement processes. The most common among these NDs are Alzheimer's disease (AD) and Parkinson's disease (PD), although several other disorders also exist. These are frontotemporal dementia (FTD), amyotrophic lateral syndrome (ALS), Huntington's disease (HD), and others; the major pathological hallmark of NDs is the proteinopathies, either of amyloid-β (Aβ), tauopathies, or synucleinopathies. Aggregation of proteins that do not undergo normal configuration, either due to mutations or through some disturbance in cellular pathway contributes to the diseases. Artificial Intelligence (AI) and deep learning (DL) have proven to be successful in the diagnosis and treatment of various congenital diseases. DL approaches like AlphaFold (AF) are a major leap towards success in CNS disorders. This 3D protein geometry modeling algorithm developed by DeepMind has the potential to revolutionize biology. AF has the potential to predict 3D-protein confirmation at an accuracy level comparable to experimentally predicted one, with the additional advantage of precisely estimating protein interactions. This breakthrough will be beneficial to identify diseases' advancement and the disturbance of signaling pathways stimulating impaired functions of proteins. Though AlphaFold has solved a major problem in structural biology, it cannot predict membrane proteins-a beneficial approach for drug designing.

Reconsidering red blood cells as the diagnostic potential for neurodegenerative disorders

BACKGROUND

Red blood cells (RBCs) are usually considered simple cells and transporters of gases to tissues.

HYPOTHESIS

However, recent research has suggested that RBCs may have diagnostic potential in major neurodegenerative disorders (NDDs).

RESULTS

This review summarizes the current knowledge on changes in RBC in Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis, and other NDDs. It discusses the deposition of neuronal proteins like amyloid-β, tau, and α-synuclein, polyamines, changes in the proteins of RBCs like band-3, membrane transporter proteins, heat shock proteins, oxidative stress biomarkers, and altered metabolic pathways in RBCs during neurodegeneration. It also highlights the comparison of RBC diagnostic markers to other in-market diagnoses and discusses the challenges in utilizing RBCs as diagnostic tools, such as the need for standardized protocols and further validation studies.

SIGNIFICANCE STATEMENT

The evidence suggests that RBCs have diagnostic potential in neurodegenerative disorders, and this study can pave the foundation for further research which may lead to the development of novel diagnostic approaches and treatments.

SIMOA Diagnostics on Alzheimer's Disease and Frontotemporal Dementia

BACKGROUND

Accurate diagnosis of Alzheimer's disease (AD) and frontotemporal dementia (FTD) represents a health issue due to the absence of disease traits. We assessed the performance of a SIMOA panel in cerebrospinal fluid (CSF) from 43 AD and 33 FTD patients with 60 matching Control subjects in combination with demographic-clinical characteristics.

METHODS

136 subjects (AD: n = 43, FTD: n = 33, Controls: n = 60) participated. Single-molecule array (SIMOA), glial fibrillary acidic protein (GFAP), neurofilament light (NfL), TAU, and ubiquitin carboxy-terminal hydrolase L1 (UCH-L1) in CSF were analyzed with a multiplex neuro 4plex kit. Receiver operating characteristic (ROC) curve analysis compared area under the curve (AUC), while the principal of the sparse partial least squares discriminant analysis (sPLS-DA) was used with the intent to strengthen the identification of confident disease clusters.

RESULTS

CSF exhibited increased levels of all SIMOA biomarkers in AD compared to Controls (AUCs: 0.71, 0.86, 0.92, and 0.94, respectively). Similar patterns were observed in FTD with NfL, TAU, and UCH-L1 (AUCs: 0.85, 0.72, and 0.91). sPLS-DA revealed two components explaining 19% and 9% of dataset variation.

CONCLUSIONS

CSF data provide high diagnostic accuracy among AD, FTD, and Control discrimination. Subgroups of demographic-clinical characteristics and biomarker concentration highlighted the potential of combining different kinds of data for successful and more efficient cohort clustering.

Altering Brain Amyloidosis by Intra-Lingual and Extra-Nasal Exposure of Aβ Aggregates

Extensive experimental and human-derived evidence suggest that misfolded Aβ particles spread similarly to infectious prions. Moreover, peripheral administration of Aβ seeds accelerates brain amyloidosis in both susceptible experimental animals and humans. The mechanisms and elements governing the transport of misfolded Aβ from the periphery to the brain are not fully understood, although circulation and retrograde axonal transport have been proposed. Here, we demonstrate that injection of Aβ seeds in the tongue, a highly innervated organ, substantially accelerates the appearance of plaques in Tg2576 mice. In addition, the extra-nasal exposure of Aβ aggregates increased amyloid pathology in the olfactory bulb. Our results show that exposing highly innervated tissues to Aβ seeds accelerates AD-like pathological features, and suggest that Aβ seeds can be transported from peripheral compartments to the brain by retrograde axonal transport. Research in this direction may be relevant on different fronts, including disease mechanisms, diagnosis, and risk-evaluation of potential iatrogenic transmission of Aβ misfolding.

Use of Laughter for the Detection of Parkinson's Disease: Feasibility Study for Clinical Decision Support Systems, Based on Speech Recognition and Automatic Classification Techniques

Parkinson's disease (PD) is an incurable neurodegenerative disorder which affects over 10 million people worldwide. Early detection and correct evaluation of the disease is critical for appropriate medication and to slow the advance of the symptoms. In this scenario, it is critical to develop clinical decision support systems contributing to an early, efficient, and reliable diagnosis of this illness. In this paper we present a feasibility study for a clinical decision support system for the diagnosis of PD based on the acoustic characteristics of laughter. Our decision support system is based on laugh analysis with speech recognition methods and automatic classification techniques. We evaluated different cepstral coefficients to identify laugh characteristics of healthy and ill subjects combined with machine learning classification models. The decision support system reached 83% accuracy rate with an AUC value of 0.86 for PD-healthy laughs classification in a database of 20,000 samples randomly generated from a pool of 120 laughs from healthy and PD subjects. Laughter could be employed for the efficient and reliable detection of PD; such a detection system can be achieved using speech recognition and automatic classification techniques; a clinical decision support system can be built using the above techniques. Significance: PD clinical decision support systems for the early detection of the disease will help to improve the efficiency of available and upcoming therapeutic treatments which, in turn, would improve life conditions of the affected people and would decrease costs and efforts in public and private healthcare systems.

α-synuclein as an emerging pathophysiological biomarker of Alzheimer's disease

INTRODUCTION

α-syn aggregates represent the pathological hallmark of synucleinopathies as well as a frequent copathology (almost 1/3 of cases) in AD. Recent research indicates a potential role of α-syn species, measured in CSF with conventional analytical techniques, in the differential diagnosis between AD and synucleinopathies (such as DLB). Pioneering studies report the detection of α-syn in blood, however, conclusive investigations are controversial. Ultrasensitive seed amplification techniques, enabling the selective quantification of α-syn seeds, may represent an effective solution to identify the α-syn component in AD and facilitate a biomarker-guided stratification.

AREAS COVERED

We performed a PubMed-based review of the latest findings on α-syn-related biomarkers for AD, focusing on bodily fluids. A dissertation on the role of ultrasensitive seed amplification assays, detecting α-syn seeds from different biological samples, was conducted.

EXPERT OPINION

α-syn may contribute to progressive AD neurodegeneration through cross-seeding especially with tau protein. Ultrasensitive seed amplification techniques may support a biomarker-drug co-development pathway and may be a pathophysiological candidate biomarker for the evolving ATX(N) system to classify AD and the spectrum of primary NDDs. This would contribute to a precise approach to AD, aimed at implementing disease-modifying treatments.