Blood RNA transcripts reveal similar and differential alterations in fundamental cellular processes in Alzheimer's disease and other neurodegenerative diseases

Ageing-related changes in the regulation of microglia and their interaction with neurons

Ageing is one of the most important risk factors for chronic health conditions, including neurodegenerative diseases. Inflammation is a feature of ageing, as well as a key pathophysiological mechanism for degenerative diseases. Microglia play multiple roles in the central nervous system; their states entail a complex assemblage of responses reflecting the multiplicity of functions they fulfil both under homeostatic basal conditions and in response to stimuli. Whereas glial cells can promote neuronal homeostasis and limit neurodegeneration, age-related inflammation (i.e. inflammaging) leads to the functional impairment of microglia and astrocytes, exacerbating their response to stimuli. Thus, microglia are key mediators for age-dependent changes of the nervous system, participating in the generation of a less supportive or even hostile environment for neurons. Whereas multiple changes of ageing microglia have been described, here we will focus on the neuron-microglia regulatory crosstalk through fractalkine (CX3CL1) and CD200, and the regulatory cytokine Transforming Growth Factor β1 (TGFβ1), which is involved in immunomodulation and neuroprotection. Ageing results in a dysregulated activation of microglia, affecting neuronal survival, and function. The apparent unresponsiveness of aged microglia to regulatory signals could reflect a restriction in the mechanisms underlying their homeostatic and reactive states. The spectrum of functions, required to respond to life-long needs for brain maintenance and in response to disease, would progressively narrow, preventing microglia from maintaining their protective functions. This article is part of the Special Issue on "Microglia".

Massive changes in gene expression and their cause(s) can be a unifying principle in the pathobiology of Alzheimer's disease

Understanding of the biology of Alzheimer's disease (AD) has long been fragmented, with various investigators concentrating on amyloid beta (Aβ) or tau, inflammation, cell death pathways, misfolded proteins, glia, and more. Yet data from multiple authors has repeatedly shown altered expression of myriad genes related to these seemingly disparate phenomena. In 2022, Morgan et al. organized the massive data on changes in AD in a meticulous survey of the literature and related these changes to Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Their data showed that 91% of the known KEGG pathways are involved in AD and that many of these pathways are represented by the known cellular/molecular phenomena of AD. Such data then raise the fundamental question: What mechanism(s) may be responsible for such widespread changes in gene expression? We review evidence for a unifying model based on sequestrations in stress granules and alteration of nucleocytoplasmic transport in AD. HIGHLIGHTS: In Alzheimer's disease (AD), critical changes take place in neurons before the appearance of plaques or tangles. Addressing these early changes provides a path to early detection and effective intervention in AD.

Distinctive whole-brain cell types predict tissue damage patterns in thirteen neurodegenerative conditions

For over a century, brain research narrative has mainly centered on neuron cells. Accordingly, most neurodegenerative studies focus on neuronal dysfunction and their selective vulnerability, while we lack comprehensive analyses of other major cell types' contribution. By unifying spatial gene expression, structural MRI, and cell deconvolution, here we describe how the human brain distribution of canonical cell types extensively predicts tissue damage in 13 neurodegenerative conditions, including early- and late-onset Alzheimer's disease, Parkinson's disease, dementia with Lewy bodies, amyotrophic lateral sclerosis, mutations in presenilin-1, and 3 clinical variants of frontotemporal lobar degeneration (behavioral variant, semantic and non-fluent primary progressive aphasia) along with associated three-repeat and four-repeat tauopathies and TDP43 proteinopathies types A and C. We reconstructed comprehensive whole-brain reference maps of cellular abundance for six major cell types and identified characteristic axes of spatial overlapping with atrophy. Our results support the strong mediating role of non-neuronal cells, primarily microglia and astrocytes, in spatial vulnerability to tissue loss in neurodegeneration, with distinct and shared across-disorder pathomechanisms. These observations provide critical insights into the multicellular pathophysiology underlying spatiotemporal advance in neurodegeneration. Notably, they also emphasize the need to exceed the current neuro-centric view of brain diseases, supporting the imperative for cell-specific therapeutic targets in neurodegeneration.

Blood-Based Transcriptomic Biomarkers Are Predictive of Neurodegeneration Rather Than Alzheimer's Disease

Alzheimer's disease (AD) is a growing global health crisis affecting millions and incurring substantial economic costs. However, clinical diagnosis remains challenging, with misdiagnoses and underdiagnoses being prevalent. There is an increased focus on putative, blood-based biomarkers that may be useful for the diagnosis as well as early detection of AD. In the present study, we used an unbiased combination of machine learning and functional network analyses to identify blood gene biomarker candidates in AD. Using supervised machine learning, we also determined whether these candidates were indeed unique to AD or whether they were indicative of other neurodegenerative diseases, such as Parkinson's disease (PD) and amyotrophic lateral sclerosis (ALS). Our analyses showed that genes involved in spliceosome assembly, RNA binding, transcription, protein synthesis, mitoribosomes, and NADH dehydrogenase were the best-performing genes for identifying AD patients relative to cognitively healthy controls. This transcriptomic signature, however, was not unique to AD, and subsequent machine learning showed that this signature could also predict PD and ALS relative to controls without neurodegenerative disease. Combined, our results suggest that mRNA from whole blood can indeed be used to screen for patients with neurodegeneration but may be less effective in diagnosing the specific neurodegenerative disease.

Multiomics and machine-learning identify novel transcriptional and mutational signatures in amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis is a fatal and incurable neurodegenerative disease that mainly affects the neurons of the motor system. Despite the increasing understanding of its genetic components, their biological meanings are still poorly understood. Indeed, it is still not clear to which extent the pathological features associated with amyotrophic lateral sclerosis are commonly shared by the different genes causally linked to this disorder. To address this point, we combined multiomics analysis covering the transcriptional, epigenetic and mutational aspects of heterogenous human induced pluripotent stem cell-derived C9orf72-, TARDBP-, SOD1- and FUS-mutant motor neurons as well as datasets from patients' biopsies. We identified a common signature, converging towards increased stress and synaptic abnormalities, which reflects a unifying transcriptional program in amyotrophic lateral sclerosis despite the specific profiles due to the underlying pathogenic gene. In addition, whole genome bisulphite sequencing linked the altered gene expression observed in mutant cells to their methylation profile, highlighting deep epigenetic alterations as part of the abnormal transcriptional signatures linked to amyotrophic lateral sclerosis. We then applied multi-layer deep machine-learning to integrate publicly available blood and spinal cord transcriptomes and found a statistically significant correlation between their top predictor gene sets, which were significantly enriched in toll-like receptor signalling. Notably, the overrepresentation of this biological term also correlated with the transcriptional signature identified in mutant human induced pluripotent stem cell-derived motor neurons, highlighting novel insights into amyotrophic lateral sclerosis marker genes in a tissue-independent manner. Finally, using whole genome sequencing in combination with deep learning, we generated the first mutational signature for amyotrophic lateral sclerosis and defined a specific genomic profile for this disease, which is significantly correlated to ageing signatures, hinting at age as a major player in amyotrophic lateral sclerosis. This work describes innovative methodological approaches for the identification of disease signatures through the combination of multiomics analysis and provides novel knowledge on the pathological convergencies defining amyotrophic lateral sclerosis.

Toward a new nosology of neurodegenerative diseases

New "omic" technologies are revealing shared and distinct biological pathways within and across neurodegenerative diseases (NDDs), allowing a better understanding of endophenotypes that exceeds the boundaries of the current diagnostic criteria. Moreover, a diagnostic framework is needed that can accommodate the co-pathology and the clinical overlap and heterogeneity of NDDs. Apart from dissecting the reasons for a revolution in how we conceive NDD, this article aims to prompt a change in how we diagnose and classify NDD, drafting a general scheme for a new nosology. As identifying a cause is the key to using the term "disease" properly, we propose using a tridimensional classification based on three axes: (1) etiology or pathogenic mechanism, (2) pathology markers and molecular biomarkers, (3) anatomic-clinical; and three hierarchical levels of etiology: (1) genetic/sporadic (2) cellular pathways and processes, and function of fluidic brain systems, and (3) risk factors.

A beginner's guide into curated analyses of open access datasets for biomarker discovery in neurodegeneration

The discovery of surrogate biomarkers reflecting neuronal dysfunction in neurodegenerative diseases (NDDs) remains an active area of research. To boost these efforts, we demonstrate the utility of publicly available datasets for probing the pathogenic relevance of candidate markers in NDDs. As a starting point, we introduce the readers to several open access resources, which contain gene expression profiles and proteomics datasets from patient studies in common NDDs, including proteomics analyses of cerebrospinal fluid (CSF). Then, we illustrate the method for curated gene expression analyses across select brain regions from four cohorts of Parkinson disease patients (and from one study in common NDDs), probing glutathione biogenesis, calcium signaling and autophagy. These data are complemented by findings of select markers in CSF-based studies in NDDs. Additionally, we enclose several annotated microarray studies, and summarize reports on CSF proteomics across the NDDs, which the readers can utilize for translational purposes. We anticipate that this "beginner's guide" will benefit the research community in NDDs, and would serve as a useful educational tool.