Understanding Neurodegenerative Diseases From the -Omics Perspective: Lessons Learnt.

We read with interest the narrative review article by Prajjwal et al. on the association between Parkinson's disease (PD), PD-plus (progressive supranuclear palsy [PSP], multisystem atrophy [MSA], corticobasal degeneration [CBD], vascular PD [VPD]), Lewy body dementia (LBD), and Alzheimer's disease (AD) [1]. It was concluded that PD, PD plus, LBD, and AD are interconnected in terms of pathophysiology, including impaired mitochondrial function, protein clumping, increased oxidative stress, and inflammatory brain processes, which are amenable to specific treatment [1]. The study is impressive, but some points require further discussion. A first point is that SPECT and PET studies have not been discussed as a means of distinguishing between PD, PD-plus LBD, and AD. The dopamine transporter (DaT) scan using the tracer ioflupan is useful for the diagnosis of PD, which shows unilateral or bilateral reduced uptake of the tracer in the striatum [2]. In LBD, perfusion SPECT typically shows severe bilateral parietotemporal and occipital hypoperfusion and DaT scan shows mildly reduced presynaptic dopamine transmission in the putamen and caudate nucleus [3]. In CBD, perfusion SPECT typically shows diffuse hypoperfusion affecting the frontal, parietal, and temporal cortex, basal ganglia and thalamus, in association with reduced striatal dopamine transmission predominantly in the putamen [3]. In AD, both perfusion SPECT and FDG PET show typical hypoperfusion and hypometabolism in the bilateral parietal cortex [3] and amyloid PET shows cortical amyloid deposits [3]. Another imaging modality that should have been discussed is ¹²³I-MIBG sympathetic imaging. ¹²³I-MIBG imaging is particularly valuable in distinguishing Lewy body diseases (including PD, LBD, and pure autonomic failure) from other PD-plus syndromes such as PSP, MSA, CBD and from AD. This imaging technique is also recognized in the ‘Diagnosis and Management of Dementia with Lewy Bodies’ guidelines: Fourth Consensus Report of the DLB Consortium’ [4]. There is also a lack of discussion on the Seed Amplification Assay (SAA), which is a powerful tool for detecting the presence of pathogenic synuclein seeds. The second point is that mitochondrial disorders (MIDs) have not been discussed as differential diagnoses of PD, PD-plus, LBD, and AD. Several syndromic and non-syndromic MIDs can mimic PD, PD plus, LBD, and AD [5]. Therefore, it is important that MIDs are thoroughly ruled out before diagnosing PD, PD-plus, LBD, and AD. Exclusion of MIDs is crucial because diagnostic and therapeutic management and outcomes can vary significantly between MIDs and the other disorders. A third point is that genetic causes of PD were not discussed in the review. Nowadays, mutations in several genes such as GBA, PRKN, SNCA, PINK1, DJ1, DNAjC6, RIC3, ATP13A2, LRRK2, MAPT, LRP10, NUS1, ARSA, TMEM230, or VPS35 are known to be associated with PD [6]. Particularly in patients with a positive family history of PD and early onset of the disease, hereditary PD should be suspected and genetic testing initiated. It is critically important to know whether PD is genetic or degenerative in nature, as therapy and outcome can vary significantly between these causes. The fourth point is that there is a discrepancy between the sentence in the abstract that PD plus is characterized by unique clinical features, imaging, and response to l-DOPA, and the sentence that these unique features make it difficult to distinguish PD from PD-plus [1]. Either PD plus manifestations are truly unique and therefore different from PD, or they are not. This discrepancy should be resolved. One form of neurodegeneration not included in the review is frontotemporal dementia. What was the reason? In summary, the interesting study has limitations that put the results and their interpretation into perspective. Removing these limitations could strengthen the conclusions and reinforce the study's message. All unresolved questions must be clarified before readers can uncritically accept the review's message. The distinction between PD, PD plus, LBD, and AD requires the use of clinical and MRI criteria as well as SPECT and PET examinations, biomarker assays, and genetic examinations. Josef Finsterer: investigation; conceptualization; validation; data curation; writing–review & editing. The author has nothing to report. The author has nothing to report. The author declares no conflicts of interest. All data are available from the corresponding author.

Neurodegenerative diseases affect millions of people worldwide and include many different types, such as Alzheimer's disease (AD), frontotemporal dementia, semantic dementia, amyotrophic lateral sclerosis, and Parkinson's disease (PD). Patients suffering from these diseases show declines in a variety of cognitive functions, as well as disruptions of brain structures and functions, in parallel with a molecular and pathological progression, such as the accumulation of specific proteins. Despite the differences in the underlying biological mechanisms, important commonalities have been observed among these diseases, leading some researchers to argue that neurodegenerative diseases are brain disorders with misfolded protein aggregates between regions over time as a consequence of diffuse network dynamics 1, 2. Recent advances in multimodal neuroimaging techniques (e.g., structural MRI, functional MRI, diffusion MRI, and positron emission tomography) provide unique opportunities to explore the structural and functional brain abnormalities in patients, in particular from a network perspective, to improve our understanding of the pathophysiological mechanisms of neurodegenerative diseases, and to assist in developing diagnostic biomarkers and assess treatment effects 3, 4. While there are still many challenging issues, such as the extraction of reliable brain connectivity from various imaging modalities and the analyses of the topological properties (e.g., the selection of global and regional metrics), network analysis of neuroimaging data in neurodegenerative diseases has significantly advanced in the past decade, mainly due to the rapid development of imaging analysis methodologies and computational models of brain networks. In this special issue, we aimed to compile works representing recent studies regarding imaging brain networks in neurodegenerative diseases. We collected eleven articles, including five review articles and six original research articles. The article topics covered various research directions in structural and functional brain network studies using neuroimaging data in neurodegenerative diseases such as AD, semantic dementia, and PD. (i) We assembled five review articles. In the first review article, Agosta et al. 5 provided a comprehensive review regarding the recent progress on the most common neurodegenerative diseases (e.g., AD, frontotemporal dementia, amyotrophic lateral sclerosis, and PD) involving the main molecular and pathological substrates of these diseases and neuroimaging findings based on structural and functional brain connectivity analyses. They highlighted the "network-based neurodegeneration" hypothesis in these disorders by representing the large-scale brain network alterations, as well as the microscopic abnormalities of structural pathways. They emphasized that characterizing network breakdown using multimodal MRI data in combination with molecular imaging techniques would be crucial to understanding the biological mechanisms of these neurodegenerative diseases, and they may even help to identify new therapeutic targets to slow or stop the disease progression. Sun et al. 6 and Li et al. 7 provided systematic reviews regarding behavioral, biochemical, and neuroimaging studies in subjective cognitive decline and mild cognitive impairment, respectively. Neurodegeneration due to AD can progress over years before dementia appears. Individuals with mild cognitive impairment have a high risk of progressing to AD, which represents the prodromal stage of this disease. Furthermore, most individuals with mild cognitive impairment are preceded by subjective cognitive decline characterized by self-reported memory complaints. By summarizing the recent progress on structural and functional imaging studies, Sun et al. 6 and Li et al. 7 highlighted that brain network dysfunctions have appeared in both subjective cognitive decline and mild cognitive impairment and emphasized the importance and necessity of considering these early stages in disease diagnosis. Given that both subjective cognitive decline and mild cognitive impairment are heterogeneous and some individuals will not convert to dementia, they also emphasized that longitudinal studies using multimodal neuroimaging techniques are vital for the discovery of diagnostic biomarkers. Yang et al. 8 conducted an exhaustive review of studies that directly or indirectly assessed the structural and functional connectivity alterations of semantic dementia. Their synthesized analyses showed that semantic dementia is associated with extensive changes in terms of both structural and functional networks, encompassing, but not restricted to, regions that are strongly atrophied in semantic dementia, such as the anterior temporal lobe. They highlighted that the relationship between structural and functional connectivity changes, and between these network changes and the core behavioral symptoms of semantic dementia, that is, semantic deficit, is not yet established, marking critical new research directions for the study of this disorder. Finally, Baggio et al. 9 systematically reviewed the recent progress on brain functional network studies using resting-state functional MRI in Parkinson's disease. Resting-state functional MRI provides a unique opportunity to explore the brain's spontaneous or intrinsic functional architecture in healthy and disease populations (e.g., AD and PD). By summarizing the findings using resting-state functional MRI, they suggested that there were a number of variabilities in the results across studies. Nonetheless, they emphasized that the resting-state fMRI technique and network analysis approaches are vital for evaluating the pathophysiological mechanisms underlying the clinical and cognitive manifestations of PD patients, as well as for discovering potential biomarkers for early diagnosis, treatment evaluation, and clinical outcomes. (ii) We assembled six original research articles. As mentioned above, the combination of resting-state functional MRI and network analysis approaches, such as graph theory, has emerged as a promising tool for characterizing the topological organization of functional brain networks in neurodegenerative diseases. In this issue, Du et al. 10 performed an important resting-state functional MRI study by systematically evaluating the test–retest reliability of various graph metrics in the high-resolution (voxel level) functional networks in 53 healthy participants. Specifically, using different nodal metrics, they identified functional hubs of the brain networks mainly located at the default-mode, salience, and executive control systems. These regions exhibited high test–retest reliability in the high-resolution functional networks, which were not sensitive to the selection of preprocessing factors such as head motion and global signal removal. Thus, this study provides valuable guidance for choosing reliable network metrics and analysis strategies in future longitudinal imaging studies of neurodegenerative diseases. Age is a key factor in neurodegenerative diseases. In this issue, Huang et al. 11 used a group-level independent component analysis and dual regression approaches to study resting-state networks derived from resting-state functional MRI data from a large group of healthy elderly participants (n = 430). They showed age-related decreases in many functional systems, including the ventral default-mode network, frontoparietal, auditory, sensorimotor, and visual medial networks. Most of these have been found to be disrupted in many neurodegenerative diseases, such as AD and PD. Thus, this work provides insights into age-related network breakdown, which would be helpful to understand the impact of aging on the biological mechanisms and clinical manifestations of neurodegenerative diseases. The hippocampus is a deep-brain structure involved in learning and memory. Structural and functional abnormalities in the hippocampus have been commonly observed in various neurodegenerative diseases. Specifically, neurodegenerative changes in AD have been suggested to begin at this structure and then to propagate in a stereotypical fashion. Automated and precise segmentation of the hippocampus is thus valuable for clinical studies. In this issue, Li et al. 12 introduced a novel segmentation method that utilized a manifold learning technique under the multiatlas-based segmentation scenario. Compared with two representative local weighted label fusion methods using structural MRI data of 28 healthy adolescents (age range: 10–17 years) and two ADNI datasets with 100 participants (age range: 60–89 years), the proposed method obtained consistent and significant improvements over the previous label fusion strategies, showing promising potential for future structural and functional connectivity studies of the hippocampus in neurodegenerative diseases. Mallio et al. 13 performed an intriguing study by investigating whether the disruption of structural connectivity in AD is centered on the hippocampus and whether this disruption propagates from this structure to others. Using diffusion-weighted imaging data, they first built structural brain networks in 14 amnestic mild cognitive impairment, 13 mild patients with AD, 15 moderate patients with AD, and healthy controls. They then calculated the percentages of affected connections directly linking to the epicenter (defined as the first ring) and to nodes with the topological distance 2 from the epicenter (defined as the second ring). They showed that both the first and the second rings were significantly affected in both the mild and the moderate AD groups but less affected in the mild cognitive impairment group, providing empirical support for the "network-based neurodegeneration" hypothesis in this disease. Numerous neuroimaging studies have suggested focal structural and functional abnormalities in many regions in patients with subcortical vascular mild cognitive impairment. However, it remains largely unknown whether and how brain network organization is disrupted in this disease. In this issue, Yi et al. 14 directly addressed this important issue by constructing brain functional networks using resting-state functional data in 21 patients with subcortical vascular mild cognitive impairment and 26 healthy controls. They found that compared with the controls, the patients exhibited disrupted global network topology with significantly increased path length and modularity. Disrupted modular structure was also observed in patients, with a notable reorganization of the executive control module. The parietal regions were split into different modules. Finally, they showed disrupted within-module and between-module connections involving the middle cingulate gyrus, anterior insula, medial prefrontal cortex, and lateral parietal regions. Collectively, this study highlighted the topological disorganization of functional brain networks in subcortical vascular mild cognitive impairment, providing implications for the biological mechanism of this disease. Postural instability with gait difficulty and tremor dominant are the two main subtypes of Parkinson's disease. Many studies have demonstrated that patients with these two subtypes exhibit different clinical manifestations, but the underlying neural substrates remain incompletely understood. In this issue, Chen et al. 15 addressed this interesting question by exploring the subtype-specific patterns of spontaneous brain activity in PD. Using resting-state functional MRI data, they measured the amplitudes of low-frequency fluctuations in 31 PD patients (12 tremor dominant /19 postural instability with gait difficulty) and 22 healthy controls. The two groups of patients showed different resting-state activities in the bilateral putamen, the cerebellar posterior lobe, and the lateral temporal and parietal regions. These differences were correlated with clinical variables (e.g., tremor score and the postural instability with gait difficulty score). Although not a direct assessment of network dysfunction, this study provided preliminary evidence for abnormal spontaneous activities in the cerebellum and putamen, which may underlie the neural substrate of the motor subtypes of PD. Collectively, the works of this special issue cover a great range and depth of the network studies of neurodegenerative diseases, constituting exciting exemplars of how the new advances in multimodal neuroimaging and network analysis can be powerful approaches to studying diseased brains. We anticipate that these works will provide critical insights into the field of neurodegenerative research. Future works are important to collect a large sample of longitudinal clinical and imaging data and develop novel network analysis approaches to fully decipher brain network dysfunction in neurodegenerative diseases. Lastly, we would like to thank all of the authors, reviewers, and the editorial office for their great contributions to this special issue. This work was supported by the National Key Basic Research Program of China (973 Project, No. 2014CB846102) and the National Science Fund for Distinguished Young Scholars (No. 81225012).

The combination of deep learning with whole-brain computational models reveals the low-dimensional representation of neurodegenerative diseases, which emerges from a highly multidimensional brain, providing valuable insight into pathological states' diagnostic, prognosis, and treatment response.

The combination of deep learning with whole-brain computational models reveals the low-dimensional representation of neurodegenerative diseases, which emerges from a highly multidimensional brain, providing valuable insight into pathological states' diagnostic, prognosis, and treatment response.

Quantification of the synaptic protein neurogranin in cerebrospinal fluid across different neurodegenerative diseases: A selective Alzheimer’s disease biomarker?

Current research lacks comprehensive investigations into the potential causal link between mitochondrial-related genes and the risk of neurodegenerative diseases (NDDs). We aimed to identify potential causative genes for five NDDs through an examination of mitochondrial-related gene expression levels. Through the integration of summary statistics from expression quantitative trait loci (eQTL) datasets (human blood and brain tissue), mitochondrial DNA copy number (mtDNA-CN), and genome-wide association studies (GWAS) datasets of five NDDs from European ancestry, we conducted a Mendelian randomization (MR) analysis to explore the potential causal relationship between mitochondrial-related genes and Alzheimer's disease (AD), Parkinson's disease (PD), amyotrophic lateral sclerosis (ALS), frontotemporal dementia (FTD), and Lewy body dementia (LBD). Sensitivity analysis and Bayesian colocalization were employed to validate this causal relationship. Through MR analysis, we have identified potential causal relationships between 12 mitochondria-related genes and AD, PD, ALS, and FTD overlapping with motor neuron disease (FTD_MND) in human blood or brain tissue. Bayesian colocalization analysis further confirms 9 causal genes, including NDUFS2, EARS2, and MRPL41 for AD; NDUFAF2, MALSU1, and METTL8 for PD; MYO19 and MRM1 for ALS; and FASTKD1 for FTD_MND. Importantly, in both human blood and brain tissue, NDUFS2 exhibits a significant pathogenic effect on AD, while NDUFAF2 demonstrates a robust protective effect on PD. Additionally, the mtDNA-CN plays a protected role in LBD (OR = 0.62, p = 0.031). This study presents evidence establishing a causal relationship between mitochondrial dysfunction and NDDs. Furthermore, the identified candidate genes may serve as potential targets for drug development aimed at preventing NDDs.

Background Observational studies showed renal function had associations with Alzheimer’s disease (AD), Parkinson’s disease (PD), Lewy body dementia (LBD) and multiple sclerosis (MS). However, it is unknown whether these associations are causal. Methods We use a two-sample Mendelian randomization (MR) analysis to investigate causal relationships between renal function and 6 neurodegenerative diseases (NDDs): AD (including familial AD), PD, LBD, frontotemporal dementia (FTD), amyotrophic lateral sclerosis (ALS) and MS. Blood urea nitrogen (BUN), chronic kidney disease (CKD) and estimated glomerular filtration rate (eGFR) were used to measure renal function. The inverse-variance weighted (IVW) was the predominant estimation method. The results were further validated using sensitivity analysis (i.e. MR Egger regression, Cochran Q statistic of IVW, and leave-one-out method). Results There was no indication of any causative relationship of BUN, CKD, or eGFR with AD, familial AD, PD, LBD, FTD and ALS (all P values >0.05). The IVW analysis demonstrated a causal relationship between eGFR and MS [odds ratio (OR), 4.89; 95% confidence interval (CI), 1.43 to 16.71; P = 0.01] that was not verified in the MR-Egger and weighted median (all P values >0.05). However, no causal association of MS with BUN (OR, 0.91; 95% CI, 0.40–2.07; P = 0.82) and CKD (OR,1.04; 95% CI, 0.88–1.23; P = 0.66) was found. There was no single SNP that affects the overall trend. Conclusions Our study showed that reduced eGFR was related to MS. The value of this study is that it provides a direction for further research on the relationship between reduced eGFR and MS.

OBJECTIVE: Motor and cognitive function were compared in patients with Lewy body dementia, Parkinson's disease, or Alzheimer's disease, to identify features that may be clinically useful in differentiating Lewy body...

Various neurodegenerative diseases fall under the umbrella term dementia: Alzheimer's disease (AD), Lewy body dementia (LBD), including Parkinson's disease dementia and dementia with Lewy bodies, and Frontotemporal dementia (FTD). Their common denominator is the pathological accumulation of misfolded proteins, affecting neuron, axon and synapse function, leading to a progressive deterioration of cognitive functioning. To define disease state, monitor disease progression and possible treatment strategies, non-invasive and pathology-sensitive neuroimaging biomarkers are highly needed. Here we showcase several studies which define the neuropathological substrates of imaging outcome measures with an unique within-subject correlative post-mortem in-situ MRI and neuropathology approach in >100 brain donors with neurodegenerative disease. The utilization of MRI ranges from cortical thickness and microstructural integrity to measures of connectome (dys) function. Digital quantitative pathology of Aβ, pTau, α-synuclein, and TDP-43 inclusions are included, as well as markers for neuroinflammation, myelin, neuro-axonal degeneration, and synaptic density. Our studies show that (ii) postmortem in-situ MRI is a good proxy for antemortem in-vivo MRI. (ii) cortical atrophy patterns in AD are positively associated with Aβ plaques and negatively with neuro-axonal damage. (iii) T1w/T2w ratio is a broader indicator of cortical integrity than just myelin. (iv) LATE co-pathology in AD and LBD has an additive effect on amygdala and hippocampal volume loss. (v) A global increase in α-synuclein pathology has a widespread effect on brain network reorganization in LBD. (vi) Hippocampal subfields are selectively vulnerable to protein aggregations and synaptic degeneration, associated with MRI volume loss and cognitive outcome in AD and LBD. (vii) cell loss in mono-aminergic and cholinergic nuclei in AD and LBD are associated with microstructural alterations within these regions and their cortical projections. By integrating multisource data (clinical, radiological, histopathological), these types of studies will result in new fundamental knowledge leading to improved interpretation of imaging datasets in the spectrum of neurodegenerative diseases.

This Special Issue comprises nine reviews offering perspectives from the development of neurodegeneration in different pathologies to neuronal protection, providing new views on the mechanism of neurodegeneration and associated processes and a summary of the progress in neuroscience. We hope you find these reviews interesting and informative and we thank the authors for these excellent contributions to The FEBS Journal.

Currently there are no effective treatments for many neurodegenerative diseases. Reliable biomarkers for identifying and stratifying these diseases will be important in the development of future novel therapies. Lewy Body Dementia (LBD) is considered an under diagnosed form of dementia for which markers are needed to discriminate LBD from other forms of dementia such as Alzheimer’s Disease (AD). This work describes a Label-Free proteomic profiling analysis of cerebral spinal fluid (CSF) from non-neurodegenerative controls and patients with LBD. Using this technology we identified several potential novel markers for LBD. These were then combined with other biomarkers from previously published studies, to create a 10 min multiplexed targeted and translational MRM-LC-MS/MS assay. This test was used to validate our new assay in a larger cohort of samples including controls and the other neurodegenerative conditions of Alzheimer’s and Parkinson’s disease (PD). Thirty eight proteins showed significantly (p < 0.05) altered expression in LBD CSF by proteomic profiling. The targeted MRM-LC-MS/MS assay revealed 4 proteins that were specific for the identification of AD from LBD: ectonucleotide pyrophosphatase/phosphodiesterase 2 (p < 0.0001), lysosome-associated membrane protein 1 (p < 0.0001), pro-orexin (p < 0.0017) and transthyretin (p < 0.0001). Nineteen proteins were elevated significantly in both AD and LBD versus the control group of which 4 proteins are novel (malate dehydrogenase 1, serum amyloid A4, GM2−activator protein, and prosaposin). Protein-DJ1 was only elevated significantly in the PD group and not in either LBD or AD samples. Correlations with Alzheimer-associated amyloid β-42 levels, determined by ELISA, were observed for transthyretin, GM2 activator protein and IGF2 in the AD disease group (r2 ≥ 0.39, p ≤ 0.012). Cystatin C, ubiquitin and osteopontin showed a strong significant linear relationship (r2 ≥ 0.4, p ≤ 0.03) with phosphorylated–tau levels in all groups, whilst malate dehydrogenase and apolipoprotein E demonstrated a linear relationship with phosphorylated-tau and total-tau levels in only AD and LBD disease groups. Using proteomics we have identified several potential and novel markers of neurodegeneration and subsequently validated them using a rapid, multiplexed mass spectral test. This targeted proteomic platform can measure common markers of neurodegeneration that correlate with existing diagnostic makers as well as some that have potential to show changes between AD from LBD.

BackgroundNeuroinflammation is involved in the pathophysiology of several neurodegenerative diseases and has been linked to faster clinical decline. This study evaluates a novel multiplex proteomic method to assess blood‐based inflammation patterns in patients with neurodegenerative diseases, including Alzheimer's disease (AD), Lewy body dementia (LBD), frontotemporal dementia (FTD) and progressive supranuclear palsy (PSP).MethodSerum samples from n = 137 patients (AD/MCI+=36, LBD=33, FTD=35, PSP=33) and n = 29 age‐matched controls were analysed with the NUcleic acid Linked Immunosorbent Assay (NULISA) Inflammation 250 panel. The panel measures ∼250 analytes, including Glial fibrillary acidic protein (GFAP) and other biomarkers of inflammation and immune response. GFAP levels were comapared across groups with a Kruskal‐Wallis test. All 250 biomarkers were compared across groups with Linear Models for Microarray and RNA‐Seq Data Analyses (LIMMA), correcting for age and sex.ResultSerum levels of GFAP were increased especially in patients with AD/MCI+ and LBD and to a lower extent in patients with PSP (Figure 1). Considering all 250 markers, patients with AD/MCI+ showed increased GFAP levels as compared to controls over and above other markers (Figure 2A). Comparing each patient group to the AD/MCI+ cohort, patients with LBD had a larger number of markers being upregulated than AD/MCI+ (Figure 2B), including Granzyme Β (GZMB) and Lysosome‐associated membrane glycoprotein 3 (LAMP‐3). Patients with FTD (Figure 2C) and PSP (Figure 2D) had higher levels in several inflammation markers as compared to AD/MCI+, including Matrix metalloproteinase‐9 (MMP9) and Hepatocyte Growth Factor (HGF).ConclusionThe NULISA Inflammation 250 panel demonstrates high sensitivity for detecting inflammatory patterns across neurodegenerative disorders. It revealed distinct condition‐specific profiles. Patients with LBD, FTD and PSP showed upregulation of many inflammation markers, as compared to controls and patients with AD.

BackgroundCurrently there are no effective treatments for many neurodegenerative diseases. Reliable biomarkers for identifying and stratifying these diseases will be important in the development of future novel therapies. Lewy Body Dementia (LBD) is considered an under diagnosed form of dementia for which markers are needed to discriminate LBD from other forms of dementia such as Alzheimer’s Disease (AD). This work describes a Label-Free proteomic profiling analysis of cerebral spinal fluid (CSF) from non-neurodegenerative controls and patients with LBD. Using this technology we identified several potential novel markers for LBD. These were then combined with other biomarkers from previously published studies, to create a 10 min multiplexed targeted and translational MRM-LC-MS/MS assay. This test was used to validate our new assay in a larger cohort of samples including controls and the other neurodegenerative conditions of Alzheimer’s and Parkinson’s disease (PD).ResultsThirty eight proteins showed significantly (p < 0.05) altered expression in LBD CSF by proteomic profiling. The targeted MRM-LC-MS/MS assay revealed 4 proteins that were specific for the identification of AD from LBD: ectonucleotide pyrophosphatase/phosphodiesterase 2 (p < 0.0001), lysosome-associated membrane protein 1 (p < 0.0001), pro-orexin (p < 0.0017) and transthyretin (p < 0.0001). Nineteen proteins were elevated significantly in both AD and LBD versus the control group of which 4 proteins are novel (malate dehydrogenase 1, serum amyloid A4, GM2−activator protein, and prosaposin). Protein-DJ1 was only elevated significantly in the PD group and not in either LBD or AD samples. Correlations with Alzheimer-associated amyloid β-42 levels, determined by ELISA, were observed for transthyretin, GM2 activator protein and IGF2 in the AD disease group (r2 ≥ 0.39, p ≤ 0.012). Cystatin C, ubiquitin and osteopontin showed a strong significant linear relationship (r2 ≥ 0.4, p ≤ 0.03) with phosphorylated–tau levels in all groups, whilst malate dehydrogenase and apolipoprotein E demonstrated a linear relationship with phosphorylated-tau and total-tau levels in only AD and LBD disease groups.ConclusionsUsing proteomics we have identified several potential and novel markers of neurodegeneration and subsequently validated them using a rapid, multiplexed mass spectral test. This targeted proteomic platform can measure common markers of neurodegeneration that correlate with existing diagnostic makers as well as some that have potential to show changes between AD from LBD.Electronic supplementary materialThe online version of this article (doi:10.1186/s13024-015-0059-y) contains supplementary material, which is available to authorized users.

Impairment of "protein quality control" in neurons is associated with etiopathogenesis of neurodegenerative diseases. The worn-out products of cell metabolism should be safely eliminated via the proteasome, autophago-lysosome and exocytosis. Insufficient activity of these degradation mechanisms within neurons leads to the accumulation of toxic protein oligomers, which represent a starting material for development of neurodegenerative proteinopathy. The spectrum of CNS linked proteinopathies is particularly broad and includes Alzheimer's disease (AD), Parkinson's disease (PD), Lewy body dementia, Pick disease, Frontotemporal dementia, Huntington disease, Amyotrophic lateral sclerosis and many others. Although the primary events in etiopathogenesis of sporadic forms of these diseases are still unknown, it is clear that aging, in connection with decreased activity of ubiquitin proteasome system, is the most significant risk factor. In this review we discuss the pathogenic role and intracellular fate of the candidate molecules associated with onset and progression of AD and PD, the protein tau and α-synuclein in context with the function of ubiquitin proteasome system. We also discuss the possibility whether or not the strategies focused to re-establishment of neuroproteostasis via accelerated clearance of damaged proteins in proteasome could be a promising therapeutic approach for treatment of major neurodegenerative diseases.

High Occurrence of Dementia in Older Adults Returning to Community From Prison