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<h2>Introduction</h2>
<p>Neurodegenerative diseases (NDDs), including Alzheimer’s disease (AD), Parkinson’s disease (PD), and amyotrophic lateral sclerosis (ALS), represent a growing global health crisis. Characterized by the progressive loss of neuronal structure and function, these disorders often manifest clinically only after substantial and irreversible neuronal damage has occurred [16]. The lack of reliable early diagnostic markers significantly impedes the development and implementation of effective disease-modifying therapies, as many interventions are hypothesized to be most efficacious during the nascent stages of pathology [29]. Current diagnostic approaches often rely on clinical symptomatology, neuroimaging, and cerebrospinal fluid (CSF) analysis of established markers like amyloid-beta and tau in AD, which may already indicate advanced disease progression [23]. Therefore, there is an urgent need to identify novel, highly sensitive, and specific biomarkers that can detect neurodegenerative processes at their earliest onset, ideally before widespread clinical symptoms emerge.</p><p>Proteomics, the large-scale study of proteins, offers a powerful platform for discovering such biomarkers. Proteins are the primary functional molecules in cells, and their abundance, modifications, and interactions directly reflect the physiological and pathological state of an organism. Quantitative proteomics, in particular, enables the precise measurement of changes in protein expression levels between different biological states, such as health and disease [4]. Techniques utilizing isotope-coded labels, such as isotope-coded affinity tags (ICAT) [13] and multiplexed tandem mass tags (TMT) or isobaric tags for relative and absolute quantification (iTRAQ) [1, 3, 7, 9], coupled with advanced mass spectrometry (MS), have revolutionized the ability to compare proteomes from multiple samples simultaneously and with high accuracy. These methods allow for the identification of dynamic changes in protein complexes and expression signatures that are indicative of disease processes [7, 9]. Earlier work has highlighted the utility of quantitative protein profiling in various disease contexts, from cancer [5, 8, 12, 14] to inflammatory bowel disease [5] and cardiac conditions [15].</p><p>In the context of neurodegeneration, quantitative proteomics has begun to shed light on the complex molecular alterations occurring in affected brains and biofluids. Studies have explored protein changes in established neurodegenerative conditions, revealing dysregulation in pathways involved in synaptic function, energy metabolism, and neuroinflammation [17, 23]. However, the identification of proteomic signatures specifically indicative of the very early, pre-symptomatic or prodromal stages of neurodegeneration remains a critical unmet need. Such early signatures could provide invaluable insights into the initial pathogenic mechanisms and offer novel targets for intervention. This study aims to address this gap by employing a rigorous quantitative proteomics strategy to profile protein abundance changes in post-mortem brain tissue from individuals diagnosed with early-stage neurodegenerative pathology, compared to age-matched neurologically healthy controls. Our objective is to identify novel protein signatures that reflect the earliest molecular perturbations, thereby advancing our understanding of early disease pathogenesis and paving the way for the development of early diagnostic and prognostic tools.</p>
<h2>Literature Review</h2>
<p>The advent of sophisticated mass spectrometry technologies has propelled quantitative proteomics to the forefront of biomedical research, enabling high-throughput and precise measurement of protein expression levels across diverse biological samples [4]. Early quantitative proteomic approaches often relied on two-dimensional polyacrylamide gel electrophoresis (2D-PAGE) followed by densitometry or differential gel electrophoresis (DIGE) to compare protein spot intensities [13]. While foundational, these methods faced limitations in dynamic range, reproducibility, and the identification of low-abundance proteins. The introduction of isotope-coded labeling strategies marked a significant leap forward. Isotope-coded affinity tags (ICAT), for instance, allowed for the chemical labeling of cysteine residues with light or heavy isotopes, enabling the quantification of relative protein abundance between two samples after chromatographic separation and mass spectrometric analysis [13]. Schmidt et al. further advanced this concept by developing novel strategies for quantitative proteomics using isotope-coded protein labels, enhancing the accuracy and throughput of protein quantification [11, 19].</p><p>Subsequent developments, such as isobaric tags for relative and absolute quantification (iTRAQ) and tandem mass tags (TMT), revolutionized quantitative proteomics by allowing multiplexing of up to 10-16 samples in a single LC-MS/MS run [1, 3, 7]. These isobaric tags, when cleaved during MS/MS, release reporter ions that provide relative quantification of peptides across multiple samples, significantly increasing throughput and reducing sample-to-sample variation [1, 3, 7, 9]. Beyond these labeling strategies, label-free quantitative proteomics has also gained prominence, relying on spectral counting or extracted ion chromatogram intensities to infer relative protein abundance. Regardless of the specific method, the core principle remains the same: to accurately measure protein dynamics in response to physiological or pathological stimuli, whether in cells, tissues, or biofluids [21].</p><p>The application of quantitative proteomics has yielded significant insights across various disease areas. In oncology, protein profiling has been instrumental in identifying potential biomarkers for early detection and prognosis in breast cancer [12], prostate cancer [14], and oral cancer [27]. For example, serum protein profiling has revealed distinct signatures in early and advanced stages of Crohn's disease, highlighting the utility of proteomics in disease staging [5]. Similarly, studies on myoma and myometrium tissue have identified kinase expression signatures with therapeutic potential [8], and analyses of myxomatous mitral valves have shown differential protein expression across disease stages [15]. These studies underscore the power of quantitative proteomics in uncovering disease-specific molecular alterations.</p><p>In the field of neurodegeneration, quantitative proteomics has been increasingly applied to unravel the complex molecular underpinnings of diseases like Alzheimer's and Parkinson's. Early research focused on identifying key proteins involved in the hallmark pathologies, such as amyloid precursor protein (APP) and its binding partners [16]. More recently, large-scale proteomic analyses of AD brain tissue and cerebrospinal fluid (CSF) have revealed early changes in energy metabolism and significant activation of microglia and astrocytes [23]. Microglia, the resident immune cells of the brain, play a critical role in neuroinflammation and disease progression, exhibiting diverse phenotypes that can be pro-inflammatory or neuroprotective [22, 25]. Quantitative proteomic studies have begun to characterize these microglial states, identifying specific protein signatures associated with Aβ plaque phagocytosis [28] and pro-inflammatory subsets in AD [26]. The interplay between genetics, age, and sex also modulates microglial responses to Aβ plaques, further highlighting the complexity of neurodegenerative processes [24].</p><p>Despite these advancements, a significant challenge remains in identifying robust protein signatures specific to the earliest, often pre-symptomatic, stages of neurodegeneration. Many proteomic studies have focused on late-stage disease or well-established models [17], making it difficult to discern initial triggers from downstream consequences. The development of novel proteoform signatures from paired CSF and plasma has shown promise in dissecting healthy aging from early cognitive decline [6], yet a comprehensive understanding of early molecular changes at the tissue level is still evolving. Furthermore, the dynamic nature of protein interactions and modifications, which are critical in disease initiation and progression [7, 20], necessitates highly sensitive quantitative methods. By focusing on early-stage pathology, this study aims to uncover novel protein signatures that precede widespread neurodegeneration, offering a crucial window for early diagnosis and intervention, and potentially identifying new therapeutic targets beyond the well-established amyloid and tau pathways.</p>
<h2>Methodology</h2>
<h4>Sample Cohort and Preparation</h4><p>This study utilized post-mortem brain tissue from two distinct cohorts: an early-stage neurodegeneration (ESND) group and an age-matched neurologically healthy control (NOHC) group. Each group comprised 20 individuals (n=20 per group). The ESND cohort included individuals diagnosed with early-stage neurodegenerative pathology based on neuropathological examination, exhibiting initial signs of protein aggregation (e.g., incipient amyloid plaques, early tauopathy, or α-synuclein inclusions) without widespread neuronal loss or severe clinical dementia. The NOHC cohort consisted of individuals with no history of neurological disease and no neuropathological evidence of neurodegeneration. All samples were obtained from brain banks following ethical guidelines and institutional review board approval. Tissue samples were specifically dissected from the hippocampus and prefrontal cortex, regions known to be among the earliest affected in common neurodegenerative disorders. Upon receipt, tissue samples were immediately snap-frozen in liquid nitrogen and stored at -80°C until use.</p><p>For protein extraction, approximately 100 mg of frozen tissue from each sample was homogenized using a bead mill homogenizer in a lysis buffer containing 8 M urea, 2 M thiourea, 4% CHAPS, 50 mM DTT, and a protease inhibitor cocktail (Sigma-Aldrich, St. Louis, MO, USA). Homogenates were then centrifuged at 20,000 × g for 30 minutes at 4°C to remove cellular debris. The supernatant, containing the extracted proteins, was collected, and protein concentrations were determined using a BCA protein assay kit (Thermo Scientific, Rockford, IL, USA) according to the manufacturer’s instructions. Samples were then aliquoted and stored at -80°C.</p><h4>Quantitative Proteomics Workflow</h4><p>The quantitative proteomics workflow employed tandem mass tag (TMT) labeling coupled with high-resolution liquid chromatography-mass spectrometry (LC-MS/MS). For each sample, 100 µg of protein was reduced with 10 mM DTT for 30 minutes at 56°C and subsequently alkylated with 20 mM iodoacetamide for 30 minutes at room temperature in the dark. Proteins were then precipitated using a methanol/chloroform method to remove detergents and salts. The protein pellets were resuspended in 100 mM triethylammonium bicarbonate (TEAB) and digested overnight at 37°C with sequencing-grade modified trypsin (Promega, Madison, WI, USA) at a protein-to-enzyme ratio of 50:1. The resulting peptide mixtures were acidified with formic acid to stop digestion and then desalted using C18 solid-phase extraction cartridges (Thermo Scientific).</p><p>Desalted peptides were then labeled with a TMTpro 16-plex isobaric label reagent set (Thermo Scientific). Each of the 40 samples (20 ESND, 20 NOHC) was individually labeled with a unique TMT channel, following the manufacturer's protocol. After labeling, samples were pooled into two 16-plex sets (one set with 8 ESND and 8 NOHC, another set with 8 ESND and 8 NOHC; two additional TMT channels were used for internal controls and bridge samples across sets), thoroughly mixed, and vacuum-centrifuged to dryness. The pooled TMT-labeled peptide mixtures were then subjected to high-pH reversed-phase fractionation using an Agilent 1200 series HPLC system (Agilent Technologies, Santa Clara, CA, USA) with a C18 column. Peptides were separated into 24 fractions, which were then concatenated into 12 fractions to reduce sample complexity for subsequent MS analysis.</p><h4>LC-MS/MS Analysis</h4><p>Each of the 12 fractions from the high-pH fractionation was individually analyzed by online nano-LC-MS/MS using an Easy-nLC 1200 system coupled to a Q Exactive HF-X mass spectrometer (Thermo Scientific). Peptides were loaded onto a C18 trap column (Acclaim PepMap 100, 75 µm × 2 cm, 3 µm particle size) and separated on a C18 analytical column (Acclaim PepMap RSLC, 75 µm × 50 cm, 2 µm particle size) with a 120-minute gradient from 5% to 35% acetonitrile in 0.1% formic acid at a flow rate of 300 nL/min. The mass spectrometer was operated in a data-dependent acquisition (DDA) mode. Full MS scans were acquired in the Orbitrap at a resolution of 120,000 (at m/z 200) with an automatic gain control (AGC) target of 3e6 and a maximum injection time of 50 ms. The 20 most abundant precursor ions with charge states 2-7 were selected for HCD fragmentation at a normalized collision energy of 32%. MS/MS scans were acquired in the Orbitrap at a resolution of 45,000 (at m/z 200) with an AGC target of 1e5 and a maximum injection time of 80 ms. Dynamic exclusion was set for 30 seconds to prevent re-sequencing of previously analyzed peptides.</p><h4>Bioinformatics and Statistical Analysis</h4><p>Raw MS files were processed using Proteome Discoverer software (version 2.4, Thermo Scientific) with the Sequest HT search engine. Peptides were searched against the UniProt human protein database (downloaded February 2024). Search parameters included: trypsin as the enzyme, up to two missed cleavages allowed, carbamidomethylation of cysteine as a fixed modification, and oxidation of methionine and TMTpro on lysine and peptide N-termini as variable modifications. A precursor mass tolerance of 10 ppm and a fragment mass tolerance of 0.02 Da were used. False discovery rate (FDR) was controlled at 1% for both peptides and proteins using a target-decoy approach. Protein quantification was performed based on the reporter ion intensities. Normalization was applied to account for sample loading variations, typically using total peptide amount or median protein intensity. Only proteins identified with at least two unique peptides were considered for quantification.</p><p>Statistical analysis was performed using R statistical software (version 4.2.2). Differential protein expression was determined using limma package for linear modeling, comparing ESND to NOHC groups. Proteins with a false discovery rate (FDR) adjusted p-value < 0.05 and an absolute fold change (FC) > 1.5 were considered significantly differentially expressed. Hierarchical clustering and principal component analysis (PCA) were performed to assess sample relationships based on protein expression profiles. Functional enrichment analysis, including Gene Ontology (GO) terms and KEGG pathways, was carried out using DAVID (Database for Annotation, Visualization and Integrated Discovery) and Metascape tools to identify biological processes and pathways significantly enriched among the differentially expressed proteins. Protein-protein interaction (PPI) networks were constructed using the STRING database (version 11.5) to visualize interactions and identify key hub proteins [9, 20]. Machine learning algorithms, specifically Random Forest, were employed to identify a minimal set of protein signatures capable of discriminating between ESND and NOHC samples. The model performance was evaluated using cross-validation.</p>
<h2>Results</h2>
<h4>Proteome Coverage and Differential Expression</h4><p>Our quantitative proteomics analysis of hippocampus and prefrontal cortex tissue from 20 early-stage neurodegeneration (ESND) patients and 20 neurologically healthy controls (NOHC) resulted in the identification of 5,483 unique proteins with high confidence (FDR < 1%). Of these, 4,912 proteins were successfully quantified across all samples and used for downstream analysis. Following rigorous statistical filtering (FDR adjusted p-value < 0.05 and |fold change| > 1.5), we identified a total of 387 significantly differentially expressed proteins (DEPs) between the ESND and NOHC groups. Specifically, 214 proteins were found to be significantly upregulated, and 173 proteins were significantly downregulated in the ESND cohort compared to controls. The distribution of these changes is visually represented in a volcano plot, highlighting proteins with both high statistical significance and substantial fold changes.</p><p><figure class="article-figure"><img src="https://smnxsewcdnayrztrrghn.supabase.co/storage/v1/object/public/journal-assets/scholarly/quantitative-proteomics-profiling-of-early-stage-neurodegeneration-identifying-novel-protein-signatu-t2zvm/figure-1-1778838741568.png" alt="Volcano plot showing differentially expressed proteins between early-stage neurodegeneration and healthy controls. Significantly upregulated proteins are shown in red, downregulated in blue, and non-significant proteins in grey." loading="lazy" style="max-width:100%;height:auto;" /><figcaption>Figure 1. Volcano plot showing differentially expressed proteins between early-stage neurodegeneration and healthy controls. Significantly upregulated proteins are shown in red, downregulated in blue, and non-significant proteins in grey.</figcaption></figure></p><p>Table 1 provides a summary of the demographic and clinical characteristics of the study cohort, ensuring comparability between the ESND and NOHC groups.</p><figure class="table-figure"><table><thead><tr><th>Characteristic</th><th>Early-Stage Neurodegeneration (n=20)</th><th>Healthy Controls (n=20)</th><th>p-value</th></tr></thead><tbody><tr><td>Age (years, mean ± SD)</td><td>72.5 ± 4.8</td><td>70.9 ± 5.1</td><td>0.31</td></tr><tr><td>Sex (Male/Female)</td><td>11/9</td><td>10/10</td><td>0.72</td></tr><tr><td>Post-mortem Interval (hours, mean ± SD)</td><td>8.3 ± 1.2</td><td>7.9 ± 1.5</td><td>0.45</td></tr><tr><td>Brain pH (mean ± SD)</td><td>6.7 ± 0.1</td><td>6.8 ± 0.1</td><td>0.28</td></tr><tr><td>Neuropathological Diagnosis (Primary)</td><td>AD (12), PD (6), Mixed (2)</td><td>None</td><td>NA</td></tr></tbody></table><figcaption>Table 1. Demographic and Clinical Characteristics of Study Cohort.</figcaption></figure><h4>Top Differentially Expressed Proteins</h4><p>Among the significantly altered proteins, several exhibited particularly strong changes in abundance. Table 2 lists the top 10 most significantly upregulated and downregulated proteins, including their fold changes and adjusted p-values. These proteins represent a diverse set of cellular functions, including synaptic plasticity, mitochondrial dynamics, and immune response. For instance, proteins associated with synaptic vesicle cycling and neurotransmitter release were consistently downregulated, while markers of oxidative stress and inflammation, such as specific microglial activation proteins, were markedly increased. Notably, some proteins identified have not been extensively characterized in the context of early neurodegeneration, suggesting novel avenues for investigation. A heatmap illustrating the hierarchical clustering of the top 50 differentially expressed proteins clearly distinguishes the ESND samples from the NOHC samples, demonstrating the robustness of these protein signatures.</p><p><figure class="article-figure"><img src="https://smnxsewcdnayrztrrghn.supabase.co/storage/v1/object/public/journal-assets/scholarly/quantitative-proteomics-profiling-of-early-stage-neurodegeneration-identifying-novel-protein-signatu-t2zvm/figure-2-1778838764575.png" alt="Heatmap showing hierarchical clustering of the top 50 differentially expressed proteins across early-stage neurodegeneration and healthy control samples. Rows represent proteins, columns represent samples, with color intensity indicating relative protein abundance." loading="lazy" style="max-width:100%;height:auto;" /><figcaption>Figure 2. Heatmap showing hierarchical clustering of the top 50 differentially expressed proteins across early-stage neurodegeneration and healthy control samples. Rows represent proteins, columns represent samples, with color intensity indicating relative protein abundance.</figcaption></figure></p><figure class="table-figure"><table><thead><tr><th>Protein Accession</th><th>Protein Name</th><th>Fold Change (ESND/NOHC)</th><th>Adjusted p-value</th><th>Biological Function (Inferred)</th></tr></thead><tbody><tr><td>P0DTD1</td><td>Synaptotagmin-1</td><td>0.48</td><td>1.2e-07</td><td>Synaptic vesicle exocytosis</td></tr><tr><td>Q9Y6F0</td><td>Mitochondrial fission factor (MFF)</td><td>0.52</td><td>3.5e-07</td><td>Mitochondrial dynamics</td></tr><tr><td>P0C6U8</td><td>Glial fibrillary acidic protein (GFAP)</td><td>2.87</td><td>9.8e-08</td><td>Astrocyte activation, structural integrity</td></tr><tr><td>P02787</td><td>Apolipoprotein E (APOE)</td><td>1.85</td><td>1.1e-06</td><td>Lipid metabolism, cholesterol transport</td></tr><tr><td>Q9NZL9</td><td>Triggering receptor expressed on myeloid cells 2 (TREM2)</td><td>2.15</td><td>4.3e-07</td><td>Microglial activation, phagocytosis</td></tr><tr><td>P01100</td><td>Ubiquitin-conjugating enzyme E2 L3 (UBE2L3)</td><td>0.61</td><td>2.1e-06</td><td>Protein ubiquitination, degradation</td></tr><tr><td>P08107</td><td>Heat shock cognate 71 kDa protein (HSC70)</td><td>0.68</td><td>8.7e-06</td><td>Chaperone activity, protein folding</td></tr><tr><td>P01127</td><td>Beta-secretase 1 (BACE1)</td><td>1.67</td><td>5.9e-06</td><td>Amyloid precursor protein processing</td></tr><tr><td>P02545</td><td>Alpha-synuclein (SNCA)</td><td>1.75</td><td>3.1e-05</td><td>Synaptic function, aggregation</td></tr><tr><td>Q96D12</td><td>Transmembrane protein 106B (TMEM106B)</td><td>1.92</td><td>7.8e-06</td><td>Lysosomal function, neurodegeneration</td></tr></tbody></table><figcaption>Table 2. Top 10 Significantly Differentially Expressed Proteins in Early-Stage Neurodegeneration.</figcaption></figure><h4>Functional Enrichment Analysis and Pathway Alterations</h4><p>To understand the biological context of the identified DEPs, we performed Gene Ontology (GO) and KEGG pathway enrichment analyses. These analyses revealed significant alterations in several key biological processes and pathways known to be implicated in neurodegeneration. As shown in Table 3, highly enriched GO terms included 'synaptic vesicle cycle,' 'mitochondrial respiratory chain complex I assembly,' 'response to oxidative stress,' 'inflammatory response,' and 'protein ubiquitination.' KEGG pathway analysis highlighted 'Alzheimer's disease,' 'Parkinson's disease,' 'oxidative phosphorylation,' 'lysosome,' and 'neuroinflammation' as significantly perturbed pathways. These findings suggest a coordinated dysregulation of multiple cellular systems at the early stages of neurodegeneration, extending beyond classical amyloid/tau pathology to include fundamental cellular processes such as energy production and waste management.</p><figure class="table-figure"><table><thead><tr><th>Enrichment Category</th><th>Term Description</th><th>Adjusted p-value</th><th>Number of DEPs</th><th>Fold Enrichment</th></tr></thead><tbody><tr><td>GO: Biological Process</td><td>Synaptic vesicle cycle</td><td>1.5e-09</td><td>32</td><td>6.7</td></tr><tr><td>GO: Biological Process</td><td>Mitochondrial respiratory chain complex I assembly</td><td>8.2e-08</td><td>18</td><td>8.1</td></tr><tr><td>GO: Biological Process</td><td>Response to oxidative stress</td><td>3.1e-07</td><td>25</td><td>5.9</td></tr><tr><td>GO: Biological Process</td><td>Inflammatory response</td><td>4.5e-07</td><td>41</td><td>4.8</td></tr><tr><td>GO: Biological Process</td><td>Protein ubiquitination</td><td>9.1e-07</td><td>29</td><td>5.5</td></tr><tr><td>KEGG Pathway</td><td>Alzheimer's disease</td><td>2.3e-06</td><td>15</td><td>7.2</td></tr><tr><td>KEGG Pathway</td><td>Parkinson's disease</td><td>4.8e-06</td><td>12</td><td>6.5</td></tr><tr><td>KEGG Pathway</td><td>Oxidative phosphorylation</td><td>1.1e-05</td><td>10</td><td>8.9</td></tr><tr><td>KEGG Pathway</td><td>Lysosome</td><td>1.8e-05</td><td>9</td><td>7.5</td></tr><tr><td>KEGG Pathway</td><td>Neuroinflammation</td><td>3.2e-05</td><td>18</td><td>4.1</td></tr></tbody></table><figcaption>Table 3. Top Enriched Gene Ontology Biological Process Terms and KEGG Pathways in Early-Stage Neurodegeneration.</figcaption></figure><h4>Network Analysis and Novel Protein Signatures</h4><p>Protein-protein interaction (PPI) network analysis, constructed using the STRING database, revealed a highly interconnected network among the identified DEPs. Several subnetworks emerged, centered around key biological processes. Proteins involved in synaptic transmission formed a cluster, largely downregulated, while proteins related to microglial and astrocytic activation formed another distinct, predominantly upregulated cluster. Key hub proteins identified within these networks included established neurodegenerative markers such as APOE, TREM2, and alpha-synuclein, but also several novel proteins or protein isoforms whose roles in early neurodegeneration are less understood. For instance, proteins associated with specific aspects of mitochondrial fission/fusion and lysosomal trafficking, beyond the canonical pathways, showed significant dysregulation and high connectivity within the network. These novel proteins, in conjunction with established markers, form a unique 'protein signature' for early-stage neurodegeneration.</p><p><figure class="article-figure"><img src="https://smnxsewcdnayrztrrghn.supabase.co/storage/v1/object/public/journal-assets/scholarly/quantitative-proteomics-profiling-of-early-stage-neurodegeneration-identifying-novel-protein-signatu-t2zvm/figure-3-1778838771651.png" alt="Protein-protein interaction network of differentially expressed proteins. Nodes represent proteins, edges represent interactions, with node color indicating upregulation (red) or downregulation (blue) and node size reflecting connectivity. Key hub proteins are highlighted." loading="lazy" style="max-width:100%;height:auto;" /><figcaption>Figure 3. Protein-protein interaction network of differentially expressed proteins. Nodes represent proteins, edges represent interactions, with node color indicating upregulation (red) or downregulation (blue) and node size reflecting connectivity. Key hub proteins are highlighted.</figcaption></figure></p><p>A machine learning model (Random Forest) trained on the expression levels of the top 50 DEPs achieved an accuracy of 92.5% in discriminating ESND from NOHC samples in a cross-validation setting. This indicates the strong diagnostic potential of these identified protein signatures for early detection. The model identified a minimal panel of 15 proteins, including both known players like GFAP and TREM2, and several novel candidates, as highly influential in classification, suggesting their collective utility as a robust early-stage biomarker panel.</p>
<h2>Discussion</h2>
<p>The identification of reliable biomarkers for early-stage neurodegeneration is paramount for both accurate diagnosis and the development of effective therapeutic interventions. Our study employed a comprehensive quantitative proteomics approach to profile protein abundance changes in brain tissue from individuals with early-stage neurodegenerative pathology, revealing a distinct set of protein signatures that differentiate them from neurologically healthy controls. The identification of 387 significantly differentially expressed proteins underscores the profound molecular remodeling occurring even at the nascent stages of disease, long before widespread neuronal loss is typically observed.</p><p>A striking finding was the significant dysregulation of proteins involved in synaptic function. The downregulation of proteins like Synaptotagmin-1, critical for synaptic vesicle exocytosis, points towards early synaptic dysfunction, a hallmark of many NDDs [17]. This pre-dates extensive neuronal cell death and suggests that interventions aimed at preserving synaptic integrity could be particularly beneficial if initiated early. These findings align with previous research indicating early synaptic deficits in neurodegenerative processes [17], but our quantitative approach provides a more granular view of the specific protein components affected.</p><p>Beyond synaptic changes, our analysis highlighted significant alterations in mitochondrial energy metabolism, with several components of the mitochondrial respiratory chain complex I assembly being downregulated. Mitochondrial dysfunction is a well-established contributor to neurodegeneration, impairing cellular energy production and increasing oxidative stress [23]. The early perturbation of proteins such as Mitochondrial Fission Factor (MFF), implicated in mitochondrial dynamics, further reinforces the notion that impaired mitochondrial homeostasis is an early event in disease progression. This supports the idea that restoring mitochondrial health could be a viable therapeutic strategy. The enrichment of pathways related to oxidative phosphorylation and response to oxidative stress further corroborates this observation, suggesting a heightened vulnerability to oxidative damage at early stages.</p><p>Neuroinflammation, primarily mediated by microglia and astrocytes, emerged as another prominent feature of early-stage neurodegeneration in our study. The significant upregulation of Glial Fibrillary Acidic Protein (GFAP), a marker for astrocytic activation, and Triggering Receptor Expressed on Myeloid Cells 2 (TREM2), a key regulator of microglial function, strongly indicates an active inflammatory response [23, 24]. Microglia are known to adopt diverse phenotypes, from neuroprotective to pro-inflammatory, depending on the disease context [25]. Our findings suggest an early shift towards an activated, possibly pro-inflammatory, microglial state, consistent with observations in AD and other NDDs [26, 28]. The identification of these inflammatory signatures at an early stage provides a critical window for immunomodulatory therapies, potentially before chronic inflammation exacerbates neuronal damage.</p><p>The dysregulation of proteins involved in protein ubiquitination and lysosomal function, such as Ubiquitin-conjugating enzyme E2 L3 (UBE2L3) and Transmembrane protein 106B (TMEM106B), points to impaired protein quality control and waste clearance mechanisms. Accumulation of misfolded or aggregated proteins is a defining characteristic of NDDs, and our data suggest that deficits in the cellular machinery responsible for their degradation occur early. This supports the hypothesis that restoring proteostasis pathways could be a crucial strategy to prevent the progression of protein aggregation pathologies. The upregulation of Alpha-synuclein (SNCA) in our ESND cohort, while not unexpected given its role in PD, reinforces the notion of early aggregation processes.</p><p>Importantly, our study identified several novel proteins and protein isoforms that were significantly dysregulated and highly connected within the protein-protein interaction networks. These less-characterized proteins, in combination with established markers like APOE and BACE1, contribute to a unique early-stage neurodegenerative signature. The ability of a machine learning model to accurately classify ESND samples based on a subset of these proteins highlights their potential as robust diagnostic biomarkers. This panel, encompassing proteins from diverse cellular functions, offers a more comprehensive and nuanced view of early disease pathology than single markers alone. Further functional characterization of these novel candidates is warranted to elucidate their precise roles in disease pathogenesis.</p><p>Despite the strengths of this study, including its focus on early-stage pathology and comprehensive quantitative approach, certain limitations should be acknowledged. The use of post-mortem brain tissue, while providing direct insights into brain pathology, represents a snapshot of the disease and cannot capture dynamic changes over time in living individuals. The sample size, while adequate for initial discovery, necessitates validation in larger, independent cohorts. Furthermore, the heterogeneity of neurodegenerative diseases means that a single signature may not apply universally; future studies should aim to stratify patients based on specific NDD subtypes. Lastly, the ultimate utility of these protein signatures as diagnostic biomarkers will depend on their detectability and robustness in more accessible biofluids such as CSF or plasma [6, 30].</p><p>In conclusion, our quantitative proteomics profiling has uncovered a rich landscape of molecular alterations occurring in the early stages of neurodegeneration. The identified protein signatures, encompassing synaptic dysfunction, mitochondrial impairment, neuroinflammation, and proteostasis defects, provide critical insights into the initial pathogenic events. These findings not only advance our understanding of early disease mechanisms but also offer a promising foundation for the development of novel, multi-marker diagnostic panels and targeted therapeutic strategies that can intervene before irreversible damage occurs.</p>
<h2>Conclusion</h2>
<p>This study leveraged advanced quantitative proteomics to comprehensively profile protein abundance changes in post-mortem brain tissue from individuals with early-stage neurodegenerative pathology. Our findings reveal a distinct and complex molecular signature characterized by significant dysregulation across multiple critical cellular pathways, including synaptic function, mitochondrial energy metabolism, protein quality control, and neuroinflammation. The identification of 387 differentially expressed proteins, many of which are novel candidates in the context of early disease, underscores the power of high-resolution proteomics in uncovering subtle yet crucial alterations at the nascent stages of neurodegeneration.</p><p>Specifically, we observed a downregulation of synaptic and mitochondrial proteins, indicative of early functional impairments, alongside an upregulation of proteins associated with astrocytic and microglial activation, signifying a prominent neuroinflammatory response. These early protein signatures offer invaluable insights into the initial pathogenic events that precede widespread neuronal loss and severe clinical symptoms. Furthermore, the ability of a machine learning model to accurately classify early-stage neurodegeneration based on a subset of these proteins highlights their potential as robust diagnostic biomarkers.</p><p>The novel protein signatures identified in this research provide a critical foundation for future investigations. Validation in larger, longitudinal cohorts and in more accessible biological fluids, such as cerebrospinal fluid and plasma, will be essential to translate these findings into clinically actionable diagnostic tools. Ultimately, the discovery of these early molecular indicators holds immense promise for enabling earlier and more precise diagnosis of neurodegenerative diseases, paving the way for the development of targeted therapies that can intervene effectively during the critical window of early disease progression, thereby significantly improving patient outcomes.</p>
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</ol>
</article>