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Neural Regeneration Research logoLink to Neural Regeneration Research
. 2023 Nov 8;19(8):1842–1848. doi: 10.4103/1673-5374.389357

Circulating proteomic biomarkers for diagnosing sporadic amyotrophic lateral sclerosis: a cross-sectional study

Lu He 1,#, Qinming Zhou 1,#, Chaoyang Xiu 2,#, Yaping Shao 3, Dingding Shen 1,4, Huanyu Meng 1, Weidong Le 5,*, Sheng Chen 1,4,6,*
PMCID: PMC10960292  PMID: 38103252

graphic file with name NRR-19-1842-g001.jpg

Keywords: amyotrophic lateral sclerosis, cathelicidin-related antimicrobial peptide, hemoglobin, label-free quantitative proteomics, multi-protein combined diagnostic panel, serum biomarkers, talin-1, translationally-controlled tumor protein, zyxin

Abstract

Biomarkers are required for the early detection, prognosis prediction, and monitoring of amyotrophic lateral sclerosis, a progressive disease. Proteomics is an unbiased and quantitative method that can be used to detect neurochemical signatures to aid in the identification of candidate biomarkers. In this study, we used a label-free quantitative proteomics approach to screen for substantially differentially regulated proteins in ten patients with sporadic amyotrophic lateral sclerosis compared with five healthy controls. Substantial upregulation of serum proteins related to multiple functional clusters was observed in patients with sporadic amyotrophic lateral sclerosis. Potential biomarkers were selected based on functionality and expression specificity. To validate the proteomics profiles, blood samples from an additional cohort comprising 100 patients with sporadic amyotrophic lateral sclerosis and 100 healthy controls were subjected to enzyme-linked immunosorbent assay. Eight substantially upregulated serum proteins in patients with sporadic amyotrophic lateral sclerosis were selected, of which the cathelicidin-related antimicrobial peptide demonstrated the best discriminative ability between patients with sporadic amyotrophic lateral sclerosis and healthy controls (area under the curve [AUC] = 0.713, P < 0.0001). To further enhance diagnostic accuracy, a multi-protein combined discriminant algorithm was developed incorporating five proteins (hemoglobin beta, cathelicidin-related antimicrobial peptide, talin-1, zyxin, and translationally-controlled tumor protein). The algorithm achieved an AUC of 0.811 and a P-value of < 0.0001, resulting in 79% sensitivity and 71% specificity for the diagnosis of sporadic amyotrophic lateral sclerosis. Subsequently, the ability of candidate biomarkers to discriminate between early-stage amyotrophic lateral sclerosis patients and controls, as well as patients with different disease severities, was examined. A two-protein panel comprising talin-1 and translationally-controlled tumor protein effectively distinguished early-stage amyotrophic lateral sclerosis patients from controls (AUC = 0.766, P < 0.0001). Moreover, the expression of three proteins (FK506 binding protein 1A, cathelicidin-related antimicrobial peptide, and hemoglobin beta-1) was found to increase with disease progression. The proteomic signatures developed in this study may help facilitate early diagnosis and monitor the progression of sporadic amyotrophic lateral sclerosis when used in combination with current clinical-based parameters.

Introduction

Amyotrophic lateral sclerosis (ALS) is a devastating neurodegenerative disorder of the motor neuron system that leads to severe disability and mortality owing to ventilatory failure (Zhou et al., 2017). The prevalence of ALS is 5 in 100,000 persons, and its incidence is 1.7 per 100,000, indicating short average survival (the median survival of patient with ALS is 2–5 years) (Oskarsson et al., 2018). Although ALS was first described in 1869, its diagnosis still depends on a history of progressive weakness and neurological examination (Oskarsson et al., 2018). Electromyography is essential for the differential diagnosis of ALS (Brown and Al-Chalabi, 2017). After the characteristic clinical symptoms and electrophysiological changes appear, ALS primarily progresses to the middle stages of neurodegeneration, with many lost motor neurons (Brown and Al-Chalabi, 2017). However, changes in biochemical parameters can occur within the early stages of neuronal loss. Thus, exploring the neurochemical signature of ALS and identifying suitable biochemical indicators could aid in early diagnosis of ALS. Exploring the biochemical changes that occur with ALS could also provide new insights into the underlying pathophysiology of the condition and help develop disease-modifying drugs. Ideally, ALS biomarkers would be used to monitor progression of the disease and predict patient prognosis.

There are two forms of ALS: familial and sporadic ALS (fALS and sALS). sALS accounts for 90% of ALS patients, while fALS accounts for the remaining 10% (Zhang et al., 2018; Xu et al., 2021; Marques and Duncan, 2022; Kürten et al., 2023; Yang et al., 2023). fALS is diagnosed by gene mutation detection and family history, which are not relevant for sALS. The causes of sALS are unknown, and different pathogenic mechanisms have been proposed (Zarei et al., 2015). Thus, there is a pressing need for the identification of diagnostic biomarkers for sALS. Cerebrospinal fluid samples most directly indicate pathological variations within the central nervous system (CNS). However, acquiring cerebrospinal fluid is quite invasive and requires high-risk surgical operations, and obtaining patient consent for repeated sample acquisition is a challenge. In contrast, blood sampling has several advantages, including simplicity, cost-effectiveness, and reduced invasiveness, making this approach more suitable for disease screening. Previous studies have identified potential blood markers for ALS diagnosis. These include various inflammatory factors, neurofilament light chain of plasma protein, and ubiquitin C-terminal hydrolase L1 (Lu et al., 2015; Li et al., 2020; Tortelli et al., 2020). Blood sampling can identify the presence of disease, and identifying circulating biochemical markers could help diagnosis and predict the prognosis of patients with ALS. However, the blood biomarkers that have been proposed for ALS remain controversial owing to the absence of validation and reproducibility studies (Moreno-Martinez et al., 2019; Sturmey and Malaspina, 2022).

Recently, proteomics has been increasingly applied to identify biomarkers and develop a better understanding of disease mechanisms. Proteomics analysis represents a high-throughput, objective, and quantitative approach to investigating proteins of interest. Proteomics methods have been used to explore biomarkers and gain mechanistic insights into the etiology of disease, and can enhance our understanding of multifactorial pathophysiological processes, as well as facilitate the development of effective therapeutic interventions. Several studies have used high-throughput proteomics methods to explore novel biomarkers (De Benedetti et al., 2017; Thompson et al., 2018). However, the reproducibility of these studies is limited owing to differing methodologies and small sample sizes. In addition, they focused on a specific pathway or a particular type of functional protein, which limited their capacity to increase our understanding of the disease. Thus, in the current study we used a proteomics-based approach to describe pathway changes and identify potential biomarkers in patients with sALS.

Methods

Study design and participant recruitment

This was a cross-sectional observation study. In total, 110 sALS patients aged 18 to 80 years were recruited from the Department and Institute of Neurology at Ruijin Hospital, affiliated with Shanghai Jiao Tong University School of Medicine, between April 2020 and September 2022. All the patients were recruited based on the revised El Escorial diagnostic criteria for ascertaining ALS (Brooks et al., 2000). The enrolled patients did not have other diseases affecting the peripheral nerves, including diabetes mellitus. During recruitment, each patient's medical history was taken, and a physical examination and biochemical analysis were performed. The ALS Functional Rating Scale-Revised (ALSFRS-R; Cedarbaum et al., 1999) was used to determine disease severity. The ALSFRS-R scale has 12 items representing four subdomains of function. These include the bulbar, fine motor, gross motor, and respiratory subdomains, with the score for each item ranging from 0 (total loss of function) to 4 (no loss of function). The total score ranged from 0 to 48, with a higher score representing better function. We also recruited 105 age- and sex-matched healthy controls from the Ruijin Hospital Physical Examination Center. The Human Ethics Committee at Ruijin Hospital approved the study (approval No. 2020-No.50 on April 1, 2020) and all participants signed an informed consent form. The study was conducted according to the requirements of the Declaration of Helsinki, and the results are reported according to the STrengthening the Reporting of OBservational studies in Epidemiology (STROBE) statement (von Elm et al., 2007).

As shown in Figure 1, ten sALS patients and five controls were randomly selected to be part of the discovery cohort, for which quantitative label-free proteomics analysis was performed. Based on the expression level of CD84, which showed the smallest inter-group difference out of the eight potential biomarkers that we identified, we calculated the number of samples needed to validate this potential biomarker with the following parameters: α = 0.05 and β = 0.9 efficiency. Next, 100 sALS patients and 100 controls were randomly assigned to the validation cohort, and enzyme-linked immunosorbent assay (ELISA) was performed to evaluate the potential biomarkers identified in the discovery cohort (the sample size of the validation cohort was calculated based on the proteomic results from the discovery cohort; Table 1). The 100 sALS patients were divided into early-stage (disease course from onset < 6 months, n = 35) and late-stage symptomatic sALS (disease course from onset > 6 months, n = 65) groups (Table 2). In addition, the patients were divided into three groups based on their ALSFRS-R scores during recruitment: low (≤ 25, n = 24), middle (26–32, n = 51), and high (≥ 33, n = 25; Table 3).

Figure 1.

Figure 1

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Overall experimental design used to develop the biomarker panel.

(A, B) First, sALS DEPs and candidate biomarkers were identified with label-free quantitative proteomics in a discovery cohort (n = 15). Then, the candidate biomarkers were validated, and the biomarker panel was further developed, using a cross-sectional cohort (n = 200). ALS: Amyotrophic lateral sclerosis; ELISA: enzyme-linked immunosorbent assay; PCA: principal multivariate component; ROC: receiver operating characteristics; sALS: sporadic amyotrophic lateral sclerosis.

Table 1.

Baseline characteristics of the participants in discovery and validation sets

Discovery set
Validation set
sALS ( n =10) Control ( n =5) P sALS ( n =100) Control ( n =100) P
Age (yr) 52.20±4.92 50.60±3.72 0.545 49.99±7.74 49.01±7.75 0.372
Male 8 (80) 3(60) 0.329 60(60) 56(56) 0.567
Disease course (mon) 29.91±9.04 18.77±11.48
ALSFRS-R 30.13±10.43 29.63±5.58
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Data are expressed as mean ± SD, and were analyzed by Student’s t-test, except for male with number (percentage) and chi-squared test. ALSFRS-R: Amyotrophic lateral sclerosis Functional Rating Scale-Revised; sALS: sporadic amyotrophic lateral sclerosis.

Table 2.

Baseline characteristics of the participants in early and late stages

Early stage ( n =35) Late stage ( n =65) P
Age (yr) 50.26±8.03 49.85±7.64 0.802
Male 17(49) 43(66) 0.087
Disease course (mon) 4.91±1.04 24.18±9.84 < 0.0001
ALSFRS-R 30.77±3.73 28.88±6.29 0.062
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Data are expressed as mean ± SD, and were analyzed by Student’s t-test, except for male with number (percentage) and chi-squared test. ALSFRS-R: Amyotrophic lateral sclerosis Functional Rating Scale-Revised.

Table 3.

Baseline characteristics of the participants with different ALSFRS-R

Low group ( n =24) Middle group ( n =51) High group ( n =25) P
Age (yr) 49.36±8.89 51.24±7.36 48.00±7.07 0.217
Male 15(63) 27(53) 18(72) 0.27
Disease course (mon) 19.96±10.31 16.24±12.37 17.38±13.59 0.461
ALSFRS-R 22.48±3.36 29.75±1.86 36.46±3.24 < 0.0001
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Data are expressed as mean ± SD, and were analyzed by one-way analysis of variance with the least significant difference post hoc test except for male with number (percentage) and chi-squared test. ALSFRS-R: Amyotrophic lateral sclerosis Functional Rating Scale-Revised.

Serum sample collection

Blood samples were collected from the forearm via venipuncture using serum-separating vacutainers. After being allowed to clot for 1–2 hours at room temperature in an upright position, the samples were centrifuged (1300 × g for 10 minutes at 4°C), and the serum was removed, aliquoted, and stored at –80°C for later proteomic analysis.

Proteomic analyses

Protein extraction

To remove cellular debris, we centrifuged the serum samples at 11,000 × g for 10 minutes at 4°C. Then, the supernatants were transferred to new centrifuge tubes. A PierceTM Spin Columns Kit for Top 12 Abundant Protein Depletion (Thermo Fisher Scientific, Hanover Park, IL, USA) was used to remove highly abundant serum proteins. A bicinchoninic acid assay was used to determine protein concentrations (Thermo Fisher Scientific) according to the manufacturer's instructions.

Trypsin digestion

After removing the highly abundant proteins, the samples were dried using a freezing vacuum concentrator (Thermo Fisher Scientific). Next, 8 M urea was added to re-solubilize the remaining proteins, dithiothreitol was added to a final concentration of 5 mM, and the proteins were allowed to reduce at 56°C for 30 minutes. Then, iodoacetamide was added to a final concentration of 11 mM, and the mixture was incubated at room temperature and protected from light for 15 minutes, after which the alkylated samples were transferred to ultrafiltration tubes (membrane with a retention molecular weight of 10 kDa, Millipore, Darmstadt, Germany) and centrifuged at 12,000 × g for 20 minutes at room temperature. The samples were resuspended in 8 M urea three times, then ammonium bicarbonate three times, centrifuging between each step, and then trypsin was added at a mass ratio of 1:50 (protease: protein, m/m), and the samples were allowed to digest overnight at 37°C. The supernatants were then collected by centrifugation at 12,000 × g for 10 minutes at room temperature, and ultrapure water was added to increase solubility. Next, the enzymatically digested peptide solution was acidified with 10% trifluoroacetic acid to pH 2–3, then centrifuged at 12,000 × g for 10 minutes at room temperature, and the supernatant was transferred to a new centrifuge tube for Stage Tip (Pierce, Thermo Fisher Scientific) desalting.

Liquid chromatography and mass spectrometry

The trypsin-digested peptides were analyzed using an EASY-nLC 1200 ultra performance liquid chromatography system (Thermo Fisher Scientific), in which mobile phase A was an aqueous solution containing 0.1% formic acid and 2% acetonitrile, and mobile phase B was an aqueous solution containing 0.1% formic acid and 90% acetonitrile. The liquid phase gradient settings were 0–96 minutes, 4–20% B; 96–114 minutes, 20–32% B; 114–117 minutes, 32–80% B; 117–120 minutes, 80% B, and the flow rate was maintained at 500.00 nL/min. The peptides were separated by the UHPLC system and then injected into the Nanospray FlexTM (Thermo Fisher Scientific) ion source for ionization. The separated peptides were detected using an Exploris 480 mass spectrometry system (Thermo Fisher Scientific). The ion source voltage was set at 2.2 kV, and the peptide parent ions and their secondary fragments were detected and analyzed using a high-resolution Orbitrap. The primary mass spectrometry scan range was set to 400–1200 m/z with a resolution of 60,000.00, while the secondary mass spectrometry scan range was fixed at 100 m/z with a resolution of 30,000.00. The data acquisition mode used a data-dependent acquisition scanning procedure, i.e., the top 15 peptide parent ions with the highest signal intensity were selected after the primary scan. The high-energy collision dissociation collision cell was sequentially entered for fragmentation using 27% fragmentation energy, and the secondary mass spectrometry was also performed sequentially. To improve the effective utilization of the mass spectrum, the automatic gain control was set to 7.5E4, the signal threshold was set to 1E4 ions/s, the maximum injection time was set to 100 ms, and the dynamic exclusion time of the tandem mass spectrometry scan was set to 30 seconds to avoid repeated scanning of the parent ions.

Database search

Secondary mass spectrometry data were retrieved using Proteome DiscovererTM 2.4 (Thermo Fisher Scientific). The search parameter settings were as follows: the database used was Homo_sapiens_9606 (20366 sequences), an inverse library was added to calculate false discovery rate caused by random matching, and a common contamination library was added to the database to eliminate the effect of contaminating proteins in the identification results; the enzyme cut mode was set to Trypsin (Full); the number of missed cut sites was set to two; the minimum peptide length was set to seven amino acid residues; the maximum number of peptide modifications was set to five; the mass error tolerance of the primary parent ion was set to 10 ppm and 5 ppm for First search and Main search, respectively, and the mass error tolerance of the secondary fragment ion was set to 0.02 Da. The cysteine alkylation Carbamidomethyl© was set to fixed modification and variable modification a‘ [‘Acetyl (Protein N-te'm)‘, ‘Oxidation 'M)‘, ‘Deamidation ('Q)']. The quantification method was set to LFQ, and the false discovery rate for protein identification and peptide spectrum match identification were set to 1%.

Functional clustering analysis

Gene Ontology (GO) functional clustering was conducted to determine the biological functions of the proteins who expression levels were significantly different in the serum of sALS patients and healthy controls (Ashburner et al., 2000). Two-tailed Fisher's exact test was used to classify proteins annotated using the GO database. In addition, the Kyoto Encyclopedia of Genes and Genomes (KEGG; https://www.genome.jp/kegg/) (Kanehisa and Goto, 2000) pathway analysis was performed to identify enriched pathways using the two-tailed Fisher's exact test. The pathways were identified by comparison to the KEGG database. The InterPro database (https://www.ebi.ac.uk/interpro) was searched for each protein category (Paysan-Lafosse et al., 2023), and a two-tailed Fisher's exact test was performed. Categories at least with one enriched cluster were included in the hierarchical clustering analysis based on functional classification (GO, Domain, and Pathway) of the differentially expressed proteins (DEPs). The function x = −log10 (P-value) was used to transform the filtered P-value matrix. Then, the x values were z-transformed for each category. The z scores were clustered using the one-way hierarchical clustering function of the Genesis software program. For cluster membership visualization, a heat map was generated using the “heatmap.2” function from the “gplots” tool of the R package software program. The molecular interactions between the DEPs were annotated using the STRING database (version 10.1; https://string-db.org) (Szklarczyk et al., 2019) and visualized using the “networkD3” tool of the R package software program (version 0.4, https://CRAN.R-project.org/package=networkD3).

Validation using ELISA

Serum levels of cathelicidin-related antimicrobial peptide (CAMP, ELK5115, ELK Biotechnology, Wuhan, China), FK506 binding protein 1A (FKBP1A, ELK4323, ELK Biotechnology), hemoglobin subunit alpha (HBA1, ELK5194, ELK Biotechnology), hemoglobin beta (HBB, ELK4071, ELK Biotechnology), CD84 (ELK8729, ELK Biotechnology), talin-1 (TLN1, ELK2345, ELK Biotechnology), translationally-controlled tumor protein (TPT1, ELK4011, ELK Biotechnology), and zyxin (ZYX, ELK4891, ELK Biotechnology) were validated using appropriate ELISA kits according to the manufacturers' instructions.

Statistical analysis

Categorical data were analyzed using the chi-squared test or Fisher's exact test, and continuous data were evaluated using either the Student's t-test or one-way analysis of variance with the least significant difference post hoc test. Multivariate logistic regression analysis was conducted to develop a diagnostic algorithm and score for sALS. The area under the receiver operating curve (area under curve, AUC) was determined and compared using the Z test. Two-tailed P-values < 0.05 were considered to indicate significant differences. SPSS software (version 18.0, SPSS Inc., Chicago, IL, USA) was used to perform the statistical analyses.

Results

Baseline characteristics of the sALS patients and controls

The baseline characteristics of the sALS and control cohorts are shown in Table 1. The sALS patients and controls were age- and sex-matched. The mean disease course from the onset of sALS patients within the discovery cohort was 29.91 ± 9.04 months, and the mean ALSFRS-R score was 30.13 ± 10.43. Moreover, the mean disease course from the onset of sALS patients in the validation cohort was 18.77 ± 11.48 months, and the mean ALSFRS-R score was 29.63 ± 5.58.

Overview of the quantitative proteomic identification and functional alterations related to sALS

Overall, 1682 proteins were detected by label-free quantitative proteomic analysis, and 387 were identified as DEPs related to sALS. Among these 387 proteins, 259 were up-regulated, and 128 were down-regulated in sALS patients compared with controls. Principal multivariate component analysis showed that the DEPs separated sALS patients from the controls. Moreover, the sALS patients could be distinguished from a clustering heatmap analysis generated from the overall DEP expression trends (Figure 2).

Figure 2.

Figure 2

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High-throughput proteomics analysis of DEPs in the serum of sALS patients and healthy controls in the discovery cohort.

(A) DEP volcano map. X-axis: Relative protein expression levels after log2 conversion; Y-axis: P values after –log10 transformation. Red dots: upregulated proteins; blue dots: downregulated proteins. (B) PCA of DEPs. (C) Clustering heatmap analysis of the DEPs. DEP: Differentially expressed protein; PCA: principal multivariate component; sALS: sporadic amyotrophic lateral sclerosis.

According to the GO analyses, the molecular functions of the DEPs were primarily associated with antioxidant, protein-disulfide reductase, carbon-oxygen lyase, hydrolase, and peroxidase activities. These results suggest that anti-oxidative stress plays an essential role in ALS pathogenesis, which is consistent with previous findings that oxidative stress damage is involved in the pathogenesis of ALS (Tam et al., 2019; Zuo et al., 2021). Cation channel activity regulation, supramolecular fiber activity, and nucleotide phosphorylation pathways were the main biological processes associated with the DEPs. DEPs were primarily located in the cortical cytoskeleton, cell cortex, and cytoplasmic region based on the cellular components term identified by the GO analysis (Figure 3A). KEGG pathway analysis revealed that DEPs were enriched in platelet activation, energy metabolism (glycolysis, tricarboxylic acid cycle, carbon metabolism, amino acid biosynthesis), and the peroxisome (Figure 3B). An interaction analysis was performed for the top 50 DEPs, and the interaction networks of the most closely interacting proteins were mapped (Figure 3C).

Figure 3.

Figure 3

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Functional clustering analysis of DEPs in the serum of sALS patients and healthy controls.

(A) Summary of GO cellular component, molecular function, and biological process functional annotation. (B) Cluster analysis of the enriched KEGG pathways. (C) Interaction network of the differentially expressed proteins. DEP: Differentially expressed protein; GO: Gene Ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; sALS: sporadic amyotrophic lateral sclerosis.

Screening for candidate biomarkers of sALS

The proteomic and functional analysis results from the discovery cohort were used to identify candidate biomarkers to accurately predict sALS onset. Next, we performed principal multivariate component dimensionality reduction analysis to identify upregulated DEPs using a false discovery rate filter of under 0.05 with a 5-fold or more difference in expression level, which yielded 42 candidate biomarker proteins. Interactome analysis of the 42 proteins was conducted to screen for biomarkers (Figure 4A). The involvement of different pathways and expression levels in the CNS were assessed, leading to the identification of eight DEPs (encoded by genes FKBP1A, CD84, CAMP, ZYX, HBA1, HBB, TLN1, and TPT1) that were considered strong biomarker candidates and were subjected to further evaluation (Figure 4B and C).

Figure 4.

Figure 4

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Identification of candidate serum biomarkers of sALS.

(A) Interaction network of selected candidate biomarkers. (B) Selection criteria used to identify candidate biomarkers. (C) Differences in expression levels of the candidate biomarkers between sALS patients and healthy controls. CNS: Central nervous system; sALS: sporadic amyotrophic lateral sclerosis.

Validation of sALS biomarkers by ELISA

The performance of the potential diagnostic biomarkers was validated in a cohort of 100 sALS patients and 100 healthy controls (Table 1). All eight of the DEPs tested were expressed at significantly higher levels in sALS patients than in controls (Figure 5A), consistent with the findings from the discovery cohort. Next, receiver operating characteristic analysis was conducted to evaluate the sensitivity and specificity of each biomarker in differentiating sALS patients from controls. The cathelicidin-related antimicrobial peptide (CAMP) protein was best at discriminating sALS from controls (AUC: 0.713, P < 0.0001) (Figure 5B and C).

Figure 5.

Figure 5

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Validation of candidate biomarkers, evaluation of diagnostic effectiveness, and establishment of a multi-protein panel.

(A) Confirmation of expression differences in the candidate biomarkers among sALS patients, early-stage sALS patients, and healthy controls, as assessed by ELISA. *P < 0.05, **P < 0.01, ****P < 0.0001 (one-way analysis of variance with the least significant difference post hoc test). (B, C) Multivariate logistic regression analysis of the eight candidate biomarkers. (D, E) Multivariate logistic regression analysis of a subset of five candidate biomarkers. AUC: Area under curve; CAMP: cathelicidin-related antimicrobial peptide; ELISA: enzyme-linked immunosorbent assay; FKBP1A: peptidyl-prolyl cis-trans isomerase FKBP1A; HBA1: hemoglobin subunit alpha; HBB: hemoglobin beta; sALS: sporadic amyotrophic lateral sclerosis; TLN1: talin-1; TPT1: translationally-controlled tumor protein; ZYX: zyxin.

Developing a diagnostic panel for sALS

Because the diagnostic efficiency of a single protein is limited, we next developed a multi-protein diagnostic panel by constructing logistic regression models using the likelihood ratio method. Ultimately, a protein panel involving five markers with high discriminating ability (HBB, CAMP, TLN1, ZYX, and TPT1) was obtained (AUC: 0.811, P < 0.0001). The five-protein-based logistic model generated a dichotomous score that could be used to accurately characterize each sample. The probability score of each sample positively diagnosed as sALS with each protein marker value was defined as Log(P) = 0.002HBB + 0.572CAMP + 0.196TLN1 + 0.900ZYX + 0.201TPT1 – 6.981.

The probability score was significantly higher in the sALS group than in the control group. The sensitivity and specificity scores for diagnosing sALS at the threshold point of 0.426 were 79% and 71%, respectively, with an AUC of 0.811 (Figure 5D and E). The expression of the five proteins (FKBP1A, TLN1, ZYX, HBA1, and TPT1) in early-stage sALS was significantly higher than in controls (Figure 5A). A logistic regression model was also established with an AUC of 0.766 for early-stage sALS involving two proteins (TLN1 and TPT1) (Figure 6A and B). In addition, the expression levels of three proteins (FKBP1A, CAMP, and HBA1) significantly differed among patients with different ALSFRS-R levels (low, middle, and high), with higher protein expression associated with lower ALSFRS-R scores (Figure 6C).

Figure 6.

Figure 6

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The multi-protein panel for sALS early diagnosis.

(A, B) Multivariate logistic regression analysis of the two-protein panel. (C) Expression level differences in the proteins, as determined by ELISA, and their correlation with different ALSFRS-R scores. *P < 0.05, **P < 0.01 (one-way analysis of variance with the least significant difference post hoc test). ALSFRS-R: Amyotrophic lateral sclerosis functional rating scale-revised; AUC: area under curve; CAMP: cathelicidin-related antimicrobial peptide; ELISA: enzyme-linked immunosorbent assay; FKBP1A: peptidyl-prolyl cis-trans isomerase FKBP1A; HBA1: hemoglobin subunit alpha; sALS: sporadic amyotrophic lateral sclerosis; TLN1: talin-1; TPT1: translationally-controlled tumor protein; ZYX: zyxin.

Discussion

The current dilemma in sALS diagnosis and treatment

sALS diagnosis is still based on clinical symptoms and neurological and electrophysiological examinations. There is a diagnostic delay owing to clinical heterogeneity and ineffective biomarkers. Once a patient meets the diagnostic criteria for sALS, they usually have already lost many motor neurons, limiting the therapeutic potential of various putative therapies (Xu et al., 2021). In addition, there are further difficulties in assessing disease progression and treatment efficacy. Some quantitative or semiquantitative functional scales have been used to evaluate disease progression and the effectiveness of therapeutics, such as the ALSFRS-R score. However, the utility of these scales is still disputed owing to their low sensitivity (Tang and Fan, 2022). Consequently, identifying candidate biomarkers could facilitate early diagnosis, early treatment initiation, accurate disease progression assessment, and treatment efficacy.

Using quantitative proteomic strategies to identify potential biomarkers for sALS

With the advancement of technology, quantitative proteomics strategies have deepened our understanding of the pathophysiology of various diseases and identified novel diagnostic and prognostic biomarkers. In our study, the abnormally upregulated protein pathways in sALS were primarily enriched in oxidative stress, energy metabolism (TCA cycle, glycolysis, carbon metabolism, amino acid biosynthesis), and ion channel activation. This is consistent with reports that disruption of these pathways is associated with sALS pathogenesis and progression (Pullen et al., 2004; Dupuis et al., 2011; Leal and Gomes, 2015; Vandoorne et al., 2018; Tam et al., 2019; Zuo et al., 2021).

The top DEPs expressed in the CNS that were upregulated more than 5-fold in patients with sALS compared with controls are involved in different pathways. Ultimately, CAMP exhibited the greatest ability to discriminate between sALS patients and controls. Previous studies of ALS biomarkers have focused mainly on a specific protein or pathway (Lu et al., 2015; Li et al., 2020; Tortelli et al., 2020). However, the diagnostic efficiency of a single protein is limited, possibly because ALS is a complex disease with numerous pathological mechanisms that are not adequately reflected by a single protein or pathway. Thus, we proposed a formula including four proteins from different pathways to help distinguish sALS patients from controls. The five-protein-based logistic model (HBB, CAMP, TLN1, ZYX, and TPT1) demonstrated significant effectiveness in differentiating sALS from controls (AUC: 0.811, P < 0.0001). Because clinical symptoms are not apparent in the early stage of sALS, neurological and electrophysiological examinations could fail to recognize it. For the diagnosis of early-stage (disease course from onset < 6 months) sALS, we developed a two-protein panel (TLN1 and TPT1). Moreover, sALS patients with lower ALSFRS-R scores exhibited higher expression of three proteins (FKBP1A, CAMP, and TPT1) than those with higher ALSFRS-R scores, which could be helpful for monitoring disease progression.

The potential biomarkers may be involved in different aspects of sALS pathophysiology

CAMP is an antimicrobial peptide expressed by humans and animals that participates in CNS innate immunity and M1 microglia activation (Bergman et al., 2005; Xu et al., 2018). Immune responses contribute to disease progression without triggering motor neuron dysfunction (Lyon et al., 2019). We found that CAMP expression was not significantly elevated in early-stage sALS but increased with the disease progression. HBA1 and HBB are iron-containing proteins that transport oxygen within erythrocytes in most vertebrates. Recently, these proteins have also been reported as being expressed in neurons and glial cells, primarily within mitochondria (Shephard et al., 2014; Freed and Chakrabarti, 2016). Hemoglobin loss in neuronal or glial mitochondria may correlate with neurodegeneration (Shephard et al., 2014; Vanni et al., 2018; Killinger et al., 2022). Reports indicate that elevated hemoglobin levels are associated with a higher incidence of neurodegenerative disease (Abbott et al., 2012; Olsson et al., 2012; Mandrioli et al., 2017). Disrupted iron homeostasis, oxidative stress, and inflammation are common pathophysiologic mechanisms of neurodegenerative disease that, both directly and indirectly, affect the affinity of hemoglobin for oxygen, leading to chronic hypoxia and secondary erythrocytosis (Graham et al., 2014). TLN1 is an adaptor protein that controls focal adhesion signaling by conjugating integrins to the cytoskeleton and localizes to stress-induced cytoplasmic stress granules (Vu et al., 2021). Defects in stress granule dynamics can induce the formation of pathological aggregates, a characteristic of ALS. Thus, increased serum levels of TLN1 in ALS patients could be associated with pathological aggregate formation. ZYX is a zinc-binding adaptor protein that translocates from focal adhesions to the nucleus to activate signal transduction and regulate pro-inflammatory pathways (Wei et al., 2019). ZYX interacts with sirtuin 1, which is associated with autophagy (Fujita et al., 2009). Previous studies have shown that autophagy is an essential component of ALS pathogenesis (Chen et al., 2012; Chen et al., 2015; Zhang et al., 2019). TPT1 is a conserved protein that interacts with many other proteins and is involved in multiple biological processes in response to cellular stresses (Bommer, 2017). FKBP1A is a chaperone protein that assists in folding/isomerizing proteins with proline residues. Similar to other neurodegenerative diseases, the aberrantly accumulated granulins in ALS patients are proline-rich, intrinsically disordered peptides with misfolded TAR DNA binding protein 43 (TDP-43) or fused in sarcoma (FUS) (Liu et al., 2006, 2014; Gerard et al., 2008; Blair et al., 2015; Caminati and Procacci, 2020). FKBP1A regulates inflammation, autophagy, apoptosis, and proliferation (Lou et al., 2022). Pathological FKBP1A aggregation in response to stress is apparent during the early stages of ALS. We suggest that a serum protein panel including TLN1 and TPT1 could facilitate the early diagnosis of sALS. Furthermore, autophagy, chronic inflammation, the immune response, and mitochondrial dysfunction account for the ALS disease progression (Taylor et al., 2016; Mejzini et al., 2019). Thus, assessing the expression levels of three proteins (FKBP1A, CAMP, and HBA1), in combination with evaluating clinical parameters, has significant potential for early diagnosis and monitoring disease progression.

CD84 expression levels were significantly higher in sALS patients than in healthy controls, despite not being included in the panels that we developed. CD84 was first considered a negative regulator of innate CD8+ T cell development. Later, it was found to promote T follicular helper cell differentiation (Wang et al., 2015) and T cell–B cell interactions (Rao et al., 2017). CD84 expression was elevated in ALS patients with low ALSFRS-R scores, suggesting that it could be involved in the late phase of ALS. Therefore, the relationship between the eight proteins and sALS remains unclear. Further investigation is needed to elucidate the underlying mechanisms.

Because ALS is a highly heterogeneous disease whose pathogenesis involves dysfunction in multiple pathways, identifying a combination of biomarkers identified by multi-omics approaches is a promising direction for future research. The DEPs identified in our study were enriched in the energy metabolism pathway. Existing evidence suggests that glucose metabolism, lipid metabolism, and ATP generation and consumption are impaired in ALS patients and animal models (Dupuis et al., 2011; Vandoorne et al., 2018). Hypermetabolism is seen in most ALS patients (Ferri and Coccurello, 2017). Overactivated AMPK, an energy sensor that is activated by decreased energy status and promotes ATP generation, was observed in the motor neurons of ALS patients (Liu et al., 2015). Lee et al. (2021) explored the transcriptomics and metabolomics profiles of spinal motor neurons of ALS patients and found that arachidonic acid metabolism was dysregulated. Thus, a panel that combines the biomarkers identified here and candidates identified by metabolomics analysis could reflect a more comprehensive neurochemical signature of ALS that would be more effective for early diagnosis, progression monitoring, and prognosis prediction than the panels described here.

Biomarkers may guide future clinical practice

In the present study we screened for potential sALS biomarkers using proteomics methods and established multi-protein panels for diagnosing sALS. The biomarkers identified here have the potential to be used for early diagnosis, disease progression monitoring, and quantitative assessment of therapeutic effects, which are critical in clinical practice. The five-protein–based logistic model (HBB, CAMP, TLN1, ZYX, and TPT1) has significant potential to facilitate sALS diagnosis. Among these five proteins, TLN1 and TPT1 are predicted to be more helpful for early diagnosis, while FKBP1A, CAMP, and HBA1, in combination with evaluation of clinical parameters, have significant potential for monitoring disease progression.

Currently, the pathogenic mechanism of ALS is unclear, and no effective therapeutic targets have been established. Our proteomics analysis provides new insight into ALS pathogenesis, which could help in improving risk prediction and identifying intervention targets.

However, there were several limitations to our study. First, the sample size from our study was relatively small, and the multi-protein panel should be validated in a larger cohort. Second, patients with other conditions that mimic ALS should be included to verify the specificity of the panel, and follow-up of the sALS patients would be required to evaluate the capacity of the biomarkers to predict survivability. Third, the identified biomarkers should be evaluated for their CNS expression levels, biological functions, and roles in ALS pathogenesis.

Acknowledgments:

We thank the patients and their families who participated in this study.

Funding Statement

Funding: This study was supported by the grants from Shanghai Shuguang Plan Project, No. 18SG15 (to SC), Shanghai Outstanding Young Scholars Project, Shanghai Talent Development Project, No. 2019044 (to SC), Medical-engineering cross fund of Shanghai Jiao Tong University, No. YG2022QN009 (to QZ), and the National Natural Science Foundation of China, No. 82201558 (to QZ).

Footnotes

Conflicts of interest: The authors have declared that no competing interest exists.

Data availability statement: The proteomics data of this study are deposited in iProX repository (www.iprox.org) under access number IPX0005516000. Summary data are available in the paper. Raw data supporting the findings of this study are available by contacting the corresponding author upon reasonable request.

C-Editor: Zhao M; S-Editors: Yu J, Li CH; L-Editors: Crow E, Yu J, Song LP; T-Editor: Jia Y

References

  1. Abbott RD, Ross GW, Tanner CM, Andersen JK, Masaki KH, Rodriguez BL, White LR, Petrovitch H. Late-life hemoglobin and the incidence of Parkinson's disease. Neurobiol Aging. 2012;33:914–920. doi: 10.1016/j.neurobiolaging.2010.06.023. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, Harris MA, Hill DP, Issel-Tarver L, Kasarskis A, Lewis S, Matese JC, Richardson JE, Ringwald M, Rubin GM, Sherlock G. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000;25:25–29. doi: 10.1038/75556. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Bergman P, Termén S, Johansson L, Nyström L, Arenas E, Jonsson AB, Hökfelt T, Gudmundsson GH, Agerberth B. The antimicrobial peptide rCRAMP is present in the central nervous system of the rat. J Neurochem. 2005;93:1132–1140. doi: 10.1111/j.1471-4159.2005.03081.x. [DOI] [PubMed] [Google Scholar]
  4. Blair LJ, Baker JD, Sabbagh JJ, Dickey CA. The emerging role of peptidyl-prolyl isomerase chaperones in tau oligomerization, amyloid processing, and Alzheimer's disease. J Neurochem. 2015;133:1–13. doi: 10.1111/jnc.13033. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Bommer UA. The translational controlled tumour protein TCTP: biological functions and regulation. Results Probl Cell Differ. 2017;64:69–126. doi: 10.1007/978-3-319-67591-6_4. [DOI] [PubMed] [Google Scholar]
  6. Brooks BR, Miller RG, Swash M, Munsat TL. El Escorial revisited: revised criteria for the diagnosis of amyotrophic lateral sclerosis. Amyotroph Lateral Scler Other Motor Neuron Disord. 2000;1:293–299. doi: 10.1080/146608200300079536. [DOI] [PubMed] [Google Scholar]
  7. Brown RH, Al-Chalabi A. Amyotrophic lateral sclerosis. N Engl J Med. 2017;377:162–172. doi: 10.1056/NEJMra1603471. [DOI] [PubMed] [Google Scholar]
  8. Caminati G, Procacci P. Mounting evidence of FKBP12 implication in neurodegeneration. Neural Regen Res. 2020;15:2195–2202. doi: 10.4103/1673-5374.284980. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Cedarbaum JM, Stambler N, Malta E, Fuller C, Hilt D, Thurmond B, Nakanishi A. The ALSFRS-R: a revised ALS functional rating scale that incorporates assessments of respiratory function. BDNF ALS Study Group (Phase III) J Neurol Sci. 1999;169:13–21. doi: 10.1016/s0022-510x(99)00210-5. [DOI] [PubMed] [Google Scholar]
  10. Chen S, Zhang X, Song L, Le W. Autophagy dysregulation in amyotrophic lateral sclerosis. Brain Pathol. 2012;22:110–116. doi: 10.1111/j.1750-3639.2011.00546.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Chen S, Zhang XJ, Li LX, Wang Y, Zhong RJ, Le W. Histone deacetylase 6 delays motor neuron degeneration by ameliorating the autophagic flux defect in a transgenic mouse model of amyotrophic lateral sclerosis. Neurosci Bull. 2015;31:459–468. doi: 10.1007/s12264-015-1539-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. De Benedetti S, Gianazza E, Banfi C, Marocchi A, Lunetta C, Penco S, Bonomi F, Iametti S. Serum Proteome in a Sporadic Amyotrophic Lateral Sclerosis Geographical Cluster. Proteomics Clin Appl. 2017;11:1700043. doi: 10.1002/prca.201700043. [DOI] [PubMed] [Google Scholar]
  13. De Calisto J, Wang N, Wang G, Yigit B, Engel P, Terhorst C. SAP-dependent and -independent regulation of innate T cell development involving SLAMF receptors. Front Immunol. 2014;5:186. doi: 10.3389/fimmu.2014.00186. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Dupuis L, Pradat PF, Ludolph AC, Loeffler JP. Energy metabolism in amyotrophic lateral sclerosis. Lancet Neurol. 2011;10:75–82. doi: 10.1016/S1474-4422(10)70224-6. [DOI] [PubMed] [Google Scholar]
  15. Ferri A, Coccurello R. What is “Hyper” in the ALS Hypermetabolism? Mediators Inflamm. 2017;2017:7821672. doi: 10.1155/2017/7821672. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Freed J, Chakrabarti L. Defining a role for hemoglobin in Parkinson's disease. NPJ Parkinsons Dis. 2016;2:16021. doi: 10.1038/npjparkd.2016.21. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Fujita Y, Yamaguchi A, Hata K, Endo M, Yamaguchi N, Yamashita T. Zyxin is a novel interacting partner for SIRT1. BMC Cell Biol. 2009;10:6. doi: 10.1186/1471-2121-10-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Gerard M, Debyser Z, Desender L, Baert J, Brandt I, Baekelandt V, Engelborghs Y. FK506 binding protein 12 differentially accelerates fibril formation of wild type alpha-synuclein and its clinical mutants A30P or A53T. J Neurochem. 2008;106:121–133. doi: 10.1111/j.1471-4159.2008.05342.x. [DOI] [PubMed] [Google Scholar]
  19. Graham J, Hobson D, Ponnampalam A. High affinity hemoglobin and Parkinson's disease. Med Hypotheses. 2014;83:819–821. doi: 10.1016/j.mehy.2014.10.016. [DOI] [PubMed] [Google Scholar]
  20. Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28:27–30. doi: 10.1093/nar/28.1.27. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Killinger BA, Marshall LL, Chatterjee D, Chu Y, Bras J, Guerreiro R, Kordower JH. In situ proximity labeling identifies Lewy pathology molecular interactions in the human brain. Proc Natl Acad Sci U S A. 2022;119:e2114405119. doi: 10.1073/pnas.2114405119. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Kürten K, Gude AC, Christian Epplen AS, Stein J, Theiss C, Matschke V. Dysregulated expression and distribution of Kif5α in neurites of wobbler motor neurons. Neural Regen Res. 2023;18:150–154. doi: 10.4103/1673-5374.343883. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Leal SS, Gomes CM. Calcium dysregulation links ALS defective proteins and motor neuron selective vulnerability. Front Cell Neurosci. 2015;9:225. doi: 10.3389/fncel.2015.00225. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Lee H, Lee JJ, Park NY, Dubey SK, Kim T, Ruan K, Lim SB, Park SH, Ha S, Kovlyagina I, Kim KT, Kim S, Oh Y, Kim H, Kang SU, Song MR, Lloyd TE, Maragakis NJ, Hong YB, Eoh H, et al. Multi-omic analysis of selectively vulnerable motor neuron subtypes implicates altered lipid metabolism in ALS. Nat Neurosci. 2021;24:1673–1685. doi: 10.1038/s41593-021-00944-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Li R, Wang J, Xie W, Liu J, Wang C. UCHL1 from serum and CSF is a candidate biomarker for amyotrophic lateral sclerosis. Ann Clin Transl Neurol. 2020;7:1420–1428. doi: 10.1002/acn3.51141. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Liu FL, Liu TY, Kung FL. FKBP12 regulates the localization and processing of amyloid precursor protein in human cell lines. J Biosci. 2014;39:85–95. doi: 10.1007/s12038-013-9400-1. [DOI] [PubMed] [Google Scholar]
  27. Liu FL, Liu PH, Shao HW, Kung FL. The intracellular domain of amyloid precursor protein interacts with FKBP12. Biochem Biophys Res Commun. 2006;350:472–477. doi: 10.1016/j.bbrc.2006.09.073. [DOI] [PubMed] [Google Scholar]
  28. Liu YJ, Ju TC, Chen HM, Jang YS, Lee LM, Lai HL, Tai HC, Fang JM, Lin YL, Tu PH, Chern Y. Activation of AMP-activated protein kinase α1 mediates mislocalization of TDP-43 in amyotrophic lateral sclerosis. Hum Mol Genet. 2015;24:787–801. doi: 10.1093/hmg/ddu497. [DOI] [PubMed] [Google Scholar]
  29. Lou D, Xing X, Liang Y. Dendrobine modulates autophagy to alleviate ox-LDL-induced oxidative stress and senescence in HUVECs. Drug Dev Res. 2022;83:1125–1137. doi: 10.1002/ddr.21937. [DOI] [PubMed] [Google Scholar]
  30. Lu CH, Macdonald-Wallis C, Gray E, Pearce N, Petzold A, Norgren N, Giovannoni G, Fratta P, Sidle K, Fish M, Orrell R, Howard R, Talbot K, Greensmith L, Kuhle J, Turner MR, Malaspina A. Neurofilament light chain: a prognostic biomarker in amyotrophic lateral sclerosis. Neurology. 2015;84:2247–2257. doi: 10.1212/WNL.0000000000001642. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Lyon MS, Wosiski-Kuhn M, Gillespie R, Caress J, Milligan C. Inflammation, Immunity, and amyotrophic lateral sclerosis: I. Etiology and pathology. Muscle Nerve. 2019;59:10–22. doi: 10.1002/mus.26289. [DOI] [PubMed] [Google Scholar]
  32. Mandrioli J, Michalke B, Solovyev N, Grill P, Violi F, Lunetta C, Conte A, Sansone VA, Sabatelli M, Vinceti M. Elevated levels of selenium species in cerebrospinal fluid of amyotrophic lateral sclerosis patients with disease-associated gene mutations. Neurodegener Dis. 2017;17:171–180. doi: 10.1159/000460253. [DOI] [PubMed] [Google Scholar]
  33. Marques RF, Duncan KE. SYNGR4 and PLEKHB1 deregulation in motor neurons of amyotrophic lateral sclerosis models: potential contributions to pathobiology. Neural Regen Res. 2022;17:266–270. doi: 10.4103/1673-5374.317960. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Mejzini R, Flynn LL, Pitout IL, Fletcher S, Wilton SD, Akkari PA. ALS genetics, mechanisms, and therapeutics: where are we now? Front Neurosci. 2019;13:1310. doi: 10.3389/fnins.2019.01310. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Moreno-Martinez L, Calvo AC, Muñoz MJ, Osta R. Are circulating cytokines reliable biomarkers for amyotrophic lateral sclerosis? Int J Mol Sci. 2019;20:2759. doi: 10.3390/ijms20112759. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Olsson MG, Davidsson S, Muhammad ZD, Lahiri N, Tabrizi SJ, Akerstrom B, Bjorkqvist M. Increased levels of hemoglobin and alpha1-microglobulin in Huntington's disease. Front Biosci (Elite Ed) 2012;4:950–957. doi: 10.2741/E432. [DOI] [PubMed] [Google Scholar]
  37. Oskarsson B, Gendron TF, Staff NP. Amyotrophic lateral sclerosis: an update for 2018. Mayo Clin Proc. 2018;93:1617–1628. doi: 10.1016/j.mayocp.2018.04.007. [DOI] [PubMed] [Google Scholar]
  38. Paysan-Lafosse T, Blum M, Chuguransky S, Grego T, Pinto BL, Salazar GA, Bileschi ML, Bork P, Bridge A, Colwell L, Gough J, Haft DH, Letunić I, Marchler-Bauer A, Mi H, Natale DA, Orengo CA, Pandurangan AP, Rivoire C, Sigrist CJA, et al. InterPro in 2022. Nucleic Acids Res. 2023;51:D418–D427. doi: 10.1093/nar/gkac993. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Pullen AH, Demestre M, Howard RS, Orrell RW. Passive transfer of purified IgG from patients with amyotrophic lateral sclerosis to mice results in degeneration of motor neurons accompanied by Ca2+ enhancement. Acta Neuropathol. 2004;107:35–46. doi: 10.1007/s00401-003-0777-z. [DOI] [PubMed] [Google Scholar]
  40. Rao DA, Gurish MF, Marshall JL, Slowikowski K, Fonseka CY, Liu Y, Donlin LT, Henderson LA, Wei K, Mizoguchi F, Teslovich NC, Weinblatt ME, Massarotti EM, Coblyn JS, Helfgott SM, Lee YC, Todd DJ, Bykerk VP, Goodman SM, Pernis AB, et al. Pathologically expanded peripheral T helper cell subset drives B cells in rheumatoid arthritis. Nature. 2017;542:110–114. doi: 10.1038/nature20810. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Shephard F, Greville-Heygate O, Marsh O, Anderson S, Chakrabarti L. A mitochondrial location for haemoglobins--dynamic distribution in ageing and Parkinson's disease. Mitochondrion. 2014;14:64–72. doi: 10.1016/j.mito.2013.12.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Sturmey E, Malaspina A. Blood biomarkers in ALS: challenges, applications and novel frontiers. Acta Neurol Scand. 2022;146:375–388. doi: 10.1111/ane.13698. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, Simonovic M, Doncheva NT, Morris JH, Bork P, Jensen LJ, Mering CV. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 2019;47:D607–D613. doi: 10.1093/nar/gky1131. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Tam OH, Rozhkov NV, Shaw R, Kim D, Hubbard I, Fennessey S, Propp N, Fagegaltier D, Harris BT, Ostrow LW, Phatnani H, Ravits J, Dubnau J, Gale Hammell M. Postmortem cortex samples identify distinct molecular subtypes of ALS: retrotransposon activation, oxidative stress, and activated glia. Cell Rep. 2019;29:1164–1177.e5. doi: 10.1016/j.celrep.2019.09.066. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Tang L, Fan D. Amyotrophic lateral sclerosis: new era, new challenges. Lancet Neurol. 2022;21:400–401. doi: 10.1016/S1474-4422(22)00084-9. [DOI] [PubMed] [Google Scholar]
  46. Taylor JP, Brown RH, Jr., Cleveland DW. Decoding ALS: from genes to mechanism. Nature. 2016;539:197–206. doi: 10.1038/nature20413. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Thompson AG, Gray E, Thézénas ML, Charles PD, Evetts S, Hu MT, Talbot K, Fischer R, Kessler BM, Turner MR. Cerebrospinal fluid macrophage biomarkers in amyotrophic lateral sclerosis. Ann Neurol. 2018;83:258–268. doi: 10.1002/ana.25143. [DOI] [PubMed] [Google Scholar]
  48. Tortelli R, Zecca C, Piccininni M, Benmahamed S, Dell'Abate MT, Barulli MR, Capozzo R, Battista P, Logroscino G. Plasma inflammatory cytokines are elevated in ALS. Front Neurol. 2020;11:552295. doi: 10.3389/fneur.2020.552295. [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Vandoorne T, De Bock K, Van Den Bosch L. Energy metabolism in ALS: an underappreciated opportunity? Acta Neuropathol. 2018;135:489–509. doi: 10.1007/s00401-018-1835-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Vanni S, Zattoni M, Moda F, Giaccone G, Tagliavini F, Haïk S, Deslys JP, Zanusso G, Ironside JW, Carmona M, Ferrer I, Kovacs GG, Legname G. Hemoglobin mRNA changes in the frontal cortex of patients with neurodegenerative diseases. Front Neurosci. 2018;12:8. doi: 10.3389/fnins.2018.00008. [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. PLoS Med. 2007;4:e296. doi: 10.1371/journal.pmed.0040296. [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Vu L, Ghosh A, Tran C, Tebung WA, Sidibé H, Garcia-Mansfield K, David-Dirgo V, Sharma R, Pirrotte P, Bowser R, Vande Velde C. Defining the caprin-1 interactome in unstressed and stressed conditions. J Proteome Res. 2021;20:3165–3178. doi: 10.1021/acs.jproteome.1c00016. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Wang N, Halibozek PJ, Yigit B, Zhao H, O'Keeffe MS, Sage P, Sharpe A, Terhorst C. Negative regulation of humoral immunity due to interplay between the SLAMF1, SLAMF5, and SLAMF6 receptors. Front Immunol. 2015;6:158. doi: 10.3389/fimmu.2015.00158. [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Wei CJ, Cui P, Li H, Lang WJ, Liu GY, Ma XF. Shared genes between Alzheimer's disease and ischemic stroke. CNS Neurosci Ther. 2019;25:855–864. doi: 10.1111/cns.13117. [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Xu X, Cai X, Zhu Y, He W, Wu Q, Shi X, Fang Y, Pei Z. MFG-E8 inhibits Aβ-induced microglial production of cathelicidin-related antimicrobial peptide: A suitable target against Alzheimer's disease. Cell Immunol. 2018;331:59–66. doi: 10.1016/j.cellimm.2018.05.008. [DOI] [PubMed] [Google Scholar]
  56. Xu X, Shen D, Gao Y, Zhou Q, Ni Y, Meng H, Shi H, Le W, Chen S, Chen S. A perspective on therapies for amyotrophic lateral sclerosis: can disease progression be curbed? Transl Neurodegener. 2021;10:29. doi: 10.1186/s40035-021-00250-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Yang F, Chen WZ, Jiang SS, Wang XH, Xu RS. A candidate protective factor in amyotrophic lateral sclerosis: heterogenous nuclear ribonucleoprotein G. Neural Regen Res. 2023;18:1527–1534. doi: 10.4103/1673-5374.357916. [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. Zarei S, Carr K, Reiley L, Diaz K, Guerra O, Altamirano PF, Pagani W, Lodin D, Orozco G, Chinea A. A comprehensive review of amyotrophic lateral sclerosis. Surg Neurol Int. 2015;6:171. doi: 10.4103/2152-7806.169561. [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Zhang J, Liu Y, Liu X, Li S, Cheng C, Chen S, Le W. Dynamic changes of CX3CL1/CX3CR1 axis during microglial activation and motor neuron loss in the spinal cord of ALS mouse model. Transl Neurodegener. 2018;7:35. doi: 10.1186/s40035-018-0138-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. Zhang X, Chen S, Lu K, Wang F, Deng J, Xu Z, Wang X, Zhou Q, Le W, Zhao Y. Verapamil ameliorates motor neuron degeneration and improves lifespan in the SOD1(G93A) mouse model of ALS by enhancing autophagic flux. Aging Dis. 2019;10:1159–1173. doi: 10.14336/AD.2019.0228. [DOI] [PMC free article] [PubMed] [Google Scholar]
  61. Zhou QM, Zhang JJ, Li S, Chen S, Le WD. n-butylidenephthalide treatment prolongs life span and attenuates motor neuron loss in SOD1(G93A) mouse model of amyotrophic lateral sclerosis. CNS Neurosci Ther. 2017;23:375–385. doi: 10.1111/cns.12681. [DOI] [PMC free article] [PubMed] [Google Scholar]
  62. Zuo X, Zhou J, Li Y, Wu K, Chen Z, Luo Z, Zhang X, Liang Y, Esteban MA, Zhou Y, Fu XD. TDP-43 aggregation induced by oxidative stress causes global mitochondrial imbalance in ALS. Nat Struct Mol Biol. 2021;28:132–142. doi: 10.1038/s41594-020-00537-7. [DOI] [PubMed] [Google Scholar]

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