Biomarkers for amyotrophic lateral sclerosis

Meta-analysis of differential gene expression in lower motor neurons isolated by laser capture microdissection from post-mortem ALS spinal cords

Introduction

ALS is a fatal neurodegenerative disease for which underlying mechanisms are incompletely understood. The motor neuron is a central player in ALS pathogenesis but different transcriptome signatures have been derived from bulk analysis of post-mortem tissue and iPSC-derived motor neurons (iPSC-MNs).

Methods

This study performed a meta-analysis of six gene expression studies (microarray and RNA-seq) in which laser capture microdissection (LCM) was used to isolate lower motor neurons from post-mortem spinal cords of ALS and control (CTL) subjects. Differentially expressed genes (DEGs) with consistent ALS versus CTL expression differences across studies were identified.

Results

The analysis identified 222 ALS-increased DEGs (FDR <0.10, SMD >0.80) and 278 ALS-decreased DEGs (FDR <0.10, SMD < -0.80). ALS-increased DEGs were linked to PI3K-AKT signaling, innate immunity, inflammation, motor neuron differentiation and extracellular matrix. ALS-decreased DEGs were associated with the ubiquitin-proteosome system, microtubules, axon growth, RNA-binding proteins and synaptic membrane. ALS-decreased DEG mRNAs frequently interacted with RNA-binding proteins (e.g., FUS, HuR). The complete set of DEGs (increased and decreased) overlapped significantly with genes near ALS-associated SNP loci (p < 0.01). Transcription factor target motifs with increased proximity to ALS-increased DEGs were identified, most notably DNA elements predicted to interact with forkhead transcription factors (e.g., FOXP1) and motor neuron and pancreas homeobox 1 (MNX1). Some of these DNA elements overlie ALS-associated SNPs within known enhancers and are predicted to have genotype-dependent MNX1 interactions. DEGs were compared to those identified from SOD1-G93A mice and bulk spinal cord segments or iPSC-MNs from ALS patients. There was good correspondence with transcriptome changes from SOD1-G93A mice (r ≤ 0.408) but most DEGs were not differentially expressed in bulk spinal cords or iPSC-MNs and transcriptome-wide effect size correlations were weak (bulk tissue: r ≤ 0.207, iPSC-MN: r ≤ 0.037).

Conclusion

This study defines a robust transcriptome signature from LCM-based motor neuron studies of post-mortem tissue from ALS and CTL subjects. This signature differs from those obtained from analysis of bulk spinal cord segments and iPSC-MNs. Results provide insight into mechanisms underlying gene dysregulation in ALS and highlight connections between these mechanisms, ALS genetics, and motor neuron biology.

Two-sample Mendelian randomization analysis of 91 circulating inflammatory protein levels and amyotrophic lateral sclerosis

Introduction

Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease with poorly understood pathophysiology. Recent studies have highlighted systemic inflammation, especially the role of circulating inflammatory proteins, in ALS.

Methods

This study investigates the potential causal link between these proteins and ALS. We employed a two-sample Mendelian Randomization(MR) approach, analyzing data from large-scale genome-wide association studies to explore the relationship between 91 circulating inflammatory proteins and ALS. This included various MR methods like MR Egger, weighted median, and inverse-variance weighted, complemented by sensitivity analyses for robust results.

Results

Significant associations were observed between levels of inflammatory proteins, including Adenosine Deaminase, Interleukin-17C, Oncostatin-M, Leukemia Inhibitory Factor Receptor, and Osteoprotegerin, and ALS risk. Consistencies were noted across different P-value thresholds. Bidirectional MR suggested that ALS risk might influence levels of certain inflammatory proteins.

Discussion

Our findings, via MR analysis, indicate a potential causal relationship between circulating inflammatory proteins and ALS. This sheds new light on ALS pathophysiology and suggests possible therapeutic targets. Further research is required to confirm these results and understand the specific roles of these proteins in ALS.

Longitudinal course of neurofilament light chain levels in amyotrophic lateral sclerosis-insights from a completed randomized controlled trial with rasagiline

BACKGROUND AND PURPOSE

Rasagiline might be disease modifying in patients with amyotrophic lateral sclerosis (ALS). The aim was to evaluate the effect of rasagiline 2 mg/day on neurofilament light chain (NfL), a prognostic biomarker in ALS.

METHODS

In 65 patients with ALS randomized in a 3:1 ratio to rasagiline 2 mg/day (n = 48) or placebo (n = 17) in a completed randomized controlled multicentre trial, NfL levels in plasma were measured at baseline, month 6 and month 12. Longitudinal changes in NfL levels were evaluated regarding treatment and clinical parameters.

RESULTS

Baseline NfL levels did not differ between the study arms and correlated with disease progression rates both pre-baseline (r = 0.64, p < 0.001) and during the study (r = 0.61, p < 0.001). NfL measured at months 6 and 12 did not change significantly from baseline in both arms, with a median individual NfL change of +1.4 pg/mL (interquartile range [IQR] -5.6, 14.2) across all follow-up time points. However, a significant difference in NfL change at month 12 was observed between patients with high and low NfL baseline levels treated with rasagiline (high [n = 13], -6.9 pg/mL, IQR -20.4, 6.0; low [n = 18], +5.9 pg/mL, IQR -1.4, 19.7; p = 0.025). Additionally, generally higher longitudinal NfL variability was observed in patients with high baseline levels, whereas disease progression rates and disease duration at baseline had no impact on the longitudinal NfL course.

CONCLUSION

Post hoc NfL measurements in completed clinical trials are helpful in interpreting NfL data from ongoing and future interventional trials and could provide hypothesis-generating complementary insights. Further studies are warranted to ultimately differentiate NfL response to treatment from other factors.

Primary lateral sclerosis: application and validation of the 2020 consensus diagnostic criteria in an expert opinion-based PLS cohort

BACKGROUND

Validation of the 2020 consensus criteria for primary lateral sclerosis (PLS) is essential for their use in clinical practice and future trials.

METHODS

In a large cohort of patients diagnosed with PLS by expert opinion prior to the new criteria with detailed clinical baseline evaluation (n=107) and longitudinal follow-up (n=63), we applied the new diagnostic criteria and analysed the clinical phenotype, electromyography (EMG), diagnostic accuracy and prognosis, adding neurofilaments and MRI as potential biomarkers.

RESULTS

The criteria for definite PLS were met by 28% and those for probable PLS by 19%, whereas 53% did not meet the full criteria at baseline, mainly due to the time, EMG and region criteria. Patients not meeting the criteria had less generalised upper motor neuron involvement but were otherwise similar in demographic and clinical characteristics. All patients with definite and probable PLS maintained PLS diagnosis during follow-up, while four patients not meeting the criteria developed clinical lower motor neuron involvement. Definite PLS cases showed improved survival compared with probable PLS and patients who did not meet the criteria. Despite a clinical PLS phenotype, fibrillation potentials/positive sharp waves and fasciculations in one or more muscles were a frequent EMG finding, with the extent and prognostic significance depending on disease duration. Serum neurofilament light and a multiparametric MRI fibre integrity Z-score correlated with clinical parameters and were identified as potential biomarkers.

CONCLUSION

Validation of the 2020 PLS consensus criteria revealed high diagnostic certainty and prognostic significance, supporting their value for research and clinical practice.

Single-Cell RNA Sequencing Analysis of Microglia Dissected the Energy Metabolism and Revealed Potential Biomarkers in Amyotrophic Lateral Sclerosis

Amyotrophic lateral sclerosis (ALS) is a common neurodegenerative disease, accompanied by the gradual loss of motor neuron, even life-threatening. However, the pathogenesis, early diagnosis, and effective strategies of ALS are not yet completely understood. In this study, the function of differentially expressed genes (DEGs) in non-neuronal cells of the primary motor cortex of ALS patients (DATA1), the brainstem of SOD1 mutant ALS mice (DATA2), and the whole blood tissue of ALS patients (DATA3) were explored. The results showed that the functions of DEGs in non-neuronal cells were mainly related to energy metabolism (such as oxidative phosphorylation) and protein synthesis. In non-neuronal cells, six upregulated DEGs (HSPA8, SOD1, CALM1, CALM2, NEFL, COX6C) and three downregulated DEGs (SNRNP70, HSPA1A, HSPA1B) might be key factors in regulating ALS. Microglia played a key role in the development of ALS. The expression of SOD1 and TUBA4A in microglia in DATA1 was significantly increased. The integration analysis of DEGs in DATA1 and DATA2 showed that SOD1 and CALM1 might be potential biomarkers. The integration analysis of DEGs in DATA1 and DATA3 showed that CALM2 and HSPA1A might be potential biomarkers. Cell interaction showed that the interaction between microglia and other cells was reduced in high oxidative phosphorylation states, which might be a risk factor in ALS. Our research provided evidence for the pathogenesis, early diagnosis, and potential targeted therapy for ALS.

Protein Biomarkers Shared by Multiple Neurodegenerative Diseases Are Calmodulin-Binding Proteins Offering Novel and Potentially Universal Therapeutic Targets

Seven major neurodegenerative diseases and their variants share many overlapping biomarkers that are calmodulin-binding proteins: Alzheimer's disease (AD), amyotrophic lateral sclerosis (ALS), frontotemporal lobar dementia (FTD), Huntington's disease (HD), Lewy body disease (LBD), multiple sclerosis (MS), and Parkinson's disease (PD). Calcium dysregulation is an early and persistent event in each of these diseases, with calmodulin serving as an initial and primary target of increased cytosolic calcium. Considering the central role of calcium dysregulation and its downstream impact on calcium signaling, calmodulin has gained interest as a major regulator of neurodegenerative events. Here, we show that calmodulin serves a critical role in neurodegenerative diseases via binding to and regulating an abundance of biomarkers, many of which are involved in multiple neurodegenerative diseases. Of special interest are the shared functions of calmodulin in the generation of protein biomarker aggregates in AD, HD, LBD, and PD, where calmodulin not only binds to amyloid beta, pTau, alpha-synuclein, and mutant huntingtin but also, via its regulation of transglutaminase 2, converts them into toxic protein aggregates. It is suggested that several calmodulin binding proteins could immediately serve as primary drug targets, while combinations of calmodulin binding proteins could provide simultaneous insight into the onset and progression of multiple neurodegenerative diseases.

Use of 3D Printing Techniques to Fabricate Implantable Microelectrodes for Electrochemical Detection of Biomarkers in the Early Diagnosis of Cardiovascular and Neurodegenerative Diseases

This Review provides a comprehensive overview of 3D printing techniques to fabricate implantable microelectrodes for the electrochemical detection of biomarkers in the early diagnosis of cardiovascular and neurodegenerative diseases. Early diagnosis of these diseases is crucial to improving patient outcomes and reducing healthcare systems' burden. Biomarkers serve as measurable indicators of these diseases, and implantable microelectrodes offer a promising tool for their electrochemical detection. Here, we discuss various 3D printing techniques, including stereolithography (SLA), digital light processing (DLP), fused deposition modeling (FDM), selective laser sintering (SLS), and two-photon polymerization (2PP), highlighting their advantages and limitations in microelectrode fabrication. We also explore the materials used in constructing implantable microelectrodes, emphasizing their biocompatibility and biodegradation properties. The principles of electrochemical detection and the types of sensors utilized are examined, with a focus on their applications in detecting biomarkers for cardiovascular and neurodegenerative diseases. Finally, we address the current challenges and future perspectives in the field of 3D-printed implantable microelectrodes, emphasizing their potential for improving early diagnosis and personalized treatment strategies.

Proteomic profiling of the brain from the wobbler mouse model of amyotrophic lateral sclerosis reveals elevated levels of the astrogliosis marker glial fibrillary acidic protein

The wobbler mouse is a widely used model system of amyotrophic lateral sclerosis and exhibits progressive neurodegeneration and neuroinflammation in association with skeletal muscle wasting. This study has used wobbler brain preparations for the systematic and mass spectrometric determination of proteome-wide changes. The proteomic characterization of total protein extracts from wobbler specimens was carried out with the help of an Orbitrap mass spectrometer and revealed elevated levels of glia cell marker proteins, i.e., glial fibrillary acidic protein and the actin-binding protein coronin. In contrast, the abundance of the actin-binding protein neurabin and the scaffolding protein named piccolo of the presynaptic cytomatrix were shown to be reduced. The increased abundance of glial fibrillary acidic protein, which is frequently used in neuropathological studies as a marker protein of glial scar formation, was confirmed by immunoblotting. In analogy, the proteomic profiling of the brain from another established murine model of motor neuron disease, the SOD1mouse, also showed increased levels of this intermediate filament protein. This suggests that neurodegenerative processes are associated with astrogliosis in both the wobbler and SOD1 brain.

Artificial intelligence and statistical methods for stratification and prediction of progression in amyotrophic lateral sclerosis: A systematic review

BACKGROUND

Amyotrophic Lateral Sclerosis (ALS) is a fatal neurodegenerative disorder characterised by the progressive loss of motor neurons in the brain and spinal cord. The fact that ALS's disease course is highly heterogeneous, and its determinants not fully known, combined with ALS's relatively low prevalence, renders the successful application of artificial intelligence (AI) techniques particularly arduous.

OBJECTIVE

This systematic review aims at identifying areas of agreement and unanswered questions regarding two notable applications of AI in ALS, namely the automatic, data-driven stratification of patients according to their phenotype, and the prediction of ALS progression. Differently from previous works, this review is focused on the methodological landscape of AI in ALS.

METHODS

We conducted a systematic search of the Scopus and PubMed databases, looking for studies on data-driven stratification methods based on unsupervised techniques resulting in (A) automatic group discovery or (B) a transformation of the feature space allowing patient subgroups to be identified; and for studies on internally or externally validated methods for the prediction of ALS progression. We described the selected studies according to the following characteristics, when applicable: variables used, methodology, splitting criteria and number of groups, prediction outcomes, validation schemes, and metrics.

RESULTS

Of the starting 1604 unique reports (2837 combined hits between Scopus and PubMed), 239 were selected for thorough screening, leading to the inclusion of 15 studies on patient stratification, 28 on prediction of ALS progression, and 6 on both stratification and prediction. In terms of variables used, most stratification and prediction studies included demographics and features derived from the ALSFRS or ALSFRS-R scores, which were also the main prediction targets. The most represented stratification methods were K-means, and hierarchical and expectation-maximisation clustering; while random forests, logistic regression, the Cox proportional hazard model, and various flavours of deep learning were the most widely used prediction methods. Predictive model validation was, albeit unexpectedly, quite rarely performed in absolute terms (leading to the exclusion of 78 eligible studies), with the overwhelming majority of included studies resorting to internal validation only.

CONCLUSION

This systematic review highlighted a general agreement in terms of input variable selection for both stratification and prediction of ALS progression, and in terms of prediction targets. A striking lack of validated models emerged, as well as a general difficulty in reproducing many published studies, mainly due to the absence of the corresponding parameter lists. While deep learning seems promising for prediction applications, its superiority with respect to traditional methods has not been established; there is, instead, ample room for its application in the subfield of patient stratification. Finally, an open question remains on the role of new environmental and behavioural variables collected via novel, real-time sensors.

Studies of Genetic and Proteomic Risk Factors of Amyotrophic Lateral Sclerosis Inspire Biomarker Development and Gene Therapy

Amyotrophic lateral sclerosis (ALS) is an incurable neurodegenerative disease affecting the upper and lower motor neurons, leading to muscle weakness, motor impairments, disabilities and death. Approximately 5-10% of ALS cases are associated with positive family history (familial ALS or fALS), whilst the remainder are sporadic (sporadic ALS, sALS). At least 50 genes have been identified as causative or risk factors for ALS. Established pathogenic variants include superoxide dismutase type 1 (SOD1), chromosome 9 open reading frame 72 (c9orf72), TAR DNA Binding Protein (TARDBP), and Fused In Sarcoma (FUS); additional ALS-related genes including Charged Multivesicular Body Protein 2B (CHMP2B), Senataxin (SETX), Sequestosome 1 (SQSTM1), TANK Binding Kinase 1 (TBK1) and NIMA Related Kinase 1 (NEK1), have been identified. Mutations in these genes could impair different mechanisms, including vesicle transport, autophagy, and cytoskeletal or mitochondrial functions. So far, there is no effective therapy against ALS. Thus, early diagnosis and disease risk predictions remain one of the best options against ALS symptomologies. Proteomic biomarkers, microRNAs, and extracellular vehicles (EVs) serve as promising tools for disease diagnosis or progression assessment. These markers are relatively easy to obtain from blood or cerebrospinal fluids and can be used to identify potential genetic causative and risk factors even in the preclinical stage before symptoms appear. In addition, antisense oligonucleotides and RNA gene therapies have successfully been employed against other diseases, such as childhood-onset spinal muscular atrophy (SMA), which could also give hope to ALS patients. Therefore, an effective gene and biomarker panel should be generated for potentially "at risk" individuals to provide timely interventions and better treatment outcomes for ALS patients as soon as possible.

Systematic Review and Meta-Analyses of Aminopeptidases as Prognostic Biomarkers in Amyotrophic Lateral Sclerosis

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease characterized by the progressive loss of motor neurons in the spinal cord, brain stem, and cerebral cortex. Biomarkers for ALS are essential for disease detection and to provide information on potential therapeutic targets. Aminopeptidases catalyze the cleavage of amino acids from the amino terminus of protein or substrates such as neuropeptides. Since certain aminopeptidases are known to increase the risk of neurodegeneration, such mechanisms may reveal new targets to determine their association with ALS risk and their interest as a diagnostic biomarker. The authors performed a systematic review and meta-analyses of genome-wide association studies (GWASs) to identify reported aminopeptidases genetic loci associated with the risk of ALS. PubMed, Scopus, CINAHL, ISI Web of Science, ProQuest, LILACS, and Cochrane databases were searched to retrieve eligible studies in English or Spanish, published up to 27 January 2023. A total of 16 studies were included in this systematic review, where a series of aminopeptidases could be related to ALS and could be promising biomarkers (DPP1, DPP2, DPP4, LeuAP, pGluAP, and PSA/NPEPPS). The literature reported the association of single-nucleotide polymorphisms (SNPs: rs10260404 and rs17174381) with the risk of ALS. The genetic variation rs10260404 in the DPP6 gene was identified to be highly associated with ALS susceptibility, but meta-analyses of genotypes in five studies in a matched cohort of different ancestry (1873 cases and 1861 control subjects) showed no ALS risk association. Meta-analyses of eight studies for minor allele frequency (MAF) also found no ALS association for the "C" allele. The systematic review identified aminopeptidases as possible biomarkers. However, the meta-analyses for rs1060404 of DPP6 do not show a risk associated with ALS.