ALS blood expression profiling identifies new biomarkers, patient subgroups, and evidence for neutrophilia and hypoxia

Copper toxicity and deficiency: the vicious cycle at the core of protein aggregation in ALS

The pathophysiology of ALS involves many signs of a disruption in copper homeostasis, with both excess free levels and functional deficiency likely occurring simultaneously. This is crucial, as many important physiological functions are performed by cuproenzymes. While it is unsurprising that many ALS symptoms are related to signs of copper deficiency, resulting in vascular, antioxidant system and mitochondrial oxidative respiration deficiencies, there are also signs of copper toxicity such as ROS generation and enhanced protein aggregation. We discuss how copper also plays a key role in proteostasis and interacts either directly or indirectly with many of the key aggregate-prone proteins implicated in ALS, such as TDP-43, C9ORF72, SOD1 and FUS as well as the effect of their aggregation on copper homeostasis. We suggest that loss of cuproprotein function is at the core of ALS pathology, a condition that is driven by a combination of unbound copper and ROS that can either initiate and/or accelerate protein aggregation. This could trigger a positive feedback cycle whereby protein aggregates trigger the aggregation of other proteins in a chain reaction that eventually captures elements of the proteostatic mechanisms in place to counteract them. The end result is an abundance of aggregated non-functional cuproproteins and chaperones alongside depleted intracellular copper stores, resulting in a general lack of cuproenzyme function. We then discuss the possible aetiology of ALS and illustrate how strong risk factors including environmental toxins such as BMAA and heavy metals can functionally behave to promote protein aggregation and disturb copper metabolism that likely drives this vicious cycle in sporadic ALS. From this synthesis, we propose restoration of copper balance using copper delivery agents in combination with chaperones/chaperone mimetics, perhaps in conjunction with the neuroprotective amino acid serine, as a promising strategy in the treatment of this incurable disease.

Adipose mesenchymal stem cells-derived extracellular vesicles exert their preferential action in damaged central sites of SOD1 mice rather than peripherally

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disorder involving motor neuron (MN) loss in the motor cortex, brainstem and spinal cord leading to progressive paralysis and death. Due to the pathogenetic complexity, there are no effective therapies available. In this context the use of mesenchymal stem cells and their vesicular counterpart is an emerging therapeutic strategy to counteract neurodegeneration. The extracellular vesicles derived from adipose stem cells (ASC-EVs) recapitulate and ameliorate the neuroprotective effect of stem cells and, thanks to their small dimensions, makes their use suitable to develop novel therapeutic approaches for neurodegenerative diseases as ALS. Here we investigate a therapeutic regimen of ASC-EVs injection in SOD1(G93A) mice, the most widely used murine model of ALS. Repeated intranasal administrations of high doses of ASC-EVs were able to ameliorate motor performance of injected SOD1(G93A) mice at the early stage of the disease and produce a significant improvement at the end-stage in the lumbar MNs rescue. Moreover, ASC-EVs preserve the structure of neuromuscular junction without counteracting the muscle atrophy. The results indicate that the intranasal ASC-EVs administration acts in central nervous system sites rather than at peripheral level in SOD1(G93A) mice. These considerations allow us to identify future applications of ASC-EVs that involve different targets simultaneously to maximize the clinical and neuropathological outcomes in ALS in vivo models.

Sex-Dependent Changes to the Intestinal and Hepatic Abundance of Drug Transporters and Metabolizing Enzymes in the SOD1G93A Mouse Model of Amyotrophic Lateral Sclerosis

Amyotrophic lateral sclerosis (ALS) is characterized by death and dysfunction of motor neurons that result in a rapidly progressing loss of motor function. While there are some data on alterations at the blood-brain barrier (BBB) in ALS and their potential impact on CNS trafficking of drugs, little is reported on the impact of this disease on the expression of drug-handling proteins in the small intestine and liver. This may impact the dosing of the many medicines that individuals with ALS are prescribed. In the present study, a proteomic evaluation was performed on small intestine and liver samples from postnatal day 120 SOD1G93A mice (a model of familial ALS that harbors a human mutant form of superoxide dismutase 1) and wild-type (WT) littermates (n = 7/genotype/sex). Untargeted, quantitative proteomics was undertaken using either label-based [tandem mass tag (TMT)] or label-free [data-independent acquisition (DIA)] acquisition strategies on high-resolution mass spectrometric instrumentation. Copper chaperone for superoxide dismutase (CCS) was significantly higher in SOD1G93A samples compared to the WT samples for both sexes and tissues, therefore representing a potential biomarker for ALS in this mouse model. Relative to WT mice, male SOD1G93A mice had significantly different proteins (Padj < 0.05, |fold-change|>1.2) in the small intestine (male 22, female 1) and liver (male 140, female 3). This included an up-regulation of intestinal transporters for dietary glucose [solute carrier (SLC) SLC5A1] and cholesterol (Niemann-Pick c1-like 1), as well as for several drugs (e.g., SLC15A1), in the male SOD1G93A mice. There was both an up-regulation (e.g., SLCO2A1) and down-regulation (ammonium transporter rh type b) of transporters in the male SOD1G93A liver. In addition, there was both an up-regulation (e.g., phosphoenolpyruvate carboxykinase) and down-regulation (e.g., carboxylesterase 1) of metabolizing enzymes in the male SOD1G93A liver. This proteomic data set identified male-specific changes to key small intestinal and hepatic transporters and metabolizing enzymes that may have important implications for the bioavailability of nutrients and drugs in individuals with ALS.

Developing a novel immune infiltration-associated mitophagy prediction model for amyotrophic lateral sclerosis using bioinformatics strategies

Background

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease, which leads to muscle weakness and eventual paralysis. Numerous studies have indicated that mitophagy and immune inflammation have a significant impact on the onset and advancement of ALS. Nevertheless, the possible diagnostic and prognostic significance of mitophagy-related genes associated with immune infiltration in ALS is uncertain. The purpose of this study is to create a predictive model for ALS using genes linked with mitophagy-associated immune infiltration.

Methods

ALS gene expression profiles were downloaded from the Gene Expression Omnibus (GEO) database. Univariate Cox analysis and machine learning methods were applied to analyze mitophagy-associated genes and develop a prognostic risk score model. Subsequently, functional and immune infiltration analyses were conducted to study the biological attributes and immune cell enrichment in individuals with ALS. Additionally, validation of identified feature genes in the prediction model was performed using ALS mouse models and ALS patients.

Results

In this study, a comprehensive analysis revealed the identification of 22 mitophagy-related differential expression genes and 40 prognostic genes. Additionally, an 18-gene prognostic signature was identified with machine learning, which was utilized to construct a prognostic risk score model. Functional enrichment analysis demonstrated the enrichment of various pathways, including oxidative phosphorylation, unfolded proteins, KRAS, and mTOR signaling pathways, as well as other immune-related pathways. The analysis of immune infiltration revealed notable distinctions in certain congenital immune cells and adaptive immune cells between the low-risk and high-risk groups, particularly concerning the T lymphocyte subgroup. ALS mouse models and ALS clinical samples demonstrated consistent expression levels of four mitophagy-related immune infiltration genes (BCKDHA, JTB, KYNU, and GTF2H5) with the results of bioinformatics analysis.

Conclusion

This study has successfully devised and verified a pioneering prognostic predictive risk score for ALS, utilizing eighteen mitophagy-related genes. Furthermore, the findings indicate that four of these genes exhibit promising roles in the context of ALS prognostic.

Association of peripheral immune activation with amyotrophic lateral sclerosis and Parkinson's disease: A systematic review and meta-analysis

BACKGROUND

Recent studies have revealed the link between immune activation and neurodegenerative diseases.

METHODS

By employing meta-analysis, we estimated the standardized mean difference (SMD) and their corresponding 95% confidence intervals (CIs) between the groups.

RESULTS

According to the pre-set criteria, a total of 21 published articles including 2377 ALS patients and 1244 HCs, as well as 60 articles including 5111 PD patients and 4237 HCs, were identified. This study provided evidence of peripheral immune activation in the pathogenesis of ALS and PD.

CONCLUSION

Our results suggested monitoring changes in peripheral blood immune cell populations, particularly lymphocyte subsets, will benefit understanding the developments and exploring reliable and specific biomarkers of these two diseases.

Unraveling the Heterogeneity of ALS-A Call to Redefine Patient Stratification for Better Outcomes in Clinical Trials

Despite tremendous efforts in basic research and a growing number of clinical trials aiming to find effective treatments, amyotrophic lateral sclerosis (ALS) remains an incurable disease. One possible reason for the lack of effective causative treatment options is that ALS may not be a single disease entity but rather may represent a clinical syndrome, with diverse genetic and molecular causes, histopathological alterations, and subsequent clinical presentations contributing to its complexity and variability among individuals. Defining a way to subcluster ALS patients is becoming a central endeavor in the field. Identifying specific clusters and applying them in clinical trials could enable the development of more effective treatments. This review aims to summarize the available data on heterogeneity in ALS with regard to various aspects, e.g., clinical, genetic, and molecular.

Fluid biomarkers for amyotrophic lateral sclerosis: a review

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease characterized by the loss of upper and lower motor neurons. Presently, three FDA-approved drugs are available to help slow functional decline for patients with ALS, but no cure yet exists. With an average life expectancy of only two to five years after diagnosis, there is a clear need for biomarkers to improve the care of patients with ALS and to expedite ALS treatment development. Here, we provide a review of the efforts made towards identifying diagnostic, prognostic, susceptibility/risk, and response fluid biomarkers with the intent to facilitate a more rapid and accurate ALS diagnosis, to better predict prognosis, to improve clinical trial design, and to inform interpretation of clinical trial results. Over the course of 20 + years, several promising fluid biomarker candidates for ALS have emerged. These will be discussed, as will the exciting new strategies being explored for ALS biomarker discovery and development.

Exploring the cuproptosis-related molecular clusters in the peripheral blood of patients with amyotrophic lateral sclerosis

BACKGROUND

Amyotrophic lateral sclerosis (ALS) is a progressive and lethal neurodegenerative disease. Several studies have suggested the involvement of cuproptosis in its pathogenesis. In this research, we intend to explore the cuproptosis-related molecular clusters in ALS and develop a novel cuproptosis-related genes prediction model.

METHODS

The peripheral blood gene expression data was downloaded from the Gene Expression Omnibus (GEO) online database. Based on the GSE112681 dataset, we investigated the critical cuproptosis-related genes (CuRGs) and pathological clustering of ALS. The immune microenvironment features of the peripheral blood in ALS patients were also examined using the CIBERSORT algorithm. Cluster-specific hub genes were determined by the WGCNA. The most accurate prediction model was selected by comparing the performance of four machine learning techniques. ROC curves and two independent datasets were applied to validate the prediction accuracy. The available compounds targeting these hub genes were filtered to investigate their efficacy in treating ALS.

RESULTS

We successfully determined four critical cuproptosis-related genes and two pathological clusters with various immune profiles and biological characteristics in ALS. Functional analysis showed that genes in Cluster1 were primarily enriched in pathways closely associated with immunity. The Support Vector Machine (SVM) model exhibited the best discrimination properties with a large area under the curve (AUC = 0.862). Five hub prediction genes (BAP1, SMG1, BCLAF1, DHX15, EIF4G2) were selected to establish a nomogram model, suggesting significant risk prediction potential for ALS. The accuracy of this model in predicting ALS incidence was also demonstrated through calibration curves, nomograms, and decision curve analysis. Finally, three drugs targeting BAP1 were determined through drug-gene interactions.

CONCLUSION

This study elucidated the complex associations between cuproptosis and ALS and constructed a satisfactory predictive model to explore the pathological characteristics of different clusters in ALS patients.

Novel Genetic and Biochemical Insights into the Spectrum of NEFL-Associated Phenotypes

Background

NEFL encodes for the neurofilament light chain protein. Pathogenic variants in NEFL cause demyelinating, axonal and intermediate forms of Charcot-Marie-Tooth disease (CMT) which present with a varying degree of severity and somatic mutations have not been described yet. Currently, 34 different CMT-causing pathogenic variants in NEFL in 174 patients have been reported. Muscular involvement was also described in CMT2E patients mostly as a secondary effect. Also, there are a few descriptions of a primary muscle vulnerability upon pathogenic NEFL variants.

Objectives

To expand the current knowledge on the genetic landscape, clinical presentation and muscle involvement in NEFL-related neurological diseases by retrospective case study and literature review.

Methods

We applied in-depth phenotyping of new and already reported cases, molecular genetic testing, light-, electron- and Coherent Anti-Stokes Raman Scattering-microscopic studies and proteomic profiling in addition to in silico modelling of NEFL-variants.

Results

We report on a boy with a muscular phenotype (weakness, myalgia and cramps, Z-band alterations and mini-cores in some myofibers) associated with the heterozygous p.(Phe104Val) NEFL-variant, which was previously described in a neuropathy case. Skeletal muscle proteomics findings indicated affection of cytoskeletal proteins. Moreover, we report on two further neuropathic patients (16 years old girl and her father) both carrying the heterozygous p.(Pro8Ser) variant, which has been identified as 15% somatic mosaic in the father. While the daughter presented with altered neurophysiology,neurogenic clump feet and gait disturbances, the father showed clinically only feet deformities. As missense variants affecting proline at amino acid position 8 are leading to neuropathic manifestations of different severities, in silico modelling of these different amino acid substitutions indicated variable pathogenic impact correlating with disease onset.

Conclusions

Our findings provide new morphological and biochemical insights into the vulnerability of denervated muscle (upon NEFL-associated neuropathy) as well as novel genetic findings expanding the current knowledge on NEFL-related neuromuscular phenotypes and their clinical manifestations. Along this line, our data show that even subtle expression of somatic NEFL variants can lead to neuromuscular symptoms.

Abnormal Brain Protein Abundance and Cross-tissue mRNA Expression in Amyotrophic Lateral Sclerosis

Due to the limitations of the present risk genes in understanding the etiology of amyotrophic lateral sclerosis (ALS), it is necessary to find additional causative genes utilizing novel approaches. In this study, we conducted a two-stage proteome-wide association study (PWAS) using ALS genome-wide association study (GWAS) data (N = 152,268) and two distinct human brain protein quantitative trait loci (pQTL) datasets (ROSMAP N = 376 and Banner N = 152) to identify ALS risk genes and prioritized candidate genes with Mendelian randomization (MR) and Bayesian colocalization analysis. Next, we verified the aberrant expression of risk genes in multiple tissues, including lower motor neurons, skeletal muscle, and whole blood. Six ALS risk genes (SCFD1, SARM1, TMEM175, BCS1L, WIPI2, and DHRS11) were found during the PWAS discovery phase, and SARM1 and BCS1L were confirmed during the validation phase. The following MR (p = 2.10 × 10-7) and Bayesian colocalization analysis (ROSMAP PP4 = 0.999, Banner PP4 = 0.999) confirmed the causal association between SARM1 and ALS. Further differential expression analysis revealed that SARM1 was markedly downregulated in lower motor neurons (p = 7.64 × 10-3), skeletal muscle (p = 9.34 × 10-3), and whole blood (p = 1.94 × 10-3). Our findings identified some promising protein candidates for future investigation as therapeutic targets. The dysregulation of SARM1 in multiple tissues provides a new way to explain ALS pathology.

Establishing mRNA and microRNA interactions driving disease heterogeneity in amyotrophic lateral sclerosis patient survival

Amyotrophic lateral sclerosis is a fatal neurodegenerative disease, associated with the degeneration of both upper and lower motor neurons of the motor cortex, brainstem and spinal cord. Death in most patients results from respiratory failure within 3-4 years from symptom onset. However, due to disease heterogeneity some individuals survive only months from symptom onset while others live for several years. Identifying specific biomarkers that aid in establishing disease prognosis, particularly in terms of predicting disease progression, will help our understanding of amyotrophic lateral sclerosis pathophysiology and could be used to monitor a patient's response to drugs and therapeutic agents. Transcriptomic profiling technologies are continually evolving, enabling us to identify key gene changes in biological processes associated with disease. MicroRNAs are small non-coding RNAs typically associated with regulating gene expression, by degrading mRNA or reducing levels of gene expression. Being able to associate gene expression changes with corresponding microRNA changes would help to distinguish a more complex biomarker signature enabling us to address key challenges associated with complex diseases such as amyotrophic lateral sclerosis. The present study aimed to investigate the transcriptomic profile (mRNA and microRNA) of lymphoblastoid cell lines from amyotrophic lateral sclerosis patients to identify key signatures that are distinguishable in those patients who suffered a short disease duration (<12 months) (n = 22) compared with those that had a longer disease duration (>6 years) (n = 20). Transcriptional profiling of microRNA-mRNA interactions from lymphoblastoid cell lines in amyotrophic lateral sclerosis patients revealed differential expression of genes involved in cell cycle, DNA damage and RNA processing in patients with longer survival from disease onset compared with those with short survival. Understanding these particular microRNA-mRNA interactions and the pathways in which they are involved may help to distinguish potential therapeutic targets that could exert neuroprotective effects to prolong the life expectancy of amyotrophic lateral sclerosis patients.

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.

A Diagnostic Gene-Expression Signature in Fibroblasts of Amyotrophic Lateral Sclerosis

Amyotrophic lateral sclerosis (ALS) is a fatal, progressive neurodegenerative disease with limited treatment options. Diagnosis can be difficult due to the heterogeneity and non-specific nature of the initial symptoms, resulting in delays that compromise prompt access to effective therapeutic strategies. Transcriptome profiling of patient-derived peripheral cells represents a valuable benchmark in overcoming such challenges, providing the opportunity to identify molecular diagnostic signatures. In this study, we characterized transcriptome changes in skin fibroblasts of sporadic ALS patients (sALS) and controls and evaluated their utility as a molecular classifier for ALS diagnosis. Our analysis identified 277 differentially expressed transcripts predominantly involved in transcriptional regulation, synaptic transmission, and the inflammatory response. A support vector machine classifier based on this 277-gene signature was developed to discriminate patients with sALS from controls, showing significant predictive power in both the discovery dataset and in six independent publicly available gene expression datasets obtained from different sALS tissue/cell samples. Taken together, our findings support the utility of transcriptional signatures in peripheral cells as valuable biomarkers for the diagnosis of ALS.

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.

Electrochemical Impedance Immunoassay for ALS-Associated Neurofilament Protein: Matrix Effect on the Immunoplatform

Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disorder, which has complex diagnostic steps. Electrochemical immunoassays may make the diagnosis simpler and faster. Here, we present the detection of ALS-associated neurofilament light chain (Nf-L) protein through an electrochemical impedance immunoassay on reduced graphene oxide (rGO) screen-printed electrodes. The immunoassay was developed in two different media, i.e., buffer and human serum, to compare the effect of the media on their figures of merit and calibration models. The label-free charge transfer resistance (R_CT) of the immunoplatform was used as a signal response to develop the calibration models. We found that exposure of the biorecognition layer to human serum improved the impedance response of the biorecognition element with significantly lower relative error. Moreover, the calibration model obtained in the human serum environment has higher sensitivity and a better limit of detection (0.087 ng/mL) than the buffer medium (0.39 ng/mL). The analyses of the ALS patient samples show that concentrations obtained from the buffer-based regression model was higher than the serum-based model. However, a high Pearson correlation (r = 1.00) between the media suggests that concentration in one medium may be useful to predict the concentration in the other medium. Moreover, the Nf-L concentration appears to increase with age in both male and female groups, while overall higher Nf-L was found in the male group than the female group.

Gene targeting in amyotrophic lateral sclerosis using causality-based feature selection and machine learning

BACKGROUND

Amyotrophic lateral sclerosis (ALS) is a rare progressive neurodegenerative disease that affects upper and lower motor neurons. As the molecular basis of the disease is still elusive, the development of high-throughput sequencing technologies, combined with data mining techniques and machine learning methods, could provide remarkable results in identifying pathogenetic mechanisms. High dimensionality is a major problem when applying machine learning techniques in biomedical data analysis, since a huge number of features is available for a limited number of samples. The aim of this study was to develop a methodology for training interpretable machine learning models in the classification of ALS and ALS-subtypes samples, using gene expression datasets.

METHODS

We performed dimensionality reduction in gene expression data using a semi-automated preprocessing systematic gene selection procedure using Statistically Equivalent Signature (SES), a causality-based feature selection algorithm, followed by Boosted Regression Trees (XGBoost) and Random Forest to train the machine learning classifiers. The SHapley Additive exPlanations (SHAP values) were used for interpretation of the machine learning classifiers. The methodology was developed and tested using two distinct publicly available ALS RNA-seq datasets. We evaluated the performance of SES as a dimensionality reduction method against: (a) Least Absolute Shrinkage and Selection Operator (LASSO), and (b) Local Outlier Factor (LOF).

RESULTS

The proposed methodology achieved 85.18% accuracy for the classification of cerebellum or frontal cortex samples as C9orf72-related familial ALS, sporadic ALS or healthy samples. Importantly, the genes identified as the most determinative have also been reported as disease-associated in ALS literature. When tested in the evaluation dataset, the methodology achieved 88.89% accuracy for the classification of sporadic ALS motor neuron samples. When LASSO was used as feature selection method instead of SES, the accuracy of the machine learning classifiers ranged from 74.07 to 96.30%, depending on tissue assessed, while LOF underperformed significantly (77.78% accuracy for the classification of pooled cerebellum and frontal cortex samples).

CONCLUSIONS

Using SES, we addressed the challenge of high dimensionality in gene expression data analysis, and we trained accurate machine learning ALS classifiers, specific for the gene expression patterns of different disease subtypes and tissue samples, while identifying disease-associated genes.

CSF oligoclonal IgG bands are not associated with ALS progression and prognosis

Introduction

Amyotrophic Lateral Sclerosis (ALS) is characterized by progressive motoneuron degeneration through cell autonomous and non-cell autonomous mechanisms; and the involvement of the innate and adaptive immune system has been hypothesized based on human and murine model data. We have explored if B-cell activation and IgG responses, as detected by IgG Oligoclonal bands (OCB) in serum and cerebrospinal fluid, were associated with ALS or with a subgroup of patients with distinct clinical features.

Methods

IgG OCB were determined in patients affected by ALS (n=457), Alzheimer Disease (n=516), Mild Cognitive Impairment (n=91), Tension-type Headache (n=152) and idiopathic Facial Palsy (n=94). For ALS patients, clinico-demographic and survival data were prospectively collected in the Register Schabia.

Results

The prevalence of IgG OCB is comparable in ALS and the four neurological cohorts. When the OCB pattern was considered (highlighting either intrathecal or systemic B-cells activation), no effect of OCB pattern on clinic-demographic parameters and overall. ALS patients with intrathecal IgG synthesis (type 2 and 3) were more likely to display infectious, inflammatory or systemic autoimmune conditions.

Discussion

These data suggest that OCB are not related to ALS pathophysiology but rather are a finding possibly indicative a coincidental infectious or inflammatory comorbidity that merits further investigation.

Drug discovery and amyotrophic lateral sclerosis: Emerging challenges and therapeutic opportunities

Amyotrophic lateral sclerosis (ALS) is characterized by the degeneration of upper and lower motor neurons (MNs) leading to paralysis and, ultimately, death by respiratory failure 3-5 years after diagnosis. Edaravone and Riluzole, the only drugs currently approved for ALS treatment, only provide mild symptomatic relief to patients. Extraordinary progress in understanding the biology of ALS provided new grounds for drug discovery. Over the last two decades, mitochondria and oxidative stress (OS), iron metabolism and ferroptosis, and the major regulators of hypoxia and inflammation - HIF and NF-κB - emerged as promising targets for ALS therapeutic intervention. In this review, we focused our attention on these targets to outline and discuss current advances in ALS drug development. Based on the challenges and the roadblocks, we believe that the rational design of multi-target ligands able to modulate the complex network of events behind the disease can provide effective therapies in a foreseeable future.

Identification of an immune-related gene prognostic index for predicting prognosis, immunotherapeutic efficacy, and candidate drugs in amyotrophic lateral sclerosis

Rationale and objectives

Considering the great insufficiency in the survival prediction and therapy of amyotrophic lateral sclerosis (ALS), it is fundamental to determine an accurate survival prediction for both the clinical practices and the design of treatment trials. Therefore, there is a need for more accurate biomarkers that can be used to identify the subtype of ALS which carries a high risk of progression to guide further treatment.

Methods

The transcriptome profiles and clinical parameters of a total of 561 ALS patients in this study were analyzed retrospectively by analysis of four public microarray datasets. Based on the results from a series of analyses using bioinformatics and machine learning, immune signatures are able to be used to predict overall survival (OS) and immunotherapeutic response in ALS patients. Apart from other comprehensive analyses, the decision tree and the nomogram, based on the immune signatures, were applied to guide individual risk stratification. In addition, molecular docking methodology was employed to screen potential small molecular to which the immune signatures might response.

Results

Immune was determined as a major risk factor contributing to OS among various biomarkers of ALS patients. As compared with traditional clinical features, the immune-related gene prognostic index (IRGPI) had a significantly higher capacity for survival prediction. The determination of risk stratification and assessment was optimized by integrating the decision tree and the nomogram. Moreover, the IRGPI may be used to guide preventative immunotherapy for patients at high risks for mortality. The administration of 2MIU IL2 injection in the short-term was likely to be beneficial for the prolongment of survival time, whose dosage should be reduced to 1MIU if the long-term therapy was required. Besides, a useful clinical application for the IRGPI was to screen potential compounds by the structure-based molecular docking methodology.

Conclusion

Ultimately, the immune-derived signatures in ALS patients were favorable biomarkers for the prediction of survival probabilities and immunotherapeutic responses, and the promotion of drug development.

Investigating Causality and Shared Genetic Architecture between Neurodegenerative Disorders and Inflammatory Bowel Disease

Published observational studies have revealed the connection between neurodegenerative disorders and inflammatory bowel disease (IBD), whereas the causal association remains largely unclear. Our study aims to assess the causality and identify the shared genetic architecture between neurodegenerative disorders and IBD. Two-sample Mendelian randomization analyses were performed to assess the causality between IBD and neurodegenerative disorders (amyotrophic lateral sclerosis [ALS], Alzheimer's disease [AD], Parkinson's disease [PD], and multiple sclerosis [MS]). Shared genetic loci, functional interpretation, and transcriptomic profiles were further investigated in ALS and IBD. We identified that genetic predisposition to IBD was suggestively associated with lower odds of ALS (odds ratio [OR] 0.96, 95% confidence interval [CI] 0.94 to 0.99). In contrast, IBD was not genetically associated with an increased risk of AD, PD, or MS (and vice versa). Two shared genetic loci (rs6571361 and rs7154847) were derived, and SCFD1, G2E3, and HEATR5A were further identified as novel risk genes with enriched functions related to membrane trafficking. G2E3 was differentially expressed and significantly correlated with SCFD1 in patients with ALS or IBD. Our study reveals the suggestively protective role of IBD on ALS, and does not support the causality of AD, PD, or MS on IBD (and vice versa). Our findings indicate possible shared genetic architecture and pathways between ALS and IBD. These results provide insights into the pathogenesis and therapeutics of IBD and neurodegenerative disorders.

Identification of potentially functional modules and diagnostic genes related to amyotrophic lateral sclerosis based on the WGCNA and LASSO algorithms

Amyotrophic lateral sclerosis (ALS) is a genetically and phenotypically heterogeneous disease results in the loss of motor neurons. Mounting information points to involvement of other systems including cognitive impairment. However, neither the valid biomarker for diagnosis nor effective therapeutic intervention is available for ALS. The present study is aimed at identifying potentially genetic biomarker that improves the diagnosis and treatment of ALS patients based on the data of the Gene Expression Omnibus. We retrieved datasets and conducted a weighted gene co-expression network analysis (WGCNA) to identify ALS-related co-expression genes. Functional enrichment analysis was performed to determine the features and pathways of the main modules. We then constructed an ALS-related model using the least absolute shrinkage and selection operator (LASSO) regression analysis and verified the model by the receiver operating characteristic (ROC) curve. Besides we screened the non-preserved gene modules in FTD and ALS-mimic disorders to distinct ALS-related genes from disorders with overlapping genes and features. Altogether, 4198 common genes between datasets with the most variation were analyzed and 16 distinct modules were identified through WGCNA. Blue module had the most correlation with ALS and functionally enriched in pathways of neurodegeneration-multiple diseases', 'amyotrophic lateral sclerosis', and 'endocytosis' KEGG terms. Further, some of other modules related to ALS were enriched in 'autophagy' and 'amyotrophic lateral sclerosis'. The 30 top of hub genes were recruited to a LASSO regression model and 5 genes (BCLAF1, GNA13, ARL6IP5, ARGLU1, and YPEL5) were identified as potentially diagnostic ALS biomarkers with validating of the ROC curve and AUC value.

Blood Transcript Biomarkers Selected by Machine Learning Algorithm Classify Neurodegenerative Diseases including Alzheimer's Disease

The clinical diagnosis of neurodegenerative diseases is notoriously inaccurate and current methods are often expensive, time-consuming, or invasive. Simple inexpensive and noninvasive methods of diagnosis could provide valuable support for clinicians when combined with cognitive assessment scores. Biological processes leading to neuropathology progress silently for years and are reflected in both the central nervous system and vascular peripheral system. A blood-based screen to distinguish and classify neurodegenerative diseases is especially interesting having low cost, minimal invasiveness, and accessibility to almost any world clinic. In this study, we set out to discover a small set of blood transcripts that can be used to distinguish healthy individuals from those with Alzheimer's disease, Parkinson's disease, Huntington's disease, amyotrophic lateral sclerosis, Friedreich's ataxia, or frontotemporal dementia. Using existing public datasets, we developed a machine learning algorithm for application on transcripts present in blood and discovered small sets of transcripts that distinguish a number of neurodegenerative diseases with high sensitivity and specificity. We validated the usefulness of blood RNA transcriptomics for the classification of neurodegenerative diseases. Information about features selected for the classification can direct the development of possible treatment strategies.

Identification of Therapeutic Targets for Amyotrophic Lateral Sclerosis Using PandaOmics – An AI-Enabled Biological Target Discovery Platform

Amyotrophic lateral sclerosis (ALS) is a severe neurodegenerative disease with ill-defined pathogenesis, calling for urgent developments of new therapeutic regimens. Herein, we applied PandaOmics, an AI-driven target discovery platform, to analyze the expression profiles of central nervous system (CNS) samples (237 cases; 91 controls) from public datasets, and direct iPSC-derived motor neurons (diMNs) (135 cases; 31 controls) from Answer ALS. Seventeen high-confidence and eleven novel therapeutic targets were identified and will be released onto ALS.AI (http://als.ai/). Among the proposed targets screened in the c9ALS Drosophila model, we verified 8 unreported genes (KCNB2, KCNS3, ADRA2B, NR3C1, P2RY14, PPP3CB, PTPRC, and RARA) whose suppression strongly rescues eye neurodegeneration. Dysregulated pathways identified from CNS and diMN data characterize different stages of disease development. Altogether, our study provides new insights into ALS pathophysiology and demonstrates how AI speeds up the target discovery process, and opens up new opportunities for therapeutic interventions.

The Big Picture of Neurodegeneration: A Meta Study to Extract the Essential Evidence on Neurodegenerative Diseases in a Network-Based Approach

The common features of all neurodegenerative diseases, including Alzheimer's disease, Parkinson's disease, Amyotrophic Lateral Sclerosis (ALS), and Huntington's disease, are the accumulation of aggregated and misfolded proteins and the progressive loss of neurons, leading to cognitive decline and locomotive dysfunction. Still, they differ in their ultimate manifestation, the affected brain region, and the kind of proteinopathy. In the last decades, a vast number of processes have been described as associated with neurodegenerative diseases, making it increasingly harder to keep an overview of the big picture forming from all those data. In this meta-study, we analyzed genomic, transcriptomic, proteomic, and epigenomic data of the aforementioned diseases using the data of 234 studies in a network-based approach to study significant general coherences but also specific processes in individual diseases or omics levels. In the analysis part, we focus on only some of the emerging findings, but trust that the meta-study provided here will be a valuable resource for various other researchers focusing on specific processes or genes contributing to the development of neurodegeneration.

Serum biomarkers of neuroinflammation and blood-brain barrier leakage in amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) is an incurable and rapidly progressive neurological disorder. Biomarkers are critical to understanding disease causation, monitoring disease progression and assessing the efficacy of treatments. However, robust peripheral biomarkers are yet to be identified. Neuroinflammation and breakdown of the blood-brain barrier (BBB) are common to familial and sporadic ALS and may produce a unique biomarker signature in peripheral blood. Using cytometric bead array (n = 15 participants per group (ALS or control)) and proteome profiling (n = 6 participants per group (ALS or control)), we assessed a total of 106 serum cytokines, growth factors, and BBB breakdown markers in the serum of control and ALS participants. Further, primary human brain pericytes, which maintain the BBB, were used as a biosensor of inflammation following pre-treatment with ALS serum. Principal components analysis of all proteome profile data showed no clustering of control or ALS sera, and no individual serum proteins met the threshold for statistical difference between ALS and controls (adjusted P values). However, the 20 most changed proteins between control and ALS sera showed a medium effect size (Cohen’s d = 0.67) and cluster analysis of their levels together identified three sample subsets; control-only, mixed control-ALS, and ALS-only. These 20 proteins were predominantly pro-angiogenic and growth factors, including fractalkine, BDNF, EGF, PDGF, Dkk-1, MIF and angiopoietin-2. S100β, a protein highly concentrated in glial cells and therefore a marker of BBB leakage when found in blood, was unchanged in ALS serum, suggesting that serum protein profiles were reflective of peripheral rather than CNS biofluids. Finally, primary human brain pericytes remained proliferative and their secretome was unchanged by chronic exposure to ALS serum. Our exploratory study suggests that individual serum cytokine levels may not be robust biomarkers in small studies of ALS, but that larger studies using multiplexed analysis of pro-angiogenic and growth factors may identify a peripheral signature of ALS pathogenesis.

Biomarkers in Human Peripheral Blood Mononuclear Cells: The State of the Art in Amyotrophic Lateral Sclerosis

Amyotrophic lateral sclerosis (ALS) is a devastating neurodegenerative disease, characterized by the progressive loss of lower motor neurons, weakness and muscle atrophy. ALS lacks an effective cure and diagnosis is often made by exclusion. Thus, it is imperative to search for biomarkers. Biomarkers can help in understanding ALS pathomechanisms, identification of targets for treatment and development of effective therapies. Peripheral blood mononuclear cells (PBMCs) represent a valid source for biomarkers compared to cerebrospinal fluid, as they are simple to collect, and to plasma, because of the possibility of detecting lower expressed proteins. They are a reliable model for patients’ stratification. This review provides an overview on PBMCs as a potential source of biomarkers in ALS. We focused on altered RNA metabolism (coding/non-coding RNA), including RNA processing, mRNA stabilization, transport and translation regulation. We addressed protein abnormalities (aggregation, misfolding and modifications); specifically, we highlighted that SOD1 appears to be the most characterizing protein in ALS. Finally, we emphasized the correlation between biological parameters and disease phenotypes, as regards prognosis, severity and clinical features. In conclusion, even though further studies are needed to standardize the use of PBMCs as a tool for biomarker investigation, they represent a promising approach in ALS research.

Circulating NAD+ Metabolism-Derived Genes Unveils Prognostic and Peripheral Immune Infiltration in Amyotrophic Lateral Sclerosis

Background: Nicotinamide adenine dinucleotide (NAD+) metabolism has drawn more attention on neurodegeneration research; however, the role in Amyotrophic Lateral Sclerosis (ALS) remains to be fully elucidated. Here, the purpose of this study was to investigate whether the circulating NAD+ metabolic-related gene signature could be identified as a reliable biomarker for ALS survival.

Methods: A retrospective analysis of whole blood transcriptional profiles and clinical characteristics of 454 ALS patients was conducted in this study. A series of bioinformatics and machine-learning methods were combined to establish NAD+ metabolic-derived risk score (NPRS) to predict overall survival for ALS patients. The associations of clinical characteristic with NPRS were analyzed and compared. Receiver operating characteristic (ROC) and the calibration curve were utilized to assess the efficacy of prognostic model. Besides, the peripheral immune cell infiltration was assessed in different risk subgroups by applying the CIBERSORT algorithm.

Results: Abnormal activation of the NAD+ metabolic pathway occurs in the peripheral blood of ALS patients. Four subtypes with distinct prognosis were constructed based on NAD+ metabolism-related gene expression patterns by using the consensus clustering method. A comparison of the expression profiles of genes related to NAD+ metabolism in different subtypes revealed that the synthase of NAD+ was closely associated with prognosis. Seventeen genes were selected to construct prognostic risk signature by LASSO regression. The NPRS exhibited stronger prognostic capacity compared to traditional clinic-pathological parameters. High NPRS was characterized by NAD+ metabolic exuberant with an unfavorable prognosis. The infiltration levels of several immune cells, such as CD4 naive T cells, CD8 T cells, neutrophils and macrophages, are significantly associated with NPRS. Further clinicopathological analysis revealed that NPRS is more appropriate for predicting the prognostic risk of patients with spinal onset. A prognostic nomogram exhibited more accurate survival prediction compared with other clinicopathological features.

Conclusions: In conclusion, it was first proposed that the circulating NAD+ metabolism-derived gene signature is a promising biomarker to predict clinical outcomes, and ultimately facilitating the precise management of patients with ALS.

Mass Spectrometry Based Protein Biomarkers and Drug Target Discovery and Clinical Diagnosis in Age-Related Progressing Neurodegenerative Diseases

Neurodegenerative diseases correspond to overly complex health disorders that are driven by intersecting pathophysiology that are often trapped in vicious cycles of degeneration and cognitive decline. The usual diagnostic route of these diseases is based on postmortem examination that involves identifying pathology that is specific to the disease in the brain. However, in such cases, accurate diagnosis of the specific disease is limited because clinical disease presentations are often complex that do not easily allow to discriminate patient's cognitive, behavioral, and functional impairment profiles. Additionally, an early identification and therapeutic intervention of these diseases is pivotal to slow the progression of neurodegeneration and extend healthy life span. Mass spectrometry-based techniques have proven to be hugely promising in biological sample analysis and discovery of biomarkers including protein and peptide biomarkers for potential drug target discovery. Recent studies on these biomarkers have demonstrated their potential for applications in early diagnostics and identifying therapeutic targets to battle against neurodegenerative diseases. In this review, we have presented principles of mass spectrometry (MS) and the associated workflows in analyzing and imaging biological samples for discovery of biomarkers. We have especially focused on age- related progressing neurodegenerative diseases such as Alzheimer's (AD) and Parkinson's disease (PD), Amyotrophic Lateral Sclerosis (ALS) and Frontotemporal dementia (FTD) and the related MS-based biomarkers developments for these diseases. Finally, we present a future perspective discussing the potential research directions ahead.

Extracellular Vesicles as Innovative Treatment Strategy for Amyotrophic Lateral Sclerosis

Amyotrophic lateral sclerosis (ALS) is a progressive and fatal motor neuron degenerative disease, and it is hard to diagnose in the early stage, and treatment means are limited, and the treatment effect is unsatisfactory. Therefore, exploring a new effective treatment strategy is urgently needed for ALS patients. Extracellular vesicles (EVs) are a heterogeneous group of natural membrane vesicles containing many bioactive substances, and they play important roles in the paracrine pathway and exhibit neuroprotection effects. A growing body of evidence shows that EVs have great application potential in diagnosis, treatment, and drug delivery in ALS, and they represent an innovative treatment strategy for ALS. In this review, we will briefly introduce the biogenesis of EVs and focus on discussing the role of EVs in ALS treatment to further enrich and boost the development of EVs as an innovative treatment strategy for ALS.

Exosomal Proteins and miRNAs as Mediators of Amyotrophic Lateral Sclerosis

Recent advances in the neurobiology and neurogenerative diseases have attracted growing interest in exosomes and their ability to carry and propagate active biomolecules as a means to reprogram recipient cells. Alterations in exosomal protein content and nucleic acid profiles found in human biological fluids have been correlated with various diseases including amyotrophic lateral sclerosis (ALS). In ALS pathogenesis, these lipid-bound nanoscale vesicles have emerged as valuable candidates for diagnostic biomarkers. Moreover, their capacity to spread misfolded proteins and functional non-coding RNAs to interconnected neuronal cells make them putative mediators for the progressive motor degeneration found remarkably apparent in ALS. This review outlines current knowledge concerning the biogenesis, heterogeneity, and function of exosomes in the brain as well as a comprehensive probe of currently available literature on ALS-related exosomal proteins and microRNAs. Lastly, with the rapid development of employing nanoparticles for drug delivery, we explore the therapeutic potentials of exosomes as well as underlying limitations in current isolation and detection methodologies.

Imaging immunological processes from blood to brain in amyotrophic lateral sclerosis

Neuropathology studies of amyotrophic lateral sclerosis (ALS) and animal models of ALS reveal a strong association between aberrant protein accumulation and motor neurone damage, as well as activated microglia and astrocytes. While the role of neuroinflammation in the pathology of ALS is unclear, imaging studies of the central nervous system (CNS) support the idea that innate immune activation occurs early in disease in both humans and rodent models of ALS. In addition, emerging studies also reveal changes in monocytes, macrophages and lymphocytes in peripheral blood as well as at the neuromuscular junction. To more clearly understand the association of neuroinflammation (innate and adaptive) with disease progression, the use of biomarkers and imaging modalities allow monitoring of immune parameters in the disease process. Such approaches are important for patient stratification, selection and inclusion in clinical trials, as well as to provide readouts of response to therapy. Here, we discuss the different imaging modalities, e.g. magnetic resonance imaging, magnetic resonance spectroscopy and positron emission tomography as well as other approaches, including biomarkers of inflammation in ALS, that aid the understanding of the underlying immune mechanisms associated with motor neurone degeneration in ALS.

Amyotrophic lateral sclerosis transcriptomics reveals immunological effects of low-dose interleukin-2

Amyotrophic lateral sclerosis is a fatal neurodegenerative disease causing upper and lower motor neuron loss and currently no effective disease-modifying treatment is available. A pathological feature of this disease is neuroinflammation, a mechanism which involves both CNS-resident and peripheral immune system cells. Regulatory T-cells are immune-suppressive agents known to be dramatically and progressively decreased in patients with amyotrophic lateral sclerosis. Low-dose interleukin-2 promotes regulatory T-cell expansion and was proposed as an immune-modulatory strategy for this disease. A randomized placebo-controlled pilot phase-II clinical trial called Immuno-Modulation in Amyotrophic Lateral Sclerosis was carried out to test safety and activity of low-dose interleukin-2 in 36 amyotrophic lateral sclerosis patients (NCT02059759). Participants were randomized to 1MIU, 2MIU-low-dose interleukin-2 or placebo and underwent one injection daily for 5 days every 28 days for three cycles. In this report, we describe the results of microarray gene expression profiling of trial participants' leukocyte population. We identified a dose-dependent increase in regulatory T-cell markers at the end of the treatment period. Longitudinal analysis revealed an alteration and inhibition of inflammatory pathways occurring promptly at the end of the first treatment cycle. These responses are less pronounced following the end of the third treatment cycle, although an activation of immune-regulatory pathways, involving regulatory T-cells and T helper 2 cells, was evident only after the last cycle. This indicates a cumulative effect of repeated low-dose interleukin-2 administration on regulatory T-cells. Our analysis suggested the existence of inter-individual variation amongst trial participants and we therefore classified patients into low, moderate and high-regulatory T-cell-responders. NanoString profiling revealed substantial baseline differences between participant immunological transcript expression profiles with the least responsive patients showing a more inflammatory-prone phenotype at the beginning of the trial. Finally, we identified two genes in which pre-treatment expression levels correlated with the magnitude of drug responsiveness. Therefore, we proposed a two-biomarker based regression model able to predict patient regulatory T-cell-response to low-dose interleukin-2. These findings and the application of this methodology could be particularly relevant for future precision medicine approaches to treat amyotrophic lateral sclerosis.

Central Nervous System Cell-Derived Exosomes in Neurodegenerative Diseases

Exosomes are a type of extracellular vesicles secreted by almost all kinds of mammalian cells that shuttle "cargo" from one cell to another, indicative of its role in cell-to-cell transportation. Interestingly, exosomes are known to undergo alterations or serve as a pathway in multiple diseases, including neurodegenerative diseases. In the central nervous system (CNS), exosomes originating from neurons or glia cells contribute to or inhibit the progression of CNS-related diseases in special ways. In lieu of this, the current study investigated the effect of CNS cell-derived exosomes on different neurodegenerative diseases.

Lipidomic traits of plasma and cerebrospinal fluid in amyotrophic lateral sclerosis correlate with disease progression

Since amyotrophic lateral sclerosis cases exhibit significant heterogeneity, we aim to investigate the association of lipid composition of plasma and CSF with amyotrophic lateral sclerosis diagnosis, its progression and clinical characteristics. Lipidome analyses would help to stratify patients on a molecular basis. For this reason, we have analysed the lipid composition of paired plasma and CSF samples from amyotrophic lateral sclerosis cases and age-matched non-amyotrophic lateral sclerosis individuals (controls) by comprehensive liquid chromatography coupled to mass spectrometry. The concentrations of neurofilament light chain-an index of neuronal damage-were also quantified in CSF samples and plasma. Amyotrophic lateral sclerosis versus control comparison, in a moderate stringency mode, showed that plasma from cases contains more differential lipids (n = 122 for raw P < 0.05; n = 27 for P < 0.01) than CSF (n = 17 for raw P < 0.05; n = 4 for P < 0.01), with almost no overlapping differential species, mainly characterized by an increased content of triacylglyceride species in plasma and decreased in CSF. Of note, false discovery rate correction indicated that one of the CSF lipids (monoacylglycerol 18:0) had high statistic robustness (false discovery rate-P < 0.01). Plasma lipidomes also varied significantly with the main involvement at onset (bulbar, spinal or respiratory). Notably, faster progression cases showed particular lipidome fingerprints, featured by decreased triacylclycerides and specific phospholipids in plasma, with 11 lipids with false discovery rate-P < 0.1 (n = 56 lipids in plasma for raw P < 0.01). Lipid species associated with progression rate clustered in a relatively low number of metabolic pathways, mainly triacylglyceride metabolism and glycerophospholipid and sphingolipid biosynthesis. A specific triacylglyceride (68:12), correlated with neurofilament content (r = 0.8, P < 0.008). Thus, the present findings suggest that systemic hypermetabolism-potentially sustained by increased triacylglyceride content-and CNS alterations of specific lipid pathways could be associated as modifiers of disease progression. Furthermore, these results confirm biochemical lipid heterogeneity in amyotrophic lateral sclerosis with different presentations and progression, suggesting the use of specific lipid species as potential disease classifiers.

Application of a bioinformatic pipeline to RNA-seq data identifies novel viruslike sequence in human blood

Numerous reports have suggested that infectious agents could play a role in neurodegenerative diseases, but specific etiological agents have not been convincingly demonstrated. To search for candidate agents in an unbiased fashion, we have developed a bioinformatic pipeline that identifies microbial sequences in mammalian RNA-seq data, including sequences with no significant nucleotide similarity hits in GenBank. Effectiveness of the pipeline was tested using publicly available RNA-seq data and in a reconstruction experiment using synthetic data. We then applied this pipeline to a novel RNA-seq dataset generated from a cohort of 120 samples from amyotrophic lateral sclerosis patients and controls, and identified sequences corresponding to known bacteria and viruses, as well as novel virus-like sequences. The presence of these novel virus-like sequences, which were identified in subsets of both patients and controls, were confirmed by quantitative RT-PCR. We believe this pipeline will be a useful tool for the identification of potential etiological agents in the many RNA-seq datasets currently being generated.

Common Factors in Neurodegeneration: A Meta-Study Revealing Shared Patterns on a Multi-Omics Scale

Neurodegenerative diseases such as Alzheimer's disease (AD), Parkinson's disease (PD), Huntington's disease (HD), and amyotrophic lateral sclerosis (ALS) are heterogeneous, progressive diseases with frequently overlapping symptoms characterized by a loss of neurons. Studies have suggested relations between neurodegenerative diseases for many years (e.g., regarding the aggregation of toxic proteins or triggering endogenous cell death pathways). We gathered publicly available genomic, transcriptomic, and proteomic data from 177 studies and more than one million patients to detect shared genetic patterns between the neurodegenerative diseases on three analyzed omics-layers. The results show a remarkably high number of shared differentially expressed genes between the transcriptomic and proteomic levels for all conditions, while showing a significant relation between genomic and proteomic data between AD and PD and AD and ALS. We identified a set of 139 genes being differentially expressed in several transcriptomic experiments of all four diseases. These 139 genes showed overrepresented gene ontology (GO) Terms involved in the development of neurodegeneration, such as response to heat and hypoxia, positive regulation of cytokines and angiogenesis, and RNA catabolic process. Furthermore, the four analyzed neurodegenerative diseases (NDDs) were clustered by their mean direction of regulation throughout all transcriptomic studies for this set of 139 genes, with the closest relation regarding this common gene set seen between AD and HD. GO-Term and pathway analysis of the proteomic overlap led to biological processes (BPs), related to protein folding and humoral immune response. Taken together, we could confirm the existence of many relations between Alzheimer's disease, Parkinson's disease, Huntington's disease, and amyotrophic lateral sclerosis on transcriptomic and proteomic levels by analyzing the pathways and GO-Terms arising in these intersections. The significance of the connection and the striking relation of the results to processes leading to neurodegeneration between the transcriptomic and proteomic data for all four analyzed neurodegenerative diseases showed that exploring many studies simultaneously, including multiple omics-layers of different neurodegenerative diseases simultaneously, holds new relevant insights that do not emerge from analyzing these data separately. Furthermore, the results shed light on processes like the humoral immune response that have previously been described only for certain diseases. Our data therefore suggest human patients with neurodegenerative diseases should be addressed as complex biological systems by integrating multiple underlying data sources.

Evaluation of blood gene expression levels in facioscapulohumeral muscular dystrophy patients

Facioscapulohumeral muscular dystrophy (FSHD) is caused by the expression of DUX4 in skeletal muscles. A number of therapeutic approaches are being developed to antagonize the events preceding and following DUX4 expression that leads to muscular dystrophy. Currently, the possibility to evaluate treatment response in clinical trials is hampered by the lack of objective molecular biomarkers connecting the disease cause to clinical performance. In this study we employed RNA-seq to examine gene expression in PAXgene tubes obtained from two independent cohorts of FSHD patients. Analysis of gene expression profiles did not lead to the identification of genes or pathways differentially expressed in FSHD patients, or associated with disease severity. In particular, we did not find evidence that the DUX4 and PAX7 signatures were differentially expressed. On the other hand, we were able to improve patient classification by including single genes or groups of genes in classification models. The best classifier was ROPN1L, a gene known to be expressed in testis, coincidentally the typical location of DUX4 expression. These improvements in patient classification hold the potential to enrich the FSHD clinical trial toolbox.

Controlling technical variation amongst 6693 patient microarrays of the randomized MINDACT trial

Gene expression data obtained in large studies hold great promises for discovering disease signatures or subtypes through data analysis. It is also prone to technical variation, whose removal is essential to avoid spurious discoveries. Because this variation is not always known and can be confounded with biological signals, its removal is a challenging task. Here we provide a step-wise procedure and comprehensive analysis of the MINDACT microarray dataset. The MINDACT trial enrolled 6693 breast cancer patients and prospectively validated the gene expression signature MammaPrint for outcome prediction. The study also yielded a full-transcriptome microarray for each tumor. We show for the first time in such a large dataset how technical variation can be removed while retaining expected biological signals. Because of its unprecedented size, we hope the resulting adjusted dataset will be an invaluable tool to discover or test gene expression signatures and to advance our understanding of breast cancer.

Principal component analysis of blood microRNA datasets facilitates diagnosis of diverse diseases

Early, ideally pre-symptomatic, recognition of common diseases (e.g., heart disease, cancer, diabetes, Alzheimer’s disease) facilitates early treatment or lifestyle modifications, such as diet and exercise. Sensitive, specific identification of diseases using blood samples would facilitate early recognition. We explored the potential of disease identification in high dimensional blood microRNA (miRNA) datasets using a powerful data reduction method: principal component analysis (PCA). Using Qlucore Omics Explorer (QOE), a dynamic, interactive visualization-guided bioinformatics program with a built-in statistical platform, we analyzed publicly available blood miRNA datasets from the Gene Expression Omnibus (GEO) maintained at the National Center for Biotechnology Information at the National Institutes of Health (NIH). The miRNA expression profiles were generated from real time PCR arrays, microarrays or next generation sequencing of biologic materials (e.g., blood, serum or blood components such as platelets). PCA identified the top three principal components that distinguished cohorts of patients with specific diseases (e.g., heart disease, stroke, hypertension, sepsis, diabetes, specific types of cancer, HIV, hemophilia, subtypes of meningitis, multiple sclerosis, amyotrophic lateral sclerosis, Alzheimer’s disease, mild cognitive impairment, aging, and autism), from healthy subjects. Literature searches verified the functional relevance of the discriminating miRNAs. Our goal is to assemble PCA and heatmap analyses of existing and future blood miRNA datasets into a clinical reference database to facilitate the diagnosis of diseases using routine blood draws.

The Peripheral Immune System and Amyotrophic Lateral Sclerosis

Amyotrophic lateral sclerosis (ALS) is a severe neurodegenerative disease that is defined by loss of upper and lower motor neurons, associated with accumulation of protein aggregates in cells. There is also pathology in extra-motor areas of the brain, Possible causes of cell death include failure to deal with the aggregated proteins, glutamate toxicity and mitochondrial failure. ALS also involves abnormalities of metabolism and the immune system, including neuroinflammation in the brain and spinal cord. Strikingly, there are also abnormalities of the peripheral immune system, with alterations of T lymphocytes, monocytes, complement and cytokines in the peripheral blood of patients with ALS. The precise contribution of the peripheral immune system in ALS pathogenesis is an active area of research. Although some trials of immunomodulatory agents have been negative, there is strong preclinical evidence of benefit from immune modulation and further trials are currently underway. Here, we review the emerging evidence implicating peripheral immune alterations contributing to ALS, and their potential as future therapeutic targets for clinical intervention.

Klotho Pathways, Myelination Disorders, Neurodegenerative Diseases, and Epigenetic Drugs

In this review we outline a rationale for identifying neuroprotectants aimed at inducing endogenous Klotho activity and expression, which is epigenetic action, by definition. Such an approach should promote remyelination and/or stimulate myelin repair by acting on mitochondrial function, thereby heralding a life-saving path forward for patients suffering from neuroinflammatory diseases. Disorders of myelin in the nervous system damage the transmission of signals, resulting in loss of vision, motion, sensation, and other functions depending on the affected nerves, currently with no effective treatment. Klotho genes and their single-pass transmembrane Klotho proteins are powerful governors of the threads of life and death, true to the origin of their name, Fates, in Greek mythology. Among its many important functions, Klotho is an obligatory co-receptor that binds, activates, and/or potentiates critical fibroblast growth factor activity. Since the discovery of Klotho a little over two decades ago, it has become ever more apparent that when Klotho pathways go awry, oxidative stress and mitochondrial dysfunction take over, and age-related chronic disorders are likely to follow. The physiological consequences can be wide ranging, potentially wreaking havoc on the brain, eye, kidney, muscle, and more. Central nervous system disorders, neurodegenerative in nature, and especially those affecting the myelin sheath, represent worthy targets for advancing therapies that act upon Klotho pathways. Current drugs for these diseases, even therapeutics that are disease modifying rather than treating only the symptoms, leave much room for improvement. It is thus no wonder that this topic has caught the attention of biomedical researchers around the world.