Whole blood transcriptome analysis in amyotrophic lateral sclerosis: A biomarker study

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.

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.

Unsupervised machine learning identifies distinct ALS molecular subtypes in post-mortem motor cortex and blood expression data

Amyotrophic lateral sclerosis (ALS) displays considerable clinical and genetic heterogeneity. Machine learning approaches have previously been utilised for patient stratification in ALS as they can disentangle complex disease landscapes. However, lack of independent validation in different populations and tissue samples have greatly limited their use in clinical and research settings. We overcame these issues by performing hierarchical clustering on the 5000 most variably expressed autosomal genes from motor cortex expression data of people with sporadic ALS from the KCL BrainBank (N = 112). Three molecular phenotypes linked to ALS pathogenesis were identified: synaptic and neuropeptide signalling, oxidative stress and apoptosis, and neuroinflammation. Cluster validation was achieved by applying linear discriminant analysis models to cases from TargetALS US motor cortex (N = 93), as well as Italian (N = 15) and Dutch (N = 397) blood expression datasets, for which there was a high assignment probability (80-90%) for each molecular subtype. The ALS and motor cortex specificity of the expression signatures were tested by mapping KCL BrainBank controls (N = 59), and occipital cortex (N = 45) and cerebellum (N = 123) samples from TargetALS to each cluster, before constructing case-control and motor cortex-region logistic regression classifiers. We found that the signatures were not only able to distinguish people with ALS from controls (AUC 0.88 ± 0.10), but also reflect the motor cortex-based disease process, as there was perfect discrimination between motor cortex and the other brain regions. Cell types known to be involved in the biological processes of each molecular phenotype were found in higher proportions, reinforcing their biological interpretation. Phenotype analysis revealed distinct cluster-related outcomes in both motor cortex datasets, relating to disease onset and progression-related measures. Our results support the hypothesis that different mechanisms underpin ALS pathogenesis in subgroups of patients and demonstrate potential for the development of personalised treatment approaches. Our method is available for the scientific and clinical community at https://alsgeclustering.er.kcl.ac.uk .

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.

RNA sequencing of peripheral blood in amyotrophic lateral sclerosis reveals distinct molecular subtypes: Considerations for biomarker discovery

AIM

Amyotrophic lateral sclerosis (ALS) is a heterogeneous neurodegenerative disease with limited therapeutic options. A key factor limiting the development of effective therapeutics is the lack of disease biomarkers. We sought to assess whether biomarkers for diagnosis, prognosis or cohort stratification could be identified by RNA sequencing (RNA-seq) of ALS patient peripheral blood.

METHODS

Whole blood RNA-seq data were generated for 96 Australian sporadic ALS (sALS) cases and 48 healthy controls (NCBI GEO accession GSE234297). Differences in sALS-control gene expression, transcript usage and predicted leukocyte proportions were assessed, with pathway analysis used to predict the activity state of biological processes. Weighted Gene Co-expression Network Analysis (WGCNA) and machine learning algorithms were applied to search for diagnostic and prognostic gene expression patterns. Unsupervised clustering analysis was employed to determine whether sALS patient subgroups could be detected.

RESULTS

Two hundred and forty-five differentially expressed genes were identified in sALS patients relative to controls, with enrichment of immune, metabolic and stress-related pathways. sALS patients also demonstrated switches in transcript usage across a small set of genes. We established a classification model that distinguished sALS patients from controls with an accuracy of 78% (sensitivity: 79%, specificity: 75%) using the expression of 20 genes. Clustering analysis identified four patient subgroups with gene expression signatures and immune cell proportions reflective of distinct peripheral effects.

CONCLUSIONS

Our findings suggest that peripheral blood RNA-seq can identify diagnostic biomarkers and distinguish molecular subtypes of sALS patients however, its prognostic value requires further investigation.

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.

Harnessing Transcriptomic Signals for Amyotrophic Lateral Sclerosis to Identify Novel Drugs and Enhance Risk Prediction

Introduction

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease. This study integrates the latest ALS genome-wide association study (GWAS) summary statistics with functional genomic annotations with the aim of providing mechanistic insights into ALS risk loci, inferring drug repurposing opportunities, and enhancing prediction of ALS risk and clinical characteristics.

Methods

Genes associated with ALS were identified using GWAS summary statistic methodology including SuSiE SNP-based fine-mapping, and transcriptome- and proteome-wide association study (TWAS/PWAS) analyses. Using several approaches, gene associations were integrated with the DrugTargetor drug-gene interaction database to identify drugs that could be repurposed for the treatment of ALS. Furthermore, ALS gene associations from TWAS were combined with observed blood expression in two external ALS case-control datasets to calculate polytranscriptomic scores and evaluate their utility for prediction of ALS risk and clinical characteristics, including site of onset, age at onset, and survival.

Results

SNP-based fine-mapping, TWAS and PWAS identified 117 genes associated with ALS, with TWAS and PWAS providing novel mechanistic insights. Drug repurposing analyses identified five drugs significantly enriched for interactions with ALS associated genes, with directional analyses highlighting α-glucosidase inhibitors may exacerbate ALS pathology. Additionally, drug class enrichment analysis showed calcium channel blockers may reduce ALS risk. Across the two observed expression target samples, ALS polytranscriptomic scores significantly predicted ALS risk (R2 = 4%; p-value = 2.1×10-21).

Conclusions

Functionally-informed analyses of ALS GWAS summary statistics identified novel mechanistic insights into ALS aetiology, highlighted several therapeutic research avenues, and enabled statistically significant prediction of ALS risk.

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.

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.

The Classification and Prediction of Ferroptosis-Related Genes in ALS: A Pilot Study

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease characterized by progressive muscle paralysis, which is followed by degeneration of motor neurons in the motor cortex of the brainstem and spinal cord. The etiology of sporadic ALS (sALS) is still unknown, limiting the exploration of potential treatments. Ferroptosis is a new form of cell death and is reported to be closely associated with Alzheimer’s disease (AD), Parkinson’s disease (PD), and ALS. In this study, we used datasets (autopsy data and blood data) from Gene Expression Omnibus (GEO) to explore the role of ferroptosis and ferroptosis-related gene (FRG) alterations in ALS. Gene set enrichment analysis (GSEA) found that the activated ferroptosis pathway displayed a higher enrichment score, and the expression of 26 ferroptosis genes showed obvious group differences between ALS and controls. Using weighted gene correlation network analysis (WGCNA), we identified FRGs associated with ALS, of which the Gene Ontology (GO) analysis displayed that the biological process of oxidative stress was the most to be involved in. KEGG pathway analysis revealed that the FRGs were enriched not only in ferroptosis pathways but also in autophagy, FoxO, and mTOR signaling pathways. Twenty-one FRGs (NR4A1, CYBB, DRD4, SETD1B, LAMP2, ACSL4, MYB, PROM2, CHMP5, ULK1, AKR1C2, TGFBR1, TMBIM4, MLLT1, PSAT1, HIF1A, LINC00336, AMN, SLC38A1, CISD1, and GABARAPL2) in the autopsy data and 16 FRGs (NR4A1, DRD4, SETD1B, MYB, PROM2, CHMP5, ULK1, AKR1C2, TGFBR1, TMBIM4, MLLT1, HIF1A, LINC00336, IL33, SLC38A1, and CISD1) in the blood data were identified as target genes by least absolute shrinkage and selection operator analysis (LASSO), in which gene signature could differentiate ALS patients from controls. Finally, the higher the expression of CHMP5 and SLC38A1 in whole blood, the shorter the lifespan of ALS patients will be. In summary, our study presents potential biomarkers for the diagnosis and prognosis of ALS.

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.

Functional characterisation of the amyotrophic lateral sclerosis risk locus GPX3/TNIP1

BACKGROUND

Amyotrophic lateral sclerosis (ALS) is a complex, late-onset, neurodegenerative disease with a genetic contribution to disease liability. Genome-wide association studies (GWAS) have identified ten risk loci to date, including the TNIP1/GPX3 locus on chromosome five. Given association analysis data alone cannot determine the most plausible risk gene for this locus, we undertook a comprehensive suite of in silico, in vivo and in vitro studies to address this.

METHODS

The Functional Mapping and Annotation (FUMA) pipeline and five tools (conditional and joint analysis (GCTA-COJO), Stratified Linkage Disequilibrium Score Regression (S-LDSC), Polygenic Priority Scoring (PoPS), Summary-based Mendelian Randomisation (SMR-HEIDI) and transcriptome-wide association study (TWAS) analyses) were used to perform bioinformatic integration of GWAS data (Ncases = 20,806, Ncontrols = 59,804) with 'omics reference datasets including the blood (eQTLgen consortium N = 31,684) and brain (N = 2581). This was followed up by specific expression studies in ALS case-control cohorts (microarray Ntotal = 942, protein Ntotal = 300) and gene knockdown (KD) studies of human neuronal iPSC cells and zebrafish-morpholinos (MO).

RESULTS

SMR analyses implicated both TNIP1 and GPX3 (p < 1.15 × 10-6), but there was no simple SNP/expression relationship. Integrating multiple datasets using PoPS supported GPX3 but not TNIP1. In vivo expression analyses from blood in ALS cases identified that lower GPX3 expression correlated with a more progressed disease (ALS functional rating score, p = 5.5 × 10-3, adjusted R2 = 0.042, Beffect = 27.4 ± 13.3 ng/ml/ALSFRS unit) with microarray and protein data suggesting lower expression with risk allele (recessive model p = 0.06, p = 0.02 respectively). Validation in vivo indicated gpx3 KD caused significant motor deficits in zebrafish-MO (mean difference vs. control ± 95% CI, vs. control, swim distance = 112 ± 28 mm, time = 1.29 ± 0.59 s, speed = 32.0 ± 2.53 mm/s, respectively, p for all < 0.0001), which were rescued with gpx3 expression, with no phenotype identified with tnip1 KD or gpx3 overexpression.

CONCLUSIONS

These results support GPX3 as a lead ALS risk gene in this locus, with more data needed to confirm/reject a role for TNIP1. This has implications for understanding disease mechanisms (GPX3 acts in the same pathway as SOD1, a well-established ALS-associated gene) and identifying new therapeutic approaches. Few previous examples of in-depth investigations of risk loci in ALS exist and a similar approach could be applied to investigate future expected GWAS findings.

Application of the 3' mRNA-Seq using unique molecular identifiers in highly degraded RNA derived from formalin-fixed, paraffin-embedded tissue

Background

Archival formalin-fixed, paraffin-embedded (FFPE) tissue samples with clinical and histological data are a singularly valuable resource for developing new molecular biomarkers. However, transcriptome analysis remains challenging with standard mRNA-seq methods as FFPE derived-RNA samples are often highly modified and fragmented. The recently developed 3′ mRNA-seq method sequences the 3′ region of mRNA using unique molecular identifiers (UMI), thus generating gene expression data with minimal PCR bias. In this study, we evaluated the performance of 3′ mRNA-Seq using Lexogen QuantSeq 3′ mRNA-Seq Library Prep Kit FWD with UMI, comparing with TruSeq Stranded mRNA-Seq and RNA Exome Capture kit. The fresh-frozen (FF) and FFPE tissues yielded nucleotide sizes range from 13 to > 70% of DV200 values; input amounts ranged from 1 ng to 100 ng for validation.

Results

The total mapped reads of QuantSeq 3′ mRNA-Seq to the reference genome ranged from 99 to 74% across all samples. After PCR bias correction, 3 to 56% of total sequenced reads were retained. QuantSeq 3′ mRNA-Seq data showed highly reproducible data across replicates in Universal Human Reference RNA (UHR, R > 0.94) at input amounts from 1 ng to 100 ng, and FF and FFPE paired samples (R = 0.92) at 10 ng. Severely degraded FFPE RNA with ≤30% of DV200 value showed good concordance (R > 0.87) with 100 ng input. A moderate correlation was observed when directly comparing QuantSeq 3′ mRNA-Seq data with TruSeq Stranded mRNA-Seq (R = 0.78) and RNA Exome Capture data (R > 0.67).

Conclusion

In this study, QuantSeq 3′ mRNA-Seq with PCR bias correction using UMI is shown to be a suitable method for gene quantification in both FF and FFPE RNAs. 3′ mRNA-Seq with UMI may be applied to severely degraded RNA from FFPE tissues generating high-quality sequencing data.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-08068-1.

Differential Epigenetic Signature of Corticospinal Motor Neurons in ALS

Corticospinal motor neurons (CSMN) are an indispensable neuron population for the motor neuron circuitry. They are excitatory projection neurons, which collect information from different regions of the brain and transmit it to spinal cord targets, initiating and controlling motor function. CSMN degeneration is pronounced cellular event in motor neurons diseases, such as amyotrophic lateral sclerosis (ALS). Genetic mutations contribute to only about ten percent of ALS. Thus understanding the involvement of other factors, such as epigenetic controls, is immensely valuable. Here, we investigated epigenomic signature of CSMN that become diseased due to misfolded SOD1 toxicity and TDP-43 pathology, by performing quantitative analysis of 5-methylcytosine (5mC) and 5-hydroxymethycytosine (5hmC) expression profiles during end-stage of the disease in hSOD1^G93A, and prpTDP-43^A315T mice. Our analysis revealed that expression of 5mC was specifically reduced in CSMN of both hSOD1^G93A and prpTDP-43^A315T mice. However, 5hmC expression was increased in the CSMN that becomes diseased due to misfolded SOD1 and decreased in CSMN that degenerates due to TDP-43 pathology. These results suggest the presence of a distinct difference between different underlying causes. These differential epigenetic events might modulate the expression profiles of select genes, and ultimately contribute to the different paths that lead to CSMN vulnerability in ALS.

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.

Different recognition modes of G-quadruplex RNA between two ALS/FTLD-linked proteins TDP-43 and FUS

Amyotrophic lateral sclerosis/frontotemporal lobar degeneration-linked proteins, TDP-43 and fused in sarcoma (FUS), bind to G-quadruplex-containing mRNAs and transport them to distal neurites for local translation. The specificity and mechanism of G4-RNA binding, however, remain largely unsolved. Using purified full-length TDP-43 and FUS and a set of seven G4-DNA/RNA, we compared their recognition properties of G4-RNAs. Both TDP-43 and FUS recognized and bound to G4-DNA/RNAs, but the target selectivity differed between two proteins. TDP-43 recognized only parallel-stranded G4-DNA/RNAs, leading to stabilize the G4 conformation. In contrast, FUS bound to all three types, parallel, hybrid, and antiparallel, of G4-DNA/RNAs, resulting in deformation of the G4 structure. We then concluded that the target selectivity and the influence on G4 RNA structure differed between TDP-43 and FUS.

Comparative evaluation for the globin gene depletion methods for mRNA sequencing using the whole blood-derived total RNAs

Background

There are challenges in generating mRNA-Seq data from whole-blood derived RNA as globin gene and rRNA are frequent contaminants. Given the abundance of erythrocytes in whole blood, globin genes comprise some 80% or more of the total RNA. Therefore, depletion of globin gene RNA and rRNA are critical steps required to have adequate coverage of reads mapping to the reference transcripts and thus reduce the total cost of sequencing. In this study, we directly compared the performance of probe hybridization (GLOBINClear Kit and Globin-Zero Gold rRNA Removal Kit) and RNAse-H enzymatic depletion (NEBNext® Globin & rRNA Depletion Kit and Ribo-Zero Plus rRNA Depletion Kit) methods from 1 μg of whole blood-derived RNA on mRNA-Seq profiling. All RNA samples were treated with DNaseI for additional cleanup before the depletion step and were processed for poly-A selection for library generation.

Results

Probe hybridization revealed a better overall performance than the RNAse-H enzymatic depletion method, detecting a higher number of genes and transcripts without 3′ region bias. After depletion, samples treated with probe hybridization showed globin genes at 0.5% (±0.6%) of the total mapped reads; the RNAse-H enzymatic depletion had 3.2% (±3.8%). Probe hybridization showed more junction reads and transcripts compared with RNAse-H enzymatic depletion and also had a higher correlation (R > 0.9) than RNAse-H enzymatic depletion (R > 0.85).

Conclusion

In this study, our results showed that 1 μg of high-quality RNA from whole blood could be routinely used for transcriptional profiling analysis studies with globin gene and rRNA depletion pre-processing. We also demonstrated that the probe hybridization depletion method is better suited to mRNA sequencing analysis with minimal effect on RNA quality during depletion procedures.

Discovery of new epigenomics-based biomarkers and the early diagnosis of neurodegenerative diseases

Treatment options for many neurodegenerative diseases are limited due to the lack of early diagnostic procedures that allow timely delivery of therapeutic agents to affected neurons prior to cell death. While notable advances have been made in neurodegenerative disease biomarkers, whether or not the biomarkers discovered to date are useful for early diagnosis remains an open question. Additionally, the reliability of these biomarkers has been disappointing, due in part to the large dissimilarities between the tissues traditionally used to source biomarkers and primarily diseased neurons. In this article, we review the potential viability of atypical epigenetic and/or consequent transcriptional alterations (ETAs) as biomarkers of early-stage neurodegenerative disease, and present our perspectives on the discovery and practical use of such biomarkers in patient-derived neural samples using single-cell level analyses, thereby greatly enhancing the reliability of biomarker application.

A Knowledge-Based Machine Learning Approach to Gene Prioritisation in Amyotrophic Lateral Sclerosis

Amyotrophic lateral sclerosis is a neurodegenerative disease of the upper and lower motor neurons resulting in death from neuromuscular respiratory failure, typically within two to five years of first symptoms. Several rare disruptive gene variants have been associated with ALS and are responsible for about 15% of all cases. Although our knowledge of the genetic landscape of this disease is improving, it remains limited. Machine learning models trained on the available protein-protein interaction and phenotype-genotype association data can use our current knowledge of the disease genetics for the prediction of novel candidate genes. Here, we describe a knowledge-based machine learning method for this purpose. We trained our model on protein-protein interaction data from IntAct, gene function annotation from Gene Ontology, and known disease-gene associations from DisGeNet. Using several sets of known ALS genes from public databases and a manual review as input, we generated a list of new candidate genes for each input set. We investigated the relevance of the predicted genes in ALS by using the available summary statistics from the largest ALS genome-wide association study and by performing functional and phenotype enrichment analysis. The predicted sets were enriched for genes associated with other neurodegenerative diseases known to overlap with ALS genetically and phenotypically, as well as for biological processes associated with the disease. Moreover, using ALS genes from ClinVar and our manual review as input, the predicted sets were enriched for ALS-associated genes (ClinVar p = 0.038 and manual review p = 0.060) when used for gene prioritisation in a genome-wide association study.

Circulating RNAs as Potential Biomarkers in Amyotrophic Lateral Sclerosis

Amyotrophic lateral sclerosis (ALS) is a complex multi-system neurodegenerative disorder with currently limited diagnostic and no therapeutic options. Despite the intense efforts no clinically applicable biomarkers for ALS are yet established. Most current research is thus focused, in particular, in identifying potential non-invasive circulating biomarkers for more rapid and accurate diagnosis and monitoring of the disease. In this review, we have focused on messenger RNA (mRNA), non-coding RNAs (lncRNAs), micro RNAs (miRNAs) and circular RNA (circRNAs) as potential biomarkers for ALS in peripheral blood serum, plasma and cells. The most promising miRNAs include miR-206, miR-133b, miR-27a, mi-338-3p, miR-183, miR-451, let-7 and miR-125b. To test clinical potential of this miRNA panel, a useful approach may be to perform such analysis on larger multi-center scale using similar experimental design. However, other types of RNAs (lncRNAs, circRNAs and mRNAs) that, together with miRNAs, represent RNA networks, have not been yet extensively studied in blood samples of patients with ALS. Additional research has to be done in order to find robust circulating biomarkers and therapeutic targets that will distinguish key RNA interactions in specific ALS-types to facilitate diagnosis, predict progression and design therapy.

Splicing Players Are Differently Expressed in Sporadic Amyotrophic Lateral Sclerosis Molecular Clusters and Brain Regions

Splicing is a tightly orchestrated process by which the brain produces protein diversity over time and space. While this process specializes and diversifies neurons, its deregulation may be responsible for their selective degeneration. In amyotrophic lateral sclerosis (ALS), splicing defects have been investigated at the singular gene level without considering the higher-order level, involving the entire splicing machinery. In this study, we analyzed the complete spectrum (396) of genes encoding splicing factors in the motor cortex (41) and spinal cord (40) samples from control and sporadic ALS (SALS) patients. A substantial number of genes (184) displayed significant expression changes in tissue types or disease states, were implicated in distinct splicing complexes and showed different topological hierarchical roles based on protein–protein interactions. The deregulation of one of these splicing factors has a central topological role, i.e., the transcription factor YBX1, which might also have an impact on stress granule formation, a pathological marker associated with ALS.

Extracellular RNAs as Biomarkers of Sporadic Amyotrophic Lateral Sclerosis and Other Neurodegenerative Diseases

Recent progress in the research for underlying mechanisms in neurodegenerative diseases, including Alzheimer disease (AD), Parkinson disease (PD), and amyotrophic lateral sclerosis (ALS) has led to the development of potentially effective treatment, and hence increased the need for useful biomarkers that may enable early diagnosis and therapeutic monitoring. The deposition of abnormal proteins is a pathological hallmark of neurodegenerative diseases, including β-amyloid in AD, α-synuclein in PD, and the transactive response DNA/RNA binding protein of 43kDa (TDP-43) in ALS. Furthermore, progression of the disease process accompanies the spreading of abnormal proteins. Extracellular proteins and RNAs, including mRNA, micro RNA, and circular RNA, which are present as a composite of exosomes or other forms, play a role in cell–cell communication, and the role of extracellular molecules in the cell-to-cell spreading of pathological processes in neurodegenerative diseases is now in the spotlight. Therefore, extracellular proteins and RNAs are considered potential biomarkers of neurodegenerative diseases, in particular ALS, in which RNA dysregulation has been shown to be involved in the pathogenesis. Here, we review extracellular proteins and RNAs that have been scrutinized as potential biomarkers of neurodegenerative diseases, and discuss the possibility of extracellular RNAs as diagnostic and therapeutic monitoring biomarkers of sporadic ALS.

Monozygotic twins and triplets discordant for amyotrophic lateral sclerosis display differential methylation and gene expression

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease characterised by the loss of upper and lower motor neurons. ALS exhibits high phenotypic variability including age and site of onset, and disease duration. To uncover epigenetic and transcriptomic factors that may modify an ALS phenotype, we used a cohort of Australian monozygotic twins (n = 3 pairs) and triplets (n = 1 set) that are discordant for ALS and represent sporadic ALS and the two most common types of familial ALS, linked to C9orf72 and SOD1. Illumina Infinium HumanMethylation450K BeadChip, EpiTYPER and RNA-Seq analyses in these ALS-discordant twins/triplets and control twins (n = 2 pairs), implicated genes with consistent longitudinal differential DNA methylation and/or gene expression. Two identified genes, RAD9B and C8orf46, showed significant differential methylation in an extended cohort of >1000 ALS cases and controls. Combined longitudinal methylation-transcription analysis within a single twin set implicated CCNF, DPP6, RAMP3, and CCS, which have been previously associated with ALS. Longitudinal transcriptome data showed an 8-fold enrichment of immune function genes and under-representation of transcription and protein modification genes in ALS. Examination of these changes in a large Australian sporadic ALS cohort suggest a broader role in ALS. Furthermore, we observe that increased methylation age is a signature of ALS in older patients.

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

BACKGROUND

Amyotrophic lateral sclerosis (ALS) is a debilitating disease with few treatment options. Progress towards new therapies requires validated disease biomarkers, but there is no consensus on which fluid-based measures are most informative.

METHODS

This study analyzed microarray data derived from blood samples of patients with ALS (n = 396), ALS mimic diseases (n = 75), and healthy controls (n = 645). Goals were to provide in-depth analysis of differentially expressed genes (DEGs), characterize patient-to-patient heterogeneity, and identify candidate biomarkers.

RESULTS

We identified 752 ALS-increased and 764 ALS-decreased DEGs (FDR < 0.10 with > 10% expression change). Gene expression shifts in ALS blood broadly resembled acute high altitude stress responses. ALS-increased DEGs had high exosome expression, were neutrophil-specific, associated with translation, and overlapped significantly with genes near ALS susceptibility loci (e.g., IFRD1, TBK1, CREB5). ALS-decreased DEGs, in contrast, had low exosome expression, were erythroid lineage-specific, and associated with anemia and blood disorders. Genes encoding neurofilament proteins (NEFH, NEFL) had poor diagnostic accuracy (50-53%). However, support vector machines distinguished ALS patients from ALS mimics and controls with 87% accuracy (sensitivity: 86%, specificity: 87%). Expression profiles were heterogeneous among patients and we identified two subgroups: (i) patients with higher expression of IL6R and myeloid lineage-specific genes and (ii) patients with higher expression of IL23A and lymphoid-specific genes. The gene encoding copper chaperone for superoxide dismutase (CCS) was most strongly associated with survival (HR = 0.77; P = 1.84e-05) and other survival-associated genes were linked to mitochondrial respiration. We identify a 61 gene signature that significantly improves survival prediction when added to Cox proportional hazard models with baseline clinical data (i.e., age at onset, site of onset and sex). Predicted median survival differed 2-fold between patients with favorable and risk-associated gene expression signatures.

CONCLUSIONS

Peripheral blood analysis informs our understanding of ALS disease mechanisms and genetic association signals. Our findings are consistent with low-grade neutrophilia and hypoxia as ALS phenotypes, with heterogeneity among patients partly driven by differences in myeloid and lymphoid cell abundance. Biomarkers identified in this study require further validation but may provide new tools for research and clinical practice.