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Circulating miR-181 is a prognostic biomarker for amyotrophic lateral sclerosis

Nature Neuroscience volume 24, pages 1534–1541 (2021)Cite this article

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Abstract

Amyotrophic lateral sclerosis (ALS) is a relentless neurodegenerative disease of the human motor neuron system, where variability in progression rate limits clinical trial efficacy. Therefore, better prognostication will facilitate therapeutic progress. In this study, we investigated the potential of plasma cell-free microRNAs (miRNAs) as ALS prognostication biomarkers in 252 patients with detailed clinical phenotyping. First, we identified, in a longitudinal cohort, miRNAs whose plasma levels remain stable over the course of disease. Next, we showed that high levels of miR-181, a miRNA enriched in neurons, predicts a greater than two-fold risk of death in independent discovery and replication cohorts (126 and 122 patients, respectively). miR-181 performance is similar to neurofilament light chain (NfL), and when combined together, miR-181 + NfL establish a novel RNA–protein biomarker pair with superior prognostication capacity. Therefore, plasma miR-181 alone and a novel miRNA–protein biomarker approach, based on miR-181 + NfL, boost precision of patient stratification. miR-181-based ALS biomarkers encourage additional validation and might enhance the power of clinical trials.

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Fig. 1: Assessment of plasma miRNA stability during ALS course.
Fig. 2: Identification of candidate miRNAs that predict the survival of patients with ALS.
Fig. 3: miR-181 is a prognostic biomarker of ALS.
Fig. 4: miR-181a-5p localizes to neuronal soma and neurites in mouse brain and spinal cord.
Fig. 5: Superior accuracy for combination of miRNA and NfL biomarkers in prognosis analysis.

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Data availability

Source data for figures are provided in supplementary tables. FASTQ.gz files with raw sequencing data, text files with raw read counts, Excel files with processed read counts and R codes are available as GSE168714 in the Gene Expression Omnibus. Source data are provided with this paper.

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Acknowledgements

We thank V. Lombardi (University College London) for technical assistance and I. Ben-Dov (Hadassah Hebrew University Medical Center) for advice on statistics. We acknowledge patients for their contributions and all ALS biomarker study coworkers and their contributions to the biobanking project, which made this study possible (REC 09/H0703/27). We also thank the North Thames Local Research Network for its support and life science editors for editorial assistance. E.H. is the Mondry Family Professorial Chair and Head of the Nella and Leon Benoziyo Center for Neurological Diseases. Imaging was performed at the de Picciotto Cancer Cell Observatory, in memory of Wolfgang and Ruth Lesser. Funding: this research was supported by the following grants: Motor Neurone Disease Association (MNDA no. 839-791), Redhill Foundation – Sam and Jean Rothberg Charitable Trust and J. and E. Moravitz. Research at the Hornstein lab is supported by the CReATe Consortium and ALSA (program: ‘Prognostic value of miRNAs in biofluids from ALS patients’); the RADALA Foundation; AFM Telethon (20576); Weizmann - Brazil Center for Research on Neurodegeneration at the Weizmann Institute of Science; the Minerva Foundation, with funding from the Federal German Ministry for Education and Research; the ISF Legacy Heritage Fund 828/17; the Israel Science Foundation 135/16 and ISF IPMP 3497/21; Target ALS 118945; the Thierry Latran Foundation for ALS Research; the European Research Council, under the European Union’s Seventh Framework Programme (FP7/2007–2013)/ERC grant agreement no. 617351; ERA-Net for Research Programmes on Rare Diseases (eRARE FP7), via the Israel Ministry of Health; Dr. Sydney Brenner and friends; A. Alfred Taubman through IsrALS; Yeda-Sela; Yeda-CEO; the Israel Ministry of Trade and Industry; the Y. Leon Benoziyo Institute for Molecular Medicine; the Kekst Family Institute for Medical Genetics; the David and Fela Shapell Family Center for Genetic Disorders Research; the Crown Human Genome Center; the Nathan, Shirley, Philip and Charlene Vener New Scientist Fund; the Julius and Ray Charlestein Foundation; the Fraida Foundation; the Wolfson Family Charitable Trust; the Adelis Foundation; Merck (United Kingdom); M. Halphen; and the estates of F. Sherr, L. Asseof and L. Fulop. P.F. is supported by a Medical Research Council Senior Clinical Fellowship and the Lady Edith Wolfson Fellowship scheme (MR/M008606/1 and MR/S006508/1). J.G. was supported in the JPND framework ONWebDUALS, and L.G. is the Graeme Watts Senior Research Fellow supported by the Brain Research Trust. N.S.Y. was supported by the Israeli Council for Higher Education via the Weizmann Data Science Research Center, by a research grant from the Estate of Tully and Michele Plesser and by Maccabim Foundation. I.M. was supported by Teva Pharmaceutical Industries as part of the Israeli National Network of Excellence in Neuroscience (fellowship no. 117941).

Author information

Author notes

  1. These authors contributed equally: Iddo Magen, Nancy Sarah Yacovzada.

Authors and Affiliations

  1. Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, Israel

    Iddo Magen, Nancy Sarah Yacovzada, Eran Yanowski & Eran Hornstein

  2. Department of Molecular Neuroscience, Weizmann Institute of Science, Rehovot, Israel

    Iddo Magen, Nancy Sarah Yacovzada, Eran Yanowski & Eran Hornstein

  3. Translational Medicine, Merck Healthcare KGaA, Darmstadt, Germany

    Anna Coenen-Stass

  4. Department of Neuromuscular Diseases, University College London, Queen Square Institute of Neurology, London, UK

    Anna Coenen-Stass, Ching-Hua Lu, Linda Greensmith, Andrea Malaspina & Pietro Fratta

  5. UCL Queen Square Motor Neuron Disease Centre, Queen Square Institute of Neurology, London, UK

    Anna Coenen-Stass, Ching-Hua Lu, Linda Greensmith, Andrea Malaspina & Pietro Fratta

  6. Precision Neurology, Department of Neurology, University of Lübeck, Lübeck, Germany

    Julian Grosskreutz

  7. Center for Healthy Aging, Department of Neurology, Jena University Hospital, Jena, Germany

    Julian Grosskreutz

  8. Neurology, School of Medicine, China Medical University and Hospital, Taichung, Taiwan

    Ching-Hua Lu

  9. Centre for Neuroscience and Trauma, Blizard Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK

    Ching-Hua Lu & Andrea Malaspina

  10. ALS Biomarkers Study, University College London, London, UK

    Ching-Hua Lu, Linda Greensmith, Andrea Malaspina & Pietro Fratta

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Contributions

I.M., P.F., A.M. and E.H. conceived the research. I.M., N.S.Y., J.G. and E.H. analyzed the data. A.M. and C.H.L. established cohorts, obtained ethics approval and collected human samples for research. E.Y. performed in situ hybridization. A.C.S. assisted with research. I.M., N.S.Y., P.F., A.M. and E.H. wrote the manuscript, with comments from and final approval by all other authors. L.G. provided resources for research and input in research development. A.M. is the corresponding author for cohorts and clinical data. P.F. and E.H. are corresponding authors for all other facets of the work.

Corresponding authors

Correspondence to Andrea Malaspina, Pietro Fratta or Eran Hornstein.

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The authors declare no competing interests.

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Peer review information Nature Neuroscience thanks the anonymous reviewers for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Analysis of miRNAs that increase during ALS course.

(a) Plasma levels of four miRNAs of the 129 miRNAs analyzed in main Fig. 1a displayed low inter-individual variability, but increased with the disease course, suggesting that, although they are not suited for prognostic use, they could potentially monitor disease progression. (miR-423/484/92a/b, t4/t1 > 1.5 SD, X-axis) (B-D) MA plots of differential miRNA expression upon repeated sampling relative to the first phlebotomy. Red features denote miRNAs with statistically significant change in levels. Temporal changes in the levels of (E) miR-423-5p, (F) miR-484, (G) miR-92a-3p, or (H) miR-92b-3p revealed correlation with time passing from enrollment (in months). Spaghetti plots of individual patient trajectories (t1-t4) denoted for (I) miR-423-5p, t21 = 4.52, p = 0.0002; (J) miR-484, t21 = 4.05,p = 0.0006; (K) miR-92a-3p, t21 = 3.85, p = 0.0009 or (L) miR-92b-3p, t21 = 4.77, p = 0.0001. Time intervals: t1-t2 6.3 ± 0.3 m.; t1-t3 13.0 ± 0.3 m.; t1-t4 32.7 ± 3 m. Disease duration: t1 28.8 ± 3 m.; t4 61.5 ± 3 m. Validation of changes to miRNA levels in an independent replication cohort (N = 26 individuals, Table 2). Spaghetti plots of individual patient trajectories (t1-t2 13.7 ± 1.6 months) in a replication cohort, for (M) miR-423-5p, t24 = 0.98, p = 0.17 (N) miR-484, t25 = 2.08, p = 0.02; (O) miR-92a-3p, t25 = 2.13, p = 0.02; or (P) miR-92b-3p, t23 = 1.55, p = 0.067. Together, miR-484 and miR-92a/b may be considered as candidate molecular biomarkers of functional decline over the course of disease. Data presented as Mean ± SEM. *p < 0.05, ***p < 0.001, paired t-test. Analysis of a single miR-423-5p sample and two miR-92b-3p samples in the replication cohort deviated from the mean according to Grubbs test and these were excluded as outliers. Correlation between the relative disease covered (rD50) in longitudinal plasma collections (X-axis) and levels of (Q) miR-423-5p, (R) miR-484, (S) miR-92a-3p, and (T) miR-92b-3p (Y-axis). The relative D50 (rD50) is a derivative of ALS Functional Rating Scale-Revised (ALSFRS-R) decay that reveals the disease covered by individual patients independent of the rate of progression24,39. For example, an rD50 of 0.0 signifies ALS onset, and 0.5 signifies the time-point where functionality is reduced by half. Longitudinal miR-484/92a/b levels in blood correlated with rD50 at the time of sampling (R-T). All statistical tests were two-sided, except for panels M-P.

Source data

Extended Data Fig. 2 Clinical features are comparable between discovery and replication cohorts.

(A) Survival from enrollment (B) survival from symptom onset (C) ALSFRS-R score at enrollment (D) progression rate at enrollment (E) sex distribution (F) onset site distribution (G) Riluzole treatment status (H) number of censored patients. Box plots in A-D are presented as median bound between minimum and maximum values. All statistical tests were two-sided. n = 252 biologically independent human samples.

Source data

Extended Data Fig. 3 Pipeline for selecting miRNAs as candidate prognostic markers.

2008 miRNAs were aligned to the human genome in the longitudinal study and out of them, 187 miRNAs, which exhibited >50 UMI counts in 60% of the samples, were included in further analysis. 125 out of the 187 miRNAs were longitudinally stable with low interindividual variability (green features in Fig. 1a). In the discovery cohort, 106 out of these 125 miRNAs passed a filtering criterion of average UMI counts >100 across all samples, and were analyzed for prognosis differences between low and high level in the discovery cohort. 19 miRNAs were further excluded after additional QC based on logrank analysis (opposite directions of prognosis differences between members of the same miRNA family, for example miR-27a and miR-27b), and the remaining 87 miRNAs were assessed for logrank and p values for prognosis differences as demonstrated in Fig. 2. 9 out of these 87 miRNAs displayed logrank p ≤ 0.01, and all of their possible pairs (9*8/2 = 36), derived from multiplication of the levels of two single miRNAs, were further assessed for prognosis differences. Nine single miRNAs and 20 miRNA pairs, each displaying a logrank p ≤ 0.01, were further subjected to feature selection by bootstrap resampling. In bootstrap resampling, features had to be selected >70% of the bootstrap samples and display statistical significance in >85% of the samples in which they were selected, in order to be tested as a prognostic marker on the replication cohort. miR-181 was the only feature fulfilling those criteria, hence it was tested in the discovery cohort, and exhibited significant survival differences and hazard ratios, both in the discovery cohort and when validated on a replication cohort that was set aside until that point.

Extended Data Fig. 4 Scatter plot, assessing agreement between separation of survival by 123 miRNA features.

The optimal threshold was calculated per miRNA in a discovery cohort of 126 patients by21. Single miRNAs (black) or miRNA pairs (green), displaying a p-value ≤0.01 (log 10 transformed values ≥ 2), for logrank test from study enrollment (logrank χ2, y-axis) or first symptoms (onset, logrank χ2, x-axis). gray: insignificant features.

Source data

Extended Data Fig. 5 miR-181 levels are predictive of survival length regardless of Riluzole treatment.

Survival by miR-181 levels in untreated patients, from enrollment (A) or onset (B), and in patients that were treated with Riluzole, from enrollment (C) or onset (D). All statistical test were two-sided. N = 248 biologically independent human samples.

Source data

Extended Data Fig. 6 miR-181 levels in expression bins.

miR-181 levels are higher in the high vs low expression bin, in both the (A) discovery cohort, t21 = 5.94, p < 0.0001 and (B) replication cohort, t21 = 2.87, p = 0.009. Plots depicting inverse correlation between miR-181 levels and survival from first phlebotomy (C), or from disease onset (D). Box plots in A,B are presented as median bound between minimum and maximum values. **p < 0.01, ***p < 0.0001, unpaired t-test with Welch’s correction. All statistical tests were two-sided. N = 248 biologically independent human samples.

Source data

Extended Data Fig. 7 miR-181 levels with respect to parameters of the D50 model.

(A) D50, a measure of disease aggressiveness, is significantly lower in high vs low miR-181 levels, indicating a more aggressive disease. t194 = 3.99, p < 0.0001. (B) Individual disease covered, reflected by rD50 values, is not different between low and high miR-181 expression bins. t72 = 1.85, p = 0.07 (C) No correlation of miR-181 levels with individual disease covered. (D) miR-181 levels are not different between different phases of disease defined by rD50 values. One-way ANOVA: F2 = 1.94, p = 0.15. Box plots in A, B and D are presented as median bound between minimum and maximum values. ***p < 0.0001, unpaired t-test with Welch’s correction. All statistical tests were two-sided. N = 248 biologically independent human samples.

Source data

Extended Data Fig. 8 miR-181 levels are not related to phenotypic properties at enrollment.

Lack of correlation between miR-181 levels at enrollment and progression rate (A), ALSFRS-R (B) and age at onset (C) in the discovery cohort, or in the replication cohort (D-F). These properties were not different between low and high miR-181 bins (G-I). Box plots in G-I are presented as median bound between minimum and maximum values All statistical test were two-sided. No adjustment for multiple comparisons was performed. N = 248 biologically independent human samples.

Source data

Extended Data Fig. 9 Cox proportional hazard analysis for continuous miR-181 and NfL values.

Multivariate Cox proportional hazard analysis for z-scores of miR-181 and NfL from enrollment (A) or onset (B) on the discovery and replication cohorts. Univariate Cox on the merged cohort (discovery + replication) for the z-scores of miR-181, NfL and the sum of the z-scores of both, from enrollment (C) or onset (D). Data are presented as median ± 95% CI. *p < 0.05, **p < 0.01, ***p < 0.001, two-tailed Wald test.

Source data

Extended Data Fig. 10 Association of miR-181 with other markers.

miR-181 is not correlated with NfL levels, either in the full cohort (A) or when NfL is broken into tertiles (B). (C-E) miR-181 is not correlated with markers of muscle integrity (CK and creatinine) or inflammatory marker (TNF-alpha). All statistical test were two-sided. No adjustment for multiple comparisons was performed. N = 248 biologically independent human samples.

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Magen, I., Yacovzada, N.S., Yanowski, E. et al. Circulating miR-181 is a prognostic biomarker for amyotrophic lateral sclerosis. Nat Neurosci 24, 1534–1541 (2021). https://doi.org/10.1038/s41593-021-00936-z

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