Early-onset sleep alterations found in patients with amyotrophic lateral sclerosis are ameliorated by orexin antagonist in mouse models
Data files
Jan 25, 2025 version files 66.35 KB
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DATA_ALS_Cohort.csv
11.58 KB
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DATA_ALS_Gene_Carriers_Cohort.csv
7.05 KB
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DATA_Mice_Baseline_PSD.csv
6.76 KB
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DATA_Mice_Baseline.csv
2.70 KB
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DATA_Mice_MCH_PSD.csv
5.66 KB
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DATA_Mice_MCH.csv
2.28 KB
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DATA_Mice_Suvorexant_PSD.csv
16.08 KB
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DATA_Mice_Suvorexant.csv
7.02 KB
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README.md
7.23 KB
Abstract
Lateral hypothalamic neurons producing melanin-concentrating hormone (MCH) and orexin/hypocretin are involved in sleep regulation. Both MCH and orexin neurons are altered in amyotrophic lateral sclerosis (ALS), the most common adult-onset motor neuron disease. However, sleep alterations are currently poorly characterized in ALS, and could represent either early symptoms or late consequences of disease progression. We characterized sleep architecture using polysomnography in cohorts of both early ALS patients without respiratory impairment and presymptomatic carriers of mutations leading to familial ALS. We observed sleep alterations, including increased wake and decreased deep sleep (non-rapid eye movement—NREM3) and increased wake correlated with worse cognitive performance, in particular, verbal fluency in both cohorts. These changes in sleep architecture were replicated in three mouse models of familial ALS, Sod1G86R, FusΔNLS/+ and TDP-43Q331K mice. Altered sleep structure in mice was fully rescued by per os administration of a dual-orexin receptor antagonist, and partially rescued by intracerebroventricular MCH.
README: Dataset for Guillot SJ, Lang C STM paper
https://doi.org/10.5061/dryad.gb5mkkx10
Description of the data and file structure
The dataset contains the raw data from which the figures were generated.
DATA_ALS_Cohort.csv: sleep parameters compared between patients with ALS and their controls
Classification: classification of patients with ALS (ALS) and their controls (CTR)
ESS: Epworth Sleepiness Scale
PSQI: Pittsburgh Sleep Quality Index sumscore
ECAS_MEM: ECAS memory subscore
ECAS_SpatConpt: ECAS spatial conception subscore
ECAS_Speak: ECAS speech subscore
ECAS_VerFlnt: ECAS verbal fluency subscore
ECAS_ExecFct: ECAS executive function subscore
ECAS_Total: ECAS total sumscore
percWake: percentage of wake episodes (%)
percREM: percentage of REM episodes (%)
percNREM: percentage of NREM episodes (%)
percNREM1: percentage of NREM1 episodes (%)
percNREM2: percentage of NREM2 episodes (%)
percNREM3:percentage of NREM3 episodes (%)
Dur_N1: duration of NREM1 (seconds)
Dur_N2: duration of NREM2 (seconds)
Dur_N3: duration of NREM3 (seconds)
Lat_N1: onset latency of NREM1 (seconds)
Lat_N2: onset latency of NREM2 (seconds)
Lat_N3: onset latency of NREM3 (seconds)
Lat_REM: onset latency of REM (seconds)
Time_in_Bed: time spent in bed (seconds)
Sleep_Total_Time: duration from first to last period of sleep (epochs)
Total_Sleep_Time: total duration of NREM and REM
Sleep_Onset_Latency: onset latency of sleep (seconds)
REM_Efficiency: efficiency of REM sleep
Sleep_Efficiency: efficiency of sleep (%)
Sleep_Maintenance_Efficiency: efficiency of sleep maintenance (%)
Sleep_Pressure: sleep pressure
Sleep_Fragmentation_Index: index of the fragmentation of sleep
DATA_ALS_Gene_Carriers_Cohort.csv: sleep parameters compared between presymptomatic gene carriers of ALS and their controls
Classification: classification of presymptomatic gene carriers of ALS (C9ORF72 and SOD1) and their controls (CTR)
ESS: Epworth Sleepiness Scale
PSQI: Pittsburgh Sleep Quality Index sumscore
ECAS_MEM: ECAS memory subscore
ECAS_SpatConpt: ECAS spatial conception subscore
ECAS_Speak: ECAS speech subscore
ECAS_VerFlnt: ECAS verbal fluency subscore
ECAS_ExecFct: ECAS executive function subscore
ECAS_Total: ECAS total sumscore
percWake: percentage of wake episodes (%)
percREM: percentage of REM episodes (%)
percNREM: percentage of NREM episodes (%)
percNREM1: percentage of NREM1 episodes (%)
percNREM2: percentage of NREM2 episodes (%)
percNREM3:percentage of NREM3 episodes (%)
Dur_N1: duration of NREM1 (seconds)
Dur_N2: duration of NREM2 (seconds)
Dur_N3: duration of NREM3 (seconds)
Lat_N1: onset latency of NREM1 (seconds)
Lat_N2: onset latency of NREM2 (seconds)
Lat_N3: onset latency of NREM3 (seconds)
Lat_REM: onset latency of REM (seconds)
Time_in_Bed: time spent in bed (seconds)
Sleep_Total_Time: duration from first to last period of sleep (epochs)
Total_Sleep_Time: total duration of NREM and REM
Sleep_Onset_Latency: onset latency of sleep (seconds)
REM_Efficiency: efficiency of REM sleep
Sleep_Efficiency: efficiency of sleep (%)
Sleep_Maintenance_Efficiency: efficiency of sleep maintenance (%)
Sleep_Pressure: sleep pressure
Sleep_Fragmentation_Index: index of the fragmentation of sleep
DATA_Mice_Baseline.csv: sleep parameters compared between transgenic ALS models with their respective controls
Genotype: genotype of the model (SOD-WT vs SOD1G86R; Fus+/+ 3m vs FusdNLS/+ 3m; Fus+/+ 10m vs FusdNLS/+ 10m; TDP-WT vs TDP-43_Q331K)
Sex: sex of the model (male or female)
percWake: percentage of wake episodes (%)
percREM: percentage of REM episodes (%)
percNREM: percentage of NREM episodes (%)
DATA_Mice_MCH.csv: sleep parameters compared between transgenic ALS models with their respective controls
Genotype: genotype of the model (SOD-WT vs SOD1G86R; Fus+/+ 3m vs FusdNLS/+ 3m; Fus+/+ 10m vs FusdNLS/+ 10m; TDP-WT vs TDP-43_Q331K)
Sex: sex of the model (male or female)
Treatment: vehicle or MCH
percWake: percentage of wake episodes (%)
percREM: percentage of REM episodes (%)
percNREM: percentage of NREM episodes (%)
DATA_Mice_Suvorexant.csv: sleep parameters compared between transgenic ALS models with their respective controls
Genotype: genotype of the model (SOD-WT vs SOD1G86R; Fus+/+ 3m vs FusdNLS/+ 3m; Fus+/+ 10m vs FusdNLS/+ 10m; TDP-WT vs TDP-43_Q331K)
Sex: sex of the model (male or female)
Treatment: vehicle or Suvorexant
percWake: percentage of wake episodes (%)
percREM: percentage of REM episodes (%)
percNREM: percentage of NREM episodes (%)
DATA_Mice_Baseline_PSD.csv: power bands compared between transgenic ALS models with their respective controls
Genotype: genotype of the model (SOD-WT vs SOD1G86R; Fus+/+ 3m vs FusdNLS/+ 3m; Fus+/+ 10m vs FusdNLS/+ 10m; TDP-WT vs TDP-43_Q331K)
Sex: sex of the model (male or female)
Delta: absolute bandpower of the Delta wave (uV^2)
Theta: absolute bandpower of the Theta wave (uV^2)
Alpha: absolute bandpower of the Alpha wave (uV^2)
Sigma: absolute bandpower of the Sigma wave (uV^2)
Beta: absolute bandpower of the Beta wave (uV^2)
Gamma: absolute bandpower of the Gamma wave (uV^2)
TotalAbsPow: total absolute power of the whole spectral power (uV^2)
DATA_Mice_MCH_PSD.csv: power bands compared between transgenic ALS models with their respective controls administered with MCH
Genotype: genotype of the model (SOD-WT vs SOD1G86R; Fus+/+ 3m vs FusdNLS/+ 3m; Fus+/+ 10m vs FusdNLS/+ 10m; TDP-WT vs TDP-43_Q331K)
Sex: sex of the model (male or female)
Treatment: vehicle or MCH
Delta: absolute bandpower of the Delta wave (uV^2)
Theta: absolute bandpower of the Theta wave (uV^2)
Alpha: absolute bandpower of the Alpha wave (uV^2)
Sigma: absolute bandpower of the Sigma wave (uV^2)
Beta: absolute bandpower of the Beta wave (uV^2)
Gamma: absolute bandpower of the Gamma wave (uV^2)
TotalAbsPow: total absolute power of the whole spectral power (uV^2)
DATA_Mice_Suvorexant_PSD.csv: power bands compared between transgenic ALS models with their respective controls administered with Suvorexant
Genotype: genotype of the model (SOD-WT vs SOD1G86R; Fus+/+ 3m vs FusdNLS/+ 3m; Fus+/+ 10m vs FusdNLS/+ 10m; TDP-WT vs TDP-43_Q331K)
Sex: sex of the model (male or female)
Treatment: vehicle or Suvorexant
Delta: absolute bandpower of the Delta wave (uV^2)
Theta: absolute bandpower of the Theta wave (uV^2)
Alpha: absolute bandpower of the Alpha wave (uV^2)
Sigma: absolute bandpower of the Sigma wave (uV^2)
Beta: absolute bandpower of the Beta wave (uV^2)
Gamma: absolute bandpower of the Gamma wave (uV^2)
TotalAbsPow: total absolute power of the whole spectral power (uV^2)
Files and variables
Uploaded files are embedded with all variables used in the analysis of this paper. These sleep parameters are referenced within the AASM Sleep Manual 2024 (v.3.0).
Patients with ALS and presymptomatic gene carriers were recorded following a one-night polysomnography.
ALS models were recorded over 24 hours and then administered with a vehicle solution and a drug, two drugs were used in this experiment, MCH and its vehicle solution, and Suvorexant and its vehicle solution.
Missing values across all datasets are indicated by an empty cell.
Methods
ALS patients were recruited from the inpatient and outpatient clinics of the neurologic department of the University Hospital of Ulm, Germany. The inclusion criteria for ALS patients included a diagnosis of definite ALS based on the revised El Escorial criteria. Presymptomatic carriers of fALS genes were recruited through the study centre of the Neurological University Hospital, through which first-degree relatives of confirmed familial ALS patients receive longitudinal follow-up and counselling. Controls were recruited from the general population at the neurology clinic, and matched to ALS patients based on age, sex, and geographical location; the requirement for this group was the exclusion of neurodegenerative diseases. All individuals in the control group were unrelated to ALS or familial ALS.
The study in the ALS patient cohort was approved by the Ethics Committee of the University of Ulm (reference 391/18), as well as the study in the presymptomatic carriers which was also approved by the Ethics Committee of the University of Ulm (reference 68/19), in compliance with the ethical standards of the current version of the revised Helsinki Declaration. All participants gave informed consent prior to enrolment.
Medical history was documented. For ALS patients, the ALSFRS-r and characteristics of disease progression were documented (site of first paresis/atrophy, date of onset). All participants also completed validated daytime sleepiness and sleep quality questionnaires, namely the Epworth Sleepiness Scale (ESS) and the Pittsburgh Sleep Quality Index (PSQI).
The exclusion criteria were intended to exclude all possible circumstances that might otherwise alter sleep architecture. For this reason, participants who had an apnoea-hypopnea index (AHI) above 20 per hour or participants who had a periodic limb movement index (PLMSI) above 50 per hour were excluded. In particular, we intended to exclude respiratory insufficiency in ALS patients. Respiratory insufficiency develops earlier or later in the progression of ALS, depending on the individual course, but is generally present in advanced stages, and is known to influence sleep architecture. For this reason, ALS patients received transcutaneous capnometry in addition to polysomnography.
Cognition was measured with the German version of the Edinburgh Cognitive and Behavioral ALS Screen (ECAS) by trained psychologists. The ECAS addresses cognitive domains of language, verbal fluency, executive functions (ALS-specific functions) and memory and visuospatial functions (ALS non-specific functions). Age and education-adjusted cut-offs were used. Behavioural changes were assessed by patient caregiver/1st-degree relative interviews on disinhibition, apathy, loss of sympathy/empathy, perseverative/stereotyped behaviour, hyperorality/altered eating behaviour and psychotic symptoms.
All participants, ALS patients, healthy controls, fALS gene carriers and fALS controls underwent a single night full polysomnography, involving monitoring of various physiological parameters including electroencephalogram (EEG), surface electromyogram (EMG), electrooculogram (EOG), respiratory effort and flow, pulse and oxygen saturation. All measurements were conducted according to the criteria of the American Academy of Sleep Medicine (AASM) guidelines. The EEG electrodes were placed according to the international 10-20 system, the following electrodes were used in each subject: Fz, C3, C4, Cz, P3, P4, Pz, O1, O2, A1, and A2. The sampling rate was 512 Hz in each case. The individually different points in time at which the participant turned off the lights and tried to sleep were marked with a "lights off" marker in each recording.
Analyses were performed using available Python packages (only compatible with Python 3.10 or newer, Python Software Foundation. Python Language Reference, version 3.12. Available at http://www.python.org) relying on MNE package. EEGs were first de-identified using the open-source Prerau Lab EDF De-identification Tool (Version 1.0; 2023) in Python (Prerau Lab EDF De-identification Tool [Computer software], 2023, Retrieved fromhttps://sleepeeg.org/edf-de-identification-tool). De-identified EEGs were then notch-filtered to remove the 50Hz powerline. Independent component analysis was performed to remove all remaining artefacts from the signal. Analyses were limited to both sensorimotor cortices (C3 and C4), which are known to be impaired at the onset of the disease, as well as nearby interhemispheric sulci (Fz, Cz and Pz). Sleep staging was performed on a 6-hour window with 30-second epochs, starting when lights were turned off, using YASA deep learning algorithm (v0.6.4), as well as the spectral analysis. Time in bed and total sleep time were calculated over the whole recording period. The automated sleep staging, hypnograms, and spectrograms were performed using Welch’s method. Sleep pressure was determined using the area under the curve (AUC) of Delta power (0.5-4Hz) of the first hour of the 6-hour window. Simpson’s rule was used to compute the AUC. REM efficiency was computed by dividing Theta power (4-8Hz) by Delta power (0.5-4Hz) specifically during REM epochs. Sleep staging and analysis were performed following the AASM’s guidelines.