DOI: 10.5061/dryad.gb5mkkx10
title: Early-onset sleep alterations found in patients with amyotrophic lateral sclerosis are ameliorated by orexin antagonist in mouse models

description: 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.
description: 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/1<sup>st</sup>-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.
description: # Dataset for Guillot SJ, Lang C STM paper
 [https://doi.org/10.5061/dryad.gb5mkkx10](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.