Author response:
The following is the authors’ response to the original reviews.
Public Reviews:
Reviewer #1 (Public review):
Summary:
The manuscript titled," Sleep-Wake Transitions Are Impaired in the AppNL-G-F Mouse Model of Early Onset Alzheimer's Disease", is about a study of sleep/wake phenomena in a knockin mouse strain carrying "three mutations in the human App gene associated with elevated risk for early onset AD". Traditional, in-depth characterization of sleep/wake states, EEG parameters, and response to sleep loss are employed to provide evidence, "supporting the use of this strain as a model to investigate interventions that mitigate AD burden during early disease stages". The sleep/wake findings of earlier studies (especially Maezono et al., 2020, as noted by the authors) were extended by several important, genotype-related observations, including age-related hyperactivity onset that is typically associated with increased arousal, a normal response to loss of sleep and to multiple sleep latency testing, and a stronger AD-like phenotype in females. The authors conclude that the AppNL-G-F mice demonstrate many of the human AD prodromal symptoms and suggest that this strain may serve as a model for prodromal AD in humans, confirming the earlier results and conclusions of Maezono et al. Finally, based on state bout frequency and duration analyses, it is suggested that the AppNL-G-F mice may develop disruptions in mechanism(s) involved in state transition.
Strengths:
The study appears to have been, technically, rigorously conducted with high quality, in-depth traditional assessment of both state and EEG characteristics, with the concordant addition of activity and temperature. The major strengths of this study derive from observations that the AppNL-G-F mice: (1) are more hyperactive in association with decreased transitions between states; (2) maintain a normal response to sleep deprivation and have normal MSLT results; and (3) display a sex specific, "stronger" insomnia-like effect of the knockin in females.
Weaknesses:
The weaknesses stem from the study's impact being limited due to its being largely confirmatory of the Maezono et al. study, with advances of importance to a potentially more focused field. Further, the authors conclude that AppNL-G-F mice have disrupted mechanism(s) responsible for state transition; however, these were not directly examined. The rationale for this conclusion is stated by the authors as based on the observations that bouts of both W and NREM tend to be longer in duration and decreased in frequency in AppNL-G-F mice. Although altered mechanism(s) of state transition (it is not clear what mechanisms are referenced here) cannot be ruled out, other explanations might be considered. For example, increased arousal in association with hyperactivity would be expected to result in increased duration of W bouts during the active phase. This would also predictably result in greater sleep pressure that is typically associated with more consolidated NREM bouts, consistent with the observations of bout duration and frequency.
Reviewer 1 succinctly summarizes the advances of this study beyond the ground-breaking Maezono et al (2020) study of this “humanized” mouse model exhibiting amyloid deposition. Whereas Maezono et al. conducted sleep/wake studies on male AppNL-G-F mice at 6 and 12 months of age, we had the unusual opportunity to study both sexes of homozygous AppNL-G-F mice and WT littermates at 14-18 months of age and to conduct a longitudinal assessment of many of the same individuals at 18-22 months. In addition to baseline sleep/wake and EEG spectral analyses, we (1) measured subcutaneous body temperature and activity to obtain a broader picture of the physiology and behavior of this strain at advanced ages; (2) assessed baseline sleepiness in this strain using the murine version of the clinically-relevant Multiple Sleep Latency Test (MSLT); (3) evaluated the response of AppNL-G-F mice and WT littermates to a 6-h perturbation of the sleep homeostat; (4) compared the sleep/wake characteristics of male vs. female AppNL-G-F mice at 18-22 months; and (5) to assess the stability of the phenotypes, analyzed these data over a continuous 14-d recording rather than the conventional 24h recordings typical of most sleep/wake studies including Maezono et al. We found that a long wake/short sleep phenotype was characteristic of homozygous AppNL-G-F mice at these advanced ages which is also evident in the Maezono et al. (2020) study at 12 months of age (but not at 6 months), although the authors do not comment on this phenotype and instead focus on the reduced REM sleep which is particularly evident in female AppNL-G-F mice in our study. Remarkably, despite being awake ~20% longer per day, we find that AppNL-G-F mice are no sleepier than WT mice as determined by the MSLT and that their sleep homeostat is intact when challenged by 6-h sleep deprivation. At both advanced ages, the long wake/short sleep phenotype is due primarily to longer Wake bouts and shorter bouts of both NREM and REM sleep during the dark phase. Moreover, hyperactivity develops in older AppNL-G-F mice, particularly females, which contributes to this phenotype. We agree with Reviewer 1 that “hyperactivity would be expected to result in increased duration of W bouts during the active phase” and that this could result in more consolidated NREM bouts. Accordingly, we have added the following sentence to the Discussion subsection Impacts of pathology on sleep/wake and activity: “Thus, the hyperactivity evident in Figures 4D, 4D’, and 5D’ could drive the longer wake bouts evident in Figure 7A and result in the longer NREM and REM sleep bouts found in male AppNL-G-F mice (Figure 12A’ and 12A”).”
The suggestion of greater sleep pressure is not borne out by our MSLT studies as we did not observe the shorter sleep latencies nor increased sleep during the nap opportunities on the MSLT that we have observed in other mouse strains. Moreover, due to their short sleep phenotype, AppNL-G-F mice should be entering the sleep deprivation study with a greater sleep debt than WT mice, yet we did not observe a stronger homeostatic response (i.e., enhanced EEG Slow Wave Activity) in this strain during recovery from sleep deprivation. Thus, we have suggested that AppNL-G-F mice are unable to transition from Wake to sleep as readily as their WT littermates. Our observations summarized above set the stage for subsequent mechanistic studies in aged AppNL-G-F mice, although realistically, mice of this age and genotype are a rare commodity.
Reviewer #2 (Public review):
Summary:
The authors have used a knock-in mouse model to explore late-in-life amyloid effects on sleep. This is an excellent model as the mutated genes are regulated by the endogenous promoter system. The sleep study techniques and statistical analyses are also first-rate.
The group finds an age-dependent increase in motor activity in advanced age in the NLGF homozygous knock-in mice (NLGF), with a parallel age-dependent increase in body temperature, both effects predominate in the dark period. Interestingly, the sleep patterns do not quite follow the sleep changes. Wake time is increased in NLGF mice, and there is no progression in increased wake over time. NREMS and REM sleep are both reduced, and there is no progression. Sleep-wake effects, however, show a robust light:dark effect with larger effects in the dark period. These findings support distinct effects of this mutation on activity and temperature and on sleep. This is the first description of the temporal pattern of these effects. NLGF mice show wake stability (longer bout durations in the dark period (their active period) and fewer brief arousals from sleep. Sleep homeostasis across the lights-on period is normal. Wake power spectral density is unaffected in NLGF mice at either age. Only REM power spectra are affected, with NLGF mice showing less theta and more delta. There are interesting sex differences, with females showing no gene difference in wake bout number, while males show a gene effect. Similarly, gene effects on NREM bout number seem larger in males than in females. Although there was no difference in homeostatic response, there was normalization of sleep-wake activity after sleep deprivation.
Strengths:
Approach (model extent of sleep phenotyping), analysis.
Weaknesses:
The weaknesses are summarized below and are viewed as "addressable".
(1) The term insomnia. Insomnia is defined as a subjective dissatisfaction with sleep, which cannot be ascertained in a mouse model. The findings across baseline sleep in NLGF mice support increased wake consolidation in the active period. The predominant sleep period (lights on) is largely unaffected, and the active period (lights off) shows increased activity and increased wake with longer bouts. There is a fantastic clue where NLGF effects are consistent with increased hypocretinergic (orexinergic) neuron activity in the dark period, and/or increased drive to hypocretin neurons from PVH.
Although the DSM-5 definition of Insomnia Disorder indeed emphasizes a subjective “complaint of dissatisfaction with sleep quantity or quality”, I think the Reviewer takes an unnecessarily narrow view of the term “insomnia”. Aside from cases of “psychological” insomnia in which there is a mismatch between subjective and objective measures of sleep, most sleep researchers would likely agree that insomnia is objectively characterized by a greater than normal wake time during the sleep period (i.e., low sleep efficiency) due to difficulty in either initiating or maintaining sleep. This view has led to efforts to identify not only the biological causes of insomnia but also animal models in which this disorder can be studied. A PubMed search on the terms “mouse” and “insomnia” retrieves 844 publications, including an authoritative 2023 review in J Sleep Research entitled "Animal Models of Human Insomnia" co-authored by a clinician-scientist who has done human sleep research throughout his career and is an authority on CBT-I, in particular. Similarly, a PubMed search on the terms “fly” and “insomnia” retrieves 18 publications. So, although our intent in the submitted version of the manuscript was to use “insomnia” as an operational term to succinctly mean “less sleep than usual”, in the revised manuscript, we have eliminated use of the term “partial insomnia” and replaced it with the term “insomnia-like phenotype”. In the Discussion section “Impacts of pathology on sleep/wake and activity”, we have revised the opening sentence to read “Insomnia in humans is typically characterized by subjective reports of reduced sleep quality and can be accompanied by objective measures of sleep fragmentation and reduced sleep amounts.”
(2) Sleep-wake transitions are impaired: This should not be termed an impairment. It could actually be beneficial to have greater state stability, especially wake stability in the dark or active period. There is reduced sleep in the model that can be normalized by short-term sleep loss. It is fascinating that recovery sleep normalized sleep in the NLGF in the immediate lights-on and light-off period. This is a key finding.
Due to the Reviewer’s objection regarding “impairment”, we have changed the title of the manuscript to “Long Wake/Short Sleep Bouts and Hyperactivity with Advanced Age in a Mouse Model of Early Onset Alzheimer’s Disease”. In Comments (1) and (2), Reviewer 2 suggests a provocative hypothesis to test. In the section “Impacts of pathology on sleep/wake and activity“, we previously stated “A hyperactive hypocretin/orexin or monoaminergic arousal system or a dysfunctional GABAergic sleep onset system could underlie the longer bouts of Wake in AppNL-G-Fmice.” We have now added this additional sentence: “Indeed, Hcrt neurons in aged mice have been shown to exhibit more frequent neuronal activity driving wake bouts and optogenetic stimulation of Hcrt neurons in aged mice results in prolonged wakefulness (Li et al., 2022).“
Reviewer #3 (Public review):
Summary:
In this study, Tisdale et al. studied the sleep/wake patterns in the biological mouse model of Alzheimer's disease. The results in this study, together with the established literature on the relationship of sleep and Alzheimer's disease progression, guided the authors to propose this mouse model for the mechanistic understanding of sleep states that translates to Alzheimer's disease patients. However, the manuscript currently suffers from a disconnect between the physiological data and the mechanistic interpretations. Specifically, the claim of "impaired transitions" is logically at odds with the observed increase in wake-state stability or possible hyperactivity. Additionally, the description of the methods, the quantification, and the figure presentation could be substantially improved. I detail some of my concerns below.
Strengths:
The selection of the knock-in model is a notable strength as it avoids the artifacts associated with APP overexpression and more closely mimics human pathology. The study utilizes continuous 14-day EEG recordings, providing a unique dataset for assessing chronic changes in arousal states. The assessment of sex as a biological variable identifies a more severe "insomniac-like" phenotype in females, which aligns with the higher prevalence and severity of Alzheimer's disease in women.
Weaknesses:
The study seems to lack a clear hypothesis-driven approach and relies mostly on explorative investigations. Moreover, lack of quantitative analytical methods as well as shaky logical conclusions, possibly not supported by data in its current form, leaves room for major improvement.
Since this paper studied sleep states, the "Methods" section is quite unclear on what specific criteria were used to classify sleep states. There is no quantitative description of classifying sleep based on clear, reproducible procedures. There are many reasonably well-characterized sleep scoring systems used in rat electrophysiological literature, which could be useful here. The authors are generally expected to describe movement speed and/or EMG and/or EEG (theta/delta/gamma) criteria used to classify these epochs. The subjective (manual) nature of this procedure provides no verifiable validation of the accuracy and interpretability of the results.
This was an oversight: the “Classification of Arousal States” section has been modified accordingly.
One of the bigger claims is that "state transition mechanism(s)" are impaired. However, Figure 7 shows that model mice exhibit significantly more long wake bouts (>260s) and fewer short wake bouts (<60s). Logically, an "impaired switch" (the flip-flop model, Saper et al., 2010) results in state fragmentation. The data here show the opposite: the wake state has become too stable. This suggests the primary defect is not in the transition mechanism itself, but possibly in a pathological increase in arousal drive (hyper-arousal), likely linked to the dark-phase hyperactivity shown in Figures 4 and 5. Also, a point to note is that this finding is not new.
Reviewers 1 and 2 also make comments conisistent with the alternative interpretation that “the wake state has become too stable.” However, I think we are using different words to say the same thing: that the transition from wake to sleep is impaired whether it is due to hyperarousal or to a defect in the flip/flop switch that results in greater Wake stability. I hope the reviewer would agree that a switch can be impaired in two directions: either it could “flicker” as seems to be the case in narcolepsy or it could get stuck in one position, which is what we suggest here based on the data in Fig. 12A, A’ and A” which show longer bouts of all states (Wake, NREM and REM) in older males. Nonetheless, the hyperarousal hypothesis suggested by the Reviewer is certainly a reasonable alternative. Consequently, we have added the following sentence to the Discussion subsection Impacts of pathology on sleep/wake and activity: “Thus, the hyperactivity evident in Figures 4D, 4D’, and 5D’ could drive the longer wake bouts evident in Figure 7A and result in the longer NREM and REM sleep bouts found in male AppNL-G-F mice.”
Figure 3 heatmaps lack color bars and units. Spectral power must be quantitatively defined and methods well-explained in the Methods section. Without these, the reader cannot discern if the "reduced power" in females is a global suppression of signal or a frequency-specific shift. Additionally, the representative example used to claim shorter sleep bouts lacks the statistical weight required for a major physiological conclusion. How does a cooler color (not clear what range and what the interpretation is) mean shorter sleep bout in female mice? The authors should clearly mark the frequency ranges that support their claims. In this figure, there is a question mark following the theta/delta range. The authors should avoid speculation and state their claims based on facts. They should also add the theta and delta ranges in the plot, such that readers can draw their own conclusions.
The Y-axis in the previous version of this figure was labelled 0-25 Hz. This figure was intended to be a descriptive illustration of how unusual the female AppNL-G-F mice are relative to WT of either sex rather than a quantitative analysis of spectral power. As suggested by Reviewer 2, we have combined this figure with the previous Fig. 14 as the revised Fig. 3 and we have modified the Y-axis labels to more explicitly indicate EEG frequencies. The question mark was legacy text from an earlier version of the manuscript; sorry for the confusion!
Figure 8 and the MSLT results show that model mice are "no sleepier than WT mice" and have a functional homeostatic rebound. This presents a logical flaw in the "insomnia" narrative. True insomnia in AD patients typically involves a failure of the homeostatic process or a debilitating accumulation of sleep debt. If these mice do not show increased sleepiness (shorter latency) despite ~19% less sleep, the authors might be describing a "reduced need" for sleep or a "hyper-aroused" state, possibly not a clinical insomnia phenotype.
Both Reviewer 2 and 3 suggest that we are using “insomnia” incorrectly, which we have used as shorthand to denote less sleep per 24h period. Reviewer 2 states that “Insomnia is defined as a subjective dissatisfaction with sleep” per DSM-5 and Reviewer 3 suggests that the mechanism underlying insomnia in AD patients is “a failure of the homeostatic process or a debilitating accumulation of sleep debt” which is not in DSM-5. Our clinical colleagues tell us that this is not established fact; some argue that the homeostat is intact and that the input(s) to the homeostat are defective. We agree that less sleep in these mice could be due to a reduced need for sleep or to hyperarousal. Consequently, we have changed the title of the manuscript to eliminate “Sleep-Wake Transitions are Impaired…” to the more objective “Long Wake/Short Sleep Bouts and Hyperactivity with Advanced Age in a Mouse Model of Early Onset Alzheimer’s Disease”.
In Figure 9, LFP power shown and compared in percentages is problematic, as LFP power distribution is known to be skewed (follows power law). This is particularly problematic here because all the frequencies above ~20 Hz seem to be totally flattened or nonexistent, which makes this comparison of power severely limited and biased towards the relative frequency in the highly skewed portion of the LFP power spectrum, i.e., very low frequency ranges like delta, theta, and possibly beta. This ignores low, mid, and high gamma as well as ripple band frequencies. NREM sleep is known to have relatively greater ripple band (100-250 Hz) power bursts in hippocampal regions, and REM sleep is known to have synchronous theta-gamma relationships.
We completely agree with the reviewer. There are at least 3 ways that spectral power data can be presented: (1) absolute power; (2) relative power (normalized to a baseline); and (3) power density. In this study, we intentionally presented results in terms of spectral power density so that our results could be compared to those in Figure 3A and 3B of Maezono et al. (2020). This was important because Maezono et al. recorded from mice of 6 and 12 months of age whereas we recorded from older mice, which allowed us to determine which parameters are likely changing with age (and, presumably, greater Ab deposition).
Recommendations for the authors:
Reviewer #1 (Recommendations for the authors):
(1) A key finding for the AppNL-G-F mouse model is the emergence of hyperactivity that may be responsible for the altered sleep architecture. Further investigation to help determine the mechanism(s) responsible might include cFos expression to help localize or provide evidence for the distributed neuronal activity increase in this model. Additionally, identification of overly active areas might provide targets for their manipulation to test the authors' hypothesis of the mechanism of the altered sleep architecture. Does chronic hyperactivity caused by other mechanisms (DREADDs, LOF of a K channel) mimic the AppNL-G-F mouse model sleep phenotype? These sorts of findings would impact the study's significance.
We agree with the Reviewer that identifying the mechanism underlying the long wake/short sleep phenotype of aged AppNL-G-Fmice would increase the study’s significance. However, we want to underscore that the opportunity to study both sexes of homozygous AppNL-G-F mice and WT littermates at 14-18 months of age and to conduct a longitudinal assessment of many of the same individuals at 18-22 months was very unusual. Our observations of the phenotype described in this manuscript set the stage for subsequent mechanistic studies in aged AppNL-G-F mice, although realistically, mice of this age and genotype are a rare commodity.
(2) A more technical area of improvement involves the presentation of the results and the associated critical statistical analyses. Relevant tables and statistics are not always reported (in the results) or properly referenced. In the mixed models, the repeated measures are "time of day", I presume.
Tables 1-6 present statistical results; these 6 Tables are referred to in the Results section a total of 14 times. The text states “The larger sample size in Experiment 2 (N=31 mice) allowed a mixed-effects model ANOVA to be conducted with Genotype, Sex, and Time as factors”. Although “Time of Day” was specified several places in the Results, thank you for pointing out omission of “of Day” from the “Data Analysis and Statistics” section; we have added this information accordingly.
(3) The model is presented as age-dependent, but there was little statistical support for this. The subjects spanned a considerable age range, and a direct quantifiable correlation between age and the various measured dependent variables could be helpful in this regard.
The long wake/short sleep phenotype characteristic of homozygous AppNL-G-F mice that we describe here is also evident in the Maezono et al. (2020) study at 12 months of age but not at 6 months in either the Maezono et al. (2020) or Calafete et al. (2023) studies, although the authors do not comment on this phenotype and instead focus on the reduced REM sleep. Thus, between these studies, there seems to be an age-dependent progression of the phenotype. We have thus added this sentence to the Discussion subsection Sleep/wake and activity phenotypes of 14-18 month vs. 18-22 month old AppNL-G-F mice: “This long wake/short sleep insomnia-like phenotype is also evident at 12 months of age (Maezono et al., 2020) but not at 6 months (Calafate et al., 2023; Maezono et al., 2020), suggesting a progression in this symptomatology.”
(4) Would a more advanced age point be helpful? Would sleep fragmentation be likely to appear with more advanced age?
The text states “Recordings collected throughout the entire 14-day period when Cohort 2 App KI and App WT mice were 21.0-24.3 months of age”. Mice on a C57BL6/J background are considered old at 18-24 months. Fig. 6B’ shows a strong trend (p=0.0558) toward shorter NREM bouts in App KI mice at 18-22 months during the dark phase at the same time that long wake bouts are evident (Fig. 6A’), strongly indicative of sleep/wake fragmentation but not quite significant with the sample size measured.
(5) How does the onset of sleep-architecture-related symptoms relate to the cognitive impairment onset in AppNL-G-F mice?
We have added this sentence to the Conclusions: “In a fear conditioning paradigm, impaired learning ability has been correlated with REM sleep duration in 13 month old but not 7 month old AppNL-G-F mice (Maezono et al., 2020).
(6) It is importantly concluded that the AppNL-G-F mouse phenotype is "stronger" in females. What is meant here by "stronger" and can this be quantified?
We have eliminated use of “stronger” and replaced with “more evident” or “more apparent”.
(7) Would ovariectomized females still show partial insomnia?
This is an interesting question, particularly because the hyperactivity evident in Figure 7C is most evident in females. The average age of cessation of estrus cyclicity in C57BL6/J mice occurs between 13-16 months of age (Nelson et al., 1982, Biol Reproduction). The female KI mice in Cohort 2 ranged from 21.0 to 23.3 months of age at the time of recording and thus can be expected to be functionally ovariectomized.
(8) The statement, "...female AppNL-G-F mice exhibited the most wakefulness and the least amount of sleep each day", sounds like a tautology.
It was an intentional statement to underscore the long wake/short sleep phenotype.
Reviewer #2 (Recommendations for the authors):
(1) Introduction:
The authors might mention in paragraph 3 that because these studies each used a mutant protein on a powerful, and not the endogenous, promoter, the effects on sleep may be skewed by overexpression in specific brain areas. In addition, they might mention that sleep homeostasis and sleep changes relative to brain temp and activity have not been examined longitudinally.
We have added the following sentences to the Limitations subsection of the Discussion: “Moreover, because studies of this strain used a mutant protein on a powerful exogenous promoter, the effects on sleep described by us and previous investigators may be skewed by overexpression in specific brain areas” and “Neither the present nor previous studies have assessed the effects of age-related changes in brain temperature on sleep/wake, sleep homeostasis or activity.”
(2) Results:
Figure 2: Images in 1B and 1B' look like IHC labeling in well over 1 and 2% of the brain for Iba-1. Are these images correct?
The use of “%” on the Y-axis was inappropriate and has been corrected. Due to variation in Iba1 immunostaining across WT mice, Iba1 measurements were normalized to WT such that the mean Iba1 area coverage for WT mice within each region of interest was set to 1. The negligible 82E1 signal in WT mice obviated the need for normalization.
Figure 3: I would move to incorporate into Figure 14 with spectra, as this is descriptive but nicely illustrates Figure 14.
Done -- thank you for this excellent suggestion!
Figure 10: The figure supports no significant estrus effects in either WT or NLGF. Could run the analysis, but important finding.
Agreed but, as indicated in the response to Reviewer 1, the average age of cessation of cyclicity in C57BL6/J mice has been reported to occur between 13-16 months of age (Nelson et al., 1982, Biol Reproduction). The female mice in the older cohort that we recorded were 18-22 months of age.
(3) Discussion:
Page 11, last paragraph: It is hard to say whether activity caused more wake or response to wake is different in these mice (anxiety and hyperactivity are both seen in Alzheimer's disease).
Hypocretin MCH is touched on but could be elaborated upon, given light/dark differences.
We agree that the directionality is difficult to ascertain. As mentioned above, we have added a discussion on hyperactivity but, having not made any assessment of anxiety in the present study, we have refrained from further speculation.
Reviewer #3 (Recommendations for the authors):
(1) Figure 9: Y-axis labels are missing on several plots.
Due to the density of info on this figure, Y-axis labels were intentionally omitted for those panels for which the Y-axis label of the panel to the left applied. Since the reviewer found this to be confusing, we have added Y-axis labels to all panels at the risk of making the figure even more dense!
(2) Figure 14: x tick labels are perplexing - why would they be labelled in such arbitrary decimal points?
As stated in the text, “EEG spectra for each state were analyzed in 0.061 Hz bins”. Consequently, X-axis labels are modulo 0.061 Hz.
(3) Figure S1 is not aligned; some plots cannot even be read.
Figure S1 has been reformatted to portrait mode from the previous landscape version (although no alignment issues were evident when viewed in landscape mode).
(4) For some reason, Tables 1-3 are horizontal, which I couldn't read.
Our apologies, some of the info in Table 1 was omitted during export. We have retained landscape mode for Table 1 and re-formatted Tables 2 and 3 in portrait mode for ease of accessibility.
