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. 2025 May 20;14:RP104979. doi: 10.7554/eLife.104979

mTOR inhibition in Q175 Huntington’s disease model mice facilitates neuronal autophagy and mutant huntingtin clearance

Philip Stavrides 1, Chris N Goulbourne 1, James Peddy 1, Chunfeng Huo 1, Mala Rao 1,2, Vinod Khetarpal 3, Deanna M Marchionini 3, Ralph A Nixon 1,2,4,5,, Dun-Sheng Yang 1,2,
Editors: Margaret S Ho6, Jonathan A Cooper7
PMCID: PMC12092004  PMID: 40392702

Abstract

Huntington’s disease (HD) is caused by the expansion of the polyglutamine stretch in huntingtin protein (HTT) resulting in hallmark aggresomes/inclusion bodies (IBs) composed of mutant huntingtin protein (mHTT) and its fragments. Stimulating autophagy to enhance mHTT clearance is considered a potential therapeutic strategy for HD. Our recent evaluation of the autophagic-lysosomal pathway (ALP) in human HD brain reveals upregulated lysosomal biogenesis and relatively normal autophagy flux in early Vonsattel grade brains, but impaired autolysosome clearance in late grade brains, suggesting that autophagy stimulation could have therapeutic benefits as an early clinical intervention. Here, we tested this hypothesis by crossing the Q175 HD knock-in model with our autophagy reporter mouse TRGL (Thy-1-RFP-GFP-LC3) to investigate in vivo neuronal ALP dynamics. In the Q175 and/or TRGL/Q175 mice, mHTT was detected in autophagic vacuoles and also exhibited a high level of colocalization with autophagy receptors p62/SQSTM1 and ubiquitin in the IBs. Compared to the robust lysosomal pathology in late-stage human HD striatum, ALP alterations in Q175 models are also late-onset but milder, that included a lowered phospho-p70S6K level, lysosome depletion, and autolysosome elevation including more poorly acidified autolysosomes and larger-sized lipofuscin granules, reflecting impaired autophagic flux. Administration of a mTOR inhibitor to 6-mo-old TRGL/Q175 normalized lysosome number, ameliorated aggresome pathology while reducing mHTT-, p62-, and ubiquitin-immunoreactivities, suggesting the beneficial potential of autophagy modulation at early stages of disease progression.

Research organism: Mouse

Introduction

HD is an autosomal dominant disorder caused by a mutation in the gene encoding HTT resulting in expansion of the polyglutamine (polyQ) stretch on its amino-terminus (Bates et al., 2015; Franklin et al., 2024; Jiang et al., 2023; Macdonald, 1993). HD pathogenesis advances in a spatiotemporal pattern, which is used to stage disease pathology severity as Grade 0–4 (HD0, HD1, HD2, etc.)(Vonsattel et al., 1985). GABA-containing medium spiny projection neurons in the striatum are most susceptible to cell death (Vonsattel, 2007), and the cerebral cortex, particularly layer 5 a, also shows cell loss (Pressl et al., 2024; Sotrel et al., 1991). Neuronal intranuclear inclusions (NIIs) and neuropil inclusions are present in HD brains and are positive for mHTT and ubiquitin (Ub) (DiFiglia et al., 1997; Gutekunst et al., 1999).

Recently, we completed a comprehensive evaluation of the ALP in the HD brain at progressive disease stages, focusing on the most affected brain region, the striatum, in comparison to a less affected region, the neocortex (Berg et al., 2024). Double fluorescence immunolabeling and immuno-electron microscopy (IEM) revealed colocalization of HTT/mHTT with the autophagy-related adaptor proteins, p62/SQSTM1 and ubiquitin, and cathepsin D (CTSD) within aggresome inclusions and autophagic compartments, documenting the involvement of ALP in HTT/mHTT turnover and the disease-related impairment of this process in late-stage disease. The temporal evolution of ALP alterations generally revealed minimal detectable impairment of upstream autophagy steps [e.g., autophagosome (AP) induction, formation, and fusion with lysosomes (LY)] and, in striatum, elevated levels of LAMP1 and LAMP2 markers suggesting modestly upregulated LY biogenesis. At late disease stages, mainly HD4, neuronal ALP dysfunction exhibited enlarged/clumped CTSD-immunoreactive autolysosomes (AL)/LY and ultrastructural evidence of autophagic vacuole (AV) fusion and transition to lipofuscin granule formation. These findings collectively suggest that relatively competent autophagy machinery is maintained during the disease progression with a compensatory upregulation in lysosomal biogenesis, which together prevents against mHTT accumulation. This situation is failing at the late disease stages when AL clearance is impeded, substrates, including mHTT and its metabolites, accumulate in AL, and aggresome inclusions increase.

A possible implication of the foregoing findings is that pharmacologic enhancement of autophagy applied at a symptomatic but early stage of disease, when the ALP clearance machinery is fully competent, may be therapeutic in clearing mHTT protein in affected neurons. By contrast, in Alzheimer’s Disease (AD) lysosomal clearance deficits develop at the earliest disease stages, suggesting enhanced autophagy induction is counterproductive.

Modulation of autophagy as a therapeutic strategy for HD has been investigated in various cell and animal models of HD (Boland et al., 2018; Jiang et al., 2023; Kim et al., 2021; Sarkar and Rubinsztein, 2008b; Yang and Zhang, 2023). Of particular relevance to our present study are those involving autophagy induction with mTOR-dependent or -independent autophagy-enhancing approaches in HD mouse models [e.g. Trehalose Tanaka et al., 2004; Rapamycin analog Temsirolimus (CCI-779) Ravikumar et al., 2004; Rilmenidine Rose et al., 2010; Rhes manipulations Baiamonte et al., 2013; Lee et al., 2015]. These studies have generally demonstrated ameliorative effects on outcome measures such as mHTT lowering and behavioral/motor function assays. However, in many cases, it is unclear whether autophagy mechanisms are directly engaged in the brain or are critical to rescue.

Thus, our study aimed to comprehensively characterize the autophagy response with a range of autophagy markers to interrogate the competence of the entire autophagy process in relationship to mHTT in the well-characterized zQ175 Knock-In HD mouse model (Q175). Towards this goal, we evaluated autophagy in neurons of Q175 after introducing by neuron-specific transgenesis the dual fluorescence-tagged autophagy probe, tandem fluorescent mRFP-eGFP-LC3 (tfLC3), an autophagy adaptor protein associated with AP and degraded via autophagy (Lee et al., 2019). We thereby generated a new Q175 cross, namely TRGL (Thy-1-RFP-GFP-LC3)/Q175. tfLC3 expression, driven postnatally by the neuron-specific Thy1-promoter, allows for selective monitoring of neuronal autophagy without the confounding influence of glial cells. Resolution and sensitivity in reporting tfLC3 signal is high compared to conventional immunofluorescence staining of LC3. The ability to ratiometrically report pH-dependent changes in fluorescence (hue angle) enables neutral pH AP to be distinguished from acidic AL, that progressively acidify intraluminally upon fusion with LY. We are further able to differentiate AL subgroups differing in their extent of acidification after LY fusion. Distinguishing properly acidified AL from those poorly acidified because of delayed or defective acidification is assisted by immunolabeling AL with a LY marker, such as CTSD, which is then detected with a third fluorophore. Together, this triple fluorescence paradigm is objectively quantified by computer-assisted deconvolution of the proportions of each label within the analyzed neuron, a reflection of their relative pH (and fusion with LY), which allows ALP organelle subtypes including LC3-negative LY to be identified and quantified for their numbers, sizes and spatial distributions in intact brain sections. The collective data provides reports on the completion (or lack thereof) of autophagy flux (ALP dynamics) and any blockage at particular steps in the ALP pathway, including the normal acidification and further maturation of AL through their successful elimination of fluorescence-tagged LC3 (Lee et al., 2019; Lee et al., 2022; Lie et al., 2021; Lie et al., 2022).

Crossing TRGL mice with a model of a neurodegenerative disease, the Q175 mouse model of HD, has enabled us to assess disease-related autophagy alterations in the Q175 and TRGL/Q175 models and their response to a pharmacological inhibition of mTOR, (mTORi) INK-128 (hereafter INK). Our data demonstrate target engagement and positive effects of the compound on rescuing Q175 phenotypes including reversal on AL/LY subtypes as reported by the tfLC3 probe and parallel reductions of mHTT-, p62- and Ub-immunoreactivity (IR), suggesting that the compound targeted the ALP to degrade mHTT.

Results

Identification of inclusions and HTT molecular species in Q175 mice

Immunohistochemistry (IHC) experiments with the antibody mEM48, which preferentially recognizes aggregated mHTT (Gutekunst et al., 1999), revealed age-dependent development of mHTT-positive profiles in the striatum of Q175KI mice. Brain sections from 2.5-mo-old Q175 (not shown) only exhibited faint and diffuse nuclear mHTT staining without identifiable aggresomes/inclusion bodies (IBs), while at 6 and 10 mo of age, mHTT-positive IBs were readily detected progressively with age (Figure 1A2–A3). There were no similar mHTT immunoreactive puncta in WT striatum (Figure 1A1). These observations are consistent with previous findings in Q175 and other HD mouse models (Carty et al., 2015; Li et al., 1999; Menalled et al., 2003). To further determine the locations of mHTT IBs in Q175, mEM48-immunostained sections were counterstained with cresyl violet (Figure 1A4) to distinguish nuclear IBs (Figure 1A4, arrowheads) from extranuclear IBs, which were localized predominantly in the neuropil (Figure 1A4, arrows) and detected, but rarely, in the cytoplasmic portion of the perikaryon. Thus, our results demonstrate an age-dependent increase in mHTT aggresomes in the Q175 model.

Figure 1. Identification of inclusions and huntingtin protein (HTT) molecular species in Q175 mice.

Figure 1.

(A) Immunohistochemistry (IHC) detects age-dependent increase in the number of mutant huntingtin protein (mHTT) inclusions. Brain sections from 6-mo- and 10-mo-old wild-type (WT) and Q175 mice were processed for IHC with antibody mEM48 (MAB5374) directed against mHTT (A). Images from sections without (A1-3) or with (A4) a cresyl violet counterstain are shown where the dark-brown puncta represent mHTT-positive inclusions. Arrowheads depict neuronal intranuclear inclusions (NIIs), determined with the assistance from the nuclear labeling by cresyl violet, while arrows indicate extranuclear inclusions, primarily the neuritic inclusion in the neuropil. Bars = 20 μm. n=4 mice/genotype, 4 sections/mouse. (B) mHTT inclusions are detected in nucleus, dendrites, and axons of Q175 brains by immuno-gold electron microscopy (IEM). Sagittal vibratome brain sections of 17-mo-old Q175 were cut and went through electron microscope (EM) processing. Small blocks were obtained from the striatal areas for ultrathin sectioning. Tissue containing grids were processed for immunogold labeling procedure with antibody mEM48, using 10 nm gold followed by silver enhancement. Structures showing high level of silver-enhanced gold labeling were considered as mHTT-positive. (C) Various forms of HTT molecules are detected with different antibodies by immunoblotting. Equal amounts of proteins from hemibrain homogenates of 17-mo-old WT, TRGL, Q175, and TRGL/Q175 (labeled as ‘Cross’) were subjected to SDS-PAGE and processed for WB with different antibodies directed against HTT/mHTT, including MAB1574 (C1, C2), mAb PHP2 (C1) and MAB5490 (C2). Images were collected by a digital gel imager (Syngene G:Box XX9). The arrowhead and arrow (C2) depict a 120 kDa and a 48 kDa fragment, respectively. (C3) Densitometry was performed with Image J for the blots shown in (C2) and the results were normalized by the immunoblot(s) of given loading control protein(s) (e.g., GAPDH). Values are the Mean ± SEM for each group (n=7 TRGL, 4 Q175, and 10 TRGL/Q175). Significant differences among the groups were analyzed by one-way ANOVA followed by Sidak’s multiple comparisons test. *p<0.05, **p<0.01.

Figure 1—source data 1. Original western blots for Figure 1C1 and C2.
Figure 1—source data 2. Original western blots for Figure 1C1 and C2, labeled for the relevant bands.

To reveal the ultrastructural locations and features of the mHTT aggregates, immunogold EM (IEM) with antibody mEM48 was performed. EM images (Figure 1B) demonstrated that the IEM with this antibody was highly specific in detecting mHTT IBs which were localized in the neuronal nuclei, dendrites, and axons. Ultrastructurally, most IBs were cotton-ball shaped and composed of fine fibrous or granular elements, somewhat similar to the unbundled short fibrils/protofibrils found in vitro with recombinant mHTT protein fragments (Ko et al., 2018; Kolla et al., 2021; Mario Isas et al., 2021), and similar to the structure of NII type inclusions we found in the human HD brain (Berg et al., 2024). Notably, however, human neuritic IBs often displayed a more heterogeneous composition, such as fine fibrils mixed with AVs or bundles composed of microtubule-like filaments, which were not found in the mouse neuritic IBs. Thus, the result suggests a homogeneous aggresome pathology in the mouse model, possibly reflecting a more rapid formation rate than in humans.

Previous immunoblotting studies have observed fragmentation of mHTT molecules in the human brain (Kim et al., 2001; Mende-Mueller et al., 2001), including our own study which detects mHTT fragments of 45–48 kDa, which predominantly exist in HD striatum (Berg et al., 2024). To assess HTT molecular species in the Q175 mouse brain, we employed multiple antibodies that preferentially detected mHTT over wild-type HTT, including MAB1574 (Clone 1C2) (Figure 1C1 and C2), its epitope containing a 38-glns stretch (Trottier et al., 1995), and mAb PHP2 (Figure 1C1), reacting with the peptide sequence QAQPLLPQP within the proline-rich domain of HTT (Ko et al., 2018). Both detected full-length mHTT and a~120 kDa fragment in the Q175 model (Figure 1C1; 1C2 left and 1C3 top two graphs). By contrast, MAB5490 (Figure 1C2 right), reacting with aa115-129 of HTT (C-terminal to the region of polyQ stretch-containing exon 1), detected both wild type and mutant forms of HTT (Figure 1C3, bottom right graph). A~48 kDa HTT fragment may correspond to the 45–48 kDa fragment seen in human brain (Berg et al., 2024), and was detected by both MAB1574 and MAB5490 antibodies in some samples (Figure 1C2) but its levels in the Q175 models and the control TRGL were not statistically significantly different (Figure 1C3, bottom left graph). Thus, the result suggests that HTT fragmentation is not obvious in the Q175 brains, unlike the far more prevalent occurrence of this phenomenon in human HD striatum.

mHTT colocalizes with p62, Ub, and CTSD in Q175 striatum

Our earlier study in human HD brains Berg et al., 2024 found a high degree of colocalization of mHTT/Ub or Ub/p62 colocalized signals in IBs, suggesting a relationship between mHTT and the autophagy machinery since p62 and Ub are adaptor proteins mediating autophagic cargo sequestration. Similarly, in Q175 mice, mHTT/Ub or Ub/p62 signals were highly colocalized in IBs, particularly in NIIs, in striatal neurons of Q175 mice (Figure 2—figure supplement 1). Additional triple IF labeling experiments (mHTT/Ub/p62) with brain sections from 6- and 10-mo-old Q175 mice identified IBs positive for mHTT, p62, and Ub within or outside the nuclei (Figure 2A, arrows and arrowheads, respectively). The 10-mo-old Q175 mice exhibited more and larger mHTT-positive IBs than 6-mo mutants (Figure 2A, first column), consistent with Figure 1A and the literature (Carty et al., 2015; Deng et al., 2021). Thus, our data demonstrate a close spatial relationship among mHTT and autophagy receptor protein p62 and Ub in the Q175 model, which is consistent with our observations in human HD brain (Berg et al., 2024).

Figure 2. Colocalization of mutant huntingtin protein (mHTT) with p62, Ub, and cathepsin D (CTSD).

(A) Triple labeling detects colocalization of mHTT with both autophagy adaptor proteins p62 and Ub. Brain sections from 6-mo- and 10-mo-old mice were immunostained with antibodies to huntingtin protein (HTT) (antibody MW8; red), p62 (green), or pan-Ub (blue), followed by an additional DAPI (cyan) labeling, and confocal images from the striatum are shown. Boxed areas are enlarged and shown in the last column. Arrows and arrowheads depict the white areas representing triply labeled inclusions containing the signals of the three proteins, where arrows are for NIIs, determined with the assistance from the nuclear DAPI labeling, while the arrowheads are for extranuclear inclusions. Bar = 10 μm. n=4 mice/genotype, 4 sections/mouse. (B) mHTT is detected in vesicles of the autophagic-lysosomal pathway (ALP). (B1) Brain sections from 10-mo-old Q175 were double-immunolabeled with antibodies to HTT (antibody MW8; green) and CTSD (red), followed by an additional DAPI (blue) labeling, and a three-color-merged confocal image from the striatum is shown. Large and small green arrows depict HTT nuclear and extranuclear inclusions, respectively, while yellow arrowheads depict yellow puncta showing HTT and CTSD signal colocalization. Bar = 10 μm. n=4 mice/genotype, 4 sections/mouse. (B2) Immuno-gold electron microscopy (IEM) with anti-HTT antibody (MAB1574) specifically detects HTT signal, represented by the silver-enhanced gold particles (red arrowheads), in autophagic vacuoles (AV)/lysosomes (LY) in cell bodies, dendrites, and axons. (B3) To demonstrate the labeling specificity of the HTT antibody in this IEM study, the number of silver-enhanced gold particles in AV/LY existing in neuronal cell bodies and neurites was counted from 69 electron microscope (EM) images from two 10-mo-old Q175 mice against the number of silver-enhanced gold particles in mitochondria on the same images, and the result is shown in the bar graph. Statistical significances between the two groups were analyzed by unpaired, two-tailed t-test. ****p<0.0001.

Figure 2.

Figure 2—figure supplement 1. Colocalization of huntingtin protein (HTT)/Ub and Ub/p62 in IBs in the striatum (STR).

Figure 2—figure supplement 1.

Brain sections from 10-mo-old mice were processed for double IF with antibodies against HTT (antibody MW8; red) and pan-Ub (green), or pan-Ub (red) and p62 (green), and confocal images from the striatum are shown. Arrowheads depict IBs showing colocalization signals.

Double IF labeling of mHTT with a LY marker CTSD (Figure 2B1) detected small punctate mHTT signal in CTSD-positive vesicles, suggesting a pool of mHTT within the ALP. Consistent with this LM finding, IEM with antibody MAB1574 directed against mHTT clearly demonstrated that the AVs in either cell bodies, dendrites, and axons were labeled with concentrated silver-enhanced gold particles (Figure 2B2), and the specificity of the IEM labeling was very high, as reflected by a much higher number of silver-enhanced gold particles associated with AVs versus the minimal number of silver-enhanced gold particles with mitochondria that represent the background labeling (Figure 2B3). Together, the LM and IEM findings suggest that mHTT molecules exist within ALP vesicles in the absence of definable aggresome/IB structures, similar to the IEM finding from a recent report (Zhou et al., 2021).

Mild late-onset alterations in the ALP revealed in 17-mo-old Q175 mice

We crossed TRGL mice (Lee et al., 2019) with Q175 to generate TRGL/Q175 and assessed AV/LY subtypes in striatal neurons with a hue angle-based analysis method (Lee et al., 2019; Lee et al., 2022). Our pilot studies in young TRGL/Q175 mice (2.5–6-mo of age) hardly detected autophagy alterations, compared to the control TRGL mice (not shown). Previous studies have reported that autophagy impairment was hardly detected in 6-mo Q175/GFP-LC3 mice (Wold et al., 2016), but alterations in a limited number of autophagy markers (e.g. protein levels of p62, LC3, p-Beclin-1) were found in brain homogenates of 12–15-mo Q175 mice (Abd-Elrahman et al., 2017; Heikkinen et al., 2021; Wold et al., 2016). Therefore, to further validate the status of the ALP and to discover potential additional autophagy alterations, we expanded our study to old mice at 17-mo of age, where we found the following ALP alterations.

Alterations in AV/LY subtypes

Confocal images from 17-mo-old TRGL/Q175 and control TRGL brain sections, triple-fluorescent labeled via an additional immunostaining with an anti-CTSD antibody and far-red emitting fluorophore, showed enhanced mRFP- and eGFP-LC3 signals in the striatal neurons of TRGL/Q175 compared to TRGL (Figure 3A1). Hue angle-based analysis confirmed the above observations by revealing significant increases in the numbers of AL and poorly-acidified AL (pa-AL, as explained in Lee et al., 2019; Lee et al., 2022). It also detected a reduction in the number of LY (Figure 3A2). Thus, these results indicate that the tfLC3 reporter can reveal alterations in the proportions of AV/LY subtypes in Q175, consistent with delayed and/or deficient acidification of AL causing deficits in the reformation of LY to replenish the LY pool.

Figure 3. Mild late-onset alterations in the autophagic-lysosomal pathway (ALP) in the striatum of 17-mo-old Q175.

(A) Quantitation of autophagic vacuoles (AV)/lysosomes (LY) subtypes of striatal neurons detect increases in AL, pa-AL, and a decrease in LY in 17-mo-old TRGL/Q175 vs. TRGL. (A1) Brain sections from TRGL and TRGL/Q175 (4 sections/mouse, 10 mice/genotype) were immunostained with an anti-CTSD antibody. Confocal images from the cranial-dorsal portion of the striatum (three images at 120 x/section) were collected and representative images for each eGFP-LC3 (green), mRFP-LC3 (red), and CTSD (blue) are shown. Arrowheads depict pa-AL. (A2) Hue angle-based analysis was performed for AV/LY subtype determination using the methods described in Lee et al., 2019 (see the Materials and methods). Data are presented as Vesicle #/Neuron (TRGL: n=713 neurons; TRGL/Q175: n=601 neurons). Statistical significances between the two groups for each vesicle type were analyzed by unpaired t-test. Two-tailed p-value: ***p<0.001, ****p<0.0001. (B) EM detects larger AL/lipofuscin granules in the Q175 striatum. Sagittal vibratome brain sections from 17-mo-old mice were cut and went through EM processing. Small blocks were obtained from the striatal area for ultrathin sectioning, followed by EM examinations of the grids. The red circle depicts NII, and the arrow depicts larger sized (>1 µm) lipofuscin granules, which were counted on randomly collected images of neurons from striatum of WT (420 neurons from n=6 mice) or Q175 (586 neurons from n=9 mice).

Figure 3.

Figure 3—figure supplement 1. Molecules involved in autophagy induction signaling of the autophagic-lysosomal pathway (ALP) are largely unchanged in 17-mo-old TRGL/Q175.

Figure 3—figure supplement 1.

Equal amounts of proteins from brain homogenates of 17-mo-old TRGL and TRGL/Q175 were subjected to SDS-PAGE and processed for western blotting (WB) with antibodies directed against a number of interested marker proteins in autophagy induction signaling of the ALP. Immunoblotting for each marker protein was performed one or more times depending on the quality of the blots. The blot of the loading control protein GAPDH is boxed and placed under each protein of interest. Images were collected by a digital gel imager (Syngene G:Box XX9). Densitometry was performed with Image J and the result for each protein of interest was normalized by its corresponding GAPDH blot and presented in the bar graph. Values are the Mean +/- SEM for each group (n = 7 TRGL and 10 TRGL/Q175). Significant differences between the two groups were analyzed by unpaired, two-tailed t-test. *p<0.05, **p<0.01, ***p<0.001.
Figure 3—figure supplement 1—source data 1. Original western blots for Figure 3—figure supplement 1.
Figure 3—figure supplement 1—source data 2. Original western blots for Figure 3—figure supplement 1, labeled for the relevant bands.
Figure 3—figure supplement 2. Molecules involved in membrane nucleation/autophagosome (AP) formation of the autophagic-lysosomal pathway (ALP) are largely unchanged in 17-mo-old TRGL/Q175.

Figure 3—figure supplement 2.

Equal amounts of proteins from brain homogenates of 17-mo-old TRGL and TRGL/Q175 were subjected to SDS-PAGE and processed for western blotting (WB) with antibodies directed against a number of interested marker proteins in membrane nucleation/AP formation of the ALP. Immunoblotting for each marker protein was performed one or more times depending on the quality of the blots. The blot of the loading control protein GAPDH is boxed and placed under each protein of interest. Images were collected by a digital gel imager (Syngene G:Box XX9). Densitometry was performed with Image J and the result for each protein of interest was normalized by its corresponding GAPDH blot and presented in the bar graph. Values are the Mean +/- SEM for each group (n = 7 TRGL and 10 TRGL/Q175). Significant differences between the two groups were analyzed by unpaired, two-tailed t-test. *p<0.05, **p<0.01, ***p<0.001.
Figure 3—figure supplement 2—source data 1. Original western blots for Figure 3—figure supplement 2.
Figure 3—figure supplement 2—source data 2. Original western blots for Figure 3—figure supplement 2, labeled for the relevant bands.
Figure 3—figure supplement 3. Autophagy adaptor proteins in the autophagic-lysosomal pathway (ALP) are largely unchanged in 17-mo-old TRGL/Q175.

Figure 3—figure supplement 3.

Equal amounts of proteins from brain homogenates of 17-mo-old TRGL and TRGL/Q175 were subjected to SDS-PAGE and processed for western blotting (WB) with antibodies directed against a number of interested autophagy adaptor proteins in the ALP. Immunoblotting for each marker protein was performed one or more times depending on the quality of the blots. The blot of the loading control protein GAPDH is boxed and placed under each protein of interest. Images were collected by a digital gel imager (Syngene G:Box XX9). Densitometry was performed with Image J and the result for each protein of interest was normalized by its corresponding GAPDH blot and presented in the bar graph. Values are the Mean +/- SEM for each group (n = 7 TRGL and 10 TRGL/Q175). Significant differences between the two groups were analyzed by unpaired, two-tailed t-test. *p<0.05, **p<0.01, ***p<0.001.
Figure 3—figure supplement 3—source data 1. Original western blots for Figure 3—figure supplement 3.
Figure 3—figure supplement 3—source data 2. Original western blots for Figure 3—figure supplement 3, labeled for the relevant bands.
Figure 3—figure supplement 4. Molecules involved in autolysosomes (AL) formation/substrate degradation of the autophagic-lysosomal pathway (ALP) are largely unchanged in 17-mo-old TRGL/Q175.

Figure 3—figure supplement 4.

Equal amounts of proteins from brain homogenates of 17-mo-old TRGL and TRGL/Q175 were subjected to SDS-PAGE and processed for western blotting (WB) with antibodies directed against a number of interested marker proteins in AL formation/substrate degradation (e.g., lysosomal hydrolases or structural components) of the ALP. Immunoblotting for each marker protein was performed one or more times depending on the quality of the blots. The blot of the loading control protein GAPDH is boxed and placed under each protein of interest. Images were collected by a digital gel imager (Syngene G:Box XX9). Densitometry was performed with Image J and the result for each protein of interest was normalized by its corresponding GAPDH blot and presented in the bar graph. Values are the Mean +/- SEM for each group (n = 7 TRGL and 10 TRGL/Q175). Significant differences between the two groups were analyzed by unpaired, two-tailed t-test. *p<0.05, **p<0.01, ***p<0.001.
Figure 3—figure supplement 4—source data 1. Original western blots for Figure 3—figure supplement 4.
Figure 3—figure supplement 4—source data 2. Original western blots for Figure 3—figure supplement 4, labeled for the relevant bands.

Mild alterations in the levels of autophagy marker proteins detected by immunoblotting

To further assess autophagy phenotypes in Q175 models, we conducted western blotting (WB) using hemibrain homogenates from 17-mo-old mice for protein markers of individual steps in the ALP (i.e. autophagy induction, membrane nucleation/AP formation, autophagy adaptor proteins and AL formation/substrate degradation) (Figure 3—figure supplements 14). At the stage of autophagy induction signaling, we did not see alterations in the levels of mTOR and p-mTOR forms including its auto-phospho-form at S2481, but we did observe a decreased level of a mTOR substrate, p-p70S6K (T389), in TRGL/Q175 compared to the control TRGL (Figure 3—figure supplement 1), implying reduced mTOR activity, similar to the finding from a previous study (Ravikumar et al., 2004). Another mTOR substrate, p-ULK1 (S757), did not exhibit changes in TRGL/Q175. However, there was an increase in the level of total ULK1, resulting in a reduced ratio of p-ULK1 (S757)/total ULK1 in TRGL/Q175 compared to TRGL (Figure 3—figure supplement 1). The data collectively imply a down-regulated mTOR activity, as expected in response to accumulated aggregate-prone protein in Q175 mice, or as a result of sequestration of mTOR by mHTT IBs, as suggested previously (Ravikumar et al., 2004).

Except for these alterations, we did not detect statistically significant differences between TRGL/Q175 and the control TRGL in all other tested marker proteins in the downstream phases of the ALP (Figure 3—figure supplements 2-4). Of special interest is the ATG14-containing VPS34/Beclin-1 complex implicated in HD pathogenesis (Park et al., 2016; Wold et al., 2016) for which we did not detect statistically significant alterations in the levels of Beclin-1, VPS34, and ATG14 and their corresponding phosphor-forms. Notably, even if some TRGL/Q175 clearly exhibited diminished signals for p-ATG14 (S29) (Figure 3—figure supplement 2), no statistical significance could be established due to large variations among samples (See Discussion). Another notable point is that we did not find alterations in levels of full-length proteins or fragments (Jamilloux et al., 2018; Norman et al., 2010; Sanchez-Garrido et al., 2018; Valionyte et al., 2022) of autophagy adaptor proteins such as p62, TRAF6 (Figure 3—figure supplement 3), in contrast to our immunoblot analysis of human HD brains (Berg et al., 2024). Together, our data suggest that, in general, autophagy alterations at the protein level, as can be detected by immunoblotting, are mild in TRGL/Q175 even at 17-mo-old (See Discussion).

Increased numbers of larger lipofuscin granules

To assess possible ultrastructural pathologies, we conducted an EM study for Q175 vs WT at 17-mo of age. As predicted, NIIs were not observed in WT neurons (Figure 3B, left panel) but were detected (Figure 3B, right panel, red circle) in ~10% striatal neurons of Q175 mice. Different from the substantial accumulation of AVs including lipofuscin granules in late stage human HD brain (e.g., HD4) (Berg et al., 2024), we did not observe gross accumulation of AVs in Q175 at this old age, except that quantitative analysis of EM images revealed that larger sized (>1 µm) lipofuscin granules (Figure 3B, right panel, arrow) modestly increased in number in Q175 compared to WT [WT: 113 lipofuscin/420 neurons (n=6 mice), i.e., 27 lipofuscin/100 neurons; Q175: 228 lipofuscin/586 neurons (n=9 mice), i.e., 39 lipofuscin/100 neurons; Unpaired t-test, Two-tailed p-value <0.01; Bar graph not shown]. Thus, these EM findings suggest that ultrastructural autophagy alterations in the ALP were mild in Q175 even at an older age.

DARPP-32-IR decreases in striatal neurons of 17-mo-old TRGL/Q175 in the absence of neuronal loss

To investigate potential neurodegeneration in our model, brain sections from 17-mo-old mice were double-labeled with the medium spiny neuron marker DARPP-32 (dopamine- and cAMP-regulated phosphoprotein, 32 kDa)(Ouimet et al., 1998) and a general neuronal marker NeuN. We found that the intensity of DARPP-32-IR significantly diminished in TRGL/Q175 compared to TRGL (Figure 4A and B). However, there were no alterations in NeuN-IR, including Area Covered and the Number of NeuN-positive neurons (Figure 4A and B), and there were clear examples of neurons showing strong NeuN signal but faint or no DARPP-32 signal (Figure 4A, arrows). Such a finding is consistent with other studies in Q175 and Q140 mice where neuronal loss was not found even though a reduction in DARPP-32 was seen in the same study, and that neuronal loss only occurred quite late, e.g., around 2 y of age (Deng et al., 2021; Hickey et al., 2008; Peng et al., 2016; Rothe et al., 2015). Thus, our data support the notion that changes in DARPP-32 may indicate alterations in its protein level and a loss of phenotype pattern, which is a potentially common prelude to neuronal loss in other neuronal cell types, e.g., basal forebrain cholinergic neurons (Jiang et al., 2022).

Figure 4. Decrease of DARPP-32-IR in striatal neurons of 17-mo-old TRGL/Q175 in the absence of a NeuN signal reduction.

Figure 4.

Brain sections from TRGL and TRGL/Q175 (n=10 mice/genotype, four sections/mouse) were double-immunostained with anti-DARPP-32 and -NeuN antibodies. Confocal images from the cranial-dorsal portion of the striatum (three images at 120 x/section) were collected and representative images are shown (A). Arrows depict examples of neurons showing strong NeuN signal while minimal DARPP-32-IR. (B) Images were quantified by Image J for Integrated Density of DARPP-32- (top) or NeuN-IR/Image field (middle), and for # of NeuN positive cells/Image field (bottom). Statistical significances between the two groups were analyzed by unpaired, two-tailed t-test. *p<0.05.

Oral mTORi INK engages mTOR target and induces downstream responses in the ALP in 7-mo-old TRGL/Q175

As a prelude to investigate effects of pharmacological autophagy stimulation with a mTORi, INK, on TRGL/Q175 mice, we first performed a safety, pharmacokinetic, and pharmacodynamic evaluation of INK in 6-mo-old WT mice, administered via oral gavage. The WB results (Figure 5—figure supplement 1A) indicated that all doses tested, from 2.5 mg/kg (mpk) to 10-mpk, exhibited target engagement as revealed by the reductions in the protein levels of mTOR targets, i.e., p-ULK1 (S757, phosphorylated by mTOR), p-p70S6K (not shown), or phosphorylated S6 ribosomal protein (p-S6, at S240/244), implying high BBB permeability of this compound and target engagement. Consistently, measurements for the levels of INK in cerebellar homogenates of WT mice treated with 1, 3, and 10-mpk INK revealed dose-dependent brain levels of INK (Figure 5—figure supplement 1B).

We then decided on an oral gavage treatment regimen for INK as 4-mpk, daily, for 3 wk, to be administered in 6-mo-old TRGL/Q175. The brains from the mice (7-mo of age after the 3 wk treatment duration) were then analyzed by multiple experimental approaches. By immunoblotting, target engagement was verified, as indicated by decreased levels of p-mTOR (S2481, autophosphorylation site), p-ULK1 (S757) and p-S6 (S240) in brain homogenates from INK-treated TRGL/Q175 compared to Veh-treated TRGL/Q175 (Figure 5A and B). Additionally, there were also INK-induced changes in marker proteins located downstream of autophagy induction, including, for example, increased levels of p-ATG14 in INK-treated samples (Figure 5A and B). It is interesting that although INK did not induce any alterations in the transgene product tfLC3-I or -II, there was a trend of decreased endogenous LC3-I, leading to a statistically significant increase in the ratio of LC3-II/I, and such a result in LC3-I reduction was reproduced in a repeated experiment (Figure 5A and B). Thus, the pilot study in WT mice and the actual study in TRGL/Q175 mice together established mTORi INK’s BBB permeability, target engagement, and ability to modify molecular events in the ALP.

Figure 5. mTORi INK exhibits target engagement and induces downstream responses in the autophagic-lysosomal pathway (ALP) in 7-mo-old TRGL/Q175.

Equal amounts of proteins from brain homogenates of 7-mo-old TRGL/Q175 mice untreated (labeled as ‘Veh’) or mechanistic target of rapamycin kinase inhibitor (mTORi) INK treated [4 mg/kg (4-mpk), daily, 3 wk; labeled as ‘INK’] were subjected to SDS-PAGE and processed for western blotting (WB) with antibodies directed against several marker proteins in the autophagy pathway, representing target engagement of INK or downstream responses. Immunoblotting for each marker protein was performed one or more times depending on the quality of the blots. Representative blots are shown on the left (A) while quantitative results of the blots are shown on the right (B). The bottom LC3 blots represent a repeated immunoblotting experiment. Values are the Mean ± SEM for each group (n=6–7 mice per condition). Significant differences between the two groups were analyzed by unpaired, two-tailed t-test. *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001. tfLC3=mRFP-eGFP-LC3; Endo-LC3=endogenous LC3.

Figure 5—source data 1. Original western blots for Figure 5A.
Figure 5—source data 2. Original western blots for Figure 5A, labeled for the relevant bands.

Figure 5.

Figure 5—figure supplement 1. Dosing tests demonstrate mechanistic target of rapamycin kinase inhibitor (mTORi) INK blood-brain barrier (BBB) penetration and target engagement even at low dosages.

Figure 5—figure supplement 1.

(A) Dosing tests for INK. INK or the vehicle was administered by oral gavage. Two dosing tests were done in 6-mo-old wild-type (WT) mice, one with 10, 8 mg/kg (mpk) every other day or 4-mpk daily for 2 wk and the other with 7.5, 5, 2.5-mpk daily for 2 wk. Equal amounts of proteins from brain homogenates of mice receiving vehicle (labeled as ‘Veh’) or INK were subjected to SDS PAGE and processed for WB with antibodies directed against marker proteins representing target engagement of INK, such as p-ULK1 (S757) and p-S6 – the drug effects are reflected by the changes within the boxed areas compared to the signals of Veh outside the boxed areas. Immunoblotting for each marker protein was performed one or more times depending on the quality of the blots. (B) INK was detected in the brains of mice receiving a single dose via oral gavage. After 6, 12 or 24 hrs post INK administration to 6-mo-old WT mice, INK was detected in cerebellum of mice treated with 1, 3 or 10-mpk INK. Total 60 mice: n = 6 mice/dose/time point, where the Vehicle control group was just tested once at the 6-hr time point.
Figure 5—figure supplement 1—source data 1. Original western blots for Figure 5—figure supplement 1.
Figure 5—figure supplement 1—source data 2. Original western blots for Figure 5—figure supplement 1, labeled for the relevant bands.

mTOR inhibition alters AV/LY subtypes

We took advantage of the tfLC3 construct reporting in vivo autophagy flux (Lee et al., 2019) to assess INK effects on AV/LY subtypes in TRGL/Q175. Confocal images from INK- or Veh-treated TRGL/Q175 and Veh-treated TRGL brain sections of 7-mo-old mice, immuno-stained with anti-CTSD antibody and detected with a third fluorophore, were collected (Figure 6A) and processed for hue-angle based quantitative analysis using the protocol described previously (Lee et al., 2019). The analysis in striatal neurons (Figure 6B) revealed reversal of the two main ALP alterations. First, the pre-existing abnormally lowered LY number in TRGL/Q175-Veh (Figure 6B, LY group, red vs green), was reversed by INK treatment (Figure 6B, LY group, red vs blue). Second, INK reduced the number of AL in TRGL/Q175-Veh compared to TRGL-Veh (Figure 6B, AL group, red vs blue), implying improved AL clearance and likely providing the basis for restoration of normal LY number. Together, the data suggest that autophagy phenotype in 7-mo Q175 is measurable but milder than that in the 17-mo-old mice shown in Figure 3A and that INK has the potential to modulate the autophagy pathway and restore normal function at this mild disease stage. mTOR inhibition mitigates mHTT-related aggresome pathology.

Figure 6. Mechanistic target of rapamycin kinase inhibitor (mTORi) INK reverses the mild alteration of autophagic vacuole (AV)/lysosomes (LY) subtypes in the striatum of 7-mo-old TRGL/Q175.

Figure 6.

(A) Brain sections from untreated or INK (4-mpk, 3 w)-treated TRGL/Q175 vs. TRGL (four sections/mouse, 5–6 mice per condition) were immunostained with an anti-cathepsin D (CTSD) antibody. Confocal images from the cranial-dorsal portion of the striatum (three images at 120 x/section) were collected and representative images for each eGFP-LC3 (green), mRFP-LC3 (red), and CTSD (blue) are shown. (B) Hue angle-based analysis was performed for AV/LY subtype determination using the methods described in Lee et al., 2019. Data are presented as Vesicle #/Neuron (TRGL-Veh: n=260 neurons; TRGL/Q175-Veh: n=218 neurons; TRGL/Q175-INK: n=287 neurons). Statistical significances among the groups were analyzed by one-way ANOVA followed by Sidak’s multiple comparisons test. **p<0.01, ****p<0.0001.

Importantly, we found that HD-like pathology such as mHTT IBs and associated protein aggregation of adaptor proteins, p62, and Ub, was ameliorated by INK treatment. Confocal images demonstrated that mHTT positive particles/inclusions were substantially reduced in INK-treated 7-mo-old TRGL/Q175, compared to Veh-treated TRGL/Q175 mice (Figure 7A). Moreover, p62-IR or Ub-IR also changed in a similar trend (Figure 7A), consistent with the high degree of colocalization of mHTT/p62/Ub signals shown in Figure 2A. The corresponding quantitative analysis, shown as Area Covered by either mHTT-, p62-, or Ub-IR on a per cell basis, confirmed statistically significant decreases in the Total Area Covered by mHTT- and p62-signals (Figure 7B1). With the assistance of the endogenous tfLC3 signal (particularly the mRFP signal) in the TRGL to identify the association status of the mHTT-, p62-, or Ub-IR with AVs, the images were additionally quantified to generate the separated ‘Area Covered by the AV-associated Form’ (Figure 7B2) and ‘Area Covered by the AV-unassociated Form’ (Figure 7B3) of mHTT-, p62-, or Ub- signals. The results collectively suggest decreased mHTT-, p62-, or Ub-IR in treated compared to untreated TRGL/Q175, implying that INK treatment was able to promote the clearance of mHTT and related receptor proteins p62 and/or Ub.

Figure 7. Mechanistic target of rapamycin kinase inhibitor (mTORi) INK reduces HTT-, Ub-, or p62-IR-covered areas parallelly in the striatum of 7-mo-old TRGL/Q175.

Figure 7.

(A) Brain sections from INK (4-mpk, 3 w)-treated or untreated TRGL/Q175 (four sections/mouse, 6–7 mice/condition) were immunostained with anti-HTT (MW8), -p62, or -pan-Ub antibodies. Confocal images from the cranial-dorsal portion of the striatum (three images at 120 x/section) were collected. Shown are single-channel images (i.e. without showing the eGFP and mRFP signals). (B1–B3) Areas covered by either the HTT-, p62-, or Ub-IR on a per cell basis are quantified (for HTT-IR, TRGL/Q175-Veh: n=196 neurons, TRGL/Q175-INK: n=385 neurons; for p62-IR, TRGL/Q175-Veh: n=311 neurons, TRGL/Q175-INK: n=378 neurons; for Ub-IR, TRGL/Q175-Veh: n=244 neurons, TRGL/Q175-INK: n=347 neurons) and grouped as ‘Total Area’ (B1), 'AV-associated Form’ (i.e. the IR which was associated with tfLC3 signals representing autophagic vacuoles, AVs) (B2) or ‘AV-unassociated Form’ (i.e. the IR which was not associated with the tfLC3 signals) (B3). Statistical significances between the two groups were analyzed by unpaired t-test. Two-tailed p-value: *p<0.05, ****p<0.0001.

mTOR inhibition does not reverse preexisting DARPP-32-IR reduction in TRGL/Q175

Finally, we evaluated neurodegenerative changes by employing DARPP-32 IHC on brain sections of Veh-treated or INK-treated TRGL/Q175. Similar to what was found in 17-mo-old mice (Figure 4), the group of TRGL/Q175-Veh, even at this young age (7-mo), already exhibited a diminished DARPP-32-IR signal compared to that in TRGL-Veh (Figure 8A and B). However, no differences between the untreated and INK-treated TRGL/Q175 groups were established by quantitative analysis (Figure 8A and B). Thus, the result suggests a lack of effectiveness of INK in reversing this pre-existing phenotype, implying that earlier intervention may be necessary (Marchionini et al., 2022).

Figure 8. INK treatment does not reverse the reduction of DARPP-32-IR in the striatum of 7-mo-old TRGL/Q175.

Figure 8.

(A-C) Sagittal brain sections containing the striatum area were immunostained with an anti-DARPP-32 antibody, and 10 x images taken from the cranial-dorsal portion of the striatum are shown (first row). Boxed areas on the first row are enlarged and shown on the second row for easy viewing of the immunostaining patterns for each condition. (D) For quantitation purposes, each whole 10 x image (excluding the areas covered by the fiber bundles which usually exhibited minimal background staining – achieved by threshold setting) was quantified by ImageJ (one image/section, four sections/mouse) and the results are expressed as the Integrated Density of DARPP-32-IR. n=6–8 mice/condition. Statistical significances among the groups were analyzed by one-way ANOVA followed by Sidak’s multiple comparisons test. **p<0.01 compared to TRGL-Veh.

Discussion

Previous studies in cell and animal models have documented roles of HTT and mHTT in autophagy in relation to HD [for reviews, see Croce and Yamamoto, 2019; Klionsky et al., 2021; Martin et al., 2015]. WT HTT participates in normal autophagy by releasing ULK1 via mTOR inhibition and serving as a scaffold to facilitate cargo sequestration by enhancing p62 interaction with LC3 and ubiquitinated cargos (Ochaba et al., 2014; Rui et al., 2015). mHTT in HD may activate autophagy by sequestering and thus inhibiting mTOR (Ravikumar et al., 2004). Most mHTT effects in cellular and mouse HD models appear to be inhibitory for autophagy induction steps, from initiation signaling and phagophore nucleation to cargo recognition/AP formation (Ashkenazi et al., 2017; Martinez-Vicente et al., 2010; Pryor et al., 2014; Rui et al., 2015; Wold et al., 2016). In addition, mHTT may disturb endolysosomal homeostasis by reducing exocytosis and promoting AL accumulation (Zhou et al., 2021). In the current study using Q175, we have found that mHTT is an ALP substrate and that the ALP is relatively intact, with some late-onset minor alterations, such as insufficient mTOR activity reflected by lowered phospho-p70S6K level and ratio of phospho-ULK1 (S757)/total ULK1, lysosome depletion, and AL/pa-AL elevation. This mild late-onset nature of ALP pathogenesis allowed us to modulate ALP with a mTOR inhibitor in 6-mo-old TRGL/Q175 which resulted in positive effects on rescuing Q175 phenotypes including normalization of AL/LY subtypes and reduction in aggresome pathology.

ALP impairments in Q175 are mild and late-onset

In the current study, we assessed ALP alterations in the Q175 model by analyzing protein markers in all phases of the ALP including autophagy induction signaling, membrane nucleation/AP formation, autophagy adaptor proteins, and AL formation/substrate degradation. By immunoblot analysis, alterations in the tested marker proteins even at 17 mo of age in TRGL/Q175 mice were limited to a reduction in p-P70S6K (T389), similar to that reported previously (Ravikumar et al., 2004), and a reduced ratio of p-ULK1 (S757)/total ULK1 as a result of increased total ULK1. While these two alterations together would suggest a lower mTOR activity and autophagy activation, consequential alterations of downstream markers, particularly proteins in the ATG14-containing Beclin-1/VPS34 complex, were not detected. Although 4 out of the 10 tested TRGL/Q175 mice exhibited very low levels of p-ATG14, implying a deficit in this complex as previously reported (Wold et al., 2016), variation among animals was too great to draw statistical conclusions. In this regard, mice used in the current study were heterozygous, which results in less autophagy alteration than that reported in homozygous mice using certain autophagy markers (Abd-Elrahman et al., 2017). It may also be noted in our experience that immunoblot analysis of brain tissue averages contributions from varied cell types that may respond to disease states in opposite directions and mask changes in a given cell population. Thus, we also applied a neuron-specific analysis using our neuron-specific tfLC3 reporter and hue-angle-based image quantification approach, which yielded more sensitive detection and findings.

In brain sections from 17-mo-old TRGL/Q175 mice, we observed increased numbers of AL including the population of pa-AL and reduced numbers of LY. Such changes in AL/pa-AL were not readily observed in TRGL/Q175 mice at 2 mo (not shown) or 7 mo of age (e.g. Figure 6B, which only exhibited a reduction in LY number), indicating that even if these alterations signify an existence of impairments in the late phase of the ALP, they do not occur until a later age (e.g. 17 mo). Importantly, this pattern of late-onset ALP pathology progression, e.g., AL/pa-AL increases, is consistent with our findings from HD human brains (Berg et al., 2024) where enlarged CTSD-positive AL accumulate and cluster in affected neuronal populations at the late disease stages (HD3 and HD4). These findings suggest that the overall autophagy function in heterozygous Q175 is largely maintained until the relatively late disease stage and provide a rationale to stimulate autophagy as a therapeutic intervention when the autophagy machinery, especially those involved in clearance, is still functional, which would generate beneficial effects. We obtained support for this concept in the subsequent study with mTORi INK when administered to TRGL/Q175 at 6 mo of age for 3 wk.

Autophagy as a primary mechanism for INK-enhanced clearance of small mHTT species

One of the key findings from this study is the reduction of mHTT-IR in neurons detected by immunolabeling after the 3 wk oral treatment with mTORi INK (Figure 7). This beneficial outcome from the drug treatment is interpreted as a primary result of the manipulation/stimulation of autophagy by the compound, leading to enhanced autophagy clearance of mHTT species including smaller, presumably soluble, species and larger aggregates such as the IBs. This interpretation is supported by the following considerations and evidence. First, in theory, the compound used for the treatment is a mTORi, so it is expected to enhance autophagy (Bensalem et al., 2021; Kim and Guan, 2015). Second, target engagement is achieved as clearly reflected by the diminished levels of mTOR-mediated phosphorylation on mTOR itself (S2481), ULK1 (S757), and p-S6 (S240/S244), along with changes in proteins downstream of mTOR, such as p-ATG14 (S29) and LC3. Third, hue-angle based confocal image analysis of AL/LY subtypes reveals a reduced AL number and an increased LY number in INK-treated TRGL/Q175 compared to Veh-treated TRGL/Q175. The findings from this analysis of AL/LY subtypes highlight the advantages of crossing TRGL with Q175 mice to evaluate autophagy modulators on the ALP, consistent with a notion of developing better tools capable of investigating autophagy flux in vivo (Nixon, 2013; Vodicka et al., 2014).

Our confocal microscopy analyses revealed mHTT signal in CTSD-positive vesicles, which can be classified as AL based on their LC3 fluorescence, and IEM detected mHTT-silver-enhanced gold particles in AP and AL including lipofuscin granules. Both findings indicate the existence of mHTT species inside the ALP as autophagy substrates. The presence of low levels of mHTT in CTSD-positive AL also implies a constitutive functional degradation event of mHTT in AL (Figure 2B1, 10-mo-old mouse). This is in contrast to the case in young AD mouse models where early-onset autophagy-stress exhibited severe substrate accumulation (e.g. reflected by strong LC3 signal) due to deficits of degradative functions within AL. (Lee et al., 2022). A reduced level in the portion of AV-associated mHTT signals (Figure 7B2) by mTORi INK further suggests that mHTT degradation has been enhanced after manipulating the ALP. This AV-associated mHTT pool is considered to be corresponding to the mHTT-silver-enhanced gold signal under IEM (Figure 2B2), i.e., amorphous and existing within the lumen of AV. Such mHTT localization and ultrastructural features are consistent with recent findings that mHTT proteins exogenously added to Q175 brain sections for incubation are recruited to multivesicular bodies/amphisomes, AL, and residual bodies (lipofuscin granules), and ultrastructurally localized to non-fibrillar, electron-lucent regions within the lumen of these organelles (Zhou et al., 2021).

Possible autophagy-related mechanism for the clearance of large mHTT IBs

It is notable that the AV-non-associated form of mHTT (i.e. did not show colocalization with the tfLC3 signal implying that they might not have contact with AVs) decrease more obviously after drug treatment, along with the reductions in p62 and Ub signals (Figure 7B3). These mHTT species are mainly larger aggresomes/IBs, including those within the nuclei. Based on our mHTT-IEM observation (Figure 1B), the IBs in Q175 brain are fibrillar aggregates in the nucleus and cytoplasm, exhibiting a homogeneous feature in shape (i.e. round/oval cotton-ball) and in content (i.e. short fine fibrils), similar to the nuclear IBs we found in human HD brains (Berg et al., 2024), whose cytoplasmic and neuritic IBs, however, also exhibit more heterogeneous compositions such as a mixture of fine fibrils with AVs, fingerprint-like structures or bundles of microtubules. In the current study, we did not observe that a whole IB is contained within an AP or AL in our EM examination, raising a question of how these larger aggregates could be degraded by the autophagy machinery after an autophagy activation by mTOR inhibition. This issue is informed by findings that the mHTT species are dynamic and the aggregates can form as different phases (liquid-like or solid-like), making it possible for mHTT species to move out or into IBs (Aktar et al., 2019; Liu et al., 2015; Peskett et al., 2018; Riguet et al., 2021) and to shuttle between the nucleus and the cytoplasm (Atwal et al., 2007; Xia et al., 2003). Thus, it is speculated that under the autophagy-activated condition induced by mTOR inhibition, the enhanced clearance of mHTT in the ALP pool would promote movements of mHTT species from the IB forms, leading to a reduction on the overall mHTT load.

Possible additional mechanisms for the reduction of mHTT species

Even if INK is a mTORi mainly targeting autophagy, we cannot completely exclude additional contributions from the UPS for the observed reduction of mHTT species given the crosstalk between the two proteolytic systems. It is well accepted that autophagy plays a more important role in the clearance of larger protein aggregates such as aggresomes/IBs (Sarkar and Rubinsztein, 2008a). For example, autophagy is involved in the degradation of p62 bodies (Bjørkøy et al., 2005) and autophagy markers are colocalized with cytoplasmic mHTT IBs (Iwata et al., 2005). However, there is also evidence for degradation of polyQ aggregates in the nuclei (Iwata et al., 2009), which contain p62 condensates as a hub for UPS-mediated nuclear protein turnover (Fu et al., 2021). We observed a high level of colocalization of mHTT with p62 and Ub in aggregates at both intranuclear and extranuclear locations (Figure 2A), which is presumably more related to the UPS hub and autophagy, respectively. It is speculated that the reduction of mHTT in the cytoplasm due to the stimulation of autophagy may partially relieve the burden of the proteasome in both the cytoplasm and the nucleus so that the nuclear proteasome operates more effectively. One of the observations reported here which may support the above speculation is the reductions of AV-non-associated forms of mHTT/p62/Ub (Figure 7B3), given that some of these aggregates should exist within the nucleus and, therefore, their reduced levels (in the nucleus) may reflect increased intranuclear UPS activity, besides the other possibility that they may travel from the nucleus to the cytosol for clearance (by autophagy) as discussed above.

Lowering mHTT via interfering protein production (e.g. through RNAi, antisense oligonucleotides) has been an attractive strategy in HD therapeutic development (Kordasiewicz et al., 2012; Tabrizi et al., 2019). Given that mTOR regulates multiple cellular pathways including protein synthesis (Magnuson et al., 2012; Wang and Proud, 2006), the inhibition of mTOR as was done in the present study would potentially affect protein synthesis as well. Thus, while our results of decreases in mHTT signals (Figure 7) can be interpreted as a result of autophagy-mediated clearance of mHTT, a possibility cannot be excluded that mTOR inhibition may result in a reduction in HTT production which may also contribute to the observed results – future studies should determine how significant such a contribution is.

In summary, Q175 models, as detected in the current study, develop ALP alterations late in the disease progression. Although milder than what is seen in late-stage HD human brain (Berg et al., 2024), there are similarities in ALP pathobiology between human HD brains and Q175 brains. These include the late-onset nature, mHTT as an ALP substrate, increased AL/pa-AL, mouse IB ultrastructural feature similar to the common fine fibril type of human IBs. The late-onset mild ALP pathology in Q175 is different from the severe AV accumulation in mouse models of AD as mentioned above (Lee et al., 2022). Together, the mild and late-onset nature of ALP alterations in Q175 provides a basis for manipulating autophagy to be a promising therapeutic strategy if the manipulations (e.g. stimulating autophagy with mTORi as in this study) are applied when the autophagy machinery is largely undamaged. This has been validated to be successful in this study as demonstrated by the INK-induced reduction of mHTT species along with other beneficial effects, consistent with findings from similar studies and supporting the general notion of lowering mHTT as a therapeutic strategy for HD (Barker et al., 2020; Cortes and La Spada, 2014; Ravikumar et al., 2004). However, it should be noted that the current study is an experimental therapeutic attempt in a mouse model which, consistent with previous reports (Ravikumar et al., 2004), is just a proof of concept for manipulating autophagy (e.g. via inhibiting mTOR in the current setting) as a potential therapeutic. The clinical implications from such studies require further rigorous verifications where early diagnosis and earlier interventions would be critical factors to be considered.

Materials and methods

Key resources table.

Reagent type (species) or resource Designation Source or reference Identifiers Additional information
Genetic reagent, KI-mouse (Mus musculus male and female) Q175 KI CHDI-81003003 N/A Knock-in Mouse
human HTT exon 1 sequence containing a ~190 CAG repeat tract
Genetic reagent,
Transgenic (Mus musculus male and female)
TRGL6 Lee et al., 2019 N/A Transgenic with Thy1 promoter
Genetic reagent,
Transgenic (Mus musculus male and female)
cross with KI (Mus musculus male and female)
Q175/TRGL This study N/A Crossed the zQ175 KI with the TRGL6 mice
Sequence-based reagent Forward primer for genotyping TRGL:
50-CTT TCC CCA CAG AAT CCA AGT CGG AAC-30
Lee et al., 2019
Sequence-based reagent Reverse primer for genotyping TRGL:
50-GCA CGA ATT CGG GCG CCG GTG GAG TGG CGG-30
Lee et al., 2019
Antibody tHTT Cell Signaling Technology Cat# 5656; RRID:AB_10827977 WB: 1:1000
Antibody ntHTT mEM48 Millipore sigma Cat# MAB5374; RRID:AB_177645 WB 1:200
IHC 1:50
Antibody HTT 1C2 Millipore sigma Cat# mab1574; RRID:AB_94263 WB 1:1000
Antibody HTT human Millipore sigma Cat# mab5490; RRID:AB_2233522 WB 1:1000
IHC 1:200
Antibody HTT MW8 Develop Studies Hybridoma Bank, University of Iowa Cat# MW8; RRID:AB_528297
Antibody HTT PHP2 CHDI/Coriell Cat# CHDI-90001516–2
Antibody MTOR Cell Signaling Technology Cat# 2983; RRID:AB_2105622 WB: 1:1000
Antibody p-MTOR S2481 Cell Signaling Technology Cat# 2874 WB 1:1000
Antibody p-MTOR S2448 Cell Signaling Technology Cat# 2971; RRID:AB_330970 WB: 1:1000
Antibody p70S6K Cell Signaling Technology Cat# 2708; RRID:AB_390722 WB: 1:1000
Antibody p-p70S6K S371 Cell Signaling Technology Cat#: 9208 WB: 1:1000
Antibody p-p70S6K T389 Cell Signaling Technology Cat# 9234; RRID:AB_2269803 WB: 1:1000
Antibody S6 ribosomal Cell Signaling Technology Cat# 2317; RRID:AB_2238583 WB: 1:1000
Antibody p-S6 ribosomal S240/S244 Cell Signaling Technology Cat# 5364; RRID:AB_10694233 WB: 1:1000
Antibody ULK1 Cell Signaling Technology Cat# 6439; RRID:AB_11178933 WB: 1:500
Antibody p-ULK S757 Cell Signaling Technology Cat# 6888; RRID:AB_10829226 WB: 1:500
Antibody p-ULK S317 Cell Signaling Technology Cat# 37762; RRID:AB_2922992 WB: 1:500
Antibody ATG5 Cell Signaling Technology Cat# 12994; RRID:AB_2630393 WB: 1:1000
Antibody ATG5 Millipore sigma Cat# abc14 WB: 1:750
IHC 1:500
Antibody ATG14 Cell Signaling Technology Cat# 5504; RRID:AB_10695397 WB: 1:1000
Antibody p-ATG14 S29 Cell Signaling Technology Cat# 92340; RRID:AB_2800182 WB: 1:1000
Antibody Beclin 1 BD Bioscience Cat# 612113; RRID:AB_399484 WB: 1:1000
Antibody p-Beclin 1 S30 Cell Signaling Technology Cat# 35955 WB: 1:1000
Antibody VPS34 Cell Signaling Technology Cat# 4263 WB 1:1000
Antibody p-VPS34 S249 Cell Signaling Technology Cat# 13857; RRID:AB_2798332 WB 1:1000
Antibody TRAF6 Cell Signaling Technology Cat# 8028 WB 1:1000
Antibody LC3 Millipore sigma Cat# abc929 WB 1:500
IHC 1:100
Antibody LC3 Novus Biologics Cat# NB100-2220; RRID:AB_10003146 WB 1:1000
IHC 1:200
Antibody p62/sqstm1 BD Biosciences Cat# 610832; RRID:AB_398152 WB 1:2000
IHC 1:200
Antibody p62/SQSTM1 c-term Progen Biotechnik Cat# GP62-C; RRID:AB_2687531 WB 1:1000
IHC 1:250
Antibody Ubiquitin Dako Agilent Cat# z0458; RRID:AB_2315524
Antibody Ubiquitin Abcam Cat# ab7780; RRID:AB_306069
Antibody LAMP1 Develop Studies Hybridoma Bank, University of Iowa Cat# H4A3; RRID:AB_2296838 IHC 1:50
Antibody CTSB Neuromics Cat# GT15047; RRID:AB_2737184 WB 1:5000
IHC 1:500
Antibody CTSD In house RU4 WB 1:10,000
IHC 1:5000
Antibody CTSD sheep In house D-2–3 WB 1:1000
IHC 1:500
Antibody DARPP32 Abcam Cat# ab40801; RRID:AB_731843 WB 1:1000
IHC 1:100
Antibody NeuN Millipore Sigma Cat# mab377; RRID:AB_2298772 IHC 1:400
Antibody β−actin Millipore Sigma Cat# A1978; RRID:AB_476692 WB 1:10,000
Antibody Goat anti-rabbit secondary Vector Laboratories Cat# 68–4140 IHC: 1:500
Antibody Goat anti-mouse secondary Vector Laboratories Cat# BA-9200 IHC: 1:500
Antibody Alexa Fluor 488-conjugated goat anti-rabbit IgG Thermo Fisher Scientific Cat# A11034; RRID:AB_2576217 IHC: 1:500
Antibody Alexa Fluor 568- goat anti-rabbit IgG Thermo Fisher Scientific Cat# A11036; RRID:AB_10563566 IHC: 1:500
Antibody Alexa Fluor 647- goat anti-rabbit IgG Thermo Fisher Scientific Cat# A21245; RRID:AB_2535813 IHC: 1:500
Antibody Alexa Fluor 405- goat anti-rabbit IgG Thermo Fisher Scientific Cat# A48254; RRID:AB_2890548 IHC: 1:500
Antibody Alexa Fluor 568- goat anti-mouse IgG Thermo Fisher Scientific Cat# A11031; RRID:AB_144696 IHC: 1:500
Antibody Alexa Fluor 647- goat anti-mouse IgG Thermo Fisher Scientific Cat# A21235; RRID:AB_2535804 IHC: 1:500
Antibody Alexa Fluor 568- goat anti-rat IgG Thermo Fisher Scientific Cat# A21247; RRID:AB_141778 IHC: 1:500
Antibody Donkey anti-Rabbit IgG HRP Jackson ImmunoResearch Cat# 711-035-152; RRID:AB_10015282 WB: 1:5000
Antibody Donkey anti-Mouse IgG HRP Jackson ImmunoResearch Cat# 712-035-150; RRID:AB_2340638 WB: 1:5000
Antibody Donkey anti-goat IgG HRP Jackson ImmunoResearch Cat# 705-035-003; RRID:AB_2340390 WB: 1:5000
Antibody 10 nm gold anti mouse IgG Electron Microscopy Sciences Cat# 25129 IEM 1:50
Antibody 10 nm gold anti rabbit IgG Electron Microscopy Sciences Cat# 25109 IEM 1:50
Chemical compound, drug INK (mTOR i) ChemScene CAS 1224844-38-5 Dissolved in 0.5% carboxymethyl cellulose (Sigma, Cat #5678) and 0.05% tween 80 in water
Chemical compound, drug PBS Thermo Fisher Scientific Cat# BP339-4
Chemical compound, drug Sodium cacodylate buffer Electron Microscopy Sciences Cat#11652 0.1 M for fixation of brains
Chemical compound, drug Paraformaldehyde Electron Microscopy Sciences Cat#15714 4% for fixation of brains
Chemical compound, drug 25% glutaraldehyde Electron Microscopy Sciences Cat# 16220
Chemical compound, drug Uranyl acetate Electron Microscopy Sciences Cat# 22400–4 EM processing
Chemical compound, drug Lead citrate Electron Microscopy Sciences Cat#22410 EM processing
Chemical compound, drug Osmium tetroxide Ted Pella Cat#18465 EM processing
Chemical compound, drug Sodium metaperiodate Sigma-Aldrich Cat#S1878-25g EM processing
Chemical compound, drug Permount Electron Microscopy Sciences Cat# 17986 Dab IHC
Chemical compound, drug Flouro-gel Electron Microscopy Sciences Cat# 17985–10 IF IHC
Chemical compound, drug FBS Thermo Fisher Scientific Cat# 26140 IHC blocking
Chemical compound, drug Horse serum Thermo Fisher Scientific Cat# 16050 IHC blocking
Chemical compound, drug Spurr resin Electron Microscopy Sciences Cat# 14300
Commercial assay kit Vectastain ABC Vector Laboratories Cat# PK-4000; RRID:AB_2336818 For DAB staining
Commercial assay kit DAB Peroxidase Substrate kit Vector Laboratories Cat# SK-4100; RRID:AB_2336382 For DAB staining
Commercial assay kit HQ silver kit NanoProbes Cat# 2012–45 ml EM processing
Software ImageJ NIH https://imagej.nih.gov/ij/
Software Excel Microsoft Microsoft 365
Software GraphPad Prism 8.0.1 GraphPad
Other Nitrocellulose membrane Whatman WB 0.2µm-pore
Other Aclar embedding film Electron Microscopy Sciences Cat# 50425–25 EM processing
Other 75 mesh nickel grids with carbon coating and formvar Electron Microscopy Sciences Cat# pi-75-ni-25 EM processing
Other EM block Electron Microscopy Sciences Cat# 25596 EM processing

The HD Q175 mouse model and the generation of a new Q175 model crossed with the autophagy reporter mouse TRGL (TRGL/Q175)

Q175 mice (B6J.zQ175 Knock-In mice, CHDI-81003003) (Menalled et al., 2012; Menalled et al., 2003) were obtained from the Jackson Lab (Stock No. 027410) on a C57BL/6 J background. The zQ175 KI allele has the mouse Htt exon 1 replaced by the human HTT exon 1 sequence containing a~190 CAG repeat tract, and the average size for the mice used in this study was 200 CAG. Q175 genotyping and CAG sizing were conducted by Laragen, Inc (Culver City, CA). The TRGL (Thy-1 mRFP-eGFP-LC3) mouse model, expressing tandem fluorescence-tagged LC3 (tfLC3 or mRFP-eGFP-LC3) in neurons, was generated, genotyped by PCR and maintained in a C57BL/6 J background as described previously (Lee et al., 2019). The TRGL was crossed with Q175 to generate the new TRGL/Q175 model featuring the tfLC3 probe as an in vivo autophagy reporter.

The mice were maintained in the Nathan Kline Institute for Psychiatric Research (NKI) animal facility and housed in a 12 hr light/dark cycle. All animal procedures were performed following the National Institutes of Health Guidelines for the Humane Treatment of Animals, with approval from the Institutional Animal Care and Use Committee at the NKI (AP2018-624). Animals of both sexes were used in this study. Details for mouse ages are in the Figure Legend and/or Results. All efforts were made to minimize animal suffering and the number of animals used. Mouse sample sizes were estimated based on similar experimental procedures in our prior studies (Lee et al., 2019; Lee et al., 2022; Yang et al., 2017; Yang et al., 2011; Yang et al., 2014), where a minimum of 5 mice per each mouse genotype per experimental condition would yield statistically significant differences in revealing alterations of autophagy in AD transgenic animal models vs wild-type mice.

mTORi INK administration to mice

INK-128 (ChemScene) was formulated in the sterile filtered vehicle (Veh) – 0.5% carboxymethyl cellulose (Sigma, Cat #5678) and 0.05% Tween 80 in water – at different concentrations to achieve the various desired dosages. The mixture was homogenized with a tissue homogenizer, stored in a sterile container at room temperature for up to 2 wk, and stirred prior to each dosing. It was administered to mice via oral gavage at a dose volume of 5 ml/kg, which was a relatively smaller volume of drug solution (generally 100–150 µl depending on the mouse body weight) aimed at avoiding the suppression of appetite. Mice receiving the Veh (also at a solution volume of 5 ml/kg) served as the control group. Randomization of grouping: mice from each pool of sex and genotype of a particular age were pulled at random and put into either vehicle or drug treatment groups to make sure of even distribution of sexes and genotypes in the vehicle and drug treatments. Oral gavage was conducted with 20 G-38mm Cadence Science Malleable Stainless-Steel Animal Feeding Needles #9921 (Fisher Sci Cat #.14-825-275). Each of the INK-128 dosing tests (ranging from 2.5 to 10 mg/kg [mpk] with various durations) and the 3 wk daily 4 mpk treatment procedures were performed once.

mTORi INK pharmacokinetics in mouse brains after oral administration

INK was formulated into 0.5% carboxymethyl cellulose and 0.05% Tween-80 in water to be administered orally. 6 mo old C57B6/J male mice were treated with compound at 1, 3, or 10 mpk or vehicle. 6, 12, or 24 hr after administration, mice were terminally anesthetized with pentobarbital, and cerebellum was dissected and flash frozen on liquid nitrogen and stored at –80 °C until analysis. Tissue was homogenized and extracted with acetonitrile (containing 0.1% formic acid). Extract was analyzed using a LC-MS/MS (Waters Xevo TQ MS) method with a lower limit of quantitation of 13 nM.

Brain tissue preparation

To obtain tissues for experiments, the animals were anesthetized with a mixture of ketamine (100 mg/kg BW) and xylazine (10 mg/kg BW). Mice for light microscopic (LM) analyses were usually fixed by cardiac perfusion using 4% paraformaldehyde (PFA) in 0.1 M sodium cacodylate buffer [pH 7.4, Electron Microscopy Sciences (EMS), Hatfield, PA]. Following perfusion fixation, the brains were immersion-fixed in the same fixative overnight at 4 °C. For transmission electron microscopic (EM) study, 4% PFA was supplemented with 2% glutaraldehyde (EMS). For biochemical analyses such as immunoblotting, the brains were flash frozen on dry ice and stored at –70 °C. When both morphological and biochemical analyses were to be performed on the same brain, the brain was removed after brief perfusion with saline. One hemisphere was frozen at –70 °C and the other half was immersion-fixed in 4% PFA for 3 d at 4 °C.

Antibodies for immunohistochemistry (IHC) and western blotting (WB)

The following primary antibodies were used in this study. (1) from Cell Signaling Technology: tHTT rabbit mAb (total HTTs, #5656), MTOR rabbit mAb (#2983); p-MTOR (S2481 autophosphorylation) pAb (#2974), p-MTOR (S2448) pAb (#2971), p70S6K rabbit mAb (#2708), p-p70S6K (S371) pAb (#9208), p-p70S6K (T389) rabbit mAb (#9235), S6 ribosomal protein mAb (#2317), p-S6 ribosomal protein (S240/244) rabbit mAb (#5364), ULK1 pAb (#4773), ULK1 rabbit mAb (#6439), p-ULK1 (S757) pAb (#6888), p-ULK1 (S317) pAb (#37762), ATG5 rabbit mAb (#12994), ATG14 pAb (#5504), p-ATG14 (S29) rabbit mAb (#92340), p-Beclin 1 (S30) rabbit mAb (#35955), VPS34 rabbit mAb (#4263), p-VPS34 (S249) pAb (#13857), TRAF6 rabbit mAb (#8028). (2) from Millipore-Sigma: ntHTT mAb (mEM48, #MAB5374; N-Terminus-specific), HTT mAb (1C2, #MAB1574; epitope: N-terminal part of the human TATA Box Binding Protein (TBP) containing a 38-glns stretch), HTT mAb (#MAB5490; epitope: human HTT aa115-129), ATG5 pAb (#ABC14), LC3 pAb (#ABC929), NeuN (#MAB377), β−actin mAb (#A1978). (3) from other vendors: HTT mAb MW8 (epitope: HD exon 1 with 67Q; Develop Studies Hybridoma Bank, University of Iowa), HTT mAb PHP2 (CHDI-90001516–2, Coriell/CHDI), Beclin 1 mAb (BD Biosciences, #612113), LC3 pAb (Novus Biologics, #NB100-2220), p62/SQSTM1 mAb (BD Biosciences, #610832) or C-term-specific p62/SQSTM1 Guinea Pig pAb (Progen Biotechnik #C-1620); ubiquitin pAb (Dako Agilent, #Z0458), ubiquitin pAb (Abcam, #ab7780), DARPP32 (Abcam, #40801), LAMP1 rat mAb (Developmental Studies Hybridoma Bank, #H4A3), CTSB goat pAb (Neuromics, #GT15047). (4) In-house made: CTSD pAb (RU4). CTSD sheep pAb (D-2–3) (Cataldo et al., 1990).

The following secondary antibodies and reagents for immunoperoxidase labeling were purchased from Vector Laboratories (Burlingame, CA): biotinylated goat anti-rabbit or -mouse IgG/IgM, Vectastain ABC kit (PK-4000), and DAB Peroxidase Substrate Kit (SK-4100). Mouse on Mouse (M.O.M) detection kit (BMK-2201), normal-goat (S-100), and normal-donkey (S-2000–20) serum blocking solution were also from Vector Lab. The following secondary antibodies for immunofluorescence were purchased from Thermo Fisher Scientific (Waltham, MA): Alexa Fluor 488-conjugated goat anti-rabbit IgG (A11034), Alexa Fluor 568- goat anti-rabbit IgG (A11036), Alexa Fluor 647- goat anti-rabbit (A21245), Alexa Fluor Plus 405- goat anti-rabbit (A48254), Alexa Fluor 568- goat anti-mouse IgG (A11031), Alexa Fluor 647- goat anti-mouse (A21235), and Alexa Fluor 647- goat anti-rat (A21247). HRP-linked secondary antibodies for immunoblotting were obtained from Jackson ImmunoResearch Laboratories (West Grove, PA): Rabbit IgG (711-035-152), Mouse IgG (711-035-150), Rat IgG (712-035-150), and Goat IgG (705-035-003).

Immunolabeling of brain sections

Immunoperoxidase and immunofluorescence IHC were performed according to the protocols previously described (Lee et al., 2019; Yang et al., 2009). Brain vibratome sections (40 μm) were blocked and incubated in primary antibody O/N (up to 3 d in some cases) at 4 °C. ABC detection method was used for immunoperoxidase labeling visualized with DAB while Alexa-Fluor conjugated secondary antibodies were used for immunofluorescence. Autofluorescence was quenched with 1% Sudan black (Sigma-Aldrich; St. Louis, MO) in 70% ethanol for 20 min. DAB-labeled sections was inspected on a Zeiss AxioSkop II equipped with a HrM digital camera (Carl Zeiss, Germany). Immunofluorescently labeled sections were examined on a Zeiss LSM880 confocal microscope. Independent investigator(s) coded animals to blind investigators when imaging and quantifying.

Confocal image collection and hue-angle based quantitative analysis for AV/LY subtypes

Confocal imaging was performed using a plan-Apochromat 20 x or 40 x/1.4 oil objective lens on a LSM880 laser scanning confocal microscope with the following parameters: eGFP (ex: 488, em: 490–560 with MBS 488), mRFP (ex: 561, em: 582–640 with MBS 458/561), Alexafluor 647 (ex: 633, em: 640–710 with MBS 488/561/633) and DAPI (ex: 405, em: 410–483), with the ‘best signal scanning mode’ which separates scanning tracks for each excitation and emission set to exclude crosstalk between each fluorophore signal. The resolution of 40 x images was 1024×1024 pixels (corresponding to an area of 212.34×212.34 μm2), and the resolution of 3 x digitally zoomed images was also 1024×1024 pixels (corresponding to an area of 70.78×70.78 μm2). Detailed settings for image collection were reported previously (Lee et al., 2019).

Hue angle-based vesicle analysis enables quantitative determination of AV/LY subtypes including AP, AL, poorly acidified AL (pa-AL), and pure LY and the method was described in detail previously (Lee et al., 2019). Briefly, high resolution confocal images containing the LC3 (red and green) and CTSD (blue) punctate signals were analyzed with the Zen Blue Image Analysis Module from Carl Zeiss Microscopy. Threshold for each of the three-color channels (red, green, blue; RGB) was set by taking the average of intensity value from 20 neuronal perikarya. The signal was segmented into discrete puncta by using the automatic watershed function to separate clumped vesicles into individual puncta. Background signal was eliminated using the size exclusion function of Zen Blue. The R, G, and B intensity values of each vesicle were calculated using the profile function of Zen Blue. The RGB ratio of each vesicle was converted into a hue angle and saturation range – which we assigned to each AV subtype and should match the desired AV color range as perceived visually (i.e. yellow for AP, blue for Ly, purple for AL and white for de-acidified AL) – by entering the values of R, G, and B for a given punctum into the formula, as follows: Hue°=IF(180/PI()*ATAN2(2*R-G-B,SQRT(3)*(G-B))<0,180/PI()*ATAN2(2*R-G-B,SQRT(3) *(G-B))+360,180/PI()*ATAN2(2*R-G-B,SQRT(3)*(G-B))). Saturation percent of the hue angle was calculated by entering the values of R, G, and B for a given punctum into the following formula = (MAX(RGB)-MIN(RGB))/SUM(MAX(RGB)+MIN(RGB))*100 (http://www.workwithcolor.com/), provided lightness is less than 1, which is the usual case for our data. The Hue angle was converted to color using the Hue color wheel. The data was pooled and categorized in Excel spreadsheets.

Ultrastructural analyses

Vibratome brain sections (50 μm) were treated with 1% osmium tetroxide in 100 mM sodium cacodylate buffer pH 7.4 for 30 min, washed in distilled water four times (10 min/wash), and then treated with 2% aqueous uranyl acetate overnight at 4 °C in the dark. Samples were then washed and sequentially dehydrated with increasing concentrations of ethanol (20, 30, 50, 70, 90, and 100%) for 30 min each, followed by three additional treatments with 100% ethanol for 20 min each. Samples were then infiltrated with increasing concentrations of Spurr’s resin (25% for 1 hr, 50% for 1 hr, 75% for 1 hr, 100% for 1 hr, 100% overnight at room temperature), and then incubated overnight at 70 °C in a resin mold. For TEM ultrastructural analysis, 70 nm sections were cut using a Leica Reichert Ultracut S ultramicrotome and a Diatome diamond knife, placed onto grids and then post-stained with 2% uranyl acetate and lead citrate. Images were taken using a Ceta Camera on a ThermoFisher Talos L120C transmission electron microscope operating at 120kV.

For post-embedding IEM, ultrathin sections were placed onto carbon formvar 75 mesh nickel grids and etched using 4% sodium metaperiodate for 10 min before being washed twice in distilled water and then blocked for 1 hr. Grids were incubated in the primary antibodies at 4 °C overnight. Next day, grids underwent seven washes in 1xPBS and were then incubated in anti-mouse or anti-rabbit 10 nm gold secondary (1/50 dilution) for 1 hr. After this, the grid was washed seven times in 1x PBS and twice in distilled water. The grids were then silver enhanced for 5 min (Nanoprobes). Grids were finally post-stained with 1% uranyl acetate for 5 min followed by two washes in water and then stained with lead citrate for 5 min followed by a final two washes in distilled water. Samples were then imaged on a ThermoFisher Talos L120C operating at 120 kV.

Western blotting

Samples for WB were prepared by homogenizing brains in a tissue-homogenizing buffer (250 mM sucrose, 20 mM Tris pH 7.4, 1 mM EDTA, 1 mM EGTA) containing protease and phosphatase inhibitors as previously described. (Schmidt et al., 2012) Following electrophoresis, proteins were transferred onto 0.2 µm-pore nitrocellulose membranes (Whatman, Florham Park, NJ) at 100 mA for 8–12 hr depending on the target protein. The blots were blocked for 1 hr in 5% non-fat milk in TBS, rinsed in TBST (TBS +0.1% Tween-20), then incubated with a primary antibody in 1% BSA/TBST overnight at 4 °C. The membrane was washed and incubated in a HRP-conjugated goat-anti-rabbit or mouse secondary antibody, diluted 1:5000 in 5% milk for 1 hr at room temperature. The membrane was again washed and then incubated in a Novex ECL (Invitrogen) for 1 min. The detection of the signals was achieved through either exposure to a film or scanning by a digital gel imager (Syngene G:Box XX9) as specified in the figure legends. Densitometry was performed with Image J and the results were normalized by the immunoblot(s) of given loading control protein(s) (usually GAPDH, unless otherwise noted).

Materials availability

Requests for resources and reagents should be directed to the lead contact, Dr. Ralph Nixon (nixon@nki.rfmh.org). The crossed TRGL/Q175 mice generated from this study are not available since the breeding had been stopped. However, Q175 mice are available from the Jackson Lab, and the TRGL mice are available from the lead contact and may require completion of a materials transfer agreement.

Acknowledgements

We are grateful to Dr. T Yoshimori (Osaka University, Japan) for the mRFP-eEGFP-LC3 construct used in the TRGL mice. This work was supported by the CHDI Foundation (RAN) and the National Institute of Aging (P01 AG017617 to RAN).

Funding Statement

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Contributor Information

Ralph A Nixon, Email: Ralph.Nixon@nki.rfmh.org.

Dun-Sheng Yang, Email: Dun-Sheng.Yang@nki.rfmh.org.

Margaret S Ho, National Yang Ming Chiao Tung University, Taiwan.

Jonathan A Cooper, Fred Hutchinson Cancer Research Center, United States.

Funding Information

This paper was supported by the following grants:

  • CHDI Foundation to Ralph A Nixon.

  • National Institutes of Health P01 AG017617 to Ralph A Nixon.

Additional information

Competing interests

No competing interests declared.

Author contributions

Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing – review and editing.

Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing – review and editing.

Data curation, Formal analysis, Investigation, Visualization.

Data curation, Investigation.

Resources, Writing – review and editing.

Data curation, Formal analysis, Investigation, Methodology.

Conceptualization, Resources, Formal analysis, Validation, Methodology, Project administration, Writing – review and editing.

Conceptualization, Resources, Supervision, Funding acquisition, Project administration, Writing – review and editing.

Conceptualization, Data curation, Formal analysis, Supervision, Validation, Investigation, Visualization, Writing – original draft, Project administration, Writing – review and editing.

Ethics

The animal procedures conducted in this study were approved by the Institutional Animal Care and Use Committee at the NKI, AP2018-624.

Additional files

MDAR checklist

Data availability

No large datasets or custom codes were generated in this study. Original western blot images are uploaded as zipped source data files. Please note that in Figure 3—figure supplements 14, there are total six protein markers (MTOR, Atg5, Beclin 1, TRAF6, CTSB and CTSD) for which the presented western blot images (the lanes arranged as TRGL, TRGL/Q175, TRGL, TRGL/Q175, TRGL, TRGL/Q175) are just representative images of the signal seen but not the blots used for quantitative analysis to generate the bar graphs (where Q175 mouse samples were included in the gels, lacking the pattern of the above TRGL, TRGL/Q175, TRGL, TRGL/Q175, TRGL, TRGL/Q175). Thus, original western blots for both situations, whose file names contain either 'used for display-only' or 'used for quantitation-only', are included in the source data files.

References

  1. Abd-Elrahman KS, Hamilton A, Hutchinson SR, Liu F, Russell RC, Ferguson SSG. mGluR5 antagonism increases autophagy and prevents disease progression in the zQ175 mouse model of Huntington’s disease. Science Signaling. 2017;10:eaan6387. doi: 10.1126/scisignal.aan6387. [DOI] [PubMed] [Google Scholar]
  2. Aktar F, Burudpakdee C, Polanco M, Pei S, Swayne TC, Lipke PN, Emtage L. The huntingtin inclusion is a dynamic phase-separated compartment. Life Science Alliance. 2019;2:e201900489. doi: 10.26508/lsa.201900489. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Ashkenazi A, Bento CF, Ricketts T, Vicinanza M, Siddiqi F, Pavel M, Squitieri F, Hardenberg MC, Imarisio S, Menzies FM, Rubinsztein DC. Polyglutamine tracts regulate beclin 1-dependent autophagy. Nature. 2017;545:108–111. doi: 10.1038/nature22078. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Atwal RS, Xia J, Pinchev D, Taylor J, Epand RM, Truant R. Huntingtin has a membrane association signal that can modulate huntingtin aggregation, nuclear entry and toxicity. Human Molecular Genetics. 2007;16:2600–2615. doi: 10.1093/hmg/ddm217. [DOI] [PubMed] [Google Scholar]
  5. Baiamonte BA, Lee FA, Brewer ST, Spano D, LaHoste GJ. Attenuation of Rhes activity significantly delays the appearance of behavioral symptoms in a mouse model of Huntington’s disease. PLOS ONE. 2013;8:e53606. doi: 10.1371/journal.pone.0053606. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Barker RA, Fujimaki M, Rogers P, Rubinsztein DC. Huntingtin-lowering strategies for Huntington’s disease. Expert Opinion on Investigational Drugs. 2020;29:1125–1132. doi: 10.1080/13543784.2020.1804552. [DOI] [PubMed] [Google Scholar]
  7. Bates GP, Dorsey R, Gusella JF, Hayden MR, Kay C, Leavitt BR, Nance M, Ross CA, Scahill RI, Wetzel R, Wild EJ, Tabrizi SJ. Huntington disease. Nature Reviews. Disease Primers. 2015;1:15005. doi: 10.1038/nrdp.2015.5. [DOI] [PubMed] [Google Scholar]
  8. Bensalem J, Fourrier C, Hein LK, Hassiotis S, Proud CG, Sargeant TJ. Inhibiting mTOR activity using AZD2014 increases autophagy in the mouse cerebral cortex. Neuropharmacology. 2021;190:108541. doi: 10.1016/j.neuropharm.2021.108541. [DOI] [PubMed] [Google Scholar]
  9. Berg MJ, Rosa CM, Kumar A, Mohan PS, Stavrides P, Marchionini DM, Yang DS, Nixon RA. Pathobiology of the autophagy-lysosomal pathway in the huntington’s disease brain. bioRxiv. 2024 doi: 10.1101/2024.05.29.596470. [DOI]
  10. Bjørkøy G, Lamark T, Brech A, Outzen H, Perander M, Overvatn A, Stenmark H, Johansen T. p62/SQSTM1 forms protein aggregates degraded by autophagy and has a protective effect on huntingtin-induced cell death. The Journal of Cell Biology. 2005;171:603–614. doi: 10.1083/jcb.200507002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Boland B, Yu WH, Corti O, Mollereau B, Henriques A, Bezard E, Pastores GM, Rubinsztein DC, Nixon RA, Duchen MR, Mallucci GR, Kroemer G, Levine B, Eskelinen E-L, Mochel F, Spedding M, Louis C, Martin OR, Millan MJ. Promoting the clearance of neurotoxic proteins in neurodegenerative disorders of ageing. Nature Reviews. Drug Discovery. 2018;17:660–688. doi: 10.1038/nrd.2018.109. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Carty N, Berson N, Tillack K, Thiede C, Scholz D, Kottig K, Sedaghat Y, Gabrysiak C, Yohrling G, von der Kammer H, Ebneth A, Mack V, Munoz-Sanjuan I, Kwak S. Characterization of HTT inclusion size, location, and timing in the zQ175 mouse model of Huntington’s disease: an in vivo high-content imaging study. PLOS ONE. 2015;10:e0123527. doi: 10.1371/journal.pone.0123527. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Cataldo AM, Thayer CY, Bird ED, Wheelock TR, Nixon RA. Lysosomal proteinase antigens are prominently localized within senile plaques of Alzheimer’s disease: evidence for a neuronal origin. Brain Research. 1990;513:181–192. doi: 10.1016/0006-8993(90)90456-l. [DOI] [PubMed] [Google Scholar]
  14. Cortes CJ, La Spada AR. The many faces of autophagy dysfunction in Huntington’s disease: from mechanism to therapy. Drug Discovery Today. 2014;19:963–971. doi: 10.1016/j.drudis.2014.02.014. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Croce KR, Yamamoto A. A role for autophagy in Huntington’s disease. Neurobiology of Disease. 2019;122:16–22. doi: 10.1016/j.nbd.2018.08.010. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Deng Y, Wang H, Joni M, Sekhri R, Reiner A. Progression of basal ganglia pathology in heterozygous Q175 knock-in Huntington’s disease mice. The Journal of Comparative Neurology. 2021;529:1327–1371. doi: 10.1002/cne.25023. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. DiFiglia M, Sapp E, Chase KO, Davies SW, Bates GP, Vonsattel JP, Aronin N. Aggregation of huntingtin in neuronal intranuclear inclusions and dystrophic neurites in brain. Science. 1997;277:1990–1993. doi: 10.1126/science.277.5334.1990. [DOI] [PubMed] [Google Scholar]
  18. Franklin GL, Teive HAG, Tensini FS, Camargo CHF, de Lima N, de Dos Santos D, Meira AT, Tabrizi SJ. The Huntington’s disease gene discovery. Movement Disorders. 2024;39:227–234. doi: 10.1002/mds.29703. [DOI] [PubMed] [Google Scholar]
  19. Fu A, Cohen-Kaplan V, Avni N, Livneh I, Ciechanover A. p62-containing, proteolytically active nuclear condensates, increase the efficiency of the ubiquitin–proteasome system. PNAS. 2021;118:62. doi: 10.1073/pnas.2107321118. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Gutekunst CA, Li SH, Yi H, Mulroy JS, Kuemmerle S, Jones R, Rye D, Ferrante RJ, Hersch SM, Li XJ. Nuclear and neuropil aggregates in Huntington’s disease: relationship to neuropathology. The Journal of Neuroscience. 1999;19:2522–2534. doi: 10.1523/JNEUROSCI.19-07-02522.1999. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Heikkinen T, Bragge T, Kuosmanen J, Parkkari T, Gustafsson S, Kwan M, Beltran J, Ghavami A, Subramaniam S, Shahani N, Ramírez-Jarquín UN, Park L, Muñoz-Sanjuán I, Marchionini DM. Global Rhes knockout in the Q175 Huntington’s disease mouse model. PLOS ONE. 2021;16:e0258486. doi: 10.1371/journal.pone.0258486. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Hickey MA, Kosmalska A, Enayati J, Cohen R, Zeitlin S, Levine MS, Chesselet M-F. Extensive early motor and non-motor behavioral deficits are followed by striatal neuronal loss in knock-in Huntington’s disease mice. Neuroscience. 2008;157:280–295. doi: 10.1016/j.neuroscience.2008.08.041. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Iwata A, Christianson JC, Bucci M, Ellerby LM, Nukina N, Forno LS, Kopito RR. Increased susceptibility of cytoplasmic over nuclear polyglutamine aggregates to autophagic degradation. PNAS. 2005;102:13135–13140. doi: 10.1073/pnas.0505801102. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Iwata A, Nagashima Y, Matsumoto L, Suzuki T, Yamanaka T, Date H, Deoka K, Nukina N, Tsuji S. Intranuclear degradation of polyglutamine aggregates by the ubiquitin-proteasome system. Journal of Biological Chemistry. 2009;284:9796–9803. doi: 10.1074/jbc.M809739200. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Jamilloux Y, Lagrange B, Di Micco A, Bourdonnay E, Provost A, Tallant R, Henry T, Martinon F. A proximity-dependent biotinylation (BioID) approach flags the p62/sequestosome-1 protein as a caspase-1 substrate. The Journal of Biological Chemistry. 2018;293:12563–12575. doi: 10.1074/jbc.RA117.000435. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Jiang Y, Alam JJ, Gomperts SN, Maruff P, Lemstra AW, Germann UA, Stavrides PH, Darji S, Malampati S, Peddy J, Bleiwas C, Pawlik M, Pensalfini A, Yang D-S, Subbanna S, Basavarajappa BS, Smiley JF, Gardner A, Blackburn K, Chu H-M, Prins ND, Teunissen CE, Harrison JE, Scheltens P, Nixon RA. Preclinical and randomized clinical evaluation of the p38α kinase inhibitor neflamapimod for basal forebrain cholinergic degeneration. Nature Communications. 2022;13:5308. doi: 10.1038/s41467-022-32944-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Jiang A, Handley RR, Lehnert K, Snell RG. From Pathogenesis to therapeutics: a review of 150 years of huntington’s disease research. International Journal of Molecular Sciences. 2023;24:13021. doi: 10.3390/ijms241613021. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Kim YJ, Yi Y, Sapp E, Wang Y, Cuiffo B, Kegel KB, Qin ZH, Aronin N, DiFiglia M. Caspase 3-cleaved N-terminal fragments of wild-type and mutant huntingtin are present in normal and Huntington’s disease brains, associate with membranes, and undergo calpain-dependent proteolysis. PNAS. 2001;98:12784–12789. doi: 10.1073/pnas.221451398. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Kim YC, Guan KL. mTOR: a pharmacologic target for autophagy regulation. The Journal of Clinical Investigation. 2015;125:25–32. doi: 10.1172/JCI73939. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Kim A, Lalonde K, Truesdell A, Gomes Welter P, Brocardo PS, Rosenstock TR, Gil-Mohapel J. New avenues for the treatment of Huntington’s disease. International Journal of Molecular Sciences. 2021;22:8363. doi: 10.3390/ijms22168363. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Klionsky DJ, Petroni G, Amaravadi RK, Baehrecke EH, Ballabio A, Boya P, Bravo-San Pedro JM, Cadwell K, Cecconi F, Choi AMK, Choi ME, Chu CT, Codogno P, Colombo MI, Cuervo AM, Deretic V, Dikic I, Elazar Z, Eskelinen E-L, Fimia GM, Gewirtz DA, Green DR, Hansen M, Jäättelä M, Johansen T, Juhász G, Karantza V, Kraft C, Kroemer G, Ktistakis NT, Kumar S, Lopez-Otin C, Macleod KF, Madeo F, Martinez J, Meléndez A, Mizushima N, Münz C, Penninger JM, Perera RM, Piacentini M, Reggiori F, Rubinsztein DC, Ryan KM, Sadoshima J, Santambrogio L, Scorrano L, Simon H-U, Simon AK, Simonsen A, Stolz A, Tavernarakis N, Tooze SA, Yoshimori T, Yuan J, Yue Z, Zhong Q, Galluzzi L, Pietrocola F. Autophagy in major human diseases. The EMBO Journal. 2021;40:e108863. doi: 10.15252/embj.2021108863. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Ko J, Isas JM, Sabbaugh A, Yoo JH, Pandey NK, Chongtham A, Ladinsky M, Wu W-L, Rohweder H, Weiss A, Macdonald D, Munoz-Sanjuan I, Langen R, Patterson PH, Khoshnan A. Identification of distinct conformations associated with monomers and fibril assemblies of mutant huntingtin. Human Molecular Genetics. 2018;27:2330–2343. doi: 10.1093/hmg/ddy141. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Kolla R, Gopinath P, Ricci J, Reif A, Rostami I, Lashuel HA. A new chemoenzymatic semisynthetic approach provides insight into the role of phosphorylation beyond exon1 of huntingtin and reveals N-terminal fragment length-dependent distinct mechanisms of aggregation. Journal of the American Chemical Society. 2021;143:9798–9812. doi: 10.1021/jacs.1c03108. [DOI] [PubMed] [Google Scholar]
  34. Kordasiewicz HB, Stanek LM, Wancewicz EV, Mazur C, McAlonis MM, Pytel KA, Artates JW, Weiss A, Cheng SH, Shihabuddin LS, Hung G, Bennett CF, Cleveland DW. Sustained therapeutic reversal of Huntington’s disease by transient repression of huntingtin synthesis. Neuron. 2012;74:1031–1044. doi: 10.1016/j.neuron.2012.05.009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Lee JH, Tecedor L, Chen YH, Monteys AM, Sowada MJ, Thompson LM, Davidson BL. Reinstating aberrant mTORC1 activity in Huntington’s disease mice improves disease phenotypes. Neuron. 2015;85:303–315. doi: 10.1016/j.neuron.2014.12.019. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Lee J-H, Rao MV, Yang D-S, Stavrides P, Im E, Pensalfini A, Huo C, Sarkar P, Yoshimori T, Nixon RA. Transgenic expression of a ratiometric autophagy probe specifically in neurons enables the interrogation of brain autophagy in vivo. Autophagy. 2019;15:543–557. doi: 10.1080/15548627.2018.1528812. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Lee J-H, Yang D-S, Goulbourne CN, Im E, Stavrides P, Pensalfini A, Chan H, Bouchet-Marquis C, Bleiwas C, Berg MJ, Huo C, Peddy J, Pawlik M, Levy E, Rao M, Staufenbiel M, Nixon RA. Faulty autolysosome acidification in Alzheimer’s disease mouse models induces autophagic build-up of Aβ in neurons, yielding senile plaques. Nature Neuroscience. 2022;25:688–701. doi: 10.1038/s41593-022-01084-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Li H, Li SH, Cheng AL, Mangiarini L, Bates GP, Li XJ. Ultrastructural localization and progressive formation of neuropil aggregates in Huntington’s disease transgenic mice. Human Molecular Genetics. 1999;8:1227–1236. doi: 10.1093/hmg/8.7.1227. [DOI] [PubMed] [Google Scholar]
  39. Lie PPY, Yang D-S, Stavrides P, Goulbourne CN, Zheng P, Mohan PS, Cataldo AM, Nixon RA. Post-Golgi carriers, not lysosomes, confer lysosomal properties to pre-degradative organelles in normal and dystrophic axons. Cell Reports. 2021;35:109034. doi: 10.1016/j.celrep.2021.109034. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Lie PPY, Yoo L, Goulbourne CN, Berg MJ, Stavrides P, Huo C, Lee J-H, Nixon RA. Axonal transport of late endosomes and amphisomes is selectively modulated by local Ca2+ efflux and disrupted by PSEN1 loss of function. Science Advances. 2022;8:eabj5716. doi: 10.1126/sciadv.abj5716. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Liu K-Y, Shyu Y-C, Barbaro BA, Lin Y-T, Chern Y, Thompson LM, James Shen C-K, Marsh JL. Disruption of the nuclear membrane by perinuclear inclusions of mutant huntingtin causes cell-cycle re-entry and striatal cell death in mouse and cell models of Huntington’s disease. Human Molecular Genetics. 2015;24:1602–1616. doi: 10.1093/hmg/ddu574. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Macdonald M. A novel gene containing a trinucleotide repeat that is expanded and unstable on Huntington’s disease chromosomes. Cell. 1993;72:971–983. doi: 10.1016/0092-8674(93)90585-E. [DOI] [PubMed] [Google Scholar]
  43. Magnuson B, Ekim B, Fingar DC. Regulation and function of ribosomal protein S6 kinase (S6K) within mTOR signalling networks. The Biochemical Journal. 2012;441:1–21. doi: 10.1042/BJ20110892. [DOI] [PubMed] [Google Scholar]
  44. Marchionini DM, Liu JP, Ambesi-Impiombato A, Kerker K, Cirillo K, Bansal M, Mushlin R, Brunner D, Ramboz S, Kwan M, Kuhlbrodt K, Tillack K, Peters F, Rauhala L, Obenauer J, Greene JR, Hartl C, Khetarpal V, Lager B, Rosinski J, Aaronson J, Alam M, Signer E, Muñoz-Sanjuán I, Howland D, Zeitlin SO. Benefits of global mutant huntingtin lowering diminish over time in a Huntington’s disease mouse model. JCI Insight. 2022;7:161769. doi: 10.1172/jci.insight.161769. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Mario Isas J, Pandey NK, Xu H, Teranishi K, Okada AK, Fultz EK, Rawat A, Applebaum A, Meier F, Chen J, Langen R, Siemer AB. Huntingtin fibrils with different toxicity, structure, and seeding potential can be interconverted. Nature Communications. 2021;12:4272. doi: 10.1038/s41467-021-24411-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Martin DDO, Ladha S, Ehrnhoefer DE, Hayden MR. Autophagy in Huntington disease and huntingtin in autophagy. Trends in Neurosciences. 2015;38:26–35. doi: 10.1016/j.tins.2014.09.003. [DOI] [PubMed] [Google Scholar]
  47. Martinez-Vicente M, Talloczy Z, Wong E, Tang G, Koga H, Kaushik S, de Vries R, Arias E, Harris S, Sulzer D, Cuervo AM. Cargo recognition failure is responsible for inefficient autophagy in Huntington’s disease. Nature Neuroscience. 2010;13:567–576. doi: 10.1038/nn.2528. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Menalled LB, Sison JD, Dragatsis I, Zeitlin S, Chesselet MF. Time course of early motor and neuropathological anomalies in a knock-in mouse model of Huntington’s disease with 140 CAG repeats. The Journal of Comparative Neurology. 2003;465:11–26. doi: 10.1002/cne.10776. [DOI] [PubMed] [Google Scholar]
  49. Menalled LB, Kudwa AE, Miller S, Fitzpatrick J, Watson-Johnson J, Keating N, Ruiz M, Mushlin R, Alosio W, McConnell K, Connor D, Murphy C, Oakeshott S, Kwan M, Beltran J, Ghavami A, Brunner D, Park LC, Ramboz S, Howland D. Comprehensive behavioral and molecular characterization of a new knock-in mouse model of Huntington’s disease: zQ175. PLOS ONE. 2012;7:e49838. doi: 10.1371/journal.pone.0049838. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Mende-Mueller LM, Toneff T, Hwang SR, Chesselet MF, Hook VYH. Tissue-specific proteolysis of huntingtin (htt) in human brain: evidence of enhanced levels of N- and C-terminal htt fragments in huntington’s disease striatum. The Journal of Neuroscience. 2001;21:1830–1837. doi: 10.1523/JNEUROSCI.21-06-01830.2001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Nixon RA. The role of autophagy in neurodegenerative disease. Nature Medicine. 2013;19:983–997. doi: 10.1038/nm.3232. [DOI] [PubMed] [Google Scholar]
  52. Norman JM, Cohen GM, Bampton ETW. The in vitro cleavage of the hAtg proteins by cell death proteases. Autophagy. 2010;6:1042–1056. doi: 10.4161/auto.6.8.13337. [DOI] [PubMed] [Google Scholar]
  53. Ochaba J, Lukacsovich T, Csikos G, Zheng S, Margulis J, Salazar L, Mao K, Lau AL, Yeung SY, Humbert S, Saudou F, Klionsky DJ, Finkbeiner S, Zeitlin SO, Marsh JL, Housman DE, Thompson LM, Steffan JS. Potential function for the Huntingtin protein as a scaffold for selective autophagy. PNAS. 2014;111:16889–16894. doi: 10.1073/pnas.1420103111. [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Ouimet CC, Langley-Gullion KC, Greengard P. Quantitative immunocytochemistry of DARPP-32-expressing neurons in the rat caudatoputamen. Brain Research. 1998;808:8–12. doi: 10.1016/s0006-8993(98)00724-0. [DOI] [PubMed] [Google Scholar]
  55. Park J-M, Jung CH, Seo M, Otto NM, Grunwald D, Kim KH, Moriarity B, Kim Y-M, Starker C, Nho RS, Voytas D, Kim D-H. The ULK1 complex mediates MTORC1 signaling to the autophagy initiation machinery via binding and phosphorylating ATG14. Autophagy. 2016;12:547–564. doi: 10.1080/15548627.2016.1140293. [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Peng Q, Wu B, Jiang M, Jin J, Hou Z, Zheng J, Zhang J, Duan W. Characterization of behavioral, neuropathological, brain metabolic and key molecular changes in zQ175 Knock-In mouse model of huntington’s disease. PLOS ONE. 2016;11:e0148839. doi: 10.1371/journal.pone.0148839. [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Peskett TR, Rau F, O’Driscoll J, Patani R, Lowe AR, Saibil HR. A liquid to solid phase transition underlying pathological huntingtin Exon1 aggregation. Molecular Cell. 2018;70:588–601. doi: 10.1016/j.molcel.2018.04.007. [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. Pressl C, Mätlik K, Kus L, Darnell P, Luo J-D, Paul MR, Weiss A, Liguore W, Carroll TS, Davis DA, McBride J, Heintz N. Selective Vulnerability of Layer 5a Corticostriatal Neurons in Huntington’s Disease. bioRxiv. 2024 doi: 10.1101/2023.04.24.538096. [DOI] [PubMed]
  59. Pryor WM, Biagioli M, Shahani N, Swarnkar S, Huang W-C, Page DT, MacDonald ME, Subramaniam S. Huntingtin promotes mTORC1 signaling in the pathogenesis of Huntington’s disease. Science Signaling. 2014;7:ra103. doi: 10.1126/scisignal.2005633. [DOI] [PubMed] [Google Scholar]
  60. Ravikumar B, Vacher C, Berger Z, Davies JE, Luo S, Oroz LG, Scaravilli F, Easton DF, Duden R, O’Kane CJ, Rubinsztein DC. Inhibition of mTOR induces autophagy and reduces toxicity of polyglutamine expansions in fly and mouse models of Huntington disease. Nature Genetics. 2004;36:585–595. doi: 10.1038/ng1362. [DOI] [PubMed] [Google Scholar]
  61. Riguet N, Mahul-Mellier A-L, Maharjan N, Burtscher J, Croisier M, Knott G, Hastings J, Patin A, Reiterer V, Farhan H, Nasarov S, Lashuel HA. Nuclear and cytoplasmic huntingtin inclusions exhibit distinct biochemical composition, interactome and ultrastructural properties. Nature Communications. 2021;12:6579. doi: 10.1038/s41467-021-26684-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
  62. Rose C, Menzies FM, Renna M, Acevedo-Arozena A, Corrochano S, Sadiq O, Brown SD, Rubinsztein DC. Rilmenidine attenuates toxicity of polyglutamine expansions in a mouse model of Huntington’s disease. Human Molecular Genetics. 2010;19:2144–2153. doi: 10.1093/hmg/ddq093. [DOI] [PMC free article] [PubMed] [Google Scholar]
  63. Rothe T, Deliano M, Wójtowicz AM, Dvorzhak A, Harnack D, Paul S, Vagner T, Melnick I, Stark H, Grantyn R. Pathological gamma oscillations, impaired dopamine release, synapse loss and reduced dynamic range of unitary glutamatergic synaptic transmission in the striatum of hypokinetic Q175 Huntington mice. Neuroscience. 2015;311:519–538. doi: 10.1016/j.neuroscience.2015.10.039. [DOI] [PubMed] [Google Scholar]
  64. Rui Y-N, Xu Z, Patel B, Chen Z, Chen D, Tito A, David G, Sun Y, Stimming EF, Bellen HJ, Cuervo AM, Zhang S. Huntingtin functions as a scaffold for selective macroautophagy. Nature Cell Biology. 2015;17:262–275. doi: 10.1038/ncb3101. [DOI] [PMC free article] [PubMed] [Google Scholar]
  65. Sanchez-Garrido J, Sancho-Shimizu V, Shenoy AR. Regulated proteolysis of p62/SQSTM1 enables differential control of autophagy and nutrient sensing. Science Signaling. 2018;11:Doi. doi: 10.1126/scisignal.aat6903. [DOI] [PubMed] [Google Scholar]
  66. Sarkar S, Rubinsztein DC. Huntington’s disease: degradation of mutant huntingtin by autophagy. The FEBS Journal. 2008a;275:4263–4270. doi: 10.1111/j.1742-4658.2008.06562.x. [DOI] [PubMed] [Google Scholar]
  67. Sarkar S, Rubinsztein DC. Small molecule enhancers of autophagy for neurodegenerative diseases. Molecular bioSystems. 2008b;4:895–901. doi: 10.1039/b804606a. [DOI] [PubMed] [Google Scholar]
  68. Schmidt SD, Nixon RA, Mathews PM. Tissue processing prior to analysis of Alzheimer’s disease associated proteins and metabolites, including Aβ. Methods in Molecular Biology. 2012;849:493–506. doi: 10.1007/978-1-61779-551-0_33. [DOI] [PubMed] [Google Scholar]
  69. Sotrel A, Paskevich PA, Kiely DK, Bird ED, Williams RS, Myers RH. Morphometric analysis of the prefrontal cortex in Huntington’s disease. Neurology. 1991;41:1117–1123. doi: 10.1212/wnl.41.7.1117. [DOI] [PubMed] [Google Scholar]
  70. Tabrizi SJ, Ghosh R, Leavitt BR. Huntingtin lowering strategies for disease modification in Huntington’s disease. Neuron. 2019;101:801–819. doi: 10.1016/j.neuron.2019.01.039. [DOI] [PubMed] [Google Scholar]
  71. Tanaka M, Machida Y, Niu S, Ikeda T, Jana NR, Doi H, Kurosawa M, Nekooki M, Nukina N. Trehalose alleviates polyglutamine-mediated pathology in a mouse model of Huntington disease. Nature Medicine. 2004;10:148–154. doi: 10.1038/nm985. [DOI] [PubMed] [Google Scholar]
  72. Trottier Y, Lutz Y, Stevanin G, Imbert G, Devys D, Cancel G, Saudou F, Weber C, David G, Tora L. Polyglutamine expansion as a pathological epitope in Huntington’s disease and four dominant cerebellar ataxias. Nature. 1995;378:403–406. doi: 10.1038/378403a0. [DOI] [PubMed] [Google Scholar]
  73. Valionyte E, Yang Y, Griffiths SA, Bone AT, Barrow ER, Sharma V, Lu B, Luo S. The caspase-6-p62 axis modulates p62 droplets based autophagy in a dominant-negative manner. Cell Death and Differentiation. 2022;29:1211–1227. doi: 10.1038/s41418-021-00912-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  74. Vodicka P, Lim J, Williams DT, Kegel KB, Chase K, Park H, Marchionini D, Wilkinson S, Mead T, Birch H, Yates D, Lyons K, Dominguez C, Beconi M, Yue Z, Aronin N, DiFiglia M. Assessment of chloroquine treatment for modulating autophagy flux in brain of WT and HD mice. Journal of Huntington’s Disease. 2014;3:159–174. doi: 10.3233/JHD-130081. [DOI] [PubMed] [Google Scholar]
  75. Vonsattel JP, Myers RH, Stevens TJ, Ferrante RJ, Bird ED, Richardson EP., Jr Neuropathological classification of Huntington’s disease. Journal of Neuropathology and Experimental Neurology. 1985;44:559–577. doi: 10.1097/00005072-198511000-00003. [DOI] [PubMed] [Google Scholar]
  76. Vonsattel JPG. Huntington disease models and human neuropathology: similarities and differences. Acta Neuropathologica. 2007;115:55–69. doi: 10.1007/s00401-007-0306-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  77. Wang X, Proud CG. The mTOR pathway in the control of protein synthesis. Physiology. 2006;21:362–369. doi: 10.1152/physiol.00024.2006. [DOI] [PubMed] [Google Scholar]
  78. Wold MS, Lim J, Lachance V, Deng Z, Yue Z. ULK1-mediated phosphorylation of ATG14 promotes autophagy and is impaired in Huntington’s disease models. Molecular Neurodegeneration. 2016;11:76. doi: 10.1186/s13024-016-0141-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  79. Xia J, Lee DH, Taylor J, Vandelft M, Truant R. Huntingtin contains a highly conserved nuclear export signal. Human Molecular Genetics. 2003;12:1393–1403. doi: 10.1093/hmg/ddg156. [DOI] [PubMed] [Google Scholar]
  80. Yang DS, Lee JH, Nixon RA. Monitoring autophagy in Alzheimer’s disease and related neurodegenerative diseases. Methods in Enzymology. 2009;453:111–144. doi: 10.1016/S0076-6879(08)04006-8. [DOI] [PubMed] [Google Scholar]
  81. Yang D-S, Stavrides P, Mohan PS, Kaushik S, Kumar A, Ohno M, Schmidt SD, Wesson D, Bandyopadhyay U, Jiang Y, Pawlik M, Peterhoff CM, Yang AJ, Wilson DA, St George-Hyslop P, Westaway D, Mathews PM, Levy E, Cuervo AM, Nixon RA. Reversal of autophagy dysfunction in the TgCRND8 mouse model of Alzheimer’s disease ameliorates amyloid pathologies and memory deficits. Brain. 2011;134:258–277. doi: 10.1093/brain/awq341. [DOI] [PMC free article] [PubMed] [Google Scholar]
  82. Yang D-S, Stavrides P, Saito M, Kumar A, Rodriguez-Navarro JA, Pawlik M, Huo C, Walkley SU, Saito M, Cuervo AM, Nixon RA. Defective macroautophagic turnover of brain lipids in the TgCRND8 Alzheimer mouse model: prevention by correcting lysosomal proteolytic deficits. Brain. 2014;137:3300–3318. doi: 10.1093/brain/awu278. [DOI] [PMC free article] [PubMed] [Google Scholar]
  83. Yang D-S, Stavrides P, Kumar A, Jiang Y, Mohan PS, Ohno M, Dobrenis K, Davidson CD, Saito M, Pawlik M, Huo C, Walkley SU, Nixon RA. Cyclodextrin has conflicting actions on autophagy flux in vivo in brains of normal and Alzheimer model mice. Human Molecular Genetics. 2017;26:843–859. doi: 10.1093/hmg/ddx001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  84. Yang J, Zhang C. Targeting the autophagy-lysosomal pathway in Huntington disease: a pharmacological perspective. Frontiers in Aging Neuroscience. 2023;15:1175598. doi: 10.3389/fnagi.2023.1175598. [DOI] [PMC free article] [PubMed] [Google Scholar]
  85. Zhou Y, Peskett TR, Landles C, Warner JB, Sathasivam K, Smith EJ, Chen S, Wetzel R, Lashuel HA, Bates GP, Saibil HR. Correlative light and electron microscopy suggests that mutant huntingtin dysregulates the endolysosomal pathway in presymptomatic Huntington’s disease. Acta Neuropathologica Communications. 2021;9:70. doi: 10.1186/s40478-021-01172-z. [DOI] [PMC free article] [PubMed] [Google Scholar]

eLife Assessment

Margaret S Ho 1

This study presents an important finding on the alterations in the autophagic-lysosomal pathway in a Huntington's disease model. The evidence supporting the claims of the authors is convincing. The original reviewers have found most of the issues previously raised have been addressed although further suggestions are given for consideration. These comments are listed below. The work will be of interest to neuroscientists working on HD.

Reviewer #1 (Public review):

Anonymous

Summary:

Huntington's disease (HD) is characterized by the expansion of polyglutamine repeats in huntingtin protein (HTT), leading to the formation of aggresomes composed of mutant huntingtin (mHTT). This study investigates the potential therapeutic strategy of enhancing autophagy to clear mHTT. The authors' evaluation of the autophagic-lysosomal pathway (ALP) in human HD brains shows that, in early stages, there is upregulated lysosomal biogenesis and relatively normal autophagy flux, while late-stage brains exhibit impaired autolysosome clearance, suggesting that early intervention may be beneficial. The authors cross the Q175 HD knock-in model with the TRGL autophagy reporter mouse to investigate ALP dynamics in vivo. In these models, mHTT is detected in autophagic vacuoles and colocalizes with autophagy receptors p62/SQSTM1 and ubiquitin. Although ALP alterations in the Q175 model are milder and later onset compared to human HD, they do show lysosome depletion and impaired autophagic flux. Treatment with an mTOR inhibitor in 6-month-old TRGL/Q175 mice normalized lysosome numbers, alleviated aggresome pathology, and reduced mHTT, p62, and ubiquitin levels. These findings suggest that autophagy modulation during the early stages of disease progression may offer potential therapeutic interventions for HD pathology.

Strengths:

Provide supportive animal evidence for mTOR inhibition in enhancing autophagy and reducing toxicity in HD animal models.

Weaknesses:

Lacks animal behavior and survival rate data, particularly regarding whether the extent of motor dysfunction in TRGL/Q175 mice is comparable to that in Q175 mice and whether the administration of mTORi INK improves these symptoms.

Reviewer #2 (Public review):

Anonymous

Summary:

In this manuscript the authors have explored the beneficial effect of autophagy upregulation in the context of HD pathology in a disease stage-specific manner. The authors have observed functional autophagy lysosomal pathway (ALP) and its machineries at the early stage in HD mouse model, whereas impairment of ALP has been documented at the later stages of the disease progression. Eventually, the authors have taken advantage of operational ALP pathway at the early stage of HD pathology, in order to upregulate ALP and autophagy flux by inhibiting mTORC1 in vivo, which ultimately reverted back multiple ALP-related abnormalities and phenotypes. Therefore, this manuscript is a promising effort to shed light on the therapeutic interventions with which HD pathology can be treated at the patient level in future.

Strengths:

The study has shown alteration of ALP in HD mouse model in a very detailed manner. Such stage dependent in vivo study will be informative and has not been done before. Also, this research provides possible therapeutic intervention in patients in future.

Weaknesses:

In this revised version of the manuscript, the authors have satisfactorily addressed all the concerns raised by the reviewers. They have also provided futuristic viewpoints towards tackling neurodegenerative disorder, especially Huntington Disease (HD).

eLife. 2025 May 20;14:RP104979. doi: 10.7554/eLife.104979.3.sa3

Author response

Philip Stavrides 1, Chris N Goulbourne 2, James Peddy 3, Chunfeng Huo 4, Mala Rao 5, Vinod Khetarpal 6, Deanna Marchionini 7, Ralph Nixon 8, Dun-Sheng Yang 9

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public review):

This study investigates alterations in the autophagic-lysosomal pathway in the Q175 HD knock-in model crossed with the TRGL autophagy reporter mouse. The findings provide valuable insights into autophagy dynamics in HD and the potential therapeutic benefits of modulating this pathway. The study suggests that autophagy stimulation may offer therapeutic benefits in the early stages of HD progression, with mTOR inhibition showing promise in ameliorating lysosomal pathology and reducing mutant huntingtin accumulation.

However, the data raises concerns regarding the strength of the evidence. The observed changes in autophagic markers, such as autolysosome and lysosome numbers, are relatively modest, and the Western blot results do not fully match the quantitative results. These discrepancies highlight the need for further validation and more pronounced effects to strengthen the conclusions. While the study suggests the potential of autophagy regulation as a long-term therapeutic strategy, additional experiments and more reliable data are necessary to confirm the broader applicability of the TRGL/Q175 mouse model.

Furthermore, the 2004 publication by Ravikumar et al. demonstrated that inhibition of mTOR by rapamycin or the rapamycin ester CCI-779 induces autophagy and reduces the toxicity of polyglutamine expansions in fly and mouse models of Huntington's disease. mTOR is a key regulator of autophagy, and its inhibition has been explored as a therapeutic strategy for various neurodegenerative diseases, including HD. Studies suggest that inhibiting mTOR enhances autophagy, leading to the clearance of mHTT aggregates. Given that dysfunction of the autophagic-lysosomal pathway and lysosomal function in HD is already well-established, and that mTOR inhibition as a therapeutic approach for HD is also known, this study does not present entirely novel findings.

Major Concerns:

(1) In Figure 3A1 and A2, delayed and/or deficient acidification of AL causes deficits in the reformation of LY to replenish the LY pool. However, in Figure S2D, there is no difference in AL formation or substrate degradation, as shown by the Western blotting results for CTSD and CTSB. How can these discrepancies be explained?

We appreciate the reviewer raising this point, and we agree with the concern. Please note that the material used for our immunoblotting was hemibrain homogenates, containing not only neurons but also glial cells, so the results for any protein, e.g., CTSD or CTSB in Fig. S2D, represented combined signals from neurons and glial cells. Our longstanding experience with western blot analysis of autophagy pathway markers is that signals from glial cells significantly interfere with/dilute the signals from neurons. By contrast, the immunofluorescence (IF) results in Fig. 3A, obtained with the assistance of tfLC3 probe and hue angle-based AV/LY subtype analysis, revealed the in situ conditions of the AL and LY within neurons selectively, which reflects the advantage of using the in vivo neuron-specific expression of the LC3 probe combined with IF with a LY marker in this study and our other related studies (Lee, Rao et al. 2019, Lee, Yang et al. 2022) as explained in the Introduction of this paper. Please also refer to a similar discussion regarding the WB-detected protein levels of p-ATG14 in L542-547.

(2) The results demonstrate that in the brain sections of 17-month-old TRGL/Q175 mice, there was an increase in the number of acidic autolysosomes (AL), including poorly acidified autolysosomes (pa-AL), alongside a decrease in lysosome (LY) numbers. These AL/pa-AL changes were not significant in 2-month-old or 7-month-old TRGL/Q175 mice, where only a reduction in lysosome numbers was observed. This indicates that these changes, representing damage to the autophagy-lysosome pathway (ALP), manifest only at later stages of the disease. Considering that the ALP is affected predominantly in the advanced stages of the disease (e.g., at 17 months), why were 6-month-old TRGL/Q175 mice selected for oral mTORi INK treatment, and why was the treatment duration restricted to just 3 weeks?

We thank the reviewer for the comment. A key outcome measure in our evaluation of mTORi treatment was amelioration of mHTT pathology, i.e., mHTT aggregates/IBs. Before conducting the mTORi treatment experiments, we had learned from our assessments of age-associated progression of mHTT aggresomes/IBs in mice of different ages (e.g., 2-, 6-, 10- and 17-mo) that there were already severe mHTT accumulations in Q175 at 10-mo-old (e.g., Fig. 2A). This is consistent with a previous report (Carty, Berson et al. 2015) showing that striatal mHTT inclusions dynamically increase from 4 to 8 months. From a therapeutic point of view, more aggregates in the mouse brain would make it more difficult for the autophagy machinery to clear these aggregates. Thus, the high degree of aggregates in 10- or 17-mo may not be modifiable by the mTORi and/or prevent reliable/sensitive measurements on mTORi-induced phenotype changes. We then preferred to apply the treatment to younger (i.e., 6-mo-old) mice when the mHTT pathology was not so severe, with detectable, albeit mild, ALP abnormality. Additionally, due to the 2-year funding limit for this project, there was insufficient time to generate a large set of old mice (e.g., ~18-mo) for another drug treatment experiment. In future studies, it might be worthy to conduct the treatment “in the advanced stages of the disease (e.g., ~18-mo)” to further examine the modification potential of the mTORi on the ALP as well as the HTT aggregations. As for the treatment duration, we were interested in an acute treatment schedule given that, in our dosing tests, we observed rapid responses to the treatment (e.g., target engagement) in a few days even with one dose, and that the 14-15-day treatments produced consistent responses (e.g., Fig. S3A). Long-term treatment, however, would be worthy testing in the future although our current study informs a therapeutic approach that has been suggested by others involving intermittent/pulsatile administration of mTOR inhibitors to minimize side effects of chronic long-term administration.

(3) Is the extent of motor dysfunction in TRGL/Q175 mice comparable to that in Q175 mice? Does the administration of mTORi INK improve these symptoms?

Unfortunately, we were unable to investigate motor functions experimentally with specific assays such as open field or rotarod tests in this study (partially affected by the falling of the funded research period within the COVID-19 pandemic peak periods in 2020). Based on our experience in handling the mice, we did not notice any obvious differences between Q175 and TRGL/Q175, and any improvements after the acute mTORi INK treatment.

(4) Why is eGFP expression not visible in Fig. 6A in TRGL-Veh mice? Additionally, why do normal (non-poly-Q) mice have fewer lysosomes (LY) than TRGL/Q175-INK mice? IHC results also show that CTSD levels are lower in TRGL mice compared to TRGL/Q175-INK mice. Does this suggest lysosome dysfunction in TRGL-Veh mice?

We appreciate the reviewer raising this point, which has been corrected (through slightly increasing the eGFP signal in the green channel and the merged channels equally for all genotypes), and the revised Fig. 6A is showing better eGFP signals. Regarding higher LY numbers/CTSD levels in TRGL/Q175-INK compared to the control TRGL-Veh mice, it does not necessarily imply LY dysfunction in TRGL mice, rather, it likely suggests mTORi treatment inducing LY biogenesis. Our original characterization of the TRGL mouse of varying ages, where low expression of the tgLC3 construct, produces only a very small increment of total LC3, resulting in no discernable functional changes in the autophagy pathway (Lee, Rao et al. 2019). The underlying mechanism, e.g., TFEB activation following mTOR inhibition, remains to be investigated in future studies.

(5) In Figure 5A, the phosphorylation of ATG14 (S29) shows minimal differences in Western blotting, which appears inconsistent with the quantitative results. A similar issue is observed in the quantification of Endo-LC3.

We welcome the reviewer’s point, and therefore bands showing bigger differences of p-ATG14 (S29) have been used in the revised Fig. 5A, making the images and the quantitative results more consistent and representative. Similar changes have also been made to the Endo-LC3 data at the bottom of Fig. 5A.

(6) In Figure S2A and Figure S2B, 17-month-old TRGL/Q175 mice show a decrease in pp70S6K and the p-ULK1/ULK1 ratio, but no changes are observed in autophagy-related markers. Do these results indicate only a slight change in autophagy at this stage in TRGL/Q175 mice? Since the mTOR pathway regulates multiple cellular mechanisms, could mTOR also influence other processes? Is it possible that additional mechanisms are involved?

We completely agree with the reviewer. As mentioned in the text at multiple locations, LAP alterations in Q175 and TRGL/Q175 mice are mild even at a relatively old age (e.g., 17-mo), especially at the protein levels detected by immunoblotting. We agree that even if the mild alterations in the levels of pp70S6K (T389) and p-ULK1/ULK1 ratio may indicate “a slight change in autophagy”, it may also imply that other cell processes are involved given that mTOR signaling regulates multiple cellular functions. In particular, the p70S6K/p-p70S6K – a mTOR substrate used as a readout for mTOR activity in this study – is a key component of the protein synthesis pathway (Wang and Proud 2006, Magnuson, Ekim et al. 2012) , so its changes may serve as readouts for alterations in not only the autophagy pathway, but also the protein synthesis pathway. [A related discussion about mTOR/protein synthesis pathways, in response to a comment from Reviewer 2, has been incorporated into the text under Discussion, L633-640]

Reviewer #2 (Public review):

Summary:

In this manuscript, the authors have explored the beneficial effect of autophagy upregulation in the context of HD pathology in a disease stage-specific manner. The authors have observed functional autophagy lysosomal pathway (ALP) and its machineries at the early stage in the HD mouse model, whereas impairment of ALP has been documented at the later stages of the disease progression. Eventually, the authors took advantage of the operational ALP pathway at the early stage of HD pathology, in order to upregulate ALP and autophagy flux by inhibiting mTORC1 in vivo, which ultimately reverted back to multiple ALP-related abnormalities and phenotypes. Therefore, this manuscript is a promising effort to shed light on the therapeutic interventions with which HD pathology can be treated at the patient level in the future.

Strengths:

The study has shown the alteration of ALP in the HD mouse model in a very detailed manner. Such stage-dependent in vivo study will be informative and has not been done before. Also, this research provides possible therapeutic interventions for patients in the future.

Weaknesses:

Some constructive comments and suggestions in order to reflect the key aspects and concepts better in the manuscript :

(1) The authors have observed lysosome number alteration in a temporally regulated disease stage-specific manner. In this scenario investigation of regulation, localization, and level of TFEB, the transcription factor required for lysosome biogenesis, would be interesting and informative.

We thank the reviewer for this point and completely agree that exploring TFEBrelated aspects would be interesting which will be investigated in future studies.

(2) For the general scientific community better clarification of the short forms will be useful. For example, in line 97, page 4, AP full form would be useful. Also 'metabolized via autophagy' can be replaced by 'degraded via autophagy'.

We appreciate the reviewer for raising this point. We introduced each abbreviation at the location where the full term first appears and, for the case of “AP”, it was introduced in (previous) Line 69 when “autophagosome” first appears. We agree with the reviewer about easy reading for the general scientific community and thus we have added an Abbreviation section after the Key Words section, listing abbreviations used in this manuscript.

Also, the word “metabolized” has been replaced with “degraded” as suggested.

(3) The nuclear vs cytosolic localization of HTT aggregates shown in Figure 2, are very interesting. The increase in cytosolic HTT aggregate formation at 10 months compared to 6 months probably suggests spatio-temporal regulation of aggregate formation. The authors could comment in a more elaborate manner, on the reason and impact of this kind of regulation of aggregate formation in the context of HD pathology.

We value the reviewer’s important point. Previous studies have well documented that mHTT aggregates exist in both intranuclear and extranuclear locations in the brains of both human HD and mouse models (DiFiglia, Sapp et al. 1997, Li, Li et al. 1999, Carty, Berson et al. 2015, Peng, Wu et al. 2016, Berg, Veeranna et al. 2024). HTT can travel between the nucleus and cytoplasm and the default location for HTT is cytoplasmic, and thus the occurrence of nuclear mHTT aggregates is considered as a result of dysfunction in the nuclear exporting system for proteins (DiFiglia, Sapp et al. 1995, Gutekunst, Levey et al. 1995, Sharp, Loev et al. 1995, Cornett, Cao et al. 2005) while other factors such as phosphorylation of HTT may also affect nuclear targeting (DeGuire, Ruggeri et al. 2018). Extranuclear aggregates of mHTT usually appear later than nuclear aggregates and develop more aggressively in terms of numbers and pace after their appearance (Li, Li et al. 1999, Carty, Berson et al. 2015, Landles, Milton et al. 2020). The fact that there are neurons containing extranuclear aggregates without having nuclear aggregates within the same cells (Carty, Berson et al. 2015) does not support a nuclear-cytoplasmic sequence for aggregate formation, implying different mechanisms controlling the formation of these two types of aggregates. It was reported that there were no significant differences in toxicity associated with the presence of nuclear compared with extranuclear aggregates (Hackam, Singaraja et al. 1999), while other studies have proposed that nuclear aggregates correlate with transcriptional dysfunction while extranuclear aggregates may impair neuronal communication and can track disease progression (Li, Li et al. 1999, Benn, Landles et al. 2005, Landles, Milton et al. 2020). Thus, the observation of a higher level of extranuclear mHTT aggregates at 10-mo compared to 6-mo from the present study is consistent with previous findings mentioned above. In addition, our EM observations of homogenous granular/short fine fibril ultrastructure of both nuclear and extranuclear aggregates are consistent with findings from mouse model studies (Davies, Turmaine et al. 1997, Scherzinger, Lurz et al. 1997), which, interestingly, is different from in vitro studies where nuclear aggregates exhibited a core and shell structure but extranuclear aggregates did not possess the shell (Riguet, Mahul-Mellier et al. 2021), reflecting differences between in vivo and in vitro conditions. Taken together, even if efforts have been made in this and previous studies in trying to understand the differences between nuclear and extranuclear aggregates, the mechanisms regarding the spatial-temporal regulation of aggregate formation have so far not been fully revealed which will require additional investigations.

(4) In this manuscript, the authors have convincingly shown that mTOR inhibition is inducing autophagy in the HD mouse model in vivo. On the other hand, mTOR inhibition would also reduce overall cellular protein translation. This aspect of mTOR inhibition can also potentially contribute to the alleviation of disease phenotype and disease symptoms by reducing protein overload in HD pathology. The authors' comments regarding this aspect would be appreciated.

We recognize the value of the reviewer’s point which we completely agree with. Lowering mHTT via interfering protein translation (e.g., through RNAi, antisense oligonucleotides) has been an attractive strategy in HD therapeutic development (Kordasiewicz, Stanek et al. 2012, Tabrizi, Ghosh et al. 2019). As mentioned above, mTOR regulates multiple cellular pathways including protein synthesis, and inhibition of mTOR as what was done in the present study is potentially affect protein synthesis as well. While our results of decreases in mHTT signals (Fig. 7) can be interpreted as a result of autophagymediated clearance of mHTT, certainly, a possibility cannot be excluded that mTOR inhibition may result in a reduction in HTT production which may also contribute to the observed results – future studies should determine how significant of such a contribution is. [The above description has been incorporated into the text under Discussion, L633-640]

(5) The authors have shown nuclear inclusion formation and aggregation of mHTT and also commented on its potential removal with the UPS system (proteasomal degradation) in vivo. As there is also a reciprocal relationship present between autophagy and proteasomal machineries, upon upregulation of autophagy machinery by mTOR inhibition proteasomal activity may decrease. How nuclear proteasomal activity increases to tackle nuclear mHTT IBs, would be interesting to understand in the context of HD pathology. Comments from the authors in this aspect would clarify the role of multiple degradation pathways in handling mutant HTT protein in HD pathology.

We appreciate the reviewer raising this point. We agree that there are reciprocal relationships between autophagy and the UPS (Korolchuk, Menzies et al. 2010, Park and Cuervo 2013). In general, failure in one pathway would lead to compensatory upregulation of the other pathway, and vice versa (Lee, Park et al. 2019). So, as the reviewer pointed out, “upon upregulation of autophagy machinery by mTOR inhibition proteasomal activity may decrease”. However, we proposed in the Discussion that “It is possible that stimulation of autophagy is reducing the mHTT in the cytoplasm and thereby partially relieves the burden of the proteasome both in the cytoplasm and in the nucleus so that the nuclear proteasome operates more effectively”, which is inconsistent with the general expectation for a decreased UPS activity. However, please note that there are also instances where two pathways may act in the same direction, e.g., autophagy inhibition disturbs UPS degradative function (Korolchuk, Mansilla et al. 2009, Park and Cuervo 2013). Anyhow, our statement is just speculation, requiring verifications with additional experiments in the future. One of the observations reported here which may support the above speculation is the reductions of AV-non-associated form of mHTT/p62/Ub (Fig. 7B3), given that some of them might exist within the nucleus, whose reduced levels may reflect increased intranuclear UPS activity, besides the other possibility that they may travel from the nucleus to the cytosol for clearance as already discussed inside the text. [The last sentence has been incorporated into the text under Discussion, L628-632]

(6) For the treatment of neurodegenerative disorders taking the temporal regulation into consideration is extremely important, as that will determine the success rate of the treatments in patients. The authors in this manuscript have clearly discussed this scenario. However, for neurodegenerative disordered patients, in most cases, the symptom manifestation is a late onset scenario. In that case, it will be complicated to initiate an early treatment regime in HD patients. If the authors can comment on and discuss the practicality of the early treatment regime for therapeutic purposes that would be impactful.

We appreciate the reviewer raising this point and we agree with the main concern that “for neurodegenerative disordered patients, in most cases, the symptom manifestation is a late onset scenario.” This is really a common challenge in the therapeutic fields for neurodegeneration diseases. It should be first noted that the current study is an experimental therapeutical attempt in a mouse model which is consistent with previous reports (Ravikumar, Vacher et al. 2004) as a proof of concept for manipulating autophagy (i.e., via inhibiting mTOR in the current setting) as a potential therapeutic, whose clinical practicality requires further verifications. Moreover, in our opinion, early diagnosis (e.g., genetic testing in individuals with higher risk for HD) may be a key in overcoming the above challenges, i.e., if early diagnosis is enabled, it would become possible for earlier interventions. [The above description has been incorporated into the text under Discussion, L654-659]

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

Minor concerns:

(1) Figures 1 and 2 should indicate the number of sections and mice/genotypes.

Thanks for the suggestion, and the info has been added in the figure legends.

(2) Figure 3A2 should explain how AP, AL, pa-AL, and LY are quantified.

Thanks for raising this point. Please note that the quantitation of AP, AL, pa-AL and LY was performed by the hue angle-based analysis which was described under “Confocal image collection and hue angle-based quantitative analysis for AV/LY subtypes” within the Materials and Methods. A phrase “(see the Materials and Methods)” has been added after the existing description “Hue angle-based analysis was performed for AV/LY subtype determination using the methods described in Lee et al., 2019” in the figure legend.

References

Benn, C. L., C. Landles, H. Li, A. D. Strand, B. Woodman, K. Sathasivam, S. H. Li, S. Ghazi-Noori, E. Hockly, S. M. Faruque, J. H. Cha, P. T. Sharpe, J. M. Olson, X. J. Li and G. P. Bates (2005). "Contribution of nuclear and extranuclear polyQ to neurological phenotypes in mouse models of Huntington's disease." Hum Mol Genet 14(20): 3065-3078.

Berg, M. J., Veeranna, C. M. Rosa, A. Kumar, P. S. Mohan, P. Stavrides, D. M. Marchionini, D.S. Yang and R. A. Nixon (2024). "Pathobiology of the autophagy-lysosomal pathway in the Huntington’s disease brain." bioRxiv: 2024.2005.2029.596470.

Carty, N., N. Berson, K. Tillack, C. Thiede, D. Scholz, K. Kottig, Y. Sedaghat, C. Gabrysiak, G. Yohrling, H. von der Kammer, A. Ebneth, V. Mack, I. Munoz-Sanjuan and S. Kwak (2015). "Characterization of HTT inclusion size, location, and timing in the zQ175 mouse model of Huntington's disease: an in vivo high-content imaging study." PLoS One 10(4): e0123527.

Cornett, J., F. Cao, C. E. Wang, C. A. Ross, G. P. Bates, S. H. Li and X. J. Li (2005). "Polyglutamine expansion of huntingtin impairs its nuclear export." Nat Genet 37(2): 198204.

Davies, S. W., M. Turmaine, B. A. Cozens, M. DiFiglia, A. H. Sharp, C. A. Ross, E. Scherzinger, E. E. Wanker, L. Mangiarini and G. P. Bates (1997). "Formation of neuronal intranuclear inclusions underlies the neurological dysfunction in mice transgenic for the HD mutation." Cell 90(3): 537-548.

DeGuire, S. M., F. S. Ruggeri, M. B. Fares, A. Chiki, U. Cendrowska, G. Dietler and H. A. Lashuel (2018). "N-terminal Huntingtin (Htt) phosphorylation is a molecular switch regulating Htt aggregation, helical conformation, internalization, and nuclear targeting." J Biol Chem 293(48): 18540-18558.

DiFiglia, M., E. Sapp, K. Chase, C. Schwarz, A. Meloni, C. Young, E. Martin, J. P. Vonsattel, R. Carraway, S. A. Reeves and et al. (1995). "Huntingtin is a cytoplasmic protein associated with vesicles in human and rat brain neurons." Neuron 14(5): 1075-1081.

DiFiglia, M., E. Sapp, K. O. Chase, S. W. Davies, G. P. Bates, J. P. Vonsattel and N. Aronin (1997). "Aggregation of huntingtin in neuronal intranuclear inclusions and dystrophic neurites in brain." Science 277(5334): 1990-1993.

Gutekunst, C. A., A. I. Levey, C. J. Heilman, W. L. Whaley, H. Yi, N. R. Nash, H. D. Rees, J. J. Madden and S. M. Hersch (1995). "Identification and localization of huntingtin in brain and human lymphoblastoid cell lines with anti-fusion protein antibodies." Proc Natl Acad Sci U S A 92(19): 8710-8714.

Hackam, A. S., R. Singaraja, T. Zhang, L. Gan and M. R. Hayden (1999). "In vitro evidence for both the nucleus and cytoplasm as subcellular sites of pathogenesis in Huntington's disease." Hum Mol Genet 8(1): 25-33.

Kordasiewicz, H. B., L. M. Stanek, E. V. Wancewicz, C. Mazur, M. M. McAlonis, K. A. Pytel, J. W. Artates, A. Weiss, S. H. Cheng, L. S. Shihabuddin, G. Hung, C. F. Bennett and D. W. Cleveland (2012). "Sustained therapeutic reversal of Huntington's disease by transient repression of huntingtin synthesis." Neuron 74(6): 1031-1044.

Korolchuk, V. I., A. Mansilla, F. M. Menzies and D. C. Rubinsztein (2009). "Autophagy inhibition compromises degradation of ubiquitin-proteasome pathway substrates." Mol Cell 33(4): 517-527.

Korolchuk, V. I., F. M. Menzies and D. C. Rubinsztein (2010). "Mechanisms of cross-talk between the ubiquitin-proteasome and autophagy-lysosome systems." FEBS Lett 584(7): 1393-1398.

Landles, C., R. E. Milton, N. Ali, R. Flomen, M. Flower, F. Schindler, C. Gomez-Paredes, M. K. Bondulich, G. F. Osborne, D. Goodwin, G. Salsbury, C. L. Benn, K. Sathasivam, E. J. Smith, S. J. Tabrizi, E. E. Wanker and G. P. Bates (2020). "Subcellular Localization And Formation Of Huntingtin Aggregates Correlates With Symptom Onset And Progression In A Huntington'S Disease Model." Brain Commun 2(2): fcaa066.

Lee, J. H., S. Park, E. Kim and M. J. Lee (2019). "Negative-feedback coordination between proteasomal activity and autophagic flux." Autophagy 15(4): 726-728.

Lee, J. H., M. V. Rao, D. S. Yang, P. Stavrides, E. Im, A. Pensalfini, C. Huo, P. Sarkar, T. Yoshimori and R. A. Nixon (2019). "Transgenic expression of a ratiometric autophagy probe specifically in neurons enables the interrogation of brain autophagy in vivo." Autophagy 15(3): 543-557.

Lee, J. H., D. S. Yang, C. N. Goulbourne, E. Im, P. Stavrides, A. Pensalfini, H. Chan, C. Bouchet-Marquis, C. Bleiwas, M. J. Berg, C. Huo, J. Peddy, M. Pawlik, E. Levy, M. Rao, M. Staufenbiel and R. A. Nixon (2022). "Faulty autolysosome acidification in Alzheimer's disease mouse models induces autophagic build-up of Abeta in neurons, yielding senile plaques." Nat Neurosci 25(6): 688-701.

Li, H., S. H. Li, A. L. Cheng, L. Mangiarini, G. P. Bates and X. J. Li (1999). "Ultrastructural localization and progressive formation of neuropil aggregates in Huntington's disease transgenic mice." Hum Mol Genet 8(7): 1227-1236.

Magnuson, B., B. Ekim and D. C. Fingar (2012). "Regulation and function of ribosomal protein S6 kinase (S6K) within mTOR signalling networks." Biochem J 441(1): 1-21.

Park, C. and A. M. Cuervo (2013). "Selective autophagy: talking with the UPS." Cell Biochem Biophys 67(1): 3-13.

Peng, Q., B. Wu, M. Jiang, J. Jin, Z. Hou, J. Zheng, J. Zhang and W. Duan (2016). "Characterization of Behavioral, Neuropathological, Brain Metabolic and Key Molecular Changes in zQ175 Knock-In Mouse Model of Huntington's Disease." PLoS One 11(2): e0148839.

Ravikumar, B., C. Vacher, Z. Berger, J. E. Davies, S. Luo, L. G. Oroz, F. Scaravilli, D. F. Easton, R. Duden, C. J. O'Kane and D. C. Rubinsztein (2004). "Inhibition of mTOR induces autophagy and reduces toxicity of polyglutamine expansions in fly and mouse models of Huntington disease." Nat Genet 36(6): 585-595.

Riguet, N., A. L. Mahul-Mellier, N. Maharjan, J. Burtscher, M. Croisier, G. Knott, J. Hastings, A. Patin, V. Reiterer, H. Farhan, S. Nasarov and H. A. Lashuel (2021). "Nuclear and cytoplasmic huntingtin inclusions exhibit distinct biochemical composition, interactome and ultrastructural properties." Nat Commun 12(1): 6579.

Scherzinger, E., R. Lurz, M. Turmaine, L. Mangiarini, B. Hollenbach, R. Hasenbank, G. P. Bates, S. W. Davies, H. Lehrach and E. E. Wanker (1997). "Huntingtin-encoded polyglutamine expansions form amyloid-like protein aggregates in vitro and in vivo." Cell 90(3): 549-558.

Sharp, A. H., S. J. Loev, G. Schilling, S. H. Li, X. J. Li, J. Bao, M. V. Wagster, J. A. Kotzuk, J. P. Steiner, A. Lo and et al. (1995). "Widespread expression of Huntington's disease gene (IT15) protein product." Neuron 14(5): 1065-1074.

Tabrizi, S. J., R. Ghosh and B. R. Leavitt (2019). "Huntingtin Lowering Strategies for Disease Modification in Huntington's Disease." Neuron 101(5): 801-819.

Wang, X. and C. G. Proud (2006). "The mTOR pathway in the control of protein synthesis." Physiology (Bethesda) 21: 362-369.

Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    Figure 1—source data 1. Original western blots for Figure 1C1 and C2.
    Figure 1—source data 2. Original western blots for Figure 1C1 and C2, labeled for the relevant bands.
    Figure 3—figure supplement 1—source data 1. Original western blots for Figure 3—figure supplement 1.
    Figure 3—figure supplement 1—source data 2. Original western blots for Figure 3—figure supplement 1, labeled for the relevant bands.
    Figure 3—figure supplement 2—source data 1. Original western blots for Figure 3—figure supplement 2.
    Figure 3—figure supplement 2—source data 2. Original western blots for Figure 3—figure supplement 2, labeled for the relevant bands.
    Figure 3—figure supplement 3—source data 1. Original western blots for Figure 3—figure supplement 3.
    Figure 3—figure supplement 3—source data 2. Original western blots for Figure 3—figure supplement 3, labeled for the relevant bands.
    Figure 3—figure supplement 4—source data 1. Original western blots for Figure 3—figure supplement 4.
    Figure 3—figure supplement 4—source data 2. Original western blots for Figure 3—figure supplement 4, labeled for the relevant bands.
    Figure 5—source data 1. Original western blots for Figure 5A.
    Figure 5—source data 2. Original western blots for Figure 5A, labeled for the relevant bands.
    Figure 5—figure supplement 1—source data 1. Original western blots for Figure 5—figure supplement 1.
    Figure 5—figure supplement 1—source data 2. Original western blots for Figure 5—figure supplement 1, labeled for the relevant bands.
    MDAR checklist

    Data Availability Statement

    No large datasets or custom codes were generated in this study. Original western blot images are uploaded as zipped source data files. Please note that in Figure 3—figure supplements 14, there are total six protein markers (MTOR, Atg5, Beclin 1, TRAF6, CTSB and CTSD) for which the presented western blot images (the lanes arranged as TRGL, TRGL/Q175, TRGL, TRGL/Q175, TRGL, TRGL/Q175) are just representative images of the signal seen but not the blots used for quantitative analysis to generate the bar graphs (where Q175 mouse samples were included in the gels, lacking the pattern of the above TRGL, TRGL/Q175, TRGL, TRGL/Q175, TRGL, TRGL/Q175). Thus, original western blots for both situations, whose file names contain either 'used for display-only' or 'used for quantitation-only', are included in the source data files.


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