Inhibiting USP16 rescues stem cell aging and memory in an Alzheimer’s model

  1. Felicia Reinitz
  2. Elizabeth Y Chen
  3. Benedetta Nicolis di Robilant
  4. Bayarsaikhan Chuluun
  5. Jane Antony
  6. Robert C Jones
  7. Neha Gubbi
  8. Karen Lee
  9. William Hai Dang Ho
  10. Sai Saroja Kolluru
  11. Dalong Qian
  12. Maddalena Adorno
  13. Katja Piltti
  14. Aileen Anderson
  15. Michelle Monje
  16. H Craig Heller
  17. Stephen R Quake
  18. Michael F Clarke  Is a corresponding author
  1. Institute of Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, United States
  2. Department of Biology, Stanford University, United States
  3. Department of Bioengineering, Stanford University, United States
  4. Sue and Bill Gross Stem Cell Research Center, University of California, Irvine, United States

Abstract

Alzheimer’s disease (AD) is a progressive neurodegenerative disease observed with aging that represents the most common form of dementia. To date, therapies targeting end-stage disease plaques, tangles, or inflammation have limited efficacy. Therefore, we set out to identify a potential earlier targetable phenotype. Utilizing a mouse model of AD and human fetal cells harboring mutant amyloid precursor protein, we show cell intrinsic neural precursor cell (NPC) dysfunction precedes widespread inflammation and amyloid plaque pathology, making it the earliest defect in the evolution of the disease. We demonstrate that reversing impaired NPC self-renewal via genetic reduction of USP16, a histone modifier and critical physiological antagonist of the Polycomb Repressor Complex 1, can prevent downstream cognitive defects and decrease astrogliosis in vivo. Reduction of USP16 led to decreased expression of senescence gene Cdkn2a and mitigated aberrant regulation of the Bone Morphogenetic Signaling (BMP) pathway, a previously unknown function of USP16. Thus, we reveal USP16 as a novel target in an AD model that can both ameliorate the NPC defect and rescue memory and learning through its regulation of both Cdkn2a and BMP signaling.

Editor's evaluation

This work by Reinitz et al. provides nice evidence of a neural stem cell (NSC) defect that precedes and appears to be independent of amyloid pathology and neuroinflammation in an AD model. The authors show that targeting USP16, a BMI antagonist, can rescue some of these NSC deficits. Furthermore, scRNA-seq and GSEA points to the BMP pathway as a major player in regulating these phenotypes; in support of their hypothesis, the authors show that inhibition of BMP signaling using small molecules also rescues NSC defects. These are interesting and novel findings that will be of interest to the field.

https://doi.org/10.7554/eLife.66037.sa0

Introduction

Alzheimer’s disease (AD) is the most common form of dementia, occurring in 10% of individuals over the age of 65 and affecting an estimated 5.5 million people in the United States (Hebert et al., 2013). Currently, there is no treatment to stop, prevent, or reverse AD, and recent advances with monoclonal antibody therapy targeting plaques, although controversial, might at best only slow progression (Huang and Mucke, 2012; Selkoe, 2019; Sevigny et al., 2016). Historically, AD has been understood by its end-stage disease phenotype, characterized clinically by dementia and pathologically by amyloid senile plaques and neurofibrillary tangles (Castellani et al., 2010). These traditional AD pathologies are thought to begin with amyloid plaque deposition that is associated with inflammation, increased reactive oxygen species (ROS), and neurodegeneration during aging (Akiyama et al., 2000; Glass et al., 2010); however, thus far, treatments to decrease formation of plaques have shown only minimal or no improvement in disease progression or outcomes (Knopman et al., 2021; Selkoe, 2019).

Adult neurogenesis is thought to be compromised in AD, contributing to early dementia (Alipour et al., 2019). The decline of neural stem/precursor cell (NPC) function in the subventricular zone (SVZ) and the hippocampus has been established in both aging (Leeman et al., 2018) and various AD mouse models (Haughey et al., 2002; López-Toledano and Shelanski, 2004; Mu and Gage, 2011; Rodríguez et al., 2009; Rodríguez and Verkhratsky, 2011; Sakamoto et al., 2014; Winner et al., 2011). However, it is still unknown whether these defects are cell-intrinsic resulting from changes inside the cells or extrinsic, resulting from factors present in the niche, such as inflammation. Here, we report that the NPC defects seen in an AD mouse model harboring Swedish, Dutch, and Iowa mutations in the amyloid precursor protein (Tg-SwDI) is initially cell-intrinsic and predates inflammation and widespread plaque deposition, which play a role later in the disease. We chose the Tg-SwDI model with mutations confined to APP because mice develop early plaque deposition and cognitive deficits, and express physiologic levels of transgenic human AβPP at approximately 50% the level of endogenous mouse APP (Davis et al., 2004). In this model, mice begin to develop amyloid deposition in brain parenchyma as early as 3 months of age and throughout the forebrain by 12 months (Davis et al., 2004; Miao et al., 2005), as well as significant cerebral amyloid angiopathy, which is thought to be a more sensitive predictor of dementia than parenchymal and amyloid plaques (Neuropathology Group. Medical Research Council Cognitive Function and Aging Study, 2001; Thal et al., 2003).

In this study, we targeted USP16, an upstream regulator of Cdkn2a, to reverse the NPC defect seen in the Tg-SwDI model. USP16 counteracts self-renewal regulator BMI1 by deubiquitinating histone H2A on Lysine 119 at the Cdkn2a locus, resulting in increased expression of protein products P16 (Ink4a) and P14 (Arf) (Adorno et al., 2013). This results in increased senescence with a concomitant decrease in self-renewal. Here, we demonstrate that inhibiting USP16 is a potential novel strategy to rescue Cdkn2a-mediated pathologies in AD induced by both p16Ink4a and p19Arf. As Cdkn2a might not be the only player in AD pathophysiology, we probed for additional pathways regulated by USP16 and identified enrichment of the BMP pathway early on in the Tg-SwDI mice. The BMP pathway has been known to play a role in NPC function. Specifically, BMPR2 is a type II receptor that heterodimerizes with BMPR1a or BMPR1b, and is responsible for transducing BMP signaling downstream to the SMAD proteins, which translocate to the nucleus and can turn on genes related to cell fate and differentiation (Chang et al., 2018). Levels of BMP2, 4, 6, and 7 expression have been found to increase in the hippocampus and SVZ with age (Yousef et al., 2015; Apostolopoulou et al., 2017). Furthermore, Bmpr2 conditional ablation in Ascl1 expressing neural stem cells (NSCs)/NPCs or treatment with BMP inhibitor Noggin results in activation of NSCs, increased cell proliferation, and a rescue of cognitive deficits to levels comparable to young mice (Meyers et al., 2016). For the first time, we show that targeting USP16 in a mouse model of AD rescues two aberrant aging pathways, Cdkn2a and BMP, which can restore self-renewal of NPCs, decrease astrogliosis, and retard cognitive decline (Figure 1).

Schematic summarizing therapeutic approaches to mitigate the effects of mutant APP through targeting of Cdkn2a, BMI1, USP16 and BMP.

Aberrant USP16/Cdkn2a/BMP signaling results in an early neural precursor cell (NPC) defect in Alzheimer’s Disease. USP16 inhibitors and/or BMPR inhibitors can be combined with current therapeutics targeting beta amyloid plaques to rescue this earlier defect that predates senescence, cognitive decline, and resulting gross inflammation.

Results

Neural precursor cell exhaustion is the earliest sign of disease in Tg-SwDI mice

Detecting disease early before fulminant pathogenesis may be crucial to develop effective diagnosis and treatment, particularly when it comes to irreversible degeneration. Therefore, we used a multimodal temporal approach, including immunofluorescence staining, in vitro neurosphere assays, Luminex assays, and behavioral studies to dissect changes at the molecular, cellular, and organismal levels in mice at varying ages. At 3 months of age, we found that proliferation of NPCs, marked by 5-ethynyl-2′-deoxyuridine (EdU) (Chehrehasa et al., 2009), SOX2, and GFAP, was increased threefold in the SVZ of Tg-SwDI mice (p=0.0153; Figure 2A). In many tissues including the blood, pancreas, intestine, and mammary gland, hyperproliferation has been linked to a premature decline in stem cell function associated with aging (Essers et al., 2009; Krishnamurthy et al., 2006; Scheeren et al., 2014). Thus, we looked at stem cell function using extreme limiting dilution analysis (ELDA) of neurosphere-formation from single cells (Hu and Smyth, 2009; Pastrana et al., 2011). We discovered that 3 and 4-month-old Tg-SwDI mice had significantly less regenerative potential of the SVZ cells than that of healthy age-matched control mice (neurosphere-initiating cell (NIC) frequencies: 1 in 14.5 versus 1 in 7.5, respectively, p=0.00166, Figure 2B and Figure 2—source data 1). As Bmi1 is required for self-renewal of stem cells in the peripheral and central nervous systems and is responsible for repressing p16Ink4a expression, we queried if decreased Bmi1 and/or increased Cdkn2a expression was concurrent with the observed decrease in regenerative potential of NPCs from Tg-SwDI mice (Molofsky et al., 2005). Indeed, we measured a significant decrease in Bmi1 expression and a significant increase in its downstream target Cdkn2a expression in neurospheres from Tg-SwDI mice compared to wild type (WT) (Figure 2C). To study whether these changes in proliferation and self-renewal capacity of NPCs occur before the well-established prominent AD phenotype of inflammation, we employed a Luminex screen to assess the presence of an array of cytokines and other inflammatory markers. We looked at the SVZ, hippocampal dentate gyrus (DG), and cortex in 3 and 4-month-old mice, but found no significant differences in inflammatory markers between Tg-SwDI and WT mice in any of these regions (Figure 2D and Figure 2—figure supplement 1A). To explore further, we measured mRNA levels of Ptgs2 (COX2), Tnf, Il6, and Il1b utilizing quantitative polymerase chain reaction (qPCR), but found no significant differences between WT and Tg-SwDI in either the SVZ, DG, or cortex for Ptgs2, whereas the remaining markers were undetectable using quantitative RT-PCR (Figure 2—figure supplement 1B).

Figure 2 with 1 supplement see all
Defects in neurosphere initiating capacity (NIC) and hyperproliferation in Tg-SwDI mice predate cognitive deficits and widespread inflammation.

(A) Representative 40× confocal images of the subventricular zone (SVZ) stains for EdU, GFAP, and SOX2 (left). Three to four-month-old mice underwent intraperitoneal injections every day for 6 days with EdU and the analysis was performed four weeks after. Count of proliferating neural precursor cells, as cells positive for EdU, GFAP, SOX2, and DAPI, is shown in the panel on the right (n = 3 mice). Data are presented as mean ± SEM. *p=0.0153 (B) Limiting dilution assays were performed using single cells derived from neurospheres from 3 to 4-month-old mice. The graph shows the percentage of neurosphere-initiating cells (NIC) ± upper and lower estimates converted to percentages from values calculated by extreme limiting dilution analysis (ELDA). **p=0.00166 Figure 2—source data 2 summarizes the lower, upper, and estimates of 1/NIC for the different genotypes calculated by ELDA. (C) Bmi1 and Cdkn2a expression levels measured by RT-qPCR in neurospheres derived from the SVZ of wild type (WT) or Tg-SwDI mice at third passage (mice aged 3 and 4 months). Data are presented as mean ± SD. ***p=0.0009 *p=0.0197 (D) Cytokine levels measured by Luminex array from the SVZ of young 3 and 4-month-old mice. No differences were observed at this age (n = 3 mice for each genotype). See also Figure 2—figure supplement 1A. Data are presented as mean ± standard deviation (SD). (E) Novel object recognition 24 hr testing in mice at 3 months of age showed no signs of cognitive impairment in the Tg-SwDI mice with a preference index comparable to that of WT indicating both genotypes had intact object discrimination (p=0.001 for WT and p=0.0099 for Tg-SwDI, n = 7–10 mice in each group). Data are presented as mean ± standard error of the mean (SEM). (F) ELDA graph of limiting dilution assay comparing human fetal neurospheres infected with pHIV-Zsgreen, APP SwI, or APP WT. Figure 2—source data 2 lists the estimated stem cell frequencies and ranges for each group, calculated using the ELDA software (n = 3 separate transductions and limiting dilution experiments) ****p=3.7e–6, *p=0.00507.

One of the hypothesized reasons for lack of efficacy observed in AD clinical trials is the late initiation of treatment. In humans, abnormal deposits of amyloid β and tau tangles as well as damage of the brain is believed to start a decade or more before cognitive decline (Ower et al., 2018). We, therefore, wanted to see if the decrease in neurosphere-initiating capacity of Tg-SwDI mice also precedes their memory impairment and progressively diminished cognitive function. Although Tg-SwDI mice are known to exhibit these features, there was no evidence of cognitive impairment in 3 and 4-month-old mice when subjected to novel object recognition (NOR) training and subsequent testing after 24 hr (Ennaceur and Delacour, 1988; Figure 2E).

Finally, given the prominent early aberrant self-renewal phenotype in the 3 and 4-month-old Tg-SwDI mice, we investigated whether or not expression of mutant APP in human NPCs might also cause a self-renewal defect. To this end, we infected human fetal neurospheres with a lentiviral construct for either pHIV-Zsgreen alone, pHIV-Zsgreen with wild type APP (APP WT), or pHIV-Zsgreen with Swedish and Indiana APP mutations (APP SwI). This model allowed us to study the potentially cell intrinsic effect of APP mutations on NPC regenerative potential in primary human cells. Although our Tg-SwDI mice employ Swedish, Dutch, and Iowa mutations, we chose a human in vitro model with Swedish and Indiana mutations as the Indiana mutation allows for an increase in the Aβ42/Aβ40 ratio rather than the increased amyloidosis of the vasculature incurred by Dutch and Iowa mutations which would have been difficult to observe in vitro. Our human NPCs expressed at least a 2-fold change increase in mutant APP compared to the baseline endogenous APP levels in the Zsgreen control (Figure 2—figure supplement 1C). Employing the same limiting dilution assay as before, we found diminished NIC frequency of mutant APP-infected human neurospheres compared to cells infected with the empty vector or with WT APP (1 in 10.44 versus 1 in 3.31 and 1 in 10.44 versus 1 in 5.26, respectively, p=3.7e–06 and p=0.00507; Figure 2F and Figure 2—source data 1). This result suggests that the self-renewal defect is cell-intrinsic and can be observed in two different AD models with different mutations.

Modest aging in Tg-SwDI accelerates NPC exhaustion prior to detectable inflammation

To explore progression of the disease with aging, we next looked at what phenotypic changes occurred in older Tg-SwDI mice, including proliferation, self-renewal, inflammation, and astrogliosis. The NPC hyperproliferation in SVZ observed in 3 and 4-month-old Tg-SwDI mice was not observed in 1-year-old mice, demonstrated by the number of EdU+SOX2+GFAP+ cells (Figure 3A). Still, the defect in self-renewal that was observed in the 3 and 4-month-old Tg-SwDI mice was exacerbated in the 1-year-old Tg-SwDI mice (p=0.00625; Figure 3B and Figure 3—source data 1).

Figure 3 with 5 supplements see all
Accelerated aging phenotype seen in Tg-SwDI mice with exacerbated self-renewal and astrogliosis.

(A) One-year-old mice underwent intraperitoneal injections every day for 6 days with EdU and the analysis was performed four weeks afterward to capture all true activated daughter stem cells that would maintain the niche without further differentiation or migration. Data are presented as counts of proliferating neural precursor cell cells positive for EdU, GFAP, SOX2, and DAPI mean ± SEM (n = 3 mice). (B) Limiting dilution assays were performed using single cells derived from neurospheres from 1-year-old mice. The bar graph shows the percentage of neurosphere-initiating cells calculated by extreme limiting dilution analysis (ELDA). *p=0.00625. Figure 3—source data 1 summarizes the lower, upper, and estimates of 1/NIC for the different genotypes calculated by ELDA. (C) Cytokine levels measured by Luminex array from the subventricular zone of 1-year-old mice. No differences were observed at this age. (n = 3 mice each genotype). See also Figure 3—figure supplement 1. Data are presented as mean ± SD. (D) Anterior sections were obtained from 2-year-old mice, stained and counted for GFAP+ cells in the cortex. Four different images per section and three sections per mouse were counted (n = 4 mice each group). A one-way ANOVA showed significant differences between the groups (****p<0.0001). Data are presented as mean ± SEM. (E) mRNA levels of Cdkn2a in the cerebral cortex of 3 and 4-month-old and 2-year-old mice were measured by RT-qPCR. Ct values were normalized to Actb. (WT = wild type littermate; 3 and 4-month-old: WT = 7, Tg-SwD n = 7, 2-year-old: WT n = 6, Tg-SwDI n = 6). A one-way ANOVA showed significant differences between the groups (p=0.0044 between 3 and 4-month-old and 2-year-old WT, p=0.0040 between 3 and 4-month-old and 2-year-old Tg-SwDI, p = 0.0438 between 2-year-old WT and Tg-SwDI, p=0.00553 between 3 and 4-month-old WT and 2-year-old Tg-SwDI). Data are presented as mean ± SD.

We hypothesized that inflammation might explain the NPC defect but may not have been easily detected at 3 months of age. However, even at 1 year old, we did not detect any overall significant differences in inflammatory cytokines in the SVZ, DG, or cortex between the WT and Tg-SwDI mice (Figure 3C, Figure 3—figure supplement 1A, B). Reactive astrogliosis, the abnormal increase and activation of astrocytes seen in AD patients and mouse models, is also a sign of inflammation that can drive degeneration of neurons and has been linked to both AD disease pathogenesis (Osborn et al., 2016) and to the BMI1/Cdkn2a pathway. Specifically, Zencak and colleagues showed increased astrogliosis in Bmi1-/- mice (Zencak et al., 2005). With the aim of evaluating astrogliosis, we performed a qPCR for A1 astrocytic markers in the cortex of 1-year-old mice, but did not observe any significant increases in mRNA expression compared to WT (Figure 3—figure supplement 2).

However, when we looked at differentiation of neurosphere cultures from the SVZ of Tg-SwDI mice and WT controls and analyzed the number of GFAP-expressing cells following differentiation, we found that cells originating from Tg-SwDI mice formed more GFAP+ cells than those from WT controls. This suggested that there was a general lineage increase in astrocytes derived from Tg-SwDI NPCs compared to WT NPCs (Figure 3—figure supplement 3).

Because inflammation and reactive astrogliosis are linked to AD, we wished to see when these events occur in our models. When we looked at 2-year-old mice by microarray analyses of SVZ, DG, and cortex, we saw an increase in Cd44, Vim, Serping1, and other markers related to pan- and A1-specific astrogliosis and a concomitant inflammatory signature of Tg-SwDI mice compared to WT mice (Figure 3—figure supplement 4A, B, respectively, Reinitz et al., 2022). We next looked for additional evidence of astrogliosis and observed a significant increase in GFAP-expressing cells in the cerebral cortex of 2-year-old Tg-SwDI mice compared to WT that was not seen in younger Tg-SwDI mice (p<0.0001; Figure 3D and Figure 3—figure supplement 5). This suggests that reactive astrogliosis is exacerbated by and strongly correlated with aging and later disease progression.

In line with our findings thus far, expression of the well-studied gene, Cdkn2a, known for its increased expression with aging and critical function of inhibiting stem cell self-renewal during development and throughout the lifespan, was increased with aging and even more so in the Tg-SwDI cortex (Figure 3E). (Molofsky et al., 2003; Park et al., 2003; Sun et al., 2004). Taken together with our data showing an increase in EdU+SOX2+GFAP+ cells in Tg-SwDI mice SVZ at an early age, our extreme limiting dilution assay in older mice, and expression changes of Cdkn2a and Bmi1, we infer a premature reduction of self-renewal capacity of NPCs, but not necessarily a decrease in the total number of neural progenitor cells in our Tg-SwDI model. These results suggest that a neural stem cell defect and early cognitive decline predate detectable inflammation and reactive astrogliosis in Tg-SwDI mice.

Self-renewal defects are rescued by Usp16 and Cdkn2a modulation

Neural precursor cells function through a number of genetic and epigenetic components, and one of the well-described master regulators is Cdkn2a, a gene tightly regulated by BMI1 (Bruggeman et al., 2005). When we crossed the Tg-SwDI mouse with a Cdkn2a knockout mouse (Tg-SwDI/Cdkn2a-/-) and performed limiting dilution assays in SVZ cells from 3-month-old mice, there was a complete restoration of the NIC frequency in the Tg-SwDI/Cdkn2a-/- cells compared to age-matched Tg-SwDI cells (p=7.7e–05; Figure 4A, Figure 4—source data 1, and Figure 4—figure supplement 1A, Figure 4—figure supplement 1—source data 1). This NIC rescue was also observed in hippocampal cells cultured from microdissection of the DG (p=2.09e–9, Figure 4B and Figure 4—source data 2). These results demonstrate that impairment of NPC regeneration, as measured by NIC frequencies, is a function of aging that is accelerated by APP mutations and is mitigated through loss of Cdkn2a, a known regulator of NPC self-renewal (Molofsky et al., 2003).

Figure 4 with 1 supplement see all
Usp16 haploinsufficiency normalizes Cdkn2a expression and restores self-renewal in Tg-SwDI NPCs.

(A) The bar graph shows the NIC frequencies in subventricular zone (p=5.5e–5 between wild type (WT) and Tg-SwDI and p=7.7e–5 between Tg-SwDI/Cdkn2a-/- and Tg-SwDI) and in the dentate gyrus (B) (p=0.00476 between wild type (WT) and Tg-SwDI and p=2.09e–9 between Tg-SwDI/Cdkn2a-/- and Tg-SwDI) as percentages of total cells with error bars indicating the upper and lower values. Mice were 3 months old when sacrificed; experiment done after third passage of neurospheres. , Figure 3—source data 1, Figure 4—source data 1 summarize the lower, upper, and estimates of 1/NIC for the different genotypes calculated by extreme limiting dilution analysis (ELDA). (C) Schematic summarizing the role of BMI1 in ubiquitinating histone H2A at different sites in the genome, including the Cdkn2a locus and the role of USP16 as its natural antagonist, suggesting that USP16 inhibition could influence neurosphere initiating capacity. (D) RT-qPCR of Cdkn2a in the cerebral cortex of 2-year-old Tg-SwDI mice shows mRNA levels were rescued by Usp16 haploinsufficiency (n = 3). Ct-values were normalized to Actb. A one-way ANOVA showed significant differences between the groups (p=0.0365 between WT and Tg-SwDI and p=0.0318 between Tg-SwDI and Tg-SwDI/Usp16+/-). Data are presented as mean ± SD. (E) Left panel shows 1× representative photographs of neurospheres grown in 96-well dish after two weeks of culture. The bar graph shows the NIC frequencies in subventricular zone as percentages of total cells comparing WT, Tg-SwDI, and Tg-SwDI/Usp16+/- mice. Mice were 3 months old. (n = 3 mice per genotype, p=0.000402 between WT and Tg-SwDI and p=0.0492 between Tg-SwDI/Usp16+/- and Tg-SwDI) (F) The bar graph shows the NIC frequencies in dentate gyrus as percentages of total cells comparing WT, Tg-SwDI, and Tg-SwDI/Usp16+/- mice. (n = 3 mice per genotype, p=9.9e–9 between WT and Tg-SwDI and p=0.00233 between Tg-SwDI/Usp16+/- and Tg-SwDI) Figure 4—source data 3 and Figure 4—source data 4 summarize the lower, upper, and estimates of 1/NIC for the different genotypes calculated by ELDA.

Unfortunately, mutations or loss of function in the Cdkn2a gene eventually leads to tumor formation, making it not feasible to perform limiting dilution experiments in 1-year-old Cdk2na knockout mice and also making it less than ideal to target therapeutically (Hussussian et al., 1994). Upstream of Cdkn2a is USP16, an antagonist of BMI1 and a de-repressor of Cdkn2a that acts through the enzymatic removal of ubiquitin from histone H2A (Figure 4C; Adorno et al., 2013; Joo et al., 2007). We predicted that downregulation of Usp16 would increase BMI1 function to counteract the effects of mutant APP similar to what we observed with knockout of Cdkn2a. This is supported by previous data that showed overexpression of USP16 in human-derived neurospheres led to a marked decrease in the formation of secondary neurospheres (Adorno et al., 2013), and overexpression of Bmi1 led to increased self-renewal and maintenance of multipotency (Fasano et al., 2009). To test this, we crossed Tg-SwDI mice with Usp16+/- mice to generate Tg-SwDI/Usp16+/- mice, which do not show tumor formation. We found that Tg-SwDI mice express greater than twofold more cortical Cdkn2a than both WT and Tg-SwDI/Usp16+/- mice, for which expression levels were very similar (p=0.0365 and p=0.0318, respectively, Figure 4D). Limiting dilution experiments of cells isolated from the SVZ and DG of the hippocampus showed that Tg-SwDI/Usp16+/- mice had significantly greater NIC frequencies, partially rescuing the self-renewal defect seen with mutant APP (p=0.0492 and p=0.00233, respectively; Figure 4E, Figure 4—source data 3 and Figure 4F, Figure 4—source data 4). Furthermore, two-year-old Tg-SwDI mice show reduced Bmi1 expression, which was rescued by Usp16 haploinsufficiency, in both the SVZ and cortex (Figure 4—figure supplement 1B, Reinitz et al., 2022). Similar to the NIC rescue in the Tg-SwDI/Cdkn2a-/- mice, these data provide further evidence of cell-intrinsic impaired self-renewal in the Tg-SwDI model of familial AD, and that reversal of this impairment is possible through targeting Cdkn2a upstream regulator, USP16.

RNA-seq reveals enriched BMP signaling in Tg-SwDI mice that is rescued by Usp16 haploinsufficiency

Previous studies using RNA-sequencing techniques have demonstrated significant genomic age-related cell intrinsic changes in self-renewing cells originating from the SVZ (Apostolopoulou et al., 2017). To delineate potential self-renewal pathways that might contribute to the defect and rescue of Tg-SwDI NPCs and Tg-SwDI/Usp16+/- NPCs, respectively, we performed single-cell RNA-seq on lineage depleted primary FACS-sorted CD31-CD45-TER119-CD24- SVZ cells from Tg-SwDI, WT, and Tg-SwDI/Usp16+/- mice at 3 and 4 months and 1 year of age (Figure 5A; Mootha et al., 2003; Subramanian et al., 2005). As CD31/CD45/TER119 denote the hematopoietic cell fraction and CD24 marks differentiated cells, we used these markers to enrich for NPCs obtained from the SVZ. A cell-type analysis using a TSNE plot surveying the top differentially expressed genes of each cluster (Tabula Muris Consortium et al., 2018; Zhang et al., 2014) did not find any new cell populations specific to the Tg-SwDI genotype (Figure 5—figure supplement 1A, B, Chen et al., 2022). This suggested to us that changes in phenotype were the result of transcriptional differences in cells rather than the addition or subtraction of an existing cell type. Like our human model, cells from the Tg-SwDI mouse model expressed approximately a 1.5-fold increase in both normalized mutant APP expression and endogenous App expression compared to WT cells (Figure 5—figure supplement 2A, Figure 5—figure supplement 2—source data 1, Chen et al., 2022). In addition, similar to the Luminex screen, we did not observe any significant transcriptional upregulation of an inflammatory signature in either 3 and 4-month-old or 1-year-old mice between Tg-SwDI and WT mice (Figure 5—figure supplement 2B, C, Figure 5—figure supplement 2—source data 2 and Figure 5—figure supplement 2—source data 3, Chen et al., 2022). It is important to note that the RNA-seq data presented here were conducted on cells enriched in the SVZ, which does not exclude the possibility of inflammation in other areas of the brain. Furthermore, when we looked at a panel of proliferation-related genes (Figure 5—figure supplement 3A, B, Figure 5—figure supplement 3—source data 1 and Figure 5—figure supplement 3—source data 2, Chen et al., 2022), there were only a few genes significantly different between WT and Tg-SwDI cells at 3 and 4 months or 1 year of age, suggesting that the hyperproliferation phenotype we observed in 3 and 4-month-old mice might involve upregulation of only a few genes such as Anapc2, Cdk4, and Pcna and downregulation of cell cycle inhibitors Cdkn1a and Cdkn1b. This analysis was limited by the fact that the same sorted cells in our scheme may not be equivalent to the cells stained for EdU, SOX2, and GFAP in Figure 2A. Other significantly upregulated and downregulated genes are shown labeled on the volcano plots in Figure 5—figure supplement 4A, B.

Figure 5 with 6 supplements see all
BMP signaling is enriched in Tg-SwDI mice and decreases with Usp16 haploinsufficiency.

(A) Lineage-CD24- NPCs were FACS-sorted from the subventricular zone of 4 mice each of the different genotypes and processed for single-cell RNA-sequencing. Figure 5—source data 1: GSEA analysis from single-cell RNA-seq data shows pathways enriched in Tg-SwDI mice compared to wild type (WT) and rescued in Tg-SwDI/Usp16+/- mice, ordered top to bottom from smallest FDR q-val (most significant) to largest FDR q-val (least significant). (n = 4 for each genotype at each time point; FDR < 25%). Pathways in common to both age groups are bolded. Figure 5—source data 2: Normalized enrichment scores of significantly enriched pathways in Tg-SwDI mice compared to WT or Tg-SwDI/Usp16+/- mice at different time points. TGFß pathway, Oxidative phosphorylation, and MYC Targets V2 were selected as they were rescued in both 3 and 4 months and 1-year-old mice by Usp16 haploinsufficiency. Highest normalized enrichment scores of each comparison are bolded. (B) Enrichment plots show TGF-ß signaling pathway as enriched in Tg-SwDI mice and rescued by Usp16 haploinsufficiency. Normalized enrichment score (NES) for left panel is 1.77 with FDR-q value = 0.008; NES for right panel is 2.30 with FDR q-value <0.001. (C) Heatmaps showing averaged log-normalized single-cell gene expression of elements of the TGF-ß pathway; elements of the BMP pathway, a sub-pathway of the TGF-ß pathway, are specifically enriched in Tg-SwDI mice.

Figure 5—source data 1

Pathways enriched in Tg-SwDI and rescued in Tg-SwDI/Usp16+/- mice.

https://cdn.elifesciences.org/articles/66037/elife-66037-fig5-data1-v2.xlsx
Figure 5—source data 2

Normalized Enrichment Scores of Significantly Enriched Pathways.

https://cdn.elifesciences.org/articles/66037/elife-66037-fig5-data2-v2.xlsx

Oftentimes, when we are looking at individual differentially expressed genes, it can be difficult to select which genes to pursue in a therapeutic context. We therefore performed gene set enrichment analysis (GSEA) in order to highlight groups of genes that are significantly enriched and related in the same pathway and can be more easily targeted. Using the GSEA Hallmark gene sets, we found only three gene sets that were enriched in Tg-SwDI mice over WT mice and were also rescued in the Tg-SwDI/Usp16+/- mice at both ages: TGF-ß pathway, oxidative phosphorylation, and Myc Targets (Figure 5—source data 1). The TGF-ß pathway consistently had the highest normalized enrichment score in pairwise comparisons between Tg-SwDI versus WT and Tg-SwDI versus Tg-SwDI/Usp16+/- of the three rescued pathways (Figure 5—source data 2). In looking specifically at the leading-edge genes contributing to the enrichment plots of the TGF-ß pathway, we found upregulation of BMP receptors and Id genes, which are known to be involved in BMP signaling, a sub-pathway of TGF-ß (Figure 5B). Heatmaps of average normalized single-cell gene expression showed BMP receptors as the most highly expressed TGF-ß receptors, with genes such as Bmpr2, Bmpr1a, Id2, and Id3 upregulated in Tg-SwDI mice and rescued in Tg-SwDI/Usp16+/- mice (Figure 5C). Furthermore, the BMP response Id genes showed stronger localization to the SLC1A3+ NPC clusters of both 3 and 4-month-old and 1-year-old mice than genes of the oxidative phosphorylation and Myc target pathways (Figure 5—figure supplements 5 and 6). These data suggest that USP16 may regulate NPC function in part through the BMP pathway.

BMPR inhibition rescues stem cell defects and abolishes increased phospho-SMAD 1/5/8

To confirm the functional significance of the BMP pathway in APP-mediated self-renewal defects, we measured the effects of modulating BMP pathway activity in vitro in human fetal NPCs expressing APP with Swedish and Indiana mutations (APP SwI). First, we measured levels of phosphorylated-SMAD (pSMAD) 1, 5, and 8, known downstream regulators of BMP activity, and found they were significantly increased in the mutant neurospheres compared to control (p=0.0001, Figure 6A). Treatment of the neurospheres with the BMP receptor inhibitor LDN-193189, a specific inhibitor of BMP-mediated SMAD1, SMAD5, and SMAD8 activation, substantially decreased pSMAD 1/5/8 in APP SwI NPCs (p<0.0001, Figure 6B C; Yu et al., 2008). Furthermore, when we treated neurospheres expressing mutant APP with LDN-193189 for a week, the number of colonies originating from those cells were similar to control cells and significantly higher than untreated mutant APP neurospheres (Figure 6D E). Notably, LDN-193189 had minimal impact on Zsgreen control neurosphere growth (Figure 6E). This finding demonstrates that the decrease in NIC frequency observed with mutant APP could be explained in part by the upregulation of BMP signaling. Moreover, BMPR inhibition rescues this defect in cells overexpressing mutant APP at doses that have minimal toxicity on healthy cells. Altogether, these data reveal that BMP signaling enrichment is recapitulated in human NPCs expressing mutant APP, and that BMPR inhibition normalizes the stem cell defect.

BMPR inhibition rescues mutant APP mediated self-renewal defects in human neurospheres.

(A) Left panel shows representative 100× images of phospho-Smad 1/5/8 staining in mutant APP-infected human fetal neurospheres compared to Zsgreen controls. Right panel shows quantification of DAPI and phospho-Smad1/5/8 co-stained cells in each group. Data are presented as mean ± SD. (B) Representative 100× images of phospho-Smad1/5/8 staining in neurospheres treated with LDN-193189 for one week. (C) Quantification of phospho-SMAD 1/5/8 after treatment with different doses of LDN-193189. A two-way ANOVA revealed significant differences between the groups (**** for p<0.0001). Data are presented as mean ± SD. (D) Representative 6× images of in vitro colonies of mutant APP- and Zsgreen-infected human fetal neurospheres after one week of LDN-193189 treatment. (E) Quantification of the colonies in (D). A two-way ANOVA revealed significant differences between groups (**** for p<0.0001 and *** for p=0.0003). Data are presented as mean ± SD.

Astrogliosis is reduced and cognitive function is restored in Tg-SwDI/Usp16+/- mice

Having identified USP16 as a target to modulate two critical pathways affected by mutations in APP, Cdkn2a, and BMP, we further investigated USP16’s potential effects on downstream pathophysiological markers of AD that are recapitulated in the Tg-SwDI model such as astrogliosis, inflammation, amyloid plaques and memory. Increased numbers of GFAP+ astrocytes were seen throughout the cortex of 9–12-month-old Tg-SwDI mice, which could mark the beginning of astrogliosis, and were significantly reduced with Usp16 haploinsufficiency (Figure 7A, Figure 7—figure supplement 1A, see also Figure 3—figure supplement 2).

Figure 7 with 3 supplements see all
Astrogliosis, cognitive deficits, but not amyloid plaque burden are some of the processes rescued in Tg-SwDI/Usp16+/- mice.

(A) Anterior sections were obtained from 9 to 12 months old mice, stained, and counted for GFAP+ cells in the cortex. Four different images per sections and three sections per mouse were counted (n = 4). Bar graph shows quantification of GFAP+ cells from cortex. A one-way ANOVA showed significant differences between the groups (p=0.0012 between wild type (WT) and Tg-SwDI and p=0.0188 between Tg-SwDI and Tg-SwDI/Usp16+/-). Data are presented as mean ± SD. See also Figure 7—figure supplement 1A. (B) Representative images of thioflavin S on the left. On the right, quantification of area covered by plaques using thioflavin S staining in Tg-SwDI and Tg-SwDI/Usp16+/- mice shows no difference between the two genotypes (10-month-old mice). Data are presented as mean ± SEM. See also Figure 7—figure supplement 1B. (C) Novel object recognition 24 hr testing in mice at 6 months of age showed the earliest signs of cognitive impairment in the Tg-SwDI mice with a preference index (PI) of 49%, while WT and Tg-SwDI/Usp16+/- mice had PIs >65% indicating intact object discrimination (p=0.001 for WT and p=0.0099 for Tg-SwDI/Usp16+/-, n = 7–10 mice). Data are presented as mean ± SEM. See also Figure 7—figure supplement 3. (D) Schematic summarizing the temporal effects of mutant APP demonstrated in this manuscript.

Amyloid plaques are one of the defining features of AD, and controversy exists concerning the effect of plaques on cognitive decline. Mutations in APP lead to amyloid plaque deposition throughout the brain as seen in 10-month-old Tg-SwDI mice (Figure 7B, Figure 7—figure supplement 1B). However, no difference was observed in plaque burden, demonstrated by Thioflavin S staining, in the age-matched Tg-SwDI/Usp16+/- mice (Figure 7B, Figure 7—figure supplement 1B). In addition, a Luminex screen of 1-year-old Tg-SwDI/Usp16+/- mice also did not reveal significant differences in the levels of inflammatory cytokines from any of the groups (Figure 7—figure supplement 2A–C).

As expected, when studying the cognitive decline in the Tg-SwDI cohort, we found that the Tg-SwDI cohort exhibited impaired performance in the NOR task as early as 6 months of age, with preference indexes (PIs) that were not significantly different 24 hr after training, indicating no memory of the familiar object (Figure 7C). The Tg-SwDI/Usp16+/- mice performed equally to their age-matched WT controls indicating memory of the familiar object with PIs in the 65–70% range (p=0.001 and p=0.0099, respectively; Figure 7C). Long-term memory impairment in Tg-SwDI mice and rescue in Tg-SwDI/Usp16+/- mice was further supported by the Barnes maze (BM) where Tg-SwDI mice spent more time exploring off-target quadrants and Tg-SwDI/Usp16+/- mice spent more time in the target quadrant (p=0.0128 and p=0.0251, respectively; Figure 7—figure supplement 3). These data indicate that although modulating Usp16 gene dosage does not affect amyloid plaque burden, it ameliorates stem cell self-renewal defects that may be the earliest indication of pathology, as well as some of the cognitive defects in these mice that occur later (Figure 7D).

Discussion

Numerous studies have sought to target processes such as inflammation, amyloid plaque accumulation, and ROS to AD pathologies in both humans and mouse models (Gjoneska et al., 2015; Sevigny et al., 2016). The lack of robust efficacy in trials that utilize therapies directed against amyloid and inflammatory pathways, even when initiated relatively early in the disease (Selkoe, 2019; Imbimbo et al., 2010), suggests that other mechanisms are at play. If so, identification of these other disease mechanisms is needed to develop effective treatments (Aisen, 2008; Doody et al., 2013; Green et al., 2009; Group et al., 2008; Salloway et al., 2014).

One of the primary findings in this study is that an NPC defect predates the development of measurable inflammation and amyloid plaque accumulation in a mutant APP model and that this defect is cell intrinsic. We further show this cell intrinsic NPC defect is reproduced in human fetal NPCs expressing APP Swedish and Indiana mutations, suggesting that our findings are translatable to other APP mutations and to human cells. The NPC defect that we discovered is partly regulated by Cdkn2a, a central component of aging responsible for decreased neurogenesis and differentiation of NPCs (Abdouh et al., 2012; Molofsky et al., 2006). Because CDKN2A expression has been correlated with sporadic AD (Arendt et al., 1996; Lüth et al., 2000; McShea et al., 1997) and targeting CDKN2A has shown benefit in improving age related and sporadic AD in models, we speculate our findings may have relevance for treatment of sporadic AD.

Current strategies to reverse neurogenesis defects include the use of drugs (‘senolytics’) that selectively remove p16Ink4a-positive senescent cells. Removal of the p16Ink4a-positive senescent cells, for instance, using a suicide gene under the regulation of the Cdkn2a promoter has been shown to attenuate progression of age-related decline and preserve cognitive function in both an accelerated aging AD mouse model and a tauopathy mouse model (Baker et al., 2011; Bussian et al., 2018). However, the use of a suicide gene is not directly translatable into humans, and other senolytics such as BCL2-inhibitors or the combination of Dasatinib and quercetin have toxicities which can limit their use (Amaya-Montoya et al., 2020; Zhu et al., 2015).

Here, we propose USP16 as a novel target that might circumvent many of the problems associated with CDKN2A inhibition or with senolytics’ treatment. When we inhibited USP16 by making Tg-SwDI mice haploinsufficient for Usp16 (Tg-SwDI/Usp16+/-), we found a rescue in the self-renewal of NPCs as early as 3 months of age. We also demonstrated a new role for USP16 in regulating the BMP pathway, a mechanism independent of Cdkn2a. Previously, Gargiulo et al. found that self-renewal gene Bmi1, whose PRC1 activity is counterbalanced by USP16, represses BMP signaling (Gargiulo et al., 2013). NPCs from Bmi1 knockout mice treated with BMP4 experience even further growth arrest than those untreated (Gargiulo et al., 2013). Furthermore, Kwak, Lohuizen, and colleagues showed that treatment of human neural stem cells with secreted APPα or overexpression of APP promoted phosphorylation of SMAD 1/5/8 and induced massive glial differentiation (by expression of GFAP) through the BMP pathway (Kwak et al., 2014). In line with this, our results reveal expression of mutant APP in human fetal NPCs induced phosphorylation of SMAD 1/5/8 and reduced neurosphere colony formation that was rescued by a BMP receptor inhibitor. Importantly, our data extends their findings of astrogliosis to an in vivo mouse model of AD. Interestingly, BMI1 regulates both Cdkn2a and the BMP pathway independently, and BMI1 expression was shown to be decreased in AD patients compared to age-matched controls (Flamier et al., 2018).

The timing of therapeutic treatment in AD seems to be crucial as studies have shown that treating the disease too late has little efficacy (Sperling et al., 2011; Yiannopoulou et al., 2019). The Tg-SwDI mice used in our study develop cognitive defects after the accumulation of amyloid beta plaques, a timeline which mimics that of humans with both dominantly inherited AD and late-onset sporadic AD (Bateman et al., 2012). In a study characterizing sporadic AD, for instance, Villemagne et al. used Pittsburgh compound B (PiB) positron emission tomography (PET) to show that 31% of healthy control subjects had high PiB retention indicating Aß deposition and of these, 25% developed mild cognitive impairment or AD by 3 years (Villemagne et al., 2011). Furthermore, studies such as those by Salloway et al. and Biogen’s EMERGE and ENGAGE trials found that treating the plaques alone did not rescue cognitive defects (Knopman et al., 2021; Salloway et al., 2014). Although the mechanism for how mutant APP causes an NPC defect is outside the scope of this work, it has been postulated that amyloid-ß oligomers may impair neurogenesis and promote gliogenesis of human NPCs through the GSK-3ß pathway which unfortunately is not amenable to inhibition as it phosphorylates a variety of substrates and incurs large cytotoxic off-target effects (Bernabeu-Zornoza et al., 2019; Lee et al., 2013). The translatability of our study therefore comes from observing that early therapeutic reduction of USP16 or BMP signaling in neural stem cells may reverse the neurogenic defect that may contribute to symptomatic AD later in life, especially if applied before cognitive deficits are present in the patient.

Understanding the pathophysiology of a disease is critical to developing therapeutic targets and designing intervening therapies. Here, we present USP16 as a potential therapeutic target acting on both BMP and Cdkn2a pathways independently. It is important to note that USP16 reduction also reduced astrocyte proliferation and restored cognitive function as measured by the NOR and Barnes Maze tests, independently of plaques and widespread inflammation. The increase in GFAP-expressing cells and impaired cognitive function seen in this AD model are purely attributable to mutant APP as Tg-SwDI mice do not develop neurofibrillary tangles that require mutations in tau (Wilcock et al., 2008). Thus, therapeutic strategies that combine targeting USP16, which effectively rescue the mutant APP-induced cell intrinsic damage, with agents that target extracellular plaque formation, neurofibrillary tangles, and/or inflammation may improve treatments for AD.

Materials and methods

Key resources table
Reagent type (species) or resourceDesignationSource or referenceIdentifiersAdditional information
Gene (Homo sapiens)Tg-SwDIDavis et al., 2004APP KM670/671NL (Swedish), APP E693Q (Dutch), APP D694N (Iowa)Transgenic mutant APP in mouse model with Swedish, Dutch, and Iowa mutations
Gene (Homo sapiens)APP SwIYoung-Pearse et al., 2007APP K595N (Swedish), APP M596L and V642F (Indiana)Mutant human APP in human neurosphere model with Swedish and Indiana mutations
Strain, strain background (Mus musculus)C57BL/6-Tg(Thy1-APPSwDutIowa)BWevn/Mmjax, C57Bl/6The Jackson LaboratoryJax Stock#: 007027; RRID:MMRRC_034843-JAXTg-SwDI mice
strain, strain background (Mus musculus)FVB/N-Usp16Tg(Tyr)2414FOve/Mmjax, FVBMutant Mouse Regional Resource CentersJax Stock#: 036225-JAX; RRID:MMRRC_036225-JAXUsp16+/- mice
Strain, strain background (Mus musculus)B6.129-Cdkn2atm1Rdp/Nci, B6.129Mouse Models of Human Cancers ConsortiumMMHCC strain:#01XB1; RRID:IMRS_NCIMR:01XB1Cdkn2a-/- mice
Transfected construct (Homo sapiens)pHIV-ZsGreenAddgeneRRID:Addgene_18121Empty Lentiviral Backbone as control
Transfected construct (Homo sapiens)APP SwIThis paper (cloned)pHIV-APPSwITo model mutant APP neurospheres
Transfected construct (Homo sapiens)APP WTThis paper (cloned)pHIV-APP695To model WT APP neurospheres
Biological sample (Mus musculus)Primary neural stem cellsThe Jackson LaboratoryTg-SwDI, WT, Tg-SwDI/Usp16+/-, Usp16+/-, Tg-SwDI/Cdkn2a-/-, Cdkn2a-/-Freshly isolated from M. musculus
Biological sample (Homo sapiens)Primary human fetal neural stem cellsUniversity of California IrvineIsolated from 18 week fetal neural tissue, enriched for CD133+ cells
Antibodyanti-SOX2 (Goat polyclonal)R&D SystemsCat# AF2018, RRID:AB_355110IHC(1:50)
AntibodyAnti-GFAP (Rabbit polyclonal)Stem Cell TechnologiesCat#:60128, RRID:AB_1118515IHC(1:500)
AntibodyAnti-pSMAD1/5/8 (Rabbit monoclonal)CSTCat#:9516, RRID:AB_491015IF(1:100)
AntibodyAnti-beta-amyloid (Mouse monoclonal)InvitrogenCat#:13–0200, RRID:AB_2532993IF(1:100)
antibodyPacific Blue anti-mouse CD31 Antibody (Rat monoclonal)BiolegendCat#:102421, RRID:AB_10613457FACS(5 µl per test)
AntibodyPacific Blue anti-mouse CD45 Antibody (Rat monoclonal)BiolegendCat#:103125, RRID:AB_493536FACS(5 µl per test)
AntibodyPacific Blue anti-mouse TER-119 Antibody (Rat monoclonal)BiolegendCat#116231, RRID:AB_2149212FACS(5 µl per test)
AntibodyFITC anti-mouse CD24 Antibody (Rat monoclonal)BiolegendCat#101805, RRID:AB_312838FACS(5 µl per test)
Recombinant DNA reagentpCAX APP Swe/Ind (plasmid)AddgeneRRID:Addgene_30145Mutant APP with Swedish, Indiana mutations
Recombinant DNA reagentpCAX APP 695AddgeneRRID:Addgene_30137Wild type APP
Recombinant DNA reagentpHIV-Zsgreen (plasmid)AddgeneRRID:Addgene_18121Lentiviral backbone
Commercial assay or kitRNeasy Lipid Tissue KitQiagenCat#: 74,804
Commercial assay or kitClick-iT EdU cell proliferation kitInvitrogenCat#: C10337
Commercial assay or kitNextera XT Library Sample Preparation KitIlluminaCat#: FC-131–1096
Chemical compound, drugLDN-193189Sigma AldrichS26185 nM and 50 nM
Software, algorithmRRRRID:SCR_001905Single cell RNA-seq
Software, algorithmGSEAhttp://www.broadinstitute.org/gsea/RRID:SCR_003199Gene set enrichment analysis
Software, algorithmELDAhttp://bioinf.wehi.edu.au/software/elda/RRID:SCR_018933Limiting dilution experiments
Software, algorithmImageJhttps://imagej.net/RRID:SCR_003070IF analysis
Software, algorithmTranscriptome Analysis ConsoleThermo FisherRRID:SCR_016519Microarray analysis
OtherDAPI stainSigma32,670IHC (1:10000)
OtherThioflavin SSigma1326-12-1IHC (1%)
OtherSytox BlueThermo FisherS11348FACS

Statistical analyses

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In all the graphs, bars show average as central values and ± SD as error bars, unless otherwise specified. P values were calculated using ANOVA in analyses with three or more groups. Tukey’s method was used for multiple test correction with 95% simultaneous confidence levels. Two-tailed t-tests were used in analyses comparing two groups, unless otherwise specified. For limiting dilution analyses, ELDA software was used to test inequality between multiple groups. Expected frequencies are reported, as well as the 95% confidence intervals (lower and upper values are indicated). *p<0.05, **p<0.01, ***p<0.001.

Mice

Tg-SwDI mice (background C57Bl/6) were purchased from Jackson Laboratories and housed in cages of 5 mice. These mice were made hemizygous for experiments after breeding with Cdkn2a-/- (C57Bl6 background) or Usp16+/- mice (back-crossed to B6EiC3). Usp16+/- mice were originally ordered from Mutant Mouse Regional Resource Centers (MMRRC) and Cdkn2a-/-(B6.129-Cdkn2atm1Rdp) were obtained from Mouse Models of Human Cancers Consortium (NCI-Frederick). WT littermates were used as control mice. Mice were maintained in cages of 5 and genotyped by traditional PCR according to animal’s provider. Mice were housed in accordance with the guidelines of Institutional Animal Care Use Committee. All animal procedures and behavioral studies involved in this manuscript are compliant to Stanford Administrative Panel on Laboratory Animal Care Protocol 10,868 pre-approved by the Stanford Institutional Animal Care and Use Committee.

Immunohistochemistry

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All animals were anesthetized with avertin and transcardially perfused with 15 ml phosphate-buffered saline (PBS). Brains were postfixed in 4% paraformaldehyde overnight at 4°C before cryoprotection in 30% sucrose. Brains were embedded in optimum cutting temperature (Tissue-Tek) and coronally sectioned at 40 µm using a sliding microtome (Leica, HM450). For immunohistochemistry, sections were stained using the Click-iT EdU cell proliferation kit and protocol (Invitrogen) to expose EdU labeling followed by incubation in blocking solution [3% normal donkey serum, 0.3% Triton X-100 in PBS] at room temperature for 1 hr. Goat antibody to Sox2 (anti-Sox2) (1:50; R&D Systems AF2018) and rabbit anti-GFAP (1:500; Stem Cell Technologies 60128) were diluted in 1% blocking solution (normal donkey serum in 0.3% Triton X-100 in PBS) and incubated overnight at 4°C. Secondary-only stains were performed as negative controls. The following day, sections were rinsed three times in ×1 PBS and incubated in secondary antibody solution (1:500) and 4′,6-diamidino-2-phenylindole (DAPI) (1:10,000) in 1% blocking solution at 4°C for 4 hr. The following secondary antibodies were used: Alexa 594 donkey anti-rabbit (Jackson ImmunoResearch), Alexa 647 donkey anti-goat (Jackson ImmunoResearch). The next day, sections were rinsed three times in PBS and mounted with ProLong Gold Antifade (Cell Signaling) mounting medium. For senile plaques, sections were incubated for 8 min in aqueous 1% Thioflavin S (Sigma) at room temperature, washed in ethanol and mounted. Total plaque area from images taken of 6 sections (1 technical replicate = 1 section) were analyzed from each mouse with n = 3 mice (1 biological replicate = 1 mouse) in each group.

Confocal imaging and quantification

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All cell counting was performed by experimenters blinded to the experimental conditions using a Zeiss LSM700 scanning confocal microscope (Carl Zeiss). For EdU stereology, all EdU-labeled cells in every 6th coronal section of the SVZ were counted by blinded experimenters at ×40 magnification. The total number of EdU-labeled cells co-labeled with SOX2 and GFAP per SVZ was determined by multiplying the number of EdU+GFAP+SOX2+ cells by 6. Cells were considered triple-labeled when they colocalized within the same plane.

Mouse neurosphere cultures

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To produce neurospheres, mice were euthanized by CO2, decapitated and the brain immediately removed. The subventricular zone was micro-dissected and stored in ice-cold PBS for further processing. The tissue was digested using Liberase DH (Roche) and DNAse I (250 U/ml) at 37°C for 20 min followed by trituration. Digested tissue was washed in ice-cold HBSS without calcium and magnesium, filtered through a 40 μm filter and immediately put into neurosphere growth media that is, Neurobasal-A (Invitrogen) supplemented with Glutamax (Life Technologies), 2% B27-A (Invitrogen), mouse recombinant epidermal growth factor (EGF; 20 ng/ml) and basic fibroblast growth factor (bFGF; 20 ng/ml) (Shenandoah Biotechnology).

For limiting dilution analysis, cells were directly plated into 96-well ultra-low adherent plates (Corning Costar) in limiting dilutions down to one cell per well. Each plating dose was done in technical replicates of up to 12 wells in each experiment, and the number of wells with neurospheres was counted after 10 days. For passaging, neurospheres were dissociated and re-plated at a density of 10 cells/µl. Experiment was repeated three times (each infection and subsequent limiting dilution experiment performed being a biological replicate).

RNA expression analyses (mouse)

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For gene expression analyses, cells were collected in Trizol (Invitrogen), and RNA was extracted following the manufacturer’s protocol. Complementary DNA was obtained using Superscript III First Strand Synthesis (Invitrogen). Real-time PCR reactions were assembled using Taqman probes (Applied Biosystems) in accordance with the manufacturer’s directions. Expression data were normalized by the expression of housekeeping gene Actb (Mm00607939_s1). Probes used in this study: Cdkn2a (Mm_00494449), Bmi1 (Mm03053308_g1), Il1b (Mm01336189_m1), Il6 (Mm99999064_m1), Tnf (Mm00443258_m1), Cox2 (Mm03294838_g1), Aspg (Mm01339695_m1), C3 (Mm01232779_m1), Cd14 (Mm00438094_g1), Cd44 (Mm01277160_m1), Clcf1 (Mm01236492_m1), Emp1 (Mm00515678_m1), Gfap (Mm01253033_m1), Ggta1 (Mm01333302_m1), S1pr3 (Mm00515669_m1), Serping1 (Mm00437835_m1), Slc10a6 (Mm00512730_m1), Srgn (Mm01169070_m1), Stat3 (Mm01219775_m1), Vim (Mm00449201_m1). Biological replicates of a minimum of 3 mice and N = 2 technical replicates for each mouse were used.

Microarray

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SVZ, DG, and Cortex were dissected from 2-year-old mice, homogenized using Qiagen TissueRupture, and RNA was extracted using RNeasy Lipid Tissue Kit (Qiagen). RNA was submitted to Stanford PAN facility where amplification of cDNA and hybridization to Mouse Gene 2.0 ST array was performed. Microarray analyses were carried out using the TAC (Transcriptome Analysis Console) from Thermofisher. TAC includes the normalization, probe summarization, and data quality control functions of Expression Console Software. The expression analysis settings were set as fold change <-2 or >2 with a p-value <0.05 using ebayes ANOVA method. The heat map clustering was generated using a gene list including the differentially expressed genes between WT and Tg-SwDI with a conditional F-test <0.05.

Brain multianalyte analysis

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The different brain regions were lysed using cell lysis buffer (Cell signaling #9803) with PMSF (Cell signaling #8553) and complete mini EDTA free protease inhibitor followed by mechanical homogenation by Tissue Ruptor (Qiagen). The samples were centrifuged at 13,000 rpm for 15 min and protein concentration calculated by BCA. Normalized samples were analyzed by the Stanford Human Immune Monitoring Center using a Luminex mouse 38-plex analyte platform that screens 38 secreted proteins using a multiplex fluorescent immunoassay. Brain homogenates were run in technical duplicates (2 wells with 200 μg each from each biology replicate) with three biological replicates (1 biological replicate = 1 brain from 1 mouse). The Luminex data (mean RFI) was generated by taking the raw fluorescence intensities of each sample and dividing by a control sample (one of the WT samples), then taking the average of the triplicated samples for each genotype.

Behavioral testing

Novel object recognition

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One behavioral test used in this study for assessing long term memory was NOR 67 carried out in arenas (50cm × 50 cm × 50 cm) resting on an infra-red emitting base. Behavior was recorded by an infrared-sensitive camera placed 2.5 m above the arena. Data were stored and analyzed using Videotrack software from ViewPoint Life Sciences, Inc (Montreal, Canada) allowing the tracking of body trajectory/speed and the detection of the nose position. On the day before NOR training, the mouse was habituated to the apparatus by freely exploring the open arena. NOR is based on the preference of mice for a novel object versus a familiar object when allowed to explore freely. For NOR training, two identical objects were placed into the arena and the animals were allowed to explore for 10 min. Testing occurred 24 hr later in the same arena but one of the familiar objects used during training was replaced by a novel object of similar dimensions, and the animal was allowed to explore freely for 7 min. The objects and the arena were cleaned with 10% ethanol between trials. Exploration of the objects was defined by the time spent with the nose in a 2.5 cm zone around the objects. The PI was calculated as the ratio of the time spent exploring the novel object over the total time spent exploring the two objects. The PI was calculated for each animal and averaged among the groups of mice by genotype. The PI should not be significantly different from 50% in the training session, but is significantly different if novelty is detected.

Barnes maze

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Another test of long-term memory that is indicative of spatial memory is the Barnes Maze similar to that described by Attar et al., 2013. The Barnes maze is a 20-hole circular platform measuring 48" in diameter with holes cut 1.75" in diameter and 1" from the edge. The platform is elevated 100 cm above the floor, and is located in the center of a room with many extra-maze and intra-maze visual cues. This task takes advantage of the natural preference of rodents for a dark environment. Motivated to escape the bright lights and the open-space of the platform, rodents search for an escape hole that leads to a dark box beneath the maze and with training they learn to use distal visual cues to determine the spatial location of the escape hole. A habituation day was followed by training over 2 days and a test day separated by 24 hr. Two downward-facing 150-watt incandescent light bulbs mounted overhead served as an aversive stimulus. Mice completed three phases of testing: habituation, training, and the probe test.

For habituation, mice were placed within the start cylinder in the middle of the maze to ensure random orientation for 15 s. The overhead lights were then turned on and mice were given 3 min to independently enter through the target hole into the escape cage. If a mouse did not enter the escape box freely, the experimenter coaxed the mouse to enter the escape box by touching the mouse’s tail.

For training, a mouse was placed in the middle of the maze in random orientation for 15 s. The overhead lights were turned on, and the tracking software was activated. The mouse was allowed up to 3 min to explore the maze and enter the escape hole. If it failed to enter within 3 min, it was gently guided to the escape hole using the start cylinder and allowed to enter the escape cage independently.

On the test/probe day, 24 hr after the last training day, the experiment was set up as described on training days, except the target hole was covered. The percent time in the correct zone and average proximity to the correct escape hole are more sensitive measures of memory than percentage visits to the correct hole. Therefore, during the probe phase, measures of time spent per quadrant and holes searched per quadrant were recorded. For these analyses, the maze was divided into quadrants consisting of five holes with the target hole in the center of the target quadrant. On day 4, latency (seconds) and path length (meters) to reach the target hole were measured. Number of pokes in each hole were calculated, time spent per quadrant and holes searched per quadrant were recorded and paired t-tests were used to compare the percentage of time spent between quadrants.

Differentiation

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Neurospheres derived from the SVZ of 1-year-old mice were dissociated into single cells and 2000 cells were cultured per well on PDL and laminin-coated adherent 96-well cell culture plates (mouse neurospheres used for differentiation). The cells were cultured in Neurobasal-A (Invitrogen) media containing 1% fetal bovine serum. After 6 days in culture, the cells were stained directly in the wells using the Stemcell Technologies protocol. Wells were incubated for 2 hr at room temperature in primary rabbit antibody to GFAP (1:200, Dako Z0334) followed by three washes in ×1 PBS and incubated in secondary antibody solution Alexa-647 goat anti-rabbit (1:500; Jackson ImmunoResearch) and 4′,6-diamidino-2-phenylindole (DAPI) (1:10,000). Cells co-positive for GFAP and DAPI were counted using ImageJ and divided by total number of DAPI-positive cells. Experiment was performed with three biological replicates in triplicate (three technical replicates and three images were taken of differing regions of each technical replicate/well).

Human neurosphere cultures

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A human fetal neural stem cell line from University of California Irvine was developed from fetal neural tissue at eighteen-week gestational age enriched for CD133+ cells. The cells were negative for mycoplasma and viral contaminants using qPCR (IDEXX BioResearch) and had normal karyotype. The use of neural progenitor cells as non-hESC stem cells in this study is compliant to Stanford Stem Cell Research Oversight (SCRO) Protocol 194 pre-approved by the Internal Review Board (IRB)/SCRO of the Stanford Research Compliance Office (RCO). Informed consent was obtained, and standard material transfer agreement signed. Cells were grown in nonadherent ultra-low attachment well plates in X-VIVO 15 media (LONZA) supplemented with LIF (10 ng/ml), N2 Supplement, N-acetylcysteine (63 ug/ml), Heparin (2 ug/ml), EGF (20 ng/ml), and FGF (20 ng/ml).

For limiting dilution analysis, cells were directly plated into 96-well ultra-low adherent plates (Corning Costar) in limiting dilutions down to one cell per well. Each plating dose was done in technical replicates of up to 12 wells in each experiment, and the number of wells with neurospheres was counted after 10  days. Experiment was repeated three times (each infection and subsequent limiting dilution experiment performed being a biological replicate).

Lentivirus production

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pCAX APP Swe/Ind was a gift from Dennis Selkoe & Tracy Young-Pearse (Addgene plasmid #30145; http://n2t.net/addgene:30145; RRID:Addgene_30145). pCAX APP 695 was a gift from Dennis Selkoe & Tracy Young-Pearse (Addgene plasmid #30137; http://n2t.net/addgene:30137; RRID:Addgene_30137). pHIV-Zsgreen was a gift from Bryan Welm & Zena Werb (Addgene plasmid #18121; http://n2t.net/addgene:18121; RRID:Addgene_18121). The cDNA for mutant APP harboring the Swedish and Indiana mutations or the cDNA for wild type APP from the plasmids listed above were each cloned into a pHIV-Zsgreen backbone obtained from Addgene (also listed above). Lipofectamine 2000 was used to transduce the construct (either pHIV-Zsgreen+mutant APP or pHIV-Zsgreen alone) into H293T cells and media was collected after 48 hr. Virus was ultra-centrifuged and resuspended in PBS then titered before infecting human fetal neurospheres.

Flow cytometry

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For single-cell RNA-sequencing, the subventricular zone of 4 mice from each genotype was micro-dissected and tissue digested using Liberase DH (Roche) and DNAse I (250 U/ml) at 37°C for 20 min followed by trituration. Digested tissue was washed in ice-cold HBSS without calcium and magnesium, filtered through a 40 μm filter, and then stained with the following antibodies for 30 min: PacBlue-CD31 (Biolegend), PacBlue-CD45 (Biolegend), PacBlue-TER119 (Biolegend), and FITC-CD24 (Biolegend). Sytox Blue was used for cell death exclusion and samples were sorted into 384 well plates prepared with lysis buffer using the Sony Sorter.

Lysis plate preparation

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Lysis plates were created by dispensing 0.4 μl lysis buffer (0.5 U Recombinant RNase Inhibitor (Takara Bio, 2,313B), 0.0625% Triton X-100 (Sigma, 93443–100 ML), 3.125 mM dNTP mix (Thermo Fisher, R0193), 3.125 μM Oligo-dT30VN (IDT, 5′-AGCAGTGGTATCAACGCAGAGTACT30VN-3′) and 1:600,000 ERCC RNA spike-in mix (Thermo Fisher, 4456740)) into 384-well hard-shell PCR plates (Biorad HSP3901) using a Tempest or Mantis liquid handler (Formulatrix).

cDNA synthesis and library preparation

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cDNA synthesis was performed using the Smart-seq2 protocol [1,2]. Illumina sequencing libraries were prepared according to the protocol in the Nextera XT Library Sample Preparation kit (Illumina, FC-131–1096). Each well was mixed with 0.8 μl Nextera tagmentation DNA buffer (Illumina) and 0.4 μl Tn5 enzyme (Illumina), then incubated at 55°C for 10 min. The reaction was stopped by adding 0.4 μl ‘Neutralize Tagment Buffer’ (Illumina) and spinning at room temperature in a centrifuge at 3220 × g for 5 min. Indexing PCR reactions were performed by adding 0.4 μl of 5 μM i5 indexing primer, 0.4 μl of 5 μM i7 indexing primer, and 1.2 μl of Nextera NPM mix (Illumina). PCR amplification was carried out on a ProFlex 2 × 384 thermal cycler using the following program: 1. 72°C for 3 min, 2. 95°C for 30 s, 3. 12 cycles of 95°C for 10 s, 55°C for 30 s, and 72°C for 1 min, and 4. 72°C for 5 min.

Library pooling, quality control, and sequencing

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Following library preparation, wells of each library plate were pooled using a Mosquito liquid handler (TTP Labtech). Pooling was followed by two purifications using ×0.7 AMPure beads (Fisher, A63881). Library quality was assessed using capillary electrophoresis on a Fragment Analyzer (AATI), and libraries were quantified by qPCR (Kapa Biosystems, KK4923) on a CFX96 Touch Real-Time PCR Detection System (Biorad). Plate pools were normalized to 2 nM and equal volumes from 10 or 20 plates were mixed together to make the sequencing sample pool. PhiX control library was spiked in at 0.2% before sequencing. Single-cell libraries were sequenced on the NovaSeq 6000 Sequencing System (Illumina) using 2 × 100 bp paired-end reads and 2 × 8 bp or 2 × 12 bp index reads with a 300-cycle kit (Illumina 20012860).

Data processing

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Sequences were collected from the sequencer and de-multiplexed using bcl2fastq version 2.19.0.316. Reads were aligned using to the mm10plus genome using STAR version 2.5.2b with parameters TK. Gene counts were produced using HTSEQ version 0.6.1p1 with default parameters, except ‘stranded’ was set to ‘false,’ and ‘mode’ was set to ‘intersection-nonempty.’ As mentioned above, four biological replicates from each genotype and at each age were combined for the single-cell RNA-seq experiment (16 samples per age group). Basic filtering of cells and genes was conducted pre-analysis using the Seurat package in R (Butler et al., 2018). Briefly, genes that were not expressed in a minimum of 5 cells were filtered out, and cells had to have a minimum of 50,000 reads and a maximum of 3,000,000 reads. Similarly, cells with less than 500 or more than 5000 genes were filtered out. This left us with the following numbers of cells below after filtering. In this experiment, a biological replicate is defined as 1 mouse of a specific genotype and a technical replicate is defined as one cell.

3 and 4 months1-year- old
WT1008980
Tg-SwDI6421089
Tg-SwDI/Usp16+/-712729
Usp16+/-651731

Gene set enrichment analysis

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Gene counts were log normalized and scaled before generating the.gct files. GSEA with the Hallmarks gene sets was run with standard parameters: 1000 permutations of type phenotype, with no collapsing to gene symbols, and weighted enrichment. Gene sets were considered significantly enriched if FDR < 25%.

Immunofluorescence (human neurospheres)

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Neurospheres were cytospun onto slides and fixed in ice-cold methanol for 5 min. Slides were rinsed three times in PBS at room temperature, followed by blocking in 3% BSA in PBS for 1 hr at room temperature. Rabbit antibody to pSMAD 1/5/8 (1:100; CST 9516) and mouse antibody to beta-amyloid (1:100; Invitrogen 13–200) were diluted in the same 3% blocking buffer and incubated overnight at 4°C. The following day, sections were rinsed three times in ×1 PBS and incubated in secondary antibody solution Cy-3 donkey anti-rabbit (1:500; Jackson ImmunoResearch) or Cy-3 donkey anti-mouse (1:500; Jackson ImmunoResearch) and 4′,6-diamidino-2-phenylindole (DAPI) (1:10,000) in 3% blocking solution at room temperature for 2 hr. Slides were then washed 3 times at room temperature in ×1 PBS and mounted. Cells positive for pSMAD 1/5/8 were counted by ImageJ. Experiment was performed three times (1 biological replicate = 1 round of infection with subsequent experiment) in triplicate (1 technical replicate = 1 slide with at least 3 neurospheres with 1 neurosphere having at least 100 cells).

Colony counts

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Human neurospheres were dissociated into single cells and infected with either a lentiviral construct containing pHIV-Zsgreen+mutant APP, pHIV-Zsgreen+wild type APP, or pHIV-Zsgreen alone and allowed to grow for a week. Thereafter, cells were again dissociated and seeded at 5000 cells/well in a 24-well plate in triplicate. Cells were fed every day with ×20 media containing the appropriate amount of LDN-19389 (Selleckchem S2618). Colonies were counted after 7 days. Experiment was performed times times (1 biological replicate = 1 round of infection with subsequent experiment) in triplicate (1 technical replicate = 1 well).

Data availability

Datasets generated are available on Dryad Digital Repository (https://doi.org/10.5061/dryad.mpg4f4qz0 and https://doi.org/10.5061/dryad.vx0k6djtf).

The following data sets were generated
    1. Chen E
    2. Jones R
    3. Clarke M
    4. Quake S
    (2022) Dryad Digital Repository
    Single-Cell RNA-sequencing of neural precursor cells from an Alzheimer's mouse model, wild-type mice, and Alzheimer's mice rescued with Usp16 haploinsufficiency.
    https://doi.org/10.5061/dryad.mpg4f4qz0
    1. Reinitz F
    2. Clarke M
    3. Nicolis di Robilant B
    (2022) Dryad Digital Repository
    Microarray analysis of subventricular zone, hippocampus, and cortex from an Alzheimer's mouse model, wild-type mice, and Alzheimer's mice rescued with Usp16 haploinsufficiency.
    https://doi.org/10.5061/dryad.vx0k6djtf

References

    1. McShea A
    2. Harris PL
    3. Webster KR
    4. Wahl AF
    5. Smith MA
    (1997)
    Abnormal expression of the cell cycle regulators P16 and CDK4 in Alzheimer’s disease
    The American Journal of Pathology 150:1933–1939.

Decision letter

  1. Jessica Young
    Reviewing Editor; Institute for Stem Cell and Regenerative Medicine (ISCRM), United States
  2. Jeannie Chin
    Senior Editor; Baylor College of Medicine, United States
  3. Jessica Young
    Reviewer; Institute for Stem Cell and Regenerative Medicine (ISCRM, United States
  4. Zachary McEachin
    Reviewer; Emory University, United States

Our editorial process produces two outputs: i) public reviews designed to be posted alongside the preprint for the benefit of readers; ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.

Decision letter after peer review:

Thank you for submitting your article “Inhibiting USP16 rescues stem cell aging and memory in an Alzheimer's model” for consideration by eLife. Your article has been reviewed by three peer reviewers, including Jessica Young as the Reviewing Editor and Reviewer #1, and the evaluation has been overseen by Matt Kaeberlein as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Zachary McEachin (Reviewer #2).

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

In general, the reviewers acknowledged the impact and novelty of the work, however concerns arose during the review process that should be addressed and/or sufficiently rebutted prior to publication in eLife. In particular, one reviewer brought up several point where the data seem over-interpreted and the descriptions over-simplifed. I have therefore combined the reviewer comments into two sets of revisions, one set to be addressed experimentally and one set to be addressed in writing, both as rebuttals to the reviewers and as clarifications and descriptions in the manuscript text.

Essential revisions:

Essential major points to be addressed experimentally prior to publication in eLife:

1. Please show the NIC/neurosphere experiments the same way in each figure. In some figures there is a table of confidence intervals, in others a graph of log fraction nonresponding and in others representative images (Figure 3E). Or please explain why the representation is different clearly in the figure legend.

2. In Figure 2: Using the please show whether this is also changed in wild type (WT) cell with age. Please show BMI expression in the APP transgenic brain and Cdkn2a expression in WT and APP mut neurospheres. Please show GFAP expression in young APP transgenic mice. It seems very surprising that already one passage abolishes Bmi1 expression (Figure 2c), and it is unclear to me how that can be interpreted as a phenotype that “accompanies aging.” Please explain.

3. In Figure 6: Please show images of GFAP staining that are represented by the quantification

4. The authors’ conclusion that Tg-SwDI/Usp16+/- rescues cognitive defects would be strengthened by additional cognition test (three chambered assay or Barnes or Morris water maze assays).

5. The authors report an initially increased proliferation of NPCs and downstream inflammatory consequences that they have measured by GFAP reactivity and astrogliosis. However, the authors have not addressed if the increased abundance of GFAP-reactive astrocytes is instead a general lineage increase in astrocytes derived from APP-mutant/over-proliferating NPCs. Could the astrocyte reactivity rate be the same in healthy animals and it is only because there are more cells generally in the fAD mouse that the authors detect this astrogliosis signature? To address this, some attempt to quantify lineage abundances by classic markers of astrocytes, neurons, and other cells differentiated from NPCs should be included, and another measure of astrocyte reactivity, like the recently reported CD49f, to supplement GFAP.

6. The study makes a very important observation in that they show that cognitive defects in an AD mouse model can be rescued independent of plaque load. However, Usp16 reduction (Tg-SwDI/Usp16+/-) also did not rescue astrocyte over-abundance (which the authors interpret as neuroinflammation). I thus feel that additional immunohistochemical and behavioral experiments are needed to dissect late disease-related phenotypes in Tg-SwDI/Usp16+/- mice, and ideally also in LDN-treated mice, to be conclusive.

7. The authors elegantly use ELDA to test the “regenerative potential” of NSCs, and they suggest that an initial hyperproliferation is followed by exhaustion. However, some critical data seems to be missing to support this claim. Is neutrospheres formation increased at 1 month for example?

8. Did the authors check for any other signs of early hyperproliferation of NPCs such as SVZ thickness, general macrocephaly, etc.? Also, it remains to be determined by the authors at what stage/age hyperproliferation stops and stem cell exhaustion sets in.

9. When neutrospheres are transduced (not infected – this term is reserved for wildtype viruses), typically only the outside rim of cells become transgenic, and thus technical variation due to different sphere sizes could be a big issue. The assay (Figure 1E) should be better characterized regarding efficiency and variations to make it more convincing.

10. The authors suggest that extensive early proliferation decreases the stem cell pool in the SVZ, but they do not show data regarding the stem cell pool in 1-year-old Tg-SwDI mice. I feel an assessment of the SVZ stained for EdU, GFAP and SOX2 should be performed at the 1-year time point, and preferentially also in the dentate gyrus to improve translatability at least to one other neural stem cell type.

11. The authors show pieces of data that lead to the conclusion that similarly reduced neurosphere numbers (Figure 2A/p6) indicate that an aging phenotype is “emulated.” Given the lack of further data comparing young and old (wt and Tg) in a rigorous manner, this statement seems overstated. In Figure 2E, it would help to interpret the age dependent nature of astrogliosis in the TgSwDI by including young animals in addition to old. Also, can the authors report neurosphere data in old mice as presented in Figure 3A/B/F?

12. In Figure 2B, it seems unclear why the authors have chosen the cortex only for this experiment. Another assay, such as a simple western blot or staining for markers for inflammation/microglial activation, would be helpful to support the interpretation.

13. The results of no increased inflammation in Figure 2F are surprising given the increased gliosis, and could be attributed to probes not included in the Luminex panel or insufficient sensitivity in the provided tissue. To address the latter, a positive control of known inflamed tissue should be included.

14. Although p16 knockdown can cause tumor formations in vivo, the authors do not have such limitations on in vitro experiments. As such, they should verify Cdkn2a’s direct involvement by specific knockdown with shRNAs and then perform neurosphere analysis.

15. The scRNAseq data are presented in a very lean manner and need more analysis. Can the authors show their scRNA data in a UMAP or tSNE plot, with cells labeled by aggregate expression of the pathways they have highlighted in Table 4? At a minimum, p-values and a volcano plot (or an appropriate alternative) of key genes should be included. Why are only gene sets presented and never individual genes? There could be some interesting changes looking at differential expression between single genes instead of aggregates of genes. With this data, the authors have an opportunity to examine the relationship between Cdkn2a and USP16 expression, do they see these more co-expression in the Tg mouse? Is there transcriptional evidence of the hyperproliferation? This dataset would also be useful to query for inflammatory markers in the NSCs.

16. In regard to the RNA-seq data: Can the authors please comment if there are other genes regulating cellular senescence that are differentially expressed? Is aberrant Cdkn2a expression replicated here?

Essential major points to be addressed in writing in the manuscript prior to publication in eLife:

1. In the discussion: please comment on how this might relate to late-onset, sporadic AD which is not caused by mutations in APP. Especially as decreased neurogenesis is seen in SAD brains (PMID: 30911133)

2. Given that Bmi-1 expression is reduced in the Tg-SwDI mice (Figure 2D) and loss of Bmi-1 results in dramatic reduction of neurosphere size (Molofsky et al., 2005, Nature), is there a difference in the size (or shape) of neurospheres from wild type (WT) and Tg-SwDI mice?

3. Is an increase in NIC capacity a general result of Cdkna2a loss? Do WT/Cdkn2a-/- show similar or increased NIC capacity as WT at 3 months (Figure 3A)?

4. In the Tg-SwDI/Usp16+/- is Bmi-1 expression changed (perhaps as a compensatory mechanism of Usp16 loss). Could this explain the only partial rescue of NIC frequency (Figure 3F).

5. In Figure 5E, is this quantification from the ELDA assay or a different assay? If different, ELDA should be used to remain consistent.

6. While the authors show quantitative data of Thioflavin staining in Figure 6B, representative images should be included in figure or supplementary. Similarly, representative images of GFAP staining for Figure 6A should be included.

7. The authors should be more overall more critical with their own data, and explain what aspects of their model system are likely useful and can be translated to a human situation, and which ones are not. I would be very hesitant to commit to what extent the mouse SVZ relates to the human situation. A phenotype detected in a three to four-month-old transgenic mice might be translatable to early human development (fetal/early life), to adult neurogenesis in early AD pathology (late in life), or not directly translatable. Mouse and human NPCs are extremely different cells (see work from Conti L. et al., Sun et al., Elkabetz et al., etc…), and this is also eident from the data here (e.g. compare scales in Figure 1B and 1E). I suggest the authors to please avoid rushed over-interpretation.

8. It is not clear how the authors come to the conclusion that this “suggests that the self-renewal defect […] not specific to the Swedish, Dutch and Iowa mutations, but also more broadly seen with other APP mutations” (Figure 1E/p5). They did not test different expression/protein levels of WT, the individual mutations, or combinations, which would be necessary to make such a statement.

9. The attempt for assessing the effect of mutant APP on NPC phenotypes in the human system is a strength of this study. In Figure 1E/p5, the authors claim that “the effects observed in NPCs derived from a genetic mouse model can be robustly recapitulated in human NPCs expressing mutant APP.” As to the APP setting, both models are very different, in that human cells possess two copies of endogenous APP. “Fetal human NPCs” are not very well defined, an no characterization of endogenous/transgenic APP expression/protein levels are provided here. The choice of this models seems suboptimal. Also, to draw meaningful conclusions, the effect should be tested on different genetic backgrounds and well-characterized NPCs (e.g. from APP-mutant iPSCs). The conclusion that the “effects observed in NPCs derived from a genetic mouse model can be robustly recapitulated in human NPCs” is thus not appropriate and probably vastly oversimplified.

10. The authors show that the neurosphere phenotype of NPCs of 3-month-old mice that was rescued by Cdkn2a-KO. This is followed by the statement (line158) that APP mutations accelerate aging through Cdkn2a. In the context pf the presented data, this is a prominent overstatement.

Reviewer #1 (Recommendations for the authors):

In general, the data supports the conclusions and I think this work brings several novel aspects to the field. The comments below are for consistency in how the data is presented.

Please show the NIC/neurosphere experiments the same way in each figure. In some figures, there is a table of confidence intervals, in others a graph of log fraction nonresponding and in others representative images (Figure 3E). Or please explain why the representation is different clearly in the figure legend.

In Figure 2: I would like to also see the NPC proliferation assays, is this changed in WT cell with age. Please show BMI expression in the APP transgenic brain if possible and Cdkn2a expression in WT and APP mut neurospheres. Please show GFAP expression in young APP transgenic mice.

In Figure 6: Please show images of GFAP staining that are represented by the quantification

In regard to the RNA-seq data: Can the authors please comment if there are other genes regulating cellular senescence that are differentially expressed? Is aberrant Cdkn2a expression replicated here?

In the discussion: please comment on how this might relate to late-onset, sporadic AD which is not caused by mutations in APP. Especially as decreased neurogenesis is seen in SAD brains (PMID: 30911133)

Reviewer #2 (Recommendations for the authors):

1) Given that Bmi-1 expression is reduced in the Tg-SwDI mice (Figure 2D) and loss of Bmi-1 results in dramatic reduction of neurosphere size (Molofsky et al., 2005, Nature), is there a difference in the size (or shape) of neurospheres from WT and Tg-SwDI mice?

2) Is an increase in NIC capacity a general result of Cdkna2a loss? Do WT/Cdkn2a-/- show similar or increased NIC capacity as WT at 3 months (Figure 3A)?

3) In the Tg-SwDI/Usp16+/- is Bmi-1 expression changed (perhaps as a compensatory mechanism of Usp16 loss). Could this explain the only partial rescue of NIC frequency (Figure 3F).

4) In Figure 5E, is this quantification from the ELDA assay or a different assay? If different, ELDA should be used to remain consistent.

5) While the authors show quantitative data of Thioflavin staining in Figure 6B, representative images should be included in figure or supplementary. Similarly, representative images of GFAP staining for Figure 6A should be included.

6) The authors’ conclusion that Tg-SwDI/Usp16+/- rescues cognitive defects would be strengthened by additional cognition test (three chambered assay or Barnes or Morris water maze assays).

Reviewer #3 (Recommendations for the authors):

In the present manuscript entitled “Inhibiting USP16 rescues stem cell aging and memory in an Alzheimer's model,” Clarke and colleagues describe early NPC defects seen in Tg-SwDI mouse model of familial AD. Interestingly, the described phenotypes, that comprise early stem cell exhaustion followed by impaired self-renewal, clearly pre-date later inflammatory, neurodegenerative classically features typically described as disease-related in mouse models. They next position Cdkn2a and BmiI at the mechanistic core of this early NPC phenotype, and present USP16 and BMP inhibition as potential therapeutic targets. The manuscript is overall well-written and figures are clear and understandable. A particular strength of the manuscript is the dogma-free approach and the general frame of the study is highly innovative, relevant and interesting. However, the authors make several assumptions that are not sufficiently backed-up by the data, and overall appear to be drawn to large overinterpretations, which have dampened this reviewer’s initial enthusiasm. Also, the manuscript reads in a way that even the authors themselves have not really decided what role they think Cdkn2a is playing here, and thus in the end it simply appears as a gene that can inhibit cell proliferation, of which they detect more in the Tg AD mouse model, and the reader is left with the feeling that probably anti-proliferative perturbation would have done the job. Further, the authors’ schematic hints at that Cdkn2a is involved in the astrogliosis process, but the current manuscript fails to present any solid data supporting that claim. Below I have listed several major points that render the paper inappropriate for publication in eLife. My points are intended to help the authors improve their manuscript for publication in eLife, but I currently feel that this would take some tremendous effort to address my points with new and conclusive experimental data.

1. The authors report an initially increased proliferation of NPCs and downstream inflammatory consequences that they have measured by GFAP reactivity and astrogliosis. However, the authors have not addressed if the increased abundance of GFAP-reactive astrocytes is instead a general lineage increase in astrocytes derived from APP-mutant/over-proliferating NPCs. Could the astrocyte reactivity rate be the same in healthy animals and it is only because there are more cells generally in the fAD mouse that the authors detect this astrogliosis signature? To address this, some attempt to quantify lineage abundances by classic markers of astrocytes, neurons, and other cells differentiated from NPCs should be included, and another measure of astrocyte reactivity, like the recently reported CD49f, to supplement GFAP.

2. The study makes a very important observation in that they show that cognitive defects in an AD mouse model can be rescued independent of plaque load. However, Usp16 reduction (Tg-SwDI/Usp16+/-) also did not rescue astrocyte over-abundance (which the authors interpret as neuroinflammation). I thus feel that additional immunohistochemical and behavioral experiments are needed to dissect late disease-related phenotypes in Tg-SwDI/Usp16+/- mice, and ideally also in LDN-treated mice, to be conclusive.

3. The authors elegantly use ELDA to test the ‘regenerative potential’ of NSCs, and they suggest that an initial hyperproliferation is followed by exhaustion. However, some critical data seems to be missing to support this claim. Is neutrospheres formation increased at 1 month for example?

4. Did the authors check for any other signs of early hyperproliferation of NPCs such as SVZ thickness, general macrocephaly, etc.? Also, it remains to be determined by the authors at what stage/age hyperproliferation stops and stem cell exhaustion sets in.

5. The authors should be more overall more critical with their own data, and explain what aspects of their model system are likely useful and can be translated to a human situation, and which ones are not. I would be very hesitant to commit to what extent the mouse SVZ relates to the human situation. A phenotype detected in a three to four-month-old transgenic mice might be translatable to early human development (fetal/early life), to adult neurogenesis in early AD pathology (late in life), or not directly translatable. Mouse and human NPCs are extremely different cells (see work from Conti L. et al., Sun et al., Elkabetz et al., etc…), and this is also eident from the data here (e.g. compare scales in Figure 1B and 1E). I suggest the authors to please avoid rushed over-interpretation.

6. It is not clear how the authors come to the conclusion that this “suggests that the self-renewal defect […] not specific to the Swedish, Dutch and Iowa mutations, but also more broadly seen with other APP mutations” (Figure 1E/p5). They did not test different expression/protein levels of WT, the individual mutations, or combinations, which would be necessary to make such a statement.

7. The attempt for assessing the effect of mutant APP on NPC phenotypes in the human system is a strength of this study. In Figure 1E/p5 the authors claim that “the effects observed in NPCs derived from a genetic mouse model can be robustly recapitulated in human NPCs expressing mutant APP.” As to the APP setting, both models are very different, in that human cells possess two copies of endogenous APP. “Fetal human NPCs” are not very well defined, an no characterization of endogenous/transgenic APP expression/protein levels are provided here. The choice of this models seems suboptimal. Also, to draw meaningful conclusions, the effect should be tested on different genetic backgrounds and well-characterized NPCs (e.g. from APP-mutant iPSCs). The conclusion that the “effects observed in NPCs derived from a genetic mouse model can be robustly recapitulated in human NPCs” is thus not appropriate and probably vastly oversimplified.

8. When neutrospheres are transduced (not infected – this term is reserved for wildtype viruses), typically only the outside rim of cells become transgenic, and thus technical variation due to different sphere sizes could be a big issue. The assay (Figure 1E) should be better characterized regarding efficiency and variations to make it more convincing.

9. The authors suggest that extensive early proliferation decreases the stem cell pool in the SVZ, but they don't show data regarding the stem cell pool in 1-year-old Tg-SwDI mice. I feel an assessment of the SVZ stained for EdU, GFAP, and SOX2 should be performed at the 1-year time point, and preferentially also in the dentate gyrus to improve translatability at least to one other neural stem cell type.

10. The authors show pieces of data that lead to the conclusion that similarly reduced neurosphere numbers (Figure 2A/p6) indicate that an aging phenotype is “emulated.” Given the lack of further data comparing young and old (WT and Tg) in a rigorous manner, this statement seems overstated. In Figure 2E, it would help to interpret the age dependent nature of astrogliosis in the TgSwDI by including young animals in addition to old. Also, can the authors report neurosphere data in old mice as presented in Figure 3A/B/F?

11. In Figure 2B, it seems unclear why the authors have chosen the cortex only for this experiment. Another assay, such as a simple western blot or staining for markers for inflammation/microglial activation, would be helpful to support the interpretation.

12. It seems very surprising that already one passage abolishes Bmi1 expression (Figure 2c), and it is unclear to me how that can be interpreted as a phenotype that “accompanies aging".” Please explain.

13. The results of no increased inflammation in Figure 2F are surprising given the increased gliosis, and could be attributed to probes not included in the Luminex panel or insufficient sensitivity in the provided tissue. To address the latter, a positive control of known inflamed tissue should be included.

14. The authors show that the neurosphere phenotype of NPCs of 3-month-old mice that was rescued by Cdkn2a-KO. This is followed by the statement (line158) that APP mutations accelerate aging through Cdkn2a. In the context pf the presented data, this is a prominent overstatement.

15. Although p16 knockdown can cause tumor formations in vivo, the authors do not have such limitations on in vitro experiments. As such, they should verify Cdkn2a’s direct involvement by specific knockdown with shRNAs and then perform neurosphere analysis.

16. The scRNAseq data are presented in a very lean manner and need more analysis. Can the authors show their scRNA data in a UMAP or tSNE plot, with cells labeled by aggregate expression of the pathways they have highlighted in Table 4? At a minimum, p-values and a volcano plot (or an appropriate alternative) of key genes should be included. Why are only gene sets presented and never individual genes? There could be some interesting changes looking at differential expression between single genes instead of aggregates of genes. With this data, the authors have an opportunity to examine the relationship between Cdkn2a and USP16 expression, do they see these more co-expression in the Tg mouse? Is there transcriptional evidence of the hyperproliferation? This dataset would also be useful to query for inflammatory markers in the NSCs.

https://doi.org/10.7554/eLife.66037.sa1

Author response

Essential revisions:

Essential Major points to be addressed experimentally prior to publication in eLife:

1. Please show the NIC/neurosphere experiments the same way in each figure. In some figures there is a table of confidence intervals, in others a graph of log fraction nonresponding and in others representative images (Figure 3E). Or please explain why the representation is different clearly in the figure legend.

This has been corrected to bar graphs with corresponding tables of confidence intervals.

2. In Figure 2: Using the please show whether this is also changed in WT cell with age. Please show BMI expression in the APP transgenic brain and Cdkn2a expression in WT and APP mut neurospheres. Please show GFAP expression in young APP transgenic mice. It seems very surprising that already one passage abolishes Bmi1 expression (Figure 2c), and it is unclear to me how that can be interpreted as a phenotype that "accompanies aging". Please explain.

When directly comparing the young vs aged WT NIC frequencies, we do not see a significant difference in self-renewal, however, there is a significant decrease in self-renewal capacity with aging in the Tg-SwDI neurospheres. We have not included this data in the updated manuscript, but have included the analysis in Author response image 1.

Author response image 1

As requested, we have measured Bmi1 expression in the Tg-SwDI transgenic brain as shown in Author response image 2; we did not see any difference in the expression of Bmi1 in the cerebral cortex. We have added Cdkn2a expression in WT and Tg-SwDI neurospheres to Figure 1C in our revised paper.

Author response image 2

We also stained brain sections from 3-4 month old WT and Tg-SwDI mice for GFAP expression. Throughout the cerebral cortex there were sparse regions of aggregated GFAP+ cells, that we called “GFAP clusters”. These GFAP clusters comprise up to 25-30 GFAP+ cells. As a possible indication of early inflammation, we counted the number of clusters, but we observed no differences between the genotypes in this age group. This data has been included in the revised manuscript (Figure 2 —figure supplement 5).We would like to clarify that when we had described a phenotype that “accompanies aging” in the text (previously line 133), we were referring to the decreased capacity of SVZ derived NPCs to form neurospheres in older mice compared to younger mice. We have updated this section of the text to use more clear descriptions that more accurately reflect the data.

Additionally, in reference to the reviewer’s concern about Bmi1 expression, others have shown an increased expression of Cdkn2a and decreased expression of Bmi1 in aging brain as well, and the citations to these references have now been added to this section of the text in the updated manuscript.

3. In Figure 6: Please show images of GFAP staining that are represented by the quantification

These have been added to Supplementary Figure 6 —figure supplement 1.

4. The authors' conclusion that Tg-SwDI/Usp16+/- rescues cognitive defects would be strengthened by additional cognition test (three chambered assay or Barnes or Morris water maze assays).

As an addition to our novel object recognition (NOR) test, we performed Barnes maze testing, and these data can be found in Supplementary Figure 6 —figure supplement 3. Consistent with the NOR experiment, this data supports long term and spatial memory deficits are present in the Tg-SwDI mice which are also rescued by Usp16 haploinsufficiency.

5. The authors report an initially increased proliferation of NPCs and downstream inflammatory consequences that they have measured by GFAP reactivity and astrogliosis. However, the authors have not addressed if the increased abundance of GFAP-reactive astrocytes is instead a general lineage increase in astrocytes derived from APP-mutant/over-proliferating NPCs. Could the astrocyte reactivity rate be the same in healthy animals and it is only because there are more cells generally in the fAD mouse that the authors detect this astrogliosis signature? To address this, some attempt to quantify lineage abundances by classic markers of astrocytes, neurons, and other cells differentiated from NPCs should be included, and another measure of astrocyte reactivity, like the recently reported CD49f, to supplement GFAP.

The reviewer brings up an important distinction between increased GFAP-reactive astrocytes versus an increase in number of cells generally in the Tg-SwDI mouse. To further investigate reactive astrogliosis and to quantify numbers of astrocytes we performed a number of studies, including qPCR of known markers of reactive astrocytes (A1 astrocytes) vs non-reactive A2 astrocytes (Zamanian et al., 2012), microarray, and differentiation studies. In the cortex of one year old Tg-SwDI mice, there was not a significant increase in A1 astrocyte markers (Figure 2 —figure supplement 2). However, using a microarray of 2 year old mice, and analyzing the same A1 and A2 astrocyte markers, we did see an increase in the A1 astrocytic markers as seen in Figure 2 —figure supplement 4, suggesting there is eventually reactive astrogliosis but further aging is required. Additionally, we placed neurospheres derived from the SVZ of Tg-SwDI mice and WT controls into differentiation conditions and analyzed the number of GFAP positive cells following differentiation (included in Figure 2 —figure supplement 3). We found that cells derived from Tg-SwDI mice formed more GFAP-positive cells than those derived from wild-type controls, suggesting at least an increase in production of astrocytes when controlling for total number of cells. All these data taken together suggest that in mutant Tg-SwDI mice, there are more astrocytes produced and that these astrocytes become reactive with aging.

The reviewer points out using CD49f as another measure of astrocyte reactivity to supplement GFAP. This marker was recently reported by Barber et al. as a “…reactivity-independent marker (expressed in both unstimulated and reactive astrocytes)…”. (Barbar et al., 2020) Thus, CD49f is a novel marker shown to be useful in identification and isolation of human and iPSC-derived astrocytes that can be stimulated to become A1-like reactive astrocytes. However, this marker cannot differentiate between reactive vs non-reactive astrocytes. Furthermore, the authors report that when they tested CD49f on “whole mouse brain of ALDH1L1eGFP, no CD49f+ astrocytes were ALDH1L1+, suggesting that CD49f could be human specific.”

To look for astrocyte reactivity, we instead analyzed mRNA using the verified A1 specific astrocyte markers as described above and differentiation followed by astrocyte quantification immunohistochemically, as shown in Figure 2—figure supplement 4.

6. The study makes a very important observation in that they show that cognitive defects in an AD mouse model can be rescued independent of plaque load. However, Usp16 reduction (Tg-SwDI/Usp16+/-) also did not rescue astrocyte over-abundance (which the authors interpret as neuroinflammation). I thus feel that additional immunohistochemical and behavioral experiments are needed to dissect late disease-related phenotypes in Tg-SwDI/Usp16+/- mice, and ideally also in LDN-treated mice, to be conclusive.

To dissect late disease-related phenotypes in the Tg-SwDI/Usp16+/- mice, we conducted a variety of assays, including immunohistochemical quantification of GFAP+ cells in the cerebral cortex, thioflavin S staining for amyloid plaques, behavioral cognitive testing with novel object recognition and Barnes Maze tests, and a Luminex assay to study inflammation. In Figure 6 we show that Usp16 reduction also decreases the number of GFAP+ cells (Figure 6A, Figure 6 —figure supplement 1) as well as cognitive defects (Figure 6C) even though there is no change in amyloid plaque burden (Figure 6B) or inflammatory markers (Figure 6 —figure supplement 2).

Although testing LDN-treated mice would be ideal, there are several factors that make this difficult as it has never been studied in the context of an intracranial application, thus a study would require an analysis of blood-brain barrier penetration, dose optimization, and determining the appropriate length/frequency of treatment. We therefore opted to use an in vitro study instead to better understand involvement of the BMP pathway, which also afforded us the advantage of using human cells.

Finally, while we agree with the reviewer that it would be ideal to continue to dissect late disease-related phenotypes in Tg-SwDI/Usp16+/- mice, the benefit of our conclusions come from identifying an earlier disease-related phenotype potentially related to cognitive decline that may be intervenable prior to the onset of late disease-related phenotypes.

7. The authors elegantly use ELDA to test the 'regenerative potential' of NSCs, and they suggest that an initial hyperproliferation is followed by exhaustion. However, some critical data seems to be missing to support this claim. Is neutrospheres formation increased at 1 month for example?

As recommended by the reviewers, we assessed neurosphere formation at 1 month old and found that there was no significant difference in the stem cell frequencies between WT and Tg-SwDI. This suggests that self-renewal changes are not detectable early in life and require some degree of aging before this phenotype can be elicited.

Author response image 3

8. Did the authors check for any other signs of early hyperproliferation of NPCs such as SVZ thickness, general macrocephaly, etc.? Also, it remains to be determined by the authors at what stage/age hyperproliferation stops and stem cell exhaustion sets in.

Given the additional ELDA experiments completed in response to number 7 above and the immunohistochemical staining of older mice (see response to number 10 below) completed since the time of this manuscript’s submission, it appears as though hyperproliferation and stem cell exhaustion begin at around 3 months old and end at about 1 year old. As shown in the response to number 7, we do not see differences in stem cell frequencies at 1 month old, but as the manuscript shows, this difference is evident at 3 months old (Figure 1B) alongside increased proliferation (Figure 1A). We no longer see differences in proliferation at 1 year old (Figure 2A), yet we continue to see decreased stem cell frequencies in cells derived from the Tg-SwDI subventricular zone (Figure 2B). Taken together, hyperproliferation extends through stem cell exhaustion, ending by approximately 1 year of age. This is not unexpected as we know that hyperproliferation and stem cell exhaustion are dynamic processes that occur over time, thus there is not necessarily a time at which one would see that hyperproliferation abruptly ends and stem cell exhaustion begins, as they are occurring simultaneously (Molofsky et al., 2003). We have updated Figure 6D to reflect this timeline.

Moreover, we performed MRI and MRS (Magnetic resonance spectroscopy), typically used to diagnose AD in patients, on our mouse models. In particular, proton magnetic resonance spectroscopy (MRS) provides a window into the biochemical changes associated with the loss of neuronal integrity that involve the brain before the manifestations of cognitive impairment in patients who are at risk for Alzheimer’s disease. Given that our Alzheimer’s mouse model, at least anatomically, does not present the same macroscopic neurodegeneration observed in patients, we sought to take advantage of MRS techniques to investigate the biochemical changes and neurodegeneration that usually appear before anatomical manifestations. We analyzed 18-22 month old mice by MRS and normalized each metabolite’s expression by the “sum of metabolites” (data were comparable when normalization was done by Creatinine). As it is possible to observe in Author response image 4, we did not observe any metabolic differences between the genotypes. NAA and myo-inositol (mIns), are the most used metabolites for detecting dementia in humans. (abbreviations: SOM: Sum of metabolites; NAA: N-acetylaspartate; mIns: Myo-Inositol; Tau: Taurine; Cho: choline; PC: phosphocholine; GLU: glutamate; GLN: glutamine)

Author response image 4

9. When neutrospheres are transduced (not infected – this term is reserved for wildtype viruses), typically only the outside rim of cells become transgenic, and thus technical variation due to different sphere sizes could be a big issue. The assay (Figure 1E) should be better characterized regarding efficiency and variations to make it more convincing.

The human fetal neural stem cells were first dissociated into single cells and then transduced with a high enough titer to ensure 100% transduction of cells. Therefore, they are green throughout (Figure 5D).

10. The authors suggest that extensive early proliferation decreases the stem cell pool in the SVZ, but they don't show data regarding the stem cell pool in 1-year-old Tg-SwDI mice. I feel an assessment of the SVZ stained for EdU, GFAP and SOX2 should be performed at the 1-year time point, and preferentially also in the dentate gyrus to improve translatability at least to one other neural stem cell type.

We thank the reviewer for making this astute observation. Since the submission of this manuscript, we have assessed the number of EdU/GFAP/SOX2 positive cells in the SVZ of 1-year old mice (see Author response image 5) and have found no significant difference between Tg-SwDI and WT mice. As discussed above in response to query number 8, the decrease in number of proliferating NPCs (or lack of hyperproliferation) at 1 year old correlates with decreased self-renewal over time beginning at 3 months old and continuing through 1 year old (Figure 1B and 2B, respectively).

Author response image 5

Total number of EdU/SOX2/GFAP triple positive cells in 1 year old mice (N = 3 WT and N = 4 Tg-SwDI mice)Shown in Author response image 6 is a representative image of the subgranular zone (SGZ) of the hippocampal dentate gyrus of 3-4 month old mice (SOX2 – red, GFAP – cyan, EdU – green, Dapi – blue). The EdU+Sox2Sox2+GFAP+ triple positive cells were counted in the subgranular zone (SGZ) using stereology shown in the graph on the right, n = 3. Unlike the SVZ, we did not observe a significant difference in proliferating NPCs marked by EdU, SOX2 and GFAP. This may be due to differences in the expression of markers that characterize NPCs that are found in the hippocampus compared to the SVZ. It is therefore challenging to directly compare NPCs of the SVZ and the SGZ of the hippocampal dentate gyrus, and we have chosen not to include this data in the manuscript.

Author response image 6

11. The authors show pieces of data that lead to the conclusion that similarly reduced neurosphere numbers (Figure 2A/p6) indicate that an aging phenotype is "emulated". Given the lack of further data comparing young and old (wt and Tg) in a rigorous manner, this statement seems overstated. In Figure 2E it would help to interpret the age dependent nature of astrogliosis in the TgSwDI by including young animals in addition to old. Also, can the authors report neurosphere data in old mice as presented in Figure 3A/B/F?

Please refer to our response to reviewer comment 2 above, which addresses the question of our data emulating an aging phenotype; there you will also find our measurement of GFAP clusters in 3-4 month old TgSwDI mice compared to WT which has been included in our revised paper as Figure 2 —figure supplement 5.

When directly comparing data from the young vs aged wild-type (WT) NIC frequencies, we did not see a significant difference in self-renewal, however, there is a significant decrease in self-renewal capacity with aging in the Tg-SwDI neurospheres (Data included in response #2 above).

Lastly, the reviewer makes an excellent suggestion to show neurosphere initiating capacity in older Cdkn2a knockout mice in Figure 3; unfortunately, these mice form tumors soon after age 3-4 months (by 9 months most of these mice have large tumors and must be euthanized). Thus, the limiting dilution experiments cannot be performed in aged mice due to the number of mice needed to survive to this age to carry out the experiment.

12. In Figure 2B. it seems unclear why the authors have chosen the cortex only for this experiment. Another assay, such as a simple western blot or staining for markers for inflammation/microglial activation, would be helpful to support the interpretation.

Cdkn2a expression can be difficult to detect in a heterogeneous population: its expression is both transient and limited to few cells, many of which are destined to die due to apoptosis and be rapidly eliminated. We tested Cdkn2a expression in three different regions of the brain in 2 year old mice as shown in Author response image 7 (n=6): SVZ, DG, and Cortex (p value between WT and Tg-SwDI DG is 0.05; p value between WT and Tg-SwDI Cortex is 0.015). Even though Tg-SwDI mice showed an increased expression in all the regions analyzed, Cdkn2a expression levels for WT mice in SVZ and DG were very low (<0.0001), making it nearly impossible to confidently detect differences in mRNA expression. As a consequence of this sensitivity issue in the SVZ and DG, we decided to present data from the cortex only, as the expression is higher overall. Our results corroborate studies done by Molofsky et al., for instance, where they were not able to detect Cdkn2a expression by PCR in 60-day-old mice and similarly, could not detect p16INK4a protein in the SVZ by western blot, consistent with other studies (Krishnamurthy et al., 2004; Molofsky et al., 2006; Zindy, Quelle, Roussel, and Sherr, 1997) due to its transient low expression level and limited sensitivity of available antibodies.

Author response image 7

13. The results of no increased inflammation in Figure 2F are surprising given the increased gliosis, and could be attributed to probes not included in the Luminex panel or insufficient sensitivity in the provided tissue. To address the latter, a positive control of known inflamed tissue should be included.

We agree with the reviewer that the result in Figure 2F was initially a surprise to us as well. To ensure that the results we were seeing were real and not due to insufficient sensitivity or insufficient probes, we tested levels of inflammation in 2 year old Tg-SwDI mice (n=3) and found that there were indeed significantly higher levels of only certain cytokines expressed over the 1 year old mice we showed in our paper. This is shown as an added positive control and has been included in Supplementary Figure 6 —figure supplement 2.

14. Although p16 knockdown can cause tumor formations in vivo, the authors do not have such limitations on in vitro experiments. As such, they should verify Cdkn2a's direct involvement by specific knockdown with shRNAs and then perform neurosphere analysis.

We did not complete the recommended experiment for several reasons: (1) by genetically knocking out Cdkn2a expression in Tg-SwDI mice and performing our neurosphere limiting dilution analysis, we have demonstrated Cdkn2a’s direct involvement in neurosphere formation in a manner that would be more effective than the use of shRNAs against Cdkn2a. To strengthen our argument, we point to similar studies (Bruggeman et al., 2005; Molofsky, He, Bydon, Morrison, and Pardal, 2005; Molofsky et al., 2006) that have demonstrated a significant increase in self-renewal of neural stem cells in both WT and Bmi1-/- mice by also genetically knocking out Cdkn2a. (2) Considering that Cdkn2a is already very lowly expressed in neurospheres, it would be difficult to verify that the knockdown occurred.

15. The scRNAseq data are presented in a very lean manner and need more analysis. Can the authors show their scRNA data in a UMAP or tSNE plot, with cells labeled by aggregate expression of the pathways they have highlighted in Table 4? At a minimum, p-values and a volcano plot (or an appropriate alternative) of key genes should be included. Why are only gene sets presented and never individual genes? There could be some interesting changes looking at differential expression between single genes instead of aggregates of genes. With this data, the authors have an opportunity to examine the relationship between Cdkn2a and USP16 expression, do they see these more co-expression in the Tg mouse? Is there transcriptional evidence of the hyperproliferation? This dataset would also be useful to query for inflammatory markers in the NSCs.

We have conducted a more thorough analysis of the data and present two additional supplementary figures addressing the reviewer’s points that show the appropriate tSNE (Figure 4 —figure supplement 1) and volcano plots (Figure 4 —figure supplement 4) with additional panels of hyperproliferation (Figure 4 —figure supplement 3) and inflammatory markers (Figure 4 —figure supplement 2). The volcano plot actually bolsters our original argument that looking at gene sets is far more ideal than looking at individual genes. Oftentimes, when we are looking at individual differentially expressed genes, it can be difficult to choose which ones to pursue in a therapeutic context. The nature of GSEA is to highlight groups of genes that are significantly enriched and related in the same pathway (which can be more easily targeted), rather than to find individual genes that are significantly enriched. Furthermore, it may be difficult to see whether individual genes that are upregulated or downregulated are due to a compensatory effect rather than contributing to the pathophysiology of the disease. With regards to Cdkn2a, neither the 3-4 month old or 1 year old WT and Tg-SwDI expressed this gene in at least 1% of cells at a minimum logFC of 0.01. While there were no significant differences in expression between 3-4 month old and 1 year old Tg-SwDI and WT mice for either Bmi1 or Usp16 (see Author response image 8) using a Wilcoxon rank Sum test for differential gene expression, we did see a mild upregulation in expression of both genes in Tg-SwDI mice compared to WT with greater expression of Usp16 over Bmi1 at both ages. When we surveyed inflammatory markers, similar to what our Luminex assay showed, we did not see a substantial increase in any of the markers at both the 3-4 month and 1 year old time point (only Csf1 and Il18 were upregulated at the 3-4 month old time point in Tg-SwDI and only Lifr and Ifngr1 were upregulated at the 1 year old time point) (Figures 4 —figure supplement 2). A possible explanation for this result could be that we were selecting for a cell population (within the SVZ) that itself does not exhibit inflammation at either time point. Finally, while we were able to survey several proliferation and cell-cycle related genes, only a few were differentially expressed. We include these in the same supplementary figures (Figure 4 —figure supplement 3). Notably, Anapc2 was significantly upregulated while cell cycle inhibitor Cdkn1a was significantly downregulated in 3-4 month old Tg-SwDI compared to WT. At 1 year of age, only Cdkn1b was significantly downregulated in 1 year old Tg-SwDI mice compared to WT.

Author response image 8

16. In regards to the RNA-seq data: Can the authors please comment if there are other genes regulating cellular senescence that are differentially expressed? Is aberrant Cdkn2a expression replicated here?

Cdkn2a expression was too lowly expressed and not differentially expressed in our single-cell RNA-seq data. In looking at a panel of genes commonly known to regulate senescence (Trp53, Cdkn1a, Mtor, Crebbp, Ep300, Mdm2, Btg2, E2f1, E2f2, E2f3, E2f4, and E2f7) (Kumari and Jat, 2021), genes that were differentially expressed in 3-4 month old mice (Tg-SwDI vs WT) included:

Author response table 1
GeneAvg_logFC difference% of Tg-SwDI cells% of WT cellsP_val_adj
Trp530.73152530.1550.1070
Cdkn1a (p21)-0.26270550.0570.1700
Mtor0.81068690.1690.1610
E2f40.35361610.1250.0853.987E-132

And in 1 year old mice:

Author response table 2
GeneAvg_logFC difference% of Tg-SwDI cells% of WT cellsP_val_adj
Crebbp-0.33030110.3940.4090

Interestingly, while Trp53, Mtor, and E2f4 were increased in young Tg-SwDI mice compared to WT mice suggesting more senescence (all of which are implicated in activating senescence), p21 expression (which is usually up in senescence) was downregulated in 3-4 month old Tg-SwDI mice. In 1 year old mice, the only senescence related gene that was differentially expressed was Crebbp which was downregulated in Tg-SwDI mice, suggesting an increase in senescence and decrease in proliferation.

Essential Major points to be addressed in writing in the manuscript prior to publication in eLife:

1. In the discussion: please comment on how this might relate to late-onset, sporadic AD which is not caused by mutations in APP. Especially as decreased neurogenesis is seen in SAD brains (PMID: 30911133)

We have updated the discussion to address this.

To expand on this, there are some encouraging features in the Tg-SwDI mouse model including the presence of cognitive defects after the accumulation of amyloid β plaques, which occurs similarly in humans with both dominantly inherited AD and late-onset AD (LOAD) (Bateman et al., 2012; Jack et al., 2010). Bateman et al., conducted a study with 128 AD mutation carriers and found that Aß42 concentrations in the CSF declined 25 years before expected symptom onset (estimated as patient’s age at onset) while Aß deposition as measured by PET scans with the use of Pittsburgh compound B was detected 15 years before expected symptom onset (Bateman et al., 2012). These same carriers did not have significant differences in mini-mental state examination scores until 5 years before expected symptom onset or significant differences in the logical memory test until 10 years before expected symptom onset (Bateman et al., 2012).

In another study characterizing sporadic AD, Villemagne et al. 2012 also used Pittsburgh compound B PET studies to study those with dementia of the Alzheimer type (DAT), those with mild cognitive impairment (MCI), and age-matched healthy controls (Villemagne et al., 2011). At baseline, 31% of healthy control subjects showed high PiB retention indicating Aß deposition and of these, 16% developed MCI or DAT by 20 months and 25% by 3 years (Villemagne et al., 2011). A similar study by Morris et al. 2009 also found that in a study of 159 participants, the mean cortical binding potential for PiB assessed with PET was also a significant predictor of progression from cognitive normality to DAT (Morris et al., 2009). Furthermore, studies such as those by Salloway et al. 2014 found that treating the plaques alone did not rescue cognitive defects (Salloway et al., 2014).

All of these studies highlight the clinical similarities of late-onset sporadic AD and the importance of early therapeutic intervention, and our work reveals a new avenue of therapeutic approach that could apply to both sporadic and familial AD based on their shared features, though confirmatory studies in humans must be pursued to confirm this.

2. Given that Bmi-1 expression is reduced in the Tg-SwDI mice (Figure 2D) and loss of Bmi-1 results in dramatic reduction of neurosphere size (Molofsky et al., 2005, Nature), is there a difference in the size (or shape) of neurospheres from WT and Tg-SwDI mice?

After performing ad hoc analysis of neurosphere diameter in 3-4 month old mice, we did not observe a significant difference between the size of neurospheres derived from the SVZ of Tg-SwDI vs WT mice, shown in Author response image 9 (n=3 biological replicates). Nor did we observe any qualitative difference in neurosphere shape (see accompanying 4x images).

Author response image 9

3. Is an increase in NIC capacity a general result of Cdkna2a loss? Do WT/Cdkn2a-/- show similar or increased NIC capacity as WT at 3 months (Figure 3A)?

NIC capacity is generally increased as a result of Cdkn2a loss. This data has been added to Figure 3 —figure supplement 1. As you can see, neurospheres from Cdkn2a-/- mice have increased NIC capacity to neurospheres from WT mice. As mentioned above, our results are corroborated by previous studies from other studies (Krishnamurthy et al., 2004; Molofsky et al., 2006; Zindy et al., 1997).

4. In the Tg-SwDI/Usp16+/- is Bmi-1 expression changed (perhaps as a compensatory mechanism of Usp16 loss). Could this explain the only partial rescue of NIC frequency (Figure 3F).

In microarray data we collected from the cortex of 2 year old mice, Bmi1 expression was reduced in Tg-SwDI mice, which is rescued by USP16 haploinsufficiency, in both the SVZ and the cortex. This data is shown in Figure 3 —figure supplement 1.

5. In Figure 5E, is this quantification from the ELDA assay or a different assay? If different, ELDA should be used to remain consistent.

The nature of our ELDA assay is not conducive to effective dosing of 1-5 cells in a 96 well format every single day. Typically, ELDA studies with drug dosing such as that in Gao et al. are limited to dosing the cells before plating and not treating the cells during growth which limits the effectiveness of the study (Gao et al., 2019). We therefore opted to perform a colony assay instead which allowed us to effectively observe differences when testing different concentrations on wild type and mutant APP neurospheres and also allowed us to accurately dose the neurospheres. In Lee et al. for example, they acknowledge these challenges and create a similar clonogenic assay to the ones we use here (Lee et al., 2014).

6. While the authors show quantitative data of Thioflavin staining in Figure 6B, representative images should be included in figure or supplementary. Similarly, representative images of GFAP staining for Figure 6A should be included.

These have been added as part of Figure 6 —figure supplement 1.

7. The authors should be more overall more critical with their own data, and explain what aspects of their model system are likely useful and can be translated to a human situation, and which ones are not. I would be very hesitant to commit to what extent the mouse SVZ relates to the human situation. A phenotype detected in a 3-4 months-old transgenic mice might be translatable to early human development (fetal/early life), to adult neurogenesis in early AD pathology (late in life), or not directly translatable. Mouse and human NPCs are extremely different cells (see work from Conti L. et al., Sun et al., Elkabetz et al., etc…), and this is also eident from the data here (e.g. compare scales in Figure 1B and 1E). I suggest the authors to please avoid rushed over-interpretation.

We recognize that all of the findings in our work may not be translatable to the human situation and have tried to adapt the text to emphasize what may or may not be translatable, however, these animal studies would need to be followed up with human studies to affirm or deny any translatability. We have also addressed this point with more detail in our revised discussion.

One important feature of our work that we suggest is translatable to humans lays in that our model system is useful in detecting an earlier phenotype present either before or at the same time as the development of the amyloid β plaques, namely the NPC self-renewal defect. While there are inherent differences between the NPCs of mice and humans, the aberrant effects on the NPCs secondary to APP mutations may similarly impact neurodegeneration and cognitive defects the mice develop later on that are not attenuated with amyloid-ß reduction. The timing of therapeutic treatment in Alzheimer’s disease seems to be crucial as studies have shown that treating the disease too late has little efficacy (Sperling et al., 2011; Yiannopoulou, Anastasiou, Zachariou, and Pelidou, 2019). We therefore hypothesize that when plaques are first visible in the patient, therapeutic reduction of Usp16 or BMP signaling may reduce the defect contributing to symptomatic AD later on in life, as seen in our mouse model. Of course, these studies would need to be performed on humans before making this claim.

8. It is not clear how the authors come to the conclusion that this "suggests that the self-renewal defect […] not specific to the Swedish, Dutch and Iowa mutations, but also more broadly seen with other APP mutations" (Figure 1E/p5). They did not test different expression/protein levels of wt, the individual mutations, or combinations, which would be necessary to make such a statement.

In the text, we have fixed this to simply state the two relevant models we have focused on: one with Swedish, Dutch, and Iowa mutations and the other with Swedish and Indiana mutations so as to not make any overstatements. Our statement was meant to highlight that with more than one type of APP mutation, we see a similar effect in NPCs. In this study, it would not be particularly useful to compare levels of expressed APP in WT and transgenic mice as a human transgene is required in this disease model; mutations in the endogenous mouse App gene do not produce clinical disease. However, we did test different expression levels of endogenous and mutant APP in both the human and murine cells, which are shown in response to reviewer point #9 below.

9. The attempt for assessing the effect of mutant APP on NPC phenotypes in the human system is a strength of this study. In Figure 1E/p5 the authors claim that "the effects observed in NPCs derived from a genetic mouse model can be robustly recapitulated in human NPCs expressing mutant APP". As to the APP setting, both models are very different, in that human cells possess two copies of endogenous APP. "Fetal human NPCs" are not very well defined, an no characterization of endogenous/transgenic APP expression/protein levels are provided here. The choice of this models seems suboptimal. Also, to draw meaningful conclusions, the effect should be tested on different genetic backgrounds and well-characterized NPCs (e.g. from APP-mutant iPSCs). The conclusion that the "effects observed in NPCs derived from a genetic mouse model can be robustly recapitulated in human NPCs" is thus not appropriate and probably vastly oversimplified.

We agree with the reviewer that our conclusion is oversimplified so we have appropriately corrected our choice of words to describe these results in our text. In selecting a human model, we wanted to choose primary cells that were easily available to us and would limit other potential sources of variation (such as different genetic backgrounds). As the reviewer stated, one strength of this study is our attempt to show effect of mutant APP on NPC phenotype. The transduction of the human fetal NPCs with either ZsGreen or mutant APP narrows down the phenotypes we see to solely the effects of the mutant APP. To further characterize our system, we show in Figure 1 —figure supplement 1 that three different transductions of human fetal NPCs have resulted in at least a 2 fold increase in APP expression in neurospheres compared to ZsGreen controls.

To provide a characterization of mutant APP in our mouse model, we can turn to our sc-RNA-seq data where we conducted a differential expression gene test and found an ~1.5 fold change between our Tg-SwDI and WT cells in both transgenic (labeled “APPSDI”) and endogenous App expression (also shown in Figure 4 —figure supplement 2). Our conclusion can thus be amended to say that the decrease in neurosphere formation in NPCs derived from our genetic mouse model has also been observed in our human fetal model with roughly the same fold increase in mutant APP expression.

10. The authors show that the neurosphere phenotype of NPCs of 3-month-old mice that was rescued by Cdkn2a-KO. This is followed by the statement (line158) that APP mutations accelerate aging through Cdkn2a. In the context pf the presented data, this is a prominent overstatement.

We have updated the manuscript to more accurately depict our findings, and the text now reads, “These results demonstrate that impairment of NPC regeneration, as measured by NIC frequencies, is a function of aging that is accelerated by APP mutations and is mitigated through loss of Cdkn2a…”

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https://doi.org/10.7554/eLife.66037.sa2

Article and author information

Author details

  1. Felicia Reinitz

    Institute of Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, United States
    Contribution
    Conceptualization, Formal analysis, Investigation, Methodology, Project administration, Validation, Visualization, Writing – original draft, Writing – review and editing
    Contributed equally with
    Elizabeth Y Chen and Benedetta Nicolis di Robilant
    Competing interests
    filer of a provisional patent: U.S.Provisional Application No. 63/124,644 titled "Modulating BMP signaling in the treatment of Alzheimer's disease"
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4952-4457
  2. Elizabeth Y Chen

    Institute of Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, United States
    Contribution
    Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Software, Validation, Visualization, Writing – original draft, Writing – review and editing
    Contributed equally with
    Felicia Reinitz and Benedetta Nicolis di Robilant
    Competing interests
    filer of a provisional patent: U.S.Provisional Application No. 63/124,644 titled "Modulating BMP signaling in the treatment of Alzheimer's disease"
  3. Benedetta Nicolis di Robilant

    Institute of Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, United States
    Contribution
    Conceptualization, Formal analysis, Investigation, Project administration, Validation, Visualization, Writing – original draft, Writing – review and editing
    Contributed equally with
    Felicia Reinitz and Elizabeth Y Chen
    Competing interests
    is the co-founder of Dorian Therapeutics. Dorian therapeutics was incorporated in June 2018 and it is an early stage anti-aging company that focuses on the process of cellular senescence. Most of the experiments were performed before the company was formed
  4. Bayarsaikhan Chuluun

    Department of Biology, Stanford University, Stanford, United States
    Contribution
    Investigation, Project administration, Validation
    Competing interests
    No competing interests declared
  5. Jane Antony

    Institute of Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, United States
    Contribution
    Formal analysis, Investigation, Validation, Writing – review and editing
    Competing interests
    filer of a provisional patent: U.S.Provisional Application No. 63/124,644 titled "Modulating BMP signaling in the treatment of Alzheimer's disease"
  6. Robert C Jones

    Department of Bioengineering, Stanford University, Stanford, United States
    Contribution
    Data curation, Project administration, Resources, Software
    Competing interests
    filer of a provisional patent: U.S.Provisional Application No. 63/124,644 titled "Modulating BMP signaling in the treatment of Alzheimer's disease"
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7235-9854
  7. Neha Gubbi

    Institute of Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, United States
    Contribution
    Investigation, Validation
    Competing interests
    No competing interests declared
  8. Karen Lee

    Institute of Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, United States
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  9. William Hai Dang Ho

    Institute of Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, United States
    Contribution
    Investigation, Resources
    Competing interests
    No competing interests declared
  10. Sai Saroja Kolluru

    Department of Bioengineering, Stanford University, Stanford, United States
    Contribution
    Project administration, Resources
    Competing interests
    No competing interests declared
  11. Dalong Qian

    Institute of Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, United States
    Contribution
    Funding acquisition, Project administration, Resources
    Competing interests
    No competing interests declared
  12. Maddalena Adorno

    Institute of Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, United States
    Contribution
    Supervision
    Competing interests
    is the co-founder of Dorian Therapeutics. Dorian therapeutics was incorporated in June 2018 and it is an early stage anti-aging company that focuses on the process of cellular senescence. Most of the experiments were performed before the company was formed
  13. Katja Piltti

    Sue and Bill Gross Stem Cell Research Center, University of California, Irvine, Irvine, United States
    Contribution
    Methodology, Resources
    Competing interests
    No competing interests declared
  14. Aileen Anderson

    Sue and Bill Gross Stem Cell Research Center, University of California, Irvine, Irvine, United States
    Contribution
    Methodology, Resources
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8203-8891
  15. Michelle Monje

    Institute of Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, United States
    Contribution
    Conceptualization, Resources, Supervision, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3547-237X
  16. H Craig Heller

    Department of Biology, Stanford University, Stanford, United States
    Contribution
    Resources, Supervision
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4479-5880
  17. Stephen R Quake

    Department of Bioengineering, Stanford University, Stanford, United States
    Contribution
    Funding acquisition, Resources, Supervision
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1613-0809
  18. Michael F Clarke

    Institute of Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, United States
    Contribution
    Conceptualization, Funding acquisition, Resources, Supervision, Writing – review and editing
    For correspondence
    mfclarke@stanford.edu
    Competing interests
    filer of a provisional patent: U.S.Provisional Application No. 63/124,644 titled "Modulating BMP signaling in the treatment of Alzheimer's disease"
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6889-4926

Funding

California Institute of Regenerative Medicine (Graduate Student Fellowship)

  • Elizabeth Y Chen

Chan Zucherberg Foundationg Biohub Initiative

  • Elizabeth Y Chen
  • Robert C Jones
  • Sai Saroja Kolluru
  • Stephen R Quake

National Institutes of Health (1R01AG059712-01)

  • Felicia Reinitz
  • Elizabeth Y Chen
  • Benedetta Nicolis di Robilant
  • Jane Antony
  • Neha Gubbi
  • Dalong Qian
  • Michael F Clarke

National Institutes of Health (AG059712)

  • Michael F Clarke

Tung Foundation

  • Felicia Reinitz

AIRC and Marie Curie Action

  • Benedetta Nicolis di Robilant

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

Acknowledgements

The authors thank several individuals, including Siddhartha Mitra, James Lennon, Sam Cheshier, Jordan Roselli, Stephen Ahn, Grace Hagiwara, Mike Alvarez, Pieter Both, Jami Wang, Ben W Dulken, Vincent M Alford, and Aisling Chaney. Flow cytometry analysis for this project was done on instruments in the Stanford Shared FACS Facility; BD FACSAriaII was purchased by NIH S10 shared instrumentation grant 1S10RR02933801. The authors thank the Stanford Neuroscience Microscopy Service, supported by NIH NS069375. Funding: California Institute of Regenerative Medicine. Chan Zuckerberg Biohub. NIH R01AG059712. Harriet and CC. Tung Foundation – FR. AIRC and Marie Curie Action – BNdR – COFUND.

Ethics

Mice were housed in accordance with the guidelines of Institutional Animal Care Use Committee. All animal procedures and behavioral studies involved in this manuscript are compliant to Stanford Administrative Panel on Laboratory Animal Care (APLAC) Protocol 10868 pre-approved by the Stanford Institutional Animal Care and Use Committee (IACUC).

Senior Editor

  1. Jeannie Chin, Baylor College of Medicine, United States

Reviewing Editor

  1. Jessica Young, Institute for Stem Cell and Regenerative Medicine (ISCRM), United States

Reviewers

  1. Jessica Young, Institute for Stem Cell and Regenerative Medicine (ISCRM, United States
  2. Zachary McEachin, Emory University, United States

Publication history

  1. Preprint posted: December 22, 2020 (view preprint)
  2. Received: December 22, 2020
  3. Accepted: March 17, 2022
  4. Accepted Manuscript published: March 21, 2022 (version 1)
  5. Version of Record published: May 20, 2022 (version 2)

Copyright

© 2022, Reinitz et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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  1. Felicia Reinitz
  2. Elizabeth Y Chen
  3. Benedetta Nicolis di Robilant
  4. Bayarsaikhan Chuluun
  5. Jane Antony
  6. Robert C Jones
  7. Neha Gubbi
  8. Karen Lee
  9. William Hai Dang Ho
  10. Sai Saroja Kolluru
  11. Dalong Qian
  12. Maddalena Adorno
  13. Katja Piltti
  14. Aileen Anderson
  15. Michelle Monje
  16. H Craig Heller
  17. Stephen R Quake
  18. Michael F Clarke
(2022)
Inhibiting USP16 rescues stem cell aging and memory in an Alzheimer’s model
eLife 11:e66037.
https://doi.org/10.7554/eLife.66037
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