Introduction
Ataxia Telangiectasia (A-T) is a rare (one in ~100,000) (Swift et al., 1986), autosomal recessive genetic disorder characterized by cancer predisposition, immune deficiency, and a highly penetrant progressive and severe ataxia linked to cerebellar atrophy (Rothblum-Oviatt et al., 2016; Boder and Sedgwick, 1958; Levy and Lang, 2018). A-T patients typically die in their second or third decade of life (Crawford et al., 2006) from lymphatic cancers, respiratory infections, or complications of ataxia—unfortunately, survivability has not dramatically changed since the 1950s (Micol et al., 2011; Rothblum-Oviatt et al., 2016). While disease progression and cause of death vary widely across patients, the highly penetrant progressive decline in motor coordination is reported as having the greatest negative impact on a patient’s quality of life (Jackson et al., 2016). Care is generally palliative, directed at reducing, limiting, or eliminating cancers or infections. No long-term therapies are available for treating the ataxia and associated cerebellar dysfunction and atrophy.
A-T is caused by deficiency or dysfunction of the ATM (A-T mutated) protein (Savitsky et al., 1995). Premature termination codon (PTC) causing nonsense mutations account for up to a half of known cases, with missense and deletion mutations also contributing (Concannon and Gatti, 1997; Sandoval et al., 1999). ATM is a serine/threonine PIKK family kinase that plays a key role in the DNA damage response (DDR), protecting cells from the tens of thousands of DNA lesions incurred each day (Lindahl and Barnes, 2000; Kastan and Bartek, 2004; Shiloh and Ziv, 2013). In the active monomeric form, ATM phosphorylates several key proteins halting the production of new DNA (cell cycle arrest) (Ando et al., 2012), and then, depending on severity of the damage, initiates DNA repair or programmed cell death (i.e., apoptosis) (Ando et al., 2012; Rashi-Elkeles et al., 2006). Several downstream DDR pathway targets of ATM have been identified, including p53, CHK2, BRCA1, SMC1, and NBS1 (Matsuoka et al., 2007). ATM’s role in DNA repair is also implicated in normal immune system development, where it is proposed to contribute to the recombination of natural DNA splicing that occurs during gene rearrangement in T- and B-lymphocyte maturation (Chao et al., 2000; Matei et al., 2006; Vacchio et al., 2007; Schubert et al., 2002). Although its roles are still emerging, ATM has also been implicated in oxidative stress homeostasis (Guo et al., 2010) and mitophagy (Valentin-Vega and Kastan, 2012; Pizzamiglio et al., 2020).
It is unclear why ATM deficiency causes ataxia, but it is far from the only DDR protein linked to ataxia, as Aprataxin (APTX) (Aicardi et al., 1988), Meiotic recombination 11 homolog 1 (MRE11) (Sedghi et al., 2018), Nibrin (NBS1) (van der Burgt et al., 1996), Senataxin (SETX) (Moreira et al., 2004), and Tyrosyl-DNA Phosphodiesterase 1 (TDP1) (Takashima et al., 2002) when absent or dysfunctional can cause cerebellar-related ataxia. This suggests that the neurological features of genome instability syndromes have a common underlying cause, although this has yet to be mechanistically demonstrated (McKinnon, 2009; Rass et al., 2007).
A major factor limiting our ability to define why the loss of DDR proteins, like ATM, selectively impacts the cerebellum and causes progressive ataxia is the lack of an animal model that recapitulates these neurological symptoms (Lavin, 2013). Several A-T rodent models have been created over the past several years by inserting gene mutations that cause protein dysfunction (lack kinase activity) or complete deficiency (Herzog et al., 1998; Xu and Baltimore, 1996; Elson et al., 1996; Spring et al., 2001; Campbell et al., 2015; Quek et al., 2016; Tal et al., 2018; Lavin, 2013); a minipig was also recently reported (Beraldi et al., 2017). However, none develop an overt, progressive ataxia with cerebellar dysfunction and atrophy that recapitulates the human disease, even though other aspects of the disorder like thyroid cancers, infertility, and immune abnormalities do develop. It remains unclear why these prior animal models fail to display the progressive ataxic phenotype (Lavin, 2013). It is possible that species-specific molecular compensations in mice provide redundancies or alternative pathways minimizing the effects of ATM deficiency in the brain (El-Brolosy and Stainier, 2017). It is also possible that the shortened lifespan of prior models (Barlow et al., 1996) is too brief for the stochastic mechanisms driving cerebellar dysfunction and atrophy to accumulate and impact motor behavior. Other challenges include potentially leaky genetic manipulations that result in low levels of ATM protein or active fragments with residual kinase activity, thus limiting neuropathology (Li et al., 2011). The impact of missing such a crucial animal model has been significant, severely limiting experimental studies from identifying the cellular and molecular mechanisms and hampering pre-clinical development and testing of much needed therapeutics.
We test here whether increasing genotoxic stress, by placing null mutations in not just the
Finally, we designed this new mouse model to test our recently developed Small Molecule Read-Through Compounds (SMRT) that enable translation through PTCs (Du et al., 2013). Thus, we inserted a PTC-causing nonsense mutation (103C>T) in the
Results
Creation of a new A-T mutant mouse model expressing a clinically relevant nonsense mutation
To create a more clinically relevant mouse model of A-T, we used a gateway recombination cloning and site-directed mutagenesis method to recapitulate a c.103C>T (p.R35X) mutation in the
Figure 1.
New A-T mouse models expressing clinically related PTCs.
(A) The
Video 1.
Pole test
Video 2.
Pole test
Video 3.
Pole test
Video 4.
Open field
ATM-deficient mice have lowered survivability and a high incidence of thymomas
We assessed the general health and development of control and experimental mice expressing different levels of ATM and APTX (Figure 2). We found that
Figure 2.
Health and survivability of single and double mutant mice.
(A)
Figure 2—figure supplement 1.
Animal weight for each time point and genotype.
(A) The average weights are plotted for each genotype at each of the indicated time points. Two-way ANOVA with age and genotype as factors excluding the double mutant mice data. Male: F(10, 226)=5.6, p<0.0001; Female: F(10, 197)=7.3, p<0.0001. (B) The survivability of each genotype of mice is plotted for male and female individually.
Both ATM and APTX deficiency are necessary to produce progressive motor dysfunction
The progressive development of severe ataxia is a hallmark characteristic of A-T that is recapitulated in the
Figure 3.
(A)
Figure 3—figure supplement 1.
(A)
We further examined behavioral differences between the
Membrane and synaptic properties are perturbed in ATM- and APTX-deficient Purkinje neurons
Purkinje neurons (PNs) are a key neuronal subtype located in the cerebellar cortex. They display considerable intrinsic excitability, firing action potentials (APs) spontaneously at rates significantly higher than most other neurons in the brain (50–100 Hz more in many cases). Their activity shapes cerebellar output via tonic inhibition of neurons of the cerebellar nuclei, which project to motor coordination centers in the forebrain, brainstem, and spinal cord. Cerebellar PN dysfunction is associated with several forms of ataxia and implicated in A-T (Hoxha et al., 2018; Cook et al., 2021; Shiloh, 2020). We therefore examined if the electrophysiological properties of PNs in the
Since PN baseline activity and responsivity to input is mediated by a baseline set of passive and active membrane properties (Figure 4), we directly recorded from and compared the membrane properties of PNs in acute cerebellar sections harvested from
Figure 4.
The biophysical properties of PNs are significantly perturbed in
(A) Schematic diagram of intracellular recording from a single Purkinje neuron (PN) in an acute cerebellar tissue slice preparation used to examine their physiological properties. (B)
Figure 4—figure supplement 1.
Current versus voltage responses significantly differ between
(A) PN voltage responses to various current steps between –500 and 2250 pA (250 pA steps) from a –70 mV holding current in
Figure 4—figure supplement 2.
Mean PN firing frequency across the cerebellum.
Average PN firing frequency (Hz) is plotted across the indicated locations at P45, P120, P210, and P400. Significance tested via two-way ANOVA with age and genotype as factors. PN, Purkinje neuron.
Figure 4—figure supplement 3.
Mean PN firing frequency across genotype and sex.
Average PN firing frequency (Hz) for all cells recorded from male and female mice is plotted for the indicated genotype. No significant differences were observed between sex. Two-way ANOVA with age and sex as factors,
Figure 4—figure supplement 4.
Coefficient of variation (CV) of PN firing frequency across the cerebellum.
Average CV of PN firing frequency is plotted across the indicated locations at P45, P120, P210, and P400. No significant differences (p<0.5) were detected across all areas using two-way ANOVA with age and genotype as factors. PN, Purkinje neuron.
Figure 4—figure supplement 5.
Mean variation between PN firing intervals across the cerebellum.
Average CV2 of PN firing frequency is plotted across the indicated locations at P45, P120, P210, and P400. No significant differences (p<0.5) were detected across all areas using two-way ANOVA with age and genotype as factors. PN, Purkinje neuron.
We next tested whether extrinsic and/or synaptic PN properties were also impacted in
ATM and APTX deficiency causes a progressive perturbation of PN neural activity that is associated with dendritic shrinking and overall cerebellar atrophy
Decreased rates of spontaneous PN AP firing, which can be indicative of PN dysfunction, have been observed in several mouse models of ataxia, including spinocerebellar ataxias (SCA) 2, 3, 5, 6, 13, and 27, several models of episodic ataxia (e.g., leaner, ducky, and tottering), and autosomal-recessive spastic ataxia of the Charlevoix-Saguenay (Hourez et al., 2011; Hansen et al., 2013; Dell’Orco et al., 2017; Kasumu and Bezprozvanny, 2012; Liu et al., 2009; Perkins et al., 2010; Shakkottai et al., 2011; Jayabal et al., 2016; Stoyas et al., 2020; Hurlock et al., 2008; Shakkottai et al., 2009; Bosch et al., 2015; Walter et al., 2006; Alviña and Khodakhah, 2010; Ady et al., 2018; Larivière et al., 2019; Cook et al., 2021). We therefore used this biomarker to characterize the progression of PN perturbation in
Using extracellular recording methods in the acute slice, we recorded spontaneous APs from 3300 PNs (Figure 4G) across 188 animals, encompassing
We found that complete deficiency of both ATM and APTX, consistent with the behavioral results, was necessary to produce a significantly reduced spontaneous PN firing frequency (Figure 4G and H). Although the trend of slower PN firing rates was observed across most regions of the cerebellum, some subregions appeared to be less or minimally impacted, including several areas of the lateral cerebellum, including the paraflocculus, paramedian, and crus I and II (Figure 4—figure supplement 4–2). Significant age-dependent changes in firing frequency were also only observed in
ATM-and APTX-deficiency induces cerebellar atrophy
In A-T patients, ataxia is usually detected between 1 and 2 years of age and is associated with little to no cerebellar atrophy (Tavani et al., 2003; Taylor et al., 2015). Significant structural changes and atrophy are usually first detected via neuroimaging between 5 and 10 years of age (Demaerel et al., 1992; Tavani et al., 2003). Postmortem clinical histopathology in A-T patients points to significant changes in PN morphology and density; however, these reports primarily detail patients at late stages of the disorder, and the relationship between the severity of PN pathology and ataxia is not clear (Verhagen et al., 2012; De León et al., 1976; Amromin et al., 1979; Monaco et al., 1988; Terplan and Krauss, 1969; Strich, 1966; Solitare, 1968; Solitare and Lopez, 1967; Aguilar et al., 1968; Paula-Barbosa et al., 1983; Gatti and Vinters, 1985).
In the
Figure 5.
Cerebellar atrophy is associated with a progressive reduction in molecular layer (ML) width and pathological changes in PN morphology but not PN cell death.
(A) Cartoon image of the brain highlighting the dorsal forebrain and cerebellar surface. Area estimates from dorsal images of the brain were used to determine the cerebellum to forebrain ratio allowing us to control for any differences in overall size of the brain. We found the cerebellum decreased in size over age in
Figure 5—figure supplement 1.
Decreased molecular layer (ML) width but not cell death is a key feature of the A-T model.
(A) Width measurements of the molecular layer and granule cell layer—ML and GCL, respectively—for each lobule across the medial intermediate and lateral areas of the cerebellum. (B)
Figure 5—figure supplement 2.
Cerebellar degeneration in the A-T model is not associated with micro glial activation or cell death markers but is associated with significant swelling of PN dendrites.
(A) Fluorescent images of anti-microglial activation (CD68) staining in
We found cerebellar atrophy to be associated with a selective reduction in the width of the molecular layer (ML) where PN dendrites reside (Figure 5B). Consistent with the temporal changes in gross cerebellar size, PN firing frequency, and behavior, ML width in
Differential disruption of thymocyte development in ATM-deficient versus APTX-deficient mice
Chronic sinopulmonary infections associated with immunodeficiency are one of the leading causes of death in A-T patients (Morrell et al., 1986; Bhatt and Bush, 2014). Immunodeficiency is linked to deficits in the generation of B- and T-lymphocytes that have been linked to defects in the antigen receptor gene rearrangement processes during the generation of these cells in the bone marrow and thymus, respectively (Staples et al., 2008). The resulting defects in mature lymphocyte numbers include decreases in CD4+ helper T-cells and killer CD8+ T-cells (Schubert et al., 2002). We therefore examined the percentages of T-cells in peripheral blood and of different subpopulations in the thymus of
In the peripheral blood, we observed a significant reduction in the total fraction of CD3+ T-cells in mice with reduced or absent ATM expression compared to wild-type mice (Figure 6). This reduction was further compounded by the concomitant deficiency of APTX. ATM and APTX deficiencies reduced T-cells in peripheral blood by over 65% compared to wild-type controls. The effect of APTX deficiency was additive to that of ATM deficiency, suggesting a different mechanism of action for each of these two proteins on T-cell generation. The reduction in the percentage of T-cells in peripheral blood was mostly associated with reduction in the CD4+ helper T-cell population (Figure 6B). Of interest, the proportion of CD8+ T-cells was increased only in
Figure 6.
T-cell deficits are found in the blood of
(A) Representative flow cytometric profiles of T-cell glycoprotein marker CD3 and summary plots indicate ATM- and/or APTX-deficient mice have decreased proportions of CD3+ T-cells in the blood. (B) Representative flow cytometric profiles of T-cell glycoprotein markers CD4 and CD8 gated on CD3+ cells and summary plots for CD8 and CD4 single positive cell proportions. ATM-deficient mice had reduced CD4+ proportions compared to mice with at least one copy of the
Given the reduction in T-cell populations in the blood, we next assessed T-cell development in the thymus. In this organ, bone marrow-derived T-cell progenitors undergo TCR gene rearrangement followed by positive selection for MHC restriction and negative selection of autoreactive clones. The phases of thymocyte development can be followed by monitoring the expression of CD4 and CD8 expression in thymocytes. The progression of this developmental program goes from double negative (CD4−CD8−) thymocytes to double positive (CD4+CD8+) thymocytes and then to single positive (CD4+ or CD8+) thymocytes. In addition, within the double negative stage, four different subpopulations can be identified, based on the expression of CD25 and CD44, known as DN1 (CD44+CD25−), DN2 (CD44+CD25+), DN3 (CD44−CD25+), and DN4 (CD44−CD25−) (Germain, 2002).
Gene rearrangement during thymocyte development occurs twice—once at the double negative thymocyte stage in the CD25+CD44− stage (Krangel, 2009) and then again in double positive thymocyte stage before progressing into separate CD4+ and CD8+ single positive populations (Livák et al., 1999). ATM deficiency has been linked to defects in both bouts of rearrangement in mice (Vacchio et al., 2007, Hathcock et al., 2013). Therefore, we compared the proportion of cells in the thymus expressing these different developmental cell surface markers in our ATM-deficient and control mice (Figure 7).
Figure 7.
ATM and APTX deficiency confer deficits in T-cell expression, but at different developmental stages.
(A) Representative flow cytometric profiles of T-cell glycoprotein markers CD44 and CD25 gated on CD4−CD8− double negative (DN) cells. Summary plots show proportions of thymocytes at DN stages 1–4 (left to right). APTX deficient mice display increased proportions for DN1–3 and decreased proportion at DN4 consistent with a deficit in ontogeny from DN3 to DN4. (B) Representative flow cytometric profiles of T-cell glycoprotein markers CD4 and CD8 gated. ATM-deficient mice display decreased proportions for CD4 and CD8 single positive cells consistent with a deficit in ontogeny from CD4+CD8+ double positive to CD4+ and CD8+ single positive fates. Statistical significances were assessed via one-way ANOVA followed by Tukey’s pairwise multiple comparisons test. Number of animals denoted at bottom of bars. Symbol/color key:
Next, we looked at the proportions of CD4+CD8+ thymocytes compared to CD4+CD8− and CD4−CD8+ single positive thymocytes in these four different strains. In agreement with our results in the blood and prior studies, we found that ATM-deficient mice but not control mice displayed decreased expression of CD4+CD8− and CD4−CD8+ single positive thymocytes (Figure 7B). These results support the role of ATM in TCR α/δgene rearrangement during thymocyte development (Bredemeyer et al., 2006), a role that is independent of the role played by APTX in early thymocyte maturation.
Read-through molecules overcome PTC to restore ATM expression
Our primary rationale for inserting a clinically relevant nonsense mutation in the
Figure 8.
ATM protein expression is restored after read-through compound exposure in explant tissues from
Spleen and cerebellar explant tissue from
Discussion
By increasing genotoxic stress through the addition of a secondary hit to the DDR pathway, we generated a novel mouse model that displays the most comprehensive set of A-T symptoms of any model to date. This includes a severe and progressive ataxia associated with cerebellar atrophy and perturbations of PN properties along with a high incidence of cancer and defects in immune cell development. Taken together, these comorbidities encompass the three leading causes of premature death in A-T—each contributing to roughly a third of deaths. Of these, the incapacitating effect of ataxia is the most penetrant and is reported by patients and caregivers as having the greatest impact on their quality of life. For this reason, the presence of ataxia and cerebellar atrophy in this new mouse model is of great significance, as it provides for the very first time a resource to not only elucidate the mechanisms of neurological dysfunction, but also a critically needed in vivo model to test severely needed A-T therapeutics, such as the read-through compounds we describe here.
We found several similarities between the overall progression of ataxia in the
The loss of motor coordination in A-T has been attributed to cerebellar degeneration due to its relatively selective neuropathology across the brain and its causal role in several different forms of ataxia (Hoche et al., 2012). Consistent with A-T patient neuroimaging studies (Wallis et al., 2007; Sahama et al., 2015; Sahama et al., 2014; Dineen et al., 2020; Tavani et al., 2003; Quarantelli et al., 2013), we find that cerebellar size in
The reason why ATM and APTX deficiency is required to generate ataxia in mice, when loss of either is sufficient to cause ataxia in humans, remains unclear. One possibility is that the rodent brain may more flexibly utilize compensatory pathways or redundant proteins while responding to the ~15k DNA lesions that impact cells each day (Lindahl and Barnes, 2000). Several forms of DNA repair exist to potentially meet this challenge, including base excision repair (BER), nucleotide excision repair (NER), as well as homologous and non-homologous end joining (HEJ and NHEJ, respectively), all of which ATM and APTX have been implicated in (Chou et al., 2015; Caglayan et al., 2017; Wakasugi et al., 2014; Tumbale et al., 2018; Chatterjee and Walker, 2017). Alternatively, it may be the case that deficiency in ATM or APTX alone does not adequately impact cell health during the mouse’s comparatively short lifespan, and thus eliminating both proteins is necessary to achieve sufficient accumulation of DNA damage to manifest over this time period. This possibility is strengthened by the fact that ATM and APTX have distinct biochemical properties and functional roles in the DDR, and therefore deficiency in both would be predicted to cause a broader, additive hit to genome stability than either alone (i.e., increased genotoxic stress).
Our finding that two genome stability pathway proteins are required to induce neurological defects in mice strongly suggests that it is the loss of ATM’s role in DNA repair, rather than potential functions in oxidative stress signaling, mitophagy, or mitochondrial function that cause the cerebellar defects (Shiloh, 2020). Alternatives, however, cannot be completely ruled out, as APTX, like ATM, has been observed within the mitochondria of brain cells, where it is thought to support the processing of mitochondrial DNA (Meagher and Lightowlers, 2014; Sykora et al., 2011). This new mouse model provides a new tool to explore these possibilities and mechanistically define how loss of ATM and APTX ultimately causes cerebellar dysfunction.
The biophysical perturbations observed in PNs recorded from the
Given the significant overlap in PN perturbations observed across many different ataxias caused by distinct cellular defects, restoring PN AP firing frequencies has been considered as a broad-based therapeutic approach. However, it remains unclear whether reduced PN firing is a causal factor of ataxia. Moreover, experimental evidence suggests changes in PN activity may in fact be a generalized response to maintain homeostasis during ongoing disease-related impairment of PN physiology (Dell’Orco et al., 2015). Thus, continued efforts across all cerebellar ataxias are needed to link the genetic, molecular, and cellular disruptions caused by disease to the specific changes in cerebellar neural signaling that ultimately generates the ataxia. Of significant importance in this effort will be determining whether disease-causing cerebellar defects commonly or differentially cause ataxia through a loss of cerebellar function (e.g., loss of coordinating signals during movement), or from a dominant negative effect (e.g., disrupting downstream neural circuits with abnormal neural output patterns). Ultimately, while a common therapeutic strategy to address cerebellar ataxias would have the greatest impact, a directed approach that addresses the distinct genetic and molecular causes of cellular dysfunction may ultimately be necessary to successfully develop an efficacious therapeutic.
The mechanistic link between deficiency in DNA stability proteins like ATM and APTX and PN dysfunction is far from clear. Our results suggest the effect of ATM and APTX loss on PNs is intrinsic, as we do not find changes in the presynaptic properties of granule cells or evidence of their cellular loss (no change in GCL thickness). Moreover, while we observed differences in short term plasticity of inferior olivary inputs in ATM- and APTX-deficient PNs and wildtype, these results likely point to a disruption in Ca2+ homeostasis potentially via reductions in Inositol 1,4,5-triphosphate receptor 1 (
In the immune system, ATM is implicated in the repair of DNA breaks that naturally occur during gene rearrangement of antigen receptor genes in B- and T-cell precursors, a phenomenon critical for antigen receptor (Ig and TCR) diversity of these cells. Our finding that T-cell proportions in the blood are significantly reduced is consistent with prior studies in humans and A-T knockout mice (Schubert et al., 2002; Hathcock et al., 2013; Chao et al., 2000; Barlow et al., 1996). This reduction of T-cells in the periphery likely correlates with a defect in both cellular and humoral immunity. Importantly, we found that expression of one copy of the ATM gene is enough to restore CD4+ deficits in the blood indicating that therapies able to restore partial ATM expression would have therapeutic efficacy. Although we have not assessed B-cell development in this paper, it is likely that similar conclusions would apply to that process given their mechanistic similarities (Marshall et al., 2018).
As expected, the reduction of T-cells in peripheral blood is correlated with defective thymocyte development. In the thymus, we found two main defects. One, induced primarily by APTX deficiency, manifests as a defect in the DN3 to DN4 transition coinciding with early rearrangement of TCR β locus. The other defect, primarily caused by ATM deficiency, correlates with decreased progression of double positive CD4+CD8+ to single positive cells, primarily CD4+ thymocytes. While the APTX finding was surprising, as its deficiency (AOA 1) is not associated with immune deficits, APTX is known to interact with TCR β gene rearrangement proteins, including XRCC4 (Clements et al., 2004). Future studies aimed at defining APTX’s role in end-joining mechanisms during TCR gene rearrangement will be important, and the possibility that alternative end-joining mechanisms, like the use of microhomologies account for the lack of an immune deficit in its absence needs further investigation (Bogue et al., 1997).
The survivability of
Given the global nature of the ATM and APTX null mutation in our mouse model, we cannot entirely rule out that extra-cerebellar defects may also contribute to the severe ataxic phenotype, and thus future examination outside the cerebellum in the forebrain, brainstem, spinal cord, and even muscle will need to be conducted. Within the cerebellum, while we found some anatomical differences in the PN firing properties within different regions of the cerebellum, we did not detect regional differences in ML width or PN density. However, there are challenges in using regional anatomy as a grouping factor in the cerebellum, as the physical folds of the tissue do not necessarily correlate with the boundaries of functional, molecular expression, or physiological property domains that have been described (Apps and Hawkes, 2009; Tsutsumi et al., 2015; Gao et al., 2012; Zhou et al., 2014). Experiments focused on examining the extent of cerebellar defects within these domains will be important in future studies and compared to the anecdotal reports of anatomical differences in A-T patients (Verhagen et al., 2012; De León et al., 1976; Amromin et al., 1979; Monaco et al., 1988; Terplan and Krauss, 1969; Strich, 1966; Solitare, 1968; Solitare and Lopez, 1967; Aguilar et al., 1968; Paula-Barbosa et al., 1983).
While we detect two potential stages in the progression of ataxia in the
Finally, pinpointing where, when, and how ATM deficiency causes cerebellar pathology and ataxia has been a challenge, as prior ATM-deficient mice generally lack the characteristic features needed to causally link cellular and molecular deficits to the ataxic phenotype. Multiple promising avenues of investigation have been defined, including those focused at the neuronal level, where ATM is implicated in oxidative stress signaling (Chen et al., 2003) and synaptic function (Li et al., 2009; Vail et al., 2016), as well as glial function, where recent evidence suggests glial pathology may be a leading factor in cerebellar pathology (Kaminsky et al., 2016; Campbell et al., 2016; Petersen et al., 2012; Weyemi et al., 2015). This novel animal model provides a new tool to test mechanistic hypotheses regarding how ATM deficiency causes cerebellar pathology and ataxia. Additionally, this model may serve most importantly as a critical preclinical tool for testing previously proposed therapeutic candidates (Browne et al., 2004; Chen et al., 2003) including our own SMRT compounds (Du et al., 2013). It cannot be overstated how severely limiting the lack of a preclinical model is for therapeutic development, especially for rare disorders like A-T and AOA1.
Materials and methods
Key resources table
| Reagent type (species) or resource | Designation | Source or reference | Identifiers | Additional information |
|---|---|---|---|---|
| Strain, strain background ( | This paper | 103C>T mutation, | Generated by Hicks laboratory. Has been backcrossed into C57b/6 9 times. Contact [email protected] | |
| Strain, strain background ( | This paper | 103C>T mutation, | Generated by Hicks laboratory. Has been backcrossed into C57b/6 9 times. Contact [email protected] | |
| Strain, strain background ( |
| Ahel et al., 2006 | MGI Cat# 3687171, RRID:MGI:3687171 | Contact [email protected] |
| Gene ( |
| MGI | MGI:107202; C030026E19Rik; ENSMUSG00000034218 | |
| Gene ( |
| OMIM | OMIM: 607585 MGI: 107202 HomoloGene: 30952; ENSG00000149311 | |
| Sequence-based reagent | Atm gene | Transnetyx | PCR primers | F- |
| Sequence-based reagent | Atm gene | Transnetyx | PCR primers | R- |
| Chemical compound/ drugs | GJ103 saltFormula: C16H14N403S | TargetMol | T3448;CAS No.: 1459687-96-7 | 100 μM in media |
| Antibody | Anti-mouse CD68 (Rat monoclonal) | Bio-Rad | Cat# MCA1957, | IF (1:400) |
| Antibody | ATM (D2E2) (Rabbit- monoclonal) | Cell Signaling Technology | Cell Signaling Technology Cat# 2873, RRID:AB_2062659 | WB (1:500)WB (1:1000) |
| Antibody | GAPDH (14C10)(Rabbit-monoclonal) | Cell Signaling Technology | Cell Signaling Technology Cat# 2118, RRID:AB_561053 | WB (1:4000) |
| Antibody | β-Actin (D6A8)(Rabbit-monoclonal) | Cell Signaling Technology | Cell Signaling Technology Cat# 8457, RRID:AB_10950489 | WB (1:5000) |
| Antibody | Anti-Rabbit IgG, HRP-linked(Goat-monoclonal-polyclonal) | Cell Signaling Technology | Cell Signaling Technology Cat# 7074, RRID:AB_2099233 | WB (1:5000) |
| Antibody | Anti-Calbindin (D-28k)(Rabbit, polyclonal) | Swant Inc. | Swant Cat# CB 38, RRID:AB_10000340 | IF (1:1000) |
| Antibody | Anti-Mouse Alexa Fluor 488 (Goat polyclonal) | Thermo Fisher Scientific; Invitrogen | Cat# A11001, | IF (1:500) |
| Antibody | Anti-mouse Cleaved Caspase-3, Asp-175 (rabbit) | Cell Signaling Technology | Cat# 9961, | IF (1:200) |
| Antibody | Anti-Rat Alexa Fluor 555 (Goat polyclonal) | Thermo Fisher Scientific; Invitrogen | Cat# A21244, | IF (1:1000) |
| Antibody | Anti-Rabbit Alexa Fluor 647 (Goat polyclonal) | Thermo Fisher Scientific; Invitrogen | Cat# A-21434, | IF (1:500) |
| Antibody | Anti-Calbindin D-28k (mouse-monoclonal) | Swant Inc. | Cat# CB300 | IF (1:500) |
| Antibody | Anti-Rabbit Alexa Fluor 488 (Goat-polyclonal) | Thermo Fisher Scientific;Invitrogen | Thermo | IF (1:1000) |
| Antibody | CD4(GK1.5)(Rat-monoclonal) | Thermo Fisher Scientific;Invitrogen | Thermo | FACS(5 µl per test) |
| Antibody | CD8(53-6.7)(Rat-monoclonal) | Thermo Fisher Scientific;Invitrogen | Thermo | FACS(5 µl per test) |
| Antibody | CD3(145-2C11)(Hamster-monoclonal) | Thermo Fisher Scientific;Invitrogen | Thermo | FACS(5 µl per test) |
| Antibody | CD44(IM7)(Rat-monoclonal) | Thermo Fisher Scientific;Invitrogen | Thermo | FACS(5 µl per test) |
| Antibody | CD25(PC61.5)(Rat-monoclonal) | Thermo Fisher Scientific;Invitrogen | Thermo | FACS(5 µl per test) |
| Other | Eosin Y (Certified Biological Stain) | Thermo Fisher Scientific (Fisher Chemical) | Cat# E511-100 | |
| Other | Hematoxylin Stain Solution, Modified Harris Formulation, Mercury Free Nuclear Stain | RICCA Chemical Company | Cat# 3530-16 | |
| Other | Permount Mounting Medium | Thermo Fisher Scientific (Fisher Chemical) | Cat# SP15-100 | |
| Other | Fluoromount-G with DAPI | Southern Biotech | Cat# 0100-20, | |
| Commercial assay or kit | BCA Protein Assay Kit | Thermo Fisher Scientific;Pierce | Cat# 23225 | Protein assay |
| Commercial assay or kit | SuperSignal West Pico Chemiluminescent Substrate | Thermo Fisher Scientific;Pierce | Cat# 34580 | Chemiluminescent substrate |
| Commercial assay or kit | Radiance plus | Azure Biosystems | Cat# AC2103 | Chemiluminescent substrate |
| Software, algorithm | FlowJo | https://www.flowjo.com/solutions/flowjo | RRID:SCR_008520 | |
| Software, algorithm | ImageJ software | ImageJ (http://imagej.nih.gov/ij/) | RRID:SCR_003070 | Version 1.53 |
| Software, algorithm | IgorPro | http://www.wavemetrics.com/products/igorpro/igorpro.htm | RRID:SCR_000325 | Version 7; Tarotools procedures |
| Software, algorithm | Neuroexpress | https://www.researchgate.net/project/NeuroExpress-Analysis-software-for-whole-cell-electrophysiological-data | https://www.researchgate.net/project/NeuroExpress-Analysis-software-for-whole-cell-electrophysiological-data | Version 21.1.13; used for sEPSC analyses |
| Software, algorithm | GraphPad, Prism | GraphPad Prism (https://graphpad.com) | RRID:SCR_015807 | Versions 8 and 9 |
| Software, algorithm | MBF, Stereo investigator | https://www.mbfbioscience.com/stereology | RRID:SCR_017667 | Version 2021 |
| Software, algorithm | Microsoft Excel | https://www.microsoft.com/en-us/microsoft-365/excel | RRID:SCR_016137 | Version 365 |
| Software, algorithm | Catwalk XT | https://www.noldus.com/catwalk-xt | RRID: SCR_021262 |
Mice
All mice were group housed and kept under a 12 hr day/night cycle with food and water available ad libitum. Animals were housed within the general mouse house population, and not in specialized pathogen-free rooms. Older animals were made available wetted food or food gel packs on the ground of the cages as ataxia developed.
These mice were created to contain the c.103C>T mutation found in a large population of North African AT patients using recombineering Gateway technology and site-directed mutagenesis. A C>T mutation at this position in the mouse
A modified Gateway R3-R4-destination vector was used to pull out the desired region of the mouse
Genotyping was performed from ear tissue samples of P8-11 mice. Real-time PCR methods conducted by Transnetyx Inc were used to determine each animals’ genotype. Animals were made identifiable via toe tattoos given at the same time as ear biopsy. Unique primers for
Animal health
Animals were weighed via a digital scale at P8, P45, P120, P210, and P400. Animal death was recorded as the day found dead, or on the day of euthanization when the animals reached a humane endpoint (animal unable to right itself within 60 s, significant hair matting indicating lack of self-grooming, or excessive distress as noted by the veterinary staff). Animal carcasses were immediately frozen upon death, and postmortem necropsies were carried out in batch. Probable cause of death was determined to the best of our ability in collaboration with the staff veterinarian (Dr. Catalina Guerra) by visual inspection of the internal organs. Some mice were cannibalized or accidentally disposed of by vivarium staff and were therefore labeled as ‘missing.’ Mice with no discernable visual cause of death were labeled ‘indeterminable.’ Mice that were found with thoracic masses near where the thymus would normally be in young mice were listed as ‘thymic cancer.’ All other identified probable causes of death (e.g., enlarged livers, urinary blockage) were labeled ‘other.’
Behavior
Before performing any behavioral test, mice were acclimated to the behavioral suite for ~20 min. Mice were tested at varying times of the day, in line with their day cycle. A battery of behavioral tests were performed on naïve double mutant mice of the indicated genotypes at various time points depending on the behavior but in the same cohort of mice. The battery of tests included Catwalk Gait assessment (P45, P120, P210, and P400) and a subset of the SmithKline-Beecham Harwell Imperial-College and Royal-London-Hospital Phenotype Assessment (SHIRPA) tests (P30 and P400). These tests were conducted by the UCLA Behavioral Core. Double mutant and control mice were additionally examined on the Vertical Pole test. All behavioral apparatuses were wiped down with ethanol (70%) between each testing each subject.
Gait analysis
We used a Noldus Catwalk Gait analysis system designed to semi-automatically measure and analyze the gait of mice during normal ambulation. Briefly, the movement of mice across a glass bottom corridor is video recorded from a ventral position. Paw prints are highlighted in the video due to light illumination across the glass walking platform. Each mouse step within a video is subsequently detected using Catwalk XT (Noldus) in a semi-automated fashion. A run for each mouse consists of three trials of consistent ambulation across the monitored platform. Only consistent trials are accepted, and mice may take up to 10 attempts to complete three compliant trials in either direction across the corridor. Compliant trials were defined as those with movement across the platform under 5-s long and with no more than 60% speed variation. Once placed onto the platform, mice generally ran back and forth without any need for experimenter prompting.
Vertical pole
Mice were placed at the top of an 80-cm-tall bolt with their nose facing down and hind paws as close to the top as possible. Mice were immediately released, and time started immediately upon placement. Time was stopped when the first forepaw touches the surface below the pole. A mouse’s natural predilection is to immediately climb down the pole, and they were given up to 60 s to traverse the pole, otherwise they were helped off the pole. A non-completed trial was automatically given a time of 30 s, as 95% of mice that did not descend within 30 s were still on the pole at the 60 s mark.
SHIRPA
Behavioral tests were conducted by the University of California, Los Angeles Behavioral Core at P30 and P400. All parameters are scored to provide a quantitative assessment, which enables the comparison of results both over time and between different laboratories. Each mouse was sequentially tested across all behaviors within ~20 min time span before moving onto the next mouse. The experimenter was blinded to animal genotype. The screen was performed as described previously (Rogers et al., 1997).
Behavioral observation
The primary screen provides a behavioral observation profile and assessment of each animal begins by observing undisturbed behavior in a viewing jar (10 cm diameter) for 5 min. In addition to the scored behaviors of body position, spontaneous activity, respiration rate, and tremor, the observer logs any instances of bizarre or stereotyped behavior and convulsions, compulsive licking, self-destructive biting, retropulsion (walking backwards), and indications of spatial disorientation.
Arena behavior
Thereafter, the mouse was transferred to the arena (30 × 50 cm2) for testing of transfer arousal and observation of normal behavior. The arena was marked into a grid of 10×10 cm2 squares to measure locomotor activity within a 30-s period. While the mouse was active in the arena, measures of startle response, gait, pelvic elevation, and tail elevation are recorded.
Supine restraint
The animal was restrained in a supine position to record autonomic behaviors. During this assessment, grip strength, body tone, pinna reflex, corneal reflex, toe pinch, wire maneuver, and heart rate were evaluated.
Balance and orientation
Finally, several measures of vestibular system function were performed. The righting reflex, contact righting reflex, and negative geotaxis tests were performed. Throughout this procedure vocalization, urination and general fear, irritability, or aggression were recorded.
Equipment used
Clear Plexiglas arena (approximate internal dimensions 55×33×18 cm3). On the floor of the arena is a Plexiglas sheet marked with 15 squares (11 cm). A rigid horizontal wire (3 mm diameter) is secured across the rear right corner such that the animals cannot touch the sides during the wire maneuver. A grid (40×20 cm2) with 12 mm mesh (approximate) is secured across the width of the box for measuring tail suspension and grip strength behavior.
A clear Plexiglas cylinder (15×11 cm2) was used as a viewing jar.
One grid floor (40×20 cm2) with 12 mm meshes on which viewing jars stand.
Four cylindrical stainless-steel supports (3 cm high × 2.5 cm diameter) to raise grids off the bench.
One square (13 cm) stainless steel plate for transfer of animals to the arena.
Cut lengths of 3/0 Mersilk held in the forceps for corneal and pinna reflex tests.
A plastic dowel rod sharpened to a pencil point to test salivation and biting.
A pair of dissecting equipment forceps, curved with fine points (125 mm forceps, Philip Harris Scientific, Cat. no. D46-174), for the toe pinch.
A stopwatch.
An IHR Click box is used for testing the startle responses. The Click Box generates a brief 20 KHz tone at 90 dB SPL when held 30 cm above the mouse. Contact Prof. K.P. Steel, MRC Institute of Hearing Research, University Park, Nottingham NG7 2RD.
A ruler.
A 30 cm clear Plexiglas tube with an internal diameter of 2.5 cm for the contact righting reflex.
Electrophysiology
Preparation of acute cerebellar slices
Acute parasagittal slices of 300 μm thickness were prepared from the cerebellum of experimental and control littermate mice by following published methods (Hansen et al., 2013). In brief, cerebella were quickly removed and immersed in an ice-cold extracellular solution with composition of (mM): 119 NaCl, 26 NaHCO3, 11 glucose, 2.5 KCl, 2.5 CaCl2, 1.3 MgCl2, and 1 NaH2PO4, pH 7.4 when gassed with 5% CO2/95% O2. Cerebella were sectioned parasagittally using a vibratome (Leica VT-1000, Leica Biosystems, Nussloch, Germany) and initially incubated at 35°C for ~30 min, and then equilibrated and stored at room temperature until use.
Extracellular electrophysiology
Extracellular and intracellular recordings were obtained from PNs in slices constantly perfused with carbogen-bubbled extracellular solution and maintained at either 37°C (extracellular) or 32°C (intracellular) ±1°C (see above). Cells were visualized with DIC optics and a water-immersion 40× objective (NA 0.75) using a Zeiss Examiner microscope. Glass pipettes of ~3 MΩ resistance (Model P-1000, Sutter Instruments, Novato, CA) were filled with extracellular solution and positioned near PN axon hillocks in order to measure AP-associated capacitive current transients in voltage clamp mode with the pipette potential held at 0 mV. For whole-cell patch-clamp recordings, pipettes were filled with an intracellular solution (mM): 140 KMeth (CH3KO3S), 10 NaCl, 2 MgCl2, 0.2 CaCl2, 10 HEPES, 14 Phosphocreatine (tris salt), 1 EGTA, 4 Mg-ATP, 0.4 Na-GTP. 100 μM Picrotoxin (Sigma-Aldrich) was added to block inhibitory GABAegeric synaptic inputs. Data was acquired using a MultiClamp 700B amplifier at 20 or 100 kHz in voltage or current clamp mode, Digidata 1440 with pClamp10 (Molecular Devices, Sunnyvale, CA), and filtered at 2–4 kHz. The series resistance was usually between 10 and 15 MΩ. Series resistance was compensated at 80% for short-term plasticity experiments only.
For extracellular recordings, a total of 20–45 PNs were recorded for each animal across all genotypes, sexes, and age groups. Recordings were distributed across both the medial-lateral and rostro-caudal axis of the cerebellum. Only cells with a ‘healthy’ look (low contrast of cellular borders) and regular, uninterrupted firing rate were recorded. During analysis, a few cells were found to have gaps in firing of greater than 2 s, and these cells were eliminated from analysis, as this type of firing is associated with being ‘unhealthy.’ Double mutant tissue did not qualitatively differ in appearance under DIC microscopy prior to recordings, nor was the number of ‘unhealthy’ cells greater than that of other genotypes (7% vs. 4–11% of all cells across control genotypes at P400). Spatial comparison of neural activity was obtained by recording from serial sections in the flocculus, lateral (2nd or 3rd), intermediate (6th or 7th), and medial (11th or 12th) slices. Lower number slices were used in the younger age groups (P45 and P110) to roughly match the relative positioning of recordings across age groups. 0–3 recordings were made from each lobule within each slice dependent on tissue quality and health. Each recording lasted for 1 min. 3–5 mice were used for each age group, and the experimenter was blinded to the genotype, age, and sex.
Intracellular recordings were obtained from PNs in either lobule III or VIII of the medial cerebellum (i.e., vermis); no statistical differences in properties were observed between lobules.
Analyses
Spontaneous AP interstimulus intervals were detected and analyzed using standard and custom routines in ClampFit (Molecular Device), IgorPro (Wavemetrics), and Excel (Microsoft). Specifically, APs were threshold detected and spiking statistics (i.e., frequency and interval length) were determined using adapted IgorPro routines (Taro Tools; https://sites.google.com/site/tarotoolsregister/). The coefficient of variation of the mean inter-spike interval (CV) and the median inter-spike interval (CV2=2 |ISIn+1−ISIn|/(ISIn+1 +ISIn)) were calculated in Excel using custom macros.
Standard membrane properties were analyzed using IgorPro. RM was determined by averaging three voltage trace responses to a –5 mV step pulse from a –80 mV holding potential and measuring the resulting current deflection between 900 and 1000 ms after onset. The membrane time constant was measured by fitting a single exponential to the initial decay phase from 90% to 10% of the peak. CM was calculated by dividing the membrane time constant by the RM. sEPSC events were recorded over a 1-min epoch and detected and measured using Neuroexpress (v21.1.13). Parallel and climbing fiber axons were stimulated using theta-glass electrodes (W.P.I.) and a TTL-controlled stimulus isolator (ISO-Flex, A.M.P.I.). Evoked EPSC amplitudes and decay time constants (one exp. for parallel and two exp. for climbing fibers) were analyzed using custom routines in IgorPro. APs were examined as part of a set of 1 s current injections between –500 and 2250 pA (250 pA steps) with a holding current adjusted to maintain an ~70 mV potential. AP waveforms were measured using custom routines in IgorPro. AP threshold was defined as the first membrane voltage in which the first derivative exceeded 30 mV/ms (Zhu et al., 2006).
Examination of cerebellar atrophy
Cerebellar size
Immediately after brain removal from the skull, a dorsal, whole-mount image was obtained. Images were then processed using Fiji (NIH). The forebrain and cerebellar sizes were assessed by outlining their two-dimensional space and then calculating area. We normalized for possible differences in overall brain size by dividing the results of the cerebellum by forebrain size to produce a relative cerebellum-to-forebrain ratio. Experimenters were blind to the genotype of the animal.
Immunohistochemistry
At the respective study endpoints (P45, P120, P210, and P400), male and female mice of all genotypes represented in this study were anesthetized with isoflurane and underwent transcardial perfusion with phosphate-buffered saline (PBS) followed by 4% (w/v) buffered paraformaldehyde (PFA) and then dissected to extract the brain. Images of the whole brain were taken immediately after removing the brain from the skull and the brains were then submerged in 4% PFA for 24 hr, and then cryoprotected in Tris-buffered saline (TBS) with 0.05% azide and 30% sucrose for 72 hr and stored at 4°C until further use. The cerebellum was separated from the forebrain and parasagittally sectioned using a sliding microtome (Microm HM 430, Thermo Fisher Scientific) set to section at 40 µm thickness. Cerebellum sections were collected in a series of six and stored in TBS-AF (TBS with 30% sucrose, 0.05% sodium azide, and 30% ethylene glycol) at 4°C or –20°C until further use. For immunofluorescent visualization of PNs, cerebellum sections of both
To quantify the number of calbindin-reactive cells in each lobule in the resulting images, we used Stereo Investigator to randomly draw two lines between 300 and 500 μm long in each lobule and manually counted the total number of PNs along the length within the 40 μm thickness of the tissue slice under 40× magnification. 2D density (# of PNs/(linear length*40 µm thickness)) of the two samples per lobule were then averaged for further comparison between lobules and animals.
Calbindin positive PN dendrite widths were measured at a predefined location in lobule VI from each animal in 25 or 40 μm thick tissue sections under 20× magnification. Dendritic widths of the primary and secondary branches were measured at the midline between the PN cell bodies and edge of the ML. Between 7 and 13 dendrites were measured per section, one section per animal.
For PN somatic measurements, Stereo Investigator was used to randomly select PNs distributed across the entire medial section under 20× magnification. The average PN width per animal was determined by averaging results across three serial sections (16–37 PNs per section). PN widths were measured perpendicular to the PN layer or to the exiting dendrite if askew by more than a few degrees.
ML and GCL (visualized with Calbindin and DAPI stains, respectively) widths were assessed in Stereo Investigator by averaging two width measurements at predefined locations for each lobule, roughly halfway along the long extent of each lobule under 2.5× magnification.
CD68 positivity in the cerebellar sections was quantified by measuring the total percent area of CD68+ positive staining across the entire medial cerebellar section. 10× stitched images were thresholded to the negative control and quantified using ImageJ, one section per animal.
To quantify the percent of Calbindin-positive PNs that were positive for cleaved Caspase-3 we counted PNs across the entire cerebellum using Stereo Investigator. Three, 20× magnification stitched images per animal were examined and the results averaged. The threshold for Caspase-3 positivity was established from control sections stained with only the secondary antibodies.
For non-fluorescent histological analysis, 25-μm-thick, free-floating tissue sections onto positively charged slides and air-dried overnight. The tissue was washed in PBS twice for 5 min, then stained sequentially with 0.1% Hematoxylin in 95% ethanol for ~25 s and 0.5% Eosin in 95% ethanol for ~3 s and washed in double-distilled water after each stain. The tissue was subsequently dehydrated for 1 min in 95% ethanol, 100% ethanol, and 100% Xylene washes, then cover slipped with Permount. Slides were imaged using a color camera (Q Imaging, MBF Biosciences) on the same Zeiss microscope and MBF acquisition software.
Experimenters were blinded to the mouse genotype in which sections were examined, and the order of examination was interleaved for all histological measurements.
Flow cytometry measurements
Flow cytometry analysis of blood and thymus cells was performed by staining with specific anti-mouse antibodies: CD4, CD8, CD3, CD44, and CD25. Briefly, whole-blood samples (50 μl) were stained using fluorescent-labeled antibodies, then red blood cells were lysed using BD lysing solution while live white blood cells were stained using a viability stain. Thymi were mechanically dissociated. 1–2 million thymus cells were similarly stained using specific antibodies for CD4, CD8, CD44, and CD25. Analysis of immuno-stained white blood cells or thymus samples was performed using FACS ARIA III and data was analyzed using FlowJo software as reported previously (Sanghez et al., 2017).
Western blots
Protein extracts (cells/tissues) were homogenized in radioimmunoprecipitation assay (RIPA) lysis buffer (150 mM NaCl, 1% Nonidet P-40 [NP-40], 0.5% deoxycholate, 0.1% SDS, 50 mM Tris, and pH 8.0) with protease inhibitors (10 μg/ml AEBSF, 10 μg/ml leupeptin, 5 μg/ml pepstatin, 5 μg/ml chymotrypsin, and 10 μg/ml aprotinin). The protein extracts were sonicated then pelleted by centrifugation at 13,000 rpm for 15 min at 4°C. BCA protein assay was used to quantify protein concentrations. Samples containing equal amounts of protein 50–100 μg per lane were separated using 4–12% gradient TGX precast gels Bio-Rad then transferred by TransBlot Semi-Dry Bio-Rad system using Nitrocellulose transfer pack. Transferred blots were stained by Ponceau S stain for equal protein loading then washed and blocked with 5% nonfat dry milk in TBST for 60 min at room temperature. Primary antibodies were incubated with shaking overnight at 4°C. Blots were probed for the following antibodies: ATM (D2E2) Rabbit mAb Cell Signaling, at 1:1000 dilution, β-Actin (D6A8) Rabbit mAb Cell Signaling, GAPDH (D16H11) Rabbit mAb Cell Signaling followed by the appropriate horseradish peroxidase-conjugated (HRP) secondary Anti-rabbit, Anti-mouse for 2 hr at room temperature. After multiple washes with TBST, protein expression was detected by Radiance Plus chemiluminescence substrate using the Azure c400 and the Bio-Rad ChemiDoc imaging systems. Densitometric analysis of the ATM was performed using ImageJ. Experiments were performed with 2 technical and 3–5 biological replicates as indicated.
Statistical assessment
The number of animals chosen for each group was based on a priori power analyses using GPower v3.1 based on an α size of 0.5, power of 0.8, and effect sizes estimated from preliminary data or prior studies. We used both parametric (one- and two-way ANOVA) for normally distributed and nonparametric (Kruskal-Wallis) statistical methods for interval data to test for differences between groups followed by pairwise multiple comparisons tests as indicated in the text. Outliers for immune data in Figures 6 and 7 were excluded via the ROUT method (Q=2%). The specific analyses used for each data set is noted in each figure legend. For all figures: ns=not significant, * p≤0.05, **p<0.01, ***p<0.001, ****p<0.0001. Data are reported as mean ± SEM and box and whisker plots indicate the minimum, first quartile, median, third quartile, and maximum data values. All figures and statistical analyses were completed using Excel (Microsoft 360) or Prism v8 and 9 (Graphpad).
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Abstract
Ataxia Telangiectasia (A-T) and Ataxia with Ocular Apraxia Type 1 (AOA1) are devastating neurological disorders caused by null mutations in the genome stability genes, A-T mutated (
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Neither ProQuest nor its licensors make any representations or warranties with respect to the translations. The translations are automatically generated "AS IS" and "AS AVAILABLE" and are not retained in our systems. PROQUEST AND ITS LICENSORS SPECIFICALLY DISCLAIM ANY AND ALL EXPRESS OR IMPLIED WARRANTIES, INCLUDING WITHOUT LIMITATION, ANY WARRANTIES FOR AVAILABILITY, ACCURACY, TIMELINESS, COMPLETENESS, NON-INFRINGMENT, MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE. Your use of the translations is subject to all use restrictions contained in your Electronic Products License Agreement and by using the translation functionality you agree to forgo any and all claims against ProQuest or its licensors for your use of the translation functionality and any output derived there from. Hide full disclaimer