Initial assessment of the spatial learning, reversal, and sequencing task capabilities of knock-in rats with humanizing mutations in the Aβ-coding region of App

Hoa Pham; Tao Yin; Luciano D’Adamio

doi:10.1371/journal.pone.0263546

. 2022 May 4;17(5):e0263546. doi: 10.1371/journal.pone.0263546

Initial assessment of the spatial learning, reversal, and sequencing task capabilities of knock-in rats with humanizing mutations in the Aβ-coding region of App

Hoa Pham ^1,^#, Tao Yin ^1,^#, Luciano D’Adamio ^1,^*

Editor: Stephen D Ginsberg²

PMCID: PMC9067689 PMID: 35507596

Abstract

Model organisms mimicking the pathogenesis of human diseases are useful for identifying pathogenic mechanisms and testing therapeutic efficacy of compounds targeting them. Models of Alzheimer’s disease (AD) and related dementias (ADRD) aim to reproduce the brain pathology associated with these neurodegenerative disorders. Transgenic models, which involve random insertion of disease-causing genes under the control of artificial promoters, are efficient means of doing so. There are confounding factors associated with transgenic approaches, however, including target gene overexpression, dysregulation of endogenous gene expression at transgenes’ integration sites, and limitations in mimicking loss-of-function mechanisms. Furthermore, the choice of species is important, and there are anatomical, physiological, and cognitive reasons for favoring the rat over the mouse, which has been the standard for models of neurodegeneration and dementia. We report an initial assessment of the spatial learning, reversal, and sequencing task capabilities of knock-in (KI) Long-Evans rats with humanizing mutations in the Aβ-coding region of App, which encodes amyloid precursor protein (App^h/h rats), using the IntelliCage, an automated operant social home cage system, at 6–8 weeks of age, then again at 4–5 months of age. These rats were previously generated as control organisms for studies on neurodegeneration involving other knock-in rat models from our lab. App^h/h rats of either sex can acquire place learning and reversal tasks. They can also acquire a diagonal sequencing task by 6–8 weeks of age, but not a more advanced serial reversal task involving alternating diagonals, even by 4–5 months of age. Thus, longitudinal behavioral analysis with the IntelliCage system can be useful to determine, in follow-up studies, whether KI rat models of Familial AD (FAD), sporadic late onset AD (LOAD), and of ADRD develop aging-dependent learning and memory deficits.

Introduction

The model organisms used to model human diseases have major implications on the phenotypic expression of disease-associated genetic mutations. In the past, our laboratory has modeled AD, the prevalent form of dementia among the elderly, and ADRD using a KI approach in mice [1–12]. The KI approach allows to model human diseases in a genetically faithful manner. More recently, we have generated rat KI models of FAD, LOAD, and ADRD [13–19]. The rat is better suited for behavioral tests and other procedures that are important in neurodegenerative diseases’ studies. Moreover, gene-expression patterns indicate that rats are better suited to model neurodegenerative diseases. Alternative splicing of MAPT [20–23], which is mutated in Frontotemporal Dementia and whose gene product tau forms neurofibrillary tangles (NFT) [24–31], leads to expression of tau isoforms with three or four microtubule binding domains (3R and 4R, respectively). Adult human and rat brains express both 3R and 4R tau isoforms [32]: in contrast, adult mouse brains express only 4R tau [33]. Thus, rats may be a better model organism for dementias with tauopathy.

Aggregated forms of Aβ, a product of APP processing, are, by many, considered the central pathogenic factor in AD. Rat and human APP differ by 3 amino-acids in the Aβ region: given that human Aβ species have higher propensity to form toxic Aβ species as compared to rodent Aβ, we produced rats carrying the humanized Aβ sequence in the endogenous App rat alleles (App^h/h rats) [13, 14]. This App^h allele allows to study pathogenic mechanisms in FAD, ADRD, and LOAD model organisms producing physiological levels of human Aβ [13–19]. In this view, App^h/h rats constitute control animals against which learning and memory performances of our FAD, ADRD, and LOAD models is measured. Whether expression of human Aβ is, per se’, sufficient to impact behavior will be addressed in future studies comparing App^h/h to App^w/w rats.

Behavioral tests are used to determine whether model organisms of AD and ADRD develop learning and memory deficits. Most studies use traditional paradigms, including novel object recognition, fear conditioning, Morris water maze, and radial arm water maze. These approaches are well established and informative. The IntelliCage system (NewBehavior AG) [34, 35] provides an additional method of assessing behavior in rodents. It has been used to study behavior in mouse models of human disease, including neurodegenerative and neuropsychiatric conditions such as Huntington’s disease and, notably, AD, with spatial learning and memory being among the most studied parameters [36, 37]. It consists of a central square home cage connected to four operant learning chambers, or corners. Every corner has two sides, each with a drinking bottle gated by a rotating door with a nosepoke sensor (Fig 1). The sides also include LEDs and air puff delivery valves as additional conditioning components. Behavioral programs are defined by the user within a visual coding platform. Subcutaneously injected transponders allow the IntelliCage to track the activity of individual animals with unique radio frequency identification tags. Among the parameters tabulated for subsequent analysis are corner visits, visit lengths, visit times, number of nosepokes per visit, and number of bottle licks per visit. This system offers a variety of advantages over standard cognitive tasks: high-throughput, unbiased data collection; minimal risk of human error; minimal perturbation of testing conditions; and uniform testing of multiple animals simultaneously in a social setting. The final point is important in the context of cognitive phenotyping because the social housing component, a distinguishing feature of this system, eliminates isolation as a confounding psychological stressor from the animals’ environment. Its stable, passive manner of data collection also mitigates stress from handling and the traumatic experience inherent in tests such as the Morris water maze [36, 38].

Fig 1 — Central home cage and four labeled corners with two drinking sides per corner.

A larger version of the IntelliCage system developed for rats has been used for studying Huntington’s disease [39]; the effect of GABA_B receptors in the insula on recognition memory [35]; and deficits in spatial learning and memory following post-weaning social isolation [40]. In this study we tested whether the IntelliCage can be used to assess learning and memory in App^h/h rats. We assessed the spatial learning, reversal, and sequencing task performance of male and females App^h/h rats at 6–8 weeks of age (peri-adolescent rats), and again at 4–5 months of age (young adult rats). We decided to start testing peri-adolescent rats based on the evidence that our FAD [13], ADRD [19], and LOAD [17, 41] rat models show synaptic plasticity and transmission alterations already at 6–8 weeks of age. Thus, knowing the performance of our control group at this early age is important. We performed a second test on the same animals at 4–5 months of age, to understand how the control App^h/h rats perform in longitudinal tests as young adults. We tested both male and female rats to determine whether there are any sex-dependent differences in performance. This is important because incidence rates of LOAD are greater in women than men after age 85 [42]. In summary, this study’s goal is to establish methods using the IntelliCage system to determine, in following studies, whether our FAD, ADRD, and LOAD models develop learning and memory deficits, the age of onset of these deficits, whether these deficits correlate with synaptic plasticity/transmission alterations, and the impact of sex.

Material and methods

Experimental animals

All experiments were done according to policies on the care and use of laboratory animals of the Ethical Guidelines for Treatment of Laboratory Animals of the NIH. Relevant protocols were approved by the Rutgers Institutional Animal Care and Use Committee (IACUC) (Protocol #201702513). All efforts were made to minimize animal suffering and reduce the number of rats used.

Rat genotyping and DNA extraction and sequencing

The insertion of humanizing mutations in App exon 16 was verified by DNA sequencing of genomic DNA PCR products that include exon 16 [13]. Fresh cut tail tissue was digested in 300 μl tail lysis buffer (100mM Tris, 5mM EDTA, 0.2% SDS, 200mM NaCl, pH 8.0) with 3 μl of 20 μg/μl protease K at 55⁰ C overnight. 100 μl of protein precipitation solution (7.5M Ammonium Acetate) was added to each sample to precipitate protein. After vortexing samples for 30 seconds, samples were centrifuged at 15000 xg for 5 min. Supernatant containing genomic DNA was mixed with 300 μl Isopropanol. After centrifugation at 15000 xg for 5 min., genomic DNA pellet was desalted with 70% ETOH and was dissolved in water for PCR and sequencing. The rats studied here were obtained by crossing App^h/h male and females. Eight breeding pairs were used. To avoid litter effects, for each cohort no more than 2 females and 2 male rats from each breeding pair were used.

Rat brain preparation

Rats were anesthetized with isoflurane and perfused via intracardiac catheterization with ice-cold PBS. Brains were extracted and homogenized using a glass-teflon homogenizer (w/v = 100 mg tissue/1 mL buffer) in 250 mM Sucrose, 20 mM Tris-base pH 7.4, 1 mM EDTA, 1mM EGTA plus protease and phosphatase inhibitors (ThermoScientific), with all steps carried out on ice or at 4°C. Samples were flash-frozen in liquid nitrogen immediately after homogenization. Total lysate was solubilized with 1% NP-40 for 30 min rotating at 4°C. Solubilized lysate was spun at 20,000 g for 10 min, the supernatant was collected and analyzed by ELISA and Western blotting.

ELISA

Aβ levels were measured with Meso Scale Discovery kit V-PLEX Plus Aβ Peptide Panel 1 (K15200G) according to the manufacturer’s recommendations. Human Aβ40 and Aβ42 were revealed using 6E10, a mouse monoclonal antibody raised against human Aβ_1–16 fragment (BioLegend 803001); rat Aβ40 and Aβ42 were revealed using M3.2, a mouse monoclonal antibody raised against rat/mouse Aβ_1–16 fragment (Biolegend 11465). Plates were read on a MESO QuickPlex SQ 120. Data were analyzed using Prism software and represented as mean ± SEM.

Western blot analysis

Protein content quantified by Bradford analysis. Ten μg of protein was brought to 15μl with PBS and LDS Sample buffer-10% β-mercaptoethanol (Invitrogen NP0007) to 1X, boiled for 1 min, cooled on ice, and loaded on a 4–12% Bis-Tris polyacrylamide gel (Biorad 3450125). Proteins were transferred onto nitrocellulose at 25V for 7min using the Trans-blot Turbo system (Biorad) and visualized by red Ponceau staining. Membranes were blocked 45min in 5%-milk (Biorad 1706404), washed extensively in PBS/0.05% Tween20. Primary antibodies were applied overnight at 4°C, at 1:1000 dilution in blocking solution (Thermo 37573). Membranes were probed with 6E10 and M3.2. Anti-mouse (Southern Biotech, OB103105) was diluted 1:1000 in 5%-milk and used against mouse and rabbit primary antibodies for 60 min at RT, with shaking. Blots were developed with West Dura ECL reagent (Thermo, PI34076) and visualized on a ChemiDoc MP Imaging System (Biorad).

Immunohistochemistry (IHC)

Intracardiac PFA-perfused rat brains were extracted and stored in 70% ethanol prior to cerebral coronal sectioning. Sections were dehydrated and paraffin embedded and the processed into 15 cross sections targeting the frontal cortex at the level of the isthmus of the corpus callosum, anterior and posterior hippocampus. IHC staining was performed in accordance with Biospective Standard Operating Procedure (SOP) # BSP‐L‐06. Slides were manually de‐paraffinized and rehydrated prior to the automated IHC. Slides initially underwent antigen retrieval, either heat‐induced epitope‐retrieval or formic acid treatment. All IHC studies were performed at room temperature on a Lab Vision Autostainer using the REVEAL Polyvalent HRP‐AEC detection system (Spring Bioscience). Briefly, slides were incubated sequentially with hydrogen peroxide for 5 minutes, to quench endogenous peroxidase, followed by 5 minutes in Protein Block, and then incubated with primary, antibodies as outlined in Table 1. Antibody binding was amplified using the Complement reagent (20 min), followed by an HRP‐conjugate (20 min), and visualized using the AEC chromogen (20 min). All IHC sections were counterstained with Acid Blue 129 and mounted with aqueous mounting medium [43]. The IHC and histology slides were digitized using an Axio Scan.Z1 digital whole‐slide scanner (Carl Zeiss). The images underwent quality control (QC) review and final images transferred to the Biospective server for qualitative image analysis.

Table 1. Primary and amplification antibodies used for IHC.

Target	Antibody	Antigen Retrieval	Dilution	Secondary & Amplification
Neurons	NeuN, Mouse monoclonal A60, Millipore	Citrate HIER	1:3000	RbαM & GtαRb- HRP
Amyloid-Beta	1–16 Aβ, Mouse monoclonal 6E10, Biolegend	80% Formic Acid	1:1000	RbαM & GtαRb-HRP
Amyloid-Beta	1–16 Aβ, Mouse monoclonal 6E10, Biolegend	80% Formic Acid	1:1000	RbαM & GtαRb-HRP
Microglia	Iba1, Rabbit polyclonal,Wako	Citrate HIER	1:200	GtαRb-HRP
Astrocytes	GFAP, Rabbit polyclonal, Thermo Scientific	Citrate HIER	1:200	DkαRb-bio & SA-HRP
Phospho-Tau	_PTau, Mouse monoclonal AT8, ThermoScientific	Citrate HIER	1:1000	Hα-bio & SA-HRP

Open in a new tab

α = anti; bio = biotin; Dk = Donkey; Gt = Goat; HIER = Heat induced antigen retrieval; H = Horse; HRP = Horseradish Peroxidase; M = Mouse; PK = Proteinase K; Rb = Rabbit; SA = Streptavidin.

Behavioral experiments and analysis

Prior to and after behavioral analysis, males were housed 2 per cage and females were housed 3 per cage under controlled laboratory conditions with a 12-hr dark/light cycle (dark from 7pm to 7am), at a temperature of 25 ± 1°C. They were anesthetized with isoflurane, tagged subcutaneously with radio frequency identification transponders, and allowed to recover for at least a week. Rats had free access to standard rodent diet and tap water while in traditional housing and were monitored for dehydration during periods of water restriction during behavioral analysis. The IntelliCage for Rats (NewBehavior AG) was used to collect behavioral data. Briefly, the program timeline was divided into three parts: (1) a period during which the animals may freely explore the IntelliCage and acclimate to a daily period of restricted water access during a time window (8:00–11:00pm) called the drinking session; (2) a period consisting of place learning and reversal programs during which every animal is assigned a drinking corner during a drinking session; and (3) a period consisting of more complex sequencing programs involving a rule that governs the designation of drinking corners based on animal activity during a drinking session. Variations in the approach toward these parts prompted the design of two parallel cohorts testing the same cognitive domains, analyzed independently. Two cohorts of App^h/h rats were studied longitudinally, A and B, housed across four IntelliCages separated by sex and cohort. Twelve rats were designated for each IntelliCage such that there would be 24 rats per cohort consisting of 12 females and 12 males each. The cohorts were run on separate program timelines, once at 6–8 weeks of age and again at 4–5 months of age (Run-1 and Run-2 through the program timeline, respectively), as outlined in Table 2 with program schematics depicted in the figures showing the results.

Table 2. IntelliCage program timeline overview for cohorts A and B.

Day	Cohort A	Cohort B
1	Free adaptation	Free adaptation
2	Nosepoke adaptation	Nosepoke adaptation
3	Time adaptation	Time adaptation
4		Time adaptation
5		Single corner restriction
6		Single corner restriction
7	Place learning (PL)	Place learning with corner switch (CS)
8
9
10	Place reversal (PR)
11		Behavioral sequencing (BS)
12
13	Behavioral sequencing (BS)
14
15
16	Serial reversal (SR)	Serial reversal (SR)
17
18
19

Open in a new tab

IntelliCage programs

Free adaptation (both cohorts, 1 day)—The rats may drink water ad libitum and explore the IntelliCage, familiarizing themselves with its layout; all bottle access doors open in response to any corner visit. Nosepoke adaptation (both cohorts, 1 day)—The rats learn they must activate a nosepoke sensor to open a water access door at any corner for seven seconds; this nosepoke mechanic remains active for every program hereafter. Time adaptation (Cohort A: 4 days, Cohort B: 2 days)—The rats may only drink between 8pm and 11pm at any corner, a time window called the drinking session. Single corner restriction (Cohort B only, 2 days)—All rats must drink from a single correct corner with the other corners being neutral during the drinking session. The correct corner changes after ninety minutes, such that the rats can drink at one corner during the first half of the drinking session and must switch to the opposite corner during the second half. Over two days, the correct corner designation follows the path 1->3 (1st day), then 2->4 (2nd day), covering all corners. Place learning (Cohort A only, 3 days)—The rats may only drink during the drinking session at a corner assigned to each of them; these assigned corners are considered correct, and the non-assigned corners are considered incorrect. Place learning with corner switch (Cohort B only, 4 days)—Each rat is assigned an initial correct corner where it can drink during the drinking session, as in place learning, with the other corners being incorrect. Every 45 minutes, the correct corner designations are switched according to the cycle (1->3->4->2[->1]). If corner 2 were the initial correct corner, the cycle would be shifted over once (2->1->3->4[->2]). After the first switch, the positions of the incorrect corners adjust accordingly. By the first 45-minute block of the next drinking session, the correct corner will have returned to its initial location. A phase refers to a 45-minute block during the drinking session in this program. The end of a phase marks when a corner switch occurs. Place reversal (Cohort A only, 3 days)—The rats may only drink during the drinking session at the corner diagonally opposite the one assigned in place learning; those reversed corners are considered correct, and the remaining corners, including the original assigned corner, are considered incorrect. Behavioral sequencing (Cohort A: 3 days, Cohort B: 5 days)—The rats must alternate between drinking at the initial learned corner and the opposite corner during the drinking session, so that one corner in the assigned diagonal is active (correct) at a time while the other is inactive (opposite); the conditions of the corners in the assigned diagonals alternate between correct and opposite, with a correct nosepoke triggering a condition switch. Visits to corners in the non-assigned diagonal are considered lateral visits. Serial reversal (both cohorts, 4 days)—The rats must alternate between a behavioral sequencing pattern on the original diagonal and the same on the other diagonal during the drinking session; the diagonal switches after every eight correct nosepokes. The corner conditions change as in the behavioral sequencing program, with lateral visits defined as before.

Corner rank comparison

To understand better the effect of social interaction on the behavior of animals in the IntelliCage, we ranked animals via a point system based on whether an animal visits the correct corner more than other animals do during single corner restriction. This may occur with the exclusion of other animals from those corners, which may bear upon the performance of paired animals in subsequent tests. As the single corner restriction program changes the correct corner every ninety minutes over two drinking sessions, we followed this workflow to produce the rankings for each animal in cohort B: 1. For each 90-minute block of the program during the drinking session, rank the animals within each IntelliCage according to the number of visits made to the appropriate correct corner; there should be four lists for each IntelliCage, corresponding to the 8:00–9:30pm and 9:30–11:00pm periods of drinking sessions 1 and 2. 2. An animal is said to out-visit another animal at a corner if it makes more visits than the other one during a given time interval. Assign four scores to each animal equal to the number of animals it out-visits, one score for each 90-minute block; each corner should be represented once as a correct corner. For example, during the period when corner 1 is considered correct, if an animal visits corner 1 more times than 4 other animals, it receives a score of 4 for that 90-minute block. 3. Use the four scores generated for each animal per Run to calculate mean scores and standard errors for statistical analyses: we compared the mean scores of animals within each IntelliCage for a given Run, performing a one-way ordinary ANOVA in Prism 9 (GraphPad, San Diego, California) followed by Tukey’s multiple comparisons tests when applicable (p < 0.05 was significant).

Learning curves

To visualize learning of all the animals in each IntelliCage as a unit, we charted the fractional accumulation of correct visits (also opposite visits for the Behavioral sequencing and Serial reversal tasks) over the course of each drinking session. With the resulting curves, we can qualitatively compare task performance according to drinking session, sex, and Run. We followed this workflow to produce the learning curves for each program: 1. For each subset of rats by sex, cohort, and Run (e.g., cohort A males in their Run-1), count the total number of visits those rats made for each drinking session. For a 3-day program, there should be 3 totals for a given subset. 2. For each subset as described in step 1, tabulate the fractional accumulation of correct (and opposite) visits over time for each drinking session, adding to each fraction, starting from 0, the value of 1 divided by the associated total count for that subset and drinking session each time a correct (or opposite) visit occurs, and 0 otherwise. The sum should be reset to 0 whenever a new drinking session occurs. 3. Match each fraction with a timestamp relative to the start of the first drinking session of a program, excluding time not belonging to a drinking session, e.g., if the nth fraction is associated with a visit that occurred during the 35^th minute of the third drinking session of a program, the fraction is matched with minute 395 (180 + 180 + 35). 4. Plot the resulting tables with time as the independent variable and fraction as the dependent variable, yielding the learning curves.

Inclusion/Exclusion criteria

During drinking sessions, animals may not visit or visit infrequently corners. Animals that did not make more than 25 visits during a drinking session in a learning and memory task were excluded from the analysis of that session. Animals that did not make sufficient visits for two consecutive drinking sessions were removed from the IntelliCage the following morning and allowed to drink water freely for an hour before being returned to the IntelliCage. Animals that died at any point during the timeline were excluded from the analyses of the current drinking session and, for obvious reasons, from all subsequent drinking sessions. Two cohort A females were excluded before the start of Run-2 because one died and the other developed hind limb paralysis. Two cohort B females died before the start of Run-1. No cohort A males were excluded from Run-1 analysis. No drinking session data points were excluded due to insufficient visits for any females of either cohort for either Run except for one during the 3^rd day of place learning, cohort A, Run-1. One cohort A male was effectively excluded from all Run-2 analysis due to its insufficient visits throughout the timeline. One cohort B male died before the start of Run-2, and another died during the first day of behavioral sequencing during Run-2. One cohort B male was mostly excluded from Run-1 analysis of behavioral sequencing and serial reversal due to insufficient visits. No other data points from cohort B males had to be excluded during Run-2 analysis. Details of data point exclusion by drinking session can be seen in the tables accompanying data figures.

Statistical analysis of area under the curves (AUC)

To assess task performance quantitatively, we used the area under the learning curves of individual animals in each IntelliCage. Every correct visit during a drinking session contributes to this area; this approach accounts not only for the total fraction of correct visits but also for the rate at which they accumulate. It also takes advantage of the large volume of data the IntelliCage collects from each program such that there is no need to approximate the rate of learning with curve fitting. For the Behavioral sequencing and the Serial reversal tasks we also calculated the AUC for the opposite corner, since the opposite corner represents the previously correct corner. Thus, calculations of these two areas indicates the speed by which a rat learns the rules regulating alternation of correct to previously correct corners. We followed this workflow for statistical analysis of learning for each program: 1. Tabulate the fractional accumulation for individual rats as described above; in other words, make the calculations necessary to generate learning curves for each rat in the IntelliCage rather than a group of them for every drinking session. 2. Calculate the area underneath each learning curve, bounded on the left and right by the start and end of each drinking session, respectively, and below by the x-axis. Before calculating the area, we completed the curve by extending it horizontally such that the final fraction at the end of the drinking session is equal to the fraction accumulated by the last visit the animal made during that drinking session. Each result is a data point representing the cumulative correct/opposite visit learning of a specific rat for a given drinking session. 3. Run statistical tests with those data points based on desired comparisons. We focused on three factors in our analysis: drinking session, sex, and Run. Given a Run and program, we performed a two-way repeated measures ANOVA in Prism 9 on the data from all animals in a cohort, organized by sex and drinking session, followed by Šídák’s multiple comparisons tests when applicable (p < 0.05 was significant). Specifically, we wanted to see whether there were significant session-wise differences within sex or sex differences within a given drinking session. If one or more drinking session data points were excluded for a given Run and program, mixed-effects analysis was performed instead with appropriate post-hoc tests. Paired t tests were performed to compare performance between Runs within a cohort for a given program, sex, and drinking session.

Results

App^h/h rats produce human Aβ species and do not develop AD-like pathology up to 14 months of age

We have previously reported that the humanizing mutations do not alter APP expression levels [13]. To verify that rats used in these experiments contain the humanizing mutations in App exon-16, we amplified by PCR the App gene exon-16 from App^w/w and App^h/h rats. Sequencing of the PCR products shows that the humanizing mutations were correctly inserted in the App^h/h genome (Fig 2A). Next, we verified whether App^h rats produce human Aβ40 and Aβ42, and whether the protein products contain the humanizing mutations. Levels of human and rat Aβ40 and Aβ42 were measured in App^w/w and App^h/h rats’ brains (5-week-old animals, 2 males and 2 females per genotype) using the species-specific detection antibodies 6E10 (human-specific) and M3.2 (rat/mouse-specific). As shown in Fig 2B, App^h/h rats produce human Aβ species, while App^w/w rats produce rat Aβ species. To complete our characterization, total brain lysates were analyzed by Western blot using the rat/mouse-specific M3.2 and the human-specific 6E10 antibodies. M3.2 detected APP only in App^w/w brains, while 6E10 APP detected APP only in App^h/h brains, confirming that APP produced by the App^h alleles contains the humanized Aβ mutations (Fig 2C).

Fig 2 — (A) To verify that the humanizing mutations were correctly inserted into *App* exon‐16, exon-16 was amplified by PCR from *App*^w/w and *App*^h/h rats. PCR products’ sequencing showed correct insertion of the humanizing mutations into the *App*^h/h genome. Nucleotides (G to C, T to A, and GC to AT) and amino acids (G to R, Y to F, and R to H) substitutions are in red. (B) The human Aβ-region specific antibody 6E10 detects Aβ40 and Aβ42 in *App*^h/h but not *App*^w/w rats’ brains. Conversely, the rat/mouse Aβ-region specific antibody M3.2 detects Aβ40 and Aβ42 in *App*^w/w but not *App*^h/h rats’ brains. (C) Western blot analysis of brain lysates shows that 6E10 detects APP in *App*^h/h but not *App*^w/w rats. Conversely, M3.2 detects APP in *App*^w/w but not *App*^h/h rats. (D) *App*^h/h rats do not show AD‐like histopathology at 14 months of age. Six male and six females rats were studied. The figure shows representative IHC in a representative male and female subject. The AT8 samples are not shown because no signal was detected.

Finally, we tested whether expression of human Aβ species is sufficient to cause AD-like pathology in rats. For this, we performed histological and immunohistochemical (IHC) analyses on 14-month-old male and female App^h/h rats. Staining with 6E10 was used to detect amyloid pathology; anti-NeuN was used to assess neuronal density and neurodegeneration; anti-IBA-1 was used to assess the activation state of microglia cells; anti-GFAP was used to assess the activation state of astrocytes; antibody AT8 was used to assess tau phosphorylation and neuronal tangle inclusion. Anti-NeuN staining did not show obvious neurodegeneration. The staining with anti-IBA-1 and anti-GFAP showed neither microglial or astrocytic activation, nor the presence of inflammatory foci and neuroinflammation. 6E10 and AT8 staining did not reveal amyloid plaques or tau pathology, respectively (Fig 2D). These results indicate that expression of human Aβ species per se’, is insufficient to prompt obvious AD-like pathology in rats (at least in 14-month-old rats). Therefore, App^h/h rats represent a valid control group when the pathogenic effects of FAD and ADRD mutations, and of LOAD gene variants, are assessed.

App^h/h rats do not visit corners more often than other App^h/h rats during single corner restriction in cohort B at either 6–8 weeks or 4–5 months of age

After IntelliCage adaptation as outlined in Table 2, rats in cohort B were started on the single corner restriction program, which tested whether the animals were able to share this corner equally among themselves for water (Fig 3A). The animals were assigned a rank during each 90-minute block of the two drinking sessions (four ranks total) based on visits to the actively correct corner, as described in the methods. The mean rank was used to compare animal learning (Fig 3B). There were no significant differences among male rats during either Run. During Run-1 one female rat (Animal 22) had a mean rank significantly lower than that of two other female rats (Animals 16 and 17), and Animal 14 had a significantly lower mean rank than that of Animal 16, but these differences were not observed during Run-2 for the same animals.

Table 3. Statistical analysis of data shown in Fig 3 for single corner restriction, cohort B.

	Ordinary one-way ANOVA
Female	Run	F (DFn, DFd)	P
	Run-1	F (9, 30) = 3.352	0.0060
	Run-2	F (9, 30) = 0.9019	0.5359
	post-hoc Tukey’s multiple comp. test	Summary	Adjusted P
	14 (Run-1) vs. 16 (Run-1)	*	0.0390
	16 (Run-1) vs. 22 (Run-1)	*	0.0186
	17 (Run-1) vs. 22 (Run-1)	*	0.0270
Male	Run	F (DFn, DFd)	P
	Run-1	F (11, 36) = 1.036	0.4366
	Run-2	F (10, 33) = 0.9633	0.4921

Open in a new tab

A p-value less than 0.05 is considered significant (*p < 0.05).

App^h/h rats in cohort A can acquire a place learning task by 6–8 weeks of age with session-wise improvement

After IntelliCage adaptation as outlined in Table 2, rats in cohort A were started on the place learning program (Fig 4A). Animal learning in this program and subsequent programs was summarized via (1) learning curves showing the fractional accumulation of correct/opposite corner visits of all animals during each drinking session, organized by sex and Run; and (2) comparisons of mean area under the learning curves of individual animals for correct/opposite visits during each drinking session, to quantify differences in task performance. Analysis of area under the curves (AUC) revealed significant session-wise increases for correct visits (C-AUC) for both sexes during Run-1. During Run-2, there were no significant session-wise differences in C-AUC for either sex (Fig 4B). There were no significant sex differences for either Run (Fig 4C), but C-AUC was significantly higher during Run-2 for all drinking sessions within females (Fig 4D). Qualitatively, a learning curve for correct visits that steepens session-wise signifies task acquisition (Fig 4E).

Table 4. Statistical analysis of data shown in Fig 4B and 4C for place learning, cohort A, Run-1, and Run-2.

Fig 4B and 4C	Mixed-effects analysis
Run-1	Source of Variation	F (DFn, DFd)	P
	Interaction	F (2, 43) = 4.389	0.0184
	Day factor(4B)	F (2, 43) = 24.08	<0.0001
	Sex factor(4C)	F (1, 22) = 3.847	0.0626
	post-hoc Sidak’s multiple comp. test	Summary	Adjusted P
	Female, day 1 vs. day 2	ns	0.9995
	Female, day 1 vs. day 3	***	0.0010
	Female, day 2 vs. day 3	**	0.0013
	Male, day 1 vs. day 2	***	0.0003
	Male, day 1 vs. day 3	****	<0.0001
	Male, day 2 vs. day 3	ns	0.2879
Run-2	Source of Variation	F (DFn, DFd)	P
	Interaction	F (2, 35) = 4.975	0.0125
	Day factor(4B)	F (2, 35) = 2.064	0.1422
	Sex factor(4C)	F (1, 20) = 0.2437	0.6269

Open in a new tab

A p-value less than 0.05 is considered significant (**p < 0.01, ***p < 0.001, ****p < 0.0001). ns = not significant.

Table 5. Statistical analysis of data shown in Fig 4D for place learning, cohort A, Run comparison.

Fig 4D	Paired t tests
Run Factor	Comparison	t, df	Summary	P
	Female, day 1	t = 5.672, df = 9	***	0.0003
	Male, day 1	t = 1.141, df = 9	ns	0.2834
	Female, day 2	t = 4.655, df = 9	**	0.0012
	Male, day 2	t = 0.9056, df = 9	ns	0.3888
	Female, day 3	t = 5.332, df = 9	***	0.0005
	Male, day 3	t = 0.01567, df = 10	ns	0.9878

Open in a new tab

A p-value less than 0.05 is considered significant (**p < 0.01, ***p < 0.001). ns = not significant.

App^h/h rats in cohort A can acquire a place reversal task by 6–8 weeks of age with session-wise improvement

After place learning, rats in cohort A were started on the place reversal program, which switches the correct corner in place learning to the one diagonally opposing it (Fig 5A). There were also significant session-wise increases in C-AUC for both sexes during Run-1, with no significant differences seen during Run-2 for either sex (Fig 5B). There were significant sex differences seen during Run-1, with C-AUC higher for males for all drinking sessions, but not during Run-2 (Fig 5C). For females, C-AUC was significantly higher during Run-2 compared to Run- 1 for the 1^st and 3^rd drinking sessions, with the value for the 2^nd drinking session being higher too but not reaching significance; there were no significant differences between runs for males (Fig 5D). Learning curves showed qualitative improvement, like those shown for place learning (Figs 4E and 5E).

Table 6. Statistical analysis of data shown in Fig 5B and 5C for place reversal, cohort A, Run-1, and Run-2.

Fig 5B and 5C	Two-way RM ANOVA
Run-1	Source of Variation	F (DFn, DFd)	P
	Interaction	F (2, 44) = 0.08855	0.9154
	Day factor(5B)	F (2, 44) = 12.26	<0.0001
	Sex factor(5C)	F (1, 22) = 15.38	0.0007
	post-hoc Sidak’s multiple comp. test	Summary	Adjusted P
	Female, day 1 vs. day 2	*	0.0277
	Female, day 1 vs. day 3	**	0.0020
	Female, day 2 vs. day 3	ns	0.7307
	Male, day 1 vs. day 2	ns	0.0713
	Male, day 1 vs. day 3	*	0.0109
	Male, day 2 vs. day 3	ns	0.8461
	Female vs. Male, day 1	**	0.0042
	Female vs. Male, day 2	**	0.0100
	Female vs. Male, day 3	*	0.0153
Run-12	Source of Variation	F (DFn, DFd)	P
	Interaction	F (2, 38) = 0.4885	0.6174
	Day factor(5B)	F (2, 38) = 4.223	0.0221
	Sex factor(5C)	F (1, 19) = 0.02749	0.8701
	post-hoc Sidak’s multiple comp. test	Summary	Adjusted P
	Female, day 1 vs. day 2	ns	0.8887
	Female, day 1 vs. day 3	ns	0.2396
	Female, day 2 vs. day 3	ns	0.6212
	Male, day 1 vs. day 2	ns	0.1257
	Male, day 1 vs. day 3	ns	0.0804
	Male, day 2 vs. day 3	ns	0.9957

Open in a new tab

A p-value less than 0.05 is considered significant (*p < 0.05, **p < 0.01). ns = not significant.

Table 7. Statistical analysis of data shown in Fig 5D for place reversal, cohort A, Run comparison.

Fig 5D	Paired t tests
Run Factor	Comparison	t, df	Summary	P
	Female, day 1	t = 5.188, df = 9	***	0.0006
	Male, day 1	t = 0.06592, df = 10	ns	0.9487
	Female, day 2	t = 1.595, df = 9	ns	0.1453
	Male, day 2	t = 0.3032, df = 10	ns	0.7680
	Female, day 3	t = 3.329, df = 9	**	0.0088
	Male, day 3	t = 0.09445, df = 10	ns	0.9266

Open in a new tab

A p-value less than 0.05 is considered significant (**p < 0.01, ***p < 0.001). ns = not significant.

App^h/h rats in cohort B can acquire a place learning with corner switching task by 4–5 months of age with session-wise improvement

Rather than progressing from place learning to place reversal as cohort A rats did, cohort B rats were started on the place learning with corner switch program after single corner restriction. This program was designed to be a faster-paced combination of place learning and place reversal, with correct corners switching every 45 minutes within a drinking session (Fig 6A). No significant session-wise differences in C-AUC were seen during Run-1 for either sex, but there were significant increases in C-AUC during Run-2 for both sexes (Fig 6B). Sex differences were significant for all drinking sessions during Run-2, with C-AUC higher for males (Fig 6C). For females during Run-2 compared to Run-1, C-AUC was significantly higher for all but the 1^st drinking session. For males during Run-2 compared to Run-1, C-AUC was significantly higher for all drinking sessions (Fig 6D). Learning curves qualitatively reflect this improvement across Runs for both sexes (Fig 6E).

Table 8. Statistical analysis of data shown in Fig 6B and 6C for place learning with corner switch, cohort B, Run-1, and Run-2.

Fig 6B and 6C	Two-way RM ANOVA
Run-1	Source of Variation	F (DFn, DFd)	P
	Interaction	F (3, 60) = 1.425	0.2444
	Day factor(6B)	F (3, 60) = 0.2456	0.8639
	Sex factor(6C)	F (1, 20) = 2.212	0.1526
Run-2	Source of Variation	F (DFn, DFd)	P
	Interaction	F (3, 57) = 1.510	0.2217
	Day factor(6B)	F (3, 57) = 12.64	<0.0001
	Sex factor(6C)	F (1, 19) = 61.50	<0.0001
	post-hoc Sidak’s multiple comp. test	Summary	Adjusted P
	Female, day 1 vs. day 2	ns	0.9993
	Female, day 1 vs. day 3	ns	>0.9999
	Female, day 1 vs. day 4	**	0.0012
	Female, day 2 vs. day 3	ns	0.9997
	Female, day 2 vs. day 4	**	0.0042
	Female, day 3 vs. day 4	**	0.0014
	Male, day 1 vs. day 2	ns	0.6828
	Male, day 1 vs. day 3	*	0.0434
	Male, day 1 vs. day 4	***	0.0004
	Male, day 2 vs. day 3	ns	0.6633
	Male, day 2 vs. day 4	*	0.0315
	Male, day 3 vs. day 4	ns	0.5969
	Female vs. Male, day 1	***	0.0004
	Female vs. Male, day 2	****	<0.0001
	Female vs. Male, day 3	****	<0.0001
	Female vs. Male, day 4	***	0.0003

Open in a new tab

A p-value less than 0.05 is considered significant (*p < 0.05, **p < 0.01, ***p < 0.001,****p < 0.0001). ns = not significant.

Table 9. Statistical analysis of data shown in Fig 6D for place learning with corner switch, cohort B, Run comparison.

Fig 6D	Paired t tests
Run Factor	Comparison	t, df	Summary	P
	Female, day 1	t = 1.200, df = 9	ns	0.2606
	Male, day 1	t = 3.216, df = 10	**	0.0092
	Female, day 2	t = 2.577, df = 9	*	0.0298
	Male, day 2	t = 4.572, df = 10	**	0.0010
	Female, day 3	t = 3.192, df = 9	*	0.0110
	Male, day 3	t = 5.835, df = 10	***	0.0002
	Female, day 4	t = 4.269, df = 9	**	0.0021
	Male, day 4	t = 8.317, df = 10	****	<0.0001

Open in a new tab

A p-value less than 0.05 is considered significant (*p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001). ns = not significant.

App^h/h rats in cohorts A and B can acquire a behavioral sequencing task by 6–8 weeks of age with session-wise improvement

After place learning and place reversal (cohort A) or place learning with corner switch (cohort B), we further tested the rats’ spatial learning capabilities with a behavioral sequencing program requiring the animals to shuttle between diagonally opposing corners for water access (Fig 7A). Visits were categorized as correct (C), lateral, or opposite (O) as described in the methods, with learning curves generated (Figs 7E and 8D) and AUC analysis performed (Figs 7B–7D and 8A–7C) similarly as in other programs. Cohort A rats of both sexes during Run-1 showed significant increases in C-AUC and decreases in O-AUC. These changes were consistent during Run-2 for females, whereas males no longer showed significant session-wise changes in C-AUC (Fig 7B). There were no significant sex differences observed for either Run (Fig 7C). For females during Run-2 compared to Run-1, C-AUC was significantly higher for the 1^st drinking session; there were no other significant differences across Runs for any drinking session (Fig 7D).

Fig 8 — **(A)** Day factor, **(B)** Sex factor and **(C)** Run factor comparisons of area under the curve (AUC) for activity curves of individual animals by sex, program day, and Run, separated by visit categories (Correct, BS-C or Opposite, BS-O). **(D)** Activity curves showing the fractional accumulation (y-axis) of correct visits or opposite visits over drinking session time (x-axis), reset every 180 minutes, by sex and Run. **(E)** For Run-1, 12 male and 10 female rats were tested since 2 females died just before the timeline started. One male was excluded from the analysis of days 1, 2, and 4, while two males were excluded from the analysis of days 3 and 5 during Run-2. These animals were excluded because they visited corners less than 25 times during those drinking sessions. For Run-2, 10 male and 10 female rats were tested, since one male died during the time between Runs, and one male died after place learning with corner switch. All 20 rats were included in the analysis of Run-2. All data represented as mean ± SEM (*p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001). See Tables 12 and 13 for statistical analysis. Males (M or ♂) are colored as red (Run-1) and light red (Run-2). Females (F or ♀) are colored as blue (Run-1) or light blue (Run-2).

Table 10. Statistical analysis of data shown in Fig 7B and 7C for behavioral sequencing, cohort A, Run-1, and Run-2.

Fig 7B and 7C	Two-way RM ANOVA
Correct Run-1	Source of Variation	F (DFn, DFd)	P
	Interaction	F (2, 44) = 3.323	0.0453
	Day factor(7B)	F (2, 44) = 22.41	<0.0001
	Sex factor(7C)	F (1, 22) = 2.906	0.1023
	post-hoc Sidak’s multiple comp. test	Summary	Adjusted P
	Female, day 1 vs. day 2	ns	0.2721
	Female, day 1 vs. day 3	*	0.0168
	Female, day 2 vs. day 3	ns	0.5330
	Male, day 1 vs. day 2	***	0.0004
	Male, day 1 vs. day 3	****	<0.0001
	Male, day 2 vs. day 3	ns	0.0775
Correct Run-2	Source of Variation	F (DFn, DFd)	P
	Interaction	F (2, 38) = 1.537	0.2281
	Day factor(7B)	F (2, 38) = 5.618	0.0073
	Sex factor(7C)	F (1, 19) = 3.569	0.0742
	post-hoc Sidak’s multiple comp. test	Summary	Adjusted P
	Female, day 1 vs. day 2	*	0.0474
	Female, day 1 vs. day 3	*	0.0278
	Female, day 2 vs. day 3	ns	0.9950
	Male, day 1 vs. day 2	ns	0.9988
	Male, day 1 vs. day 3	ns	0.1598
	Male, day 2 vs. day 3	ns	0.2075
Opposite Run-1	Source of Variation	F (DFn, DFd)	P
	Interaction	F (2, 44) = 0.09638	0.9083
	Day factor(7B)	F (2, 44) = 15.98	<0.0001
	Sex factor(7C)	F (1, 22) = 2.116	0.1599
	post-hoc Sidak’s multiple comp. test	Summary	Adjusted P
	Female, day 1 vs. day 2	**	0.0055
	Female, day 1 vs. day 3	**	0.0039
	Female, day 2 vs. day 3	ns	0.9992
	Male, day 1 vs. day 2	**	0.0088
	Male, day 1 vs. day 3	**	0.0011
	Male, day 2 vs. day 3	ns	0.8555
Opposite Run-2	Source of Variation	F (DFn, DFd)	P
	Interaction	F (2, 38) = 0.7340	0.4867
	Day factor(7B)	F (2, 38) = 12.12	<0.0001
	Sex factor(7C)	F (1, 19) = 0.009860	0.9219
	post-hoc Sidak’s multiple comp. test	Summary	Adjusted P
	Female, day 1 vs. day 2	*	0.0395
	Female, day 1 vs. day 3	***	0.0006
	Female, day 2 vs. day 3	ns	0.3448
	Male, day 1 vs. day 2	ns	0.1123
	Male, day 1 vs. day 3	*	0.0379
	Male, day 2 vs. day 3	ns	0.9526

Open in a new tab

A p-value less than 0.05 is considered significant (*p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001). ns = not significant.

Table 11. Statistical analysis of data shown in Fig 7D for behavioral sequencing, cohort A, Run comparison.

Fig 7D	Paired t tests
Run Factor Correct	Comparison	t, df	Summary	P
	Female, day 1	t = 2.491, df = 8	*	0.0375
	Male, day 1	t = 0.2282, df = 11	ns	0.8237
	Female, day 2	t = 1.032, df = 8	ns	0.3324
	Male, day 2	t = 1.474, df = 11	ns	0.1686
	Female, day 3	t = 1.177, df = 8	ns	0.2728
	Male, day 3	t = 1.844, df = 11	ns	0.0923
Run Factor Opposite	Comparison	t, df	Summary	P
	Female, day 1	t = 0.9581, df = 8	ns	0.3661
	Male, day 1	t = 0.9490, df = 11	ns	0.3630
	Female, day 2	t = 0.6758, df = 8	ns	0.5182
	Male, day 2	t = 1.496, df = 11	ns	0.1628
	Female, day 3	t = 0.4350, df = 8	ns	0.6751
	Male, day 3	t = 0.7209, df = 11	ns	0.4860

Open in a new tab

A p-value less than 0.05 is considered significant (*p < 0.05). ns = not significant.

Table 12. Statistical analysis of data shown in Fig 8A and 8B for behavioral sequencing, cohort B, Run-1, and Run-2.

Fig 8A and 8B	Mixed-effects analysis
Correct Run-1	Source of Variation	F (DFn, DFd)	P
	Interaction	F (4, 73) = 1.712	0.1565
	Day factor(8A)	F (4, 73) = 6.442	0.0002
	Sex factor(8B)	F (1, 20) = 2.837	0.1077
	post-hoc Sidak’s multiple comp. test	Summary	Adjusted P
	Male, day 1 vs. day 4	**	0.0013
	Male, day 1 vs. day 5	***	0.0003
	Male, day 2 vs. day 4	*	0.0367
	Male, day 2 vs. day 5	*	0.0102
Correct Run-2	Source of Variation	F (DFn, DFd)	P
	Interaction	F (4, 72) = 30.28	<0.0001
	Day factor(8A)	F (4, 72) = 43.82	<0.0001
	Sex factor(8B)	F (1, 18) = 76.20	<0.0001
	post-hoc Sidak’s multiple comp. test	Summary	Adjusted P
	Male, day 1 vs. day 2	****	<0.0001
	Male, day 1 vs. day 3	****	<0.0001
	Male, day 1 vs. day 4	****	<0.0001
	Male, day 1 vs. day 5	****	<0.0001
	Male, day 2 vs. day 3	****	<0.0001
	Male, day 2 vs. day 4	****	<0.0001
	Male, day 2 vs. day 5	****	<0.0001
	Female vs. Male, day 2	****	<0.0001
	Female vs. Male, day 3	****	<0.0001
	Female vs. Male, day 4	****	<0.0001
	Female vs. Male, day 5	****	<0.0001
Opposite Run-1	Source of Variation	F (DFn, DFd)	P
	Interaction	F (4, 73) = 0.5797	0.6783
	Day factor(8A)	F (4, 73) = 0.5545	0.6963
	Sex factor(8B)	F (1, 20) = 0.0003232	0.9858
Opposite Run-2	Source of Variation	Summary	Adjusted P
	Interaction	F (4, 72) = 3.377	0.0137
	Day factor(8A)	F (4, 72) = 12.53	<0.0001
	Sex factor(8B)	F (1, 18) = 0.5148	0.4823
	post-hoc Sidak’s multiple comp. test	Summary	Adjusted P
	Male, day 1 vs. day 2	*	0.0490
	Male, day 1 vs. day 3	**	0.0016
	Male, day 1 vs. day 4	****	<0.0001
	Male, day 1 vs. day 5	****	<0.0001
	Male, day 2 vs. day 5	**	0.0025

Open in a new tab

A p-value less than 0.05 is considered significant (*p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001). ns not shown.

Table 13. Statistical analysis of data shown in Fig 8C for behavioral sequencing, cohort B, Run comparison.

Fig 8C	Paired t tests
Run Factor Correct	Comparison	t, df	Summary	P
	Female, day 1	t = 2.393, df = 9	*	0.0403
	Male, day 1	t = 2.692, df = 8	*	0.0274
	Female, day 2	t = 0.8613, df = 9	ns	0.4114
	Male, day 2	t = 10.57, df = 8	****	<0.0001
	Female, day 3	t = 0.8399, df = 9	ns	0.4227
	Male, day 3	t = 10.76, df = 8	****	<0.0001
	Female, day 4	t = 1.226, df = 9	ns	0.2512
	Male, day 4	t = 8.341, df = 8	****	<0.0001
	Female, day 5	t = 1.809, df = 9	ns	0.1039
	Male, day 5	t = 7.820, df = 8	****	<0.0001
Run Factor Opposite	Comparison	t, df	Summary	P
	Female, day 1	t = 3.144, df = 9	*	0.0119
	Male, day 1	t = 3.395, df = 8	**	0.0094
	Female, day 2	t = 1.403, df = 9	ns	0.1942
	Male, day 2	t = 3.111, df = 8	*	0.0144
	Female, day 3	t = 2.459, df = 9	*	0.0362
	Male, day 3	t = 1.404, df = 8	ns	0.1976
	Female, day 4	t = 0.4780, df = 9	ns	0.6441
	Male, day 4	t = 0.5876, df = 8	ns	0.5730
	Female, day 5	t = 0.08419, df = 9	ns	0.9347
	Male, day 5	t = 0.7311, df = 8	ns	0.4856

Open in a new tab

A p-value less than 0.05 is considered significant (*p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001). ns = not significant.

Cohort B rats exhibited a different learning profile: for females, the session-wise differences in C-AUC and O-AUC were insignificant during both Runs, whereas for males, there were many significant session-wise differences, especially during Run-2 with increases in C-AUC and decreases in O-AUC (Fig 8A). Sex differences were insignificant during Run-1, whereas C-AUC was significantly higher in males during Run-2 from the 2^nd to the 5^th drinking sessions (Fig 8B). Significant differences found between Runs for females were sporadic for C-AUC (1^st drinking session, higher in Run-1) and O-AUC (1^st and 3^rd drinking sessions, higher in Run-2); in contrast, for males, C-AUC was significantly higher for every drinking session during Run-2 compared to Run-1, with O-AUC higher for the 1^st and 2^nd drinking sessions (Fig 8C).

App^h/h rats in cohort A and B may not be able to acquire a serial reversal task by 4–5 months of age

We ended the timeline for both cohorts with a serial reversal program designed to add a layer of complexity to behavioral sequencing by requiring the rats to alternate diagonals after every eight successive, but not necessarily consecutive, correct nosepokes correct nosepokes (Fig 9A). For cohort A, qualitatively, learning curves for both sexes did not show much difference between Runs or session-wise improvement (Fig 9E). Session-wise differences in AUC were minimal for both sexes during both Runs (Fig 9B). Sex differences were insignificant for both Runs as well (Fig 9C). The only significant difference between Runs was in O-AUC for females during the 1^st drinking session, which was lower in Run-2 (Fig 10C). For cohort B, the learning curves suggest a possible difference between Run-1 and Run-2 for males, but no session-wise differences (Fig 10D). AUC analysis revealed no significant session-wise differences (Fig 10A). Compared to females during Run-2, C-AUC was significantly higher for all drinking sessions in males, with O-AUC significantly lower for the 3^rd and 4^th drinking sessions (Fig 10B). For males during Run-2 compared to Run-1, C-AUC was significantly higher for every drinking session, and O-AUC was significantly lower for the 1^st drinking session; for females, there were no significant differences between Runs (Fig 10C).

Fig 10 — **(A)** Day factor, **(B)** Sex factor and **(C)** Run factor comparisons of area under the curve (AUC) for activity curves of individual animals by sex, program day, and Run, separated by visit categories (Correct, SR-C or Opposite, SR-O). **(D)** Activity curves showing the fractional accumulation (y-axis) of correct visits or opposite visits over drinking session time (x-axis), reset every 180 minutes, by sex and Run. **(E)** For Run-1, 12 male and 10 female rats were tested since 2 females died just before the timeline started. Two males were excluded from the analysis of day 1, three males were excluded from the analysis of day 2, and four males were excluded from the analysis of days 3 and 4 during Run-1. These animals were excluded because they visited corners less than 25 times during the drinking session. For Run-2, 10 male and 10 female rats were tested, since one male died during the time between Runs and one male died after place learning with corner switch. All 20 rats were included in the analysis of Run-2. All data represented as mean ± SEM (*p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001). See Tables 16 and 17 for statistical analysis. Males (M or ♂) are colored as red (Run-1) and light red (Run-2). Females (F or ♀) are colored as blue (Run-1) or light blue (Run-2).

Table 14. Statistical analysis of data shown in Fig 9B and 9C for serial reversal, cohort A, Run-1, and Run-2.

Fig 9B and 9C	Two-way RM ANOVA
Correct Run-1	Source of Variation	F (DFn, DFd)	P
	Interaction	F (3, 66) = 0.3061	0.8209
	Day factor(9B)	F (3, 66) = 0.4277	0.7338
	Sex factor(9C)	F (1, 22) = 0.5099	0.4827
Correct Run-2	Source of Variation	F (DFn, DFd)	P
	Interaction	F (3, 53) = 0.6830	0.5664
	Day factor(9B)	F (3, 53) = 2.568	0.0641
	Sex factor(9C)	F (1, 19) = 5.754	0.0269
Opposite Run-1	Source of Variation	F (DFn, DFd)	P
	Interaction	F (3, 66) = 0.4999	0.6837
	Day factor(9B)	F (3, 66) = 2.051	0.1152
	Sex factor(9C)	F (1, 22) = 0.06284	0.8044
Opposite Run-2	Source of Variation	F (DFn, DFd)	P
	Interaction	F (3, 53) = 2.117	0.1090
	Day factor(9B)	F (3, 53) = 1.178	0.3271
	Sex factor(9C)	F (1, 19) = 1.600	0.2212

Open in a new tab

A p-value less than 0.05 is considered significant.

Table 15. Statistical analysis of data shown in Fig 9D for serial reversal, cohort A, Run comparison.

Fig 9D	Paired t tests
Run Factor Correct	Comparison	t, df	Summary	P
	Female, day 1	t = 0.1544, df = 9	ns	0.8807
	Male, day 1	t = 0.0315, df = 9	ns	0.9756
	Female, day 2	t = 0.1535, df = 9	ns	0.8814
	Male, day 2	t = 1.396, df = 11	ns	0.1902
	Female, day 3	t = 0.6986, df = 9	ns	0.5024
	Male, day 3	t = 0.8514, df = 8	ns	0.4193
	Female, day 4	t = 0.2108, df = 9	ns	0.8377
	Male, day 4	t = 0.9781, df = 9	ns	0.3536
Run Factor Opposite	Comparison	t, df	Summary	P
	Female, day 1	t = 2.629, df = 9	*	0.0274
	Male, day 1	t = 0.2406, df = 10	ns	0.8147
	Female, day 2	t = 0.8891, df = 9	ns	0.3971
	Male, day 2	t = 0.1268, df = 10	ns	0.9016
	Female, day 3	t = 0.6236, df = 9	ns	0.5483
	Male, day 3	t = 0.1706, df = 8	ns	0.8688
	Female, day 4	t = 0.7629, df = 9	ns	0.4651
	Male, day 4	t = 0.4479, df = 9	ns	0.6648

Open in a new tab

A p-value less than 0.05 is considered significant (*p < 0.05). ns = not significant.

Table 16. Statistical analysis of data shown in Fig 10A and 10B for serial reversal, cohort B, Run-1, and Run-2.

Fig 10A and 10B	Mixed-effects analysis
Correct Run-1	Source of Variation	F (DFn, DFd)	P
	Interaction	F (3, 49) = 1.404	0.2527
	Day factor(10A)	F (3, 49) = 0.4026	0.7518
	Sex factor(10B)	F (1, 18) = 0.2942	0.5942
Correct Run-2	Source of Variation	F (DFn, DFd)	P
	Interaction	F (3, 54) = 1.273	0.2928
	Day factor(10A)	F (3, 54) = 1.481	0.2300
	Sex factor(10B)	F (1, 18) = 61.77	<0.0001
	post-hoc Sidak’s multiple comp. test	Summary	Adjusted P
	Female vs. Male, day 1	****	<0.0001
	Female vs. Male, day 2	****	<0.0001
	Female vs. Male, day 3	****	<0.0001
	Female vs. Male, day 4	****	<0.0001
Opposite Run-1	Source of Variation	F (DFn, DFd)	P
	Interaction	F (3, 49) = 0.1940	0.9000
	Day factor(10A)	F (3, 49) = 0.7972	0.5014
	Sex factor(10B)	F (1, 18) = 0.7083	0.4111
Opposite Run-2	Source of Variation	F (DFn, DFd)	P
	Interaction	F (3, 54) = 0.6292	0.5993
	Day factor(10A)	F (3, 54) = 0.05791	0.9815
	Sex factor(10B)	F (1, 18) = 18.17	0.0005
	post-hoc Sidak’s multiple comp. test	Summary	Adjusted P
	Female vs. Male, day 3	*	0.0242
	Female vs. Male, day 4	**	0.0056

Open in a new tab

(*p < 0.05, **p < 0.01, ****p < 0.0001). A p-value less than 0.05 is considered significant.

Table 17. Statistical analysis of data shown in Fig 10C for serial reversal, cohort B, Run comparison.

Fig 10C	Paired t tests
Run Factor Correct	Comparison	t, df	Summary	P
	Female, day 1	t = 0.8066, df = 9	ns	0.4407
	Male, day 1	t = 4.498, df = 8	**	0.0020
	Female, day 2	t = 0.5204, df = 9	ns	0.6153
	Male, day 2	t = 5.283, df = 7	**	0.0011
	Female, day 3	t = 0.4795, df = 9	ns	0.6430
	Male, day 3	t = 5.137, df = 6	**	0.0021
	Female, day 4	t = 0.7748, df = 9	ns	0.4583
	Male, day 4	t = 3.550, df = 6	*	0.0121
Run Factor Opposite	Comparison	t, df	Summary	P
	Female, day 1	t = 0.6327, df = 9	ns	0.5427
	Male, day 1	t = 2.358, df = 8	*	0.0461
	Female, day 2	t = 0.3549, df = 9	ns	0.7308
	Male, day 2	t = 1.223, df = 7	ns	0.2608
	Female, day 3	t = 0.3089, df = 9	ns	0.7644
	Male, day 3	t = 1.474, df = 6	ns	0.1909
	Female, day 4	t = 0.0202, df = 9	ns	0.9843
	Male, day 4	t = 2.396, df = 6	ns	0.0536

Open in a new tab

A p-value less than 0.05 is considered significant (*p < 0.05, **p < 0.01). ns = not significant.

Discussion

Place learning with reversal is the most frequently used learning protocol for the IntelliCage [36]. It has been applied to male Sprague-Dawley rats, also 6–8 weeks old, to explore the relationship between GABA_B receptors and recognition memory [35]. In another study, an IntelliCage place learning task was performed alongside more traditional paradigms, namely, the forced swimming test, open field test, and Morris water maze, to validate a novel multi-function closed maze designed to detect learning, memory, and affective disorders in post-weaning socially isolated rats [40]. The results of the place learning task agreed with those of the Morris water maze with respect to spatial learning and memory deficits in socially isolated rats, supporting the IntelliCage as a valid methodology beside traditional ones. In our study, App^h/h rats of both sexes were able to adapt to the IntelliCage and acquire a place learning and reversal task, as well as a more complex behavioral sequencing task, by 6–8 weeks of age. Males tended to perform better than females at 4–5 months of age in place learning with corner switch—essentially a quicker version of the place learning with reversal paradigm—and behavioral sequencing. This result differs from that of an IntelliCage study wherein female mice around one year old performed better than males in place learning with reversal [44]. The results of the single corner restriction program for cohort B suggest that although individual variance exists among the rats, it is small enough that animals can be approximated as identical subjects for these IntelliCage experiments. Generating learning curves with aggregate cohort data is one way to reduce the impact of this variance on interpretation of cohort performance. Using AUC as a metric for comparing learning between groups is a natural extension of using linear fits on learning curves to estimate learning rate and takes full advantage of the data volume the IntelliCage offers. Task acquisition can be characterized by performance parameters—in this case, AUC—that are greater or less than the value that would be expected through chance alone, depending on the visit category. Chance C-AUC/O-AUC would be equal to the area of a right triangle with base of length 180 (number of minutes in a drinking session) and height of 0.25 (probability of visiting a correct/opposite corner at random), or 22.5. Significant session-wise differences in the appropriate direction can reflect task acquisition too, as seen with increases in C-AUC accompanied by decreases in O-AUC. These characteristics were observed for all the spatial learning programs except serial reversal, suggesting that the program is too complex for the rats to learn by 4–5 months of age. Acquisition of behavioral sequencing and serial reversal tasks in the form of diagonal shuttling and switching, respectively, has been well established as an IntelliCage paradigm for mice by Endo et al. [34]; however, the diagonal switches were originally programmed to occur automatically, independently of nosepokes, on a timescale of 4–7 3-hour drinking sessions per switch, making our version of the serial reversal task considerably more difficult. In general, a task that challenges the animals without being impossible to acquire would be ideal for identifying possible cognitive deficits in models of neurodegeneration and dementia. Task acquisition of behavioral sequencing but not serial reversal suggests that a program of intermediate difficulty using a sequence involving all four corners (in clockwise motion, for example) rather than just two in a single diagonal, might be worth testing in future studies. Yet, such a program could be affected by development of pervasive strategy (to visit each next corner) that can collapse the cognitive demand of this particular protocol.

By these measures, this study establishes a baseline spatial learning profile for App^h/h control rats while exploring analytic methods involving aggregate cohort learning and use of AUC as a metric for task performance in the IntelliCage.

In summary, the longitudinal behavioral analysis tested here with the IntelliCage system can be useful to determine, in follow-up studies, whether knock-in rat models of FAD, LOAD, and ADRD develop aging-dependent learning and memory deficits, whether these deficits correlate with either early synaptic plasticity/transmission alterations or potential AD-like pathology, and the impact of sex.

Data Availability

All relevant data are within the manuscript.

Funding Statement

LD Project Number1R01AG073182-01 Contact PI D'ADAMIO, LUCIANO Project Number5R01AG063407-02 Contact PI D'ADAMIO, LUCIANO Project Number1RF1AG064821-01 Contact PI D'ADAMIO, LUCIANO All 3 grants are from: National Institutes of Health, National Institute on Aging. USA The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

References

1.Barbagallo AP, Wang Z, Zheng H, D’Adamio L. The intracellular threonine of amyloid precursor protein that is essential for docking of Pin1 is dispensable for developmental function. PLoS One. 2011;6(3):e18006. doi: 10.1371/journal.pone.0018006 [DOI] [PMC free article] [PubMed] [Google Scholar]
2.Barbagallo AP, Wang Z, Zheng H, D’Adamio L. A single tyrosine residue in the amyloid precursor protein intracellular domain is essential for developmental function. J Biol Chem. 2011;286(11):8717–21. doi: 10.1074/jbc.C111.219873 [DOI] [PMC free article] [PubMed] [Google Scholar]
3.Barbagallo AP, Weldon R, Tamayev R, Zhou D, Giliberto L, Foreman O, et al. Tyr(682) in the intracellular domain of APP regulates amyloidogenic APP processing in vivo. PLoS One. 2010;5(11):e15503. doi: 10.1371/journal.pone.0015503 [DOI] [PMC free article] [PubMed] [Google Scholar]
4.Garringer HJ, Murrell J, D’Adamio L, Ghetti B, Vidal R. Modeling familial British and Danish dementia. Brain Struct Funct. 2010;214(2–3):235–44. doi: 10.1007/s00429-009-0221-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
5.Giliberto L, Matsuda S, Vidal R, D’Adamio L. Generation and initial characterization of FDD knock in mice. PLoS One. 2009;4(11):e7900. doi: 10.1371/journal.pone.0007900 [DOI] [PMC free article] [PubMed] [Google Scholar]
6.Tamayev R, Matsuda S, Fa M, Arancio O, D’Adamio L. Danish dementia mice suggest that loss of function and not the amyloid cascade causes synaptic plasticity and memory deficits. Proc Natl Acad Sci U S A. 2010;107(48):20822–7. doi: 10.1073/pnas.1011689107 [DOI] [PMC free article] [PubMed] [Google Scholar]
7.Tamayev R, Giliberto L, Li W, d’Abramo C, Arancio O, Vidal R, et al. Memory deficits due to familial British dementia BRI2 mutation are caused by loss of BRI2 function rather than amyloidosis. J Neurosci. 2010;30(44):14915–24. doi: 10.1523/JNEUROSCI.3917-10.2010 [DOI] [PMC free article] [PubMed] [Google Scholar]
8.Tamayev R, Matsuda S, Giliberto L, Arancio O, D’Adamio L. APP heterozygosity averts memory deficit in knockin mice expressing the Danish dementia BRI2 mutant. EMBO J. 2011;30(12):2501–9. doi: 10.1038/emboj.2011.161 [DOI] [PMC free article] [PubMed] [Google Scholar]
9.Tamayev R D ’Adamio L. Memory deficits of British dementia knock-in mice are prevented by Aβ-precursor protein haploinsufficiency. J Neurosci. 2012;32(16):5481–5. doi: 10.1523/JNEUROSCI.5193-11.2012 [DOI] [PMC free article] [PubMed] [Google Scholar]
10.Tamayev R, Akpan N, Arancio O, Troy CM, D’Adamio L. Caspase-9 mediates synaptic plasticity and memory deficits of Danish dementia knock-in mice: caspase-9 inhibition provides therapeutic protection. Mol Neurodegener. 2012;7:60. doi: 10.1186/1750-1326-7-60 [DOI] [PMC free article] [PubMed] [Google Scholar]
11.Tamayev R, Matsuda S, Arancio O, D’Adamio L. β- but not γ-secretase proteolysis of APP causes synaptic and memory deficits in a mouse model of dementia. EMBO Mol Med. 2012;4(3):171–9. doi: 10.1002/emmm.201100195 [DOI] [PMC free article] [PubMed] [Google Scholar]
12.Tamayev R, D’Adamio L. Inhibition of γ-secretase worsens memory deficits in a genetically congruous mouse model of Danish dementia. Mol Neurodegener. 2012;7:19. doi: 10.1186/1750-1326-7-19 [DOI] [PMC free article] [PubMed] [Google Scholar]
13.Tambini MD, Yao W, D’Adamio L. Facilitation of glutamate, but not GABA, release in Familial Alzheimer’s APP mutant Knock-in rats with increased β-cleavage of APP. Aging Cell. 2019;18(6):e13033. doi: 10.1111/acel.13033 [DOI] [PMC free article] [PubMed] [Google Scholar]
14.Tambini MD, Norris KA, D’Adamio L. Opposite changes in APP processing and human Aβ levels in rats carrying either a protective or a pathogenic APP mutation. eLife. 2020;9:e52612. doi: 10.7554/eLife.52612 [DOI] [PMC free article] [PubMed] [Google Scholar]
15.Tambini MD, D’Adamio L. Trem2 Splicing and Expression are Preserved in a Human Aβ-producing, Rat Knock-in Model of Trem2-R47H Alzheimer’s Risk Variant. Sci Rep. 2020;10(1):4122. doi: 10.1038/s41598-020-60800-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
16.Tambini MD, D’Adamio L. Knock-in rats with homozygous PSEN1 ^L435F Alzheimer mutation are viable and show selective γ-secretase activity loss causing low Aβ40/42 and high Aβ43. J Biol Chem. 2020;295(21):7442–51. doi: 10.1074/jbc.RA120.012542 [DOI] [PMC free article] [PubMed] [Google Scholar]
17.Ren S, Yao W, Tambini MD, Yin T, Norris KA, D’Adamio L. Microglia TREM2 ^R47H Alzheimer-linked variant enhances excitatory transmission and reduces LTP via increased TNF-α levels. Elife. 2020;9. doi: 10.7554/eLife.57513 [DOI] [PMC free article] [PubMed] [Google Scholar]
18.Ren S, Breuillaud L, Yao W, Yin T, Norris KA, Zehntner SP, et al. TNF-α-mediated reduction in inhibitory neurotransmission precedes sporadic Alzheimer’s disease pathology in young Trem2 R^47H rats. J Biol Chem. 2020;296:100089. doi: 10.1074/jbc.RA120.016395 [DOI] [PMC free article] [PubMed] [Google Scholar]
19.Yin T, Yao W, Lemenze AD, D’Adamio L. Danish and British dementia ITM2b/BRI2 mutations reduce BRI2 protein stability and impair glutamatergic synaptic transmission. J Biol Chem. 2021;296:100054. doi: 10.1074/jbc.RA120.015679 [DOI] [PMC free article] [PubMed] [Google Scholar]
20.Andreadis A. Tau gene alternative splicing: expression patterns, regulation and modulation of function in normal brain and neurodegenerative diseases. Biochim Biophys Acta. 2005;1739(2–3):91–103. doi: 10.1016/j.bbadis.2004.08.010 [DOI] [PubMed] [Google Scholar]
21.Janke C, Beck M, Stahl T, Holzer M, Brauer K, Bigl V, et al. Phylogenetic diversity of the expression of the microtubule-associated protein tau: implications for neurodegenerative disorders. Brain Res Mol Brain Res. 1999;68(1–2):119–28. doi: 10.1016/s0169-328x(99)00079-0 [DOI] [PubMed] [Google Scholar]
22.Hong M, Zhukareva V, Vogelsberg-Ragaglia V, Wszolek Z, Reed L, Miller BI, et al. Mutation-specific functional impairments in distinct tau isoforms of hereditary FTDP-17. Science. 1998;282(5395):1914–7. doi: 10.1126/science.282.5395.1914 [DOI] [PubMed] [Google Scholar]
23.Roberson ED, Scearce-Levie K, Palop JJ, Yan F, Cheng IH, Wu T, et al. Reducing endogenous tau ameliorates amyloid beta-induced deficits in an Alzheimer’s disease mouse model. Science. 2007;316(5825):750–4. doi: 10.1126/science.1141736 [DOI] [PubMed] [Google Scholar]
24.Spillantini MG, Goedert M. Tau protein pathology in neurodegenerative diseases. Trends Neurosci. 1998;21(10):428–33. doi: 10.1016/s0166-2236(98)01337-x [DOI] [PubMed] [Google Scholar]
25.Goedert M, Crowther RA, Spillantini MG. Tau mutations cause frontotemporal dementias. Neuron. 1998;21(5):955–8. doi: 10.1016/s0896-6273(00)80615-7 [DOI] [PubMed] [Google Scholar]
26.Grundke-Iqbal I, Iqbal K, Tung YC, Quinlan M, Wisniewski HM, Binder LI. Abnormal phosphorylation of the microtubule-associated protein tau (tau) in Alzheimer cytoskeletal pathology. Proc Natl Acad Sci U S A. 1986;83(13):4913–7. doi: 10.1073/pnas.83.13.4913 [DOI] [PMC free article] [PubMed] [Google Scholar]
27.Hutton M, Lendon CL, Rizzu P, Baker M, Froelich S, Houlden H, et al. Association of missense and 5’-splice-site mutations in tau with the inherited dementia FTDP-17. Nature. 1998;393(6686):702–5. doi: 10.1038/31508 [DOI] [PubMed] [Google Scholar]
28.Stanford PM, Shepherd CE, Halliday GM, Brooks WS, Schofield PW, Brodaty H, et al. Mutations in the tau gene that cause an increase in three repeat tau and frontotemporal dementia. Brain. 2003;126(Pt 4):814–26. doi: 10.1093/brain/awg090 [DOI] [PubMed] [Google Scholar]
29.Yasuda M, Takamatsu J, D’Souza I, Crowther RA, Kawamata T, Hasegawa M, et al. A novel mutation at position +12 in the intron following exon 10 of the tau gene in familial frontotemporal dementia (FTD-Kumamoto). Ann Neurol. 2000;47(4):422–9. [PubMed] [Google Scholar]
30.Kowalska A, Hasegawa M, Miyamoto K, Akiguchi I, Ikemoto A, Takahashi K, et al. A novel mutation at position +11 in the intron following exon 10 of the tau gene in FTDP-17. J Appl Genet. 2002;43(4):535–43. [PubMed] [Google Scholar]
31.Grover A, England E, Baker M, Sahara N, Adamson J, Granger B, et al. A novel tau mutation in exon 9 (1260V) causes a four-repeat tauopathy. Exp Neurol. 2003;184(1):131–40. doi: 10.1016/s0014-4886(03)00393-5 [DOI] [PubMed] [Google Scholar]
32.Hanes J, Zilka N, Bartkova M, Caletkova M, Dobrota D, Novak M. Rat tau proteome consists of six tau isoforms: implication for animal models of human tauopathies. J Neurochem. 2009;108(5):1167–76. doi: 10.1111/j.1471-4159.2009.05869.x [DOI] [PubMed] [Google Scholar]
33.McMillan P, Korvatska E, Poorkaj P, Evstafjeva Z, Robinson L, Greenup L, et al. Tau isoform regulation is region- and cell-specific in mouse brain. J Comp Neurol. 2008;511(6):788–803. doi: 10.1002/cne.21867 [DOI] [PMC free article] [PubMed] [Google Scholar]
34.Endo T, Maekawa F, Voikar V, Haijima A, Uemura Y, Zhang Y, et al. Automated test of behavioral flexibility in mice using a behavioral sequencing task in IntelliCage. Behav Brain Res. 2011;221(1):172–81. doi: 10.1016/j.bbr.2011.02.037 [DOI] [PubMed] [Google Scholar]
35.Wu N, Wang F, Jin Z, Zhang Z, Wang LK, Zhang C, et al. Effects of GABAB receptors in the insula on recognition memory observed with intellicage. Behav Brain Funct. 2017;13(1):7. doi: 10.1186/s12993-017-0125-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
36.Kiryk A, Janusz A, Zglinicki B, Turkes E, Knapska E, Konopka W, et al. IntelliCage as a tool for measuring mouse behavior—20 years perspective. Behav Brain Res. 2020;388:112620. doi: 10.1016/j.bbr.2020.112620 [DOI] [PubMed] [Google Scholar]
37.Iman IN, Yusof NAM, Talib UN, Ahmad NAZ, Norazit A, Kumar J, et al. The IntelliCage System: A Review of Its Utility as a Novel Behavioral Platform for a Rodent Model of Substance Use Disorder. Front Behav Neurosci. 2021;15:683780. doi: 10.3389/fnbeh.2021.683780 [DOI] [PMC free article] [PubMed] [Google Scholar]
38.Masuda A, Kobayashi Y, Itohara S. Automated, Long-term Behavioral Assay for Cognitive Functions in Multiple Genetic Models of Alzheimer’s Disease, Using IntelliCage. J Vis Exp. 2018(138). doi: 10.3791/58009 [DOI] [PMC free article] [PubMed] [Google Scholar]
39.Urbach YK, Raber KA, Canneva F, Plank AC, Andreasson T, Ponten H, et al. Automated phenotyping and advanced data mining exemplified in rats transgenic for Huntington’s disease. J Neurosci Methods. 2014;234:38–53. doi: 10.1016/j.jneumeth.2014.06.017 [DOI] [PubMed] [Google Scholar]
40.Cao G, Wei X, Li W, Yin H, Lang W, Wei P, et al. Verification of a multi-function closed maze for the detection of affective disorder and spatial cognitive impairment in post-weaning socially isolated rats. Neurosci Lett. 2021;763:136192. doi: 10.1016/j.neulet.2021.136192 [DOI] [PubMed] [Google Scholar]
41.Ren S, Breuillaud L, Yao W, Yin T, Norris KA, Zehntner SP, et al. TNFα reduces inhibitory transmission in young Trem2^R47H Sporadic Alzheimer rats before observable Aβ and brain pathology. bioRxiv 2020. [Google Scholar]
42.Beam CR, Kaneshiro C, Jang JY, Reynolds CA, Pedersen NL, Gatz M. Differences Between Women and Men in Incidence Rates of Dementia and Alzheimer’s Disease. J Alzheimers Dis. 2018;64(4):1077–83. doi: 10.3233/JAD-180141 [DOI] [PMC free article] [PubMed] [Google Scholar]
43.Zehntner SP, Chakravarty MM, Bolovan RJ, Chan C, Bedell BJ. Synergistic tissue counterstaining and image segmentation techniques for accurate, quantitative immunohistochemistry. J Histochem Cytochem. 2008;56(10):873–80. doi: 10.1369/jhc.2008.950345 [DOI] [PMC free article] [PubMed] [Google Scholar]
44.Mifflin MA, Winslow W, Surendra L, Tallino S, Vural A, Velazquez R. Sex differences in the IntelliCage and the Morris water maze in the APP/PS1 mouse model of amyloidosis. Neurobiol Aging. 2021;101:130–40. doi: 10.1016/j.neurobiolaging.2021.01.018 [DOI] [PMC free article] [PubMed] [Google Scholar]

PLoS One. doi: 10.1371/journal.pone.0263546.r001

Decision Letter 0

Stephen D Ginsberg

15 Mar 2022

PONE-D-22-01698Initial assessment of the spatial learning, reversal, and sequencing task capabilities of knock-in rats with humanizing mutations in the Aß-coding region of AppPLOS ONE

Dear Dr. D'Adamio,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration by 2 Reviewers and an Academic Editor, all of the critiques of both Reviewers, especially Reviewer #2, must be addressed in detail in a revision to determine publication status. Please pay particular attention to the critiques regarding lack of validation/controls and confusing structure of the Figures as well as a general lack of description of the Intellicage. If you are prepared to undertake the work required, I would be pleased to reconsider my decision, but revision of the original submission without directly addressing the critiques of the Reviewers does not guarantee acceptance for publication in PLOS ONE. If the authors do not feel that the queries can be addressed, please consider submitting to another publication medium. A revised submission will be sent out for re-review. The authors are urged to have the manuscript given a hard copyedit for syntax and grammar.

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

Please submit your revised manuscript by Apr 29 2022 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Stephen D. Ginsberg, Ph.D.

Section Editor

PLOS ONE

Journal Requirements:

1. When submitting your revision, we need you to address these additional requirements.

Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

2. In your Data Availability statement, you have not specified where the minimal data set underlying the results described in your manuscript can be found. PLOS defines a study's minimal data set as the underlying data used to reach the conclusions drawn in the manuscript and any additional data required to replicate the reported study findings in their entirety. All PLOS journals require that the minimal data set be made fully available. For more information about our data policy, please see http://journals.plos.org/plosone/s/data-availability.

Upon re-submitting your revised manuscript, please upload your study’s minimal underlying data set as either Supporting Information files or to a stable, public repository and include the relevant URLs, DOIs, or accession numbers within your revised cover letter. For a list of acceptable repositories, please see http://journals.plos.org/plosone/s/data-availability#loc-recommended-repositories. Any potentially identifying patient information must be fully anonymized.

Important: If there are ethical or legal restrictions to sharing your data publicly, please explain these restrictions in detail. Please see our guidelines for more information on what we consider unacceptable restrictions to publicly sharing data: http://journals.plos.org/plosone/s/data-availability#loc-unacceptable-data-access-restrictions. Note that it is not acceptable for the authors to be the sole named individuals responsible for ensuring data access.

We will update your Data Availability statement to reflect the information you provide in your cover letter.

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: No

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: No

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: No

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The manuscript is well written, timely, and is important to establish the use of behavioral apparatuses (Intellicage) that remove variables that may contaminate results. Major concerns noted below include not validating pathology in the rat model, missing details in the methods section, and elaborating in the discussion on how this is related to already published work in the AD field related to rodent models and Intellicage testing.

Major concerns:

1) Methods: Key methods on the rat model used are missing. How did they verify the genetic background of the rats? PCR?

2) There is no validation of pathology in this rat model. If the point is that this model is superior than mouse models over-expressing APP, data on pathology needs to be presented. At the very least, measures of abeta soluble and insoluble fractions should be included and correlated with performance in the intellicage. It is also unclear if rats would have pathology at this age, so ages selected is not clear.

3) There is no mention of what the protocol is if an animal does not drink during the testing period. In various studies, animals have been shown not to drink in the Intellicage, it is a frequent occurrence. How many rats were excluded because of this? This should be elaborated in the methods section and is critically important for animal ethical standards.

4) The Discussion lacks detail. There is no discussion on how performance of the rats compares to 1) other behavioral tasks (ie., MWM, object recognition, etc.) or 2) to performance of mice in the IntellICage. There are many recent papers on this topic in the last 2 years.

5) Why were the ages selected and how do they relate to pathology?

Minor concerns:

1. The introduction can benefit from a paragraph on what is the intellicage, its background, and how it differs from standard cognitive tasks.

2. The abstract needs concluding sentences to summarize the key findings of this work and the impact to the field of AD rodent modeling.

Reviewer #2: This is an interesting and important study using humanized model of APP in rats (APP KI) carrying humanized mutations in the Ab region of APP. The main focus of the study is on utilization of IntelliCage to delineate possible cognitive phenotypes of this APP KI model.

Below are some of my comments to improve this otherwise interesting manuscript.

Suggestions/Concerns:

- The introduction seems to introduce many general topics and it might be helpful to somewhat re-structure it to align it with main focus of this particular study.

- The description of the rat model in the Methods section is not sufficient. More details are needed to guide readers' understanding which mutations are introduced and whether the levels of APP and/or Ab are affected in this model.

- Please state explicitly and visibly in the Methods section the total number of rats used for each protocol/cohort/sex as well as a number of rats per IntelliCage.

- Figures 1-2 are helpful showing the nature of the tasks used. It would be nice to put the names of different protocols in the panels and include easily accessible information on a time frame for each stage.

- The description of IntelliCage outcomes in the Methods section is convoluted and too complex to follow. Some explanations of why such outcomes were chosen with their general meaning would be helpful.

- Statistical analyses section does not address statistical analyses but rather consist of further workflow description of how to receive the outcomes.

- The major issue is the lack of the control group. From reading the Methods section, It is unclear what comparisons are of interest in this study. There is no description of what types of comparisons for which factors are planned.

- Results are presented in terms of cohorts, and it is unclear what meaning these descriptions have. For example, why the results started from Cohort B? What is the meaning of comparing one female ra to two other female rats at different passes?

- If " activity curves show.. the fractional accumulation of corner visits" as stated in Results, then the results for correct and incorrect activity curves are dependent on each other and do not need to be represented as separate outcomes.

- There are 10 multi-panel figures accompanied by more than 14 tables with stat results. How the effect of multiple comparisons in this dataset been addressed is not clear.

- The figures could be edited to present more easily perceived information. Names of the task, scheme of the task, etc.

- The discussion/intro do not place this study in any background of what has been done in this environment. The choice of the tasks is not discussed as well as the choice of the outcomes used. All of these shortcomings significantly decrease enthusiasm to this potentially interesting study.

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: Yes: Alena Savonenko

PLoS One. 2022 May 4;17(5):e0263546. doi: 10.1371/journal.pone.0263546.r002

Author response to Decision Letter 0

30 Mar 2022

Reviewer #1:

Major concerns:

1) Methods: Key methods on the rat model used are missing. How did they verify the genetic background of the rats? PCR?

Response: Thank you for this comment. To address these questions, we have included a new Figure (Fig. 2). In this figure we show the mutations introduced to humanize the Aβ region of rat App and the sequencing method used to verify the genotype of rats (Fig. 2A). To verify whether the protein products of the Apph/h allele contain the humanizing mutations, we performed the following experiments. 1) an ELISA assay using as detection antibody either M3.2 (a mouse monoclonal raised against the rat APP sequence between the β‐ and α‐secretase cleavage sites DAEFGHDSGFEVRHQK, which only recognize APP molecules containing the rat Aβ sequence) or 6E10 (a mouse monoclonal raised against the corresponding domain of human APP DAEFRHDSGYEVHHQK, which only recognize APP molecules containing the human Aβ sequence). This ELISA shows that Appw/w rats produce rat Aβ while Apph/h rats produce human Aβ (Fig. 2B). 2) Western blot analysis with M3.2 and 6E10. As shown in Fig. 2C, M3.2 detects APP only in Appw/w, conversely, 6E10 detects APP only in Apph/h rats. Altogether, these experiments demonstrate that products of the Apph allele, contain the humanized Aβ sequence. Methods related to the new Figure 4 have been added to the revised manuscript. We believe these experiments address the reviewer’s question.

2) There is no validation of pathology in this rat model. If the point is that this model is superior to mouse models over-expressing APP, data on pathology needs to be presented. At the very least, measures of abeta soluble and insoluble fractions should be included and correlated with performance in the intellicage. It is also unclear if rats would have pathology at this age, so ages selected is not clear.

Response: Thank you for this question. In the original version, we did not satisfactorily explain the purpose behind the generation of this animal. We do not consider the Apph/h rats a model of late onset AD (LOAD). We are aware that recently Dr. LaFerla and Jackson Laboratory have produced a "humanized" App knock-in mouse which is being used by many as a model of LOAD. However, we consider our "humanized" App knock-in rat as a control animal to be used to determine the effect of familial genetic mutation causing early onset AD, or other genetic variant that increase the risk of LOAD. With this Apph allele, we can test the pathogenic mechanisms of familial and sporadic mutations/variants in the context of normal levels of human Aβ expression. We do not expect this model to develop AD-like pathology (especially not at these young ages). To proof or disproof this prediction, in this revised manuscript we show IHC analysis of 14 months old Apph/h rats: unsurprisingly, Apph/h rats show no signs of AD-like pathology, even at 14 moths of age (Fig. 2D). Methods related to the new Figure 2 have been added to the revised manuscript. We believe these experiments address the reviewer’s question.

As for the ages selected, we based this selection on the evidence that LOAD, FAD and ADRD knock-in rat models that we have created show synaptic plasticity and transmission alterations already at 6-8 weeks of age. Thus, the first time point was chosen because, in future experiments we will test whether these familial and sporadic pathogenic mutations cause deficits in learning and memory as compared to control Apph/h rats starting at ~8 weeks of age, when these “AD” animals show synaptic plasticity/transmission alterations. We performed a second longitudinal test of the same animals because, in these future experiments, we intend to perform longitudinal studies in our FAD and LOAD rat model organisms. Therefore, we tested whether longitudinal studies using the IntelliCage are informative. In this revised version, we have included modifications in all sections to explain these concepts better.

Response: Thank you for this question. We apologize for not having provided this important information in the first version of the paper. We have added a new section in the Methods entitled Inclusion/exclusion criteria to address this criticism. In addition, Details of data point exclusion by drinking session can be seen in the tables accompanying data figures.

4) The Discussion lacks detail. There is no discussion on how performance of the rats compares to 1) other behavioral tasks (i.e., MWM, object recognition, etc.) or 2) to performance of mice in the IntellICage. There are many recent papers on this topic in the last 2 years.

Response: Thank you for this question. We have revised the discussion to address these points.

5) Why were the ages selected and how do they relate to pathology?

Response: See response to Criticism #2.

Minor concerns:

1. The introduction can benefit from a paragraph on what is the intellicage, its background, and how it differs from standard cognitive tasks.

Response: Thank you for this suggestion. This paragraph has been added to the

Introduction.” Behavioral tests consent to determine whether model organism of AD and ADRD develop learning & memory deficits. Most studies use traditional paradigms, including novel object recognition, Fear conditioning, Morris water maze, Radial arm water maze. These approaches are well established and informative. The IntelliCage system (NewBehavior AG) provides an additional method of assessing behavior in rodents. It has been used to study behavior in mouse models of human disease, including neurodegenerative and neuropsychiatric conditions such as Huntington’s disease and, notably, AD, with spatial learning and memory being among the most studied parameters. It consists of a central square home cage connected to four operant learning chambers, or corners. Every corner has two sides, each with a drinking bottle gated by a rotating door with a nosepoke sensor (Figure 1). The sides also include LEDs and air puff delivery valves as additional conditioning components. Behavioral programs are defined by the user within a visual coding platform. Subcutaneously injected transponders allow the IntelliCage to track the activity of individual animals with unique radio frequency identification tags. Among the parameters tabulated for subsequent analysis are corner visits, visit lengths, visit times, number of nosepokes per visit, and number of bottle licks per visit. This system offers a variety of advantages over standard cognitive tasks: high-throughput, unbiased data collection; minimal risk of human error; minimal perturbation of testing conditions; and uniform testing of multiple animals simultaneously in a social setting. The final point is important in the context of cognitive phenotyping because the social housing component, a distinguishing feature of this system, eliminates isolation as a confounding psychological stressor from the animals’ environment. Its stable, passive manner of data collection also mitigates stress from handling and the traumatic experience inherent in tests such as the Morris water maze.”

2. The abstract needs concluding sentences to summarize the key findings of this work and the impact to the field of AD rodent modeling.

Response: Thank you for this suggestion. The concluding sentence has been added to the Abstract.

Reviewer #2:

Suggestions/Concerns:

- The introduction seems to introduce many general topics and it might be helpful to somewhat re-structure it to align it with main focus of this particular study.

Response: Thank you for the suggestion. Based on this suggestion, we have extensively changed the Introduction.

Response: See response to Criticism #2 of Reviewer #1. A direct comparison between Aβ levels cannot be done since the detection antibodies are different (6E10 for human Aβ and M3.2 for rat Aβ). We do not use 4G8 because 4G8 can also detect P3 (the α-γ secretase mini- Aβ peptide) and cannot accurately measure Aβ. We have previously shown that APP expression levels are unchanged by the humanizing mutations.

- Please state explicitly and visibly in the Methods section the total number of rats used for each protocol/cohort/sex as well as a number of rats per IntelliCage.

Response: Thank you for this suggestion. We have added the requested information in the methods section. In addition, for each experiment we indicate the # of animals tested as well as the number of animals included in the analyses in the Figures and Figures legends. See also response to Criticism #3 of Reviewer #1.

Response: Thank you for this suggestion. We have added paragraphs describing the general rationale for outcomes (i.e., corner rank, AUC, activity curves, Statistical analysis of AUC) in the Methods section.

Corner rank: "To understand better the effect of social interaction on the behavior of animals in the IntelliCage, we ranked animals via a point system based on whether an animal visits the correct corner more than other animals do during single corner restriction. This may occur with the exclusion of other animals from those corners and be considered a proxy for social dominance. As the single corner restriction program changes the correct corner every ninety minutes over two drinking sessions, we might expect a dominant animal to score highly for all corners."

Activity curves: "To visualize the activity of all the animals in each IntelliCage as a unit, we charted the fractional accumulation of correct/opposite visits over the course of each drinking session. With the resulting curves, we can qualitatively compare task performance according to drinking session, sex, and pass."

Statistical analysis of AUC: "To assess task performance quantitatively, we used the area under the activity curves of individual animals in each IntelliCage. Every correct/opposite visit during a drinking session contributes to this area; this approach accounts not only for the total fraction of correct/opposite visits but also for the rate at which they accumulate. It also takes advantage of the large volume of data the IntelliCage collects from each program such that there is no need to approximate the rate of learning with curve fitting."

- Statistical analyses section does not address statistical analyses but rather consist of further workflow description of how to receive the outcomes.

Response: Thank you. We have listed which tests we used and the comparisons we were interested in for each part of the analysis in the Methods section.

Corner rank: "[We] compared the mean scores of animals within each IntelliCage for a given pass, performing a one-way ordinary ANOVA in Prism 9 (GraphPad, San Diego, California) followed by Tukey’s multiple comparisons tests when applicable (p < 0.05 was significant)."

AUC: "We focused on three factors in our analysis: drinking session, sex, and pass. Given a pass and program, we performed a two-way repeated measures ANOVA in Prism 9 on the data from all animals in a cohort, organized by sex and drinking session, followed by Šídák's multiple comparisons tests when applicable (p < 0.05 was significant). Specifically, we wanted to see whether there were significant session-wise differences within sex or sex differences within a given drinking session. If one or more drinking session data points were excluded for a given pass and program, mixed-effects analysis was performed instead with appropriate post-hoc tests. Paired t tests were performed to compare performance between passes within a cohort for a given program, sex, and drinking session."

Response: Thank you for this question. In the original version, we did not satisfactorily explain the rationale for the experimental design. In this version, we more explicitly address these points ate the end of the introduction: “In this study we tested whether the IntelliCage can be used to assess learning and memory in Apph/h rats. We assessed the spatial learning, reversal, and sequencing task performance of male and females Apph/h rats at 6-8 weeks of age (peri-adolescent rats), and again at 4-5 months of age (young adult rats). We decided to start testing peri-adolescent rats based on the evidence that our FAD, ADRD and LOAD rat models show synaptic plasticity and transmission alterations already at 6-8 weeks of age. Thus, knowing the performance of our control group at this early age is important. We performed a second test on the same animals at 4-5 months of age, to understand how the control Apph/h rats perform in longitudinal tests as young adults. We tested both male and female rats to determine whether there are any sex-dependent differences in performance. This is important because incidence rates of LOAD are greater in women than men after age 85 . In summary, this study’s goal is to establish methods using the IntelliCage system to determine, in following studies, whether our FAD, ADRD and LOAD models develop learning and memory deficits, the age of onset of these deficits, whether these deficits correlate with synaptic plasticity/transmission alterations, and the impact of sex.” We have also restructured Figures and analyses to better show these comparisons of interest.

Response: Thank you for these questions. In the methods section under "IntelliCage" we have described what we mean by "cohort" and the rationale for designing two of them. " Briefly, the program timeline was divided into three parts: (1) a period during which the animals may freely explore the IntelliCage and acclimate to a daily period of restricted water access during a time window (8:00-11:00pm) called the drinking session; (2) a period consisting of place learning and reversal programs during which every animal is assigned a drinking corner during a drinking session; and (3) a period consisting of more complex sequencing programs involving a rule that governs the designation of drinking corners based on animal activity during a drinking session. Variations in the approach toward these parts prompted the design of two parallel cohorts testing the same cognitive domains, analyzed independently. Two cohorts of Apph/h rats were studied longitudinally, A and B, housed across four IntelliCages separated by sex and cohort. Twelve rats were designated for each IntelliCage such that there would be 24 rats per cohort consisting of 12 females and 12 males each. The cohorts were run on separate program timelines, once at 6-8 weeks of age and again at 4-5 months of age (the first pass and second pass through the program timeline, respectively), as outlined in Table 2 with program descriptions in included in the Figures showing the results."

The only reason the results start with reference to cohort B is because the program whose results are being described--single corner restriction--comes before place learning and reversal (cohort A) overall on the shared timeline. Emphasis on the longitudinal aspect of this study and the rationale behind corner rank comparison should address the concern of why it is relevant to compare an animal to another across passes; in this case, it was to make a point that any significant differences in corner rank that appeared during the first pass were not significant during the second pass.

- If " activity curves show. the fractional accumulation of corner visits" as stated in Results, then the results for correct and incorrect activity curves are dependent on each other and do not need to be represented as separate outcomes.

Response: Thank you for this question. We have removed activity curves for opposite (incorrect) visits from PL, PR, and CS figures. For BS and SR figures he has removed activity curves for lateral visits and kept those for correct and opposite visits.

- There are 10 multi-panel figures accompanied by more than 14 tables with stat results. How the effect of multiple comparisons in this dataset been addressed is not clear.

Response: Thank you for this question. We are not sure we understand exactly what is being asked. But if this question relates to appropriate adjustment of p values with multiple comparisons, this is addressed in the updated statistical analysis section. The tables also mention that we used post-hoc tests addressing correction for multiple comparisons.

- The figures could be edited to present more easily perceived information. Names of the task, scheme of the task, etc.

Responses: Thank you for these questions. We have changed the figures to include the abbreviation of the task name in the inclusion/exclusion table. We have also moved the panels from previous Figure 2 describing the program to each data figure.

Response: Thank you for these questions. The updated introduction partially addresses this, "[The IntelliCage] has been used to study behavior in mouse models of human disease, including neurodegenerative and neuropsychiatric conditions such as Huntington’s disease and, notably, AD, with spatial learning and memory being among the most studied parameters. [...] A larger version of the IntelliCage system developed for rats has been used for studying Huntington’s disease(40); the effect of GABAB receptors in the insula on recognition memory(36); and deficits in spatial learning and memory following post-weaning social isolation(41)." The choice of outcomes is discussed in the methods section.

In addition, we have changed the discussion to further address this point.

Attachment

Submitted filename: Point-by-point response to reviewers.docx

Click here for additional data file.^{(28.3KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0263546.r003

Decision Letter 1

Stephen D Ginsberg

18 Apr 2022

PONE-D-22-01698R1Initial assessment of the spatial learning, reversal, and sequencing task capabilities of knock-in rats with humanizing mutations in the Aß-coding region of AppPLOS ONE

Dear Dr. D'Adamio,

Thank you for resubmitting your work to PLOS ONE. Please make the corrections posed by Reviewer #2 so I can render a decision on this manuscript.

==============================

Please submit your revised manuscript by Jun 02 2022 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

We look forward to receiving your revised manuscript.

Kind regards,

Stephen D. Ginsberg, Ph.D.

Section Editor

PLOS ONE

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Reviewer #1: Thank you for addressing all raised comments by this reviewer.

Reviewer #2: The revised version of the manuscript is much improved. I have some comments that I believe are easily addressable:

Corner rank: The interpretation of a corner task as a measure of social dominance, although it is very interesting data, is not supported by data on social hierarchy. It should be discussed as a limitation of the study.

Activity curves: The name of this variable (activity curves) is misleading as this variable measures the fraction of correct responses (relative to visits to the opposite corner) rather than "activity", a word that strongly associates with motor/exploratory activity. The choice of this measure (proportion of correct to opposite corner) to characterize cognitive performance is not clearly explained. Why the visits to two other corners were excluded is not explained.

The analyses of reversal learning would benefit from analyses of correct vs previously baited corners.

The idea that "Apph/h rats constitute control animals" should be proven by data showing comparison of this model to wt rats. For example, we do know that presence of wild type human tau in mice does have consequences on a number of biological levels. This should be discussed as a limitation/future direction of the study.

Discussion section is much improved. A particular discussion on chance levels in different tasks is a particularly important addition. I would suggest introducing definitions of chance level for appropriate variables (including AUC) in the Method section, and, what would be really great, putting these levels as a line in appropriate figures. The issue of chance levels is especially significant for this study as its goal is to prove that rats learned the tasks.

In the discussion, it is stated that "...a program of intermediate difficulty using a sequence involving all four corners (in clockwise motion, for example) rather than just two in a single diagonal, might be worth testing in future studies." Although the main idea here (that a task of intermediate difficulty would be the most useful) sounds great, a particular example (visiting all four corners in clockwise motion) might be easily affected by development of pervasive strategy (to visit each next corner) that can collapse the cognitive demand of this particular protocol.

Minor:

- abbreviations should be explained before their first use - a KI approach in mice...

- a statement in the Introduction that " ...The KI approach ... makes no assumption about pathogenic mechanisms (except the unbiased genetic one)" is rather unclear and not needed.

- The figures are improved; but there is still a lot of information that is unreasonably hidden. For example, if FP = 1st pass, SP = 2nd pass, the figures would be much more readable if "Run 1" and "Run 2" would be used instead of FP and SP. The same could be said about other abbreviations (in the figures and Tables) which could be easily avoided altogether (BS-O, BS-C, SR, ...). While these comments may sound like non-essential for investigators who worked with these tasks, the relative novelty of ItelliCage, tasks and variables to overwhelming majority of other researchers makes it very important to spent time and make data presentation as clear as possible.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: Yes: Alena Savonenko

PLoS One. 2022 May 4;17(5):e0263546. doi: 10.1371/journal.pone.0263546.r004

Author response to Decision Letter 1

19 Apr 2022

Q: Corner rank: The interpretation of a corner task as a measure of social dominance, although it is very interesting data, is not supported by data on social hierarchy. It should be discussed as a limitation of the study.

R: We have eliminated any reference to this test as a measure of social dominance. The methods now read

“Corner rank comparison. To understand better the effect of social interaction on the behavior of animals in the IntelliCage, we ranked animals via a point system based on whether an animal visits the correct corner more than other animals do during single corner restriction. This may occur with the exclusion of other animals from those corners, which may bear upon the performance of paired animals in subsequent tests.”

Q: Activity curves: The name of this variable (activity curves) is misleading as this variable measures the fraction of correct responses (relative to visits to the opposite corner) rather than "activity", a word that strongly associates with motor/exploratory activity. The choice of this measure (proportion of correct to opposite corner) to characterize cognitive performance is not clearly explained. Why the visits to two other corners were excluded is not explained.

R: We have changed the name activity curves into Learning curves.

As for measuring the correct versus opposite, we realize that the writing in the methods was confusing. The sections describing the methods and rationale now read:

“Learning curves. To visualize learning of all the animals in each IntelliCage as a unit, we charted the fractional accumulation of correct visits (also opposite visits for the Behavioral sequencing and Serial reversal tasks) over the course of each drinking session. With the resulting curves, we can qualitatively compare task performance according to drinking session, sex, and Run. We followed this workflow to produce the learning curves for each program: 1. For each subset of rats by sex, cohort, and Run (e.g., cohort A males in their Run-1), count the total number of visits those rats made for each drinking session. For a 3-day program, there should be 3 totals for a given subset. 2. For each subset as described in step 1, tabulate the fractional accumulation of correct (and opposite) visits over time for each drinking session, adding to each fraction, starting from 0, the value of 1 divided by the associated total count for that subset and drinking session each time a correct (or opposite) visit occurs, and 0 otherwise.”

“Statistical analysis of area under the curves (AUC). To assess task performance quantitatively, we used the area under the learning curves of individual animals in each IntelliCage. Every correct visit during a drinking session contributes to this area; this approach accounts not only for the total fraction of correct visits but also for the rate at which they accumulate. It also takes advantage of the large volume of data the IntelliCage collects from each program such that there is no need to approximate the rate of learning with curve fitting. For the Behavioral sequencing and the Serial reversal tasks we also calculated the AUC for the opposite corner, since the opposite corner represents the previously correct corner. Thus, calculations of these two areas indicates the speed by which a rat learns the rules regulating alternation of correct to previously correct corners.”

As for the analysis of all for corners (i.e. the incorrect corners), reviewer 2 pointed out during the first round of revision, the following:

If " activity curves show.. the fractional accumulation of corner visits" as stated in Results, then the results for correct and incorrect activity curves are dependent on each other and do not need to be represented as separate outcomes.

Based on this comment we eliminated the analysis of the other corners when revising the manuscript.

Q: The analyses of reversal learning would benefit from analyses of correct vs previously baited corners.

R: Thank you for the suggestion but this analysis, which is conceptually identical to the analysis of Opposite in Behavioral sequencing and Serial reversal, is not informative in this case.

Q: The idea that "Apph/h rats constitute control animals" should be proven by data showing comparison of this model to wt rats. For example, we do know that presence of wild type human tau in mice does have consequences on a number of biological levels. This should be discussed as a limitation/future direction of the study.

R: We have added this sentence to the introduction.

“Whether expression of human Aβ is, per se’, sufficient to impact behavior will be addressed in future studies comparing Apph/h to Appw/w rats.”

Q: Discussion section is much improved. A particular discussion on chance levels in different tasks is a particularly important addition. I would suggest introducing definitions of chance level for appropriate variables (including AUC) in the Method section, and, what would be really great, putting these levels as a line in appropriate figures. The issue of chance levels is especially significant for this study as its goal is to prove that rats learned the tasks.

R: Thank you for the suggestion but we think that the chance level is sufficiently explained in the discussion and very intuitive for a system with 4 options.

Q: In the discussion, it is stated that "...a program of intermediate difficulty using a sequence involving all four corners (in clockwise motion, for example) rather than just two in a single diagonal, might be worth testing in future studies." Although the main idea here (that a task of intermediate difficulty would be the most useful) sounds great, a particular example (visiting all four corners in clockwise motion) might be easily affected by development of pervasive strategy (to visit each next corner) that can collapse the cognitive demand of this particular protocol.

R: We have added this sentence to the discussion.

“Yet, such a program could be affected by development of pervasive strategy (to visit each next corner) that can collapse the cognitive demand of this particular protocol.”

Minor:

Q: - abbreviations should be explained before their first use - a KI approach in mice...

R: We have added the KI abbreviation in the Abstract

Q: - a statement in the Introduction that " ...The KI approach ... makes no assumption about pathogenic mechanisms (except the unbiased genetic one)" is rather unclear and not needed.

R: The sentence has been deleted

Q: - The figures are improved; but there is still a lot of information that is unreasonably hidden. For example, if FP = 1st pass, SP = 2nd pass, the figures would be much more readable if "Run 1" and "Run 2" would be used instead of FP and SP. The same could be said about other abbreviations (in the figures and Tables) which could be easily avoided altogether (BS-O, BS-C, SR, ...). While these comments may sound like non-essential for investigators who worked with these tasks, the relative novelty of ItelliCage, tasks and variables to overwhelming majority of other researchers makes it very important to spent time and make data presentation as clear as possible.

R: First pass and second pass have need changed to Run-1 and Run-2 in text and figure.

Abbreviations have been eliminated.

Attachment

Submitted filename: response to the revision of the revision.docx

Click here for additional data file.^{(19.3KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0263546.r005

Decision Letter 2

Stephen D Ginsberg

22 Apr 2022

Initial assessment of the spatial learning, reversal, and sequencing task capabilities of knock-in rats with humanizing mutations in the Aß-coding region of App

PONE-D-22-01698R2

Dear Dr. D'Adamio,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Stephen D. Ginsberg, Ph.D.

Section Editor

PLOS ONE

PLoS One. doi: 10.1371/journal.pone.0263546.r006

Acceptance letter

Stephen D Ginsberg

25 Apr 2022

PONE-D-22-01698R2

Initial assessment of the spatial learning, reversal, and sequencing task capabilities of knock-in rats with humanizing mutations in the Aβ-coding region of App

Dear Dr. D'Adamio:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Stephen D. Ginsberg

Section Editor

PLOS ONE

Associated Data

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

Supplementary Materials

Attachment

Submitted filename: Point-by-point response to reviewers.docx

Click here for additional data file.^{(28.3KB, docx)}

Attachment

Submitted filename: response to the revision of the revision.docx

Click here for additional data file.^{(19.3KB, docx)}

Data Availability Statement

All relevant data are within the manuscript.

[pone.0263546.ref001] 1.Barbagallo AP, Wang Z, Zheng H, D’Adamio L. The intracellular threonine of amyloid precursor protein that is essential for docking of Pin1 is dispensable for developmental function. PLoS One. 2011;6(3):e18006. doi: 10.1371/journal.pone.0018006 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0263546.ref002] 2.Barbagallo AP, Wang Z, Zheng H, D’Adamio L. A single tyrosine residue in the amyloid precursor protein intracellular domain is essential for developmental function. J Biol Chem. 2011;286(11):8717–21. doi: 10.1074/jbc.C111.219873 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0263546.ref003] 3.Barbagallo AP, Weldon R, Tamayev R, Zhou D, Giliberto L, Foreman O, et al. Tyr(682) in the intracellular domain of APP regulates amyloidogenic APP processing in vivo. PLoS One. 2010;5(11):e15503. doi: 10.1371/journal.pone.0015503 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0263546.ref004] 4.Garringer HJ, Murrell J, D’Adamio L, Ghetti B, Vidal R. Modeling familial British and Danish dementia. Brain Struct Funct. 2010;214(2–3):235–44. doi: 10.1007/s00429-009-0221-9 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0263546.ref005] 5.Giliberto L, Matsuda S, Vidal R, D’Adamio L. Generation and initial characterization of FDD knock in mice. PLoS One. 2009;4(11):e7900. doi: 10.1371/journal.pone.0007900 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0263546.ref006] 6.Tamayev R, Matsuda S, Fa M, Arancio O, D’Adamio L. Danish dementia mice suggest that loss of function and not the amyloid cascade causes synaptic plasticity and memory deficits. Proc Natl Acad Sci U S A. 2010;107(48):20822–7. doi: 10.1073/pnas.1011689107 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0263546.ref007] 7.Tamayev R, Giliberto L, Li W, d’Abramo C, Arancio O, Vidal R, et al. Memory deficits due to familial British dementia BRI2 mutation are caused by loss of BRI2 function rather than amyloidosis. J Neurosci. 2010;30(44):14915–24. doi: 10.1523/JNEUROSCI.3917-10.2010 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0263546.ref008] 8.Tamayev R, Matsuda S, Giliberto L, Arancio O, D’Adamio L. APP heterozygosity averts memory deficit in knockin mice expressing the Danish dementia BRI2 mutant. EMBO J. 2011;30(12):2501–9. doi: 10.1038/emboj.2011.161 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0263546.ref009] 9.Tamayev R D ’Adamio L. Memory deficits of British dementia knock-in mice are prevented by Aβ-precursor protein haploinsufficiency. J Neurosci. 2012;32(16):5481–5. doi: 10.1523/JNEUROSCI.5193-11.2012 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0263546.ref010] 10.Tamayev R, Akpan N, Arancio O, Troy CM, D’Adamio L. Caspase-9 mediates synaptic plasticity and memory deficits of Danish dementia knock-in mice: caspase-9 inhibition provides therapeutic protection. Mol Neurodegener. 2012;7:60. doi: 10.1186/1750-1326-7-60 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0263546.ref011] 11.Tamayev R, Matsuda S, Arancio O, D’Adamio L. β- but not γ-secretase proteolysis of APP causes synaptic and memory deficits in a mouse model of dementia. EMBO Mol Med. 2012;4(3):171–9. doi: 10.1002/emmm.201100195 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0263546.ref012] 12.Tamayev R, D’Adamio L. Inhibition of γ-secretase worsens memory deficits in a genetically congruous mouse model of Danish dementia. Mol Neurodegener. 2012;7:19. doi: 10.1186/1750-1326-7-19 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0263546.ref013] 13.Tambini MD, Yao W, D’Adamio L. Facilitation of glutamate, but not GABA, release in Familial Alzheimer’s APP mutant Knock-in rats with increased β-cleavage of APP. Aging Cell. 2019;18(6):e13033. doi: 10.1111/acel.13033 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0263546.ref014] 14.Tambini MD, Norris KA, D’Adamio L. Opposite changes in APP processing and human Aβ levels in rats carrying either a protective or a pathogenic APP mutation. eLife. 2020;9:e52612. doi: 10.7554/eLife.52612 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0263546.ref015] 15.Tambini MD, D’Adamio L. Trem2 Splicing and Expression are Preserved in a Human Aβ-producing, Rat Knock-in Model of Trem2-R47H Alzheimer’s Risk Variant. Sci Rep. 2020;10(1):4122. doi: 10.1038/s41598-020-60800-1 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0263546.ref016] 16.Tambini MD, D’Adamio L. Knock-in rats with homozygous PSEN1 ^L435F Alzheimer mutation are viable and show selective γ-secretase activity loss causing low Aβ40/42 and high Aβ43. J Biol Chem. 2020;295(21):7442–51. doi: 10.1074/jbc.RA120.012542 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0263546.ref017] 17.Ren S, Yao W, Tambini MD, Yin T, Norris KA, D’Adamio L. Microglia TREM2 ^R47H Alzheimer-linked variant enhances excitatory transmission and reduces LTP via increased TNF-α levels. Elife. 2020;9. doi: 10.7554/eLife.57513 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0263546.ref018] 18.Ren S, Breuillaud L, Yao W, Yin T, Norris KA, Zehntner SP, et al. TNF-α-mediated reduction in inhibitory neurotransmission precedes sporadic Alzheimer’s disease pathology in young Trem2 R^47H rats. J Biol Chem. 2020;296:100089. doi: 10.1074/jbc.RA120.016395 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0263546.ref019] 19.Yin T, Yao W, Lemenze AD, D’Adamio L. Danish and British dementia ITM2b/BRI2 mutations reduce BRI2 protein stability and impair glutamatergic synaptic transmission. J Biol Chem. 2021;296:100054. doi: 10.1074/jbc.RA120.015679 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0263546.ref020] 20.Andreadis A. Tau gene alternative splicing: expression patterns, regulation and modulation of function in normal brain and neurodegenerative diseases. Biochim Biophys Acta. 2005;1739(2–3):91–103. doi: 10.1016/j.bbadis.2004.08.010 [DOI] [PubMed] [Google Scholar]

[pone.0263546.ref021] 21.Janke C, Beck M, Stahl T, Holzer M, Brauer K, Bigl V, et al. Phylogenetic diversity of the expression of the microtubule-associated protein tau: implications for neurodegenerative disorders. Brain Res Mol Brain Res. 1999;68(1–2):119–28. doi: 10.1016/s0169-328x(99)00079-0 [DOI] [PubMed] [Google Scholar]

[pone.0263546.ref022] 22.Hong M, Zhukareva V, Vogelsberg-Ragaglia V, Wszolek Z, Reed L, Miller BI, et al. Mutation-specific functional impairments in distinct tau isoforms of hereditary FTDP-17. Science. 1998;282(5395):1914–7. doi: 10.1126/science.282.5395.1914 [DOI] [PubMed] [Google Scholar]

[pone.0263546.ref023] 23.Roberson ED, Scearce-Levie K, Palop JJ, Yan F, Cheng IH, Wu T, et al. Reducing endogenous tau ameliorates amyloid beta-induced deficits in an Alzheimer’s disease mouse model. Science. 2007;316(5825):750–4. doi: 10.1126/science.1141736 [DOI] [PubMed] [Google Scholar]

[pone.0263546.ref024] 24.Spillantini MG, Goedert M. Tau protein pathology in neurodegenerative diseases. Trends Neurosci. 1998;21(10):428–33. doi: 10.1016/s0166-2236(98)01337-x [DOI] [PubMed] [Google Scholar]

[pone.0263546.ref025] 25.Goedert M, Crowther RA, Spillantini MG. Tau mutations cause frontotemporal dementias. Neuron. 1998;21(5):955–8. doi: 10.1016/s0896-6273(00)80615-7 [DOI] [PubMed] [Google Scholar]

[pone.0263546.ref026] 26.Grundke-Iqbal I, Iqbal K, Tung YC, Quinlan M, Wisniewski HM, Binder LI. Abnormal phosphorylation of the microtubule-associated protein tau (tau) in Alzheimer cytoskeletal pathology. Proc Natl Acad Sci U S A. 1986;83(13):4913–7. doi: 10.1073/pnas.83.13.4913 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0263546.ref027] 27.Hutton M, Lendon CL, Rizzu P, Baker M, Froelich S, Houlden H, et al. Association of missense and 5’-splice-site mutations in tau with the inherited dementia FTDP-17. Nature. 1998;393(6686):702–5. doi: 10.1038/31508 [DOI] [PubMed] [Google Scholar]

[pone.0263546.ref028] 28.Stanford PM, Shepherd CE, Halliday GM, Brooks WS, Schofield PW, Brodaty H, et al. Mutations in the tau gene that cause an increase in three repeat tau and frontotemporal dementia. Brain. 2003;126(Pt 4):814–26. doi: 10.1093/brain/awg090 [DOI] [PubMed] [Google Scholar]

[pone.0263546.ref029] 29.Yasuda M, Takamatsu J, D’Souza I, Crowther RA, Kawamata T, Hasegawa M, et al. A novel mutation at position +12 in the intron following exon 10 of the tau gene in familial frontotemporal dementia (FTD-Kumamoto). Ann Neurol. 2000;47(4):422–9. [PubMed] [Google Scholar]

[pone.0263546.ref030] 30.Kowalska A, Hasegawa M, Miyamoto K, Akiguchi I, Ikemoto A, Takahashi K, et al. A novel mutation at position +11 in the intron following exon 10 of the tau gene in FTDP-17. J Appl Genet. 2002;43(4):535–43. [PubMed] [Google Scholar]

[pone.0263546.ref031] 31.Grover A, England E, Baker M, Sahara N, Adamson J, Granger B, et al. A novel tau mutation in exon 9 (1260V) causes a four-repeat tauopathy. Exp Neurol. 2003;184(1):131–40. doi: 10.1016/s0014-4886(03)00393-5 [DOI] [PubMed] [Google Scholar]

[pone.0263546.ref032] 32.Hanes J, Zilka N, Bartkova M, Caletkova M, Dobrota D, Novak M. Rat tau proteome consists of six tau isoforms: implication for animal models of human tauopathies. J Neurochem. 2009;108(5):1167–76. doi: 10.1111/j.1471-4159.2009.05869.x [DOI] [PubMed] [Google Scholar]

[pone.0263546.ref033] 33.McMillan P, Korvatska E, Poorkaj P, Evstafjeva Z, Robinson L, Greenup L, et al. Tau isoform regulation is region- and cell-specific in mouse brain. J Comp Neurol. 2008;511(6):788–803. doi: 10.1002/cne.21867 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0263546.ref034] 34.Endo T, Maekawa F, Voikar V, Haijima A, Uemura Y, Zhang Y, et al. Automated test of behavioral flexibility in mice using a behavioral sequencing task in IntelliCage. Behav Brain Res. 2011;221(1):172–81. doi: 10.1016/j.bbr.2011.02.037 [DOI] [PubMed] [Google Scholar]

[pone.0263546.ref035] 35.Wu N, Wang F, Jin Z, Zhang Z, Wang LK, Zhang C, et al. Effects of GABAB receptors in the insula on recognition memory observed with intellicage. Behav Brain Funct. 2017;13(1):7. doi: 10.1186/s12993-017-0125-4 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0263546.ref036] 36.Kiryk A, Janusz A, Zglinicki B, Turkes E, Knapska E, Konopka W, et al. IntelliCage as a tool for measuring mouse behavior—20 years perspective. Behav Brain Res. 2020;388:112620. doi: 10.1016/j.bbr.2020.112620 [DOI] [PubMed] [Google Scholar]

[pone.0263546.ref037] 37.Iman IN, Yusof NAM, Talib UN, Ahmad NAZ, Norazit A, Kumar J, et al. The IntelliCage System: A Review of Its Utility as a Novel Behavioral Platform for a Rodent Model of Substance Use Disorder. Front Behav Neurosci. 2021;15:683780. doi: 10.3389/fnbeh.2021.683780 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0263546.ref038] 38.Masuda A, Kobayashi Y, Itohara S. Automated, Long-term Behavioral Assay for Cognitive Functions in Multiple Genetic Models of Alzheimer’s Disease, Using IntelliCage. J Vis Exp. 2018(138). doi: 10.3791/58009 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0263546.ref039] 39.Urbach YK, Raber KA, Canneva F, Plank AC, Andreasson T, Ponten H, et al. Automated phenotyping and advanced data mining exemplified in rats transgenic for Huntington’s disease. J Neurosci Methods. 2014;234:38–53. doi: 10.1016/j.jneumeth.2014.06.017 [DOI] [PubMed] [Google Scholar]

[pone.0263546.ref040] 40.Cao G, Wei X, Li W, Yin H, Lang W, Wei P, et al. Verification of a multi-function closed maze for the detection of affective disorder and spatial cognitive impairment in post-weaning socially isolated rats. Neurosci Lett. 2021;763:136192. doi: 10.1016/j.neulet.2021.136192 [DOI] [PubMed] [Google Scholar]

[pone.0263546.ref041] 41.Ren S, Breuillaud L, Yao W, Yin T, Norris KA, Zehntner SP, et al. TNFα reduces inhibitory transmission in young Trem2^R47H Sporadic Alzheimer rats before observable Aβ and brain pathology. bioRxiv 2020. [Google Scholar]

[pone.0263546.ref042] 42.Beam CR, Kaneshiro C, Jang JY, Reynolds CA, Pedersen NL, Gatz M. Differences Between Women and Men in Incidence Rates of Dementia and Alzheimer’s Disease. J Alzheimers Dis. 2018;64(4):1077–83. doi: 10.3233/JAD-180141 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0263546.ref043] 43.Zehntner SP, Chakravarty MM, Bolovan RJ, Chan C, Bedell BJ. Synergistic tissue counterstaining and image segmentation techniques for accurate, quantitative immunohistochemistry. J Histochem Cytochem. 2008;56(10):873–80. doi: 10.1369/jhc.2008.950345 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0263546.ref044] 44.Mifflin MA, Winslow W, Surendra L, Tallino S, Vural A, Velazquez R. Sex differences in the IntelliCage and the Morris water maze in the APP/PS1 mouse model of amyloidosis. Neurobiol Aging. 2021;101:130–40. doi: 10.1016/j.neurobiolaging.2021.01.018 [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

Initial assessment of the spatial learning, reversal, and sequencing task capabilities of knock-in rats with humanizing mutations in the Aβ-coding region of App

Hoa Pham

Tao Yin

Luciano D’Adamio

Roles

Abstract

Introduction

Fig 1. IntelliCage schematic.

Material and methods

Experimental animals

Rat genotyping and DNA extraction and sequencing

Rat brain preparation

ELISA

Western blot analysis

Immunohistochemistry (IHC)

Table 1. Primary and amplification antibodies used for IHC.

Behavioral experiments and analysis

Table 2. IntelliCage program timeline overview for cohorts A and B.

IntelliCage programs

Corner rank comparison

Learning curves

Inclusion/Exclusion criteria

Statistical analysis of area under the curves (AUC)

Results

Apph/h rats produce human Aβ species and do not develop AD-like pathology up to 14 months of age

Fig 2. Characterization of Apph/h rats.

Apph/h rats do not visit corners more often than other Apph/h rats during single corner restriction in cohort B at either 6–8 weeks or 4–5 months of age

Fig 3. Single corner restriction, cohort B.

Table 3. Statistical analysis of data shown in Fig 3 for single corner restriction, cohort B.

Apph/h rats in cohort A can acquire a place learning task by 6–8 weeks of age with session-wise improvement

Fig 4. Place learning, cohort A.

Table 4. Statistical analysis of data shown in Fig 4B and 4C for place learning, cohort A, Run-1, and Run-2.

Table 5. Statistical analysis of data shown in Fig 4D for place learning, cohort A, Run comparison.

Apph/h rats in cohort A can acquire a place reversal task by 6–8 weeks of age with session-wise improvement

Fig 5. Place reversal, cohort A.

Table 6. Statistical analysis of data shown in Fig 5B and 5C for place reversal, cohort A, Run-1, and Run-2.

Table 7. Statistical analysis of data shown in Fig 5D for place reversal, cohort A, Run comparison.

Apph/h rats in cohort B can acquire a place learning with corner switching task by 4–5 months of age with session-wise improvement

Fig 6. Place learning with corner switch, cohort B.

Table 8. Statistical analysis of data shown in Fig 6B and 6C for place learning with corner switch, cohort B, Run-1, and Run-2.

Table 9. Statistical analysis of data shown in Fig 6D for place learning with corner switch, cohort B, Run comparison.

Apph/h rats in cohorts A and B can acquire a behavioral sequencing task by 6–8 weeks of age with session-wise improvement

Fig 7. Behavioral sequencing, cohort A.

Fig 8. Behavioral sequencing, cohort B.

Table 10. Statistical analysis of data shown in Fig 7B and 7C for behavioral sequencing, cohort A, Run-1, and Run-2.

Table 11. Statistical analysis of data shown in Fig 7D for behavioral sequencing, cohort A, Run comparison.

Table 12. Statistical analysis of data shown in Fig 8A and 8B for behavioral sequencing, cohort B, Run-1, and Run-2.

Table 13. Statistical analysis of data shown in Fig 8C for behavioral sequencing, cohort B, Run comparison.

Apph/h rats in cohort A and B may not be able to acquire a serial reversal task by 4–5 months of age

Fig 9. Serial reversal, cohort A.

Fig 10. Serial reversal, cohort B.

Table 14. Statistical analysis of data shown in Fig 9B and 9C for serial reversal, cohort A, Run-1, and Run-2.

Table 15. Statistical analysis of data shown in Fig 9D for serial reversal, cohort A, Run comparison.

Table 16. Statistical analysis of data shown in Fig 10A and 10B for serial reversal, cohort B, Run-1, and Run-2.

Table 17. Statistical analysis of data shown in Fig 10C for serial reversal, cohort B, Run comparison.

Discussion

Data Availability

Funding Statement

References

Decision Letter 0

Stephen D Ginsberg

Roles

Author response to Decision Letter 0

Decision Letter 1

Stephen D Ginsberg

Roles

Author response to Decision Letter 1

Decision Letter 2

Stephen D Ginsberg

Roles

Acceptance letter

Stephen D Ginsberg

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

App^h/h rats produce human Aβ species and do not develop AD-like pathology up to 14 months of age

Fig 2. Characterization of App^h/h rats.

App^h/h rats do not visit corners more often than other App^h/h rats during single corner restriction in cohort B at either 6–8 weeks or 4–5 months of age

App^h/h rats in cohort A can acquire a place learning task by 6–8 weeks of age with session-wise improvement

App^h/h rats in cohort A can acquire a place reversal task by 6–8 weeks of age with session-wise improvement

App^h/h rats in cohort B can acquire a place learning with corner switching task by 4–5 months of age with session-wise improvement

App^h/h rats in cohorts A and B can acquire a behavioral sequencing task by 6–8 weeks of age with session-wise improvement

App^h/h rats in cohort A and B may not be able to acquire a serial reversal task by 4–5 months of age