Integrated analysis of behavioral, epigenetic, and gut microbiome analyses in AppNL-G-F, AppNL-F, and wild type mice

There was no overall genotype difference in circadian activity level when only comparing AppNL-G-F and AppNL-F genotypes.

“Consistent with an earlier study, we did not observe an overall genotype differences in circadian activity level when only comparing the AppNL-G-F and AppNL-F genotypes39.”

• cites [39] Latif-Hernandez, A. et al. Subtle behavioral changes and increased prefrontal-hippocampal network synchronicity in APPNL−G−F mice before prominent plaque deposition. Behav. Brain Res. 364, 431–441 (2019). sourceDisputed

  Section 3.2 (page unavailable) reports significantly higher 23-hour activity in APP NL-G-F mice than APP NL mice at 3 and 10 months, contrary to the claimed absence of an overall genotype difference. It found no difference at 6 months. Our copy labels the comparison group APP NL, not AppNL-F. (LLM, conf=0.90)

  Checked by a reasoning model — may be wrong.

  Experimental — accuracy not yet measured

• AssumptionRests on an assumption: The AppNL-G-F and AppNL-F mouse models recapitulate aspects of human AD relevant to the study.

The effects of the neurotoxin MPTP on cognitive performance may be, at least in part, mediated by the gut microbiome.

“We have shown that the effects of the neurotoxin 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) on cognitive performance may be, at least in part, mediated by the gut microbiome8.”

• cites [8] Torres, E. et al. Effects of sub-chronic MPTP exposure on behavioral and cognitive performance and the microbiome of wild-type and mGlu8 knockout female and male mice. Front. Behav. Neurosci. 12, 140 (2018). sourceSupported

These genotype-dependent associations include members of the Lachnospiraceae and Ruminococcaceae families.

“These genotype-dependent associations include members of the Lachnospiraceae and Ruminococcaceae families.”

• Also stated in: § Discussion · plain

  “These genotype-dependent associations include members of the Lachnospiraceae and Ruminococcaceae families, which our prior mouse model research has also linked to behavior and cognitive performance8,47,48.”

• cites [8] Torres, E. et al. Effects of sub-chronic MPTP exposure on behavioral and cognitive performance and the microbiome of wild-type and mGlu8 knockout female and male mice. Front. Behav. Neurosci. 12, 140 (2018). sourcePartly supported

  The Results > Microbiome section (page unavailable) reports genotype-related differences in overall microbial diversity and an association between Lachnospiraceae NK4A136 and fear-learning performance. It does not link that taxon’s association to genotype, and a search for Ruminococcaceae found no passage naming it. Thus the paper supports related microbiome findings, but not the claimed genotype-dependent associations for both families. (LLM, conf=0.85)

  Experimental — accuracy not yet measured
• cites [47] Magnusson, K. et al. Relationships bbetween diet-related changes in the gut microbiome and cognitive flexibility. Neuroscience 300, 128–140 (2015). sourceNot checked — no full text of the source
• cites [48] Raber, J. et al. Effects of six sequential charged particle beams on behavioral and cognitive performance in B6D2F1 female and male mice. Front. Physiol. 11, 959 (2020). sourceCould not verify

  The Results > Microbiome passages report sex- and radiation-dose-dependent associations, including some involving Lachnospiraceae, but do not report genotype-dependent associations. The passages found for “genotype” and “Ruminococcaceae” do not identify Ruminococcaceae as part of such an association; our copy therefore does not verify the claim. (LLM, conf=0.74)

  Experimental — accuracy not yet measured

Transplants of fecal microbiota of genotype- and age-matched male mice partially restored Aβ pathology and microglial morphology.

“and transplants of fecal microbiota of genotype- and age-matched male mice partially restored Aβ pathology and microglial morphology9.”

• cites [9] Dodiya, H. et al. Sex-specific effects of microbiome perturbations on cerebral Aβ amyloidosis and microglia phenotypes. J. Exp. Med. 216, 1542 (2019). sourceSupported

MPTP treatment affected the diversity of the gut microbiome.

“MPTP treatment affected the diversity of the gut microbiome.”

• cites [8] Torres, E. et al. Effects of sub-chronic MPTP exposure on behavioral and cognitive performance and the microbiome of wild-type and mGlu8 knockout female and male mice. Front. Behav. Neurosci. 12, 140 (2018). sourceSupported

The human orthologous regulatory element may be involved in AD in humans.

“Of note, this region also includes one of the SNPs (rs10119) significantly associated with AD in the National Human Genome Research Institute catalog of published genome-wide association studies (NHGRI-GWAS33), indicating that this regulatory element may be involved in AD in humans (Fig. 6B).”

• cites [33] Buniello, A. et al. The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019. Nucl. Acids Res. 47, D1005–D1012 (2019). sourceSupported

Several CTCF binding sites were reported at the human orthologous region.

“Furthermore, several CTCF binding sites were reported at this region28”

• cites [28] Lesurf, R. et al. ORegAnno 3.0: a community-driven resource for curated regulatory annotation. Nucl. Acids Res. 44, D126–D132 (2016). sourceCould not verify

  The Results sections on New records and Applications describe curated transcription-factor binding sites generally, but do not identify CTCF sites at the human orthologous region. Searches for “CTCF” and “CTCF binding sites human orthologous region” found no passage addressing that specific claim. (LLM, conf=0.76)

  Experimental — accuracy not yet measured

Sex differences in AD-related genes are not limited to risk factors like APOE but are pertinent to dominant AD mutations as well.

“Together with the sex differences in pathology seen in mice transgenically expressing App with the Swedish mutation and presenilin 1 with the A246E mutation37, these data indicate the sex differences in AD-related genes are not limited to risk factors like APOE but pertinent to dominant AD mutations as well (for review, see38).”

• cites [37] Wang, J., Tanila, H., Puloivali, J., Kadish, I. & Groen, T. Gender differences in the amount and deposition of amyloidin APPswe and PS1 double transgenic mice. Neurobiol. Dis. 14, 318–327 (2003). sourceNot checked — no full text of the source
• cites [38] Gamache, J., Yun, Y. & Chiba-Falek, O. Sex-dependent effect of APOE on Alzheimer’s disease and other age-related neurodegenerative disorders. Dis. Model Mech. 13, 045211 (2020). sourceSupported

DMR_394 displays epigenetic signatures of transcription start and enhancer activity in hippocampus tissue of adult wild-type mice.

“By overlaying public chromatin state characterizations27, we found that DMR_394 displayed epigenetic signatures of transcription start and enhancer activity in hippocampus tissue of adult wild-type mice.”

• cites [27] Gjoneska, E. et al. Conserved epigenomic signals in mice and humans reveal immune basis of Alzheimer’s disease. Nature 518, 365–369 (2015). sourceSupported

• AssumptionRests on an assumption: Public mouse hippocampus chromatin state and CTCF ChIP-seq data are representative of the study mice.

DMR_394 overlaps known regulatory and transcription factor binding sites from the Open Regulatory Annotation Database.

“Consistently, we found this DMR to overlap known regulatory and transcription factor binding sites from the Open Regulatory Annotation Database28.”

• cites [28] Lesurf, R. et al. ORegAnno 3.0: a community-driven resource for curated regulatory annotation. Nucl. Acids Res. 44, D126–D132 (2016). sourceSupported

The putative role of the human orthologous region as a hippocampal regulatory element is conserved in human.

“Furthermore, several CTCF binding sites were reported at this region28 suggesting the putative role of this region as a hippocampal regulatory element is conserved in human (Fig. 6B).”

• cites [28] Lesurf, R. et al. ORegAnno 3.0: a community-driven resource for curated regulatory annotation. Nucl. Acids Res. 44, D126–D132 (2016). sourceCould not verify

  The citation is attached to the report of several CTCF binding sites at this region; the proposed conserved hippocampal role is the citing authors’ inference. Our copy’s Results discuss curated regulatory annotations generally, but neither those passages nor searches for CTCF binding sites and hippocampal conservation identify binding sites at this particular region. The cited paper therefore does not verify the region-specific report in the citing sentence. (LLM, conf=0.73)

  Experimental — accuracy not yet measured

There was no effect of genotype on contextual fear memory.

“We did not detect an effect of genotype on contextual fear memory, consistent with an earlier study43.”

• cites [43] Sakakibara, Y., Sekiya, M., Saito, T., Saido, T. & Ijima, K. Cognitive and emotional alterations in App knock-in mouse models of Aβ amyloidosis. BMC Neurosci. 19, 46 (2018). sourceSupported

• AssumptionRests on an assumption: The AppNL-G-F and AppNL-F mouse models recapitulate aspects of human AD relevant to the study.

The result in female mice is consistent with the cognitive phenotype in the Y maze reported in this genotype.

“Our result in female mice is consistent with the cognitive phenotype in the Y maze reported in this genotype25,42.”

• cites [25] Saito, T. et al. Single App knock-in mouse models of Alzheimer’ disease. Nat. Neurosci. 17, 661–663 (2014). sourceNot checked — no full text of the source
• cites [42] Nilsson, P., Saito, T. & Saido, T. New mouse model of Alzheimer’s. ACS Chem. Neurosci. 5, 499–502 (2014). sourceSupported

These taxa may interface with conserved aspects of mammalian neurophysiology.

“These associations also exist in human clinical populations—for example, members of the Lachnospiraceae are frequently associated with diagnoses of major depressive disorder across studies60–63—suggesting that these taxa may interface with conserved aspects of mammalian neurophysiology.”

• cites [60] Jiang, H. et al. Altered fecal microbiota composition in patients with major depressive disorder. Brain Behav. Immun. 48, 186–194 (2015). sourceNot checked — no full text of the source
• cites [61] Zheng, P. et al. Gut microbiome remodeling induces depressive-like behaviors through a pathway mediated by the host’s metabolism. Mol. Psychiatry 21, 786–796 (2016). sourceNot checked — no full text of the source
• cites [62] Cheung, S. et al. Systematic Review Of Gut Microbiota And Major Depression. Front.Psychiatry 10, 34 (2019). sourceSupported
• cites [63] Vacca, M. et al. The contraversioal role of human gut Lachnopiraceae. Microorganisms 8, 573 (2020). sourcePartly supported

  Section 4.5, “Intestinal Dysbiosis Associated with the Gut–Brain Axis,” links several Lachnospiraceae taxa to major depressive disorder and describes possible neural and hormonal routes between the gut microbiome and brain. This supports the cautious suggestion that the taxa may interface with neurophysiology, but the paper does not establish that their relationship with it is conserved across mammals. (LLM, conf=0.79)

  Experimental — accuracy not yet measured

The AppNL-F and AppNL-G-F alleles impact the gut microbiome in ways that link to cognitive performance.

“Collectively, these results suggest that the AppNL-F and AppNL-G-F alleles impact the gut microbiome in ways that link to cognitive performance, and support the hypothesis that AD risk alleles may elicit their impact on cognition at least in part by altering the microbiome’s contribution to neurophysiology49–52.”

• cites [49] Seo, D.-O., Boros, B. & Holtzman, D. The microbiome: a target for Alzheimer disease?. Cell. Res. 29, 779–780 (2019). sourcePartly supported

  The main text (pp. 779–780) links gut-microbiome changes with cognitive impairment in 5xFAD and APP/PS1 mouse models. It does not discuss the AppNL-F or AppNL-G-F alleles, so it does not support the allele-specific claim. (LLM, conf=0.86)

  Experimental — accuracy not yet measured
• cites [50] Askarova, S. et al. The links between the gut microbiome, aging, modern lifestyle and Alzheimer’s disease. Front. Cell. Infect. Microbiol. 10, 104 (2020). sourcePartly supported

  The “Brain-Gut-Microbiota Axis and Alzheimer's Disease” section (page unavailable) reports microbiome differences in APP/PS1 transgenic mice compared with wild-type mice. It does not identify the AppNL-F or AppNL-G-F alleles or link microbiome changes in those mice to cognitive performance. (LLM, conf=0.83)

  Experimental — accuracy not yet measured
• cites [51] Kowalski, K. & Mulak, A. Brain-gut-microbiota axis in Alzheimer’s disease. J. Neurogastroenterol. Motil. 25, 48–60 (2019). sourcePartly supported

  The “Gut Microbiota in Animal Models of Alzheimer’s Disease” section reports altered gut microbiota in APP transgenic mice and effects of microbiota on brain amyloid pathology. It does not identify the AppNL-F or AppNL-G-F alleles or establish that microbiome changes associated with those alleles link to cognitive performance. No page number is available in our copy. (LLM, conf=0.84)

  Experimental — accuracy not yet measured
• cites [52] Haran, J. et al. Alzheimer’s disease microbiome is associated with dysregulation of the anti-inflammatory p-glycoprotein pathway. mBio 10, e00632-e1619 (2019). sourcePartly supported

  The Results section reports that gut microbiome composition distinguishes older adults with Alzheimer’s disease from those without dementia, supporting a general microbiome–dementia association. It does not examine the AppNL-F or AppNL-G-F alleles or show that those alleles alter the microbiome in ways linked to cognitive performance. (LLM, conf=0.81)

  Experimental — accuracy not yet measured

AD risk alleles may elicit their impact on cognition at least in part by altering the microbiome's contribution to neurophysiology.

“Collectively, these results suggest that the AppNL-F and AppNL-G-F alleles impact the gut microbiome in ways that link to cognitive performance, and support the hypothesis that AD risk alleles may elicit their impact on cognition at least in part by altering the microbiome’s contribution to neurophysiology49–52.”

• cites [49] Seo, D.-O., Boros, B. & Holtzman, D. The microbiome: a target for Alzheimer disease?. Cell. Res. 29, 779–780 (2019). sourcePartly supported

  The main text (pp. 779–780) reports microbiome changes and cognitive impairment in 5xFAD mice, and says that modulating the gut microbiota partly improved cognition in APP/PS1 mice. This supports a possible microbiome–cognition link, but the paper does not test the AppNL-F or AppNL-G-F alleles or establish that AD risk alleles generally affect cognition through the microbiome. (LLM, conf=0.84)

  Experimental — accuracy not yet measured
• cites [50] Askarova, S. et al. The links between the gut microbiome, aging, modern lifestyle and Alzheimer’s disease. Front. Cell. Infect. Microbiol. 10, 104 (2020). sourcePartly supported

  In “Brain-Gut-Microbiota Axis and Alzheimer's Disease” (page unavailable), the review reports altered gut microbiota in APP/PS1 transgenic mice and a change in brain amyloid deposits after microbiota transfer. It also discusses microbiome-related changes in memory in other mice. These passages support a possible genetic–microbiome–brain link, but do not show that AD risk alleles affect cognition through microbiome-mediated changes in neurophysiology. (LLM, conf=0.84)

  Experimental — accuracy not yet measured
• cites [51] Kowalski, K. & Mulak, A. Brain-gut-microbiota axis in Alzheimer’s disease. J. Neurogastroenterol. Motil. 25, 48–60 (2019). sourcePartly supported

  The Introduction identifies APP mutations as a cause of early-onset AD, and the “Gut Microbiota in Animal Models of Alzheimer’s Disease” section reports altered microbiota in APP transgenic mice and microbiota-dependent changes in brain amyloid pathology. These findings support a possible gene–microbiota–brain link, but the review does not establish that AD risk alleles affect cognition by altering the microbiome’s contribution to neurophysiology. No page numbers were provided for these passages. (LLM, conf=0.83)

  Experimental — accuracy not yet measured
• cites [52] Haran, J. et al. Alzheimer’s disease microbiome is associated with dysregulation of the anti-inflammatory p-glycoprotein pathway. mBio 10, e00632-e1619 (2019). sourcePartly supported

  The Introduction and Discussion link the gut microbiome to Alzheimer’s disease and describe a possible pathway through intestinal inflammation, supporting the broad microbiome–neurophysiology hypothesis. But the paper compares elders by dementia status; it does not show that AD risk alleles alter the microbiome or that allele-driven changes affect cognitive performance. (LLM, conf=0.84)

  Experimental — accuracy not yet measured

Compound Poisson distributions appropriately model the over-dispersion and zero-inflation often observed in microbiome count data.

“Compound Poisson distributions have been demonstrated to appropriately model the over-dispersion and zero-inflation often observed in microbiome count data.”

• cites [90] Sharpton, T. et al. Development of inflammatory bowel disease is linked to a longitudinal restructuring of the gut metagenome in mice. mSystems 5, 2 (2017). sourceSupported

CLR transformation avoids spurious associations caused by analyzing raw counts of compositional microbiome data.

“Prior to microbiome composition (beta-diversity), analyses we applied a centered log-ratio (CLR) transformation on the raw ASV counts in order to account for the fact that microbiome composition is inherently compositional data, and such a transformation has been demonstrated to avoid spurious associations caused by analyzing the raw counts of such data89.”

• cites [89] Gloor, G., Macklaim, J., Pawlowsky, G. V. & Egozcue, J. Microbiome datasets are compositions: And this is not optional. Front. Microbol. 8, 2224 (2017). sourcePartly supported

  Section 3 recommends CLR transformation for compositional microbiome counts and says CLR values are scale-invariant. Sections 2 and 3 discuss spurious correlations and false positives from methods that ignore compositionality, but do not demonstrate that CLR transformation alone avoids spurious associations; the reported false-positive reduction concerns ALDEx2, which also models a probability distribution. (LLM, conf=0.84)

  Experimental — accuracy not yet measured

A 1 Kb region that overlapped the 3′UTR of the Tomm40 gene and the promoter region of the Apoe gene was significantly more methylated in the hippocampus of AppNL-G-F than WT mice.

“we identified a 1 Kb region that overlapped ing 3′UTR of the Tomm40 gene and the promoter region of the Apoe gene that and was significantly more methylated in the hippocampus of AppNL-G-F than WT mice.”

Differences in genotype modulated the relationships between the gut microbiome and mouse behavioral and cognitive performance.

“The biodiversity and composition of the gut microbiome linked to various aspects of mouse behavioral and cognitive performance but differences in genotype modulated these relationships.”

The biodiversity and composition of the gut microbiome linked to various aspects of mouse behavioral and cognitive performance.

“The biodiversity and composition of the gut microbiome linked to various aspects of mouse behavioral and cognitive performance but differences in genotype modulated these relationships.”

The genotype effects observed in behavioral tests were test-dependent.

“The genotype effects observed in behavioral tests were test-dependent.”

There was an effect of genotype on movement during the two shocks in females.

“there was an effect of genotype in females (F(2, 33) = 5.994, p = 0.006 Fig. 2J).”

The number of entries in AppNL-G-F female mice was lower than in WT females.

“The number of entries in AppNL-G-F female mice was lower than in WT (p = 0.030, Tukey’s) and AppNL-F females (p = 0.032, Tukey’s).”

There was an effect of genotype on activity levels in the elevated zero maze in males.

“However, there was an effect of genotype on activity levels in the males (F(2,37) = 3.926, p = 0.0284, Fig. 1H).”

Repeated measures ANOVA of time spent in the center in open field days 1 through 3 revealed an effect of genotype in females.

“Repeated measures ANOVA of time spent in the center in open field days 1 through 3 revealed an effect of genotype in females (F(2,32) 6.668, p = 0.004, Fig. 2G).”

Of these genes, 57 had fully overlapping DMRs.

“Of these genes, 57 had fully overlapping DMRs (i.e., methylation change occurring in the exact same region of the gene; Fig. 4A).”

In females, AppNL-G-F mice moved more during the shocks than WT mice.

“In females, AppNL-G-F mice moved more during the shocks than WT mice (p = 0.005, Tukey’s).”

AppNL-F males had a higher reach score than AppNL-G-F males.

“with a higher reach score in AppNL-F than AppNL-G-F males (p = 0.0415, Dunnett’s).”

AppNL-G-F females trended towards spending more time in the center than WT females.

“and AppNL-G-F females trended towards a similar pattern (p = 0.063, Tukey’s).”

AppNL-F females had a higher reach score than AppNL-G-F females.

“AppNL-F females had a higher reach score than AppNL-G-F mice (p = 0.0047, Dunnett’s) and WT females (p = 0.0065).”

The percent spontaneous alternation was lower in AppNL-G-F than WT females.

“The percent spontaneous alternation was lower in AppNL-G-F than WT females (p = 0.0166, Tukey’s).”

There was an effect of genotype on nest building change score in males.

“There was also an effect of genotype in the males (F(2,36) = 4.44, p = 0.0189, Fig. 1F).”

There was an effect of genotype on the number of entries in the Y maze in females.

“There was an effect of genotype on the number of entries in the Y maze in the females (F(2,33) = 4.825, p = 0.0145, Fig. 2C).”

The number of entries in AppNL-G-F female mice was lower than in AppNL-F females.

“The number of entries in AppNL-G-F female mice was lower than in WT (p = 0.030, Tukey’s) and AppNL-F females (p = 0.032, Tukey’s).”

The fall score was higher in AppNL-F than AppNL-G-F females.

“The fall score was higher in AppNL-F than AppNL-G-F females (p = 0.0079, Tukey’s).”

A little under half (49/114) of the ASVs with significant behavioral associations belong to genera within the Lachnospiraceae family.

“A little under half 49/114) of the ASVs with significant behavioral associations belong to genera within the Lachnospiraceae family.”

The Horn's Parallel Analysis for component retention yielded three factors with eigenvalues > 1.0, which explained a total of 46.244% of the variance among the measures entered into the model.

“The Horn's Parallel Analysis for component retention yielded three factors with eigenvalues > 1.0, which explained a total of 46.244% of the variance among the measures entered into the model (Table 1).”

The percent time freezing during the first tone of the acquisition phase of fear conditioning showed a significant positive association with alpha-diversity.

“The percent time freezing during the first tone of the acquisition phase of fear conditioning showed a significant positive association with alpha-diversity (e.g., observed ASVs: F = 5.49, p = 0.024).”

We identified a total of 628 unique significant differentially methylated regions (DMRs) in AppNL-G-F mice compared to WT controls.

“We compared genome-wide DNA methylation levels in pair-wise comparisons of each of the AppNL-G-F and AppNL-F mice to age-matched wild-type controls, and identified a total of 628 and 562 unique significant differentially methylated regions (DMRs; q-value < 0.05, Table S2), respectively.”

There was no effect of genotype on spontaneous alternations in males.

“In contrast, there was no effect of genotype on spontaneous alternations in the males (Fig. 2B).”

There was a trend towards an effect of genotype on the number of entries in the Y maze in males.

“There was a trend towards an effect of genotype on the number of entries in the Y maze in the males (F(2,37) = 2.575, p = 0.0897, Fig. 2D)”

App genotype associated with the types of taxa that comprise the gut microbiome.

“a PERMANOVA analysis based on the Aitchison distance (Euclidean distance on centered log-ratio transformed counts) revealed that App genotype associated with the types of taxa that comprise the gut microbiome (Table 2); F = 3.03, p = 0.017).”

AppNL-F females were lighter than WT females.

“with AppNL-F females being lighter than wild type (WT) females (p = 0.0039, Dunnett’s).”

In total, 67 DMR-containing genes were shared between AppNL-G-F and AppNL-F mice.

“In total, 67 DMR-containing genes were shared between AppNL-G-F and AppNL-F mice.”

AppNL-F females spent more time in the center than WT females.

“AppNL-F females spent more time in the center than WT females (p = 0.003, Tukey’s)”

There was no effect of genotype on contextual fear memory in females or males.

“There was no effect of genotype on contextual fear memory in females or males (Fig. S1J,K).”

There was no effect of genotype on fall scores in males.

“In contrast, there was no effect of genotype of fall scores in the males (Fig. 1L).”

There was no effect of genotype on measures of anxiety in females or males.

“There was no effect of genotype on measures of anxiety, assessed as the percent time spent in the open areas of the maze in females or males (Fig. S1A,B).”

There was an effect of genotype on cued fear memory in males.

“However, there was an effect of genotype on cued fear memory in males (F(2, 37) = 3.599, p = 0.037 Fig. 2K).”

AppNL-G-F females moved significantly less across all three days than AppNL-F females.

“such that WT (p = 0.022, Tukey’s) and AppNL-G-F (p = 0.023, Tukey’s) females moved significantly less across all three days than AppNL-F females.”

During the tones of fear conditioning acquisition, there was no effect of genotype on freezing in females or males.

“During the tones of fear conditioning acquisition, there was no effect of genotype on freezing in females or males (Fig. S1F,G).”

No such genotype differences in total distance traveled in the open field were observed in males.

“No such genotype differences were observed in the males (Fig. 2F).”

There was no significant effect of genotype on movement during the two shocks in males.

“While an analysis of movement during the two shocks demonstrated no significant effect of genotype in males”

Components 1, 2 and 3 explained 17.985%, 16.085% and 12.173% of the variance respectively.

“Components 1, 2 and 3 explained 17.985%, 16.085% and 12.173% of the variance respectively.”

The direction and amount of methylation change in shared DMRs were highly similar between AppNL-G-F and AppNL-F mice.

“Surprisingly, the direction (i.e., hyper- or hypomethylation) and amount of methylation change in these shared DMRs were highly similar between AppNL-G-F and AppNL-F mice (R2 = 0.92, p < 2.2e−16; Fig. S4).”

There was no effect of genotype on body weights in male mice.

“In contrast to the female mice, there was no effect of genotype in the male mice (Fig. 1B).”

There were no effects of genotype on baseline motion in females.

“There were no effects of genotype on baseline motion in the females (Fig. S1E).”

There was a significant effect of genotype on body weights in female mice.

“There was a significant effect of genotype on body weights in female mice (F(2,33) = 5.876, p = 0.0066, Fig. 1A)”

Repeated measures ANOVA of total distance traveled in the open field on days 1 through 3 revealed an effect of genotype in females.

“Repeated measures ANOVA of total distance traveled in the open field on days 1 through 3 revealed an effect of genotype in females (F(2, 33) = 5.225, p = 0.011 Fig. 2E)”

AppNL-G-F males spent less time in the center than AppNL-F males.

“AppNL-G-F males spent less time in the center than AppNL-F males (p = 0.0364, Tukey’s).”

There was a significant effect of genotype on the percent spontaneous alternations in the Y maze in females.

“There was a significant effect of genotype on the percent spontaneous alternations in the females (F(2,33) = 4.279, p = 0.0223, Fig. 2A).”

AppNL-F females had a higher reach score than WT females.

“AppNL-F females had a higher reach score than AppNL-G-F mice (p = 0.0047, Dunnett’s) and WT females (p = 0.0065).”

After multiple test correction (Bonferroni, p = 0.001), linear models revealed 138 significant taxon-behavior relationships comprised of combinations of 114 different taxa and 10 different behavior scores.

“After multiple test correction (Bonferroni, p = 0.001), these linear models revealed 138 significant taxon-behavior relationships comprised of combinations of 114 different taxa and 10 different behavior scores (Table S5).”

There was an effect of genotype on time spent in the center of the open field in males.

“There was also an effect of genotype on time spent in the center of the open field in males (F(2,37) = 4.261 p = 0.0216, Fig. 2H).”

AppNL-G-F male mice moved less than WT male mice during baseline.

“AppNL-G-F male mice moved less than WT male mice (p = 0.01, Tukey’s).”

AppNL-G-F males had a higher dark-to-light activity ratio than WT males.

“AppNL-G-F (p = 0.0161, Dunnett’s) and AppNL-F males (p = 0.0035, Dunnett’s) had a higher dark-to-light activity ratio than WT males.”

Linear models revealed that, for all four metrics assessed, the biodiversity of the gut microbiome (i.e., alpha-diversity) associated consistently with several cognitive measures.

“Linear models revealed that, for all four metrics assessed, the biodiversity of the gut microbiome (i.e., alpha-diversity) associated consistently with several cognitive measures (Table 2).”

A negative change score in nest building was seen in AppNL-G-F females and males.

“A negative change score was seen in AppNL-G-F females (Fig. 1E) and males (Fig. 1F).”

There was an effect of genotype on reaches score in females.

“For the reaches score, there was an effect of genotype in females (F(2,33) = 6.926, p = 0.0031, Fig. 1I).”

There was an effect of genotype on the ratio of circadian activity in male mice.

“When the ratio of circadian activity, defined as activity during the dark period divided by that in the light period, was analyzed, there was an effect of genotype in the males (F(2,25) = 5.398, p = 0.0059, Fig. 1D).”

Time spent exploring the novel object significantly linked to alpha-diversity in an App-genotype dependent manner.

“Furthermore, two behavioral scores, hippocampus-dependent contextual fear memory (percent freezing in the environment 24 h following training) and time spent exploring the novel object, significantly linked to alpha-diversity in an App-genotype dependent manner.”

There was an effect of genotype on nest building change score in females.

“In females, there was an effect of genotype (F(2,32) = 7.382, p = 0.0079, Fig. 1E).”

AppNL-G-F females had a significantly lower nest building change score than WT females.

“AppNL-G-F females had a significantly lower change score than WT females (p = 0.0042, Sidak’s).”

AppNL-F males had a higher dark-to-light activity ratio than WT males.

“AppNL-G-F (p = 0.0161, Dunnett’s) and AppNL-F males (p = 0.0035, Dunnett’s) had a higher dark-to-light activity ratio than WT males.”

There was an effect of genotype on fall scores in females.

“For falls, there was an effect of genotype in the females (F(2,33) = 5.285, p = 0.0102, Fig. 1K).”

There was a trend towards an effect of genotype for reaches in males.

“There was a trend towards an effect of genotype for reaches in the males (F(2,37) = 3.009, p = 0.0615, Fig. 1J)”

During fear learning, there was a significant effect of genotype on baseline activity levels in males.

“During fear learning, there was a significant effect of genotype on baseline activity levels (baseline average motion in males (F(2, 37) = 4.760, p = 0.014 Fig. 2I).”

We identified a total of 562 unique significant differentially methylated regions (DMRs) in AppNL-F mice compared to WT controls.

“We compared genome-wide DNA methylation levels in pair-wise comparisons of each of the AppNL-G-F and AppNL-F mice to age-matched wild-type controls, and identified a total of 628 and 562 unique significant differentially methylated regions (DMRs; q-value < 0.05, Table S2), respectively.”

WT females moved significantly less across all three days than AppNL-F females.

“such that WT (p = 0.022, Tukey’s) and AppNL-G-F (p = 0.023, Tukey’s) females moved significantly less across all three days than AppNL-F females.”

The composition of the gut microbiome linked to wire hang reach independent of genotype.

“The composition of the gut microbiome linked to cognitive performance measures, such as wire hang reach (number of times the animal reached to one of the poles on either end of the wire; F = 1.73, p = 0.046), independent of genotype (Fig. 3A).”

AppNL-G-F males had a significantly lower nest building change score than AppNL-F males.

“AppNL-G-F males had a significantly lower change score than AppNL-F males (p = 0.0140, Fig. 1F).”

Contextual freezing, cued freezing, center duration in the open field, wire hang fall score and wire hang reach score all loaded onto factor 1.

“Contextual freezing, cued freezing, center duration in the open field, wire hang fall score and wire hang reach score all loaded onto factor 1”

There was a trend towards a lower number of entries in AppNL-G-F than WT males.

“with a trend towards a lower number of entries in AppNL-G-F than WT males (p = 0.0537, Dunnett’s).”

AppNL-G-F males moved less than WT males in the elevated zero maze.

“AppNL-G-F males moved less than WT males (p = 0.0176, Dunnett’s).”

Hippocampus-dependent contextual fear memory (percent freezing in the environment 24 h following training) significantly linked to alpha-diversity in an App-genotype dependent manner.

“Furthermore, two behavioral scores, hippocampus-dependent contextual fear memory (percent freezing in the environment 24 h following training) and time spent exploring the novel object, significantly linked to alpha-diversity in an App-genotype dependent manner.”

Male AppNL-F mice froze significantly more during the tone in the cued fear memory test than WT male mice.

“Male AppNL-F mice froze significantly more during the tone in the cued fear memory test than WT male mice (p = 0.031, Tukey’s).”

There was no effect of genotype on freezing during the inter-stimulus intervals in females or males.

“There was no effect of genotype on freezing during the ISIs in females or males (Fig. S1H,I).”

Microbiome composition linked to time spent exploring the novel object in an App genotype-dependent manner.

“Microbiome composition also linked to time spent exploring the novel object (F = 1.69, p = 0.005) in an App genotype-dependent manner (Fig. 3B).”

There was no effect of genotype on object recognition in female or male mice.

“There was no effects of genotype on object recognition in female or male mice (Fig. S1C,D).”

There was no effect of genotype on total distance travelled in the elevated zero maze in females.

“For total distance travelled in the elevated zero maze, there was no effect of genotype in the females (Fig. 1G).”

There was no effect of genotype on cued fear memory in females.

“There was no effect of genotype on cued fear memory in females (Fig. S1L).”

• Also stated in: § Discussion · plain

  “In our study, there was no effect of genotype on cued fear memory in females but there was an effect of genotype on cued fear memory in males with male AppNL-F mice freezing more during the tone in the cued fear memory test than WT male mice.”

• AssumptionRests on an assumption: The AppNL-G-F and AppNL-F mouse models recapitulate aspects of human AD relevant to the study.

The majority of ASVs that manifest an association with one of these DMRs belong to genera within the Lachnospiraceae family (n = 11).

“The majority of ASVs that manifest an association with one of these DMRs belong to genera within the Lachnospiraceae family (n = 11; tab taxon-methylations selected LMs)”

DMR linked to Cerkl positively associated with a Lachnoclostridium ASV (ASV0178; F = 11.269, p = 0.012).

“For example, a DMR linked to CERamide Kinase Like (Cerkl) positively associated with a Lachnoclostridium ASV (ASV0178; F = 11.269, p = 0.012)”

AppNL-F mice displayed significant bias towards having more hypomethylated DMRs.

“AppNL-F mice displayed significant bias towards having more hypomethylated DMRs (Fisher’s exact test p-value = 0.03)”

In the open field, WT females moved across less than AppNL-F females.

“In the open field, WT females moved across less than AppNL-F females.”

• AssumptionRests on an assumption: The AppNL-G-F and AppNL-F mouse models recapitulate aspects of human AD relevant to the study.

There was an effect of genotype on time spent in the center of the open field in males, with AppNL-G-F males spending less time in the center than AppNL-F males.

“There was also an effect of genotype on time spent in the center of the open field in males, with AppNL-G-F males spending less time in the center than AppNL-F males.”

• AssumptionRests on an assumption: The AppNL-G-F and AppNL-F mouse models recapitulate aspects of human AD relevant to the study.

There was no genotype difference in cued fear memory comparing AppNL-G-F and WT female or male mice.

“We did not see a genotype difference in cued fear memory comparing AppNL-G-F and WT female or male mice.”

• AssumptionRests on an assumption: The AppNL-G-F and AppNL-F mouse models recapitulate aspects of human AD relevant to the study.

DMR_394 is a 1 Kb region overlapping the 3′UTR of Tomm40 and the promoter region of Apoe.

“One of the DMRs that stood out from our analysis because of its potential connection to AD was a 1 Kb region overlapping 3′UTR of the Tomm40 gene and the promoter region of the Apoe gene (Fig. 6A).”

After data reduction, model selection, and multiple test correction, linear regression models identified 21 taxa whose CLR-transformed abundance in the gut significantly predicted one or more set of DMR scores.

“However, after data reduction, model selection, and multiple test correction, linear regression models identified 21 taxa whose CLR-transformed abundance in the gut significantly predicted one or more set of DMR scores.”

• AssumptionRests on an assumption: The 14 DMRs selected based on proximity to AD-associated genes are representative of AD-relevant epigenetic changes.

DMR_394 is approximately 21% more methylated in the hippocampus of AppNL-G-F mice compared to WT controls (q-value = 1.04e−21).

“This Tomm40-Apoe DMR (DMR_394) is ~ 21% more methylated in the hippocampus of AppNL-G-F mice, compared to WT controls (q-value = 1.04e−21).”

• Also stated in: § Discussion · plain

  “Our analysis revealed 1 Kb a significant Differentially Methylated Region (DMR) overlapping 3′UTR of the Tomm40 gene and the promoter region of the Apoe gene that is ~ 21% more methylated in the hippocampus of AppNL-G-F than WT mice.”

There was no genotype difference in measures of anxiety in the elevated zero maze.

“We did not see a genotype difference in measures of anxiety in the elevated zero maze either.”

• AssumptionRests on an assumption: The AppNL-G-F and AppNL-F mouse models recapitulate aspects of human AD relevant to the study.

In the open field, AppNL-F females spent more time in the center than WT females.

“In the open field, AppNL-F females spent more time in the center than WT females and AppNL-G-F females trended towards a similar pattern.”

• AssumptionRests on an assumption: The AppNL-G-F and AppNL-F mouse models recapitulate aspects of human AD relevant to the study.

A similar trend in females did not reach significance.

“While a similar trend was seen in females, it did not reach significance.”

• AssumptionRests on an assumption: The AppNL-G-F and AppNL-F mouse models recapitulate aspects of human AD relevant to the study.

There was no genotype difference comparing the time AppNL-G-F and WT spent in the center of the open field.

“We did not see a genotype difference comparing the time AppNL-G-F and WT spent in the more anxiety-provoking center.”

• AssumptionRests on an assumption: The AppNL-G-F and AppNL-F mouse models recapitulate aspects of human AD relevant to the study.

Hypomethylation was not more common than hypermethylation among AppNL-F DMRs that overlapped intergenic regions.

“but not in DMRs overlapping intergenic regions (p-value = 0.35; Fig. 4C).”

We found broadly similar patterns of overlap distribution between AppNL-F and AppNL-G-F mice.

“We found broadly similar patterns of overlap distribution between AppNL-F and AppNL-G-F mice (Fig. 4C).”

We did not find significant enrichment of GO terms related to AD among AppNL-F DMR-containing genes.

“but not among AppNL-F DMR-containing genes (Fig. 5A, Table S3).”

None of the 14 prioritized DMRs had significant associations with microbiome composition for any beta-diversity metric beyond the variance already explained by mouse genotype.

“After stepwise model optimization, none of the 14 DMRs had significant associations with microbiome composition for any beta-diversity metric beyond the variance already explained by mouse genotype.”

• AssumptionRests on an assumption: The 14 DMRs selected based on proximity to AD-associated genes are representative of AD-relevant epigenetic changes.

There was an effect of genotype on movement during the shocks in females, with female AppNL-G-F mice moving more during the shocks than WT mice.

“There was also an effect of genotype on movement during the shocks in females, with female AppNL-G-F mice moving more during the shocks than WT mice.”

• AssumptionRests on an assumption: The AppNL-G-F and AppNL-F mouse models recapitulate aspects of human AD relevant to the study.

We found significant enrichment of several GO terms related to AD among the AppNL-G-F DMRs.

“We found significant enrichment of several GO terms related to AD among the AppNL-G-F DMRs (for example, Regulation of long-term synaptic potentiation (GO:1900271) and Positive regulation of synaptic transmission (GO:0050806); p-value < 0.05, Table S3)”

In AppNL-G-F mice, the ratios of hypo- and hypermethylated DMRs were not significantly deviant from an even split, regardless of the genetic structures they overlapped.

“In AppNL-G-F mice, the ratios of hypo- and hypermethylated DMRs were not significantly deviant from an even split, regardless of the genetic structures they overlapped (Fisher’s exact test, p-value > 0.1; Fig. 4C).”

In the Y maze, the percent spontaneous alternation was lower in AppNL-G-F than WT females.

“In the Y maze, the percent spontaneous alternation was lower in AppNL-G-F than WT females.”

• AssumptionRests on an assumption: The AppNL-G-F and AppNL-F mouse models recapitulate aspects of human AD relevant to the study.

AppNL-G-F females trended towards spending more time in the center of the open field.

“In the open field, AppNL-F females spent more time in the center than WT females and AppNL-G-F females trended towards a similar pattern.”

• AssumptionRests on an assumption: The AppNL-G-F and AppNL-F mouse models recapitulate aspects of human AD relevant to the study.

Hypomethylation was more common than hypermethylation among AppNL-F DMRs that overlapped genic structures.

“Hypomethylation was more common than hypermethylation among AppNL-F DMRs that overlapped genic structures (Fisher’s exact test, p-value < 0.05)”

Public CTCF ChIP-seq data from adult mouse hippocampus provide evidence that the Tomm40-Apoe DMR is bound by CTCF.

“In addition, using public CTCF chromatin immunoprecipitating sequencing (ChIP-seq) data from adult mouse hippocampus29 we found evidence that this Tomm40-Apoe DMR may be bound by CTCF (Fig. 6A), a protein with crucial functions in regulation of genome organization and gene regulation30.”

• Also stated in: § Discussion · plain

  “In addition, using public CTCF chromatin immunoprecipitating sequencing (ChIP-seq) data from adult mouse hippocampus29, this Tomm40-Apoe DMR was found to be bound by CTCF.”

• cites [29] Sams, D. et al. Neuronal CTCF is necessary for basal and experience-dependent gene regulation, memory formation, and genomic structure of BDNF and arc. Cell. Rep. 17, 2418–2430 (2016). sourceNot checked — no full text of the source

• AssumptionRests on an assumption: Public mouse hippocampus chromatin state and CTCF ChIP-seq data are representative of the study mice.

There was no effect of genotype on spontaneous alternations in the males.

“In contrast, there was no effect of genotype on spontaneous alternations in the males in our study, similar to the lack if genotype differences reported previously in the Y maze41.”

• cites [41] Whyte, L. et al. Reduction in open field activity in the absence of memory deficits in the App NL-G-F knock-in mouse model of Alzheimer’s disease. Behav. Brain Res. 336, 177–181 (2018). sourceNot checked — no full text of the source

• AssumptionRests on an assumption: The AppNL-G-F and AppNL-F mouse models recapitulate aspects of human AD relevant to the study.

The human hippocampus chromatin state at the region orthologous to the mouse Tomm40-Apoe DMR shows signatures of transcription and enhancer activity.

“Investigation of human hippocampus chromatin state from publicly available data at the region orthologous to the mouse Tomm40-Apoe DMR, revealed signatures of transcription and enhancer activity (Fig. 6B).”

DMR linked to apolipoprotein C2 positively associated with two Lachnospiraceae ASVs (ASV0048; F = 12.638, p = 0.009; and ASV0056; F = 15.759, p = 0.005).

“as well as apolipoprotein C2, which positively associated with two Lachnospiraceae ASVs (ASV0048; F = 12.638, p = 0.009; and ASV0056; F = 15.759, p = 0.005).”

During fear learning, there was a significant effect of genotype on baseline activity levels in males, with male AppNL-G-F mice moving less than WT males.

“However, during fear learning, there was a significant effect of genotype on baseline activity levels in males, with male AppNL-G-F mice moving less than WT males.”

• AssumptionRests on an assumption: The AppNL-G-F and AppNL-F mouse models recapitulate aspects of human AD relevant to the study.

AppNL-G-F DMRs showed a roughly even split between hypo- and hypermethylated DMRs.

“while AppNL-G-F DMRs showed a roughly even split between hypo- and hypermethylated DMRs (Fisher’s exact test p-value = 0.241; Fig. 4B).”

There was an effect of genotype on the ratio of circadian activity in males, with a higher ratio of activity in AppNL-G-F and AppNL-F males than WT males.

“We observed an effect of genotype on the ratio of circadian activity in the males, with a higher ratio of activity in AppNL-G-F and AppNL-F males than WT males.”

• AssumptionRests on an assumption: The AppNL-G-F and AppNL-F mouse models recapitulate aspects of human AD relevant to the study.

ASV0007 from Muribaculaceae negatively associated with a DMR linked to Slc5a10 (F = 37.966, p = 0.004).

“while an ASV from the Muribaculaceae (ASV0007) negatively associated with a DMR linked to Solute Carrier Family 5 (Sodium/Glucose Cotransporter), Member 10 (Slc5a10) (F = 37.966, p = 0.004)”

Four taxa (ASV0007, ASV0038, ASV0051, and ASV0178) also significantly associated with behavioral scores.

“Of particular note, 4 of these taxa (ASV0007, ASV0038, ASV0051, and ASV0178) also significantly associated with behavioral scores as described above.”

ASV0007 from Muribaculaceae negatively associated with a DMR linked to GLp2r (F = 26.465, p = 0.001).

“as well as a DMR linked to Glucagon-Like Peptide-2 Receptor (GLp2r) (F = 26.465, p = 0.001).”

A Bacteroides-assigned ASV (ASV0038) negatively associates with the Apoe DMR.

“For example, a Bacteroides-assigned ASV (ASV0038) negatively associates with this DMR.”

Beta-diversity analysis revealed baseline differences in the microbiome based on genotype.

“In this study, we found evidence in support of this notion, but also observed that differences in mouse genotype can modulate these relationships, as evidenced by our beta-diversity analysis that reveal baseline differences in the microbiome based on genotype.”

There was a negative relationship between the relative abundance of two Lachnospiraceae ASVs (ASV0048 & ASV0056) and methylation at a DMR nearby apolipoprotein C2.

“For instance, there was a negative relationship between the relative abundance of two Lachnospiraceae ASVs (ASV0048 & ASV0056) and methylation at a DMR nearby apolipoprotein C2, a gene implicated in metabolism of triglyceride-rich lipoproteins (TRLs) by acting as a cofactor of lipoprotein lipase (LPL), the main enzyme that hydrolyses plasma triglycerides on TRL59.”

The biodiversity of AppNL-F and AppNL-G-F genotype mice manifested positive associations with the amount of time spent with a novel object.

“However, the biodiversity of AppNL-F and AppNL-G-F genotype mice manifested positive associations with this same measure.”

Integration of gut microbiome data with hippocampal DNA methylation information at DMRs associated with AD-relevant genes revealed linkages between various ASVs in the gut and a DMR overlapping the Apoe gene.

“Integration of gut microbiome data with hippocampal DNA methylation information at DMRs associated with AD-relevant genes revealed linkages between various ASVs in the gut and a DMR overlapping the Apoe gene, which is involved in Reelin signaling.”

In this study, we found evidence in support of the notion that the biodiversity and composition of the gut microbiome are linked to mouse cognition and behavior.

“In this study, we found evidence in support of this notion, but also observed that differences in mouse genotype can modulate these relationships, as evidenced by our beta-diversity analysis that reveal baseline differences in the microbiome based on genotype.”

The biodiversity of the gut microbiome negatively associated with the amount of time WT mice spent with a novel object.

“For example, the biodiversity of the gut microbiome negatively associated with the amount of time WT mice spent with a novel object, which is an indicator of object recognition memory.”

We observed associations between gut microbiome and hippocampal methylation nearby more genes implicated in lipoprotein metabolism.

“We also observed associations between gut microbiome and hippocampal methylation nearby more genes implicated in lipoprotein metabolism.”

Two ASVs from the Muribaculaceae family manifest associations with the Apoe DMR, but in opposing directions.

“Additionally, two ASVs from the Muribaculaea family manifest associations with this same DMR, but in opposing directions.”

The association between the composition of the microbiome and the time a mouse spent exploring a novel object differed in a genotype-dependent manner.

“Moreover, the association between the composition of the microbiome and the time a mouse spent exploring a novel object differed in a genotype-dependent manner.”

ASV0004 negatively associates with the Apoe DMR.

“Specifically, ASV0004 negatively associates with this DMR while ASV0137 positively associates with this DMR.”

The composition of the gut microbiome manifested striking differences as a function of mouse genotype, with all three genotypes eliciting distinct microbiome compositions from one another.

“Commensurate with this observation, the composition of the gut microbiome manifested striking differences as a function of mouse genotype, with all three genotypes eliciting distinct microbiome compositions from one another.”

We uncovered positive relationships between two Lactobacillus ASVs (ASV0040 & ASV0028) and DMRs overlapping the genes GSK3b and Igfbp4, respectively.

“Additionally, we uncovered positive relationships between two Lactobacillus ASVs (ASV0040 & ASV0028) and DMRs overlapping the genes Glycogen synthase kinase 3b (GSK3b) and Igfbp4, respectively.”

ASV0137 positively associates with the Apoe DMR.

“Specifically, ASV0004 negatively associates with this DMR while ASV0137 positively associates with this DMR.”

APP genotype affected the relationship between the relative abundance of specific phylotypes in the gut and various behavioral and cognitive measures.

“Accordingly, APP genotype also affected the relationship between the relative abundance of specific phylotypes in the gut and various behavioral and cognitive measures.”

DMR-containing genes in AppNL-GF, but not AppNL-F, mice were significantly enriched for genes associated with several AD-related phenotypes, including cerebrospinal fluid levels of Aβ42 and phosphorylated and total tau, immediate and delayed recall of logical memory, and hippocampal volume.

“In addition, DMR-containing genes in AppNL-GF, but not AppNL-F, mice were significantly enriched for genes associated with several AD-related phenotypes, including cerebrospinal fluid levels of Aβ42 and phosphorylated and total tau, immediate and delayed recall of logical memory, and hippocampal volume.”

Cage effects do not appear to underlie the association between microbiome composition and behavior.

“Additionally, cage effects do not appear to underlie this association (Suppl. Methods and Suppl. Fig. S4).”

There are genotype- and sex-dependent effects on behavioral measures in the AppNL-F and AppNL-G-F mice.

“Thus, there are genotype- and sex-dependent effects on behavioral measures in the AppNL-F and AppNL-G-F mice.”

A common underlying ability is being assessed in contextual freezing, cued freezing, center duration in the open field, wire hang fall score and wire hang reach score.

“indicating a common underlying ability is being assessed in these tasks.”

The genotype effects on cognitive measures in the AppNL-F and AppNL-G-F mice are sex-dependent.

“These data show that the genotype effects on cognitive measures in the AppNL-F and AppNL-G-F mice are also sex-dependent.”

Taxa derived from the Lachnospiraceae family may manifest relatively common associations with hippocampal epigenetic architecture.

“indicating that taxa derived from this family of microbes may manifest relatively common associations with hippocampal epigenetic architecture.”

DMR_394 likely contains a regulatory element that is normally active in adult mouse hippocampus.

“All together, these findings indicated that DMR_394 likely contains a regulatory element that is normally active in adult mouse hippocampus.”

• AssumptionRests on an assumption: Public mouse hippocampus chromatin state and CTCF ChIP-seq data are representative of the study mice.

The fact that these relatively closely related ASVs exhibit opposing relationships with this DMR underscores the potential for host-microbe interactions to rapidly diversify.

“The fact that these relatively closely related ASVs exhibit opposing relationships with this DMR underscores the potential for host-microbe interactions to rapidly diversify and suggests that caution should be employed when leveraging knowledge about close relatives to infer a gut microbe’s biology.”

These data highlight the importance of mouse apoE in human APP KI mouse models.

“These data highlight the importance of mouse apoE in human APP KI mouse models.”

Differences in mouse genotype can modulate the relationships between the gut microbiome and cognition/behavior.

“In this study, we found evidence in support of this notion, but also observed that differences in mouse genotype can modulate these relationships, as evidenced by our beta-diversity analysis that reveal baseline differences in the microbiome based on genotype.”

Our novel finding of a linkage between an Apoc2 hippocampal DMR and the relative abundance of these taxa clarifies the potential mechanisms underpinning these longstanding observations.

“Our novel finding of a linkage between an Apoc2 hippocampal DMR and the relative abundance of these taxa clarifies the potential mechanisms underpinning these longstanding observations.”

Our results extend prior observations by revealing connections between behavioral and cognitive performance, epigenetic architecture of the brain, and gut microbiota.

“Our results extend these prior observations by revealing connections between behavioral and cognitive performance, epigenetic architecture of the brain, and gut microbiota.”