Christoph A. Thaiss’s research while affiliated with Stanford University and other places

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Publications (127)


Gut Feelings: The Critical Role of Interoception in Obesity and Disorders of Gut-Brain Interaction
  • Article

April 2025

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2 Reads

Gastroenterology

Lin Chang

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Christoph A. Thaiss

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Zachary Knight

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Sahib Khalsa

Overview of DRiDO study and microbiome dataset
a, At 6 months of age, genetically diverse mice started one of five dietary interventions. They were extensively phenotyped and stool was collected for microbiome profiling. b, Lifespan per dietary group. This analysis includes n = 924 mice: n = 937 mice were alive at the start of dietary restriction at 6 months, and n = 13 mice were omitted from analysis owing to accidental death during technician handling. P values were calculated with pairwise log-rank tests against the AL group and adjusted with the Benjamini–Hochberg procedure. P value symbols are defined as follows: *P < 0.05; ***P < 0.001; ****P < 0.0001. c, Microbiome data generation consisted of extracting DNA from stool samples, preparing libraries, performing shotgun metagenomic sequencing, performing quality control, and finally, taxonomic and functional classification. After all quality control procedures, the cohort consisted of 2,997 stool samples. d, PCoA plot of n = 2,997 quality-controlled metagenomes. Ordination based on Bray–Curtis distances of genus-level relative abundances. The colours denote the dietary groups, and the sizes of the circles denote the mouse ages at the time of stool collection. Box plots along the sides show PCoA1 (top) and PCoA2 (left) coordinates per dietary group. Bar plots along the sides show the mean age of stool samples within each bin of PCoA1 (bottom) and PCoA2 (right) coordinates. PCoA1 and PCoA2 explain 35% and 8% of overall variance, respectively. For box plots in b and d, boxes extend from the 25th to 75th percentiles, whiskers extend to 1.5 times the interquartile range and the centre line is the median.
Host age influences the microbiome
a, Effect of age on genera. Age coefficients and standard errors were calculated with a linear mixed model (model 1). P values were calculated with a conditional Wald test and adjusted with the Benjamini–Hochberg procedure. b, Bifidobacterium increases with age (n = 2,988 metagenomes with age ≤40 months). The vertical dashed line at 6 months represents the start of dietary restriction. c, Uniqueness increases with age (n = 2,988 metagenomes with age ≤40 months). d, Host age prediction using 573 genus-level metagenomes from AL mice. The green line represents the line of best fit, and the grey shading represents the 95% confidence interval (linear regression). The black dashed line at y = x represents perfect accuracy. e, Top 10 most important genera for age prediction. Each dot is one of 10 cross-validation folds. The x axis shows the percentage increase in MSE when that particular genus is excluded from a tree within the random forest regressor. In b and c, data are presented as mean ± standard error of the mean (s.e.m.).
Universality of age-associated microbiome changes
a, We compared 573 samples from DO AL mice with 141 samples from a different mouse ageing cohort (‘B6’) and with 4,101 human gut microbiome samples. b, Percentage of genera associated with age (Benjamini–Hochberg-adjusted P < 0.1) based on linear mixed models (models 5–7) within each dataset. c, Comparison of age-associated taxonomic changes across datasets. Each pairwise comparison shows all features that passed prevalence filtration in both datasets. Spearman correlation and corresponding P value are shown above each plot. Features associated with age and with the same sign in the pairwise comparison are shown in green. d, Taxonomic uniqueness increases with age in all three datasets. Each panel includes the line of best fit and 95% confidence interval. e, Schematic of the cohousing and separation experiments. Y, young always housed with young; O, old always housed with old; CY, young housed with old; CO, old housed with young; exCY, formerly CY that were separated from old; exCO, formerly CO that were separated from young. f, PCoA (genus-level Bray–Curtis distances) of samples at baseline and 1 month of cohousing. The plus sign denotes the group centroid. g, Bray–Curtis distances (n = 863) between previously cohoused mice (exCY, exCO) and non-cohoused controls (Y, O). h, Random forest classifier trained on baseline samples and evaluated on cohousing and separation samples. Accuracy is the percentage of samples within each group correctly classified as young or old. i, Uniqueness split by age and cohousing status (n = 264 samples). B, baseline; C, cohousing; S2, 2 weeks of separation; S4, 4 weeks of separation; and so on. In g and i, the significance of group differences was evaluated with a t-test, and P value symbols are defined as follows: NS, P ≥ 0.05; ***P < 0.001; ****P < 0.0001.
Genetic influence on the microbiome
a, Heritability of n = 107 genus-level features as calculated by a linear mixed model (model 1). P values were calculated with a likelihood ratio test and adjusted with the Benjamini–Hochberg procedure. The yellow vertical dashed line shows mean heritability for heritable features. b, Percentage of heritable taxa (as reported by the authors) in other studies30,33,51,82,84. The number of samples per study is indicated. The colour of each bar indicates whether the study was performed in DO mice, agricultural animals or humans. c, Proportion of variance explained (PVE) by all experimental variables for 107 genus-level features (model 10). P values were calculated using a likelihood ratio test and adjusted with the Benjamini–Hochberg procedure. The horizontal lines show the mean PVE. d, Genome-wide results for the six age-specific significant QTL with Benjamini–Hochberg-adjusted P < 0.1 (P values calculated by permutation). Markers with LOD greater than 7.5 are coloured red.
Effects of dietary restriction on the microbiome
a, Effect of DR on 107 genus-level features. DR coefficients and standard errors were calculated with a linear mixed model (model 1). P values were calculated with a conditional Wald test and adjusted with the Benjamini–Hochberg procedure. The horizontal dashed grey lines are visual aids to help compare dietary groups. b, UMGS1815 was increased by DR (n = 2,988 metagenomes). c,Ligilactobacillus was increased by DR (n = 2,988 metagenomes). d, Absolute magnitude of DR coefficients (n = 107 genus-level features). The grey lines connect the same genus in different dietary groups. The horizontal bars show the mean. Statistical significance was evaluated by a paired t-test. e, Comparison of DR coefficients. The Pearson correlation and P value are indicated above each scatter plot. Lines of best fit and 95% confidence intervals (linear regression) are shown in purple. f, Mean CR (red) versus mean fasting (blue) coefficients are connected by vertical lines. Genera with opposite signs are opaque, while genera with the same sign are transparent. Dashed horizontal line at 0. g, Emergencia is decreased only by CR (n = 2,988 metagenomes). The AL group median is designated by a horizontal dashed grey line. h, Roseburia is decreased by fasting and increased by CR (n = 2,988 metagenomes). i, Receiver operating characteristic (ROC) curves for binary DR prediction. Each grey line is the ROC curve for one of five cross-validation folds. The purple line is the mean ROC curve. The diagonal dashed line represents no predictive accuracy. j, Predicting the dietary group before (grey) and after (purple) DR initiation. Each dot represents the prediction accuracy in 1 of 10 cross-validation folds. The horizontal dashed line at 20% represents expected accuracy by chance. Statistical significance was evaluated by a one-sided t-test (testing whether the mean accuracy is greater than 20%). k, Prediction accuracy stratified by dietary group. Only predictions after the start of DR were considered. l, Age prediction with a random forest regressor trained on AL samples (n = 2,618 metagenomes from 5, 10, 16, 22 and 28 months). The vertical dashed line at 6 months represents DR initiation; the diagonal dashed line represents perfect prediction. Statistical significance was evaluated by a t-test between AL and DR predictions at each age. m, UBA11957 decreases with both age and DR (n = 2,988 metagenomes). n, Ligilactobacillus increases with both age and DR (n = 2,988 metagenomes). In b, c, g and h, statistical significance was evaluated by a t-test against the AL group. For box plots in b, c, g and h, boxes extend from the 25th to 75th percentiles, whiskers extend to 1.5 times the interquartile range and the centre line is the median. In m and n, data are presented as mean ± s.e.m. In b–e, g,h, j and l, P value symbols are defined as follows: NS, P ≥ 0.05; *P < 0.05; ****P < 0.0001.

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Gut metagenomes reveal interactions between dietary restriction, ageing and the microbiome in genetically diverse mice
  • Article
  • Publisher preview available

March 2025

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85 Reads

Nature Microbiology

Lev Litichevskiy

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[...]

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Christoph A. Thaiss

The gut microbiome changes with age and has been proposed to mediate the benefit of lifespan-extending interventions such as dietary restriction. However, the causes and consequences of microbiome ageing and the potential of such interventions remain unclear. Here we analysed 2,997 metagenomes collected longitudinally from 913 deeply phenotyped, genetically diverse mice to investigate interactions between the microbiome, ageing, dietary restriction (caloric restriction and fasting), host genetics and a range of health parameters. Among the numerous age-associated microbiome changes that we find in this cohort, increased microbiome uniqueness is the most consistent parameter across a second longitudinal mouse experiment that we performed on inbred mice and a compendium of 4,101 human metagenomes. Furthermore, cohousing experiments show that age-associated microbiome changes may be caused by an accumulation of stochastic environmental exposures (neutral theory) rather than by the influence of an ageing host (selection theory). Unexpectedly, the majority of taxonomic and functional microbiome features show small but significant heritability, and the amount of variation explained by host genetics is similar to ageing and dietary restriction. We also find that more intense dietary interventions lead to larger microbiome changes and that dietary restriction does not rejuvenate the microbiome. Lastly, we find that the microbiome is associated with multiple health parameters, including body composition, immune components and frailty, but not lifespan. Overall, this study sheds light on the factors influencing microbiome ageing and aspects of host physiology modulated by the microbiome.

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Gut Microbiome-Produced Bile Acid Metabolite Lengthens Circadian Period in Host Intestinal Cells

March 2025

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3 Reads

Host circadian signaling, feeding, and the gut microbiome are tightly interconnected. Changes in the gut microbial community can affect the expression of core clock genes, but the specific metabolites and molecular mechanisms that mediate this relationship remain largely unknown. Here, we sought to identify gut microbial metabolites that impact circadian signaling. Through a phenotypic screen of a focused library of gut microbial metabolites, we identified a bile acid metabolite, lithocholic acid (LCA), as a circadian modulator. LCA lengthened the circadian period of core clock gene hPer2 transcription in a dose-responsive manner in human colonic cells. We found evidence that LCA modulates the casein kinase 1δ/ϵ(CK1δ/ϵ)-protein phosphatase 1 (PP1) feedback loop and stabilizes core clock protein cryptochrome 2 (CRY2). Furthermore, we showed that LCA feeding alters circadian transcription in mouse distal ileum and colon. Taken together, our work identifies LCA as a molecular link between host circadian biology and the microbiome. Because bile acids are secreted in response to feeding, our work provides potential mechanistic insight into the molecular nature of the food-entrainable oscillator by which peripheral clocks adapt to the timing of food intake. Given the association between circadian rhythm, feeding, and metabolic disease, our insights may offer a new avenue for modulating host health.



Dietary manipulation of intestinal microbes prolongs survival in a mouse model of Hirschsprung disease

February 2025

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46 Reads

Enterocolitis is a common and potentially deadly manifestation of Hirschsprung disease (HSCR) but disease mechanisms remain poorly defined. Unexpectedly, we discovered that diet can dramatically affect the lifespan of a HSCR mouse model ( Piebald lethal , sl/sl ) where affected animals die from HAEC complications. In the sl/sl model, diet alters gut microbes and metabolites, leading to changes in colon epithelial gene expression and epithelial oxygen levels known to influence colitis severity. Our findings demonstrate unrecognized similarity between HAEC and other types of colitis and suggest dietary manipulation could be a valuable therapeutic strategy for people with HSCR. Abstract Hirschsprung disease (HSCR) is a birth defect where enteric nervous system (ENS) is absent from distal bowel. Bowel lacking ENS fails to relax, causing partial obstruction. Affected children often have “Hirschsprung disease associated enterocolitis” (HAEC), which predisposes to sepsis. We discovered survival of Piebald lethal ( sl/sl ) mice, a well-established HSCR model with HAEC, is markedly altered by two distinct standard chow diets. A “Protective” diet increased fecal butyrate/isobutyrate and enhanced production of gut epithelial antimicrobial peptides in proximal colon. In contrast, “Detrimental” diet-fed sl/sl had abnormal appearing distal colon epithelium mitochondria, reduced epithelial mRNA involved in oxidative phosphorylation, and elevated epithelial oxygen that fostered growth of inflammation-associated Enterobacteriaceae . Accordingly, selective depletion of Enterobacteriaceae with sodium tungstate prolonged sl/sl survival. Our results provide the first strong evidence that diet modifies survival in a HSCR mouse model, without altering length of distal colon lacking ENS. Highlights Two different standard mouse diets alter survival in the Piebald lethal ( sl/sl ) mouse model of Hirschsprung disease, without impacting extent of distal colon aganglionosis (the region lacking ENS). Piebald lethal mice fed the “Detrimental” diet had many changes in colon epithelial transcriptome including decreased mRNA for antimicrobial peptides and genes involved in oxidative phosphorylation. Detrimental diet fed sl/sl also had aberrant-appearing mitochondria in distal colon epithelium, with elevated epithelial oxygen that drives lethal Enterobacteriaceae overgrowth via aerobic respiration. Elimination of Enterobacteriaceae with antibiotics or sodium tungstate improves survival of Piebald lethal fed the “Detrimental diet”. Graphical abstract



MISO workflow for analysis of spatial multi-omics dataset with paired histology image
MISO starts by constructing an adjacency matrix for each modality. This adjacency matrix and the spot-level features for that modality are used as input for a multilayer perceptron that is trained to minimize spectral clustering and reconstruction loss functions. The modality-specific embeddings are extracted from the multilayer perceptrons, and interactions between each pair of modalities are computed. The features of low-quality modalities are removed, and the final group of embeddings is used as input for the k-means clustering algorithm.
Analysis of a 10x Visium bladder cancer spatial transcriptomics dataset
a, H&E-stained histology image of the analyzed tissue section with TLS and HEV annotation. b, TLS score across all Visium spots. c, TLS score across all super-pixels following gene expression resolution enhancement using iStar. d, Shown from left to right are the clustering results for TLS1 and TLS2 provided by MISO, MUSE and SpatialGlue, with masks showing the true-positive and false-positive HEV super-pixels for MISO and SpatialGlue.
Analysis of large-scale spatial transcriptomics datasets
a–e, Analysis of a 10x Xenium gastric cancer spatial transcriptomics dataset. a, Pathologist manual annotation. b, Histology image patches of tissue from mucosa and carcinoma regions. c, MISO clustering results. d, Spatial gene expression plot of ERBB2, a gastric cancer marker gene that shows similar levels of expression in the carcinoma and mucosa regions. e, MISO extracted tissue histology image features that enabled the identification of mucosal and carcinoma clusters. f–i, Analysis of a 10x Visium HD CRC spatial transcriptomics dataset. f, Pathologist annotation of tissue section. g, MISO clustering results. h, Spatial gene expression plot of CEACAM6, a CRC marker gene that shows similar levels of expression across all annotated invasive cancer regions. i, Spatial gene expression plot of SPP1, a tumor-specific macrophage marker gene that is colocalized with cluster 4 identified by MISO.
Analysis of a spatial ATAC–RNA-seq dataset from a mouse at E13
a, H&E-stained histology image of an adjacent tissue section. b, Magnification and annotation of telencephalon. c, Shown from left to right are the clustering results from MISO, MUSE and SpatialGlue. d, Shown from left to right are the spatial gene expression plots of marker genes for the VZ, subpallium stratum and pallium stratum of the telencephalon. e, Spots plotted according to their RNA or ATAC t-SNE coordinates and colored by the MISO clustering results.
Analysis of spatial multi-omics datasets with three modalities
a–c, Clustering results for a coronal mouse brain spatial transcriptomics and metabolomics dataset. a, H&E-stained histology image of an analyzed tissue section, with magnification of hippocampus. b, Shown from left to right are the clustering results of the super-pixels in the hippocampus from MISO (RNA, metabolite and tissue histology), MUSE (RNA and tissue histology) and SpatialGlue (RNA and metabolite). c, Shown from left to right are the MERFISH distribution of CA2 and CA3 glutamatergic neurons and spatial gene expression plots of Amigo2 and Chrm3 in the pyramidal layer of the hippocampus. d–h, Clustering results for a human tonsil spatial gene and protein expression dataset. d, Germinal center annotation of the tissue section. e, MISO clustering results when taking all three modalities (that is, RNA, tissue histology and protein), two modalities (RNA and tissue histology) and two modalities (RNA and protein), as input. f, MUSE clustering results when taking RNA and tissue histology data as input. g, SpatialGlue clustering results when taking RNA and protein data as input. h, F1 score for germinal center localization for all methods when taking each possible combination of modalities as input.
Resolving tissue complexity by multimodal spatial omics modeling with MISO

January 2025

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156 Reads

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5 Citations

Nature Methods

Spatial molecular profiling has provided biomedical researchers valuable opportunities to better understand the relationship between cellular localization and tissue function. Effectively modeling multimodal spatial omics data is crucial for understanding tissue complexity and underlying biology. Furthermore, improvements in spatial resolution have led to the advent of technologies that can generate spatial molecular data with subcellular resolution, requiring the development of computationally efficient methods that can handle the resulting large-scale datasets. MISO (MultI-modal Spatial Omics) is a versatile algorithm for feature extraction and clustering, capable of integrating multiple modalities from diverse spatial omics experiments with high spatial resolution. Its effectiveness is demonstrated across various datasets, encompassing gene expression, protein expression, epigenetics, metabolomics and tissue histology modalities. MISO outperforms existing methods in identifying biologically relevant spatial domains, representing a substantial advancement in multimodal spatial omics analysis. Moreover, MISO’s computational efficiency ensures its scalability to handle large-scale datasets generated by subcellular resolution spatial omics technologies.


Microglia replacement by ER-Hoxb8 conditionally immortalized macrophages provides insight into Aicardi-Goutières Syndrome neuropathology

January 2025

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10 Reads

Microglia, the brain’s resident macrophages, can be reconstituted by surrogate cells - a process termed “microglia replacement.” To expand the microglia replacement toolkit, we here introduce estrogen-regulated (ER) homeobox B8 (Hoxb8) conditionally immortalized macrophages, a cell model for generation of immune cells from murine bone marrow, as a versatile model for microglia replacement. We find that ER-Hoxb8 macrophages are highly comparable to primary bone marrow-derived (BMD) macrophages in vitro, and, when transplanted into a microglia-free brain, engraft the parenchyma and differentiate into microglia-like cells. Furthermore, ER-Hoxb8 progenitors are readily transducible by virus and easily stored as stable, genetically manipulated cell lines. As a demonstration of this system’s power for studying the effects of disease mutations on microglia in vivo, we created stable, Adar1 -mutated ER-Hoxb8 lines using CRISPR-Cas9 to study the intrinsic contribution of macrophages to Aicardi-Goutières Syndrome (AGS), an inherited interferonopathy that primarily affects the brain and immune system. We find that Adar1 knockout elicited interferon secretion and impaired macrophage production in vitro, while preventing brain macrophage engraftment in vivo - phenotypes that can be rescued with concurrent mutation of Ifih1 (MDA5) in vitro, but not in vivo. Lastly, we extended these findings by generating ER-Hoxb8 progenitors from mice harboring a patient-specific Adar1 mutation (D1113H). We demonstrated the ability of microglia-specific D1113H mutation to drive interferon production in vivo, suggesting microglia drive AGS neuropathology. In sum, we introduce the ER-Hoxb8 approach to model microglia replacement and use it to clarify macrophage contributions to AGS.


Microglia replacement by ER-Hoxb8 conditionally immortalized macrophages provides insight into Aicardi-Goutières Syndrome neuropathology

January 2025

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13 Reads

Microglia, the brain’s resident macrophages, can be reconstituted by surrogate cells - a process termed “microglia replacement.” To expand the microglia replacement toolkit, we here introduce estrogen-regulated (ER) homeobox B8 (Hoxb8) conditionally immortalized macrophages, a cell model for generation of immune cells from murine bone marrow, as a versatile model for microglia replacement. We find that ER-Hoxb8 macrophages are highly comparable to primary bone marrow-derived (BMD) macrophages in vitro, and, when transplanted into a microglia-free brain, engraft the parenchyma and differentiate into microglia-like cells. Furthermore, ER-Hoxb8 progenitors are readily transducible by virus and easily stored as stable, genetically manipulated cell lines. As a demonstration of this system’s power for studying the effects of disease mutations on microglia in vivo, we created stable, Adar1 -mutated ER-Hoxb8 lines using CRISPR-Cas9 to study the intrinsic contribution of macrophages to Aicardi-Goutières Syndrome (AGS), an inherited interferonopathy that primarily affects the brain and immune system. We find that Adar1 knockout elicited interferon secretion and impaired macrophage production in vitro, while preventing brain macrophage engraftment in vivo - phenotypes that can be rescued with concurrent mutation of Ifih1 (MDA5) in vitro, but not in vivo. Lastly, we extended these findings by generating ER-Hoxb8 progenitors from mice harboring a patient-specific Adar1 mutation (D1113H). We demonstrated the ability of microglia-specific D1113H mutation to drive interferon production in vivo, suggesting microglia drive AGS neuropathology. In sum, we introduce the ER-Hoxb8 approach to model microglia replacement and use it to clarify macrophage contributions to AGS.



Citations (78)


... In this study, we conducted benchmarking of spaMGCN against the latest methods-SpatialGlue [41], SSGATE [42], GraphST [43], GAAEST [44], SpaGIC [45], MISO [46], and scMDC [47]-using different tests with default parameters. SpatialGlue, MISO, SSGATE, GraphST, GAAEST, and SpaGIC are spatial domain identification methods, while scMDC is a single-cell multi-omics clustering method. ...

Reference:

spaMGCN: a graph convolutional network with autoencoder for spatial domain identification using multi-scale adaptation
Resolving tissue complexity by multimodal spatial omics modeling with MISO

Nature Methods

... These interactions are bidirectional and are governed by complex signaling pathways that allow for communication and metabolic support. Astrocytes secrete gliotransmitters that may modulate neuronal excitability and synaptic transmission; in turn, neurons communicate with glial cells through neurotransmitter signaling [11,12]. In addition, neuronal activity would promote the proliferation and differentiation of oligodendrocyte precursor cells (OPCs) into myelinating oligodendrocytes. ...

Mind the GAPS: Glia associated with psychological stress
  • Citing Article
  • October 2024

... Consider that an intervention's specific MOA likely targets mechanisms upstream of many other genes, processes, or mechanisms, that contribute to the clinical phenotype of interest that could be perturbed 66 . This last issue was recently exposed in a large study of mice subjected to different forms of rigorous dietary restriction (DR) to explore the geroprotective effects of DR on lifespan 67 . Interestingly, not only did not all the mice benefit from DR, but genetic factors explained about 3 times more of the variation in lifespan than did DR 67,68 . ...

Dietary restriction impacts health and lifespan of genetically diverse mice

Nature

... Rather, they herald a profound shift in how we understand the (micro)biological underpinnings of criminal responsibility. Auto-brewery syndrome adds to rapidly accumulating research demonstrating that the human microbiome is a crucial mediator of brain function and behaviour (Devason et al. 2024). Indeed, microbes may play an important role in so-called System-1 ...

Neuromicrobiology Comes of Age: The Multifaceted Interactions between the Microbiome and the Nervous System
  • Citing Article
  • August 2024

ACS Chemical Neuroscience

... When Foxa1 and Foxa2 are knocked out in intestinal epithelial cells, the glycosylase network is disrupted, leading to drastic changes in microbial composition and spontaneous IBD. 102 ...

The evolutionarily ancient FOXA transcription factors shape the murine gut microbiome via control of epithelial glycosylation
  • Citing Article
  • May 2024

Developmental Cell

... Although germ-free mice demonstrated reduced sebaceous gland activity, microbial association was insufficient to immediately rescue this phenotype. Instead, rescue required transgenerational effects, illustrating the complexity of host-microbe crosstalk at the skin barrier (77). ...

The microbiota and T cells non-genetically modulate inherited phenotypes transgenerationally
  • Citing Article
  • April 2024

Cell Reports

... However, obesity is a pro-inflammatory state that may influence neurotoxicity through several mechanisms. Previous studies suggest that obesity is associated with increased systemic inflammation and metabolic dysregulation, characterized by elevated levels of IL-6 and TNF-α, which can impair the integrity of the blood-brain barrier (BBB) [19]. Also, obesity has been linked to chronic neuroinflammation and microglial activation, which may predispose patients to more severe ICANS symptoms [20]. ...

Targeting lipid nanoparticles to the blood brain barrier to ameliorate acute ischemic stroke
  • Citing Article
  • March 2024

Molecular Therapy

... We did not observe a general loss in core Bacteroides species as reported in Wilmanski et al. 27 , which could be a function of our Asian cohorts, but may also be due to methodological differences (e.g., covariate adjustment). Also, our results suggest that the trends reported for richness 22,94 and uniqueness 27 may be variable across cohorts 95 . Further studies are needed across different global populations to explore the factors impacting this variability using consistently generated datasets and potentially using long-read sequencing to enable more reliable detection/assembly of rare taxa. ...

Interactions between the gut microbiome, dietary restriction, and aging in genetically diverse mice

... Flow cytometry analysis shows that they mediate the intrahepatic infiltration of CD8 + T cells through the CCL3/CCL4-CCR5 axis and enhance the cytotoxicity of CD8 + T cells through the CD40/CD40L costimulatory signal and the secretion of IL-4/IL-13 [17]. NKT cells can also enhance the expansion and cytotoxicity of CD8 + T cells by inhibiting the negative feedback regulation of the STAT6 signalling pathway and enhancing the cross-priming efficiency of cytotoxic T lymphocytes (CTLs) [18]. NKT cells activate Tregs by releasing regulatory factors such as IL-2, IL-10, and Transforming Growth Factor-β (TGF-β). ...

Conditional NKT Cell Depletion in Mice Reveals a Negative Feedback Loop That Regulates CTL Cross-Priming

The Journal of Immunology

... Several interventions are known to extend mouse lifespan (Miller et al. 2007) and healthspan, including multiple approaches to dietary restriction (DR) (Weindruch et al. 1986;Anderson, Shanmuganayagam, and Weindruch 2009;Mitchell et al. 2019) and several compounds, including the drug rapamycin (Heitman, Movva, and Hall 1991;Bjedov et al. 2010;Harrison et al. 2009). These interventions are robust across mouse genotypes, as evidenced by their validation in genetically heterogeneous populations (Di Francesco et al. 2023). Despite their robustness, consensus on the physiological mechanisms-of-action for these interventions is lacking (Green, Lamming, and Fontana 2022;Papadopoli et al. 2019). ...

Regulators of health and lifespan extension in genetically diverse mice on dietary restriction