experimental validation
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Investigating the nonlinear complex dynamics of the tuft cell-microbiome cross-talk: the impact of feedback loops on immune regulation, microbial modulation and response to tissue insults
Project Abstract Tuft cells (TCs) are specialized chemosensory epithelial cells that are emerging as critical regulators of intestinal homeostasis. Named over 70 years ago based on their distinct morphology, a defined function for TCs was only elucidated in the last decade. TCs in the small intestine sense succinate from helminths to initiate type 2 immune responses that mediate parasite expulsion. Recently, we discovered a novel physiologic function for TCs in the colon, where their role had been considered minimal. Succinate, a key microbial metabolite, is produced by colonic microbiota as both a precursor to other metabolites and a cross-feeding fuel source for pathogens. TCs respond to succinate by secreting interleukin-25 (IL-25), which activates type 2 cytokine- producing lymphocytes (T2Ls), amplifying TC expansion and reinforcing barrier function. We recently demonstrated that this SPB–TC–IL-25–T2L feedback loop is essential for protection against pathogen-induced colitis. Our preliminary data further suggest that TCs actively promote colonization by succinate-producing bacteria (SPBs), establishing positive feedback on TC-supporting microbes, while other epithelial cells such as goblet cells (GCs) and Paneth cells (PCs) may exert complementary or counterbalancing influences. Supported by new modeling insights, we hypothesize that these epithelial–immune–microbiome interactions form coordinated feedback loops that collectively optimize intestinal resilience. These loops may create a dynamic, multi-stable system that flexibly transitions between homeostatic and hyperplastic states, buffering against microbial fluctuations and pathogenic insults while preventing uncontrolled type 2 inflammation. Using a combination of mathematical modeling and experimental validation, we will develop a multi- layered systems framework to explore how epithelial–immune–microbial feedbacks shape resilience or breakdown in clinically relevant models of colonic infection and inflammation. Our three Aims will (1) develop, calibrate, and validate a mathematical model that integrates TCs, GCs, PCs, SPBs, and SCBs; (2) define the immunological circuits governing epithelial–microbiome equilibrium; and (3) determine how epithelial feedbacks regulate microbial community structure and resilience. In line with NIH’s new initiative to prioritize human-based research, our proposal combines computational modeling, human colonic organoids, and complementary mouse models. Organoid experiments will provide human-relevant data for model calibration, while in vivo studies validate systemic predictions, ensuring both rigor and translational relevance while minimizing reliance on animal models. This work will generate interoperable models that integrate epithelial, microbial, and immune networks, providing predictive insight into intestinal outcomes under homeostatic, infectious, and inflammatory conditions and informing therapeutic strategies for microbiome-targeted interventions.
Uncovering genetic determinants of carbapenem resistance in Klebsiella pneumoniae
Carbapenem-resistant Klebsiella pneumoniae represents an urgent global health threat due to its increasing prevalence and high mortality rates, necessitating a comprehensive understanding of its resistance mechanisms. While key resistance mechanisms and their genetic determinants are known, such as beta- lactamases and porin mutations, the cause of resistance in many strains remains elusive. Moreover, other strains that carry known genetic carbapenem-resistance factors have been found to still be susceptible to carbapenems for unclear reasons. Further, strains can carry genetic elements which, while not conferring resistance directly, can promote resistance indirectly by accelerating its acquisition, such as through mutations in DNA repair systems or mobile genetic elements. To address these knowledge gaps, we propose a genome-wide association study (GWAS), with the aim of maximizing the discovery of gene variants associated with meropenem resistance, with experimental validation of candidates to identify true causal variants. We will overcome limitations of prior studies in the following ways: 1) We have compiled an expanded data set of publicly available K. pneumoniae genomes from strains isolated across a wide distribution of countries, with in hand access to >100 isolates upon which experimental validation studies will be performed. 2) We will perform comprehensive capture of genetic variants by employing a reference-free GWAS, utilizing unitigs, stretches of DNA sequence that represent the entire spectrum of genetic variation. 3) We will enhance statistical power to detect genetic variants with even subtle effects on resistance by using a quantitative, continuous minimum inhibitory concentration (MIC) phenotype to meropenem rather than a binary designation of resistant or susceptible. 4) We will reduce the number of false positives arising from correlation, or linkage disequilibrium (LD), with known carbapenemase and other known resistance factors by performing a conditional GWAS, where known factors are included as covariates. 5) We will further mitigate confounding effects due to population structure and LD, which cause non-random relationships between variants, by utilizing a pangenome-wide regression with an elastic net penalty. 6) Crucially, we will functionally validate our findings, which will include genetic variants associated with increased resistance, whether through direct or indirect mechanisms, as well as those that may restore susceptibility in strains already possessing known resistance factors. We will bridge the gap between GWAS findings and functional validation by leveraging our high-throughput experimental capabilities. This integrated approach promises to uncover novel mechanisms of carbapenem resistance, its acquisition, and susceptibility in K. pneumoniae, with the potential to inform the development of future diagnostics or therapeutic strategies.
Cognitive Psychometrics: Statistical Modeling of Individual Differences in Latent Processes
Many psychological theories assume that qualitatively different cognitive processes can result in identical responses. Multinomial processing tree (MPT) models allow researchers to disentangle latent cognitive processes based on observed response frequencies. Recently, MPT models have been extended to explicitly account for participant and item heterogeneity. These hierarchical Bayesian MPT models provide the opportunity to connect two traditionally isolated disciplines. Whereas cognitive psychology has often focused on the experimental validation of MPT model parameters on the group level, psychometrics provides the necessary concepts and tools for measuring differences in MPT parameters on the item or person level. Moreover, MPT parameters can be regressed on covariates to model latent processes as a function of personality traits or other person characteristics.
Contextual inference underlies the learning of sensorimotor repertoires
Humans spend a lifetime learning, storing and refining a repertoire of motor memories. However, it is unknown what principle underlies the way our continuous stream of sensori-motor experience is segmented into separate memories and how we adapt and use this growing repertoire. Here we develop a principled theory of motor learning based on the key insight that memory creation, updating, and expression are all controlled by a single computation – contextual inference. Unlike dominant theories of single-context learning, our repertoire-learning model accounts for key features of motor learning that had no unified explanation and predicts novel phenomena, which we confirm experimentally. These results suggest that contextual inference is the key principle underlying how a diverse set of experiences is reflected in motor behavior.
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