Imbalance in Glucose Metabolism Regulates the Transition of Microglia from Homeostasis to Disease-Associated Microglia Stage 1.

Microglia undergo two-stage activation in neurodegenerative diseases, known as disease-associated microglia (DAM). TREM2 mediates the DAM2 stage transition, but what regulates the first DAM1 stage transition is unknown. We report that glucose dyshomeostasis inhibits DAM1 activation and PKM2 plays a role. As in tumors, PKM2 was aberrantly elevated in both male and female human AD brains, but unlike in tumors, it is expressed as active tetramers, as well as among TREM2+ microglia surrounding plaques in 5XFAD male and female mice. snRNAseq analyses of microglia without Pkm2 in 5XFAD mice revealed significant increases in DAM1 markers in a distinct metabolic cluster, which is enriched in genes for glucose metabolism, DAM1, and AD risk. 5XFAD mice incidentally exhibited a significant reduction in amyloid pathology without microglial Pkm2 Surprisingly, microglia in 5XFAD without Pkm2 exhibited increases in glycolysis and spare respiratory capacity, which correlated with restoration of mitochondrial cristae alterations. In addition, in situ spatial metabolomics of plaque-bearing microglia revealed an increase in respiratory activity. These results together suggest that it is not only glycolytic but also respiratory inputs that are critical to the development of DAM signatures in 5XFAD mice.

COLMARppm: A Web Server Tool for the Accurate and Rapid Prediction of 1H and 13C NMR Chemical Shifts of Organic Molecules and Metabolites.

Despite rapid progress in metabolomics research, a major bottleneck is the large number of metabolites whose chemical structures are unknown or whose spectra have not been deposited in metabolomics databases. Nuclear magnetic resonance (NMR) spectroscopy has a long history of elucidating chemical structures from experimentally measured 1H and 13C chemical shifts. One approach to characterizing the chemical structures of an unknown metabolite is to predict the 1H and 13C chemical shifts of candidate compounds (e.g., metabolites from the Human Metabolome Database (HMDB)) and compare them with chemical shifts of the unknown. However, accurate prediction of NMR chemical shifts in aqueous solution is challenging due to limitations of experimental chemical shift libraries and the high computational cost of quantum chemical methods. To improve NMR prediction accuracy and applicability, an empirical prediction strategy is introduced here to provide an accurately predicted chemical shift for organic molecules and metabolites within seconds. Unique features of COLMARppm include (i) the training library exclusively consisting of high quality NMR spectra measured under standard conditions in aqueous solution, (ii) utilization of NMR motif information, and (iii) leveraging of the improved prediction accuracy for the automated assignment of experimental chemical shifts for candidate structures. COLMARppm is demonstrated in terms of accuracy and speed for a set of 20 compounds taken from the HMDB for chemical shift prediction and resonance assignment. COLMARppm is applicable to a wide range of small molecules and can be directly incorporated into metabolomics workflows.

Predicting protein flexibility with AlphaFold.

AlphaFold2 has revolutionized protein structure prediction from amino-acid sequence. In addition to protein structures, high-resolution dynamics information about various protein regions is important for understanding protein function. Although AlphaFold2 has neither been designed nor trained to predict protein dynamics, it is shown here how the information returned by AlphaFold2 can be used to predict dynamic protein regions at the individual residue level. The approach, which is termed cdsAF2, uses the 3D protein structure returned by AlphaFold2 to predict backbone NMR NH S2 order parameters using a local contact model that takes into account the contacts made by each peptide plane along the backbone with its environment. By combining for each residue AlphaFold2's pLDDT confidence score for the structure prediction accuracy with the predicted S2 value using the local contact model, an estimator is obtained that semi-quantitatively captures many of the dynamics features observed in experimental backbone NMR NH S2 order parameter profiles. The method is demonstrated for a set nine proteins of different sizes and variable amounts of dynamics and disorder.

Quantitative prediction of ensemble dynamics, shapes and contact propensities of intrinsically disordered proteins.

Intrinsically disordered proteins (IDPs) are highly dynamic systems that play an important role in cell signaling processes and their misfunction often causes human disease. Proper understanding of IDP function not only requires the realistic characterization of their three-dimensional conformational ensembles at atomic-level resolution but also of the time scales of interconversion between their conformational substates. Large sets of experimental data are often used in combination with molecular modeling to restrain or bias models to improve agreement with experiment. It is shown here for the N-terminal transactivation domain of p53 (p53TAD) and Pup, which are two IDPs that fold upon binding to their targets, how the latest advancements in molecular dynamics (MD) simulations methodology produces native conformational ensembles by combining replica exchange with series of microsecond MD simulations. They closely reproduce experimental data at the global conformational ensemble level, in terms of the distribution properties of the radius of gyration tensor, and at the local level, in terms of NMR properties including 15N spin relaxation, without the need for reweighting. Further inspection revealed that 10-20% of the individual MD trajectories display the formation of secondary structures not observed in the experimental NMR data. The IDP ensembles were analyzed by graph theory to identify dominant inter-residue contact clusters and characteristic amino-acid contact propensities. These findings indicate that modern MD force fields with residue-specific backbone potentials can produce highly realistic IDP ensembles sampling a hierarchy of nano- and picosecond time scales providing new insights into their biological function.

ARCHE-NOAH: NMR supersequence with five different CEST experiments for studying protein conformational dynamics.

An NMR NOAH-supersequence is presented consisting of five CEST experiments for studying protein backbone and side-chain dynamics by 15N-CEST, carbonyl-13CO-CEST, aromatic-13Car-CEST, 13Cα-CEST, and methyl-13Cmet-CEST. The new sequence acquires the data for these experiments in a fraction of the time required for the individual experiments, saving over four days of NMR time per sample.

Fundamental and practical aspects of machine learning for the peak picking of biomolecular NMR spectra.

Rapid progress in machine learning offers new opportunities for the automated analysis of multidimensional NMR spectra ranging from protein NMR to metabolomics applications. Most recently, it has been demonstrated how deep neural networks (DNN) designed for spectral peak picking are capable of deconvoluting highly crowded NMR spectra rivaling the facilities of human experts. Superior DNN-based peak picking is one of a series of critical steps during NMR spectral processing, analysis, and interpretation where machine learning is expected to have a major impact. In this perspective, we lay out some of the unique strengths as well as challenges of machine learning approaches in this new era of automated NMR spectral analysis. Such a discussion seems timely and should help define common goals for the NMR community, the sharing of software tools, standardization of protocols, and calibrate expectations. It will also help prepare for an NMR future where machine learning and artificial intelligence tools will be common place.

COLMARq: A Web Server for 2D NMR Peak Picking and Quantitative Comparative Analysis of Cohorts of Metabolomics Samples.

Highly quantitative metabolomics studies of complex biological mixtures are facilitated by the resolution enhancement afforded by 2D NMR spectra such as 2D 13C-1H HSQC spectra. Here, we describe a new public web server, COLMARq, for the semi-automated analysis of sets of 2D HSQC spectra of cohorts of samples. The workflow of COLMARq includes automated peak picking using the deep neural network DEEP Picker, quantitative cross-peak volume extraction by numerical fitting using Voigt Fitter, the matching of corresponding cross-peaks across cohorts of spectra, peak volume normalization between different spectra, database query for metabolite identification, and basic univariate and multivariate statistical analyses of the results. COLMARq allows the analysis of cross-peaks that belong to both known and unknown metabolites. After a user has uploaded cohorts of 2D 13C-1H HSQC and optionally 2D 1H-1H TOCSY spectra in their preferred format, all subsequent steps on the web server can be performed fully automatically, allowing manual editing if needed and the sessions can be saved for later use. The accuracy, versatility, and interactive nature of COLMARq enables quantitative metabolomics analysis, including biomarker identification, of a broad range of complex biological mixtures as is illustrated for cohorts of samples from bacterial cultures of Pseudomonas aeruginosa in both its biofilm and planktonic states.

Observation of Sub-Microsecond Protein Methyl-Side Chain Dynamics by Nanoparticle-Assisted NMR Spin Relaxation.

Amino-acid side-chain properties in proteins are key determinants of protein function. NMR spin relaxation of side chains is an important source of information about local protein dynamics and flexibility. However, traditional solution NMR relaxation methods are most sensitive to sub-nanosecond dynamics lacking information on slower ns-µs time-scale motions. Nanoparticle-assisted NMR spin relaxation (NASR) of methyl-side chains is introduced here as a window into these ns-µs dynamics. NASR utilizes the transient and nonspecific interactions between folded proteins and slowly tumbling spherical nanoparticles (NPs), whereby the increase of the relaxation rates reflects motions on time scales from ps all the way to the overall tumbling correlation time of the NPs ranging from hundreds of ns to µs. The observed motional amplitude of each methyl group can then be expressed by a model-free NASR S2 order parameter. The method is demonstrated for 2H-relaxation of CH2D methyl moieties and cross-correlated relaxation of CH3 groups for proteins Im7 and ubiquitin in the presence of anionic silica-nanoparticles. Both types of relaxation experiments, dominated by either quadrupolar or dipolar interactions, yield highly consistent results. Im7 shows additional dynamics on the intermediate time scales taking place in a functionally important loop, whereas ubiquitin visits the majority of its conformational substates on the sub-ns time scale. These experimental observations are in good agreement with 4-10 µs all-atom molecular dynamics trajectories. NASR probes side-chain dynamics on a much wider range of motional time scales than previously possible, thereby providing new insights into the interplay between protein structure, dynamics, and molecular interactions that govern protein function.

2D NMR-Based Metabolomics with HSQC/TOCSY NOAH Supersequences.

Sensitivity-improved versions of two-dimensional (2D) 13C-1H HSQC (heteronuclear single quantum coherence) and HSQC-TOCSY (HSQC-total correlation spectroscopy) NMR experiments optimized for small biological molecules and their complex mixtures encountered in metabolomics are presented that preserve the magnetization of 1H spins not directly attached to 13C spins. This allows (i) the application of rapid acquisition techniques to substantially shorten measurement time and (ii) their incorporation into supersequences (NOAH-NMR by ordered acquisition using 1H detection) for the compact acquisition of multiple 2D NMR data sets with significant gains in sensitivity, resolution, and/or time. The new pulse sequences, which are demonstrated for both metabolite model mixtures and mouse urine, offer an attractive approach for the efficient measurement of multiple 2D NMR spectra (HSQCsi and/or HSQCsi-TOCSY and TOCSY) of metabolomics samples in a single experiment for the accurate and comprehensive identification and quantitation of metabolites. These new methods bring to bear the advantages of 2D NMR to metabolomics studies with larger cohorts of samples.

Broadband Dynamics of Ubiquitin by Anionic and Cationic Nanoparticle Assisted NMR Spin Relaxation.

The quantitative and comprehensive description of the internal dynamics of proteins is critical for understanding their function. Nanoparticle-assisted 15 N NMR spin relaxation spectroscopy is a new method for the observation of picosecond to microsecond dynamics of proteins when transiently interacting with the surface of the nanoparticles (NPs). The method is applied here to the protein ubiquitin in the presence of anionic and cationic silica NPs (SNPs) of different sizes. The backbone dynamics profiles are reproducible and strikingly similar to each other, indicating that specific protein-SNP interactions are unimportant. The dynamics profiles closely match the sub-nanosecond dynamics S2 values observed by model-free analysis of standard 15 N relaxation of ubiquitin in free solution, indicating that the bulk of the ubiquitin backbone dynamics in solution is confined to sub-nanosecond timescales and, hence, it is dynamically more restrained than previous NMR studies have suggested.

COLMAR Lipids Web Server and Ultrahigh-Resolution Methods for Two-Dimensional Nuclear Magnetic Resonance- and Mass Spectrometry-Based Lipidomics.

Accurate identification of lipids in biological samples is a key step in lipidomics studies. Multidimensional nuclear magnetic resonance (NMR) spectroscopy is a powerful analytical tool for this purpose as it provides comprehensive structural information on lipid composition at atomic resolution. However, the interpretation of NMR spectra of complex lipid mixtures is currently hampered by limited spectral resolution and the absence of a customized lipid NMR database along with user-friendly spectral analysis tools. We introduce a new two-dimensional (2D) NMR metabolite database "COLMAR Lipids" that was specifically curated for hydrophobic metabolites presently containing 501 compounds with accurate experimental 2D 13C-1H heteronuclear single quantum coherence (HSQC) chemical shift data measured in CDCl3. A new module in the public COLMAR suite of NMR web servers was developed for the (semi)automated analysis of complex lipidomics mixtures (http://spin.ccic.osu.edu/index.php/colmarm/index2). To obtain 2D HSQC spectra with the necessary high spectral resolution along both 13C and 1H dimensions, nonuniform sampling in combination with pure shift spectroscopy was applied allowing the extraction of an abundance of unique cross-peaks belonging to hydrophobic compounds in complex lipidomics mixtures. As shown here, this information is critical for the unambiguous identification of underlying lipid molecules by means of the new COLMAR Lipids web server, also in combination with mass spectrometry, as is demonstrated for Caco-2 cell and lung tissue cell extracts.

Quantitative Cooperative Binding Model for Intrinsically Disordered Proteins Interacting with Nanomaterials.

Intrinsically disordered proteins (IDPs) can display a broad spectrum of binding modes and highly variable binding affinities when interacting with both biological and nonbiological materials. A quantitative model of such behavior is important for the better understanding of the function of IDPs when encountering inorganic nanomaterials with the potential to control their behavior in vivo and in vitro. Depending on their amino acid composition and chain length, binding properties can vary strongly between different IDPs. Moreover, due to differences in the physical chemical properties of clusters of amino acid residues along the IDP primary sequence, individual residues can adopt a wide range of bound state populations. Quantitative experimental binding affinities with synthetic silica nanoparticles (SNPs) at residue-level resolution, which were obtained for a set of IDPs by solution NMR relaxation experiments, are explained here by a first-principle analytical statistical mechanical model termed SILC. SILC quantitatively predicts residue-specific binding affinities to nanoparticles and it expresses binding cooperativity as the cumulative result of pairwise residue effects. The model, which was parametrized for anionic SNPs and applied to experimental data of four IDP systems with distinctive binding behavior, successfully predicts differences in overall binding affinities, fine details of IDP-SNP affinity profiles, and site-directed mutagenesis effects with a spatial resolution at the individual residue level. The SILC model provides an analytical description of such types of fuzzy IDP-SNP complexes and may help advance understanding nanotoxicity and in vivo targeting of IDPs by specifically designed nanomaterials.

Extreme Nonuniform Sampling for Protein NMR Dynamics Studies in Minimal Time.

NMR spectroscopy is an extraordinarily rich source of quantitative dynamics of proteins in solution using spin relaxation or chemical exchange saturation transfer (CEST) experiments. However, 15N-CEST measurements require prolonged multidimensional, so-called pseudo-3D HSQC experiments where the pseudo dimension is a radio frequency offset Δω of a weak 15N saturation field. Nonuniform sampling (NUS) approaches have the potential to significantly speed up these measurements, but they also carry the risk of introducing serious artifacts and the systematic optimization of nonuniform sampling schedules has remained elusive. It is demonstrated here how this challenge can be addressed by using fitted cross-peaks of a reference 2D HSQC experiment as footprints, which are subsequently used to reconstruct cross-peak amplitudes of a pseudo-3D data set as a function of Δω by a linear least-squares fit. It is shown for protein Im7 how the approach can yield highly accurate CEST profiles based on an absolutely minimally sampled (AMS) data set allowing a speed-up of a factor 20-30. Spectrum-specific optimized nonuniform sampling (SONUS) schemes based on the Cramer-Rao lower bound metric were critical to achieve such a performance, revealing also more general properties of optimal sampling schedules. This is the first systematic exploration and optimization of NUS schedules for the dramatic speed-up of quantitative multidimensional NMR measurements that minimize unwanted errors.

Real-Time Pure Shift HSQC NMR for Untargeted Metabolomics.

Sensitivity and resolution are key considerations for NMR applications in general and for metabolomics in particular, where complex mixtures containing hundreds of metabolites over a large range of concentrations are commonly encountered. There is a strong demand for advanced methods that can provide maximal information in the shortest possible time frame. Here, we present the optimization and application of the recently introduced 2D real-time BIRD 1H-13C HSQC experiment for NMR-based metabolomics of aqueous samples at 13C natural abundance. For mouse urine samples, it is demonstrated how this real-time pure shift sensitivity-improved heteronuclear single quantum correlation method provides broadband homonuclear decoupling along the proton detection dimension and thereby significantly improves spectral resolution in regions that are affected by spectral overlap. Moreover, the collapse of the scalar multiplet structure of cross-peaks leads to a sensitivity gain of about 40-50% over a traditional 2D HSQC-SI experiment. The experiment works well over a range of magnetic field strengths and is particularly useful when resonance overlap in crowded regions of the HSQC spectra hampers accurate metabolite identification and quantitation.

Accurate and Efficient Determination of Unknown Metabolites in Metabolomics by NMR-Based Molecular Motif Identification.

Knowledge of the chemical identity of metabolite molecules is critical for the understanding of the complex biological systems to which they belong. Since metabolite identities and their concentrations are often directly linked to the phenotype, such information can be used to map biochemical pathways and understand their role in health and disease. A very large number of metabolites however are still unknown; i.e., their spectroscopic signatures do not match those in existing databases, suggesting unknown molecule identification is both imperative and challenging. Although metabolites are structurally highly diverse, the majority shares a rather limited number of structural motifs, which are defined by sets of 1H and 13C chemical shifts of the same spin system. This allows one to characterize unknown metabolites by a divide-and-conquer strategy that identifies their structural motifs first. Here, we present the structural motif-based approach "SUMMIT Motif" for the de novo identification of unknown molecular structures in complex mixtures, without the need for extensive purification, using NMR in tandem with two newly curated NMR molecular structural motif metabolomics databases (MSMMDBs). For the identification of structural motif(s), first, the 1H and 13C chemical shifts of all the individual spin systems are extracted from 2D and 3D NMR spectra of the complex mixture. Next, the molecular structural motifs are identified by querying these chemical shifts against the new MSMMDBs. One database, COLMAR MSMMDB, was derived from experimental NMR chemical shifts of known metabolites taken from the COLMAR metabolomics database, while the other MSMMDB, pNMR MSMMDB, is based on predicted chemical shifts of metabolites of several existing large metabolomics databases. For molecules consisting of multiple spin systems, spin systems are connected via long-range scalar J-couplings. When this motif-based identification method was applied to the hydrophilic extract of mouse bile fluid, two unknown metabolites could be successfully identified. This approach is both accurate and efficient for the identification of unknown metabolites and hence enables the discovery of new biochemical processes and potential biomarkers.

The Intracellular Loop of the Na+/Ca2+ Exchanger Contains an "Awareness Ribbon"-Shaped Two-Helix Bundle Domain.

The Na+/Ca2+ exchanger (NCX) is a ubiquitous single-chain membrane protein that plays a major role in regulating the intracellular Ca2+ homeostasis by the counter transport of Na+ and Ca2+ across the cell membrane. Other than its prokaryotic counterpart, which contains only the transmembrane domain and is self-sufficient as an active ion transporter, the eukaryotic NCX protein possesses in addition a large intracellular loop that senses intracellular calcium signals and controls the activation of ion transport across the membrane. This provides a necessary layer of regulation for the more complex function of eukaryotic cells. The Ca2+ sensor in the intracellular loop is known as the Ca2+-binding domain (CBD12). However, how the signaling of the allosteric intracellular Ca2+ binding propagates and results in transmembrane ion transportation still lacks a detailed explanation. Further structural and dynamics characterization of the intracellular loop flanking both sides of CBD12 is therefore imperative. Here, we report the identification and characterization of another structured domain that is N-terminal to CBD12 in the intracellular loop using solution nuclear magnetic resonance (NMR) spectroscopy. The atomistic structure of this domain reveals that two tandem long α-helices, connected by a short linker, form a stable crossover two-helix bundle (THB), resembling an "awareness ribbon". Considering the highly conserved amino acid sequence of the THB domain, the detailed structural and dynamics properties of the THB domain will be common among NCXs from different species and will contribute toward the understanding of the regulatory mechanism of eukaryotic Na+/Ca2+ exchangers.

Time-Resolved Protein Side-Chain Motions Unraveled by High-Resolution Relaxometry and Molecular Dynamics Simulations.

Motions of proteins are essential for the performance of their functions. Aliphatic protein side chains and their motions play critical roles in protein interactions: for recognition and binding of partner molecules at the surface or serving as an entropy reservoir within the hydrophobic core. Here, we present a new NMR method based on high-resolution relaxometry and high-field relaxation to determine quantitatively both motional amplitudes and time scales of methyl-bearing side chains in the picosecond-to-nanosecond range. We detect a wide variety of motions in isoleucine side chains in the protein ubiquitin. We unambiguously identify slow motions in the low nanosecond range, which, in conjunction with molecular dynamics computer simulations, could be assigned to transitions between rotamers. Our approach provides unmatched detailed insight into the motions of aliphatic side chains in proteins and provides a better understanding of the nature and functional role of protein side-chain motions.

Carbohydrate Background Removal in Metabolomics Samples.

NMR-based metabolomics is a powerful tool to comprehensively monitor chemical processes in biological systems. Key to its success is the accurate and complete metabolite identification and quantification. Due to the inherent complexity of most metabolic mixtures, NMR peak overlap can make data analysis of 1D or even 2D NMR spectra challenging, especially for the 1H spectral region from 3.2-4.5 ppm that is dominated by carbohydrates and their derivatives. To address this problem, we present an effective method for carbohydrate signal removal in complex metabolomics samples by oxidation via the addition of sodium periodate (NaIO4). In an optional step, reaction products can be removed with hydrazide beads. The treated samples show substantially simplified 1D and 2D NMR spectra with their carbohydrate peaks removed, whereas noncarbohydrate peaks remain mostly unaffected. This allows the unrestricted detection of those metabolites that are otherwise obscured by carbohydrate signals. The method was first tested for metabolite model mixtures and then applied to urine and serum samples. It revealed a significant number of noncarbohydrates that were made unambiguously observable and identifiable by this method. The proposed protocol is simple and it is suitable for high-throughput sample treatment for the comprehensive metabolite identification in a broad range of samples.

Statistical database analysis of the role of loop dynamics for protein-protein complex formation and allostery.

MOTIVATION: Protein loops show rich conformational dynamics properties on a wide range of timescales as they play an essential role for many cellular functions during protein-protein interactions and recognition processes. However, little is known about the detail behavior of loops upon protein binding including allostery. RESULTS: We report the loop motions and their dominant timescales for a library of 230 proteins that form protein-protein complexes using the ToeLoop predictor of loop dynamics. We applied the analysis to proteins in both their complex and free state and relate specific loop properties to their role in protein recognition. We observe a strong tendency of loops that move on relatively slow timescales of tens of ns to sub-µs to be directly involved in binding and recognition processes. Complex formation leads to a significant reduction in loop flexibility at the binding interface, but in a number of cases it can also trigger increased flexibility in distal loops in response to allosteric conformational changes. The importance of loop dynamics and allostery is highlighted by a case study of an antibody-antigen complex. Furthermore, we explored the relationship between loop dynamics and experimental binding affinities and found that a prevalence of high loop rigidity at the binding interface is an indicator of increased binding strength. AVAILABILITY AND IMPLEMENTATION: http://spin.ccic.ohio-state.edu/index.php/toeloopppi. CONTACT: bruschweiler.1@osu.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

miRNA-122 Protects Mice and Human Hepatocytes from Acetaminophen Toxicity by Regulating Cytochrome P450 Family 1 Subfamily A Member 2 and Family 2 Subfamily E Member 1 Expression.

Acetaminophen toxicity is a leading cause of acute liver failure (ALF). We found that miRNA-122 (miR-122) is down-regulated in liver biopsy specimens of patients with ALF and in acetaminophen-treated mice. A marked decrease in the primary miR-122 expression occurs in mice on acetaminophen overdose because of suppression of its key transactivators, hepatocyte nuclear factor (HNF)-4α and HNF6. More importantly, the mortality rates of male and female liver-specific miR-122 knockout (LKO) mice were significantly higher than control mice when injected i.p. with an acetaminophen dose not lethal to the control. LKO livers exhibited higher basal expression of cytochrome P450 family 2 subfamily E member 1 (CYP2E1) and cytochrome P450 family 1 subfamily A member 2 (CYP1A2) that convert acetaminophen to highly reactive N-acetyl-p-benzoquinone imine. Upregulation of Cyp1a2 primary transcript and mRNA in LKO mice correlated with the elevation of aryl hydrocarbon receptor (AHR) and mediator 1 (MED1), two transactivators of Cyp1a2. Analysis of ChIP-seq data in the ENCODE (Encyclopedia of DNA Element) database identified association of CCCTC-binding factor (CTCF) with Ahr promoter in mouse livers. Both MED1 and CTCF are validated conserved miR-122 targets. Furthermore, depletion of Ahr, Med1, or Ctcf in Mir122-/- hepatocytes reduced Cyp1a2 expression. Pulse-chase studies found that CYP2E1 protein level is upregulated in LKO hepatocytes. Notably, miR-122 depletion sensitized differentiated human HepaRG cells to acetaminophen toxicity that correlated with upregulation of AHR, MED1, and CYP1A2 expression. Collectively, these results reveal a critical role of miR-122 in acetaminophen detoxification and implicate its therapeutic potential in patients with ALF.