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.

NMR 1H, 13C, 15N backbone resonance assignments of wild-type human K-Ras and its oncogenic mutants G12D and G12C bound to GTP.

Human K-Ras protein, which is a member of the GTPase Ras family, hydrolyzes GTP to GDP and concomitantly converts from its active to its inactive state. It is a key oncoprotein, because several mutations, particularly those at residue position 12, occur with a high frequency in a wide range of human cancers. The K-Ras protein is therefore an important target for developing therapeutic anti-cancer agents. In this work we report the almost complete sequence-specific resonance assignments of wild-type and the oncogenic G12C and G12D mutants in the GTP-complexed active forms, including the functionally important Switch I and Switch II regions. These assignments serve as the basis for a comprehensive functional dynamics study of wild-type K-Ras and its G12 mutants.

DEEP Picker1D and Voigt Fitter1D: a versatile tool set for the automated quantitative spectral deconvolution of complex 1D-NMR spectra.

The quantitative deconvolution of 1D-NMR spectra into individual resonances or peaks is a key step in many modern NMR workflows as it critically affects downstream analysis and interpretation. Depending on the complexity of the NMR spectrum, spectral deconvolution can be a notable challenge. Based on the recent deep neural network DEEP Picker and Voigt Fitter for 2D NMR spectral deconvolution, we present here an accurate, fully automated solution for 1D-NMR spectral analysis, including peak picking, fitting, and reconstruction. The method is demonstrated for complex 1D solution NMR spectra showing excellent performance also for spectral regions with multiple strong overlaps and a large dynamic range whose analysis is challenging for current computational methods. The new tool will help streamline 1D-NMR spectral analysis for a wide range of applications and expand their reach toward ever more complex molecular systems and their mixtures.

Excited-state observation of active K-Ras reveals differential structural dynamics of wild-type versus oncogenic G12D and G12C mutants.

Despite the prominent role of the K-Ras protein in many different types of human cancer, major gaps in atomic-level information severely limit our understanding of its functions in health and disease. Here, we report the quantitative backbone structural dynamics of K-Ras by solution nuclear magnetic resonance spectroscopy of the active state of wild-type K-Ras bound to guanosine triphosphate (GTP) nucleotide and two of its oncogenic P-loop mutants, G12D and G12C, using a new nanoparticle-assisted spin relaxation method, relaxation dispersion and chemical exchange saturation transfer experiments covering the entire range of timescales from picoseconds to milliseconds. Our combined experiments allow detection and analysis of the functionally critical Switch I and Switch II regions, which have previously remained largely unobservable by X-ray crystallography and nuclear magnetic resonance spectroscopy. Our data reveal cooperative transitions of K-Ras·GTP to a highly dynamic excited state that closely resembles the partially disordered K-Ras·GDP state. These results advance our understanding of differential GTPase activities and signaling properties of the wild type versus mutants and may thus guide new strategies for the development of therapeutics.

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.

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.

Differential metabolism between biofilm and suspended Pseudomonas aeruginosa cultures in bovine synovial fluid by 2D NMR-based metabolomics.

Total joint arthroplasty is a common surgical procedure resulting in improved quality of life; however, a leading cause of surgery failure is infection. Periprosthetic joint infections often involve biofilms, making treatment challenging. The metabolic state of pathogens in the joint space and mechanism of their tolerance to antibiotics and host defenses are not well understood. Thus, there is a critical need for increased understanding of the physiological state of pathogens in the joint space for development of improved treatment strategies toward better patient outcomes. Here, we present a quantitative, untargeted NMR-based metabolomics strategy for Pseudomonas aeruginosa suspended culture and biofilm phenotypes grown in bovine synovial fluid as a model system. Significant differences in metabolic pathways were found between the suspended culture and biofilm phenotypes including creatine, glutathione, alanine, and choline metabolism and the tricarboxylic acid cycle. We also identified 21 unique metabolites with the presence of P. aeruginosa in synovial fluid and one uniquely present with the biofilm phenotype in synovial fluid. If translatable in vivo, these unique metabolite and pathway differences have the potential for further development to serve as targets for P. aeruginosa and biofilm control in synovial fluid.

Quantitative Multistate Binding Model of Silica Nanoparticle-Protein Interactions Obtained from Multinuclear Spin Relaxation.

Nanoparticle-assisted NMR spin relaxation (NASR), which makes internal protein dynamics in solution directly observable on nanosecond to microsecond time scales, has been applied to different nuclei and relaxation processes of the same protein system. A model is presented describing the transient interaction between ubiquitin and anionic silica nanoparticles for the unified interpretation of a wealth of experimental data including 2H, 13C, and 15N relaxation of methyl side chain and backbone moieties. The best model, implemented using a stochastic Liouville equation, describes the exchange process via an intermediary encounter state between free and fully nanoparticle-bound protein. The implication of the three-state binding model on the interpretation of NASR data is discussed.

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.

NOAH-(15N/13C)-CEST NMR supersequence for dynamics studies of biomolecules.

An NMR supersequence is introduced for the rapid acquisition of 15N-CEST and methyl-13C-CEST experiments in the same pulse sequence for applications to proteins. The high sensitivity and accuracy allows the simultaneous quantitative characterization of backbone and side-chain dynamics on the millisecond timescale ideal for routine screening for alternative protein states.

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.

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.

Cadaverine Is a Switch in the Lysine Degradation Pathway in Pseudomonas aeruginosa Biofilm Identified by Untargeted Metabolomics.

There is a critical need to accurately diagnose, prevent, and treat biofilms in humans. The biofilm forming P. aeruginosa bacteria can cause acute and chronic infections, which are difficult to treat due to their ability to evade host defenses along with an inherent antibiotic-tolerance. Using an untargeted NMR-based metabolomics approach, we identified statistically significant differences in 52 metabolites between P. aeruginosa grown in the planktonic and lawn biofilm states. Among them, the metabolites of the cadaverine branch of the lysine degradation pathway were systematically decreased in biofilm. Exogenous supplementation of cadaverine caused significantly increased planktonic growth, decreased biofilm accumulation by 49% and led to altered biofilm morphology, converting to a pellicle biofilm at the air-liquid interface. Our findings show how metabolic pathway differences directly affect the growth mode in P. aeruginosa and could support interventional strategies to control biofilm formation.

DEEP picker is a deep neural network for accurate deconvolution of complex two-dimensional NMR spectra.

The analysis of nuclear magnetic resonance (NMR) spectra for the comprehensive and unambiguous identification and characterization of peaks is a difficult, but critically important step in all NMR analyses of complex biological molecular systems. Here, we introduce DEEP Picker, a deep neural network (DNN)-based approach for peak picking and spectral deconvolution which semi-automates the analysis of two-dimensional NMR spectra. DEEP Picker includes 8 hidden convolutional layers and was trained on a large number of synthetic spectra of known composition with variable degrees of crowdedness. We show that our method is able to correctly identify overlapping peaks, including ones that are challenging for expert spectroscopists and existing computational methods alike. We demonstrate the utility of DEEP Picker on NMR spectra of folded and intrinsically disordered proteins as well as a complex metabolomics mixture, and show how it provides access to valuable NMR information. DEEP Picker should facilitate the semi-automation and standardization of protocols for better consistency and sharing of results within the scientific community.

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.

NMR Spin Relaxation Theory of Biomolecules Undergoing Highly Asymmetric Exchange with Large Interaction Partners.

The transient interactions of proteins and other molecules with much larger structures, such as synthetic or biological nanoparticles, lead to certain types of enhanced nuclear magnetic resonance (NMR) spin relaxation effects, which can be accurately measured by multidimensional solution NMR techniques. These relaxation effects provide new information about the nanostructures and the protein, their interactions, internal dynamics, and associated kinetic and thermodynamic parameters, such as exchange rates and populations. Although theoretical treatments exist that cover either the fast or slow exchange limits, a theoretical treatment that applies to all practically relevant exchange processes is still missing. A unified theoretical framework is presented for this purpose based on a stochastic Liouville equation (SLE). It covers nuclear spin dynamics, overall rotational diffusion of both the protein and the nanostructure, the exchange process between a free state and a bound state, and internal protein dynamics. Although the numerical implementation of the SLE typically involves large matrices, it is shown here that it is computationally still tractable for situations relevant in practice. Application of the theory demonstrates how transverse relaxation is substantially impacted by the kinetics of binding on a wide range of exchange timescales. It is further shown that when exchange occurs on the appropriate timescale, transverse relaxation is able to report on internal dynamics far slower than observable by traditional transverse relaxation experiments. The SLE will allow the realistic and quantitative interpretation of experimental NMR data reporting about transient protein-nanoparticle interactions, thereby providing a powerful tool for the characterization of protein dynamics modes on a vast range of timescales including motions that may be functionally relevant.

Systematic Differences between Current Molecular Dynamics Force Fields To Represent Local Properties of Intrinsically Disordered Proteins.

The prevalence of intrinsically disordered proteins (IDPs) and protein regions in structural biology has prompted the recent development of molecular dynamics (MD) force fields for the more realistic representations of such systems. Using experimental nuclear magnetic resonance backbone scalar 3J-coupling constants of the intrinsically disordered proteins α-synuclein and amyloid-ß in their native aqueous environment as a metric, we compare the performance of four recent MD force fields, namely, AMBER ff14SB, CHARMM C36m, AMBER ff99SB-disp, and AMBER ff99SBnmr2, by partitioning the polypeptides into an overlapping series of heptapeptides for which a cumulative total of 276 µs MD simulations were performed. The results show substantial differences between the different force fields at the individual residue level. Except for ff99SBnmr2, the force fields systematically underestimate the scalar 3J(HN,Hα)-couplings due to an underrepresentation of ß-conformations and an overrepresentation of either α- or PPII conformations. The study demonstrates that the incorporation of coil library information in modern MD force fields, as shown here for ff99SBnmr2, provides substantially improved performance and more realistic sampling of the local backbone dihedral angles of IDPs as reflected by the good accuracy of the computed scalar 3J(HN,Hα)-couplings with less than 0.5 Hz error. Such force fields will enable a better understanding of how structural dynamics and thermodynamics influence the IDP function. Although the methodology based on heptapeptides used here does not allow the assessment of potential intramolecular long-range interactions, its computational affordability permits well-converged simulations that can be easily parallelized. This should make the quantitative validation of intrinsic disorder observed in MD simulations of polypeptides with experimental scalar J-couplings widely applicable.

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.