A single-cell atlas of human and mouse white adipose tissue.

White adipose tissue, once regarded as morphologically and functionally bland, is now recognized to be dynamic, plastic and heterogenous, and is involved in a wide array of biological processes including energy homeostasis, glucose and lipid handling, blood pressure control and host defence1. High-fat feeding and other metabolic stressors cause marked changes in adipose morphology, physiology and cellular composition1, and alterations in adiposity are associated with insulin resistance, dyslipidemia and type 2 diabetes2. Here we provide detailed cellular atlases of human and mouse subcutaneous and visceral white fat at single-cell resolution across a range of body weight. We identify subpopulations of adipocytes, adipose stem and progenitor cells, vascular and immune cells and demonstrate commonalities and differences across species and dietary conditions. We link specific cell types to increased risk of metabolic disease and provide an initial blueprint for a comprehensive set of interactions between individual cell types in the adipose niche in leanness and obesity. These data comprise an extensive resource for the exploration of genes, traits and cell types in the function of white adipose tissue across species, depots and nutritional conditions.

Glycosylation and Cardiovascular Diseases.

Cardiovascular disease (CVD) is the leading cause of death worldwide, accounting for approximately 18 million deaths in 2017. Coronary artery disease is the predominant cause of death from CVD, followed by stroke. Owing to recent technological advancements, glycans and glycosylation patterns of proteins have been investigated in association with CVD risk factors and clinical events. These studies have found significant associations of glycans as biomarkers of systemic inflammation and major CVD risk factors and events. While more limited, studies have also shown that glycans may be useful for monitoring response to anti-inflammatory therapies and may be responsive to changes in lifestyle, particularly in patients with chronic inflammatory diseases. Glycans capture summative risk information related to inflammatory, immune, and signaling pathways and are promising biomarkers for CVD risk prediction and therapeutic monitoring.

I-PINE web server: an integrative probabilistic NMR assignment system for proteins.

Various methods for understanding the structural and dynamic properties of proteins rely on the analysis of their NMR chemical shifts. These methods require the initial assignment of NMR signals to particular atoms in the sequence of the protein, a step that can be very time-consuming. The probabilistic interaction network of evidence (PINE) algorithm for automated assignment of backbone and side chain chemical shifts utilizes a Bayesian probabilistic network model that analyzes sequence data and peak lists from multiple NMR experiments. PINE, which is one of the most popular and reliable automated chemical shift assignment algorithms, has been available to the protein NMR community for longer than a decade. We announce here a new web server version of PINE, called Integrative PINE (I-PINE), which supports more types of NMR experiments than PINE (including three-dimensional nuclear Overhauser enhancement and four-dimensional J-coupling experiments) along with more comprehensive visualization of chemical shift based analysis of protein structure and dynamics. The I-PINE server is freely accessible at http://i-pine.nmrfam.wisc.edu . Help pages and tutorial including browser capability are available at: http://i-pine.nmrfam.wisc.edu/instruction.html . Sample data that can be used for testing the web server are available at: http://i-pine.nmrfam.wisc.edu/examples.html .

NMR-STAR: comprehensive ontology for representing, archiving and exchanging data from nuclear magnetic resonance spectroscopic experiments.

The growth of the biological nuclear magnetic resonance (NMR) field and the development of new experimental technology have mandated the revision and enlargement of the NMR-STAR ontology used to represent experiments, spectral and derived data, and supporting metadata. We present here a brief description of the NMR-STAR ontology and software tools for manipulating NMR-STAR data files, editing the files, extracting selected data, and creating data visualizations. Detailed information on these is accessible from the links provided.

Applications of Parametrized NMR Spin Systems of Small Molecules.

We have developed technology for producing accurate spectral fingerprints of small molecules through modeling of NMR spin system matrices to encapsulate their chemical shifts and scalar couplings. We describe here how libraries of these spin systems utilizing unique and reproducible atom numbering can be used to improve NMR-based ligand screening and metabolomics studies. We introduce new Web services that facilitate the analysis of NMR spectra of mixtures of small molecules to yield their identification and quantification. The library of parametrized compounds has been expanded to cover simulations of 1H NMR spectra at a variety of magnetic fields of more than 1100 compounds, included are many common metabolites and a library of drug-like molecular fragments used in ligand screening. The compound library and related Web services are freely available from http://gissmo.nmrfam.wisc.edu/ .

NMReDATA, a standard to report the NMR assignment and parameters of organic compounds.

Even though NMR has found countless applications in the field of small molecule characterization, there is no standard file format available for the NMR data relevant to structure characterization of small molecules. A new format is therefore introduced to associate the NMR parameters extracted from 1D and 2D spectra of organic compounds to the proposed chemical structure. These NMR parameters, which we shall call NMReDATA (for nuclear magnetic resonance extracted data), include chemical shift values, signal integrals, intensities, multiplicities, scalar coupling constants, lists of 2D correlations, relaxation times, and diffusion rates. The file format is an extension of the existing Structure Data Format, which is compatible with the commonly used MOL format. The association of an NMReDATA file with the raw and spectral data from which it originates constitutes an NMR record. This format is easily readable by humans and computers and provides a simple and efficient way for disseminating results of structural chemistry investigations, allowing automatic verification of published results, and for assisting the constitution of highly needed open-source structural databases.

Spin System Modeling of Nuclear Magnetic Resonance Spectra for Applications in Metabolomics and Small Molecule Screening.

The exceptionally rich information content of nuclear magnetic resonance (NMR) spectra is routinely used to identify and characterize molecules and molecular interactions in a wide range of applications, including clinical biomarker discovery, drug discovery, environmental chemistry, and metabolomics. The set of peak positions and intensities from a reference NMR spectrum generally serves as the identifying signature for a compound. Reference spectra normally are collected under specific conditions of pH, temperature, and magnetic field strength, because changes in conditions can distort the identifying signatures of compounds. A spin system matrix that parametrizes chemical shifts and coupling constants among spins provides a much richer feature set for a compound than a spectral signature based on peak positions and intensities. Spin system matrices expand the applicability of NMR spectral libraries beyond the specific conditions under which data were collected. In addition to being able to simulate spectra at any field strength, spin parameters can be adjusted to systematically explore alterations in chemical shift patterns due to variations in other experimental conditions, such as compound concentration, pH, or temperature. We present methodology and software for efficient interactive optimization of spin parameters against experimental 1D-1H NMR spectra of small molecules. We have used the software to generate spin system matrices for a set of key mammalian metabolites and are also using the software to parametrize spectra of small molecules used in NMR-based ligand screening. The software, along with optimized spin system matrix data for a growing number of compounds, is available from http://gissmo.nmrfam.wisc.edu/ .

Defining a two-pronged structural model for PB1 (Phox/Bem1p) domain interaction in plant auxin responses.

Phox/Bem1p (PB1) domains are universal structural modules that use surfaces of different charge for protein-protein association. In plants, PB1-mediated interactions of auxin response factors (ARF) and auxin/indole 3-acetic acid inducible proteins regulate transcriptional events modulated by the phytohormone auxin. Here we investigate the thermodynamic and structural basis for Arabidopsis thaliana ARF7 PB1 domain self-interaction. Isothermal titration calorimetry and NMR experiments indicate that key residues on both the basic and acidic faces of the PB1 domain contribute to and organize coordinately to stabilize protein-protein interactions. Calorimetric analysis of ARF7PB1 site-directed mutants defines a two-pronged electrostatic interaction. The canonical PB1 interaction between a lysine and a cluster of acidic residues provides one prong with an arginine and a second cluster of acidic residues defining the other prong. Evolutionary conservation of this core recognition feature and other co-varying interface sequences allows for versatile PB1-mediated interactions in auxin signaling.

Probabilistic validation of protein NMR chemical shift assignments.

Data validation plays an important role in ensuring the reliability and reproducibility of studies. NMR investigations of the functional properties, dynamics, chemical kinetics, and structures of proteins depend critically on the correctness of chemical shift assignments. We present a novel probabilistic method named ARECA for validating chemical shift assignments that relies on the nuclear Overhauser effect data . ARECA has been evaluated through its application to 26 case studies and has been shown to be complementary to, and usually more reliable than, approaches based on chemical shift databases. ARECA is available online at http://areca.nmrfam.wisc.edu/.

Integrative NMR for biomolecular research.

NMR spectroscopy is a powerful technique for determining structural and functional features of biomolecules in physiological solution as well as for observing their intermolecular interactions in real-time. However, complex steps associated with its practice have made the approach daunting for non-specialists. We introduce an NMR platform that makes biomolecular NMR spectroscopy much more accessible by integrating tools, databases, web services, and video tutorials that can be launched by simple installation of NMRFAM software packages or using a cross-platform virtual machine that can be run on any standard laptop or desktop computer. The software package can be downloaded freely from the NMRFAM software download page ( http://pine.nmrfam.wisc.edu/download_packages.html ), and detailed instructions are available from the Integrative NMR Video Tutorial page ( http://pine.nmrfam.wisc.edu/integrative.html ).

ADAPT-NMR 3.0: utilization of BEST-type triple-resonance NMR experiments to accelerate the process of data collection and assignment.

ADAPT-NMR (Assignment-directed Data collection Algorithm utilizing a Probabilistic Toolkit in NMR) is a software package whose Bayesian core uses on-the-fly chemical shift assignments to guide data acquisition by non-uniform sampling from a panel of through-bond NMR experiments. The new version of ADAPT-NMR (ADAPT-NMR v3.0) has the option of utilizing 2D tilted-plane versions of 3D fast spectral acquisition with BEST-type pulse sequences, while also retaining the capability of acquiring and processing data from tilted-plane versions of conventional sensitivity-enhanced experiments. The use of BEST experiments significantly reduces data collection times and leads to enhanced performance by ADAPT-NMR.

NMRFAM-SDF: a protein structure determination framework.

The computationally demanding nature of automated NMR structure determination necessitates a delicate balancing of factors that include the time complexity of data collection, the computational complexity of chemical shift assignments, and selection of proper optimization steps. During the past two decades the computational and algorithmic aspects of several discrete steps of the process have been addressed. Although no single comprehensive solution has emerged, the incorporation of a validation protocol has gained recognition as a necessary step for a robust automated approach. The need for validation becomes even more pronounced in cases of proteins with higher structural complexity, where potentially larger errors generated at each step can propagate and accumulate in the process of structure calculation, thereby significantly degrading the efficacy of any software framework. This paper introduces a complete framework for protein structure determination with NMR--from data acquisition to the structure determination. The aim is twofold: to simplify the structure determination process for non-NMR experts whenever feasible, while maintaining flexibility by providing a set of modules that validate each step, and to enable the assessment of error propagations. This framework, called NMRFAM-SDF (NMRFAM-Structure Determination Framework), and its various components are available for download from the NMRFAM website (http://nmrfam.wisc.edu/software.htm).

Genome-wide association analysis and Mendelian randomization proteomics identify drug targets for heart failure.

We conduct a large-scale meta-analysis of heart failure genome-wide association studies (GWAS) consisting of over 90,000 heart failure cases and more than 1 million control individuals of European ancestry to uncover novel genetic determinants for heart failure. Using the GWAS results and blood protein quantitative loci, we perform Mendelian randomization and colocalization analyses on human proteins to provide putative causal evidence for the role of druggable proteins in the genesis of heart failure. We identify 39 genome-wide significant heart failure risk variants, of which 18 are previously unreported. Using a combination of Mendelian randomization proteomics and genetic cis-only colocalization analyses, we identify 10 additional putatively causal genes for heart failure. Findings from GWAS and Mendelian randomization-proteomics identify seven (CAMK2D, PRKD1, PRKD3, MAPK3, TNFSF12, APOC3 and NAE1) proteins as potential targets for interventions to be used in primary prevention of heart failure.

Discovering cellular programs of intrinsic and extrinsic drivers of metabolic traits using LipocyteProfiler.

A primary obstacle in translating genetic associations with disease into therapeutic strategies is elucidating the cellular programs affected by genetic risk variants and effector genes. Here, we introduce LipocyteProfiler, a cardiometabolic-disease-oriented high-content image-based profiling tool that enables evaluation of thousands of morphological and cellular profiles that can be systematically linked to genes and genetic variants relevant to cardiometabolic disease. We show that LipocyteProfiler allows surveillance of diverse cellular programs by generating rich context- and process-specific cellular profiles across hepatocyte and adipocyte cell-state transitions. We use LipocyteProfiler to identify known and novel cellular mechanisms altered by polygenic risk of metabolic disease, including insulin resistance, fat distribution, and the polygenic contribution to lipodystrophy. LipocyteProfiler paves the way for large-scale forward and reverse deep phenotypic profiling in lipocytes and provides a framework for the unbiased identification of causal relationships between genetic variants and cellular programs relevant to human disease.

Inherited basis of visceral, abdominal subcutaneous and gluteofemoral fat depots.

For any given level of overall adiposity, individuals vary considerably in fat distribution. The inherited basis of fat distribution in the general population is not fully understood. Here, we study up to 38,965 UK Biobank participants with MRI-derived visceral (VAT), abdominal subcutaneous (ASAT), and gluteofemoral (GFAT) adipose tissue volumes. Because these fat depot volumes are highly correlated with BMI, we additionally study six local adiposity traits: VAT adjusted for BMI and height (VATadj), ASATadj, GFATadj, VAT/ASAT, VAT/GFAT, and ASAT/GFAT. We identify 250 independent common variants (39 newly-identified) associated with at least one trait, with many associations more pronounced in female participants. Rare variant association studies extend prior evidence for PDE3B as an important modulator of fat distribution. Local adiposity traits (1) highlight depot-specific genetic architecture and (2) enable construction of depot-specific polygenic scores that have divergent associations with type 2 diabetes and coronary artery disease. These results - using MRI-derived, BMI-independent measures of local adiposity - confirm fat distribution as a highly heritable trait with important implications for cardiometabolic health outcomes.

SARS2 simplified scores to estimate risk of hospitalization and death among patients with COVID-19.

Although models have been developed for predicting severity of COVID-19 from the medical history of patients, simplified models with good accuracy could be more practical. In this study, we examined utility of simpler models for estimating risk of hospitalization of patients with COVID-19 and mortality of these patients based on demographic characteristics (sex, age, race, median household income based on zip code) and smoking status of 12,347 patients who tested positive at Mass General Brigham centers. The corresponding electronic records were queried (02/26-07/14/2020) to construct derivation and validation cohorts. The derivation cohort was used to fit generalized linear models for estimating risk of hospitalization within 30 days of COVID-19 diagnosis and mortality within approximately 3 months for the hospitalized patients. In the validation cohort, the model resulted in c-statistics of 0.77 [95% CI 0.73-0.80] for hospitalization, and 0.84 [95% CI 0.74-0.94] for mortality among hospitalized patients. Higher risk was associated with older age, male sex, Black ethnicity, lower socioeconomic status, and current/past smoking status. The models can be applied to predict the absolute risks of hospitalization and mortality, and could aid in individualizing the decision making when detailed medical history of patients is not readily available.

Historical Controls in Randomized Clinical Trials: Opportunities and Challenges.

Randomized control trials (RCTs) with placebo are the gold standard for determining efficacy of novel pharmaceutical treatments. Since their inception, over 75 years ago, researchers have amassed a large body of underutilized data on outcomes in the placebo control arms of these trials. Although rare disease indications have used these historical placebo data as synthetic controls to reduce burden on patients and accelerate drug discovery, broad use of historical controls is in its infancy. Large-scale historical placebo data could be leveraged to benefit both drug developers and patients if warehoused and made more available to guide trial design and analysis. Here, we examine challenges in utilizing historical controls related to heterogeneity in trial design, outcome ascertainment, patient characteristics, and unmeasured pharmacogenomic effects. We then discuss the advantages and disadvantages of current approaches and propose a path forward to broader use of historical controls in RCTs.

NUScon: a community-driven platform for quantitative evaluation of nonuniform sampling in NMR.

Although the concepts of nonuniform sampling (NUS​​​​​​​) and non-Fourier spectral reconstruction in multidimensional NMR began to emerge 4 decades ago , it is only relatively recently that NUS has become more commonplace. Advantages of NUS include the ability to tailor experiments to reduce data collection time and to improve spectral quality, whether through detection of closely spaced peaks (i.e., "resolution") or peaks of weak intensity (i.e., "sensitivity"). Wider adoption of these methods is the result of improvements in computational performance, a growing abundance and flexibility of software, support from NMR spectrometer vendors, and the increased data sampling demands imposed by higher magnetic fields. However, the identification of best practices still remains a significant and unmet challenge. Unlike the discrete Fourier transform, non-Fourier methods used to reconstruct spectra from NUS data are nonlinear, depend on the complexity and nature of the signals, and lack quantitative or formal theory describing their performance. Seemingly subtle algorithmic differences may lead to significant variabilities in spectral qualities and artifacts. A community-based critical assessment of NUS challenge problems has been initiated, called the "Nonuniform Sampling Contest" (NUScon), with the objective of determining best practices for processing and analyzing NUS experiments. We address this objective by constructing challenges from NMR experiments that we inject with synthetic signals, and we process these challenges using workflows submitted by the community. In the initial rounds of NUScon our aim is to establish objective criteria for evaluating the quality of spectral reconstructions. We present here a software package for performing the quantitative analyses, and we present the results from the first two rounds of NUScon. We discuss the challenges that remain and present a roadmap for continued community-driven development with the ultimate aim of providing best practices in this rapidly evolving field. The NUScon software package and all data from evaluating the challenge problems are hosted on the NMRbox platform.

Quantum approximate Bayesian computation for NMR model inference.

Recent technological advances may lead to the development of small scale quantum computers capable of solving problems that cannot be tackled with classical computers. A limited number of algorithms has been proposed and their relevance to real world problems is a subject of active investigation. Analysis of many-body quantum system is particularly challenging for classical computers due to the exponential scaling of Hilbert space dimension with the number of particles. Hence, solving problems relevant to chemistry and condensed matter physics are expected to be the first successful applications of quantum computers. In this paper, we propose another class of problems from the quantum realm that can be solved efficiently on quantum computers: model inference for nuclear magnetic resonance (NMR) spectroscopy, which is important for biological and medical research. Our results are based on three interconnected studies. Firstly, we use methods from classical machine learning to analyze a dataset of NMR spectra of small molecules. We perform a stochastic neighborhood embedding and identify clusters of spectra, and demonstrate that these clusters are correlated with the covalent structure of the molecules. Secondly, we propose a simple and efficient method, aided by a quantum simulator, to extract the NMR spectrum of any hypothetical molecule described by a parametric Heisenberg model. Thirdly, we propose a simple variational Bayesian inference procedure for extracting Hamiltonian parameters of experimentally relevant NMR spectra.