Emergence of structures in neuronal network activities.

Nonlinear responses of individual neurons are both experimentally established and considered fundamental for the functioning of neuronal circuitry. Consequently, one may envisage the collective dynamics of large networks of neurons exhibiting a large repertoire of nonlinear behaviors. However, an ongoing and central challenge in the modeling of neural dynamics involves the trade-off between tractability and biological realism.

Perspective: use and reuse of NMR-based metabolomics data: what works and what remains challenging.

Background: The National Cancer Institute issued a Request for Information (RFI; NOT-CA-23-007) in October 2022, soliciting input on using and reusing metabolomics data. This RFI aimed to gather input on best practices for metabolomics data storage, management, and use/reuse.

Aim Of Review: The nuclear magnetic resonance (NMR) Interest Group within the Metabolomics Association of North America (MANA) prepared a set of recommendations regarding the deposition, archiving, use, and reuse of NMR-based and, to a lesser extent, mass spectrometry (MS)-based metabolomics datasets.

Whole blood RNA extraction efficiency contributes to variability in RNA sequencing data sets.

Numerous methodologies are used for blood RNA extraction, and large quantitative differences in recovered RNA content are reported. We evaluated three archived data sets to determine how extraction methodologies might influence mRNA and lncRNA sequencing results. The total quantity of RNA recovered /ml of blood affects RNA sequencing by impacting the recovery of weakly expressed mRNA, and lncRNA transcripts.

Biological Magnetic Resonance Data Bank.

The Biological Magnetic Resonance Data Bank (BMRB, https://bmrb.io) is the international open data repository for biomolecular nuclear magnetic resonance (NMR) data. Comprised of both empirical and derived data, BMRB has applications in the study of biomacromolecular structure and dynamics, biomolecular interactions, drug discovery, intrinsically disordered proteins, natural products, biomarkers, and metabolomics.

NMR and Metabolomics-A Roadmap for the Future.

Metabolomics investigates global metabolic alterations associated with chemical, biological, physiological, or pathological processes. These metabolic changes are measured with various analytical platforms including liquid chromatography-mass spectrometry (LC-MS), gas chromatography-mass spectrometry (GC-MS) and nuclear magnetic resonance spectroscopy (NMR). While LC-MS methods are becoming increasingly popular in the field of metabolomics (accounting for more than 70% of published metabolomics studies to date), there are considerable benefits and advantages to NMR-based methods for metabolomic studies.

Spectrum dependency to rate and spike timing in neuronal spike trains.

Background: Spike trains are series of interspike intervals in a specific order that can be characterized by their probability distributions and order in time which refer to the concepts of rate and spike timing features. Periodic structure in the spike train can be reflected in oscillatory activities. Thus, there is a direct link between oscillator activities and the spike train.

Merging NMR Data and Computation Facilitates Data-Centered Research.

The Biological Magnetic Resonance Data Bank (BMRB) has served the NMR structural biology community for 40 years, and has been instrumental in the development of many widely-used tools. It fosters the reuse of data resources in structural biology by embodying the FAIR data principles (Findable, Accessible, Inter-operable, and Re-usable). NMRbox is less than a decade old, but complements BMRB by providing NMR software and high-performance computing resources, facilitating the reuse of software resources.

Anomalous amide proton chemical shifts as signatures of hydrogen bonding to aromatic sidechains.

Hydrogen bonding between an amide group and the p- cloud of an aromatic ring was first identified in a protein in the 1980s. Subsequent surveys of high-resolution X-ray crystal structures found multiple instances, but their preponderance was determined to be infrequent. Hydrogen atoms participating in a hydrogen bond to the p- cloud of an aromatic ring are expected to experience an upfield chemical shift arising from a shielding ring current shift.

Strategies for detecting and identifying biological signals amidst the variation commonly found in RNA sequencing data.

Background: RNA sequencing analysis focus on the detection of differential gene expression changes that meet a two-fold minimum change between groups. The variability present in RNA sequencing data may obscure the detection of valuable information when specific genes within certain samples display large expression variability. This paper develops methods that apply variance and dispersion estimates to intra-group data to identify genes with expression values that diverge from the group envelope.

Coordinated analysis of exon and intron data reveals novel differential gene expression changes.

RNA-Seq expression analysis currently relies primarily upon exon expression data. The recognized role of introns during translation, and the presence of substantial RNA-Seq counts attributable to introns, provide the rationale for the simultaneous consideration of both exon and intron data. We describe here a method for the coordinated analysis of exon and intron data by investigating their relationship within individual genes and across samples, while taking into account changes in both variability and expression level.

Probabilistic identification of saccharide moieties in biomolecules and their protein complexes.

The chemical composition of saccharide complexes underlies their biomedical activities as biomarkers for cardiometabolic disease, various types of cancer, and other conditions. However, because these molecules may undergo major structural modifications, distinguishing between compounds of saccharide and non-saccharide origin becomes a challenging computational problem that hinders the aggregation of information about their bioactive moieties. We have developed an algorithm and software package called "Cheminformatics Tool for Probabilistic Identification of Carbohydrates" (CTPIC) that analyzes the covalent structure of a compound to yield a probabilistic measure for distinguishing saccharides and saccharide-derivatives from non-saccharides.

Fragment screening targeting Ebola virus nucleoprotein C-terminal domain identifies lead candidates.

The Ebola Virus is a causative agent of viral hemorrhagic fever outbreaks and a potential global health risk. The outbreak in West Africa (2013-2016) led to 11,000+ deaths and 30,000+ Ebola infected individuals. The current outbreak in the Democratic Republic of Congo (DRC) with 3000+ confirmed cases and 2000+ deaths attributed to Ebola virus infections provides a reminder that innovative countermeasures are still needed.

Metabolic Changes in Synaptosomes in an Animal Model of Schizophrenia Revealed by H and H,C NMR Spectroscopy.

Synaptosomes are isolated nerve terminals that contain synaptic components, including neurotransmitters, metabolites, adhesion/fusion proteins, and nerve terminal receptors. The essential role of synaptosomes in neurotransmission has stimulated keen interest in understanding both their proteomic and metabolic composition. Mass spectrometric (MS) quantification of synaptosomes has illuminated their proteomic composition, but the determination of the metabolic composition by MS has been met with limited success.

Tools for Enhanced NMR-Based Metabolomics Analysis.

Metabolomics is the study of profiles of small molecules in biological fluids, cells, or organs. These profiles can be thought of as the "fingerprints" left behind from chemical processes occurring in biological systems. Because of its potential for groundbreaking applications in disease diagnostics, biomarker discovery, and systems biology, metabolomics has emerged as a rapidly growing area of research.

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.

Automated evaluation of consistency within the PubChem Compound database.

Identification of discrepant data in aggregated databases is a key step in data curation and remediation. We have applied the ALATIS approach, which is based on the international chemical shift identifier (InChI) model, to the full PubChem Compound database to generate unique and reproducible compound and atom identifiers for all entries for which three-dimensional structures were available. This exercise also served to identify entries with discrepancies between structures and chemical formulas or InChI strings.

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.

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.

Caloric Restriction Engages Hepatic RNA Processing Mechanisms in Rhesus Monkeys.

Caloric restriction (CR) extends lifespan and delays the onset of age-related disorders in diverse species. Metabolic regulatory pathways have been implicated in the mechanisms of CR, but the molecular details have not been elucidated. Here, we show that CR engages RNA processing of genes associated with a highly integrated reprogramming of hepatic metabolism.

NMR-Based Identification of Metabolites in Polar and Non-Polar Extracts of Avian Liver.

Metabolites present in liver provide important clues regarding the physiological state of an organism. The aim of this work was to evaluate a protocol for high-throughput NMR-based analysis of polar and non-polar metabolites from a small quantity of liver tissue. We extracted the tissue with a methanol/chloroform/water mixture and isolated the polar metabolites from the methanol/water layer and the non-polar metabolites from the chloroform layer.

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.

Unique identifiers for small molecules enable rigorous labeling of their atoms.

Rigorous characterization of small organic molecules in terms of their structural and biological properties is vital to biomedical research. The three-dimensional structure of a molecule, its 'photo ID', is inefficient for searching and matching tasks. Instead, identifiers play a key role in accessing compound data.

NMRbox: A Resource for Biomolecular NMR Computation.

Advances in computation have been enabling many recent advances in biomolecular applications of NMR. Due to the wide diversity of applications of NMR, the number and variety of software packages for processing and analyzing NMR data is quite large, with labs relying on dozens, if not hundreds of software packages. Discovery, acquisition, installation, and maintenance of all these packages is a burdensome task.

Increasing rigor in NMR-based metabolomics through validated and open source tools.

The metabolome, the collection of small molecules associated with an organism, is a growing subject of inquiry, with the data utilized for data-intensive systems biology, disease diagnostics, biomarker discovery, and the broader characterization of small molecules in mixtures. Owing to their close proximity to the functional endpoints that govern an organism's phenotype, metabolites are highly informative about functional states. The field of metabolomics identifies and quantifies endogenous and exogenous metabolites in biological samples.