Disease related changes in ATAC-seq of iPSC-derived motor neuron lines from ALS patients and controls.

Amyotrophic Lateral Sclerosis (ALS), like many other neurodegenerative diseases, is highly heritable, but with only a small fraction of cases explained by monogenic disease alleles. To better understand sporadic ALS, we report epigenomic profiles, as measured by ATAC-seq, of motor neuron cultures derived from a diverse group of 380 ALS patients and 80 healthy controls. We find that chromatin accessibility is heavily influenced by sex, the iPSC cell type of origin, ancestry, and the inherent variance arising from sequencing. Once these covariates are corrected for, we are able to identify ALS-specific signals in the data. Additionally, we find that the ATAC-seq data is able to predict ALS disease progression rates with similar accuracy to methods based on biomarkers and clinical status. These results suggest that iPSC-derived motor neurons recapitulate important disease-relevant epigenomic changes.

In Silico Discovery of Group II Intron RNA Splicing Inhibitors.

Here, we describe the discovery of compounds that inhibit self-splicing in group II introns. Using docking calculations, we targeted the catalytic active site within the Oceanobacillus iheyensis group IIC intron and virtually screened a library of lead-like compounds. From this initial virtual screen, we identified three unique scaffolds that inhibit splicing in vitro. Additional tests revealed that an analog of the lead scaffold inhibits splicing in an intron-dependent manner. Furthermore, this analog exhibited activity against the group II intron from a different class: the yeast ai5γ IIB intron. The splicing inhibitors we identified could serve as chemical tools for developing group II intron-targeted antifungals, and, more broadly, our results highlight the potential of in silico techniques for identifying bioactive hits against structured and functionally complex RNAs.

Structural basis for control of bacterial RNA polymerase pausing by a riboswitch and its ligand.

Folding of nascent transcripts can be modulated by the RNA polymerase (RNAP) that carries out their transcription, and vice versa. A pause of RNAP during transcription of a preQ1 riboswitch (termed que-PEC) is stabilized by a previously characterized template consensus sequence and the ligand-free conformation of the nascent RNA. Ligand binding to the riboswitch induces RNAP pause release and downstream transcription termination; however, the mechanism by which riboswitch folding modulates pausing is unclear. Here, we report single-particle cryo-electron microscopy reconstructions of que-PEC in ligand-free and ligand-bound states. In the absence of preQ1, the RNA transcript is in an unexpected hyper-translocated state, preventing downstream nucleotide incorporation. Strikingly, on ligand binding, the riboswitch rotates around its helical axis, expanding the surrounding RNAP exit channel and repositioning the transcript for elongation. Our study reveals the tight coupling by which nascent RNA structures and their ligands can functionally regulate the macromolecular transcription machinery.

Collaborative Learning Communities With Medical Students as Teachers.

In recent years, peer-assisted learning has emerged as a new and effective medical education modality. Near-peer tutoring utilizes a senior student serving as an instructor to a junior student. In 2019, the University of California, Irvine, School of Medicine (UCISOM) implemented a near-peer tutoring model beginning with first-year anatomy and physiology curricula. Following a successful pilot program, UCISOM launched a full-fledged near-peer tutoring program in 2020 named Collaborative Learning Communities (CLC) with Medical Students as Teachers. The rollout of CLC occurred in phases. In 2020, second-year medical students led the program for first-year students; in 2021, an additional program was led by third-year medical students for second-year students; in 2022, the program expanded to third-year medical students led by fourth-year students. Each program serves the unique learning needs of each student class, utilizing evidence-based teaching practices while allowing the opportunity for mentorship, interclass connectedness, and refinement of the tutor's teaching skills. In this paper, we describe the creation of CLC, its goals, leadership and curricular structure, and its various benefits, challenges, and limitations.

Lifetime of ground conformational state determines the activity of structured RNA.

Biomolecules continually sample alternative conformations. Consequently, even the most energetically favored ground conformational state has a finite lifetime. Here, we show that, in addition to the 3D structure, the lifetime of a ground conformational state determines its biological activity. Using hydrogen-deuterium exchange nuclear magnetic resonance spectroscopy, we found that Zika virus exoribonuclease-resistant RNA (xrRNA) encodes a ground conformational state with a lifetime that is ~105-107 longer than that of canonical base pairs. Mutations that shorten the apparent lifetime of the ground state without affecting its 3D structure decreased exoribonuclease resistance in vitro and impaired virus replication in cells. Additionally, we observed this exceptionally long-lived ground state in xrRNAs from diverse infectious mosquito-borne flaviviruses. These results demonstrate the biological significance of the lifetime of a preorganized ground state and further suggest that elucidating the lifetimes of dominant 3D structures of biomolecules may be crucial for understanding their behaviors and functions.

Per- and polyfluoroalkyl substances fate and transport at a wastewater treatment plant with a collocated sewage sludge incinerator.

This study aims to understand the fate and transport of per- and polyfluoroalkyl substances (PFAS) and inorganic fluoride (IF) at an undisclosed municipal wastewater treatment plant (WWTP) operating a sewage sludge incinerator (SSI). A robust statistical analysis characterized concentrations and mass flows at all WWTP and SSI primary influents/effluents, including thermal-treatment derived airborne emissions. WWTP-level net mass flows (NMFs) of total PFAS were not statistically different from zero. SSI-level NMFs indicate that PFAS, and specifically perfluoroalkyl acids (PFAAs), are being broken down. The NMF of perfluoroalkyl sulfonic acids (PFSAs; -274 ± 34 mg/day) was statistically significant. The observed breakdown primarily occurred in the sewage sludge. However, the total PFAS destruction and removal efficiency of 51 % indicates the SSI may inadequately remove PFAS. The statistically significant IF source (NMF = 16 ± 4.2 kg/day) compared to the sink of PFAS as fluoride (NMF = -0.00036 kg/day) suggests that other fluorine-containing substances are breaking down in the SSI. WWTP PFAS mass discharges were primarily to the aquatic environment (>99 %), with <0.5 % emitted to the atmosphere/landfill. Emission rates for formerly phased-out PFOS and PFOA were compared to previously reported levels. Given the environmental persistence of these compounds, the observed decreases in PFOS and PFOA discharge rates from prior reports implies regional/local differences in emissions or possibly their accumulation elsewhere. PFAS were observed in stack gas emissions, but modestly contributed to NMFs and showed negligible contribution to ambient air concentrations observed downwind.

Large-scale differentiation of iPSC-derived motor neurons from ALS and control subjects.

Using induced pluripotent stem cells (iPSCs) to understand the mechanisms of neurological disease holds great promise; however, there is a lack of well-curated lines from a large array of participants. Answer ALS has generated over 1,000 iPSC lines from control and amyotrophic lateral sclerosis (ALS) patients along with clinical and whole-genome sequencing data. The current report summarizes cell marker and gene expression in motor neuron cultures derived from 92 healthy control and 341 ALS participants using a 32-day differentiation protocol. This is the largest set of iPSCs to be differentiated into motor neurons, and characterization suggests that cell composition and sex are significant sources of variability that need to be carefully controlled for in future studies. These data are reported as a resource for the scientific community that will utilize Answer ALS data for disease modeling using a wider array of omics being made available for these samples.

Using Selectively Scaled Molecular Dynamics Simulations to Assess Ligand Poses in RNA Aptamers.

Predicting the structure (or pose) of RNA-ligand complexes is an important problem in RNA structural biology. Although one could use computational docking to rapidly sample putative poses of RNA-ligand complexes, accurately discriminating the native-like poses from non-native, decoy poses remains a formidable challenge. Here, we started from the assumption that native-like RNA-ligand poses are less likely to dissociate during molecular dynamics simulations, and then we used enhanced simulations to promote ligand unbinding for diverse poses of a handful of RNA aptamer-ligand complexes. By fitting unbinding profiles obtained from the simulations to a single exponential, we identified the characteristic decay time (τ) as particularly effective at resolving native poses from decoys. We also found that a simple regression model trained to predict the simulation-derived parameters directly from structure could also discriminate ligand poses for similar RNA aptamers. Characterizing the unbinding properties of individual poses may thus be an effective strategy for enhancing pose prediction for ligands interacting with RNA aptamers. A similar strategy might be applicable to other ligandable RNAs; however, further analysis will be required to confirm this hypothesis.

Using Unassigned NMR Chemical Shifts to Model RNA Secondary Structure.

NMR-derived chemical shifts are sensitive probes of RNA structure. However, the need to assign NMR spectra hampers their utility as a direct source of structural information. In this report, we describe a simple method that uses unassigned 2D NMR spectra to model the secondary structure of RNAs. As in the case of assigned chemical shifts, we could use unassigned chemical shift data to reweight conformational libraries such that the highest weighted structure closely resembles their reference NMR structure. Furthermore, the application of our approach to the 3'- and 5'-UTR of the SARS-CoV-2 genome yields structures that are, for the most part, consistent with the secondary structure models derived from chemical probing data. Therefore, we expect the framework we describe here will be useful as a general strategy for rapidly generating preliminary structural RNA models directly from unassigned 2D NMR spectra. As we demonstrated for the 337-nt and 472-nt UTRs of SARS-CoV-2, our approach could be especially valuable for modeling the secondary structures of large RNA.

Answer ALS, a large-scale resource for sporadic and familial ALS combining clinical and multi-omics data from induced pluripotent cell lines.

Answer ALS is a biological and clinical resource of patient-derived, induced pluripotent stem (iPS) cell lines, multi-omic data derived from iPS neurons and longitudinal clinical and smartphone data from over 1,000 patients with ALS. This resource provides population-level biological and clinical data that may be employed to identify clinical-molecular-biochemical subtypes of amyotrophic lateral sclerosis (ALS). A unique smartphone-based system was employed to collect deep clinical data, including fine motor activity, speech, breathing and linguistics/cognition. The iPS spinal neurons were blood derived from each patient and these cells underwent multi-omic analytics including whole-genome sequencing, RNA transcriptomics, ATAC-sequencing and proteomics. The intent of these data is for the generation of integrated clinical and biological signatures using bioinformatics, statistics and computational biology to establish patterns that may lead to a better understanding of the underlying mechanisms of disease, including subgroup identification. A web portal for open-source sharing of all data was developed for widespread community-based data analytics.

An integrated multi-omic analysis of iPSC-derived motor neurons from C9ORF72 ALS patients.

Neurodegenerative diseases are challenging for systems biology because of the lack of reliable animal models or patient samples at early disease stages. Induced pluripotent stem cells (iPSCs) could address these challenges. We investigated DNA, RNA, epigenetics, and proteins in iPSC-derived motor neurons from patients with ALS carrying hexanucleotide expansions in C9ORF72. Using integrative computational methods combining all omics datasets, we identified novel and known dysregulated pathways. We used a C9ORF72 Drosophila model to distinguish pathways contributing to disease phenotypes from compensatory ones and confirmed alterations in some pathways in postmortem spinal cord tissue of patients with ALS. A different differentiation protocol was used to derive a separate set of C9ORF72 and control motor neurons. Many individual -omics differed by protocol, but some core dysregulated pathways were consistent. This strategy of analyzing patient-specific neurons provides disease-related outcomes with small numbers of heterogeneous lines and reduces variation from single-omics to elucidate network-based signatures.

Navigating Chemical Space by Interfacing Generative Artificial Intelligence and Molecular Docking.

Here, we report the implementation and application of a simple, structure-aware framework to generate target-specific screening libraries. Our approach combines advances in generative artificial intelligence (AI) with conventional molecular docking to explore chemical space conditioned on the unique physicochemical properties of the active site of a biomolecular target. As a demonstration, we used our framework, which we refer to as sample-and-dock, to construct focused libraries for cyclin-dependent kinase type-2 (CDK2) and the active site of the main protease (Mpro) of the SARS-CoV-2 virus. We envision that the sample-and-dock framework could be used to generate theoretical maps of the chemical space specific to a given target and so provide information about its molecular recognition characteristics.

Probabilistic Modeling of RNA Ensembles Using NMR Chemical Shifts.

NMR-derived chemical shifts are structural fingerprints that are sensitive to the underlying conformational distributions of molecules. Thus, chemical shift data are now routinely used to infer the dynamical or conformational ensembles of peptides and proteins. However, for RNAs, techniques for inferring their conformational ensembles from chemical shift data have received less attention. Here, we used chemical shift data and the Bayesian/maximum entropy (BME) approach to model the secondary structure ensembles of several single-stranded RNAs. Inspection of the resulting ensembles indicates that the secondary structure of the highest weighted (most probable) conformer in the ensemble typically resembled the known NMR structure. Furthermore, using apo chemical shifts measured for the HIV-1 TAR RNA, we found that our framework reproduces the expected structure yet predicts the existence of a previously unobserved base pair, which we speculate may be sampled transiently. We expect that the chemical shift-based BME (CS-BME) framework we describe here should find utility as a general strategy for modeling RNA ensembles using chemical shift data.

Quantum Mechanics Helps Uncover Atypical Recognition Features in the Flavin Mononucleotide Riboswitch.

Estimating the binding energies of small molecules to RNA could help uncover their molecular recognition characteristics and be used to rationally design RNA-targeting chemical probes. Here, we leveraged the ability of the fragment molecular orbital (FMO) method to provide detailed pairwise energetic information to examine the interactions between the aptamer domain of the flavin mononucleotide (FMN)-responsive riboswitch and small-molecule ligands. After developing an efficient protocol for executing high-level FMO calculations on RNA-ligand complexes, we applied our protocol to nine FMN-aptamer-ligand complexes. We then used the results to identify "hot-spots" within the aptamer and decomposed pairwise interactions between the hot-spot residues and the ligands. Interestingly, we found that several of these hot-spot residues interact with the ligands via atypical CH···O hydrogen bonds and anion-π contacts, as well as (face-to-edge) T-shaped π-π interactions. We envision that our results should pave the way for the wider and more prominent use of FMO calculations to study structure-energy relationships in diverse RNA-ligand systems, which in turn may provide a basis for dissecting the molecular recognition characteristics of RNAs.

Mining for Ligandable Cavities in RNA.

Identifying potential ligand binding cavities is a critical step in structure-based screening of biomolecular targets. Cavity mapping methods can detect such binding cavities; however, for ribonucleic acid (RNA) targets, determining which of the detected cavities are "ligandable" remains an unsolved challenge. In this study, we trained a set of machine learning classifiers to distinguish ligandable RNA cavities from decoy cavities. Application of our classifiers to two independent test sets demonstrated that we could recover ligandable cavities from decoys with an AUC > 0.83. Interestingly, when we applied our classifiers to a library of modeled structures of the HIV-1 transactivation response (TAR) element RNA, we found that several of the conformers that harbored cavities with high ligandability scores resembled known holo-TAR structures. On the basis of our results, we envision that our classifiers could find utility as a tool to parse RNA structures and prospectively mine for ligandable binding cavities and, in so doing, facilitate structure-based virtual screening efforts against RNA drug targets.

CS-Annotate: A Tool for Using NMR Chemical Shifts to Annotate RNA Structure.

Here, we introduce CS-Annotate, a tool that uses assigned NMR chemical shifts to annotate structural features in RNA. At its core, CS-Annotate is a deployment of a multitask deep learning model that simultaneously classifies the solvent exposure, base-stacking and -pairing status, and conformation of individual RNA residues from their chemical shift fingerprint. Here, we briefly describe how we trained and tested the classifier and demonstrate its application to a model RNA system. CS-Annotate can be accessed via the SMALTR (Structure-based MAchine Learning Tools for RNA) Science Gateway (smaltr.org).

RNA Ensembles from Solvent Accessibility Data: Application to the SAM-I Riboswitch Aptamer Domain.

Riboswitches are regulatory ribonucleic acid (RNA) elements that act as ligand-dependent conformational switches that recognize their cognate ligand via a binding pocket located in their aptamer domain. In the apo form, the aptamer domain is dynamic, requiring an ensemble representation of its structure. Here, as a proof-of-concept, we used solvent accessibility information to construct a pair of dynamical ensembles of the aptamer domain of the well-studied S-adenosylmethionine (SAM) class-I riboswitch in the absence (-SAM) and presence (+SAM) of SAM. To achieve this, we first generated a large conformational library and then reweighted conformers in the library using solvent-accessible surface area (SASA) data derived from recently reported light-activated structural examination of RNA (LASER) reactivities measured in the -SAM and +SAM states of the riboswitch. The differences in the resulting -SAM and +SAM ensembles are consistent with a SAM-dependent reshaping of the free-energy landscape of the aptamer domain. Within our -SAM ensemble, we identified a "transient" state that is missing a critical long-range contact, leading us to speculate that it may be representative of a folding intermediate. Further structural analysis also revealed that the transient state harbors a hidden binding pocket that is distinct from the SAM-binding pocket and is predicted by docking calculations to selectively bind small-molecule ligands. The SASA-based method we applied to the SAM-I riboswitch aptamer domain is general and could be used to construct dynamical ensembles for other riboswitch aptamer domains and, more broadly, other classes of structured RNAs.

Correction: Paszkiewicz et al. A Peripheral CB1R Antagonist Increases Lipolysis, Oxygen Consumption Rate, and Markers of Beiging in 3T3-L1 Adipocytes Similar to RIM, Suggesting That Central Effects Can Be Avoided. Int. J. Mol. Sci. 2020, 21, 6639.

The authors wish to make the following corrections to this paper [...].

Endocannabinoid Receptor-1 and Sympathetic Nervous System Mediate the Beneficial Metabolic Effects of Gastric Bypass.

The exact mechanisms underlying the metabolic effects of bariatric surgery remain unclear. Here, we demonstrate, using a combination of direct and indirect calorimetry, an increase in total resting metabolic rate (RMR) and specifically anaerobic RMR after Roux-en-Y gastric bypass (RYGB), but not sleeve gastrectomy (SG). We also show an RYGB-specific increase in splanchnic sympathetic nerve activity and "browning" of visceral mesenteric fat. Consequently, selective splanchnic denervation abolishes all beneficial metabolic outcomes of gastric bypass that involve changes in the endocannabinoid signaling within the small intestine. Furthermore, we demonstrate that administration of rimonabant, an endocannabinoid receptor-1 (CB1) inverse agonist, to obese mice mimics RYGB-specific effects on energy balance and splanchnic nerve activity. On the other hand, arachidonoylethanolamide (AEA), a CB1 agonist, attenuates the weight loss and metabolic signature of this procedure. These findings identify CB1 as a key player in energy regulation post-RYGB via a pathway involving the sympathetic nervous system.

A Peripheral CB1R Antagonist Increases Lipolysis, Oxygen Consumption Rate, and Markers of Beiging in 3T3-L1 Adipocytes Similar to RIM, Suggesting that Central Effects Can Be Avoided.

With the increased prevalence of obesity and related co-morbidities, such as type 2 diabetes (T2D), worldwide, improvements in pharmacological treatments are necessary. The brain- and peripheral-cannabinoid receptor 1 (CB1R) antagonist rimonabant (RIM) has been shown to induce weight loss and improve glucose homeostasis. We have previously demonstrated that RIM promotes adipose tissue beiging and decreased adipocyte cell size, even during maintenance on a high-fat diet. Given the adverse side-effects of brain-penetrance with RIM, in this study we aimed to determine the site of action for a non-brain-penetrating CB1R antagonist AM6545. By using in vitro assays, we demonstrated the direct effects of this non-brain-penetrating CB1R antagonist on cultured adipocytes. Specifically, we showed, for the first time, that AM6545 significantly increases markers of adipose tissue beiging, mitochondrial biogenesis, and lipolysis in 3T3-L1 adipocytes. In addition, the oxygen consumption rate (OCR), consisting of baseline respiratory rate, proton leak, maximal respiratory capacity, and ATP synthase activity, was greater for cells exposed to AM6545, demonstrating greater mitochondrial uncoupling. Using a lipolysis inhibitor during real-time OCR measurements, we determined that the impact of CB1R antagonism on adipocytes is driven by increased lipolysis. Thus, our data suggest the direct role of CB1R antagonism on adipocytes does not require brain penetrance, supporting the importance of focus on peripheral CB1R antagonism pharmacology for reducing the incidence of obesity and T2D.