Small nuclear RNAs (snRNAs) are core spliceosome components and mediate pre-mRNA splicing. Here we show that snRNAs contain a regulated and reversible nucleotide modification causing them to exist as two different methyl isoforms, m 1 and m 2 , reflecting the methylation state of the adenosine adjacent to the snRNA cap. We find that snRNA biogenesis involves the formation of an initial m 1-isoform with a single-methylated adenosine (2'-O-methyladenosine, Am), which is then converted to a dimethylated m 2-isoform (N 6 ,2'-O-dimethyladenosine, m 6 Am). The relative m 1-and m 2-isoform levels are determined by the RNA demethylase FTO, which selectively demethylates the m 2-isoform. We show FTO is inhibited by the oncometabolite D-2hydroxyglutarate, resulting in increased m 2-snRNA levels. Furthermore, cells that exhibit high m 2-snRNA levels show altered patterns of alternative splicing. Together, these data reveal that FTO controls a previously unknown central step of snRNA processing involving reversible methylation, and suggest that epitranscriptomic information in snRNA may influence mRNA splicing.

In rodents, obesity and aging impair nicotinamide adenine dinucleotide (NAD+) biosynthesis, which contributes to metabolic dysfunction. Nicotinamide mononucleotide (NMN) availability is a rate-limiting factor in mammalian NAD+ biosynthesis. We conducted a 10-week, randomized, placebo-controlled, double-blind trial to evaluate the effect of NMN supplementation on metabolic function in postmenopausal women with prediabetes who were overweight or obese. Insulin-stimulated glucose disposal, assessed by using the hyperinsulinemic-euglycemic clamp, and skeletal muscle insulin signaling [phosphorylation of protein kinase AKT and mechanistic target of rapamycin (mTOR)] increased after NMN supplementation but did not change after placebo treatment. NMN supplementation up-regulated the expression of platelet-derived growth factor receptor β and other genes related to muscle remodeling. These results demonstrate that NMN increases muscle insulin sensitivity, insulin signaling, and remodeling in women with prediabetes who are overweight or obese (clinicaltrial.gov NCT 03151239).

Untargeted metabolomics aims to quantify the complete set of metabolites within a biological system, most commonly by liquid chromatography/mass spectrometry (LC/MS). Since nearly the inception of the field, compound identification has been widely recognized as the rate-limiting step of the experimental workflow. In spite of exponential increases in the size of metabolomic databases, which now contain experimental MS/MS spectra for over a half a million reference compounds, chemical structures still cannot be confidently assigned to many signals in a typical LC/MS dataset. The purpose of this Perspective is to consider why identification rates continue to be low in untargeted metabolomics. One rationalization is that many naturally occurring metabolites detected by LC/MS are true “novel” compounds that have yet to be incorporated into metabolomic databases. An alternative possibility, however, is that research data do not provide database matches because of informatic artifacts, chemical contaminants, and signal redundancies. Increasing evidence suggests that, for at least some sample types, many unidentifiable signals in untargeted metabolomics result from the latter rather than new compounds originating from the specimen being measured. The implications of these observations on chemical discovery in untargeted metabolomics are discussed.

In the present study, the application of mass spectrometry (MS) binding assays as a tool for library screening is reported. For library generation, dynamic combinatorial chemistry (DCC) was used. These libraries can be screened by means of MS binding assays when appropriate measures are taken to render the libraries pseudostatic. That way, the efficiency of MS binding assays to determine ligand binding in compound screening with the ease of library generation by DCC is combined. The feasibility of this approach is shown for γ-aminobutyric acid (GABA) transporter 1 (GAT1) as a target, representing the most important subtype of the GABA transporters. For the screening, hydrazone libraries were employed that were generated in the presence of the target by reacting various sets of aldehydes with a hydrazine derivative that is delineated from piperidine-3-carboxylic acid (nipecotic acid), a common fragment of known GAT1 inhibitors. To ensure that the library generated is pseudostatic, a large excess of the nipecotic acid derivative is employed. As the library is generated in a buffer system suitable for binding and the target is already present, the mixtures can be directly analyzed by MS binding assays-the process of library generation and screening thus becoming simple to perform. The binding affinities of the hits identified by deconvolution were confirmed in conventional competitive MS binding assays performed with single compounds obtained by separate synthesis. In this way, two nipecotic acid derivatives exhibiting a biaryl moiety, 1-{2-[2'-(1,1'-biphenyl-2-ylmethylidene)hydrazine]ethyl}piperidine-3-carboxylic acid and 1-(2-{2'-[1-(2-thiophenylphenyl)methylidene]hydrazine}ethyl)piperidine-3-carboxylic acid, were found to be potent GAT1 ligands exhibiting pK(i) values of 6.186 ± 0.028 and 6.229 ± 0.039, respectively. This method enables screening of libraries, whether generated by conventional chemistry or DCC, and is applicable to all kinds of targets including membrane-bound targets such as G protein coupled receptors (GPCRs), ion channels and transporters. As such, this strategy displays high potential in the drug discovery process.

Mass spectrometric (MS) binding assays, a powerful tool to determine affinities of single drug candidates toward chosen targets, were recently demonstrated to be suitable for the screening of compound libraries generated with reactions of dynamic combinatorial chemistry when rendering libraries pseudostatic. Screening of small hydrazone libraries targeting γ-aminobutyric acid transporter 1 (GAT1), the most abundant γ-aminobutyric acid (GABA) transporter in the central nervous system, revealed two nipecotic acid derived binders with submicromolar affinities. Starting from the biphenyl carrying hit as lead structure, the objective of the present study was to discover novel high affinity GAT1 binders by screening of biphenyl focused pseudostatic hydrazone libraries formed from hydrazine 10 and 36 biphenylcarbaldehydes 11c-al. Hydrazone 12z that carried a 2',4'-dichlorobiphenyl residue was found to be the most potent binder with low nanomolar affinity (pK(i) = 8.094 ± 0.098). When stable carba analogues of representative hydrazones were synthesized and evaluated, the best binder 13z was again displaying the 2',4'-dichlorobiphenyl moiety (pK(i) = 6.930 ± 0.021).

N1-methyladenosine (m1A) was proposed to be a highly prevalent modification in mRNA 5’UTRs based on mapping studies using an m1A-binding antibody. We developed a bioinformatic approach to discover m1A and other modifications in mRNA throughout the transcriptome by analyzing preexisting ultra-deep RNA-Seq data for modification-induced misincorporations. Using this approach, we detected appreciable levels of m1A only in one mRNA: the mitochondrial MT-ND5 transcript. As an alternative approach, we also developed an antibody-based m1A-mapping approach to detect m1A at single-nucleotide resolution, and confirmed that the commonly used m1A antibody maps sites to the transcription-start site in mRNA 5’UTRs. However, further analysis revealed that these were false-positives caused by binding of the antibody to the m7G-cap. A different m1A antibody that lacks cap-binding cross-reactivity does not show enriched binding in 5’UTRs. These results demonstrate that high-stoichiometry m1A sites are exceedingly rare in mRNAs and that previous mappings of m1A to 5’UTRs were the result of antibody cross-reactivity to the 5’ cap.

There is an urgent need to identify which COVID-19 patients will develop life-threatening illness so that medical resources can be optimally allocated and rapid treatment can be administered early in the disease course, when clinical management is most effective. To aid in the prognostic classification of disease severity, we perform untargeted metabolomics on plasma from 339 patients, with samples collected at six longitudinal time points. Using the temporal metabolic profiles and machine learning, we build a predictive model of disease severity. We discover that a panel of metabolites measured at the time of study entry successfully determine disease severity. Through analysis of longitudinal samples, we confirm that the majority of these markers are directly related to disease progression and that their levels return to baseline upon disease recovery. Finally, we validate that these metabolites are also altered in a hamster model of COVID-19.

No abstract