Design, construction, and functional characterization of a tRNA neochromosome in yeast.

Here, we report the design, construction, and characterization of a tRNA neochromosome, a designer chromosome that functions as an additional, de novo counterpart to the native complement of Saccharomyces cerevisiae. Intending to address one of the central design principles of the Sc2.0 project, the ∼190-kb tRNA neochromosome houses all 275 relocated nuclear tRNA genes. To maximize stability, the design incorporates orthogonal genetic elements from non-S. cerevisiae yeast species. Furthermore, the presence of 283 rox recombination sites enables an orthogonal tRNA SCRaMbLE system. Following construction in yeast, we obtained evidence of a potent selective force, manifesting as a spontaneous doubling in cell ploidy. Furthermore, tRNA sequencing, transcriptomics, proteomics, nucleosome mapping, replication profiling, FISH, and Hi-C were undertaken to investigate questions of tRNA neochromosome behavior and function. Its construction demonstrates the remarkable tractability of the yeast model and opens up opportunities to directly test hypotheses surrounding these essential non-coding RNAs.

Quantification of tRNA m1A modification by templated-ligation qPCR.

N1-methyladenosine (m1A) is a widespread modification in all eukaryotic, many archaeal, and some bacterial tRNAs. m1A is generally located in the T loop of cytosolic tRNA and between the acceptor and D stems of mitochondrial tRNAs; it is involved in the tertiary interaction that stabilizes tRNA. Human tRNA m1A levels are dynamically regulated that fine-tune translation and can also serve as biomarkers for infectious disease. Although many methods have been used to measure m1A, a PCR method to assess m1A levels quantitatively in specific tRNAs has been lacking. Here we develop a templated-ligation followed by a qPCR method (TL-qPCR) that measures m1A levels in target tRNAs. Our method uses the SplintR ligase that efficiently ligates two tRNA complementary DNA oligonucleotides using tRNA as the template, followed by qPCR using the ligation product as the template. m1A interferes with the ligation in specific ways, allowing for the quantitative assessment of m1A levels using subnanogram amounts of total RNA. We identify the features of specificity and quantitation for m1A-modified model RNAs and apply these to total RNA samples from human cells. Our method enables easy access to study the dynamics and function of this pervasive tRNA modification.

Single-read tRNA-seq analysis reveals coordination of tRNA modification and aminoacylation and fragmentation.

Transfer RNA (tRNA) utilizes multiple properties of abundance, modification, and aminoacylation in translational regulation. These properties were typically studied one-by-one; however, recent advance in high throughput tRNA sequencing enables their simultaneous assessment in the same sequencing data. How these properties are coordinated at the transcriptome level is an open question. Here, we develop a single-read tRNA analysis pipeline that takes advantage of the pseudo single-molecule nature of tRNA sequencing in NGS libraries. tRNAs are short enough that a single NGS read can represent one tRNA molecule, and can simultaneously report on the status of multiple modifications, aminoacylation, and fragmentation of each molecule. We find correlations among modification-modification, modification-aminoacylation and modification-fragmentation. We identify interdependencies among one of the most common tRNA modifications, m1A58, as coordinators of tissue-specific gene expression. Our method, SingLe-read Analysis of Crosstalks (SLAC), reveals tRNAome-wide networks of modifications, aminoacylation, and fragmentation. We observe changes of these networks under different stresses, and assign a function for tRNA modification in translational regulation and fragment biogenesis. SLAC leverages the richness of the tRNA-seq data and provides new insights on the coordination of tRNA properties.

Hiding in Plain Sight: The Issue of Hidden Variables.

Here we discuss "hidden variables", which are typically introduced during an experiment as a consequence of the application of two independent variables together to create a stimulus. With increased sophistication in modern chemical biology tools and related precision interrogation techniques, hidden variables have become integral to many chemical biologists' routine experiments. For instance, they can appear in the use of light-activatable chemical probes (e.g., µMap, T-REX), or stimulus-induced enzyme activation (e.g., APEX). Unfortunately, control experiments assess only how independent variables affect measured outcomes and not the multiple differences between the two independent variables and the twain. We outline ways to account for potential hidden variables in experimental design and data interpretation as a means to aid developers of new methods, particularly those involving light-driven techniques, chemical activation, or biorthogonal chemistries, to better incorporate well-controlled procedures.