Removing Intron from pre tRNA is essential in all three kingdoms of life. In humans, this process is mediated by tRNA splicing Endonuclease (TSEN), which includes four subunits: TSEN2, TSEN15, TSEN34, and TSEN54.

On April 6, 2023, Shi Yigong's team of Westlake University published a research paper entitled "Structural basis of pre tRNA intron removal by human tRNA splicing endonuclease" online in Molecular Cell (IF=19), which revealed the structural basis of human tRNA splicing Endonuclease to remove the introns in the tRNA precursor. This study reported the cryoelectron microscopy structure of human TSEN binding to full-length pre tRNA at an average resolution of 2.94 and 2.88 Å before and after catalysis, respectively.

The human TSEN has an extended surface groove that accommodates l-type pre tRNA. The mature domain of pretRNA is recognized by the conserved structural elements of TSEN34, TSEN54, and TSEN2. This recognition locates the anti codon stem of pre tRNA and places the 30 splice site and 50 splice site at the catalytic centers of TSEN34 and TSEN2, respectively. Most Intron sequences have no direct interaction with TSEN, which explains why pre tRNAs of different Intron can be accommodated and cleaved. This structure reveals the molecular scale mechanism of TSEN pre tRNA cleavage.

Transport RNA (tRNA) is crucial for the flow of genetic information, which allows Ribosome to translate mRNA into proteins. Mature tRNAs are generated from tRNAs precursors (pre tRNAs) through a series of post-transcriptional processing and modification steps. In the three kingdoms of life, for a subset of pre tRNAs, Intron sequences exist and must be removed by splicing. Among the predicted tRNA genes in the Human genome, at least 28 contain Intron with different lengths and sequences. In archaea and Eukaryote, Intron are removed by tRNA splicing Endonuclease (TSENs), and then two released Exon are connected through a multi-step process involving specific tRNA Ligase.

Eukaryote TSEN includes two catalytic subunits TSEN34 and TSEN2, two structural subunits TSEN54 and TSNE15, TSEN34 and TSEN2 cut pre tRNA at 30 splice site (30SS) and 50 splice site (50SS) respectively. In mammals, the polynucleotide kinase CLP1 is co purified with TSEN. Although CLP1 is optional before in vitro tRNA cleavage, mutations in CLP1 and all TSEN subunits have been associated with tRNA metabolic changes and neuropathy.

Mechanism pattern diagram (from Molecular Cell)

Since the discovery of tRNA Intron in the 1970s, extensive biochemical and Crystallography studies have gained considerable understanding of the pre tRNA cleavage of various types of TSENs. In particular, the structure of the TSEN and BHB RNA motif complexes in archaea reveals some key interactions that are necessary for pre tRNA recognition and cleavage. However, the slow emergence of structural information about Eukaryote TSEN severely limits the understanding of the pre tRNA cleavage mechanism. For example, TSEN is believed to use a molecular scale mechanism to recognize the two site cleavage of pre tRNA, but its foundation is still insufficient due to the lack of structural information on the full length pre tRNA bound by TSEN. In addition, it is still unclear how the four TSEN subunits are organized into a complete Endonuclease with two independent Active site.

This study reported two high-resolution structures of human TSEN binding to full-length pre tRNA: one in the pre catalytic state and the other in the post catalytic state. In order to capture the pre catalytic state, the author introduced two missense mutations in TSEN: H255A in TSEN34 and H377A in TSNE. In summary, the structure of the human TSEN/CLP1/pretRNA complex studied in the pre and post catalytic states provides a framework for understanding the mechanism of pre tRNA splicing.

Scientists have long known that mitochondria play an important role in the metabolism and energy production of cancer cells. However, as of now, researchers are not clear about the relationship between the structural organization of mitochondrial networks and their functional bioenergy activity at the entire tumor level.

Recently, in a research report entitled "Spatial mapping of mitochondrial networks and bioenergetics in lung cancer" published on the international journal Nature, scientists from UCLA and other institutions combined Positron emission tomography (PET) and electron microscope through research, A three-dimensional super resolution map of mitochondrial network was generated in the Lung tumor of genetically engineered mice.

In the article, researchers used an artificial intelligence technique called deep learning to classify and analyze tumors based on mitochondrial activity and other factors, and quantified the structure of hundreds of cells and thousands of mitochondria throughout the entire tumor. Researchers studied two major subtypes of non-small cell lung cancer (NSCLC) - Adenocarcinoma of the lung and squamous cell carcinoma, and found different mitochondrial network subsets in these tumors; More importantly, they also found that mitochondria can often be organized together with Organelle such as lipid droplets and produce special subcellular structures, while supporting tumor cell metabolism and mitochondrial activity.

Image source: Nature (2023) DOI: 10.1038/s41586-023-05793-3

Researchers speculate that the mitochondrial population in human cancer samples does not repel their respective tumor subtypes, but rather has an activity spectrum; These research findings may provide key information for understanding the function of mitochondria in cancer cells and have the potential to help develop new cancer therapies. Shackelford, the researcher, said that our research represents the key first step to generate a highly detailed three-dimensional map of Lung tumor using a genetically engineered mouse model; Using these maps, we can generate a blueprint of the structure and function of Lung tumor, and provide valuable clues to reveal how tumor cells structurally organize their cellular architecture to respond to the high metabolic demands of tumor growth. Our research findings may help guide and improve current cancer treatment strategies, and also clarify the new direction of scientists' treatment of lung cancer.

This study reveals a new discovery of metabolic flux of Lung tumor, and clarifies that the preference of cancer cells for nutrition may be determined by the regions of mitochondria and other Organelle in their cells, that is, they either depend on glucose or free fatty acids. The research results of this article are of great significance for developing effective anticancer therapies that can target tumor specific nutritional preferences. This multimodal imaging method can also prompt researchers to uncover previously unknown aspects of cancer metabolism. Researchers believe that this may also be applied to research on other types of cancer.

In summary, the results of this study indicate that in non-small cell lung cancer, mitochondrial networks can be divided into different subpopulations and dominate the tumor's bioenergy capacity.

RNA is a genetic information carrier that exists in biological cells, some viruses and Viroid. Different types of RNA have different functions. Transfer RNA (tRNA) plays an important role in protein translation. If there is incorrect or missing modification in tRNA, it will result in incorrect or incomplete proteins.

It has been found that mutations of a variety of tRNA modifying enzymes are associated with various human diseases, including neural Degenerative disease, Metabolic disorder and cancer. However, studying tRNA has always faced many challenges, partly due to the lack of a simple method for quantifying its abundance and chemical modification simultaneously. Because the current nanopore sequencing setup discards the vast majority of tRNA reads, the sequencing yield is low, and there is a bias in the representation of tRNA abundance based on transcript length.

Recently, a research team and collaborators from the Barcelona Institute of Science and Technology in Spain published a research paper titled "Quantitative analysis of tRNA abundance and modifications by nanopore RNA sequencing" in Nature Biotechnology. The research team has developed a new tRNA nanopore sequencing method called Nano-tRNAseq, which can directly accurately sequence natural tRNAs, accurately quantify tRNA abundance, and simultaneously capture tRNA modification changes. The research team used Nano tRNAseq to successfully detect the crosstalk and interdependence between different tRNA modification types within the same molecule of Saccharomyces cerevisiae tRNA population, as well as the changes in the response of tRNA population to oxidative stress.

Figure 1. Article published in Nature Biotechnology

The Direct RNA Sequencing (DRS) platform developed by Oxford Nanopore Technology (ONT) is a promising alternative to NGS technology for describing tRNAs. This technology allows for direct sequencing of natural RNA molecules, so in principle, tRNA modification and tRNA abundance can be detected and quantified without the need for reverse transcription or PCR. Previous studies have shown that nanopores can capture tRNA using solid-state or biological (ONT) nanopores. By connecting connectors to extend tRNA molecules, tRNA can be sequenced, labeled, and localized through biological nanopores. However, the sequencing yield of tRNA molecules using the above method is relatively low, and there is no report on whether this method reproduces existing in vivo tRNA abundance and/or tRNA modification.

The research team found that reprocessing the original nanopore signal strength and filling the 5 'and 3' tRNA ends with RNA connectors can accurately determine and map the base, capture the entire tRNA sequence, increase the number of tRNA reads by 12 times, and obtain accurate tRNA abundance. This method based on nanopores is named Nano-tRNAseq (Figure 1) and can be used to sequence natural tRNAs and obtain quantitative estimates of tRNA abundance and modification kinetics in a single experiment. The research team stated that the Nano-tRNAseq method is the most successful method for DRS using nanopores in vitro and natural tRNA molecular sequencing, base determination, and mapping.

Figure 2. Nano tRNAseq can effectively sequence natural tRNA

The short and highly modified nature of natural tRNAs makes their comparison challenging. The inaccurate determination of modified bases in the DRS dataset also resulted in a large proportion of mismatched bases in the original tRNA detection. Due to these incorrect bases, using the commonly used long read mapping tool minimap2 (- ax map ont-k15) with recommended settings can only compare a small portion of reads (2.56%). In order to improve the mapping ability of Nano tRNAseq reads, the research team tested the short reads mapping algorithm BWA and found that the BWA MEM comparison using recommended parameters outperformed minimap2 in mapping readas.

When using BWA-MEM with parameters - W13 k6 xont2d T20, it was found that there was an optimal balance between increased mapping reads and error alignment, with 54.63% of reads mapped and very little error alignment (0.19%). When comparing the performance of two mapping algorithms in natural tRNA molecules, the comparison is more pronounced. Subsequently, the researchers evaluated whether the mappability of Nano tRNAseq reads was influenced by the lengths of 5 'and 3' RNA junctions. Research has found that even without RNA connectors in the reference sequence, short and unmodified sequences can be effectively aligned, while short and modified reads benefit greatly from extended sequences with connectors, indicating that extended sequence molecules with RNA connectors are crucial for guiding correct alignment of "mismatched" short reads, such as short reads from natural tRNA.