Pol3Base: a resource for decoding the interactome, expression, evolution, epitranscriptome and disease variations of Pol III-transcribed ncRNAs.

RNA polymerase III (Pol III) transcribes hundreds of non-coding RNA genes (ncRNAs), which involve in a variety of cellular processes. However, the expression, functions, regulatory networks and evolution of these Pol III-transcribed ncRNAs are still largely unknown. In this study, we developed a novel resource, Pol3Base (http://rna.sysu.edu.cn/pol3base/), to decode the interactome, expression, evolution, epitranscriptome and disease variations of Pol III-transcribed ncRNAs. The current release of Pol3Base includes thousands of regulatory relationships between ∼79 000 ncRNAs and transcription factors by mining 56 ChIP-seq datasets. By integrating CLIP-seq datasets, we deciphered the interactions of these ncRNAs with >240 RNA binding proteins. Moreover, Pol3Base contains ∼9700 RNA modifications located within thousands of Pol III-transcribed ncRNAs. Importantly, we characterized expression profiles of ncRNAs in >70 tissues and 28 different tumor types. In addition, by comparing these ncRNAs from human and mouse, we revealed about 4000 evolutionary conserved ncRNAs. We also identified ∼11 403 tRNA-derived small RNAs (tsRNAs) in 32 different tumor types. Finally, by analyzing somatic mutation data, we investigated the mutation map of these ncRNAs to help uncover their potential roles in diverse diseases. This resource will help expand our understanding of potential functions and regulatory networks of Pol III-transcribed ncRNAs.

tsRFun: a comprehensive platform for decoding human tsRNA expression, functions and prognostic value by high-throughput small RNA-Seq and CLIP-Seq data.

tRNA-derived small RNA (tsRNA), a novel type of regulatory small noncoding RNA, plays an important role in physiological and pathological processes. However, the understanding of the functional mechanism of tsRNAs in cells and their role in the occurrence and development of diseases is limited. Here, we integrated multiomics data such as transcriptome, epitranscriptome, and targetome data, and developed novel computer tools to establish tsRFun, a comprehensive platform to facilitate tsRNA research (http://rna.sysu.edu.cn/tsRFun/ or http://biomed.nscc-gz.cn/DB/tsRFun/). tsRFun evaluated tsRNA expression profiles and the prognostic value of tsRNAs across 32 types of cancers, identified tsRNA target molecules utilizing high-throughput CLASH/CLEAR or CLIP sequencing data, and constructed the interaction networks among tsRNAs, microRNAs, and mRNAs. In addition to its data presentation capabilities, tsRFun offers multiple real-time online tools for tsRNA identification, target prediction, and functional enrichment analysis. In summary, tsRFun provides a valuable data resource and multiple analysis tools for tsRNA investigation.

Reducing the structure bias of RNA-Seq reveals a large number of non-annotated non-coding RNA.

The study of RNA expression is the fastest growing area of genomic research. However, despite the dramatic increase in the number of sequenced transcriptomes, we still do not have accurate estimates of the number and expression levels of non-coding RNA genes. Non-coding transcripts are often overlooked due to incomplete genome annotation. In this study, we use annotation-independent detection of RNA reads generated using a reverse transcriptase with low structure bias to identify non-coding RNA. Transcripts between 20 and 500 nucleotides were filtered and crosschecked with non-coding RNA annotations revealing 111 non-annotated non-coding RNAs expressed in different cell lines and tissues. Inspecting the sequence and structural features of these transcripts indicated that 60% of these transcripts correspond to new snoRNA and tRNA-like genes. The identified genes exhibited features of their respective families in terms of structure, expression, conservation and response to depletion of interacting proteins. Together, our data reveal a new group of RNA that are difficult to detect using standard gene prediction and RNA sequencing techniques, suggesting that reliance on actual gene annotation and sequencing techniques distorts the perceived architecture of the human transcriptome.

Isolation of Yasminevirus, the First Member of Klosneuvirinae Isolated in Coculture with Vermamoeba vermiformis, Demonstrates an Extended Arsenal of Translational Apparatus Components.

The family of giant viruses is still expanding, and evidence of a translational machinery is emerging in the virosphere. The Klosneuvirinae group of giant viruses was first reconstructed from in silico studies, and then a unique member was isolated, Bodo saltans virus. Here we describe the isolation of a new member in this group using coculture with the free-living amoeba Vermamoeba vermiformis This giant virus, called Yasminevirus, has a 2.1-Mb linear double-stranded DNA genome encoding 1,541 candidate proteins, with a GC content estimated at 40.2%. Yasminevirus possesses a nearly complete translational machinery, with a set of 70 tRNAs associated with 45 codons and recognizing 20 amino acids (aa), 20 aminoacyl-tRNA synthetases (aaRSs) recognizing 20 aa, as well as several translation factors and elongation factors. At the genome scale, evolutionary analyses placed this virus in the Klosneuvirinae group of giant viruses. Rhizome analysis demonstrated that the genome of Yasminevirus is mosaic, with ∼34% of genes having their closest homologues in other viruses, followed by ∼13.2% in Eukaryota, ∼7.2% in Bacteria, and less than 1% in Archaea Among giant virus sequences, Yasminevirus shared 87% of viral hits with Klosneuvirinae. This description of Yasminevirus sheds light on the Klosneuvirinae group in a captivating quest to understand the evolution and diversity of giant viruses.IMPORTANCE Yasminevirus is an icosahedral double-stranded DNA virus isolated from sewage water by amoeba coculture. Here its structure and replicative cycle in the amoeba Vermamoeba vermiformis are described and genomic and evolutionary studies are reported. This virus belongs to the Klosneuvirinae group of giant viruses, representing the second isolated and cultivated giant virus in this group, and is the first isolated using a coculture procedure. Extended translational machinery pointed to Yasminevirus among the quasiautonomous giant viruses with the most complete translational apparatus of the known virosphere.

DNA damage-induced cell death relies on SLFN11-dependent cleavage of distinct type II tRNAs.

Transcriptome analysis reveals a strong positive correlation between human Schlafen family member 11 (SLFN11) expression and the sensitivity of tumor cells to DNA-damaging agents (DDAs). Here, we show that SLFN11 preferentially inhibits translation of the serine/threonine kinases ATR and ATM upon DDA treatment based on distinct codon usage without disrupting early DNA damage response signaling. Type II transfer RNAs (tRNAs), which include all serine and leucine tRNAs, are cleaved in a SLFN11-dependent manner in response to DDAs. Messenger RNAs encoded by genes with high TTA (Leu) codon usage, such as ATR, display utmost susceptibility to translational suppression by SLFN11. Specific attenuation of tRNA-Leu-TAA sufficed to ablate ATR protein expression and restore the DDA sensitivity of SLFN11-deficient cells. Our study uncovered a novel mechanism of codon-specific translational inhibition via SLFN11-dependent tRNA cleavage in the DNA damage response and supports the notion that SLFN11-deficient tumor cells can be resensitized to DDAs by targeting ATR or tRNA-Leu-TAA.

Convolutional neural networks for classification of alignments of non-coding RNA sequences.

Motivation: The convolutional neural network (CNN) has been applied to the classification problem of DNA sequences, with the additional purpose of motif discovery. The training of CNNs with distributed representations of four nucleotides has successfully derived position weight matrices on the learned kernels that corresponded to sequence motifs such as protein-binding sites. Results: We propose a novel application of CNNs to classification of pairwise alignments of sequences for accurate clustering of sequences and show the benefits of the CNN method of inputting pairwise alignments for clustering of non-coding RNA (ncRNA) sequences and for motif discovery. Classification of a pairwise alignment of two sequences into positive and negative classes corresponds to the clustering of the input sequences. After we combined the distributed representation of RNA nucleotides with the secondary-structure information specific to ncRNAs and furthermore with mapping profiles of next-generation sequence reads, the training of CNNs for classification of alignments of RNA sequences yielded accurate clustering in terms of ncRNA families and outperformed the existing clustering methods for ncRNA sequences. Several interesting sequence motifs and secondary-structure motifs known for the snoRNA family and specific to microRNA and tRNA families were identified. Availability and implementation: The source code of our CNN software in the deep-learning framework Chainer is available at http://www.dna.bio.keio.ac.jp/cnn/, and the dataset used for performance evaluation in this work is available at the same URL.

High-level phylogeny of the Coleoptera inferred with mitochondrial genome sequences.

The Coleoptera (beetles) exhibits tremendous morphological, ecological, and behavioral diversity. To better understand the phylogenetics and evolution of beetles, we sequenced three complete mitogenomes from two families (Cleridae and Meloidae), which share conserved mitogenomic features with other completely sequenced beetles. We assessed the influence of six datasets and three inference methods on topology and nodal support within the Coleoptera. We found that both Bayesian inference and maximum likelihood with homogeneous-site models were greatly affected by nucleotide compositional heterogeneity, while the heterogeneous-site mixture model in PhyloBayes could provide better phylogenetic signals for the Coleoptera. The amino acid dataset generated more reliable tree topology at the higher taxonomic levels (i.e. suborders and series), where the inclusion of rRNA genes and the third positions of protein-coding genes improved phylogenetic inference at the superfamily level, especially under a heterogeneous-site model. We recovered the suborder relationships as (Archostemata+Adephaga)+(Myxophaga+Polyphaga). The series relationships within Polyphaga were recovered as (Scirtiformia+(Elateriformia+((Bostrichiformia+Scarabaeiformia+Staphyliniformia)+Cucujiformia))). All superfamilies within Cucujiformia were recovered as monophyletic. We obtained a cucujiform phylogeny of (Cleroidea+(Coccinelloidea+((Lymexyloidea+Tenebrionoidea)+(Cucujoidea+(Chrysomeloidea+Curculionoidea))))). This study showed that although tree topologies were sensitive to data types and inference methods, mitogenomic data could provide useful information for resolving the Coleoptera phylogeny at various taxonomic levels by using suitable datasets and heterogeneous-site models.

Dissecting tRNA-derived fragment complexities using personalized transcriptomes reveals novel fragment classes and unexpected dependencies.

We analyzed transcriptomic data from 452 healthy men and women representing five different human populations and two races, and, 311 breast cancer samples from The Cancer Genome Atlas. Our studies revealed numerous constitutive, distinct fragments with overlapping sequences and quantized lengths that persist across dozens of individuals and arise from the genomic loci of all nuclear and mitochondrial human transfer RNAs (tRNAs). Surprisingly, we discovered that the tRNA fragments' length, starting and ending points, and relative abundance depend on gender, population, race and also on amino acid identity, anticodon, genomic locus, tissue, disease, and disease subtype. Moreover, the length distribution of mitochondrially-encoded tRNAs differs from that of nuclearly-encoded tRNAs, and the specifics of these distributions depend on tissue. Notably, tRNA fragments from the same anticodon do not have correlated abundances. We also report on a novel category of tRNA fragments that significantly contribute to the differences we observe across tissues, genders, populations, and races: these fragments, referred to as i-tRFs, are abundant in human tissues, wholly internal to the respective mature tRNA, and can straddle the anticodon. HITS-CLIP data analysis revealed that tRNA fragments are loaded on Argonaute in a cell-dependent manner, suggesting cell-dependent functional roles through the RNA interference pathway. We validated experimentally two i-tRF molecules: the first was found in 21 of 22 tested breast tumor and adjacent normal samples and was differentially abundant between health and disease whereas the second was found in all eight tested breast cancer cell lines.

A 3' external transcribed spacer in a tRNA transcript acts as a sponge for small RNAs to prevent transcriptional noise.

During ribosomal and transfer RNA maturation, external transcribed spacer (ETS) and internal transcribed spacer (ITS) sequences are excised and, as non-functional by-products, are rapidly degraded. However, we report that the 3'ETS of the glyW-cysT-leuZ polycistronic tRNA precursor is highly and specifically enriched by co-purification with at least two different small regulatory RNAs (sRNAs), RyhB and RybB. Both sRNAs are shown to base pair with the same region in the 3'ETS of leuZ (3'ETS(leuZ)). Disrupting the pairing by mutating 3'ETS(leuZ) strongly increased the activity of sRNAs, even under non-inducing conditions. Our results indicate that 3'ETS(leuZ) prevents sRNA-dependent remodeling of tricarboxylic acid (TCA) cycle fluxes and decreases antibiotic sensitivity when sRNAs are transcriptionally repressed. This suggests that 3'ETS(leuZ) functions as a sponge to absorb transcriptional noise from repressed sRNAs. Additional data showing RybB and MicF sRNAs are co-purified with ITS(metZ-metW) and ITS(metW-metV) strongly suggest a wide distribution of this phenomenon.

Selenium metabolism in Trypanosoma: characterization of selenoproteomes and identification of a Kinetoplastida-specific selenoprotein.

Proteins containing the 21st amino acid selenocysteine (Sec) are present in the three domains of life. However, within lower eukaryotes, particularly parasitic protists, the dependence on the trace element selenium is variable as many organisms lost the ability to utilize Sec. Herein, we analyzed the genomes of Trypanosoma and Leishmania for the presence of genes coding for Sec-containing proteins. The selenoproteomes of these flagellated protozoa have three selenoproteins, including distant homologs of mammalian SelK and SelT, and a novel multidomain selenoprotein designated SelTryp. In SelK and SelTryp, Sec is near the C-terminus, and in all three selenoproteins, it is within predicted redox motifs. SelTryp has neither Sec- nor cysteine-containing homologs in the human host and appears to be a Kinetoplastida-specific protein. The use of selenium for protein synthesis was verified by metabolically labeling Trypanosoma cells with 75Se. In addition, genes coding for components of the Sec insertion machinery were identified in the Kinetoplastida genomes. Finally, we found that Trypanosoma brucei brucei cells were highly sensitive to auranofin, a compound that specifically targets selenoproteins. Overall, these data establish that Trypanosoma, Leishmania and likely other Kinetoplastida utilize and depend on the trace element selenium, and this dependence is due to occurrence of selenium in at least three selenoproteins.

Transfer RNA: A comparison by gel electrophoresis of the tRNA in HeLa cytoplasm, HeLa mitochondrial fraction, and E. coli.

A class of transfer RNA separable from the majority of transfer RNA by gel electrophoresis has been observed in E. coli and in the mitochondrial fraction of HeLa cells but not in HeLa cytoplasm. The size of this class of transfer RNA, as judged by its mobility on polyacrylamide gel, is intermediate between the major part of the transfer RNA and 5S RNA. It exists in part associated with the ribosomes, and its amino acid acceptor activity is different from the major class of transfer RNA.