Accurate microRNA annotation of animal genomes using trained covariance models of curated microRNA complements in MirMachine

The annotation of microRNAs depends on the availability of transcriptomics data and expert knowledge. This has led to a gap between the availability of novel genomes and high-quality microRNA complements. Using >16,000 microRNAs from the manually curated microRNA gene database MirGeneDB, we generated trained covariance models for all conserved microRNA families. These models are available in our tool MirMachine, which annotates conserved microRNAs within genomes. We successfully applied MirMachine to a range of animal species, including those with large genomes and genome duplications and extinct species, where small RNA sequencing is hard to achieve. We further describe a microRNA score of expected microRNAs that can be used to assess the completeness of genome assemblies. MirMachine closes a long-persisting gap in the microRNA field by facilitating automated genome annotation pipelines and deeper studies into the evolution of genome regulation, even in extinct organisms.

HPV16 and HPV18 type-specific APOBEC3 and integration profiles in different diagnostic categories of cervical samples

Human papillomavirus (HPV) 16 and 18 are the most predominant types in cervical cancer. Only a small fraction of HPV infections progress to cancer, indicating that additional factors and genomic events contribute to the carcinogenesis, such as minor nucleotide variation caused by APOBEC3 and chromosomal integration. We analysed intra-host minor nucleotide variants (MNVs) and integration in HPV16 and HPV18 positive cervical samples with different morphology. Samples were sequenced using an HPV whole genome sequencing protocol TaME-seq. A total of 80 HPV16 and 51 HPV18 positive samples passed the sequencing depth criteria of 300× reads, showing the following distribution: non-progressive disease (HPV16 n = 21, HPV18 n = 12); cervical intraepithelial neoplasia (CIN) grade 2 (HPV16 n = 27, HPV18 n = 9); CIN3/adenocarcinoma in situ (AIS) (HPV16 n = 27, HPV18 n = 30); cervical cancer (HPV16 n = 5). Similar numbers of MNVs in HPV16 and HPV18 samples were observed for most viral genes, with the exception of HPV18 E4 with higher numbers across clinical categories. APOBEC3 signatures were observed in HPV16 lesions, while similar mutation patterns were not detected for HPV18. The proportion of samples with integration was 13% for HPV16 and 59% for HPV18 positive samples, with a noticeable portion located within or close to cancer-related genes.

A 10-year prediagnostic follow-up study shows that serum RNA signals are highly dynamic in lung carcinogenesis

The majority of lung cancer (LC) patients are diagnosed at a late stage, and survival is poor. Circulating RNA molecules are known to have a role in cancer; however, their involvement before diagnosis remains an open question. In this study, we investigated circulating RNA dynamics in prediagnostic LC samples, focusing on smokers, to identify if and when disease-related signals can be detected in serum. We sequenced small RNAs in 542 serum LC samples donated up to 10 years before diagnosis and 519 matched cancer-free controls coming from 905 individuals in the Janus Serum Bank. This sample size provided sufficient statistical power to independently analyze time to diagnosis, stage, and histology. The results showed dynamic changes in differentially expressed circulating RNAs specific to LC histology and stage. The greatest number of differentially expressed RNAs was identified around 7 years before diagnosis for early-stage LC and 1-4 years prior to diagnosis for locally advanced and advanced-stage LC, regardless of LC histology. Furthermore, NSCLC and SCLC histologies have distinct prediagnostic signals. The majority of differentially expressed RNAs were associated with cancer-related pathways. The dynamic RNA signals pinpointed different phases of tumor development over time. Stage-specific RNA profiles may be associated with tumor aggressiveness. Our results improve the molecular understanding of carcinogenesis. They indicate substantial opportunity for screening and improved treatment and will guide further research on early detection of LC. However, the dynamic nature of the RNA signals also suggests challenges for prediagnostic biomarker discovery.

TaME-seq: An efficient sequencing approach for characterisation of HPV genomic variability and chromosomal integration

HPV genomic variability and chromosomal integration are important in the HPV-induced carcinogenic process. To uncover these genomic events in an HPV infection, we have developed an innovative and cost-effective sequencing approach named TaME-seq (tagmentation-assisted multiplex PCR enrichment sequencing). TaME-seq combines tagmentation and multiplex PCR enrichment for simultaneous analysis of HPV variation and chromosomal integration, and it can also be adapted to other viruses. For method validation, cell lines (n = 4), plasmids (n = 3), and HPV16, 18, 31, 33 and 45 positive clinical samples (n = 21) were analysed. Our results showed deep HPV genome-wide sequencing coverage. Chromosomal integration breakpoints and large deletions were identified in HPV positive cell lines and in one clinical sample. HPV genomic variability was observed in all samples allowing identification of low frequency variants. In contrast to other approaches, TaME-seq proved to be highly efficient in HPV target enrichment, leading to reduced sequencing costs. Comprehensive studies on HPV intra-host variability generated during a persistent infection will improve our understanding of viral carcinogenesis. Efficient identification of both HPV variability and integration sites will be important for the study of HPV evolution and adaptability and may be an important tool for use in cervical cancer diagnostics.

A comprehensive profile of circulating RNAs in human serum

Non-coding RNA (ncRNA) molecules have fundamental roles in cells and many are also stable in body fluids as extracellular RNAs. In this study, we used RNA sequencing (RNA-seq) to investigate the profile of small non-coding RNA (sncRNA) in human serum. We analyzed 10 billion Illumina reads from 477 serum samples, included in the Norwegian population-based Janus Serum Bank (JSB). We found that the core serum RNA repertoire includes 258 micro RNAs (miRNA), 441 piwi-interacting RNAs (piRNA), 411 transfer RNAs (tRNA), 24 small nucleolar RNAs (snoRNA), 125 small nuclear RNAs (snRNA) and 123 miscellaneous RNAs (misc-RNA). We also investigated biological and technical variation in expression, and the results suggest that many RNA molecules identified in serum contain signs of biological variation. They are therefore unlikely to be random degradation by-products. In addition, the presence of specific fragments of tRNA, snoRNA, Vault RNA and Y_RNA indicates protection from degradation. Our results suggest that many circulating RNAs in serum can be potential biomarkers.

Avoidance of stochastic RNA interactions can be harnessed to control protein expression levels in bacteria and archaea

A critical assumption of gene expression analysis is that mRNA abundances broadly correlate with protein abundance, but these two are often imperfectly correlated. Some of the discrepancy can be accounted for by two important mRNA features: codon usage and mRNA secondary structure. We present a new global factor, called mRNA:ncRNA avoidance, and provide evidence that avoidance increases translational efficiency. We also demonstrate a strong selection for the avoidance of stochastic mRNA:ncRNA interactions across prokaryotes, and that these have a greater impact on protein abundance than mRNA structure or codon usage. By generating synonymously variant green fluorescent protein (GFP) mRNAs with different potential for mRNA:ncRNA interactions, we demonstrate that GFP levels correlate well with interaction avoidance. Therefore, taking stochastic mRNA:ncRNA interactions into account enables precise modulation of protein abundance.

DOI:http://dx.doi.org/10.7554/eLife.13479.001

Many genes carry information for making proteins. To make a protein, a working copy of the information stored in DNA is first copied into a molecule of messenger RNA. These RNA messages are then interpreted by the ribosome, the molecular machine that makes proteins. Many messages are produced from each gene, and each message can be read multiple times. Thus, it should follow that the number of messages produced dictates the number of proteins made. However, this is not the case and the number of proteins produced cannot be completely predicted from knowing the number of messenger RNAs.

Cells control how much of a given protein they produce through interactions between the messenger RNAs and other regulatory RNAs. The regulatory RNAs bind directly to a message and impede protein production. Because there are millions of RNAs in a cell, these interactions have evolved to be highly specific. Nevertheless, it seems inevitable that messenger RNAs would encounter other RNAs too, which could short-circuit gene regulation and lead to less protein being produced.

Umu et al. have now asked if such short-circuit events are selected against during evolution. Computational tools were used to predict the strength of binding between the RNAs found in the dominant forms of microbial life on Earth: the bacteria and the archaea. This approach revealed that the majority of messenger RNAs bind more weakly to the most common RNA molecules found in cells than would be expected by chance. Weakened binding should prevent the RNA molecules from becoming tangled with each other and ensure that protein levels are not perturbed by unintended interactions between highly expressed messages and other RNAs.

To test this hypothesis further, Umu et al. generated versions of the gene for a green fluorescent protein that differed only in how well their messenger RNAs could avoid interacting with the most abundant RNAs in E. coli cells. Those messengers that were designed to avoid interacting with other RNAs yielded far more protein than those that were not. The findings show that taking this kind of avoidance into account can improve predictions about how much protein will be produced and should therefore make it easier to control protein production in experimental systems.

Finally, the messenger RNAs of some bacteria do not show such clear avoidance. However, these bacteria have a more complex internal cell structure. This finding hints at an alternative means for avoiding short-circuiting events that could be used by more complicated cells, such of those of animals and plants, which also contain much larger numbers of RNAs.

DOI:http://dx.doi.org/10.7554/eLife.13479.002

Robust identification of noncoding RNA from transcriptomes requires phylogenetically-informed sampling

Noncoding RNAs are integral to a wide range of biological processes, including translation, gene regulation, host-pathogen interactions and environmental sensing. While genomics is now a mature field, our capacity to identify noncoding RNA elements in bacterial and archaeal genomes is hampered by the difficulty of de novo identification. The emergence of new technologies for characterizing transcriptome outputs, notably RNA-seq, are improving noncoding RNA identification and expression quantification. However, a major challenge is to robustly distinguish functional outputs from transcriptional noise. To establish whether annotation of existing transcriptome data has effectively captured all functional outputs, we analysed over 400 publicly available RNA-seq datasets spanning 37 different Archaea and Bacteria. Using comparative tools, we identify close to a thousand highly-expressed candidate noncoding RNAs. However, our analyses reveal that capacity to identify noncoding RNA outputs is strongly dependent on phylogenetic sampling. Surprisingly, and in stark contrast to protein-coding genes, the phylogenetic window for effective use of comparative methods is perversely narrow: aggregating public datasets only produced one phylogenetic cluster where these tools could be used to robustly separate unannotated noncoding RNAs from a null hypothesis of transcriptional noise. Our results show that for the full potential of transcriptomics data to be realized, a change in experimental design is paramount: effective transcriptomics requires phylogeny-aware sampling.

We have analysed more than 400 public transcriptomes, generated using RNA-seq, from almost 40 strains of Bacteria and Archaea. We discovered that the capacity to identify noncoding RNA outputs from this data is strongly dependent on phylogenetic sampling. Our results show that, for the full potential of transcriptomics data as a discovery tool to be realized, a change in experimental design is critical: effective comparative transcriptomics requires phylogeny-aware sampling. We also examined how comparative transcriptomics experiments can be used to effectively identify RNA elements. We find that, for RNA element discovery, a phylogeny-informed sampling approach is more effective than analyses of individual species. Phylogeny-informed sampling reveals a narrow ‘Goldilocks Zone’ (where species are not too similar and not too divergent) for RNA identification using clusters of related species. In stark contrast to protein-coding genes, not only is the phylogenetic window for the effective use of comparative methods for noncoding RNA identification perversely narrow, but few existing datasets sit within this Goldilocks Zone: by aggregating public datasets, we were only able to create one phylogenetic cluster where comparative tools could be used to confidently separate unannotated noncoding RNAs from transcriptional noise.