Investigating prokaryotic transcriptomes and the impact of crosstalk between noncoding RNA and messenger RNA interactions

Prokaryotes have a complex noncoding RNA (ncRNA) based regulatory system, resembling that of eukaryotes. Recent transcriptomics studies also point out the abundance of highly expressed uncharacterized RNAs in archaea and bacteria. However, despite the recent advances indicating the prevalence of ncRNAs in prokaryotes, it is still unknown to what extent these uncharacterized transcripts are functional. Therefore, we have proposed a phylogeny informed approach to design new RNA sequencing (RNAseq) experiments, which increases the information harnessed from transcriptome data for ncRNA detection. Many regulatory ncRNAs engage in RNARNA interactions, where RNA molecules bind to form a duplex. Predictions of true targets for an RNA enables a successful functional characterization, these can be estimated by bioinformatics methods. However, the algorithms developed to date are imperfect and it is an open question as to which ones perform well and whether these can be improved upon. Towards this goal we performed a computational benchmark study to find reliable algorithms for RNARNA interaction prediction. We found that energy based methods, which include the accessibility of interaction regions, are currently the most accurate. Many ncRNAs, including housekeeping ncRNA genes, are highly expressed. The abundances of interacting RNA molecules enable RNARNA duplex formation. In chapter IV we explore the impact of high abundance RNAs on protein expression due to crosstalk RNARNA interactions between mRNAs and ncRNAs. With extensive RNARNA interaction predictions we reveal that RNA avoidance is an evolutionarily conserved phenomenon among prokaryotes, which means that core mRNAs have evolved to avoid crosstalk interactions with abundant ncRNAs. Our predictions also reveal that RNA avoidance may influence protein expression. To test this, we investigated the stability of interactions between mRNAs and core ncRNAs. These predictions show that the RNA avoidance influences the final protein abundances. In conclusion, the primary aims of this study are to investigate the prokaryotic transcriptome for novel ncRNA genes and examine the effects of crosstalk RNA interactions. We present a method to increase information gained from transcriptome in prokaryotes for ncRNA identification. We also present the most comprehensive benchmark of RNARNA interaction prediction algorithms to date. Lastly, we introduce and test a ‘RNA avoidance hypothesis’ that shows the influence of crosstalk RNA interactions on protein expression in bacteria

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

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

Biyotrofik bitki pataojenlerinde (pas ve küf) aday efektör tespiti.

Biotrophic plant pathogens lead to huge crop losses and they have great economical drawbacks on wheat and barley production. These pathogens rely on formation of haustoria and transfer of effector proteins into the host cells for generating disease. The main role of effector proteins is to disable plant defense mechanisms. Effector proteins contain N-terminal signal peptides and they have little sequence similarity between each other. It is vital to detect as many effector proteins as possible to understand infection and disease formation processes of biotrophic plant pathogens. To this end, sequencing of pathogen genomes are being emerged, the data will be invaluable for identifying the candidate effectors in terms of biological and biochemical roles in infection and more. There are some bioinformatics based methods available that can be utilized to classify and distinguish effectors from other pathogenic genes. It is important to understand how candidate effectors can be searched from Expressed Sequence Tags or transcriptome sequences. Hereby, our attempt is to present a pipeline in establishing a methodology. As a consequence, here we propose new candidate effectors. In plant-pathogen interactions also miRNAs are too proving to be an important factor which cannot be neglected. During disease infection, expression levels of miRNAs are varying which in turn may be a proof of miRNA regulation of pathogen genes. Therefore, cross-kingdom RNA interference may take place between plant and pathogen. We have tested plant pathogens for possible plant miRNA availability and found their most likely targets with in the pathogen genes.M.S. - Master of Scienc

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

Serum RNA profiling in the 10-year period prior to diagnosis of testicular germ cell tumour

Although testicular germ cell tumor (TGCT) overall is highly curable, patients may experience late effects after treatment. An increased understanding of the mechanisms behind the development of TGCT may pave the way for better outcome for patients. To elucidate molecular changes prior to TGCT diagnosis we sequenced small RNAs in serum from 69 patients who were later diagnosed with TGCT and 111 matched controls. The deep RNA profiles, with on average 18 million sequences per sample, comprised of nine classes of RNA, including microRNA. We found that circulating RNA signals differed significantly between cases and controls regardless of time to diagnosis. Different levels of TSIX related to X-chromosome inactivation and TEX101 involved in spermatozoa production are among the interesting findings. The RNA signals differed between seminoma and non-seminoma TGCT subtypes, with seminoma cases showing lower levels of RNAs and non-seminoma cases showing higher levels of RNAs, compared with controls. The differentially expressed RNAs were typically associated with cancer related pathways. Our results indicate that circulating RNA profiles change during TGCT development according to histology and may be useful for early detection of this tumor type

Circulating small non-coding RNAs associated with age, sex, smoking, body mass and physical activity

Small non-coding RNAs (sncRNA) are regulators of cell functions and circulating sncRNAs from the majority of RNA classes are potential non-invasive biomarkers. Understanding how common traits influence ncRNA expression is essential for assessing their biomarker potential. In this study, we identify associations between sncRNA expression and common traits (sex, age, self-reported smoking, body mass, self-reported physical activity). We used RNAseq data from 526 serum samples from the Janus Serum Bank and traits from health examination surveys. Ageing showed the strongest association with sncRNA expression, both in terms of statistical significance and number of RNAs, regardless of RNA class. piRNAs were abundant in the serum samples and they were associated to sex. Interestingly, smoking cessation generally restored RNA expression to non-smoking levels, although for some sncRNAs smoking-related expression levels persisted. Pathway analysis suggests that smoking-related sncRNAs target the cholinergic synapses and may therefore potentially play a role in smoking addiction. Our results show that common traits influence circulating sncRNA expression. It is clear that sncRNA biomarker analyses should be adjusted for age and sex. In addition, for specific sncRNAs, analyses should also be adjusted for body mass, smoking, physical activity and technical factors

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

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

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