non-coding genome | the Non-Coding RNA Group

HCMV

Sat, 10 Oct 2020 00:00:00 +0000

HCMV is present in humans with seroprevalence ranging from 45 to 96% globally (about 96% in China). HCMV infection occurs throughout the body, is always mild and asymptomatic in healthy populations, but is symptomatic or even lethal in immunocompromised/immune-immature people, such as HIV-infected patients, transplant recipients and new-borns infected in utero. The virus can enter latency after primary infection, but can be re-activated at any time.

In the general population, the immune system restricts HCMV infection to latency and is asymptomatic. However, HCMV infection causes severe diseases in immune-immature and immune-compromised patients. This includes transplant recipients, who often suffer severe complications including pneumonia, hepatitis and gastrointestinal ulcerations after transplantation. This is a consequence of their impaired immune system which allows the latent HCMV infection to be reactivated. Consequently, transplant recipients always receive pre-emptive or prophylactic antiviral therapy but nevertheless have a high risk of developing drug resistance and HCMV reactivation.

To date, no effective vaccine is available for HCMV, and only four licensed drugs (ganciclovir/valganciclovir, cidofovir and foscarnet, which all target viral DNA replication) are available. Most recently, in the US in 2017, letermovir, which targets viral DNA packaging, was licenced for prophylaxis in bone marrow patients. However, all of these drugs can cause severe side effects and, in the long-term, drug resistance inevitably occurs due to accumulation of mutations in the HCMV genome. In spite of these problems, HCMV drug resistance has not been well characterized, leaving a huge gap in options for clinical therapy and treatment.

HCMV studies to date have primarily been experimental and have used “classical” approaches to identify key proteins and clarify their roles in the infection process. Computational methods can assist in genome characterization and is widely used in the study of other viral and bacterial genomes, but this approach has not been widely adopted in the investigation of the HCMV genome. We are developing computational methods to perform genome wide comparison of HCMV strains, including both coding and non-coding (miRNA) regions. In this work, we collaboratiing with the lab of Luo Minhua in Wuhan Institute of Virology and hospitals in Beijing and Changsha who have been collecting viral isolates from transplant patients. This work is supported by a Scientia Fellowship from UiO

Ethnicity

Wed, 17 Jun 2020 00:00:00 +0000

A 2016 report on the ethnic composition of WES and WGS studies found that 84% of studies involved Europeans, only 14% & 3% of samples originated from Asia and Africa respectively. The bias is even more pronounced for studies of the non-coding genome, with only a handful of reports on population SNVs. Even though the situation is improving, the majority of studies remain biased towards Caucasian populations. This lack of variation among populations can impact awareness of how efficacious a drug may be or how likely it is to cause adverse events. However, the advent of large-scale WGS with broader populations means it is now possible to consider population specific variation within NC genomic regions.

In this work we are using publicly available data from the 1000 genomes project and the African Variome Project and combining it with data from collaborators in Africa and China to identify variations in specific populations that occur within miRNA associated regions. This work is funded by a Research Council of Norway FRIPRO grant.

miRNAs

Mon, 27 Apr 2020 00:00:00 +0000

There are more than 45 000 miRNA related publications in PubMed. While some studies investigate miRNA biogenesis, function and decay, the majority of work has focused on identifying specific miRNAs with roles in disease and developmental processes. In the most common approach, miRNA association studies are performed using microarray or Next Generation Sequencing (NGS) platforms to compare two conditions (e.g. healthy versus cancer) and identify miRNAs that have statistically significant differences in expression levels. The mRNA targets of these miRNAs are predicted using computational tools such as TargetScan3 and functionally interesting ones may be also experimentally verified.

However, these studies implicitly assume an oversimplistic model of miRNA function

Annotation: miRNA studies are dependent on annotation and the primary reference resource is miRBase. The quality of this resource is variable and different versions can return different results in miRNA expression studies (i.e. identification of differentially expressed miRNAs). A further complication is that miRBase includes highly similar or duplicate miRNAs, and miRNAs that have multiple copies.
isomiRs: In most miRNA expression studies it is assumed that miRNAs exist as a well-defined and stable entity, i.e., the single sequence specified in miRBase is the exact form in which a miRNA is expressed. In reality, a miRNA is expressed as a series of highly similar isoforms, or isomiRs, which have demonstrated functional roles18. Microarray based studies are unable to capture this variation, and most NGS Small RNA Sequencing (Small RNA Seq) studies generally fail to consider such deviations.
Ethnicity: This is rarely considered in miRNA studies. miRBase annotation is based on the standard reference genome, GrCh38.p13, but ethnicity can impact miRNA studies by (i) failing to map reads to features containing population specific SNVs, and (ii) failing to incorporate population specific variation in the 3’UTR targets.
Targeting: Due to cost and throughput issues, determining miRNA targets is heavily dependent on computational prediction tools. Many of these are rule based, i.e., they incorporate knowledge into the prediction process - in particular, they require the presence of seed region binding (nt2 to nt7/8 in a miRNA). Even machine learning based approaches used by tools such as TargetScan incorporate this information into their models. While this helps to improve model performance, it biases the model to identify targeting events based on existing knowledge, rather than providing new insight into the targeting process.

Jasmine: a Java pipeline for isomiR characterization in miRNA-seq Data

Sun, 01 Mar 2020 00:00:00 +0000

miRAW: A deep learning-based approach to predict microRNA targets by analyzing whole microRNA transcripts

Sun, 01 Jul 2018 00:00:00 +0000