Skip to main content
Premium Trial:

Request an Annual Quote

University of Florida’s Rolf Renne Discusses Viral microRNAs and Tumorigenesis

Name: Rolf Renne
Position: Associate professor, genetics/microbiology, University of Florida
Background: Assistant professor, hematology/oncology, Case Western Reserve University — 1999-2004
Postdoc, University of California, San Francisco — 1995-1998
Postdoc, Albert Ludwigs University of Freiburg — 1994-1995
PhD, retrovirology/foamyviruses, Albert Ludwigs University of Freiburg — 1993
MA, biology/genetics, Albert Ludwigs University of Freiburg — 1989

Researchers from the University of Florida last week published in the online version of PLoS Pathogens a paper showing that the expression of 10 microRNAs within the genome of Kaposi sarcoma–associated herpesvirus in 293 cells led to significant down-regulation of a number of genes.
One of those genes, thrombospondin 1, is highly anti-angiogenic and known to be down-regulated in Kaposi sarcoma tumors, suggesting that “virally encoded miRNAs may directly contribute to pathogenesis and potentially tumorigenesis in the infected host,” according to the paper’s authors.
This week, RNAi News spoke with Rolf Renne, the senior author of the PLoS Pathogens paper, about the findings.
Can you talk about the research focus of your lab?
My lab is highly focused on Kaposi sarcoma herpesvirus, and the main focus had been until two years ago on the latency-associated nuclear antigen of KSHV, which is an origin-binding protein required for latent DNA replication.
Two years ago, we started working on microRNAs and we were one of three labs that reported on the presence of KSHV microRNAs in the genome. We reported this in the Journal of Virology in 2005, but there were two other groups … that reported this [around the same time].
Regarding this paper that just came out, can you talk about the questions you were looking to answer?
The underlying interest for us is that these microRNAs are expressed at the same time during latency, [as are] a lot of other genes that are thought to be important for pathogenesis/tumorigenesis of the virus. For all microRNAs, the big question is, “What genes are targeted?”
The problematic thing for microRNAs, in contrast to siRNAs, is that mainly the seed sequence determines binding specificity. But since the seed sequence is pretty short — only seven base pairs out of 23 — every microRNA can bind to many target genes, so it’s hard to just predict them by computer algorithms.
What we thought when we started working on this is that the virus gives us a nice opportunity in that we can ectopically express [the KSHV] microRNAs because they should not interfere with any cellular microRNAs. In eukaryotic cells, we either have to delete or over-express [a microRNA, but in KSHV] we just bring something in that is novel to the cell, then ask, “What happens to the gene expression profile of the cell?”
We did this within a model system, 293 cells, which were chosen because they can be easily manipulated — in other words, they are highly transfectable — and you can do confirmation assays in these cells, but not so much in primary cells. We looked at which genes are changed in response to the microRNAs.
What’s special about those microRNAs is that they’re a cluster of 10, which are expressed at the same time within the same [KSHV] cells. Rather than express one at a time, we chose to over-express the entire cluster in 293 cells and then do the array experiments.
What did you find?
We found that 205 probes sets out of about 40,000 were changed. When we did initial data trimming, it turned out that about 62 genes were down-regulated and nine genes were up-regulated. That was a good indication that we had something that had to do with the microRNAs because most genes were down-regulated, not up-regulated.
What we did then was team up with a bioinformatics person in our department, Alberto Riva, and we did a prediction of seed sequences within the 3’ UTRs of those genes we had found to be changed in response to microRNA expression. We found that seed sequence matches were highly enriched in these 3’ UTRs.
We looked through the list and started working on those genes that were most changed in response to microRNA expression first, in particular three genes: SPP1, thrombospondin, and PRG1. We did qRT-PCR to confirm that indeed they were changed, and they were.
To really [demonstrate] microRNA-dependent regulation, one thing you have to do is look at whether … you can see the same gene down-regulate … if you put the 3’ UTR for a specific gene downstream of a reporter gene — in this case luciferase — and then … over-express the microRNAs again. That’s what we did for the three genes and we showed that they were regulated by these microRNAs.
When we did a Western blot, we found that thrombospondin … was reduced more than 10-fold in the cells in which the microRNAs were expressed. However, for the two other genes, that was not the case, which was disheartening, but that’s the data the way we found it.
So we focused in on thrombospondin, which turns out to be kind of an important gene in the context of Kaposi sarcoma because a hallmark feature of [the disease] is that it is very angiogenic, and thrombospondin’s function is to be very anti-angiogenic. So the idea here would be that the virus brings in genes that can inhibit an anti-angiogenic factor [to] support growth of blood vessels into the tumor.
What I want to convey is that this is the first step of the analysis. What, of course, needs to be done is follow up with this in cells that are more relevant for the biology of the virus, which are lymphoid cells and endothelial cells.
You said that you looked at these microRNAs together as a cluster. Are there plans to start looking at them on their own?
That’s what we’re doing right now in the laboratory. What we have done in this paper is create a cell that expressed all of the microRNAs at the same time. However, we have already in the laboratory cell lines from different origins that express each of those microRNAs by themselves, and we’re doing similar studies now.
But these studies are kind of expensive, and that was why we first started doing [them as we did in our recent paper]. There’s also [another issue], which isn’t the price, but the biology. We did expression studies in primary fusion lymphoma cells and asked … whether [the miRNAs] are differentially expressed or whether they are expressed all at the same time. As of now, we have no indication that different microRNAs of KSHV are differentially expressed.
In other words, they are all expressed at the same time, so they will have their biological effect at the same time. In addition to this, a surprising finding was that the gene we found to be best targeted, thrombospondin, was, in an inhibiting experiment using antagomirs … found to be targeted by at least six out of the nine microRNAs in our experiment.
So it’s not just one microRNA regulating one gene, and that is in line with recently published work [suggesting that] mammalian microRNAs seem not to work like C. elegans microRNAs where you have [a] one microRNA-one gene linear relationship.

The Scan

Genetic Tests Lead to Potential Prognostic Variants in Dutch Children With Dilated Cardiomyopathy

Researchers in Circulation: Genomic and Precision Medicine found that the presence of pathogenic or likely pathogenic variants was linked to increased risk of death and poorer outcomes in children with pediatric dilated cardiomyopathy.

Fragile X Syndrome Mutations Found With Comprehensive Testing Method

Researchers in Clinical Chemistry found fragile X syndrome expansions and other FMR1 mutations with ties to the intellectual disability condition using a long-range PCR and long-read sequencing approach.

Team Presents Strategy for Speedy Species Detection in Metagenomic Sequence Data

A computational approach presented in PLOS Computational Biology produced fewer false-positive species identifications in simulated and authentic metagenomic sequences.

Genetic Risk Factors for Hypertension Can Help Identify Those at Risk for Cardiovascular Disease

Genetically predicted high blood pressure risk is also associated with increased cardiovascular disease risk, a new JAMA Cardiology study says.