A Research Study Progress Report

microRNA Deregulation in Mesenchymal Transformation and Sarcomagenesis

Eva Hernando, PhD

Assistant Professor

Co-Director MD/PhD Program

Department of Pathology

New York University School of Medicine

NYU Langone Medical Center

[Editor's Note: The research study that Dr. Hernando reports on here was co-funded by the Liddy Shriver Sarcoma Initiative and Leiomyosarcoma Direct Research Foundation (a.k.a., LMSdr and LMSarcoma Direct Research Foundation). This research study represents the first attempt to classify leiomyosarcomas based on their miRNA profile, and to explore the contribution of miRNAs to the smooth-muscle oncogenic transformation process. She reports on the two main objectives of the study which are stated below.]

1. Development and Characterization of in vitro SMC Differentiation of Mesenchymal Stem Cells (MSCs)

To characterize the molecular basis of LMS, it is crucial to understand the normal biology of SMC differentiation. To this end, we have developed and characterized an in vitro model of SMC differentiation, identified the miRNA ‘signature’ (Figure 1) of this differentiation and have begun to develop tools to investigate the role that these miRNAs (and their targets) play in SMC differentiation. 

You can click on any of the figures in this report to view a larger image.

We have developed an in vitro smooth-muscle cell (SMC) differentiation system from human mesenchymal stem cells (hMSCs) to SMCs adapting a previously published protocol. Briefly, hMSCs acquired from Dr. Prockop’s Lab (Tulane University) were cultured to near confluency, at which time smooth muscle differentiation medium (SMDM) was added. At various timepoints after induction of differentiation (0, 1, 2, 5, 7, 10, 14, 21, 28, 35, and 42 days) cells are collected for RNA extraction, and glass slides seeded with cells are fixed for future immunofluorescence (IF) studies. To date, we have successfully reproduced and characterized the differentiation of hMSCs to SMCs no less than 5 times. The differentiation state of SMCs is determined by morphology of cells as examined by phase contrast microscopy, as well as reverse transcriptase polymerase chain reaction (RT-PCR), IF, and fluorescence-activated cell sorting (FACS) analysis of specific markers. Additionally, the contractile function of differentiated SMCs was assayed using established protocols.

Our data demonstrates that by 14 days after induction of differentiation, the hMSCs begin to form “hills and valleys” and visible fibers can be seen within the cytoplasm of the cells, both characteristics of cultured SMCs (Figure 2).

This distinct SMC morphology coincides with induction, as assayed by RT-PCR, of multiple SMC markers including SM-MHC and reduced expression of hMSC markers, including CD73 (Figure 3). 

Induction of ASMA and loss of CD105 can also be seen by IF (Figure 4). Functional assays indicate that after 21 days in SMDM the cells have acquired the capacity to contract.

To define the miRNA ‘signature’ of SMC differentiation, total RNA was extracted from various timepoints during differentiation and hybridized to ‘early release’ Agilent miRNA microarrays containing the mature miRNA sequences for all miRNAs according to Sanger miRBase Version 9.2. Raw intensity values were obtained from the arrays with Agilent’s Feature Extractor software and translated to ‘total gene signal’ values by subtracting background. Total gene signals found to be less than background were flagged as ‘undetectable’. The preliminary data consists of two independent timecourses. Corresponding timepoints for each timecourse are being used as biological duplicates. Our collaborator, Dr. Nicholas D. Socci (Computational Biology Center, Memorial Sloan-Kettering Cancer Center) then performed quantile normalization and generalized log transformation to produce normalized values for analysis. At this point, any miRNA found to be ‘undetectable’ in 50% or more of the samples across the timecourse was eliminated from further analysis. To determine which miRNAs are altered during differentiation, a J.T. trend test was performed. A comparison was made between the first timepoint (t=0) and the end timepoint (in this case t=28 for one timecourse and t=35 for the biological duplicate). All trend test results were limited to a false discovery rate (FDR) of less than 0.09. Because of low sample numbers, we have been unable, at this time, to run computational analyses for more dynamic expression patterns, ie, miRNAs with a biphasic expression pattern. Additional samples have been submitted which will allow more complex statistical analyses.

Preliminary data has identified 13 miRNAs either significantly upregulated or downregulated during SMC differentiation. To enhance the significance of our data, two more timecourses have been submitted as additional biological replicates.

After defining the subset of miRNAs that are differentially expressed, the next step will be to determine, at the molecular level, which role these miRNAs may play in the process of SMC differentiation. Based on predicted targets (TargetScan.org), literature searches, and comparing the SMC differentiation ‘signature’ to the LMS ‘signature’ we have selected few a miRNAs to begin testing for a functional role in the SMC differentiation process. To this end, we have cloned the pre-miRNA sequences of candidate miRNAs into the inducible lentiviral vector pTRIPZ (Open Biosystems), and we are currently testing their functions.

2.- Identification of the LMS miRNA ‘Signature’

The molecular basis of LMS genesis and progression is widely unknown. We have identified a subset of miRNAs dysregulated between normal and tumor samples, and are developing tools to be used to determine the role that these miRNAs and their targets might play in LMS.

Briefly, total RNA was extracted from frozen blocks of 10 normal myometrial samples, 10 fibroid (benign uterine leiomyomas) and 9 LMS tumor samples. The samples were then hybridized to ‘early release’ Agilent miRNA microarray slides and normalized as described above. In this analysis, miRNAs found to be ‘undetectable’ in 25% or more of the samples were eliminated from further analysis. After normalization, a pairwise t-test comparing tumor versus benign and normal samples was performed to determine which miRNAs were significantly dysregulated. The results were limited to an FDR of less than 0.01. Preliminary analysis of the miRNA microarray data has identified a distinct miRNA ‘signature’ for LMS.

Based on hierarchical clustering, the miRNA ‘signature’ of LMS was able to faithfully cluster most of the tumor samples. An unsupervised hierarchical clustering was also performed to investigate the ability of the miRNA signatures of normal and tumor samples to faithfully classify the tissue type (Figure 5).

Using the same means as outlined in the preliminary data for Aim 1, several miRNAs from the LMS ‘signature’ have been chosen for investigation into their functional role in LMS. Higher significance is given to miRNAs that demonstrated a trend during SMC differentiation in addition to being dysregulated in LMS. We have successfully cloned the pre-miRNA sequences of some miRNA candidates into the inducible lentiviral vector pTRIPZ (Open Biosystems), and are starting to test their function in LMS-genesis and progression, by using primary SMCs and LMS cell lines.

Acknowledgements

Experimental work has been conducted by Laura Danielson (Graduate Student) assisted by Silvia Menendez (Research Technician), under the supervision of Dr. Eva Hernando. This study was done in collaboration with Dr. Douglas Levine and Dr. Nicholas D. Socci (Memorial Sloan-Kettering Cancer Center).

V5N3 ESUN Copyright © 2008 Liddy Shriver Sarcoma Initiative.