Journal Club

The role of extracellular vesicles (EVs) in cancer has recently become a promising area of research. A primary function of EVs is to deliver molecules from a donor cell to regulate cellular processes in a target cell. Little research has investigated what effect departing EVs may have on the donor cell.

In cancer, EVs from tumor cells can deviate from their original purpose. Altering vesicular content could benefit tumors, for example by increasing tumor proliferation or strengthening drug resistance.
 

Exosomal Packaging
Could donor tumor cells selectively shunt cancer-fighting molecules into secreted vesicles to escape their effects? Teng et al. thought this might be the case. They hypothesized that tumor cells specifically secrete tumor suppressor miRNA, namely miR-193a, into exosomes, a class of EV secreted via the endocytic membrane transport pathway, while oncogenic miRNAs are kept.

To test this theory, they validated miR-193a’s function in a mouse model. Specifically, they found that miR-193a targets Caprin1, a cell-cycle-associated protein, and arrests the cell cycle in phase G1. Thus, secreting miR-193a from the cell in exosomes would restart the cell cycle and enable a tumor cell to proliferate.

After studying miR-193a’s primary function, the authors explored what might facilitate its secretion. They showed that MVP (major vault protein) complexes with miR-193a and that knock-down of MVP leads to higher levels of miR-193a in the cell and less miR-193a in exosomes. They concluded that MVP mediates the sorting of miR-193a into exosomes. They also found that a higher level of MVP in the cell correlates with lower levels of miR-193a and higher levels of Caprin1, indicating that MVP aids in cell proliferation. Lastly, the researchers determined in a mouse model that export of miR-193a by MVP promotes metastasis of colon cancer to the liver.

Figure 1 shows a model of these interactions. In the pre-metastatic cell, miR-193a is freely expressed. In the tumor metastatic cell, MVP has complexed with miR-193a, driving it into exosomes to be secreted.

Figure 1: Model for the mechanism of colon cancer metastasis to the liver. Tumor suppressor mir-193a is sorted into exosomes by Major Vault Protein (MVP) and then secreted from the cell.

Figure 1: Model for the mechanism of colon cancer metastasis to the liver. Tumor suppressor mir-193a is sorted into exosomes by Major Vault Protein (MVP) and then secreted from the cell.


 

Cancer Biomarker
This finding implicates exosomal miR-193a as a potential biomarker for colon cancer. Could higher levels of exosomal miR-193a indicate a more aggressive disease? To answer these questions, the authors applied their findings in the clinical setting. Teng et al. examined the livers of mice with metastatic colon cancer, performing an extensive characterization of the levels of tumor suppressive and oncogenic miRNAs in normal and tumor cells and in the exosomes secreted by both. From that extensive analysis, they chose three miRNAs upregulated (miR-193a, miR-126 and miR-148a) and one miRNA downregulated (miR-196b) in exosomes from tumor cells and examined the levels of those miRNAs in exosomes purified from the blood (plasma) of 40 colon cancer patients, 15 with metastasis to the liver and 25 without. They found the same upregulation and downregulation of the 4 miRNAs in the patient population, with higher levels in the population with liver metastasis.

This exciting study identifies potential biomarkers for colon cancer. The two main findings highlight the importance of exosomes in cancer proliferation. First, the authors found that tumor suppressing miRNAs are packed into exosomes, while oncogenic miRNAs remain in the cell. Next, they found that there are higher amounts of tumor suppressing miRNAs in tumor-derived exosomes relative to exosomes from healthy cells. They also found that MVP acts as a mediator of these differences between metastatic and healthy cells. Teng et al. believe that once we develop a dependable method to purify exosomes, scientists may uncover further roles for exosomes in cancer progression.

Recent research from members of the ExRNA Communication Consortium (ERCC) suggests that extracellular RNAs (exRNAs) circulating in plasma play an active role in insulin resistance (IR). Insulin resistance is an incurable but manageable syndrome where the body stops reacting efficiently to the insulin hormone, which stimulates the uptake of glucose in the blood into cells and inhibits the body from using fat for energy, resulting in high blood sugar levels. The study, by Shav et al., points to certain exRNAs, particularly miR-122 and miR-192, as indicators and active players in IR regardless of the age, sex, or BMI of a person, which suggests that they may serve as more than metabolic markers and that they perhaps have functional, trans-organ roles in mediating IR.

Previous research has demonstrated that exRNAs have different functions in pathways relating to metabolic syndrome, which is a series of conditions that increase the risk of heart disease, stroke, and diabetes. For example, there is measurable miRNA dysregulation in obesity and in the progression of cardiometabolic disease. Other miRNAs are involved in brown/white fat specification, adipose tissue inflammation (Karbiener and Scheideler, 2014), and hepatic steatosis (fatty liver disease, Becker et al., 2015). Although these studies have identified specific exRNAs and miRNA networks that also have roles in IR, they have had either small sample sizes and lack validation in large populations or have been carried out in non-human models. In order to validate these data, the authors carried out a large-scale human translational study in which they analyzed detailed obesity-related phenotypic data from over 2,500 participants (most of whom were non-diabetic) from the Framingham Heart Study (FHS), an unrelated cardiovascular disease study (Feinleib et al., 1975).

To begin, the investigators analyzed blood samples from 2,317 non-diabetic study participants and quantified the plasma extracellular circulating exRNAs. They looked at RNAs [including piwi-interacting RNA (piRNA) and small nucleolar RNA (snoRNA)] expressed above a threshold level and excluded RNAs that were not found in at least 100 participants. From the resulting panel of 391 exRNAs, the investigators identified 16 microRNAs (miRNA), 1 piRNA, and 1 snoRNA that were associated with insulin after controlling for age, sex, and BMI. Of note, the abundance of miR-122 was shown to increase in a stepwise fashion as levels of insulin increased across the population. Higher levels of both miR-122 and miR-192 in the plasma were also consistently associated with a series of metabolic phenotypes, such as greater BMI and waist circumference, visceral fat quantity and quality, and liver attenuation. On the other hand, neither miRNA was associated with subcutaneous fat. These results were consistent whether the analysis included only the non-diabetic participants or the entire FHS population.

miRNAs function to regulate gene expression, so the authors conducted a pathway analysis to determine the targets of the 16 identified miRNAs. Almost unsurprisingly, all 16 miRNAs target insulin signaling pathways such that there is ample crosstalk and targeting of multiple IR-related genes by multiple miRNAs. This analysis validated findings from previous studies that implicated several miRNA target genes in the pathogenesis of IR, notably protein tyrosine phosphatase, nonreceptor type 1 (PTP1B) (Stull et al., 2012), mitogen-activated protein kinases (MAPKs) (Wang, Goalstone & Draznin, 2004), and 5′ adenosine monophosphate-activated protein kinase (AMPK) (Ruderman et al., 2013).

For the second part of the study, the investigators determined whether the miR-122 and miR-192 associations to age, sex, and BMI held true in a separate study population, a cohort of 90 overweight or obese young adults involved in the POOL study. Analyses of the youths’ plasma samples indicated that miR-122 (but not miR-192) was associated with greater IR after adjusting for age, sex, and BMI, and that this association remained even after the miRNA was analyzed independently of age, sex, BMI, or metabolite profile.

This study provides additional evidence and translational support for the role of exRNAs in IR. The findings indicate not only an association of exRNAs with insulin levels, but that the exRNAs may be playing an active role in the development or sustainment of IR. It is therefore critical to conduct further mechanistic investigations into the role of exRNAs in the metabolic architecture of IR.

The study of RNAs that do not produce proteins, so-called noncoding RNAs, has been an active area of research for many years. Recently, new kinds of non-coding RNAs have been described that have poorly defined activities. Circular RNA (circRNAs) are one of these more enigmatic biomolecules. They are formed when the 5′ head and 3′ tail of a messenger RNA precursor are spliced together. Next-generation sequencing studies have recently shown that circRNAs are abundant and widely expressed in mammals. While other non-coding RNAs have been shown to play critical roles in cancer, the association between circRNAs and cancer is largely unknown. In addition, the degree to which circRNAs are secreted outside the cell has not been well explored.

To study the presence and regulated release of circRNAs during colorectal cancer (CRC) progression, we used three related colon cancer cell lines that differ only in the mutation status of KRAS, an enzyme that acts at the beginning of a wide array of cellular signaling pathways. The parental cell line (DLD-1) contains both wild-type and G13D mutant KRAS alleles, whereas the derivative cell lines contain only a mutant KRAS (DKO-1) or wild-type KRAS (DKs-8) allele (Shirasawa et al. 1993). The G13D mutation locks KRAS into an active state. KRAS mutations occur in approximately 34–45% of CRCs and have been associated with a wide range of tumor-promoting effects (Vogelstein et al. 1988, Wong and Cunningham 2008). We performed deep RNA-Seq analysis of ribosomal RNA-depleted total RNA libraries to characterize circRNA expression in these cell lines and in the exosomes they release. The results from this study were recently published in the journal Scientific Reports (Dou et al. 2016).

Using a unique pipeline developed by our group, we identified hundreds of high-quality candidate circRNAs in each cell line. Remarkably, circRNAs were significantly down-regulated at a global level in the cell lines with mutant KRAS alleles (DLD-1 and DKO-1) compared to wild type (DKs-8), indicating a widespread effect of mutant KRAS on circRNA abundance (see Figure 1). This finding was confirmed in another pair of cell lines. In all of these cell lines, circRNAs were found associated with secreted exosomes, and circRNAs were more abundant there than in cells. Although circRNAs were down-regulated in cell lines with mutant KRAS alleles, it is difficult to conclude that KRAS directly regulates circRNAs. Nevertheless, our analysis did show that down-regulation of circRNAs in KRAS mutant cells was not caused by their increased export to exosomes.
 

Figure 1.

Figure 1. The blue highlight shows that expression of most circRNAs is lower in the KRAS-mutant cell lines than in the KRAS wild-type cell line.
FDR = False Discovery Rate; a higher number indicates a more confident prediction of a difference in expression.

There are complex regulatory mechanisms for expression of both circRNA and the host genes from which they derive. Figure 2 shows that lower expression of circRNA in the mutant KRAS vs. wild-type cell lines was not matched by a similar lower expression of host gene mRNA. We found a similar lack of correlation in circRNA and host gene mRNA expression level in all the exosome populations we studied. These results imply that regulation of circRNAs can occur independent of their host genes, and different regulatory processes might direct secretion of circRNA and host gene mRNA.
 

Figure 2.

Figure 2. The blue highlight shows that while expression of most circRNAs is lower in the KRAS-mutant than in the wild-type cell line, host gene mRNA expression shows no such pattern.

To further delineate how circRNA biogenesis could be affected by mutant KRAS, we also examined the expression levels of the RNA-editing enzyme ADAR and the RNA-binding protein QKI, which have been reported as circRNA regulators (Ivanov et al. 2015, Conn et al. 2015) (see Figure 3). Here we obtained contradictory results. The level of ADAR was decreased in the KRAS mutant cells; reduced ADAR activity could lead to an increase of circRNAs. QKI was also down-regulated in KRAS mutant cells, which could lead to a decrease of circRNAs.
 

Figure 3. Effect of ADAR and QKI on pre-mRNA circularization

Figure 3. Effect of ADAR and QKI on pre-mRNA circularization

More broadly, we studied the expression levels of all RNA-binding proteins within the RBPDB database (Cook et al. 2011). Six were found to be significantly differentially expressed in KRAS mutant cell lines compared with wild-type KRAS cell lines (ELAVL2, RBMS3, BICC1, MSI1, RBM44, and LARP6). These genes may serve as candidate circRNA regulators. However, our previous work shows that the correlation between mRNA and protein expression level is low for RNA-binding proteins (Zhang et al. 2014), and thus RNA levels for these RNA-binding proteins might not reflect their true protein levels. Further investigation will be needed to precisely define how circRNAs are regulated. Nevertheless, our results show that oncogenic mutations can change circRNA composition in cells and exosomes and suggest that circRNAs may serve as promising cancer biomarkers.

References

Conn, S.J., et al. The RNA binding protein Quaking regulates formation of circRNAs. Cell (2015) 160: 1125-1134. PMID 25768908.

Cook, K.B., et al. RBPDB: a database of RNA-binding specificities. Nucleic Acids Res (2011) 39: D301-D308. PMID 21036867.

Dou, Y., et al. Circular RNAs are down-regulated in KRAS mutant colon cancer cells and can be transferred to exosomes. Sci Rep (2016) 6: 37982. PMID 27892494.

Ivanov, A., et al. Analysis of intron sequences reveals hallmarks of circular RNA biogenesis in animals. Cell Rep (2015) 10:170-177. PMID 25558066.

Shirasawa et al. Altered growth of human colon cancer cell lines disrupted at activated Ki-ras. Science (1993) 260:85-88. PMID 8465203.

Vogelstein, B., et al. Genetic alterations during colorectal-tumor development. N Engl J Med (1988) 319:525-532. PMID 2841597.

Wong, R. and Cunningham, D. Using predictive biomarkers to select patients with advanced colorectal cancer for treatment with Epidermal Growth Factor Receptor antibodies. J Clin Oncol (2008) 26:5668-5670. PMID 19001346.

Zhang, B., et al. Proteogenomic characterization of human colon and rectal cancer. Nature (2014) 513:382-387. PMID 25043054.

Alzheimer’s Disease (AD) accounts for a large number of dementia cases resulting in impaired memory, thinking, and behavior. Risk factors for AD include age and family history, but unfortunately there is not yet a definitive way to predict if an individual will develop the disease. There are reference biomarkers that can indicate a higher risk of developing AD, such as APOE genotype. Carriers of the APOE4 allele, present in ~20% of the population, are at increased risk for AD. Cerebrospinal fluid (CSF) is a body fluid found in the brain and spine that cushions and protects the brain from injury. CSF protein biomarkers, such as Aβ42, tau and phospho-tau, are important in screening for brain disease, but these reference markers often lack the sensitivity and specificity necessary for clinical utility.

Extracellular RNA, specifically microRNA (miRNA), has been found in CSF and may serve as a useful resource for improved AD biomarkers. In a recently published study, the Saugstad lab from Oregon Health and Science University examined CSF from a large group of living donors to identify unique miRNA biomarkers enriched in AD patients. In the study, miRNA expression levels from 50 AD and 49 control subjects were assessed using TaqMan Low Density Arrays containing probes for 754 validated miRNAs. Each miRNA was given a “Multitest Score” combining the results of four statistical tests, and miRNAs that passed two or more of the tests were considered for further analyses.

Two statistical tests, log-rank and logistic regression, were used to identify candidates that were twice as likely to be associated with AD status as not. The other tests were two variants of random forest classifier, CART and CHAID, designed to select biomarker candidates able to reliably distinguish AD from non-AD status when grouped with random subsets of other miRNAs. 36 miRNA biomarker candidates were identified by at least two of these analyses. The researchers found that linear combinations of subsets of miRNA, and the addition of ApoE genotyping status, further increased the sensitivity and specificity of AD detection (Figure 1).

 

Figure 1. CSF miRNA biomarkers and APOE genotype predict AD status better together. AUC - Area Under the Curve; higher AUC indicates higher predictive power.

Figure 1. CSF miRNA biomarkers and APOE genotype predict AD status better together. AUC – Area Under the Curve; higher AUC indicates higher predictive power.

Reprinted with permission from IOS Press.


 

This study shows the potential use of miRNAs isolated from CSF as AD biomarkers. The stringent statistical analyses and large sample size together provided strength to these initial studies. These 36 candidate biomarkers are currently being tested in further validation studies in CSF from a new group of 120 donors, which will also include APOE genotyping and Aβ42 and tau protein levels. Ultimately, a combination of miRNA CSF biomarkers with existing reference biomarkers (APOE, Aβ42, tau) may provide a specific and sensitive tool for the diagnosis of AD in the clinic.

Citation:
MicroRNAs in Human Cerebrospinal Fluid as Biomarkers for Alzheimer’s Disease
Lusardi T, Phillips J, Wiedrick, J, Harrington C, Lind B, Lapidus J, Quinn J, Saugstad J. Journal of Alzheimer’s Disease (2017) 55: 1223-1233. doi: 10.3233/JAD-160835

Multiple sclerosis (MS) is an autoimmune demyelinating disease of the central nervous system (CNS). Currently, magnetic resonance imaging (MRI) is the most commonly used method to diagnose and monitor MS, but there is a poor correlation between MRI disease measures and clinical disability or disease progression in MS. MRI is also an expensive tool that might carry potential risks due to brain accumulation of contrast material (Kanda et al., 2015). In the last few years, a lot of effort has been invested in the identification of biomarkers for MS; however, to date, few of these findings have proven clinically useful. Thus, there is a strong unmet clinical need for objective body fluid biomarkers to assist in early diagnosis, predicting long-term prognosis, monitoring treatment response, and predicting potential adverse effects in MS.

Circulating miRNAs have been detected in several body fluids (Cortez et al., 2011) where they are highly stable as they are resistant to circulating ribonucleases (Mitchell et al., 2008). Their stability, along with the development of sensitive methods for their detection and quantification (Guerau-de-Arellano M. et al., 2012), makes them ideal candidates for biomarkers. We previously reported changes in circulating plasma miRNAs in MS patients (Gandhi R. et al., 2013). In a new study, our group investigated serum miRNAs as biomarkers in MS as part of an NCATS-funded UH2 initiative. We found that several serum miRNAs were differentially expressed in MS, were associated with disease stage, and correlated with disability.

Study Design (Figure 1): Serum from 296 participants including patients with MS, other neurologic diseases (Alzheimer’s disease and amyotrophic lateral sclerosis), inflammatory diseases (rheumatoid arthritis and asthma), and healthy controls (HC) were tested. miRNA profiles were determined using LNA (locked nucleic acid) based qPCR. MS patients were categorized according to disease stage and disability. In the discovery phase, 652 miRNAs were measured from the serum of 26 MS patients and 20 healthy controls. Those miRNAs from the discovery set that were significantly differentially expressed (p <0.05) in cases vs controls were validated using qPCR in 58 MS patients and 30 healthy controls.

 

Serum miRNA biomarkers in MS - Figure 1

 

Note: Results in the current study were normalized to the four most stably expressed miRNA across all the subjects. We agree with other blogs posted on exRNA.org suggesting that there is an immediate need to identify reference miRNA/exRNA that could be used for data normalization.

 

Figure 2: Differentially expressed circulating miRNAs as biomarkers in Multiple Sclerosis (MS). Up to top five miRNAs with p<0.05 are represented for each group comparison; a) MS, b) relapsing remitting MS (RRMS) and secondary progressive (SPMS) compared to the healthy control (HC), c) RRMS vs. SPMS, and d) the correlation of miRNA with the expanded disease severity scale (EDSS).

 

Results: We found 7 miRNAs (p<0.05 in both discovery phase and validation) that differentiate MS patients from healthy controls; miR-320a up-regulation was the most significantly changing serum miRNA in MS patients. We found 8 miRNAs that differentiated relapsing-remitting MS (RRMS) from HC. Among these, miR-484 up-regulation in RRMS patients showed the strongest association. When comparing secondary progressive MS (SPMS) patients to HC, 34 miRNAs significantly differentiated between the groups in both phases, with miR-320a up-regulation showing the strongest link. We also identified two miRNAs linked to disease progression, with miR-27a-3p being the most significant. Ten miRNAs correlated with degree of disability according to the Kurtzke Expanded Disability Status Scale (EDSS), of which miR-199a-5p had the strongest correlation with disability. Of the 15 unique miRNAs we identified in the different group comparisons, 12 have previously been reported to be associated with MS, but not in serum. Kegg Pathway Analysis showed that significant and differentially expressed miRNAs target important immune functions and are related to the maintenance of neuronal homeostasis. For example, miR-27a-3p, the strongest miRNA to distinguish RRMS from SPMS and progressive MS (PMS) (up-regulated in the relapsing form as compared to the progressive forms) shows a strong link to both the neurotrophin signaling pathway and the T cell receptor signaling pathway. Other studies have shown that miR-27a-3p targets multiple proteins of intracellular signaling networks that regulate the activity of NF-κB and MAPKs 6. As a consequence, miR-27a inhibits differentiation of Th1 and Th17 cells and promotes the accumulation of Tr1 and Treg cells (Min S. et al., 2012). It has also been shown that miRa-27-3p is up-regulated in MS active brain lesions and that the level of miR-27a-3p in CSF is reduced in patients with dementia due to Alzheimer’s disease (AD) (Frigerio C.S. et al., 2013). Of all the miRNAs, miR-486-5p was identified in the largest number of comparisons. It correlates with EDSS and is up-regulated in MS compared to HC, to other neurological diseases, and to other inflammatory diseases. This particular miRNA was found to be associated with TGF-beta signaling pathways and is a known tumor suppressor (Oh H.K. et al., 2011). miR-320a has been previously described to be highly expressed in B cells of MS patients and was suggested to contribute to increased blood-brain barrier permeability due to regulation of MMP-9 (Aung L.L. et al., 2015). Pathway analysis links this miRNA to cell-to-cell adhesion pathways, another indication that it may be linked to blood-brain barrier permeability.

The current study is the most comprehensive evaluation to date of the role of serum miRNAs as biomarkers in MS, with the largest sample size and employing two independent cohort designs. One limitation of our study is that participant subject samples were collected from a single MS center. Further external validation of our results will require investigating samples from patients at other centers. We are currently performing such multicenter studies, which may also increase the power of our results. A second limitation of our study is the relatively small number of participants who contributed to each group comparison. Future work will require larger sample sizes to ensure that we have sufficient power to detect miRNAs with smaller effect sizes. Although miRNAs have been studied in cells and the CNS of MS patients, ours is the first comprehensive investigation of serum miRNAs.

Conclusions: Our findings identify circulating serum miRNAs (Figure 2) as potential biomarkers to diagnose and monitor disease status in MS. These findings are now being tested using patient samples obtained from other international MS centers. We are now investigating the role of miRNA as biomarkers for disease prognosis and treatment response in MS.

Acknowledgements: This study is a highly collaborative project, and I thank my whole team at the Ann Romney Center for Neurologic Diseases & MS Center for their contribution. The grant TR000890 is supported by the NIH Common Fund, through the Office of Strategic Coordination / Office of the NIH Director.

Cell culture is a staple of modern biology, and Fetal Bovine Serum (FBS) is an essential component of many cell culture protocols. A specific use for FBS is to supply nutrients to cells and to stimulate their growth. Another role of FBS in cell culture research is to represent the complexities and functionality of endogenous biological environments; however, precisely this complexity has long been a potential confounding factor for researchers. For example, cytokines in FBS can lead to the stimulation of cells, thus producing unintended experimental environments. Despite these disadvantages, FBS retains a prominent role in modern cell culture, with estimated sales as high as 700,000 liters per year. Because of its ubiquity in cell culture research, it is critical to investigate how the components of FBS may be influencing experiments and downstream analysis.

Variability and uncertainty in the composition of FBS is especially problematic for studies that evaluate cellular secretions. For example, to successfully determine the array of RNA secreted by cultured cells, we need to know the extent to which the medium is contaminated by exogenous RNA. Additionally, extracellular RNA (exRNA) is not only found distributed freely throughout the liquid medium, but it is also often found packaged inside of extracellular vesicles (EVs) or lipoprotein complexes. Therefore, in a paper released online yesterday, Wei et al. evaluated how the RNA composition of FBS might be confounding research.

The authors first evaluated exogenous RNA contamination. They grew cultures of a cell type known not to express a particular RNA, then evaluated the presence of that RNA in the culture media. If that RNA was found, its origin was probably the media itself. For example, the authors demonstrated that miR-122, a liver-specific miRNA, is present in media from cultured glioma cells, suggesting that its source is likely FBS itself. They then attempted to deplete RNA from FBS via ultracentrifugation, but despite a 24 hour spin at 100,000g, about 75% of total RNA remained in the supernatant. This result has also been found by researchers attempting to deplete FBS of RNA-containing EVs and emphasizes the difficulty of producing media truly free from contaminating RNA.

These results led the authors to ask whether existing studies have wrongly attributed the presence of exRNA to a particular experimental procedure or cell type, when it should be recognized as a component of the FBS in the cell culture media. To answer this question, the authors first broadly profiled the RNA composition of FBS using RNA sequencing. They determined that between 9% and 22% of FBS RNA mapped to the human genome, depending on the stringency of the mapping algorithm and FBS preparation. They also checked for the presence of bovine-specific RNA in existing human cell culture exRNA datasets, finding levels as high as 17%, with samples from exosomes (a type of EV) containing particularly high levels. Finally, they demonstrated experimentally that bovine-specific transcripts are taken up into cells, interfering not only with exRNA analysis but also with intracellular RNA studies.

Moving forward, a significant remaining issue is deciding how to treat conserved RNA known to be present in both FBS and the cell line under study. Switching from FBS to purely chemically defined media can help with this problem, but it is not possible for all cell types and experimental conditions. Alternatively, a quantitative analysis of the chemical composition of the media might make it possible to estimate which RNAs are secreted by the cells of interest by filtering out known FBS RNAs from the total RNA pool.

This research cautions us to be careful in the design and interpretation of experiments to identify extracellular RNAs that use FBS in culture media. The paper, Fetal Bovine Serum RNA Interferes with the Cell Culture derived Extracellular RNA, released in Scientific Reports yesterday, is authored by Zhiyun Wei, Arsen O. Batagov, David R. F. Carter, and Anna M. Krichevsky.

Secreted RNAs leave the intracellular environment by associating with diverse vesicular and protein components. Secreted vesicles are heterogeneous and follow various routes of egress from the cell (1). Subclasses of such vesicles contain distinct cell surface proteins (2). In order to fully understand the diversity of vesicles that contain RNA, it is necessary to analyze and sort vesicle populations (3). One way to do this is by flow sorting such vesicles based on the presence of distinct vesicular surface proteins.

The ability to perform flow cytometric analysis and sorting of exosomes has been an ongoing area of controversy due to the small size of exosomes, which range in size from 40-130nm, near or below the diffraction limit of light. Nevertheless, a variety of groups have used this technique to analyze different subsets of small vesicles successfully (4-14), including proteomic analyses (4, 15-17). The efficacy of these flow-sorting experiments has been cross-validated by a variety of means, including western blots and co-localization of coincidently expressed factors. Fluorescence-Activated Vesicle Sorting (FAVS) uses light scattering properties of vesicles to analyze and sort individual exosomes using fluorescent labels. (See a previous blog on FAVS here.)

In the paper, “Identification and Characterization of EGF Receptor in Individual Exosomes by Fluorescence-Activated Vesicle Sorting (FAVS)”, published in the Journal of Extracellular Vesicles (JEV), Higginbotham and colleagues have used FAVS to analyze exosomal subsets that express varying amounts of EGFR in different cell-culture and in vivo contexts. This was done using DiFi cells, a human colorectal cancer (CRC) cell line, and A431, an epidermoid cancer cell line, which express approximately 5×106 and 2.5×106 EGFRs per cell, respectively (18, 19). The FAVS results showed that DiFi exosomes contain far more EGFR than do A431 exosomes, far exceeding the two-fold difference in EGFR levels present in these cell lines. Furthermore, using an antibody that recognizes an active form of EGFR, mAb806 (20-22), the amount of active EGFR was also found to be dramatically higher in DiFi exosomes than in A431 exosomes.

FAVS was also used to sort EGFR/CD9 double-positive and double-negative exosome populations, allowing enrichment of both subsets by post-sort analysis as well as western blot validation of the sorted exosomes (see Figure). Using human-specific reagents, FAVS was able to detect DiFi exosomes in the plasma of mice bearing DiFi xenografts. FAVS was also used to demonstrate that EGFR and one of its ligands, amphiregulin (AREG) are present in the plasma of normal individuals.

 

Results from the JEV paper derived from Fig 2. DiFi exosomes were flow sorted using antibodies to EGFR and CD9. Sorted purified double-negative vesicles (blue box/arrow) and double-positive vesicles (red box/arrow) were probed by western blot for markers as shown. These results validate the flow sorting enrichment of these different classes of vesicles.  Also shown is a STORM image of an individual flow sorted double-positive vesicle.

Results from the JEV paper derived from Fig 2. DiFi exosomes were flow sorted using antibodies to EGFR and CD9. Sorted purified double-negative vesicles (blue box/arrow) and double-positive vesicles (red box/arrow) were probed by western blot for markers as shown. These results validate the flow sorting enrichment of these different classes of vesicles. Also shown is a STORM image of an individual flow sorted double-positive vesicle.

 

This work joins flow-sorting work done by other labs using somewhat different techniques (6-14) and has implications for similar kinds of work done by other members of this consortium (23-25). Common to all these techniques was the use of lipid and/or specific extracellular vesicle markers to identify classes of secreted vesicles. Unlike FAVS, many sorting methods trigger vesicular events based on fluorescence rather than scatter. In all of these cases, analysis of secreted vesicle populations was performed. In some cases vesicle sorting was also achieved.

Thus, FAVS appears to be a promising technique to identify and purify distinct subsets of exosomes for discovery studies. It also holds promise for the detection of biomarkers in disease states including subsets of associated secreted RNAs.

References

1. Yang JM, Gould SJ. The cis-acting signals that target proteins to exosomes and microvesicles. Biochem Soc Trans (2013) 41:277-82. PMID 23356297.

2. Kowal J, et al. Proteomic comparison defines novel markers to characterize heterogeneous populations of extracellular vesicle subtypes. Proc Natl Acad Sci USA (2016) 113:E968-77. PMID 26858453.

3. Lunavat TR, et al. Small RNA deep sequencing discriminates subsets of extracellular vesicles released by melanoma cells–Evidence of unique microRNA cargos. RNA Biol (2015) 12:810-23. PMID 26176991.

4. Cao Z, et al. Use of fluorescence-activated vesicle sorting for isolation of Naked2-associated, basolaterally targeted exocytic vesicles for proteomics analysis. Mol Cell Proteomics (2008) 7:1651-67. PMID 18504258.

5. Higginbotham JN, et al. Amphiregulin exosomes increase cancer cell invasion. Curr Biol (2011) 21:779-86. PMID 21514161.

6. Poncelet P, et al. Standardized counting of circulating platelet microparticles using currently available flow cytometers and scatter-based triggering: Forward or side scatter? Cytometry A (2016) 89:148-58. PMID 25963580.

7. Erdbrugger U, Lannigan J. Analytical challenges of extracellular vesicle detection: A comparison of different techniques. Cytometry A (2016) 89:123-34. PMID 26651033.

8. Chandler WL, Yeung W, Tait JF. A new microparticle size calibration standard for use in measuring smaller microparticles using a new flow cytometer. J Thromb Haemost (2011) 9:1216-24. PMID 21481178.

9. van der Pol E, et al. Single vs. swarm detection of microparticles and exosomes by flow cytometry. J Thromb Haemost (2012) 10:919-30. PMID 22394434.

10. Arraud N, Gounou C, Turpin D, Brisson AR. Fluorescence triggering: A general strategy for enumerating and phenotyping extracellular vesicles by flow cytometry. Cytometry A (2016) 89:184-95. PMID 25857288.

11. Pospichalova V, et al. Simplified protocol for flow cytometry analysis of fluorescently labeled exosomes and microvesicles using dedicated flow cytometer. J Extracell Vesicles (2015) 4:25530. PMID 25833224.

12. Nolte-‘t Hoen EN, et al. Quantitative and qualitative flow cytometric analysis of nanosized cell-derived membrane vesicles. Nanomedicine (2012) 8:712-20. PMID 22024193.

13. van der Vlist EJ, et al. Fluorescent labeling of nano-sized vesicles released by cells and subsequent quantitative and qualitative analysis by high-resolution flow cytometry. Nat Protoc (2012) 7:1311-26. PMID 22722367.

14. Groot Kormelink T, et al. Prerequisites for the analysis and sorting of extracellular vesicle subpopulations by high-resolution flow cytometry. Cytometry A (2016) 89:135-47. PMID 25688721.

15. Demory Beckler M, et al. Proteomic analysis of exosomes from mutant KRAS colon cancer cells identifies intercellular transfer of mutant KRAS. Mol Cell Proteomics (2013) 12:343-55. PMID 23161513.

16. McConnell RE, et al. The enterocyte microvillus is a vesicle-generating organelle. J Cell Biol (2009) 185:1285-98. PMID 19564407.

17. Shifrin DA, et al. Enterocyte microvillus-derived vesicles detoxify bacterial products and regulate epithelial-microbial interactions. Curr Biol (2012) 22:627-31. PMID 22386311.

18. Gross ME, et al. Cellular growth response to epidermal growth factor in colon carcinoma cells with an amplified epidermal growth factor receptor derived from a familial adenomatous polyposis patient. Cancer Res (1991) 51:1452-9. PMID 1847663.

19. Kawamoto T, et al. Growth stimulation of A431 cells by epidermal growth factor: identification of high-affinity receptors for epidermal growth factor by an anti-receptor monoclonal antibody. Proc Natl Acad Sci USA (1983) 80:1337-41. PMID 6298788.

20. Walker F, et al. Ligand binding induces a conformational change in epidermal growth factor receptor dimers. Growth Factors (2012) 30:394-409. PMID 23163584.

21. Gan HK, et al. Targeting of a conformationally exposed, tumor-specific epitope of EGFR as a strategy for cancer therapy. Cancer Res (2012) 72:2924-30. PMID 22659454.

22. Reilly EB, et al. Characterization of ABT-806, a Humanized Tumor-Specific Anti-EGFR Monoclonal Antibody. Mol Cancer Ther (2015) 14:1141-51. PMID 25731184.

23. Stoner SA, et al. High sensitivity flow cytometry of membrane vesicles. Cytometry A (2016) 89:196-206. PMID 26484737.

24. Nolan JP. Flow Cytometry of Extracellular Vesicles: Potential, Pitfalls, and Prospects. Curr Protoc Cytom (2015) 73:13.4.1-6. PMID 26132176.

25. Danielson KM, et al. Diurnal Variations of Circulating Extracellular Vesicles Measured by Nano Flow Cytometry. PLoS ONE (2016) 11:e0144678. PMID 26745887.

For researchers who have just begun studying extracellular vesicles (EVs) and their contents, including extracellular RNA, the Extracellular RNA Communication consortium (ERCC) published a protocols decision tree today, designed to help select a set of protocols for isolating EVs and exRNA from several biofluids of interest. Some of the protocols in the decision tree have been developed by ERCC researchers, but many relevant methods were published before the ERCC existed, and the versions on the ExRNA Portal include modifications and comments made by ERCC members in the course of their experiments, and are being periodically updated. In this blog, we introduce the ERCC protocols decision tree and discuss some of the nuanced differences between classes of EV isolation methods, highlighting methods that have existed in the field for some time.

There are several methods and commercial kits available to isolate extracellular vesicles (EVs) and extracellular RNA (exRNA) from human biofluids. Multiple studies have reported on a variety of these methods, but to date there is not one method or kit that suits all studies. The type of biofluid, the sample volume, and the fraction of exRNAs of interest are some of the criteria used to determine what method should be used for a particular study. Here, we have compiled a list of methods and kits most widely reported in the literature for isolation of EVs, exRNA or other components of biofluids. Broadly speaking, all methods can be classified into five categories (see Table), the most widely used being ultracentrifugation.

Drawbacks of ultracentrifugation include the need for expensive instrumentation (ultracentrifuges and rotors) and the belief that the EV population after ultracentrifugation will be contaminated with cell-free DNA and proteins. Depending on the speed and time of ultracentrifugation, some ribonucleoprotein (RNP) and lipoprotein (LPP) complexes may also sediment. After ultracentrifugation, some researchers further purify the EV population using density gradients or size exclusion chromatography. Sucrose gradients have been used widely for several years but are being replaced more recently by iodixanol (OptiPrep) gradients because some groups have reported that sucrose may inhibit the biological effects of EVs, while EVs prepared with OptiPrep better retain their biological activity.

Size exclusion chromatography is also widely used and is suitable for fractionation of sedimented EVs, as well as unprocessed biofluids. Recently Izon introduced a commercial kit to speed up this method, with relatively good results. Size exclusion chromatography yields a very clean population of EVs with the drawback being loss of EVs during the multi-step purification process.

The first commercial EV isolation kit (ExoquickTM) was launched on the market about six years ago, and is based on the principle of polyethylene glycol/sodium chloride precipitation, which has long been used for concentration of viruses. Since that time, several other kits using a precipitation strategy were launched by other manufacturers. Each of these kits (SBI, Life Technologies, Norgen Biotek, and Exiqon) have slightly different proprietary approaches to EV precipitation. Drawbacks to EV precipitation kits include co-precipitation of other unwanted molecules found in the biofluids and the difficulty of isolating EVs from large volumes of starting material. Ultrafiltration (e.g. using Millipore Amicon filters) is often used by researchers to concentrate large volumes, either before or after EV purification.

Filtration based methods which isolate specific size ranges of EVs can also be performed using commercially available devices. In addition, several kits and protocols for affinity purification have been developed by biotechnology companies and academic research laboratories. Antibody-based affinity methods (ExoCap, Microfluids, µNMR), and heparin-coated agarose or magnetic beads have been shown to bind subpopulations of EVs from cell culture media, plasma and serum efficiently. Another affinity kit, the METE kit, includes a proprietary peptide that, according to the manufacturer, binds to heat shock proteins found on the surface of the plasma membrane, suggesting a possible method to enrich for EVs with high levels of heat shock proteins. All of these methods yield a pure sample of EVs and can be scaled up, although scale-up costs can be significant, particularly in the case of antibody-based methods. These methods are limited to isolating a subset of EVs that express a specific antigen. For researchers who are interested in targeting a specific population EVs that displays one of these antigens, this may be a good option. However, at this point, for most cases, the biology is unclear on the diversity of EVs released by cells. Therefore, one antibody, or a pool of three to four antibodies, may not isolate all relevant EVs present in the sample.

ExoRNeasy isolates EVs based on their affinity to a proprietary membrane. ERCC members using this kit have reported that it can efficiently separate EVs (they bind to the membrane with high affinity) from other exRNA-containing particles, such as RNP complexes, which can be collected in the flow through, so this kit offers the extra advantage of efficiently separating EVs from RNP complexes. The ExoRNeasy/exoEasy kit yields a pure EV population and can isolate EVs from a volume as low as 200 μL or as high as 4 mL of plasma or serum with one loading per column, and up to 100 mL of cultured media per column by loading the column 3-4 times. Larger volumes require loading multiple filters.

Two kits available on the market are based on a two-step procedure where the sample is first either filtered (e.g. PureExo) or concentrated (e.g. Exo-Spin) and then resuspended and allowed to bind to proprietary beads. Intact EVs are then eluted in PBS and can be used for a variety of downstream assays. These kits do not offer feasible approaches to scale up to larger volumes.

Many other combinations and variations on these methods have also been reported in the literature, and this list is not meant to comprehensively encompass all reports in the field. It is a simple overview of the major classes of EV and RNA isolation methods present in the EV isolation field.

EV protocols table

Thery C. et al. 2006 Lobb et al. 2015 Boing et al. 2014 Lobb et al. 2015 Taylor DD. et al. 2011 Vlassov AV. et al. 2012 Hudson MB. et al. 2014 Lasser C. et al. 2012 Bryant, R. J. et al. 2012 jsrmicro.com Chen C. et al. 2010 Shao H. et al. 2012 Balaj et al. 2015 Enderle et al. 2015 Korbelik et al. 2015 cellgs.com Ghosh et al. 2014

Extracellular vesicles (EVs) play an important role in cell-to-cell communication. Recently, EVs have been shown to be involved in immune modulation, tumor biology, and tissue regeneration. The mechanisms of action of EVs are associated with their ability to stimulate target cells directly and to transfer proteins, biologically active lipids, and nucleic acids to the target cells. In fact, mRNAs, long non-coding RNAs (lncRNAs), and microRNAs (miRNAs) can be compartmentalized into EVs, escape enzymatic degradation, and be delivered to target cells. This horizontal transfer of extracellular RNAs carried by EVs can induce epigenetic alterations in recipient cells. The result is a change of phenotype or even a genetic and functional reprogramming of the recipient cells. Furthermore, EVs carry a selection of miRNAs different from the miRNAs most expressed in the cells of origin. However, little is known about the mechanisms of miRNA enrichment in EVs.

Argonaute2

Argonaute2


 
Alix

Alix

We hypothesized a possible interaction between the Alix and Argonaute 2 (Ago2) proteins. The resulting complex may have a role in miRNA transport into EVs. Ago2 is a reasonable candidate to play a role in miRNA packaging within multivesicular bodies during EV biogenesis because of its central role in miRNA maturation. We observed that Ago2, as well as several other ribonucloproteins involved in RNA storage and stability, is expressed in EVs derived from adult human liver stem-like cells (HLSCs). Cells which express mesenchymal and embryonic markers.
 

Alix Figure 1

Alix is a multifunctional protein commonly used as a marker of EVs. It is an accessory protein of the Endosomal Sorting Complex Required for Transport (ESCRT), and several studies indicate that ESCRT is involved in the biogenesis of EVs.

We observed that HLSC-derived EVs express both Alix and Ago2. Co-immunoprecipitation (Co-IP) experiments with Alix or Ago2 antibody showed that the two proteins are associated. We also found that the miRNAs enriched in HLSC-EVs precipitate with the Alix – Ago2 complex. After the incubation of HLSC-EVs with human endothelial cells, we observed that miRNAs from HLSC-EVs are transferred to these cells.
 

Alix Figure 4

After the silencing of Alix expression in HLSCs, we observed the absence of both Alix and Ago2 proteins in EVs derived from the knockdown HLSCs and a strong reduction in the number of miRNAs normally enriched in HLSC-EVs. On the other hand, EV size, surface expression of CD63 and Tsg101, and the number of released EVs were not affected. After incubation with endothelial cells, EVs derived from Alix-knockdown HLSC do not transfer miRNAs to cells.

Alix is known to be involved in endocytic membrane trafficking and cytoskeletal remodeling. It is also associated with the ESCRT machinery, which participates in processes of vesiculation and cargo sorting, including multivesicular body biogenesis. Our data suggest that Alix binds Ago2 and drives it into EVs together with the associated miRNAs.
 

Alix Figure 5

This might be a general mechanism of miRNA transport into EVs, common to other cell types. Enrichment of a selected set of miRNAs might also depend on the affinity of miRNAs for carrier proteins such as Ago2.

Source: Iavello A, Frech VS, Gai C, Deregibus MC, Quesenberry PJ, Camussi G. Role of Alix in miRNA packaging during extracellular vesicle biogenesis. Int J Mol Med. 2016 37:958-966. doi: 10.3892/ijmm.2016.2488. PMID: 26935291.

The potential of extracellular vesicle (EV) RNA as biomarkers of disease is increasingly being recognized. Circulating extracellular RNAs can potentially indicate the presence of disease without the need for an invasive biopsy of diseased tissue. A recent study by Yuan et al (Scientific Reports, Jan 2016) performed a systematic analysis of circulating exRNA in plasma obtained from 50 healthy individuals and 142 cancer patients. The authors conducted the largest RNA sequencing study reported to date for profiling circulating extracellular RNA (exRNA) species in order to provide useful insights into their baseline expression level. High-throughput multivariate statistical analysis identified a set of RNA candidates that were associated with age, sex, and cancer type.

The study directly addressed a major challenge in the field of exRNA, namely the lack of a baseline reference to accurately determine RNA abundance. Currently qRT-PCR is the most common method used to quantitate gene expression, but it relies on well-established reference controls for normalization. However, information on reference controls has not been established for exRNA. Control RNAs used for normalization in qRT-PCR experiments of cellular RNA cannot be reliably used for exRNA. Exogenous spike-in RNAs such as those derived from species like C. elegans cannot be used to normalize across biological or pathological states. Here the authors surveyed a pool of almost 200 exRNA profiles to identify a few potential reference candidates for exRNA quantification. Most candidate were miRNAs like miR-99a-5p. The study also provided convincing evidence for the presence in plasma of species of RNA other than miRNA, such as piwiRNA (see figure).

The dataset generated by this study is available in the exRNA Atlas for other researchers to explore. Future studies will be needed to validate these findings; however, with the current study we have taken a big leap towards the goal of determining the biomarker potential of exRNA for human diseases.
 
 

RNA species detected in the plasma of 142 cancer patients and 50 healthy controls

RNA species detected in the plasma of 142 cancer patients and 50 healthy controls
Source: Scientific Reports / CC BY 4.0