Other

In the last 8 years, extracellular vesicles (EVs) have attracted significant interest among scientists for their proposed role in intercellular communication, as reservoirs for disease biomarkers, and as targeted drug delivery vehicles. Multiple groups have reported the secretion of EVs and characterised their transcriptomic, proteomic and lipidomic content. In order to compare the data generated with other studies, researchers used to perform the cumbersome task of compiling the data from published EV studies. Since the launch of ExoCarta (https://www.exocarta.org) (Mathivanan and Simpson, 2009), a manually curated exosome-centric database that catalogs RNA, proteins and lipids, the process of comparison between datasets has been made easier.

However, growing interest in EVs has made updating and maintaining an online database a daunting task. Surveys of online links in the biomedical literature report that over 20% of database links are not active after their initial publication (Wren, 2004; Wren, 2008; Ducut et al, 2008). In addition, more than 50% of databases are never updated after initial publication, limiting their usability (Wren, 2008). The underlying reasons for this database decay are often lack of personnel and continued funding. A self-sustaining system that allows for users and researchers to contribute and update the databases may be a long term solution to the problem.

This daunting task of updating databases regularly and the need for a compendium for all types of EVs prompted the development of Vesiclepedia (https://www.microvesicles.org) in 2012 (Kalra et al, 2012). The Vesiclepedia compendium allows for community annotation. Since its initial release, through the active participation of the EV research community, the amount of data contained in Vesiclepedia has doubled. In addition to hosting mammalian data, Vesiclepedia also now hosts data from all organisms including prokaryotes. Furthermore, another database driven by community annotation, EVpedia (https://www.evpedia.info) (Kim et al, 2014), which catalogues EV data from prokaryotes and eukaryotes, was also recently initiated.

While semi-automatic measures are in place to allow for community annotation, the onus is on the research community in general to drive this effort forward. Researchers should develop a culture of depositing datasets to online resources to increase the visibility of the data. They should also develop the habit of participating in community annotations. Peer-reviewed journals also need to mandate the deposition of datasets to an online resource prior to publication (some journals indeed do this already).

Specific extracellular RNAs (exRNAs) have been shown in basic and clinical studies to regulate key processes central to the pathogenesis of cardiovascular disease (CVD). A growing number of smaller human studies or studies examining a limited number of miRNAs have associated exRNA with CVD and its risk factors. In our study, we performed RNA sequencing (RNA-seq) on previously stored plasma samples from Framingham Heart Study (FHS) participants (Offspring Exam 8), including those with and without CVD, to determine if there was a multimarker predictor using exRNAs to discriminate between CVD and disease-free individuals. From these data, we plan to study a profile of approximately 600 exRNAs in almost 3000 additional participants of the FHS. Thus far, we have sequenced 20 CVD and 20 matched non-CVD plasma samples using an Ion Proton platform. Sequencing data was processed in the Genboree Sequencing pipeline and comparative analysis was performed.

Specifically, RNAs samples were isolated from plasma using a miRCURY RNA Isolation Kit –Biofluids (Exiqon, Denmark). Ion Total RNA-Seq Kit v2 (Life Technologies, USA) was used for creating libraries for sequencing. Ion Chef System and Ion PI IC 200 kits were used for template preparation, and sequencing was performed on Ion PI Chip Kit v2 BC and Ion Proton System (Life Technologies, USA).

From these data, we identified a total of 688 small RNAs above ≥5 Reads Per Million (RPM). The small RNAs were comprised of 426 miRNAs, 36 piRNAs, 24 snoRNAs and 202 tRNAs. miR-223-3p and miR451a were the top 2 most expressed miRNAs. We observed strong correlation in gene expression between the CVD and non-CVD groups. Only miR-589-3p expression was significantly changed in the CVD group compared to the non-CVD group. We have utilized these findings to develop our target exRNA list and are completing measurements by high-throughput RT-PCR in the remaining participants of the Offspring 8 cohort.

Purpose

This Funding Opportunity Announcement (FOA) invites research grant applications focused on defining the central role of exosomes in the neuropathogenesis of Human Immunodeficiency Virus (HIV)-1 Associated Neurocognitive Disorders (HAND) and determining the potential use of exosomes as biomarkers for HAND or as delivery vehicles for CNS targeted therapeutics. Basic and translational research in domestic and international settings is of interest. Multidisciplinary research teams and collaborative alliances are encouraged but not required.

Background
HIV-Associated Neurocognitive Disorders (HAND) remain prevalent despite the widespread use of potent anti-retroviral drug regimens. Low levels of viral replication and chronic inflammation continue to persist in the central nervous system (CNS) in well treated patients. There remain considerable gaps in our understanding of the pathophysiologic mechanisms driving HIV-1 associated neurocognitive decline in the setting of low level viral replication. The release of inflammatory mediators by macrophages/microglial cells and astrocytes contribute to the pathogenesis of HAND. The mechanisms by which HIV-related inflammation spreads within the CNS compartment is an area that requires further study. In particular there is a great need to define the communication pathways between macrophages, astrocytes and neuronal cells within the CNS in the setting of HIV-infection.
Exosomes have emerged as novel conduits for cell-cell communication and they have been shown to play a role in cancer biology and neurodegenerative diseases (Parkinson’s, Alzheimer’s disease and amyotrophic lateral sclerosis). Exosomes are small vesicles (30-100 nm) released from cells that carry RNA, protein or lipid to a distant cell with the potential to effect phenotypic changes within the recipient cell. The role of exosomes in cell-to-cell communication is an emerging area of biology that has been recognized as critical in understanding regulation of the innate and adaptive immune response, cancer cell biology, and neurological disorders.

In the context of HIV infection there is evidence that HIV-1 proteins regulate exosome release in vitro. For example, co-exposure of astrocytes to HIV-1 tat protein and morphine induces the release of exosomes that carry microRNA29. When applied to neurons, these exosomes carrying microRNA29 decrease neuronal viability. Another example are exosomes that are packaged and released with the trans-activation response element (TAR) microRNA. These exosomes have been found to be released from HIV-1 infected cells in culture and they have also been purified from human sera derived from HIV-infected individuals. When applied to cultured astroglioma cells, these exosomes carrying TAR microRNA increase the susceptibility of the cells to HIV-1 infection. While these studies suggest the impact of HIV infection on exosome release and cargo content, this initiative encourages further research to examine whether normal exosome physiology is altered in the setting of HAND.

Exosomal cargo may prove useful as clinical biomarkers for diagnosing HAND and also as CNS delivery vehicles for a therapeutic approach to HAND treatment. Exosomes are found in virtually all body fluids including blood, saliva, cerebrospinal fluid, breast milk and urine. Thus, diagnostic methods that use these fluids as sources of exosomes may be devised. In addition to body fluids, tissue sources of exosomes may also have biomarker potential where biopsies are possible. Exosomes have also been demonstrated to serve as delivery vehicles for treatment of inflammatory disorders. While the possibility of CNS-targeted delivery of exosomes has been described in the literature, further research on using this approach for treatment of HAND is needed.

The R01 version of the FOA can be found at https://grants.nih.gov/grants/guide/rfa-files/RFA-MH-16-100.html.
The R21 version of the FOA can be found at https://grants.nih.gov/grants/guide/rfa-files/RFA-MH-16-110.html.

The secretion of biomolecules into the extracellular milieu is a common phenomenon in biology. Similar to mammalian cells which secrete and use extracellular vesicles to communicate between cells using the contained biomolecules; bacteria, e.g. Escherichia coli, also secrete outer membrane vesicles (OMVs (*); Table 1) containing distinct biomolecules which are thought to be involved in intra-species communication, in inter-kingdom exchanges and pathogenicity (1–5). To date, such exported molecules such as small molecules, DNA, peptides and proteins, have been well-studied in bacteria (2,5,6). The bacterial extracellular RNA complement has only been very recently characterized. More specifically, a very recent report has found that Prochlorococcus, the numerically dominant marine cyanobacterium, continuously releases lipid vesicles containing proteins, DNA and RNA into its extracellular milieu (7). In total, 89% of the genome of Prochlorococcus was represented at least once in the RNA-associated libraries from the OMV fraction (7).

Using a combination of physical characterization and RNA sequencing, our group has recently analyzed the extracellular RNA complement of both OMV-associated and OMV-free RNA of the enteric Gram-negative model bacterium Escherichia coli K-12 substrain MG1655, and we have compared it to its intracellular RNA complement (8). Our results demonstrate that when MG1655 is cultured in LB, rich bacterial media, a large part of the extracellular RNA complement is in the size range between 15 and 40 nucleotides (Figure 1) and is derived from specific intracellular RNA species.

Figure 1: Size distribution of extracellular RNA released by Escherichia coli K-12. In red the size distribution of RNA associated with OMVs is represented and in green RNA extracted from the OMV-free bacterial supernatant is shown. (RFU: relative fluorescence unit).

In addition, we demonstrated that RNA is specifically associated with OMVs (Figure 2) and that the relative abundances of RNA biotypes in the intracellular, OMV and OMV-free fractions are quite distinct (Figure 3).

Figure 2: Confocal microscopy analysis of OMVs, stained with lipid tracer dye, DiD (red) and RNA specific dye, SYTO RNASelect (green). The area highlighted within the white rectangular box is magnified in the inset. The scale bar is equivalent to 5 µm in the main images and equivalent to 500 nm in the magnified images. Each individual colour dot in the images likely represents the aggregation of several OMVs, as the typical sizes of OMVs are below the limit of resolution of confocal microscopy.

Figure 3: Annotated profile of RNA extracted from OMVs obtained from Escherichia coli cultures (OMV), of RNA extracted from OMV-depleted Escherichia coli culture supernatant (OMV-f) and RNA extracted from the bacterial cells themselves (Int).

Apart from rRNA fragments, a significant portion of the extracellular RNA complement is composed of specific cleavage products of functionally important structural, non-coding RNAs, including tRNAs, 4.5S RNA, 6S RNA and tmRNA (transfer-messenger RNA: bacterial RNA with dual tRNA-like and mRNA-like properties).

The function of these exported small RNAs is still unknown, but it is important to note that OMVs can be taken up by human host cells. Thus, they may even contribute to pathogenesis, as demonstrated for OMVs derived from Helicobacter pylori, which are taken up by gastric epithelial cells (9). In this context, OMVs have been found to enhance the carcinogenic potential of this specific bacterium (10).

As the number of microorganisms living in and on the human body outnumbers the total number of human cells by at least an order of magnitude and knowing that the vesicular secretion by bacteria is a commonly observed phenomenon (11–14), it is not surprising that first reports have shown that OMVs can influence human health and specific disease states (Table 1). OMVs released by gastrointestinal pathogens can be harmful or even lethal, as OMVs derived from pathogenic bacteria transport diverse virulence factors to the host cells, enabling them to modulate host defense and response in order to assure their survival and replication (11,13,15,16) (Table 1). On the other hand, it has only recently become appreciated that OMV-induced signaling by commensal bacteria is of utmost importance for the host (5). A recent study shows that polysaccharide A enriched OMVs derived from a human commensal (Bacteroides fragilis) mediate host immune regulation and prevent colitis in a mouse model (5).

Our results, described in Ghosal et al., suggest a selective export of specific RNA biotypes by Escherichia coli (8). For example, specific fragments of specific tRNAs are found in the OMV, and are different from those found outside the OMVs. In addition, our unpublished data suggests that Salmonella enterica serovar Typhimurium also secretes small RNA-enriched OMVs (personal communication). These initial results seem promising, but require more detailed and mechanistic studies in order to ascertain if bacterial secreted small RNAs do play a role in intercellular communication, and what those roles may be. This is entirely analogous to findings in eukaryotes, where small RNA can be delivered via vesicles to their target cell and trigger a response (17–19). Indeed, a previous study in bacteria seems to underscore this hypothesis as it demonstrates that extracellular RNA secreted by Listeria spp. is a key component for the host developing an immune response against the bacterial infection (20). Moreover, small extracellular RNA fragments of Mycobacterium tuberculosis (comprised primarily of tRNA and rRNA fragments), induce early apoptosis in human monocytes (21). Whether these molecules are delivered to their target via OMVs still needs to be conclusively established.

Understanding the role of bacteria-derived exogenous RNA in host-microbe interactions, in pathogenesis as well as in mutualism, will elucidate new mechanisms and perhaps allow the identification of new drug targets and/or the development of RNA-based vaccines. Further investigations in the field of extracellular bacterial RNAs are clearly needed to shed light on their potential role as mediators of microbe-microbe and host-microbe intercellular communication, and on the specific mechanisms of these effects. This will be an exciting advance that could provide entirely new approaches to human disease therapy and prevention.

*: OMV definition and functions
Outer membrane vesicles (OMV) are produced by all Gram-negative bacteria. They are thought to form when buds from the outer membrane (OM) of cells encapsulate periplasmic material and pinch off from the OM to form spheroid particles with a size range of 10-300 nm in diameter. By the inclusion of cargos (small molecules, peptides and proteins, DNA, RNA) into OMVs, the cargos may benefit from : protection from degradation, long-distance delivery, specificity in host-cell targeting, evasion of the hosts immune response and coordinated secretion with other bacterial effectors (22).

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The 2015 meeting of the International Society for Extracellular Vesicles (4/23-26) and the joint Educational Day of the NIH Extracellular RNA Communication Consortium and ISEV (4/22) are rapidly approaching.

Late-breaking abstracts for ISEV2015 are now being accepted through Monday, March 16th through this website. Registration discounts are available to those who join ISEV/ISEV-Americas for a nominal fee.

We expect up to 1000 EV researchers during the event, beginning with the joint Educational Day on April 22nd. The Educational Day will be followed by the main ISEV2015 meeting, with four days of abstract-driven presentations and poster sessions. Plenary speakers include NIH Director Francis Collins, Nobel Laureate James Rothman, EV pioneer Xandra Breakefield, and miRNA co-discoverer Gary Ruvkun. A Potomac River cruise on Friday evening, April 24th, will provide an unforgettable networking opportunity. Please consider joining us and urging your colleagues and group members to submit abstracts!

Purpose
The purpose of this Funding Opportunity Announcement (FOA) is to invite applications that explore new research on the potential role of exosomes in cell-to-cell communication relevant to the impact of exosomes on HIV transmission, innate or adaptive immune responses to HIV, or HIV pathogenesis. This FOA solicits early-stage, exploratory projects with little to no preliminary data. Please note, there are research topics that are NOT supported by this FOA such as projects that focus on HIV hijacking the exosome release pathway for viral egress.

Background
Exosomes are small vesicles (30-100 nm) released from cells that were first described in the early 1980s. Since then, exosomes have been found to carry RNA, protein or lipid to a distant cell with the potential to change the phenotype of the recipient cell. The role of exosomes in cell-to-cell communication is an emerging area of biology that has been recognized as critical towards understanding regulation of the innate and adaptive immune response, cancer cell biology, and neurological disorders.
In the early to mid-2000’s, a large body of research focused on understanding how HIV hijacks the cellular exosome release pathway for viral egress. This avenue of research identified many virus-host interactions and identified viral egress pathways in T-cells and macrophages.
A current gap in our understanding is how exosomes carrying biologically active cargo may influence cell-to-cell communication relevant to HIV pathogenesis, the host response to HIV, and/or transmission of HIV. Studies looking at the function of exosomes in acute infection, or in chronic infection in individuals on fully suppressive antiretroviral regimens are encouraged.

The FOA is available at https://grants.nih.gov/grants/guide/pa-files/PA-15-107.html.

We continue to curate relevant pathways for the exRNA portal at WikiPathways, highlighting the mechanisms of exRNA signaling and regulation. The latest set of pathways represent a range of topics, including microRNAs in osteoclastogenesis (WP2866) and new findings in RNA interference (WP2805). Several pathways describe results from studies conducted using exRNA as a research tool, including 1) the effects of a high fat diet on megakaryocyte and platelet function (WP2865), 2) extracellular vesicles as mediators of signal transduction (WP2870), and 3) the effects of tumor nutrient utilization on ovarian cancer progression (WP2868). Additionally, one study provided a network view of its findings on Notch3 apoptosis-related changes in ovarian cancer (WP2864). This pathway represents a great candidate for further curation.

The curation process involves transferring findings represented as a figure by creating a new pathway in gpml format, using the WikiPathways plugin for PathVisio. After upload to WikiPathways, the content is tagged with appropriate curation and ontology tags, to increase its utility and exposure.

The goal of the Wikipathways exRNA portal is to build a collection of pathway models for exRNA researchers to use for illustration, data visualization, and analysis. Each pathway is a self-contained data model that connects to identifier and annotation databases. In addition to providing static images for figures and presentations, these pathways can also be used by bioinformatics and network analysis packages such as Cytoscape and PathVisio. Furthermore, as a wiki, anyone can sign up to improve and grow the content. We invite you all to edit, fix, and add to the pathway models in the exRNA portal at WikiPathways.

The Wikipathways exRNA portal was developed by the Data Management and Resource Repository group, serving the data coordination and scientific outreach needs of the consortium. If you have questions and/or feedback on ways to improve the exRNA portal at WikiPathways, please contact info@exrna.org or Alex Pico at apico@gladstone.uscf.edu.

The NIH Extracellular RNA Communication Consortium (ERCC) and the International Society for Extracellular Vesicles (ISEV) will hold a joint Educational Day on April 22nd, 2015 at the Bethesda North Marriott. Following the Educational Day, the fourth annual meeting of ISEV will provide three-and-a-half days of cutting edge research presentations, plus keynote speakers including NIH Director Francis Collins and EV pioneer Xandra Breakefield.

The fast-paced joint Educational Day will feature topics such as EV isolation, low-input RNA profiling, standards and spike-ins, and RNA-mediated communication. If you are a member of ERCC, you are already signed up for the Educational Day. If not, registration options are found here: https://www.isevmeeting.org/registration1.html.

Abstract submission for the subsequent ISEV2015 meeting closes on January 16th (https://www.isevmeeting.org/abstracts.html), and the early registration deadline is January 26th (https://www.isevmeeting.org/registration1.html). Please note that several travel scholarships will be available for presenters who are young investigators or members of under-represented minorities in the sciences. Interested individuals can indicate their status through the abstract submission process.

I have been thinking a lot during the past year about biomarkers… actually, more specifically about the data needed to develop the best biomarker panels, the sample sizes needed, and how to convince patients – and even healthy individuals – to participate in research to help develop these biomarker panels. I have come to the conclusion that we might all be doing it wrong. In fact, I think we all KNOW we are doing it wrong deep down inside.

First, don’t we all believe that the best biomarker panels are going to be based on longitudinal analysis of biomolecules? In other words, the eventual successful biomarker approaches will mostly measure “rate of change” of the biomarker panel and not just presence/absence or something similar like quantitative level differences. Are there examples of good biomarker panels that AREN’T rooted in longitudinal measures? Feel free to post your favorite example of a good clinical biomarker/panel in the comments section. Think of the PSA test as an example. A single PSA level measurement isn’t generally accepted as a good indicator for surgical intervention as it can be biased by other things. The utility of PSA testing may primarily reside in an examination of the longitudinal measurements. Work in the NEJM suggested that the rate of rise of PSA during the years prior to a diagnosis of prostate cancer can indicate altered severity of the disease (D’Amico et al, N Engl J Med 2004). So, why aren’t we all focused on collecting longitudinal samples? Is it because our biospecimen of choice isn’t all that really “non-invasive” even though we like to try and convince ourselves and our colleagues that it is?

Coming from a neuroscientist this is crazy talk bordering on sacrilege. Everyone knows that cerebrospinal fluid (CSF) is the best place to go looking for CNS biomarkers. Come on, they say, a spinal tap isn’t that bad. We have the approach down – no pain at all. We can even get samples from age-matched healthy controls without too much issue. I believe all of this, of course. I know there are excellent clinical teams out there that can achieve precisely those things… the problem is that there aren’t enough people out there willing to undergo it just for the sake of research. We have what I would describe as a chicken:egg problem here. Of course folks would be willing to donate CSF immediately if they knew that the biomarker panel would tell them something about their personal risk for disease AND if that knowledge would help their treating physician “change their future”. But we aren’t there yet… we are still in the phase of asking folks to participate in research with the hope of developing those idealized biomarker panels. Couple that with the fact that CSF collection is costly – in both donor time and clinical costs – and we are left with an underpowered study. Look, I still think it is the best place to look for CNS-disease biomarkers, but I lose sleep at night worrying about our actual statistical power to detect biomarker panels with anything but a pretty large effect size.

Secondly, are we really getting as rich of a data collection on our research participants as we should be (or could be)? How many of us are using data in Excel sheets that was transcribed from medical notes? Or maybe we are “cutting edge” by getting all of our patient data directly from the clinic’s electronic medical records… achieving that level of integration is definitely to be applauded. But how much data are we leaving “on the table”? I suggest that we are leaving a whole lot of meaningful data out there. This came to the front of my mind recently when I was traveling with a colleague from our hotel to the site of our mutual meeting. She was trying to remember exactly what she had for dinner the night before so she could log it into a diet tracking app on her smartphone. As luck would have it, I was sitting next to her and we had dinner together the night before so we essentially tag-teamed our remembrances of what she ate… it probably would have been easier without the wine we had WITH dinner. Anyway, my point is not mind-bending as researchers have known about it for a long time… self-report data becomes pretty unreliable quickly. So, if I wanted to include data about her dietary choices a few days after she made them it would probably be a very incomplete picture. But the remedy for this is simple – we should be doing more interacting with our study subjects via their already existing habits for tracking data in real time. You are probably thinking that this daily tracking of diet information isn’t all that common… not so fast. MyFitnessPal, an app that tracks activity and dietary choices, has over 40 million users. The new iPhone has an activity tracking feature built into the phone. The whole area of “wearables” and the “internet of things” is gaining significant momentum. Jawbone’s UP sleep tracking bracelet is worn by millions of people each night as they drift off to sleep. We are collectively leaving this data “on the table” and it could be really important when we start to attempt to identify the best biomarkers associated with our disease or condition of interest.

Last, do we need to wait for an official clinical visit to phenotype our patients / subjects? Couldn’t we collect data on a daily basis by interacting with them in their own home through the internet? Many useful pieces of data could be collected by simply asking a few questions or having them complete a few web-based tasks once a day or once a week or once a month even. We wouldn’t have to go for months without assessing them during that clinical follow up visit. And what happens during that clinical visit? Is that truly the “average” snapshot of the patient and their progress? Or are they changed – and their habits changed – simply by the need to go to the doctor that day. White coat hypertension is a great example of how a patient can respond differently phenotypically when at the doctor’s office. We can do better.

Thinking about these three points my laboratory has started to do some work to change our path in our biomarker studies. We have started to reduce our RNA sequencing assays to practice in smaller and smaller samples and in biospecimen types that can be collected by subjects on their own in the comfort of their own home and mailed to our laboratory. The idea is simple – we need larger sample sets to develop the best biomarker panels; therefore we need to utilize biospecimens that are easier and less invasive to collect. I concede that it could be that our biomarker may never even end up in the fluid that we are assaying, but that is exactly what we need to explore. We have started to partner with companies producing wearable devices that measure various parameters. Take the sleep data for example… it may not be as rigorously collected as one can achieve during an overnight stay at a sleep study center, but we are betting it is “good enough” since we can get data for 30 days in a row or more. To me, it just seems logical that it will generate a much clearer picture of the study subject. Eventually we will have years and years of data from the same subject measured with the exact same device in the privacy of their own home. Additionally we have started to explore the area of phenotypic assessment over the internet. This is easier to do in some fields compared to others. My lab is interested in the genomics of cognition. We developed a short web-based visual reaction time and episodic memory task at our study site – www.mindcrowd.org. We have had almost 50,000 unique test takers during the past two years. The insights provided by this large dataset and the various slices and dices we can do of the data across different demographic variables (even the rare combinations) is pretty staggering. Soon we will begin to collect two year decline data on thousands of test takers who participated in our study during the first month we started.

I guess research is in a constant state of “doing it wrong” actually. We are always learning. However, I really feel strongly that the speed at which “personal technology” – like cell phones – changes today is so fast that we in the clinical translational research area have been left behind a little bit. We definitely need to catch up, because we are leaving a lot of data on the table.

Alzheimer’s disease (AD) is the most common form of dementia, a general term for loss of memory and other intellectual abilities serious enough to interfere with daily life. AD accounts for 60 – 80% of dementia cases, and is the sixth leading cause of death in the United States. The greatest known risk factor for AD is increasing age; most people with AD are 65 and older. As AD is a progressive disease, and dementia symptoms gradually worsen over several years, costs increase in parallel with dementia and behavioral disturbances, which require increased caregiving time and intensity. The cost of AD in the United States today is estimated to be $100 billion each year; the greatest costs are for long-term care by health care professionals and institutionalization, with significant family costs for direct caregiving and lost earnings. Current AD treatments do not stop disease progression, but they can slow the progression of dementia symptoms and improve quality of life for those with AD and their caregivers, and thus provide tremendous economic and social benefits. However, there is no clinical diagnostic test for AD, nor any that distinguishes early AD from age-related dementia. Such a tool would be invaluable in guiding clinicians towards early interventional efforts.

The existence of extracellular RNAs (exRNAs) in biofluids represents a fertile molecular landscape from which diagnostic and prognostic biomarkers may be accessed, characterized, and exploited. The mission of the Extracellular RNA Communication Program (ERCP) is to further basic and translational studies on exRNAs in a wide range of biofluids and human diseases. Our studies are focused on the identification of exRNAs in cerebrospinal fluid (CSF) that can be used as biomarkers for AD. CSF is produced in the brain and circulates through the ventricles and spinal cord, providing a buffer against chemical imbalances and mechanical injury, and a means to clear metabolic waste from the brain. The brain produces ~500 mL of CSF per day, with ~150 mL present at any time. Biological components in CSF provide a sensitive indicator of changes in the brain. For example, prominent protein biomarkers for AD include beta-amyloid and tau. Combined measures of beta-amyloid fragment and phosphorylated tau show high sensitivity for AD (90%), but low specificity (64%), which limits their clinical utility. To date there are no clinical biomarkers that can predict the onset of AD, distinguish AD from mild cognitive impairment, or distinguish the individual stages of AD (latent, mild, moderate, severe). Further, there are no studies to date to examine the utility of exRNAs in CSF from living human donors as AD biomarkers.

MicroRNAs (miRNAs) are one example of exRNAs that are increasingly found in circulating biofluids such as CSF, plasma, serum, and placental tissue, where their expression is correlated with several diseases including brain injury (traumatic brain injury, ischemia), degenerative disease (multiple sclerosis and AD), and mental health disorders (bipolar disorder). MiRNAs are members of the non-protein-coding family of RNAs that serve as regulators of post-transcriptional gene expression. Importantly, miRNAs are stable in circulating fluids, presumably because they are contained within vesicles or ribonucleoprotein complexes, which likely affords them protection against ribonuclease digestion. MiRNAs bind to specific proteins to form RNA-induced silencing complexes (RISCs), and together they can bind messenger RNA (mRNA) to either repress mRNA translation, or degrade mRNA transcripts, to ultimately regulate the expression of target proteins in the cell. Given that AD is generally characterized by an over-accumulation of proteins, we proposed that a loss of miRNAs might lead to overexpression and accumulation of proteins in AD brain.

To examine this hypothesis, we obtained miRNAs in CSF from 47 AD and 47 control living human donors from the Oregon Alzheimer’s Disease Center (OADC). The OADC uses standardized methods for CSF collection that are consistent with other dementia research centers. Total RNA was isolated from CSF, and miRNA detection was performed using PCR based methods that are in widespread clinical use. Each miRNA cycle threshold (Ct) value was normalized to U6 snRNA internal controls (ΔCt). Of 332 CSF miRNAs, we found that 19 had significant differential expression between AD and control, and these 19 miRNAs were detected less often in AD versus control CSF. Logistic regression models and receiver operating characteristic (ROC) curves were then used to evaluate combinations of the 19 top-performing miRNA to accurately classify subjects into AD and control. Figure 1 shows that area under the ROC curves demonstrate 63% classification accuracy for 1 miRNA, 73% for two miRNAs, and 81% for 4 miRNAs. Thus, combinations of miRNAs show better discrimination between AD and control CSF. Together, our studies support that miRNAs in human CSF show potential utility as biomarkers for AD, and suggest that CSF exRNAs may be used as biomarkers for other dementia and neurodegenerative diseases.

Figure 1: Combinations of miRNAs better discriminate between AD and control CSF.