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Graner. Extracell Vesicles Circ Nucleic Acids 2020;1:3-19 I http://dx.doi.org/10.20517/evcna.2020.08 Page 17

and in certain cases, with observable effects (transient growth in soft agar, presence of micronuclei). From
the perspective of the cancer cell, vesiculation and extracellular release of materials is programmed and is a
necessary part of the cancer cell’s existence. There is another side, however: recipient cells who bind and take
up cancer cell EVs, and process the contents. Janusz’s lab pursued this in the form of mutant RAS DNA in
the blood components of tumor-bearing mice. They were able to find circulating RAS DNA, cell-free and in
EVs, but the most abundant depot of the DNA was in white blood cells. Neutrophils in particular seemed to
control the amounts of tumor DNA in blood; if those cells are eliminated, circulating tumor DNA and DNA
in EVs increased. These data suggest a possible new form of liquid biopsy - leukobiopsy, where leukocytes
may harbor the informative characteristics of circulating tumor materials and probed for those tumor
entities, be they DNA, RNA, oncoproteins, or oncometabolites.

As introduced earlier by NIH/NIDA’s John Satterlee, there is a genuine need for biomarkers for drugs of
addiction and substance use disorders. Here, Ursula S. Sandau (Oregon Health and Science University, US)
described her work in EVs regarding methamphetamine use and treatment monitoring with EV cargo as
a metric for recovery. Methamphetamine use is increasing globally, and can have damaging effects across
organ systems, including adverse neuropsychiatric effects. Methamphetamine acts at synaptic dopamine
transporters to block dopamine re-uptake, leaving dopamine in the dopaminergic neuronal synapse
and stimulating a reward response. The drug also drives neuroinflammatory responses and has multiple
implications. Brain microRNAs are altered in response to methamphetamine dosing, and these in turn, can
affect proteins implicated in addiction; some of these miRs become diminished in plasma. Certain miRs are
also implicated in blood-brain barrier permeability, allowing vesicle release into the blood compartment. In a
collaborative study, Ursula’s group aimed to characterize plasma EVs in active methamphetamine users, and
identify miRs with altered expression in that population. Using clinical data gathered as part of the study,
the goal was to follow plasma EV miR changes in the context of clinical and neuropsychological changes
in users.They performed vesicle flow cytometry to calculate particle concentrations in the subjects’ plasma,
and found finding that measures of lifetime exposure showed some correlation to particle quantity. These
correlations were maintained when sorting for EVs vs. all particles. Purification of EVs and quantification
of miRs showed that 20 miRs were significantly increased, and 69 miRs decreased. Following statistical
management, they narrowed the numerically relevant miR numbers to six. When correlating these to
subjects’ clinical features, characteristics of lifetime exposure were significant and included frequency for
three of the six miRs. Pathway analysis based on miR targets revealed pathway involved in cardiac, liver, and
kidney disease, as well as neurological function. These also correlated with behavioral function related to
methamphetamine use. The results of the study suggest that plasma EVs and their miR content may serve as
biomarkers in methamphetamine use disorder and may correlate with clinical features.

The final speaker of the session, and of the conference, was Andy Hill (LaTrobe University, Australia),
regarding EV-based biomarkers of neurodegenerative disease. Neurodegenerative diseases present with a
range of signs and symptoms, and with clinical decline before diagnosis. Causes are also variable, but often
involve protein misfolding and deposit in the brain. As diagnosis may involve sophisticated brain imaging
techniques not routinely available, the search for blood-based biomarkers is reasonable. EV microRNAs
have been sought as potential biomarkers for years, particularly in accessible, minimally-invasive sources,
such as blood. However, blood-based biomarkers might not represent the actual pathology of the brain,
so Andy’s group delved into techniques to isolate vesicles from actual brain tissues (both healthy controls
and AD patients). The EV cargo was then compared to the blood (serum) EV cargo they had previously
identified as putative AD biomarkers. There were indeed miRs that were found at higher levels in AD brain
EVs vs. control brain EVs, but also some that were different between serum and brain. Thus, the serum EV
miRs were not exactly the same as the brain EV miRs, but could nonetheless be useful. The next step was to
compare the miRs predictive of AD with imaging studies. Employing machine learning (Association Rule
Mining, ARM), EV miR levels could predict imaging positive (AD) and imaging negative (healthy control)