The field on ncRNAs in the mammalian inner ear is still very much in development. While there has been tremendous progress in the last decade, there are areas that are still in their infancy. One such area is that of lincRNAs. lincRNAs are relatively long stretches of RNA larger than 200nt (Ponting et al., 2009). They were identified relying on knowledge from proteincoding transcripts. Both coding and non-coding transcripts have particular chromatin signatures consisting of H3K4me3 and H3K36me3. By identifying K4–K36 domains that lay outside known protein-coding loci, lincRNAs could be methodically identified (Guttman et al., 2009). Unlike miRNAs, lincRNAs have no shared structural characteristics; their biogenesis and processing is unique, as well as their mode of action. Therefore it is not straightforward to identify and study them. An additional factor that hampers the research in the field of these new species is that lincRNAs are extremely cell- and tissue-specific, making their discovery particular to each system. Although there is some evolutionary conservation between species, it is much less prominent than the one observed in coding RNA transcripts, adding to the complexity of their identification. As opposed to miRNAs, the biological functions of lincRNAs are largely unknown. Moreover, some of the already described roles of the lincRNA are variable and are not necessarily mutually exclusive (Da Sacco et al., 2012). lincRNAs have been found to act as gene activators, gene suppressors, cis and trans gene expression regulators, and chromatin modificators.

LincRNAs have been shown to play a critical role in the development and regulation of the sensory systems. As for the long ncRNA species, lincRNAs have been found expressed in the mouse retina (Mustafi et al., 2013) and suggested to be associated with retinal and visual maintenance in mammals. Another study showed that the taurine upregulated gene 1 (Tug1) lincRNA is required for differentiation of the murine retina acting via regulation of the cell cycle (Young et al., 2005). Two studies have reported lincRNAs in the inner ear. MEG3, a lincRNA implicated in leukemia (Heuston et al., 2011), was examined following microarray detection experiment of enriched transcripts in a mouse inner ear library (Manji et al., 2006). Following detection of its expression in the developing otocyst, spiral ganglion, stria vascularis, Reissner's membrane, and greater epithelial ridge (GER), as well as hair and supporting cells, MEG3 was hypothesized to play a role in pattern specification and differentiation during otocyst development and maintenance terminally differentiated cochlear cells. *Rubie* was identified as an inner-ear specific lincRNA upstream of the *Bmp4* gene (Roberts et al., 2012). Rubie was predicted to be the gene mutated in epistatic circler (*Ecl*) mice, contributing to its vestibular phenotype. There is clearly room for a comprehensive investigation of lincRNAs in the inner ear.

One of the most exciting developments in the field has been the generation of a novel *in vitro* model system for the inner ear (Koehler et al., 2013). Mouse embryonic stem cells (ESC) were differentiated in a step-wise manner into a 3D culture of vestibular sensory epithelia. These cells showed characteristics of innate hair cells; they were able to take up FM1-43 dye and exhibited voltage-dependent currents. Moreover, ribbon synapses were formed between the hair cells and neighboring neurons in the 3D culture. These cells may serve as a substrate for investigating additional aspects of RNA regulation and lead to identification of additional RNA species in the inner ear.

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#### **CONCLUSION**

miRNAs are being developed as therapeutics for breast cancer (Piva et al., 2013), rheumatic diseases (Pers and Jorgensen, 2013), and hepatitis C virus infection (Lindow and Kauppinen, 2012; Janssen et al., 2013), which are already involved in Phase 2 clinical trials. The major limitations in miRNA research in the inner ear are the lack of robust cell lines and the inability to gain access to human tissue in an efficient manner. Nevertheless, the identification of hundreds of miRNAs in the auditory system and the elucidation of the function of many of these miRNAs and their targets holds promise for their use in therapeutics one day. A deeper understanding of the regulatory elements involved in the diseased state of the ear, including hearing impairment, cholesteatoma, OM, and vestibular schwannomas can be reached, with miRNAs serving as a potential source of regeneration therapies and relevant pharmaceutical studies. The ability to generate stem cells may open up further avenues for RNA regulation studies.

#### **ACKNOWLEDGMENTS**

Research in the Avraham laboratory is supported by the Israel Science Foundation 1320/11, National Institutes of Health (NIDCD) R01DC011835, I-CORE Gene Regulation in Complex Human Disease Center No. 41/11, Human Frontier Science Program RGP0012/2012, and Ministry of Immigrant Absorption (Kathy Ushakov).

#### **REFERENCES**

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**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 19 September 2013; paper pending published: 17 October 2013; accepted: 04 December 2013; published online: 23 December 2013.*

*Citation: Ushakov K, Rudnicki A and Avraham KB (2013) MicroRNAs in sensorineural diseases of the ear. Front. Mol. Neurosci. 6:52. doi: 10.3389/fnmol.2013.00052*

*This article was submitted to the journal Frontiers in Molecular Neuroscience.*

*Copyright* © *2013 Ushakov, Rudnicki and Avraham. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

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# MicroRNA responses to focal cerebral ischemia in male and female mouse brain

*Theresa A. Lusardi 1†, Stephanie J. Murphy2†, Jay I. Phillips 2, Yingxin Chen2, Catherine M. Davis 2, Jennifer M. Young2, Simon J. Thompson1 and Julie A. Saugstad2 \**

*1 Dow Neurobiology Laboratories, Legacy Research Institute, Portland, OR, USA*

*2 Department of Anesthesiology and Perioperative Medicine, Oregon Health and Science University, Portland, OR, USA*

#### *Edited by:*

*Hermona Soreq, The Hebrew University of Jerusalem, Israel*

#### *Reviewed by:*

*Enrico Tongiorgi, University of Trieste, Italy Alessandro Vercelli, University of Torino, Italy*

#### *\*Correspondence:*

*Julie A. Saugstad, Department of Anesthesiology and Perioperative Medicine, Oregon Health and Science University, 3181 SW Sam Jackson Park Road, HRC5N, Portland, OR 97239-3098, USA e-mail<EMAIL_ADDRESS>
*†These authors have contributed equally to this work.*

Stroke occurs with greater frequency in men than in women across diverse ethnic backgrounds and nationalities. Work from our lab and others have revealed a sex-specific sensitivity to cerebral ischemia whereby males exhibit a larger extent of brain damage resulting from an ischemic event compared to females. Previous studies revealed that microRNA (miRNA) expression is regulated by cerebral ischemia in males; however, no studies to date have examined the effect of ischemia on miRNA responses in females. Thus, we examined miRNA responses in male and female brain in response to cerebral ischemia using miRNA arrays. These studies revealed that in male and female brains, ischemia leads to both a universal miRNA response as well as a sexually distinct response to challenge. Target prediction analysis of the miRNAs increased in male or female ischemic brain reveal sex-specific differences in gene targets and protein pathways. These data support that the mechanisms underlying sexually dimorphic responses to cerebral ischemia includes distinct changes in miRNAs in male and female brain, in addition to a miRNA signature response to ischemia that is common to both.

**Keywords: microRNA, cerebral ischemia, sex-differences, array analysis, qRT-PCR, pathway analysis, stroke**

#### **INTRODUCTION**

Stroke occurs more frequently in men than women across diverse ethnic backgrounds and nationalities (Bushnell, 2008; Reeves et al., 2008; Saini and Shuaib, 2008; Appelros et al., 2009; Persky et al., 2010; Ovbiagele et al., 2013; Towfighi et al., 2013). Our lab and others have shown that sensitivity to cerebral ischemia, i.e., the extent of brain damage resulting from ischemic insult, is sex-specific, with female animals being less sensitive than males (Murphy et al., 2004; Koerner et al., 2007; Lang and McCullough, 2008; Reeves et al., 2008; Vagnerova et al., 2008; Cheng and Hurn, 2010; Siegel et al., 2010). Sex-specific responses are also observed in response to focal cerebral ischemia in isoflurane preconditioned mice (Kitano et al., 2007) and in immune responses to ischemia (Banerjee et al., 2013). Furthermore, sex differences in ischemic sensitivity have been extended to the cellular level as our lab and others have shown that astrocytes (Liu et al., 2007, 2008) and neurons (Li et al., 2005; Johnsen and Murphy, 2011) from male newborn rodents are more sensitive to oxygen-glucose deprivation (an *in vitro* model of ischemia) than cells from female newborn rodents. These observations suggest that the male brain exhibits a more "ischemia-sensitive" phenotype than the female brain. However, the underlying molecular mechanisms for this sexually dimorphic response to ischemia are not well understood.

We examined a role for miRNAs in ischemic responses in the male and female brain. MiRNAs are short, non-coding RNA sequences that regulate post-transcriptional gene expression via translational repression or mRNA degradation (Ambros, 2004; Murchison and Hannon, 2004; Niwa and Slack, 2007; Guarnieri and DiLeone, 2008; Chua et al., 2009). MiRNAs have been implicated in the regulation of numerous physiological and pathological processes such as brain differentiation (Feng and Feng, 2011), neurological disorders (Saugstad, 2010), ischemic preconditioning (Lusardi et al., 2010), and stroke (Rink and Khanna, 2011; Tan et al., 2011).

For these studies we focused on miRNA expression at 8 h after ischemia, based on our previous miRNA studies in rodent brain showing that this reperfusion time is optimal for robust change in miRNA expression levels. Two previous studies revealed little or no changes in miRNA expression at 2 and 4 h after treatment, robust changes 8 h after treatment, and a return to levels comparable to naïve controls by 24 h after treatment (Lusardi et al., 2010, 2012). These studies suggest that the treatments used (ischemia or glutamate activation) induced transcriptional changes in miRNA expression, or alterations in the miRNA processing pathway, that were optimally detected 8 h after the treatment. This time course would be consistent with miRNAs as early mediators of mRNA translation and protein expression that in turn lead to cellular changes that develop within 24–72 h after ischemia.

Our miRNA profiling studies revealed that there are sexspecific differences in miRNA responses to ischemia as well as a universal, ischemia-induced miRNA signature equally present in both male and female brains. Our findings reveal a novel mechanism, namely the differential regulation of miRNA responses, for sex differences in ischemic sensitivity mediated by sex-specific miRNA pathways in male and female brain.

#### **MATERIALS AND METHODS**

#### **EXPERIMENTAL GROUPS**

Experiments were carried out in male and female C57BL/6 mice (Charles River Laboratories, Wilmington, MA, USA), 8–14 weeks of age and weighing 20–25 g. Experiments were carried out in accordance with the National Institutes of Health guidelines for research animal care and approved by the Oregon Health and Science University Animal Care and Use Committee. All mice were maintained on a 12/12 h light-dark cycles and permitted *ad libitum* access to food and water. Male and female mice were randomized to one of the following experimental groups: control (experimentally naïve), sham surgery, or transient focal cerebral ischemia.

#### **TRANSIENT FOCAL CEREBRAL ISCHEMIA**

All surgeries were conducted under aseptic conditions by a single surgeon. Transient focal cerebral ischemia was induced in male and female mice for 60 min by reversible right middle cerebral artery occlusion (MCAO) under isoflurane anesthesia, followed by 8 h of reperfusion as previously described (Chen et al., 2012). Peri-ischemic head and body temperature were controlled at 36.5 ± 1.0◦C (mean ± standard deviation) with warm water pads and a heating lamp. The common carotid artery was temporarily occluded while a 6-0 nylon monofilament surgical suture (ETHICON, Inc., Somerville, NJ, USA) with a silicone-coated (Xantopren Comfort Light, Heraeus Kulzer, Germany) tip was inserted via an external carotid artery stump distal to the internal carotid artery to the origin of the middle cerebral artery. After 60 min of MCAO, the filament was withdrawn to allow for reperfusion. All incisions were the closed with 6-0 surgical sutures (ETHICON, Inc., Somerville, NJ, USA) before each mouse was awakened and recovered in a separate cage with a warm water pad. For sham surgeries, the filament was placed but not advanced to achieve MCAO. Occlusion and reperfusion were verified in each mouse by laser Doppler flowmetry (LDF) (Model DRT4, Moor Instruments Inc. Wilmington, USA). Mice were excluded if intra-ischemic LDF (% pre-ischemic LDF baseline) was greater than 25%. Neurological deficit scores were determined at 1 h of reperfusion to confirm the presence of ischemic injury using a 0–4 point scale as follows: 0, no neurological dysfunction; 1, failure to extend left forelimb fully when lifted by tail; 2, circling to the contralateral side; 3, falling to the left; and 4, no spontaneous movement or in a comatose state (Chen et al., 2012). Any animal without a deficit at 1 h of reperfusion was excluded from the study. Eight hours following either sham surgery or focal cerebral ischemia, mice were anesthetized with isoflurane and euthanized by decapitation. Experimentally naïve mice were also anesthetized with isoflurane and euthanized by decapitation. Right and left cortices were sub-dissected from each mouse brain, and the tissues were frozen in 2 methyl-2-butane on dry ice, then stored at −80◦C.

#### **RNA ISOLATION**

To correlate with the right MCAO model, RNA was isolated from the right mouse brain cortex with the mirVana miRNA Isolation Kit (Life Technologies, Carlsbad, CA, USA), following the recommended protocol for total RNA isolation from frozen tissue. The RNA isolation did not include the "Enrichment Procedure for Small RNA" in the protocol provided with the kit. Total RNA was eluted with 100µL of Elution Solution provided with the RNA isolation kit, and the RNA concentrations quantified by spectroscopic measurement of A260. RNA samples were stored at −80◦C until further use.

#### **MICRORNA ARRAY PROFILING**

Mouse MicroRNA Genome V2.0 PCR Arrays (MAM-200C; SABiosciences/Qiagen, Valencia, CA) were used to quantitatively assay miRNA expression in mouse brain. The arrays consisted of the 528 most abundantly expressed and well-characterized miRNA sequences in the mouse genome, as annotated by the Sanger miRBase Release 14. For qRT-PCR array analysis we used total RNA samples representing control male and female mice, and ischemic male and female mice. The RT2 miRNA First Strand Kit (SABiosciences) was used for Reverse Transcription (RT) of the RNAs, as per the manufacturer's instructions. Total RNA from control and ischemic male and female mice (*n* = 3 mice/group) were pooled, then 2µg of the pooled total RNA was incubated in RT buffers at 37◦C for 2 h, followed by 95◦C for 5 min to degrade the RNA and to inactivate the reverse transcriptase. The first-strand cDNA samples were chilled on ice then diluted with RNase-free water. The RT2 SYBR green master mix (PA-012) was used for the qRT-PCR reactions, as per the manufacturer's instructions (SABiosciences). Briefly, the diluted first-strand cDNA was combined with master mix, then 25µL was aliquoted into each well of six 96 well plates. The six plates were briefly centrifuged then stored at −20◦C. For amplification, individual plates were removed from −20◦C, defrosted for 5 min at RT, briefly centrifuged and placed into a 7500 Fast Real-Time PCR System (Applied Biosystems, Foster City, CA). Parameters were set to: (i) one cycle at 95◦C for 10 min, (ii) 40 cycles at 95◦C for 15 s, 60◦C for 40 s, and 72◦C for 30 s. A dissociation step set to: (iii) 95◦C for 15 s; 60◦C for 1 min and 95◦C for 15 s was performed to ensure that all PCRs generated a single product. Normalized delta CT (-Ct) values were calculated with respect to the average of all Ct values, and --Ct was calculated as (-Ct-Ischemic minus -Ct-Control) for all of the male and female miRNAs.

#### **SABIOSCIENCES qRT-PCR MICRORNA ARRAY NORMALIZATION**

We calculated the -Ct for each miRNA in a given experiment based on the average Ct value of all miRNAs in that experimental condition: -Ctmir = Ctavg − Ctmir. We then determined the response to ischemia for males and females separately, and calculated the change in miRNA expression as --Ctmir = -Ctmirischemia − -Ctmircontrol. We defined statistical significance as a multiple of a standard deviation from the mean --Ct (SD).

#### **REAL-TIME qRT-PCR DETECTION**

We analyzed miRNA expression in individual mice (each group) from control (*n* = 5), sham (*n* = 5), and ischemic (*n* = 4) cortices. Sham surgery groups were added after the initial miRNA array profiling to account for any effects due to anesthesia and surgical stress. Detection of miRNAs was completed with a 2 step qRT-PCR assay, using the miScript PCR System (Qiagen, Valencia, CA). One microgram of total RNA was converted to cDNA, for RNA from each individual sample, with the miScript II RT Kit, following the standard protocol with the miScript HiSpec Buffer. Negative control samples included those with no reverse transcriptase (No RT). The qRT-PCR assays were prepared for each individual using 1µg of total RNA in the same reverse transcription reaction protocol, omitting the RT enzyme in the reaction mix. The resulting 20µL of RT products (or No RT controls) were diluted to a total volume of 100µL with the addition of 80µL RNase/ DNase Free water, and stored at −80◦C before use in PCR assays. Two micro litres of cDNA was assayed in each PCR reaction well. miScript PCR Primer Assays (Qiagen) were used for detection of mature miRNA, using the miScript SYBR Green PCR Kit for qRT-PCR assays. The following primer sets were used:

Assays were performed with the standard recommended reaction mix (25µL volume per reaction) in 96-well reaction plates, using a ViiA 7 Real Time PCR Detection System (Life Technologies). Each miRNA assay was performed in triplicate for individual samples. The Ct values were calculated using the same cycle threshold and baseline for all reactions. The -Ctmir = Ctnon-changers − Ctmir, responses to ischemia for males and females were calculated the change in miRNA expression as --Ctmir = -Ctmirischemia − -Ctmircontrol, and -Ctmirischemia − -Ctmirsham.

#### **MICRORNA TARGET PREDICTION**

We used the miRmap target prediction program (http://mirmap. ezlab.org/, accessed November 2013) to query the predicted targets of miRNAs increased by ischemia in male and female brain (Vejnar et al., 2013). We then used the predicted protein targets identified in miRmap to query the PANTHER v8.1 program (http://www.pantherdb.org/, accessed November 2013). The PANTHER (Protein ANalysis THrough Evolutionary Relationships) classification system was designed to classify proteins (and their genes) according to: family and subfamily, molecular function, biological process, and pathway that explicitly specifies the relationships between the interacting molecules (Mi and Thomas, 2009; Mi et al., 2013).

#### **RESULTS**

#### **STATISTICAL ANALYSIS OF MICRORNA ARRAYS**

We used the Mouse MicroRNA Genome V2.0 PCR Arrays to profile the expression of mouse cortical miRNAs in both male and female brains. For the array studies, we pooled RNA samples isolated from control and ischemic male and female cortices. For array normalization, we first considered the endogenous controls included on the PCR arrays (Snord85, Snord68, Snord66, and Rnu6). We found that within a given treatment group, the endogenous controls were similar from plate-to-plate. However, across experimental groups the response of the endogenous controls was not consistent for sex or stroke groupings. We therefore calculated the -Ct for each miRNA in a given experiment (control or ischemia) based on the average Ct value of all miRNAs in that experimental condition: -CtmiR = Ctavg − CtmiR, consistent with the assumption that most miRNAs would not be altered by focal ischemia as shown by linear regression analysis in **Figure 1**.

We then determined the response to ischemia for males and females separately, and calculated the change in miRNA expression as --CtmiR = -CtmiRischemia − -CtmiRcontrol. We defined significance as a multiple of a standard deviation from the mean --Ct (SD), as shown in **Figure 2**. A miRNA was considered to be significantly decreased in response to ischemia if --Ctmir (≤−1.5 *SD*); the specific miRNAs are listed in **Table 1A**. A miRNA was considered to be significantly increased in response to ischemia if --Ctmir (≥1.5 *SD*); specific miRNAs are listed in **Table 1B**. Approximately half of the 528 miRNAs did not change in response to ischemia based on the cutoff criterion (−0.5 *SD*) < --Ctmir < (0.5 *SD*). An additional group of mi-RNAs possibly changed in response to ischemia, with a (0.5 *SD*) < |--Ctmir| < (1.5 × *SD*), but did not meet the criterion for significance and were excluded from further analysis in this study.

For initial studies, miRNA was considered significantly changed if --Ct was greater than 1.5 *SD* (standard deviation) from the mean --Ct. The studies revealed two profiles: (1) sexdependent responses wherein ischemia-regulated miRNAs were unique to male or female brain, and (2) a sex-independent response in which miRNAs are equally present and equally regulated in both male and female brains (the focus of another study). Of the significantly increased miRNAs, >50% were present in both male and female brain (**Table 1**).

#### **INDIVIDUAL MICRORNA VALIDATION**

Individual miRNAs representing down- and up-regulated species in male and female mice were further validated by Taqman qRT-PCR assays (Qiagen). Although the initial miRNA array studies did not include a sham surgery group, we did include sham surgery groups in the individual qRT-PCR validation studies to account for any treatment effects due to anesthesia and surgical stress. As indicated in **Table 1**, we examined miR-883b-3p that decreased in males and increased in females, miR-296-5p that decreased in males and females, miR-509-3p that decreased in females (1A). We also examined miR-682 that increased in males, miR-686 and miR-1224 that increased in males and females, and miR-883a-3p that increased in females (1B). Bold font in **Table 1** highlights the miRNAs that were chosen for individual qRT-PCR

validation, while italic font indicates the miRNAs that showed significant changes in the opposite direction in male and female brain.

We calculated the --Cts using both control (*n* = 5 each male and female) and sham surgery (*n* = 5 each male and female) -Ct values, relative to ischemia (*n* = 4 each male and female). Based on the array results, we selected miR-15b∗ and miR-125b-3p as candidates for a normalizing factor for our qRT-PCR validation. The rationale for this choice is as follows. We compared the raw Ct values for each of these miR across experimental conditions (control, sham, and ischemic) using a 2-Way repeated measures ANOVA (Prism, GraphPad Software, Inc. La Jolla, CA). The results showed: (1) there is no effect of the experimental condition (i.e., ischemia vs. sham vs. control) (*p* = 0.1429), there is

**FIGURE 2 | Classification of miRNAs altered by ischemia.** The figure shows the distribution of the miRNAs altered by ischemia relative to control in male and female mouse cortex. For initial studies we focused on miRNAs that were significantly decreased (<−1.5 *SD*) or increased (>1.5 *SD*) in the male and female mouse cortex.

a significant difference between miR-15b∗ and miR-125b-3p (*p* < 0.0001), and (3) there was no interaction between the experimental condition and the miRNAs (*p* = 0.1272). The within subject matching is significant (*p* = 0.0004), suggesting that variation across is due to individual variation, not to chance. These analyses support that neither miR-125b-1-3p nor miR-15b∗ is sensitive to the experimental manipulations (**Figure 3**) in males (3A) or females (3B), thus the average of both was used as a normalizing factor.

We then examined whether any of the selected miRNAs were sensitive to the sham surgery. Since the original miRNA array comparison was performed on control vs. ischemic conditions, we examined whether there was any significant influence of a surgical sham on miRNA expression. Thus, we compared the normalized Ct values (-Ct) for each miRNA in Control and Sham experimental groups using a 2-Way repeated measures ANOVA (Prism, GraphPad Software). The results show: (1) there is no effect of the experimental group (*p* = 0.1130), (2) there is a significant effect of miRNA (*p* < 0.0001), (3) there is no interaction between miRNA and experimental group (*p* = 0.1344), and (4) there is a significant within-subject matching (*p* = 0.0116), suggesting variations are due to individual subject variation, rather than random. The conclusions from these analyses are that there is no influence of the surgery alone on any of the miRNAs examined here. Thus, **Figure 4** shows the changes in select miRNAs expressed in male (**Figure 4A**) of female (**Figure 4B**) cortex in control vs. ischemic treated cortex.

After accounting for the above, the results show that many but not all of the miRNAs under study showed changes in the same direction (up or down) in the individual qRT-PCR assays, consistent with the changes in expression detected by the pooled samples in the miRNA arrays (**Figure 5**). MiR-686 and miR-1224 both showed increased expression in males and females, as predicted by the miRNA arrays. In addition, miR-296-5p decreased

miRNAs are not altered by ischemia.

#### **Table 1 | MicroRNAs Regulated By Ischemia.**

*MiRNAs significantly regulated by ischemia in male and female mouse brain.*

*(A) Lists the* --*Cts for miRNAs significantly decreased in ischemia (*<−*1.5 SD) in male and female cortex.*

*(B) Lists the* --*Cts for miRNAs significantly increased by ischemia (*>*1.5 SD) in male and female cortex.*

*The bold indicates the miRNAs in the table that were used for subsequent studies, the data for which is presented in Figure 4. The italics indicate those miRNAs that were found to be oppositely regulated in male and female brain.*

in males and females, and miR-125b-3p showed no changes in male or female brain, consistent with the miRNA arrays. MiR-682 showed increased expression in males, consistent with the miRNA arrays, but there was increased expression in females that was opposite from the arrays. MiR-883a-3p showed increased expression in females, consistent with the miRNA arrays, but there was a slight increase in the expression in males relative to the small decrease in expression that was detected in the arrays. Also, miR-509-3p increased in the males, consistent with the miRNA arrays, but decreased significantly in the females which was opposite from the arrays.

These outcomes support that miRNAs are regulated by ischemia in male and female brain. However, they also show that the use of different array formats, or pooled vs. individual qRT-PCR samples, likely influences the expression results (Git et al., 2010). The miRNAs with the most robust changes in

expression in the arrays showed consistent changes in the individual arrays, particularly for those miRNAs that increased in expression. This observation suggests that decreases in miRNA expression from RNA degradation, low abundance of miRNAs, or pooling samples, may affect the array outcomes and data interpretation.

#### **MICRORNA TARGET PREDICTION**

We used the miRmap target prediction program (http://mirmap. ezlab.org/) to query the predicted targets of miRNAs increased by ischemia in male and female brain (Vejnar et al., 2013). We used miRmap to query the targets of the 17 miRNAs increased by ischemia in male brain. The results show target genes and the top number of miRNAs predicted to target this gene with a cutoff of 6 (**Table 2A**). We also used miRmap to query the targets of the 9 miRNAs increased by ischemia in female brain. The results show target genes and the top number of miRNAs predicted to target this gene with a cutoff of 4 (**Table 2B**). The results show that the miRNAs increased by ischemia in male and female brain are distinct, and only 2 targets are detected in common in the groups, CD73 and PKN2. These findings suggest that differential pathways are targeted by miRNAs increased by ischemia and that these pathways may lead to differential outcomes to ischemia may underlie the sexually dimorphic responses to ischemia, wherein females are afforded greater protection against ischemia insult

than males. PANTHER target analysis revealed that there are 36 pathways of the genes targeted by miRNAs increased in male brain, and there are 69 pathways for the genes targeted by miRNAs in the female brain (Table 3—Supplemental Data). These findings support that distinct pathways are targeted by miRNA responses to ischemia, and provide an opportunity to focus studies on specific protein and protein pathways that may have been overlooked in prior studies.

#### **DISCUSSION**

Previous studies have shown that miRNAs are regulated in the brain in response to stress, including cerebral ischemia (Fasanaro et al., 2010; Saugstad, 2010; Rink and Khanna, 2011; Liu et al., 2013; Ouyang et al., 2013). However, to our knowledge, we are the first to show miRNA responses to ischemia that are sex dependent, i.e., there are differential responses in male or female brain following ischemia. For this study we focused on those miRNAs differentially expressed between males and females greater than 1.5 *SD* from the mean --Ct in order to look at the most robust changers in response to ischemia. However, mRNAs can be targeted by many miRNAs (Doench and Sharp, 2004) and even small, subtle changes in miRNA levels can lead to significant changes in mRNA translation or stability. Thus, we are currently evaluating those differentially expressed miRNAs that

show a change between 1.0 and 1.5 *SD* from the mean to identify additional proteins/pathways that could underlie differential responses to ischemia in male and female brain.

Our studies also revealed a signature miRNA response to ischemia that is common to both males and females. These key findings provide a mechanism, based on differential miRNA expression in male and female brain, to identify cellular or molecular targets that could underlie the sex differences in responses to ischemia. Such insight is likely to have implications for therapeutic strategies for the treatment of stroke in men vs. women. In addition, these studies also provide the ability to examine new targets that might contribute to ischemic injury in both males and females. Thus, we are focused on identifying the cellular/molecular targets of the ischemic-regulated miRNAs to determine how these miRNAs produce differential outcomes to ischemic injury. The genes identified in this study as predicted target of the miRNAs increased by ischemia in male and female ischemic cortex (**Table 2**) are quite distinct, and support that these miRNAs are likely initiating changes in distinct proteins and pathways that lead to altered phenotypes in in response to **Table 2 | Predicted Proteins Targeted By MicroRNAs Significantly Increased Following Ischemia.**

*Proteins predicted to be targeted by miRNAs differentially regulated by ischemia in male and female brain. The table lists show the gene name and the top number of miRNAs in each group predicted to target the gene (6–9 of 17 miRNAs in male, 4–6 miRNAs in female).*

ischemia in male and female brain. PANTHER pathway analysis of the gene targets of miRNAs increased by ischemia in male and female (Table 3—Supplemental Data) support that distinct pathways are induced in each gender, and future studies are focused on clarifying the importance of these pathways in differential responses to ischemia in male and female brain.

One limitation of the present studies is that miRNA target prediction is still evolving. However, we used the miRmap target prediction program (http://mirmap.ezlab.org/) to query the predicted targets of miRNAs increased by ischemia in male and female brain (Vejnar et al., 2013). The miRmap open source software library employs eleven predictor features, three of which are novel, as well as common features of target prediction including thermodynamic, evolutionary, probabilistic, and sequence-based features. This program allows the examination of feature correlations and comparison of their predictive power in an unbiased way using high throughput experimental data. Overall, target site accessibility appears to be the most predictive feature. Methods for identifying real miRNA targets are also evolving, such as using RISCtraps to stabilize and purify targets of specific miRNAs (Cambronne et al., 2012), but will be essential for future studies identifying the targets of the miRNAs regulated by ischemia in male and female brain. Another limitation is that we examined miRNA expression in the whole ischemic cortex, which may have diluted the expression of distinct miRNAs due to inclusion of both the core and penumbra. However, at 8 h reperfusion time, there are no apparent changes in cell death and thus no reliable method to indicate the boundary between core and penumbra. Thus, we will examine the regional/cellular changes in differentially expressed miRNAs to identify their potential role as mediators of cell death in the ischemic core.

In conclusion, we have shown that the sex-related differences in the response to ischemic insult observed in males and female mice is also characterized by a corresponding difference in expression levels of a specific set of miRNAs. Thus, current and future studies in our laboratory are focused on elucidating the roles of ischemia-regulated miRNAs that show distinct changes in male or female brain, along with those miRNAs that are regulated in a sex-independent fashion. We anticipate that these studies will clarify the mechanisms underlying responses to ischemia, both sex-related and non-sex-related. We also trust that they will provide guidance for the future design of therapeutic strategies to treat stroke specifically tailored to male and female patients. For example, a recent study of miRNAs in cardiac ischemia revealed that cardiomyocyte proliferation could be stimulated by the exogenous administration of miRNAs, and more importantly, that this treatment could restore cardiac mass and promote functional recovery after myocardial infarction in adult rats (Eulalio et al., 2012). We propose that future therapies could similarly be developed for cerebral ischemia, whereby administration of mi-RNAs designed specifically for males or females would promote functional recovery after stroke in humans.

#### **AUTHOR CONTRIBUTIONS**

Stephanie J. Murphy and Julie A. Saugstad conceptualized the project. Yingxin Chen performed all of the mouse middle cerebral artery occlusion surgeries and brain sub-dissections. Simon J. Thompson, Catherine M. Davis, and Jennifer M. Young isolated the RNAs and performed the SABiosciences miRNA arrays. Theresa A. Lusardi and Julie A. Saugstad analyzed the miRNA array data. Jay I. Phillips isolated RNA and performed the individual qRT-PCR assays. Theresa A. Lusardi, Julie A. Saugstad, and Jay I. Phillips analyzed the miRNA qRT-PCR data. Theresa A. Lusardi, Stephanie J. Murphy, and Julie A. Saugstad prepared the manuscript.

#### **ACKNOWLEDGMENTS**

This work was support by the National Institutes of Health (R21NS078581, Stephanie J. Murphy; R01NS064270, Julie A. Saugstad).

#### **SUPPLEMENTARY MATERIAL**

The Supplementary Material for this article can be found online at: http://www.frontiersin.org/journal/10.3389/fnmol.2014. 00011/abstract

#### **REFERENCES**

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 20 September 2013; accepted: 23 January 2014; published online: 11 February 2014.*

*Citation: Lusardi TA, Murphy SJ, Phillips JI, Chen Y, Davis CM, Young JM, Thompson SJ and Saugstad JA (2014) MicroRNA responses to focal cerebral ischemia in male and female mouse brain. Front. Mol. Neurosci. 7:11. doi: 10.3389/fnmol.2014.00011 This article was submitted to the journal Frontiers in Molecular Neuroscience.*

*Copyright © 2014 Lusardi, Murphy, Phillips, Chen, Davis, Young, Thompson and Saugstad. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

# Circulating microRNAs in Alzheimer's disease: the search for novel biomarkers

#### *Véronique Dorval1,2\*, Peter T. Nelson3 and Sébastien S. Hébert1,2\**

*1 Axe Neurosciences, Centre de Recherche du Centre Hospitalier Universitaire de Québec (Centre Hospitalier de l'Université Laval), Québec, QC, Canada*

*2 Département de Psychiatrie et de Neurosciences, Université Laval, Québec, QC, Canada*

*3 Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY, USA*

#### *Edited by:*

*Hermona Soreq, The Hebrew University of Jerusalem, Israel*

*Reviewed by: Noam Shomron, Tel Aviv University, Israel*

*Elizabeth Mary McNeill, Harvard Medical School, USA*

#### *\*Correspondence:*

*Sébastien S. Hébert and Véronique Dorval, Axe Neurosciences, Centre de Recherche du Centre Hospitalier Universitaire de Québec (Centre Hospitalier de l'Université Laval), 2705 Boulevard Laurier, RC-9800, QC G1V 4G2, Canada e-mail: sebastien.hebert@ neurosciences.ulaval.ca<EMAIL_ADDRESS>
Alzheimer's disease (AD) is the most common neurodegenerative disease in the elderly. While advancements have been made in understanding the genetic and molecular basis of AD, the clinical diagnosis of AD remains difficult, and post-mortem confirmation is often required. Furthermore, the onset of neurodegeneration precedes clinical symptoms by approximately a decade. Consequently, there is a crucial need for an early and accurate diagnosis of AD, which can potentially lead to strategies that can slow down or stop the progression of neurodegeneration and dementia. Recent advances in the non-coding RNA field have shown that microRNAs (miRNAs) can function as powerful biomarkers in human diseases. Studies are emerging suggesting that circulating miRNAs in the cerebrospinal fluid and blood serum have characteristic changes in AD patients. Whether miRNAs can be used in AD diagnosis, alone or in combination with other AD biomarkers (e.g., amyloid and tau), warrants further investigation.

**Keywords: microRNA, Alzheimer's disease, biomarker, diagnosis, mild cognitive impairment**

#### **INTRODUCTION**

Alzheimer's disease (AD) is a prevalent, devastating, and progressive neurodegenerative disorder. Epidemiological studies predict that over 35 million people worldwide will be affected by 2050, thus significantly increasing social and economical burdens. There is no cure at hand, and only a few medications aimed at slowing down memory deficits and clinical symptoms are available, with limited benefits. Consequently, there is an urgent need for the identification of biomarkers that will allow the detection of AD at early (prodromal) stages, potentially leading to novel diagnostic or therapeutic strategies.

Pathologically, AD is characterized by the gradual, widespread loss of neurons, synapses, and neuropil, culminating in ∼40% loss of brain mass in end-stage disease (West et al., 1994; Gomez-Isla et al., 1996). There are two main AD pathological hallmarks: extracellular amyloid (senile) plaques and intracellular neurofibrillary tangles (NFTs; Hyman et al., 2012). The amyloid plaques comprise aggregated amyloid-beta (Aβ) peptides that are generated by sequential cleavage of amyloid precursor protein (APP) by β-secretase/BACE1 and the presenilin (PSEN) containing γ-secretase complex (Kang et al., 1987; Wolfe, 2006). The NFTs result from the abnormal aggregation of hyperphosphorylated microtubule-associated protein, tau. The reason for tau aggregation into tangles remains under investigation, but may result from an imbalance in the delicate regulation of tau kinases and phosphatases. Whereas approximately 1–5% of AD cases can be explained genetically by mutations in APP or PSEN genes, the exact cause(s) of sporadicAD remains obscure. Most experts agree, however, that sporadic AD is caused by a combination of genes and environmental factors (multifactorial), perhaps exacerbated by oxidative stress and inflammation.

Biomarkers are used to measure or indicate the effects or progress of a disease or condition. A subtype of biomarkers relates to specific and traceable biochemical molecules or compounds found in body fluids. Detection of these substances may indicate disease states or allow correlations with the progression or the susceptibility to a disease or a given treatment. They can be measured in, for instance, saliva, sweat, breath, blood/serum, urine, and cerebrospinal fluid (CSF). The collection of these biological fluids is significantly less invasive than biopsies, an important and practical issue when studying neurodegenerative disorders like AD.

Accumulating evidence suggests that circulating biomarkers may be used in AD diagnosis, the most common being Aβ peptides (Aβ40 and Aβ42, the latter being more prone to aggregation) and tau/phospho-tau (Thr181 being one of the common phosphoepitopes). While this area of research continues to progress (Tarawneh and Holtzman, 2010; Holtzman, 2011), large variability exists in the literature, hampering or delaying their routine use in the clinic (Ingelson et al., 1999). Moreover, their potential use as prodromal AD biomarkers remains uncertain. Therefore, most experts agree that additional biomarkers are required for an accurate and early diagnosis of AD vs. other potential causes of dementia. In this review, we discuss recent studies suggesting that miRNAs could function as novel, non-invasive biomarkers in AD.

#### **miRNAs AS BIOMARKERS**

The miRNAs are a class of small (∼22 nt) non-protein-coding RNAs crucially involved in the post-transcriptional regulation

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of gene expression. They are important for multiple biological processes such as development, proliferation, inflammation, and apoptosis (Xu et al., 2004; Pasquinelli et al., 2005; Thounaojam et al., 2013). The biogenesis and role(s) of the miRNA pathway have been recently and thoroughly reviewed by Treiber et al. (2012, and references therein). In brief, miRNAs function by binding with partial complementarity to messenger RNA (mRNA) sequences, mainly in the 3 untranslated region (3 UTR). This targeting leads to either degradation or translational repression of the mRNA template(s), causing an overall downregulation in protein output. The miRNAs can target several disease-related genes involved in neurodegeneration (Delay et al., 2012; Abe and Bonini, 2013).

The precise mechanism(s) involved in miRNA release from cells remain largely unknown, but may involve the ceramide-dependent secretory machinery (Kosaka et al., 2010). Alternatively, there may be a passive leakage from necrotic or apoptotic cells (Zernecke et al., 2009). In any case, these small RNAs are highly stable in body fluids such as plasma and CSF (Mraz et al., 2009), making them attractive biomarkers. There are several factors involved in modulating (distant) circulating miRNAs. These small RNAs are transported in free forms, exosomes, liposomes, or high-density lipoproteins, which protect them from degradation (Vickers et al., 2011; Hu et al., 2012; the stable packaging, processing, and functionality of miRNAs in biofluids is a fascinating and important area of research mostly beyond what is addressed in the current review). While some miRNAs are ubiquitously expressed, others are present in specific cells or tissues, including the central nervous system (CNS; Landgraf et al., 2007). Furthermore, bioinformatics studies suggest that miRNA abundance is directly correlated with mRNA target activity (Dorval et al., 2012).

Interestingly, miRNAs have been described as epigenetic contributors to age-related cognitive changes (Kosik et al., 2012). It has been suggested that dysregulation of these miRNA-dependent epigenetic functions in vulnerable brain regions may lead to cognitive impairments. Accordingly, the past few years have witnessed an explosion of papers linking miRNAs to disease states, and current research efforts establish that miRNA expression profiles are altered in a variety of pathogenic conditions. This is particularly recognized in the cancer field (Sayed and Abdellatif, 2011). Interestingly, the various changes in miRNA levels are observable not only in cells/tissues directly related to disease (e.g., tumors vs. adjacent tissues), but often in the periphery or distant biological systems (e.g., tumors vs. blood). It is noteworthy that most peripheral miRNAs are also found in the brain, albeit at various levels (Hebert et al., 2013).

#### **CIRCULATING miRNA BIOMARKERS IN AD CEREBROSPINAL FLUID**

Cerebrospinal fluid is a clear fluid that flows within the ventricles and around the surface of the brain and spinal cord. One primary function of CSF is to circulate nutrients within the CNS and, in turn, to act as a waste remover. The CSF is an attractive source of biomarkers as it is in direct and constant contact with the extracellular space of the brain, and can reflect biochemical and/or physiological changes that occur inside the brain.

In a pioneer study by Cogswell et al. (2008), the group performed a large-scale expression analysis of miRNAs in control and AD CSF. About 201 (out of 242 tested) miRNAs were detected above background levels, as measured by quantitative reverse transcriptase-polymerase chain reaction (qRT-PCR) using Taq-Man probes (Applied Biosystems). They identified 60 miRNAs, including let-7i, that were significantly altered in AD CSF (Braak V stage) when compared to healthy elderly controls (Braak I stage; *n* = 10 per group, *P* < 0.05). Using biological pathway enrichment algorithms, the group observed an association between misregulated miRNAs and the immune system, including pathways such as *innate immunity* (e.g., miR-146b) and *T cell activation and differentiation* (e.g., miR-181a, miR-142-5p). Putative targets for these miRNAs include IRAK1, TRAF6 (Lindsay, 2008), and Bcl-2 family members (Ouyang et al., 2012). The authors suggested that abnormally expressed miRNAs in the CSF were likely derived from immune cells. This was the first study demonstrating that miRNAs can be detected in the CSF (even when initially frozen) and are altered in neurodegenerative disease conditions.

van Harten et al. (2011) confirmed that it was technically feasible to perform genome-wide expression analyses of circulating miRNAs in control and AD CSF. The authors used two stem-loop qRT-PCR methods, including: (1) an individual miRNA TaqMan qRT-PCR and (2) a Megaplex modified microarray. Using this latter approach, the authors detected 667 miRNAs from one control and oneAD subject (note that more than 2,000 human miRNAs are currently registered in the miRNA database – www.mirbase.org). The authors specifically quantified and validated changes in neuronal miR-802, a suppressor of caveolin-1 (Lin et al., 2011), in the CSF of control (*n* = 8) and AD (*n* = 14) patients. Clinical tests, combined with Aβ42, t-tau, and p-tau-181 measurements in the CSF were globally consistent with the diagnosis of either group. Whether other miRNAs were misregulated in AD conditions was not evaluated.

Only recently have two critical questions been addressed in relation to CSF miRNAs in neurodegenerative diseases. The first natural question relates to *why* miRNAs are stably present in this biofluid. After all, RNAs are notoriously unstable in solution, and yet there presence has been reliably affirmed. Thus, there is a tantalizing possibility that the miRNAs in solution – and in biochemical packaging as described above – may be playing a role in the CNS. In an elegant study, Lehmann et al. (2012) demonstrated that circulating miRNAs, and in particular let-7b, could exacerbate brain damage and neurodegeneration by binding directly to the Tolllike receptor 7 (TLR7). As measured by miRNA qRT-PCR, AD CSF (*n* = 13) contained significantly higher levels of let-7b when compared to controls (*n* = 11). Here, AD patients were selected, in part, on the basis of Aβ42 and t-tau levels. Unfortunately, no correlation between these AD markers and let-7b levels was provided. However, this study demonstrates that miRNAs in CNS are bioactive, and may have paracrine/hormonal-like functions, which, if generally true, provides a novel and potentially incredibly important context for miRNA function (and pathological impact) in the brain.

A second key question is more practical, and was addressed by Alexandrov et al. (2012): is there a correlation between Aβ peptides and miRNA levels in the CSF? In this study, the patient

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groups consisted of six AD and six age-matched controls. Consistent with previous studies using enzyme-linked immunosorbent assay (ELISA), they reported a decrease in Aβ40 and Aβ42 in AD CSF, although this observation did not reach statistical significance (*P* ∼ 0.06). Interestingly, the authors measured higher (greater than 100-fold) levels of total miRNAs (total mass) when compared to Aβ peptides, and this, both in control and AD CSF. Fluorescence-based miRNA microarrays indicated that the proinflammatory miRNAs miR-9, miR-125b, miR-146a, and miR-155 were significantly increased in AD CSF. These observations were further validated by a highly sensitive light-emitting diode (LED) based Northern dot-blot analysis. This increase of specific miRNAs was extended to *in vitro* paradigms, where primary human neuronal/glial cells treated with AD-derived extracellular fluid lead to an increase of the same set of miRNAs. Significant negative correlations were observed between Aβ42 peptides and miR-137 (*r* = −0.75, *P* = 0.003), miR-181c (*r* = –0.57, *P* = 0.037), miR-9 (*r* = –0.7, *P* = 0.007), miR-29a (*r* = –0.64, *P* = 0.01), and miR-29b-1 (*r* = –0.569, *P* = 0.037), and this, in both control and AD patients. Based on these observations, it is tempting to speculate that miRs, alone or in combination with known AD biomarkers, could provide a better assessment of AD diagnosis.

#### **BLOOD**

Blood circulates in the principal vascular system, composed of arteries and veins, to carry oxygen to and carbon dioxide from tissues. The combination of lymphocytes, monocytes, and macrophages composes the peripheral blood mononuclear cells (PBMCs) population. These blood cells are critical components in the immune system.

Schipper (2007) assessed miRNA levels in blood mononuclear cells (BMCs) derived from sporadic AD and age-matched controls (*n* = 16 per group), using a microarray chip containing 462 human miRNAs. Several miRNAs were identified to be significantly altered in AD BMCs. A large number of miRNAs, including miR-34a, miR-181b, and let-7f, were validated by miRNA qRT-PCR. Interestingly, miR-34 targets include p53 (He et al., 2007), Notch (Bu et al., 2013), and Bcl-2 (Cole et al., 2008). The let-7 targets the oncogene Ras protein, thus promoting tumorigenesis (Johnson et al., 2005). Inversely, let-7 expression is regulated by the oncogenic Myc protein (Chang et al., 2008), suggesting a regulatory feedback loop. Together, these observations highlight the importance of these miRNAs in cell/tissue homeostasis.

Geekiyanage and Chan (2011) showed by miRNA qRT-PCR a decrease in miR-137, miR-181c, miR-9, and miR-29a/b levels in the neocortical region of controls (*n* = 7) and AD subjects (*n* = 7), which negatively correlated with Aβ42 levels in post-mortem brain tissues. In a follow-up study, using the same technical approach, the group reported that the same miRNAs were also present in the blood, albeit at lower basal levels (Geekiyanage et al., 2012). They were found to be downregulated in the blood serum of mild cognitive impairment (MCI; *n* = 7) and "probable" AD patients (*n* = 7) when compared controls (*n* = 7).

Villa et al. (2013) provided further evidence that dysregulation of peripheral miRNAs might contribute to AD development. In isolated PBMCs, they first showed that the transcription factor Sp1 was regulated at a post-transcriptional level by miR-29b. Interestingly, Sp1 regulates the expression of AD-related genes such as APP (La Fauci et al., 1989) and tau (Heicklen-Klein and Ginzburg, 2000). In a cohort of 393 AD patients and 412 healthy controls, the group observed an inverse relationship between Sp1 mRNA and miR-29b levels in PBMCs (*p* = 0.002). To our knowledge, this is the first report suggesting that changes in miRNA levels (e.g., miR-29b) and its/their target(s) (e.g., Sp1) may serve as cooperative biomarkers for AD diagnosis. Whether a genuine interaction between both molecules occurs in the blood remains to be validated.

Very recently, Bekris et al. (2013)reported in an elegant 3-phase study including post-mortem brain arrays and qRT-PCR validation that plasma miR-15a correlated with neuritic plaque score and Braak stages in AD. This particular miRNA was predicted to modulate 9 AD-relevant genes, including APP (Liu et al., 2012) and tau (Hebert et al., 2010).

#### **CIRCULATING miRNA BIOMARKERS IN MILD COGNITIVE IMPAIRMENT, AND CHALLENGES IN PATHOLOGICAL SPECIFICITY**

Mild cognitive impairment is a term often conflated with indicating early clinical manifestation of AD, and many do indeed progress to full-blownAD clinically, although many other pathologies than AD underlie the clinical state of MCI (see below). Nevertheless, it is essential to develop tools that can accurately discriminate between normal aging, MCI, AD, and likely other cognitive disabilities. An attractive approach has recently been proposed, namely miRNA "pairs." This concept uses, following single qRT-PCR TaqMan assays, bioinformatics to analyze the ratios of all measured miRNAs, and select the most promising pair(s) of biomarkers (Sheinerman et al., 2012). In a pilot study, 13 miRNA pairs allowed to discriminate between AD and age-matched controls, as well as between MCI and age-matched controls (*n* = 10 per group), and this, with up to 90% accuracy. The proposed sets of miRNAs could detect pre-symptomatic MCI 1–5 years before the diagnosis in 70% of cases. Finally, the same pairs of miRNAs have been able to discriminate between aged and young healthy controls (*n* = 20 per group).

There are two practical issues that are important to the clinical relevance of any biomarker: sensitivity and specificity. The issue of sensitivity is basic and relates to the fact that by the time AD is manifest as MCI, it may be too late for (at least some) therapeutic interventions. The Aβ/tau CSF studies have now shown that, as expected (Nelson et al., 2009), up to one-third of nondemented subjects harbor some AD-type pathology (Nelson et al., 2012). It is increasingly appreciated that these are the patients that should be targeted for biomarker studies as well as clinical trials.

Aspects of biomarker specificity are perhaps paramount, and often under-appreciated. Although MCI is often used to indicate an early stage of AD, MCI was originally defined according to neuropsychological features (Portet et al., 2006), which have been recognized to entail "multiple sources of heterogeneity."As such, it

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is quite usual for MCI to be associated with brain pathologies other than AD: dementia with Lewy bodies (DLB), vascular pathologies, hippocampal sclerosis (HS-Aging), frontotemporal lobar dementia (FTLD), and other conditions may cause or contribute to MCI, as to dementia (Jicha et al., 2006). This highlights an important aspect of AD-related biomarkers: they are not only used in predicting whom will become demented, but also for specifying which subtype of dementia will be predominant; the importance of this specificity for clinical trials is obvious. Novel insights, relevant to this consideration, were obtained by deep sequencing miRNAs from brains of individuals with multiple different pathological diseases (AD, DLB, FTLD, and HS-Aging; Hebert et al., 2013). Although not a biomarker study *per se*, this showed that some miRNAs (particularly miR-132-5p) are downregulated in neurodegenerative diseases non-specifically. In the future, it is hoped that more specific miRNA "fingerprints" may help to distinguish the individual subtypes of neurodegenerative diseases before their earliest manifestations.

#### **CONCLUSION AND PERSPECTIVES**

To date, most researchers have relied on the combination of Aβ peptides, total-tau, and phospho-tau (Thr181) ratios to provide the best discriminative values for individuals with or without AD. However, in most cases, large variability and differences between studied groups did not reach statistical significance, leaving inconsistencies. Without excluding the amyloid and tau biological markers, a combination of biomarkers may provide a better tool for AD diagnosis, therefore improving their clinical usefulness. Known examples include structural (e.g., hippocampal shrinkage), functional (e.g., glucose metabolism), and molecular imaging (e.g., fluorescent Pittsburgh compound B; Chintamaneni and Bhaskar, 2012).

Small non-coding RNAs, and in particular miRNAs, have come a long way in the past two decades. As discussed herein, circulating miRNAs provide an exciting and emerging research area in the biomarker field. As of now, long lists of miRNAs

#### **REFERENCES**

A microRNA miR-34a-regulated bimodal switch targets notch in colon cancer stem cells. *Cell Stem Cell* 12, 602–615. doi: 10.1016/j.stem.2013. 03.002

potentially misregulated in disease conditions have been reported, although finding overlaps is challenging (note that this is also the case for miRNA profiling studies in the brain). However, some AD-specific miRNAs were "consistently" identified, including some let-7 family members (let-7f, let-7b, and let-7i), miR-9, miR-181, and miR-29 (Maes et al., 2009). These miRNAs seem involved in processes previously associated with AD, that is to say inflammation and immunological response. Perhaps expectedly, several miRNAs and their functions as biomarkers have been patented or in the process thereof (see, e.g., www.freepatentsonline.com or www.patentlawlinks.com). Although very attractive, the applicability of miRNAs as diagnostic tools into the clinic for AD (or MCI) will require extensive validation and follow-up studies in larger cohorts of patients. This is important as AD is a heterogeneous, multifactorial disease, with often display overlapping pathologies (e.g., Aβ deposits and Lewy bodies; Gomperts et al., 2008) and/or co-morbid diagnoses (e.g., diabetes, stroke). Obviously, the ultimate goal is to provide a sensitive, reproducible, and accurate detection of AD neuropathological changes prior to the onset of the disease and the appearance of the clinical symptoms. To this end, future studies will require better neuropathological validations as well as, ultimately, far greater sample sizes for robust statistical power.

In conclusion, circulating miRNAs are amongst the promising next generation of biomarkers for AD, and ultimately the discrimination between neurodegenerative diseases. They may be small molecules, but miRNAs certainly provide a big potential for the diagnosis of human diseases.

Note: While this work was in progress, a report has been published with regards to a circulating miRNA signature in AD patients (Leidinger et al., 2013).

#### **ACKNOWLEDGMENTS**

This work was supported by the Alzheimer's Society of Canada and the Canadian Institutes of Health Research.

in turn amyloid beta, novel targets in sporadic Alzheimer's disease. *J. Neurosci.* 31, 14820–14830. doi: 10.1523/JNEUROSCI.3883-11. 2011

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regulated by the let-7 microRNA family. *Cell* 120, 635–647. doi: 10.1016/j.cell.2005.01.014

to potentially modulate Alzheimer'sassociated pathogenesis in SAMP8 mice. *Neurobiol. Aging* 33, 522–534. doi: 10.1016/j.neurobiolaging.2010. 04.034

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bodies induces CXCL12-dependent vascular protection. *Sci. Signal.* 2, ra81. doi: 10.1126/scisignal. 2000610

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Received: 04 June 2013; paper pending published: 26 June 2013; accepted: 11 August 2013; published online: 30 August 2013.*

*Citation: DorvalV, Nelson PT and Hébert SS (2013) Circulating microRNAs in* *Alzheimer's disease: the search for novel biomarkers. Front. Mol. Neurosci. 6:24. doi: 10.3389/fnmol.2013.00024*

*This article was submitted to the journal Frontiers in Molecular Neuroscience. Copyright © 2013 Dorval, Nelson and Hébert. This is an open-access article distributed under the terms of the Creative*

*Commons Attribution License (CC BY).*

*The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

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# Erratum: Circulating microRNAs in Alzheimer's disease: the search for novel biomarkers

#### *Veronique Dorval 1,2\**

*1 Axe Neurosciences, Centre de recherche du CHU de Québec (CHUL), Quebec, QC, Canada*

*2 Département de psychiatrie et de neurosciences, Université Laval, Quebec, QC, Canada*

*\*Correspondence<EMAIL_ADDRESS>
#### *Edited by:*

*Hermona Soreq, The Hebrew University of Jerusalem, Israel*

**Keywords: microRNA, Alzheimer's disease, biomarker, diagnosis, mild cognitive impairment**

#### **A commentary on**

**Circulating microRNAs in Alzheimer's disease: the search for novel biomarkers** *by Dorval, V., Nelson, P. T., and Hébert, S. S. (2013). Front. Mol. Neurosci. 6:24. doi: 10.3389/fnmol.2013.00024*

An error has been pointed out under the section CSF after our mini-review was published. At the end of the last paragraph, the reported correlations have been wrongly assigned to Alexandrov et al. The correct citation for this work is "Geekiyanage, H., and Chan, C. (2011). MicroRNA-137/181c regulates serine palmitoyltransferase and in turn amyloid beta, novel targets in sporadic Alzheimer's disease. *J. Neurosci.* 31, 14820–14830. doi: 10.1523/JNEUROSCI.3883-11.2011." However, these results were obtained from the study of frontal cortices of Alzheimer's patients, not the CSF. Thus, caution should be taken with regards to this work, which is unfortunately no longer in the scope of the present mini-review on circulating microRNAs as biomarkers.

*Received: 23 October 2013; accepted: 24 October 2013; published online: 11 November 2013.*

*Citation: Dorval V (2013) Erratum: Circulating microRNAs in Alzheimer's disease: the search for novel biomarkers. Front. Mol. Neurosci. 6:38. doi: 10.3389/ fnmol.2013.00038*

*This article was submitted to the journal Frontiers in Molecular Neuroscience.*

*Copyright © 2013 Dorval. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

# Increased microRNA-34c abundance in Alzheimer's disease circulating blood plasma

#### *Shephali Bhatnagar 1, Howard Chertkow2,3,4,5, Hyman M. Schipper 2,4,5, Zongfei Yuan1, Vikranth Shetty1, Samantha Jenkins 1, Timothy Jones 1 and Eugenia Wang1 \**

*1 Advanced Genomic Technology, LLC, Louisville, KY, USA*

*2 Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada*

*3 Memory Clinic, Department of Medicine, Jewish General Hospital, Montreal, QC, Canada*

*4 Bloomfield Centre for Research in Aging, Lady Davis Institute, Montreal, QC, Canada*

*5 Division of Geriatric Medicine and Department of Clinical Neurosciences, Jewish General Hospital, Montreal, QC, Canada*

#### *Edited by:*

*Hermona Soreq, The Hebrew University of Jerusalem, Israel*

#### *Reviewed by:*

*Hermona Soreq, The Hebrew University of Jerusalem, Israel Samuil R. Umansky, DiamiR, LLC, USA*

#### *\*Correspondence:*

*Eugenia Wang, Advanced Genomic Technology, 5100 US Highway 42, Suite 433, Louisville, KY 40241, USA e-mail<EMAIL_ADDRESS>
Circulating microRNAs, present either in the cellular component, peripheral blood mononuclear cells (PBMC), or in cell-free plasma, have emerged as biomarkers for age-dependent systemic, disease-associated changes in many organs. Previously, we have shown that microRNA (miR)-34a is increased in circulating PBMC of Alzheimer's disease (AD) patients. In the present study, we show that this microRNA's sister, miR-34c, exhibits even greater increase in both cellular and plasma components of AD circulating blood samples, compared to normal age-matched controls. Statistical analysis shows the accuracy of levels of miR-34c assayed by receiver operating characteristic (ROC) analysis: the area under the curve is 0.99 (*p* < 0.0001) and the 95% confidence level extends from 0.97 to 1. Pearson correlation between miR-34c levels and mild and moderate AD, as defined by the mini-mental state examination (MMSE), shows an *r*-value of −0.7, suggesting a relatively strong inverse relationship between the two parameters. These data show that plasma levels of microRNA 34c are much more prominent in AD than those of its sister, miR-34a, or than its own level in PBMC. Transfection studies show that miR-34c, as does its sister miR-34a, represses the expression of several selected genes involved in cell survival and oxidative defense pathways, such as Bcl2, SIRT1, and others, in cultured cells. Taken together, our results indicate that increased levels of miR-34c in both PBMC and plasma may reflect changes in circulating blood samples in AD patients, compared to age-matched normal controls.

#### **Keywords: peripheral blood mononuclear cells (PBMC), plasma microRNA, miR-34a, miR-34c**

#### **INTRODUCTION**

Recently, microRNAs found in circulating blood, especially in cell-free plasma, have been noted functionally for intercellular and/or inter-organ communication. Circulating plasma microRNAs seem to be largely released from the cellular compartment, i.e., peripheral blood mononuclear cells (PBMC), either associated with specific protein or lipid molecules, or released in vesicles known as exosomes, with some portion derived from the cell debris of apoptotic bodies (Smalheiser, 2007; De Smaelea et al., 2010; Etheridge et al., 2011). Not surprisingly, differentially expressed plasma microRNAs have been noted as powerful biomarkers for several central nervous system disorders, from bipolar disorder to schizophrenia, Huntington's disease and Alzheimer's disease (AD) (Gaughwin et al., 2011; Rong et al., 2011; Suarez-Gomez et al., 2011; Geekiyanage et al., 2012; Sheinerman et al., 2012; Shi et al., 2012). Our own work has shown that lead microRNAs are differentially regulated in PBMC of AD patients compared with age-matched controls (Schipper et al., 2007; Maes et al., 2010). Therefore, unique microRNAs offer another aspect, in addition to specific transcriptome and serum protein profiling: blood biomarker discovery, for instance for AD.

Among many microRNAs, the miR-34 family, composed of three members, miR-34a, -34b, and -34c, is relatively wellunderstood. MicroRNA-34b, a plasma biomarker for Huntington disease (Gaughwin et al., 2011), and its sister, miR-34c, are linked as a bi-cistronic transcriptional unit (Liang et al., 2009); together with the other sister, miR-34a, they participate functionally in at least two signaling pathways: (1) Bcl2 for cell survival/apoptosis; and (2) SIRT1 deacetylase for p53 or neuroprotection signaling. SIRT1, p53, and miR-34a are involved in a positive feedback loop for miR-34a expression: acetylated p53 binds the promoter to activate this microRNA's transcription (He et al., 2007; Yamakuchi et al., 2008). Increased miR-34a suppresses SIRT1 expression, thereby diminishing its deacetylation of p53, leading to an increase in acetylated p53 transcriptional activity, resulting in the

**Abbreviations:** miR or miRNA, microRNA; AD, Alzheimer's disease; NEC, normal elderly control; MMSE, Mini-Mental State Examination; MMSE score (4–9), severe group; MMSE score (10–20), moderate group; MMSE score (21–24), mild group; Bcl2, B-cell lymphoma 2; SIRT1, sirtuin1; Psen1, presenilin-1.

continued up-regulation of this microRNA (Yamakuchi et al., 2008).

Many reports, including our own work in aged mice, observe parallel changes during aging between differentially regulated circulating microRNAs and changes in the central nervous system (Li et al., 2011a). During aging, accumulating oxidative stressactivated p53 may tilt the balance toward age-dependent increase of miR-34a, observed in the brains of old rats and mice (Li et al., 2011b,c); and a reduction in age-dependent increase in miR-34a in brain has been observed in calorie-restricted mice (Khanna et al., 2011). In animal models of AD, increased miR-34a levels are observed in brains of mouse models bearing both the APPswe and presenilin transgenes (Wang et al., 2009). MicroRNA 34-c, a sister of miR-34a, is also observed in the hippocampal region of AD animal models to be functionally connected to cognitive decline, because inhibition of this microRNA rescues memory impairment in AD transgenic mice, with corresponding regained SIRT1 levels (Zovoilis et al., 2011). These results suggest that increased expression of miR-34a and -34c may repress SIRT1 and Bcl2 expression, one of the many underlying causes for dysregulation of oxidative defense and neuronal cell survival in the brain of AD transgenic animals.

The main objective of this study is to test the hypothesis that unique microRNA changes in AD patients, specifically those of the microRNA-34 family, can be identified in circulating blood samples in both the cellular component, PBMC, and cell-free plasma. To test this hypothesis, we have expanded beyond our previous observation of increased miR-34a in PBMC of AD patients (Schipper et al., 2007) to include also miR-34c in our study of specimens of both blood components. Here, we report that miR-34c in circulating plasma and PBMC indeed exhibits increased levels of expression in AD patients, compared with age-matched normal elderly controls (NEC). Transfection study shows that miR-34c, similar to reported findings for miR-34a, functionally represses Bcl2, SIRT1, and other proteins, all key genes whose decreases are functionally associated with weakened oxidative defense and cell survival.

#### **MATERIALS AND METHODS**

#### **SUBJECTS AND CLINICAL EVALUATION**

Informed consent was obtained from all participants, following the Institutional Review Board protocol approved by the Sir Mortimer B. Davis Jewish General Hospital (JGH) Research Ethics Committee. Blood samples were obtained from subjects at the Memory Clinic at the JGH, from 110 AD patients (age: 56–90) and 123 NEC (age: 61–90) without cognitive impairment. NEC were recruited by newspaper advertisements and public lectures, scored less than 4 on the Subjective Memory Scale of Schmand (Schmand et al., 1996), lacked other medical or neurological illnesses, and scored within 1 standard deviation (SD) of age and education means on memory screening tests. These consist of scores of 26 or more on the Montreal Cognitive Assessment (MoCA) (Nasreddine et al., 2005), a Mini-Mental State Examination (MMSE) scoring 25 or above (Folstein et al., 1975), and normal range scores on the delayed paragraph recall component of the Logical Memory Test of the WAIS-R (Wechsler, 2008). Patients with sporadic late onset AD were screened and assessed at the JGH McGill University Memory Clinic in Montréal; the diagnosis was made according to standard NINCDS-ADRDS criteria (McKhann et al., 1984), and conformed to the more recent revised criteria for probable AD as well (McKhann et al., 2011). Severity of dementia was stratified according to MMSE results: mild (21–24), moderate (10–20), or severe (4–9).

#### **BLOOD COLLECTION AND ISOLATION AND QUALITY EVALUATION OF RNA SAMPLES**

Approximately 30 ml of blood per donor was collected in EDTA vacutainers, and processed to isolate the plasma and PBMC fractions, using Ficoll-Plaque Plus (GE Healthcare, Piscataway, NJ). PBMC and plasma samples, stored in RNAlater buffer solution (Ambion, Austin, TX), were processed for RNA and protein isolation by Trizol/chloroform and centrifugation, to separate the upper RNA-containing aqueous phase from the lower proteinaceous phase, for isolation of microRNA from the plasma specimens, as previously described (Li et al., 2011a). Prior to RNA isolation, 0.625 ng of synthetic miRNA-39 from *Caenorhabditis elegans* (cel-miRNA-39) was added to the Trizol as a spike-in control for purification efficiency and cDNA synthesis quality validation during qPCR assays, as described below (Kroh et al., 2010) (Exiqon, #203203). RNA integrity was assessed using a Nanodrop spectrophotometer (Thermo Fischer Scientific, Barrington, IL) and Agilent 2100 bioanalyzer (Agilent Technologies, Waldbronn, Germany).

#### **TAQMAN MicroRNA REAL TIME qPCR**

Plasma microRNAs were used to generate cDNA using the Taqman® MicroRNA Reverse Transcription Kit (Applied Biosystems, Carlsbad, CA), with specific miRNA stem-loop primers for miR-34a, -34b, -34c, and -16, by MultiScribe Reverse Transcriptase; the reactions were carried out in a GeneAmp PCR System 9700 (Applied Biosystems). These cDNA samples were processed to assess mature miRNA levels by real time polymerase chain reactions (qPCR) using the Taqman® Universal PCR Master Mix kit (Applied Biosystems). The qPCR was conducted in a 7500 real time PCR system (Applied Biosystems) under the following conditions: 95◦C 10 min, 60 cycles of 95◦C 15 s, and 60◦C 1 min. The levels of mature cel-miR-39 mRNA were measured using individual TaqMan microRNA Assays (Applied Biosystems) according to the manufacturer's instructions (Exiqon, #203203); cel-miR-39 was used to normalize miRNA levels. A mean Ct was calculated for *C. elegans* miRNA for each sample, followed by calculating the median of all mean *C. elegans* synthetic miRNA Cts, taking all samples into consideration. Then a normalization factor was calculated for each sample by subtracting the mean *C. elegans* synthetic miRNA Ct of the sample of interest from the median value calculated earlier. This normalization factor was then integrated into the calculation of the raw Ct value obtained for each sample, which was further normalized by reference to Ct values for miR-16.

#### **WESTERN BLOT ANALYSES**

Western blot analyses were performed as previously described (Bates et al., 2010; Li et al., 2011c), using an actin band for transfection cell lysates, to check equal loading across all lanes (Pendyala et al., 2010). The antibodies used were mouse anti-Bcl2 (1:1000, 692, Abcam Inc., Cambridge, MA), rabbit anti-Psen1 (1:500, 71181;Abcam), rabbit anti-Onecut2 (1:500, 28466;Abcam), rabbit anti-SIRT1 (1:500, 110304;Abcam), and rabbit anti-β-actin (1:1000, 8226;Abcam). Goat anti-mouse (31403, Thermo Fischer Scientific, Barrington, IL) was used for Bcl2 (1:2000), and goat anti-rabbit (31460, Thermo Scientific) for β-actin, SIRT1, Psen1, and Onecut2 (1:1000) as secondary antibodies. Intensities of antibody reactive bands were detected by Enhanced Chemiluminescence (ECL), (Pierce Biotechnology, Rockford, IL), and quantified by densitometry using ImageJ software (Public domain, NIH, USA).

#### **CONSTRUCTION OF hsa34a AND hsa34c GFP RECOMBINANTS, AND FUNCTIONAL TARGET SUPPRESSION STUDY**

Micro-34a and -34c were amplified from DNA purified from human embryonic kidney cells (HEK 293 cell line; ATCC# CRL 1573) with the following primers:

miR-34a forward 5 -tctagaGAG TCC CCT CCG GAT GCC GTG, reverse 5 -ggatccCCA CCC ACCG TGG CGC AG, 229 bp; miR-34c forward 5 -tctagaAGC CCC TCC ATC CAT GTA ACG GT, reverse 5 -ggatccAAC ACC CCT CTT CCC CAC GCA, 328 bp.

Amplified PCR products were purified and cloned by the Qiagen PCR Cloning kit (Qiagen, Valencia, CA), and subcloned into the pCDH-CMV-MCS-EF1-copGFP vector (System Biosciences, Mountain View, CA), which was then used for functional assays by transfecting human embryonic kidney cells (HEK 293), as previously described (Bates et al., 2010).

#### **STATISTICAL ANALYSES**

All statistical analyses were performed using MS Excel 2010, SPSS 17.0 statistical software package (IBM), or SAS version 9.2 (SAS Institute Inc., Cary, NC). Student's *t*-test and one-way analysis of variance (ANOVA) were performed to determine significant differences for two-way or more than two groups. For multiple comparisons, Fisher's Least Significant Difference (LSD) test was used, following one-way ANOVA, in order to assign statistical significance, and then the Scheffé test was applied to reduce Type 1 errors. Levels of miR-34c, miR-34b, and miR-34a were normalized with regard to miR-16, as recommended by the manufacturer as well as a previous report of Huntington disease plasma biomarker discovery (Gaughwin et al., 2011), as a reference gene, using the comparative Delta Ct method. The -Ct value is the difference between the Ct of the target microRNA and that of the reference microRNA (-Ct = Ct miR34× − Ct miR16). To include the spike-in results in this calculation, the Ct values were first normalized against *Cel-39* and then against miR-16, according to the equation: normalizing factor = median (Avg.Ct*Cel*all − Avg.Ct*Cel*sample); normalized Ctsample = Ctsample + normalizing factor (Livak and Schmittgen, 2001). Correlation between MMSE scores and expression levels of miR-34c and miR-34a in AD and NEC patients was calculated using the Pearson correlation coefficient (r) (Taylor, 1990); a correlation coefficient value of 0.8 or above indicates strong correlation, whereas a value around 0.5 is indicative of moderate correlation.

Receiver-Operating Characteristic (ROC) curves were used to determine the accuracy of the test differentiating AD from NEC individuals for miR-34c and miR-34a in age-matched cohorts (Zweig and Campbell, 1993; Fawcett, 2006). We calculated the sensitivity, specificity and accuracy as follows;

Sensitivity = TP/(TP + FN) = (Number of true positive assessments)/(Number of all positive assessments); Specificity = TN/(TN + FP) = (Number of true negative assessments)/(Number of all negative assessments) Accuracy = (TN + TP)/(TN + TP + FN + FP) = (Number of correct assessments)/(Number of all assessments)

A true positive (TP) is defined as an individual showing concordance between disease presence and a diagnostic test result, while a true negative (TN) represents disease absence with test result also negative. Contrary to these scenarios is discordance between these two categories: i.e., a false positive (FP) is defined as disease absence in an individual whose diagnostic test is positive, and a false negative (FN) characterizes disease presence in an individual whose diagnostic test is negative. All four counts, i.e., TP, TN, FP, and FN, were calculated with a cut-off point base (Li and Chung, 2013).

#### **RESULTS**

#### **SELECTION OF SAMPLES FOR BEST RNA QUALITY, AND CONTROLS FOR CALCULATION OF miR-34a AND miR-34c LEVELS**

From blood samples of 110 AD patients and 123 NEC, only 78 AD and 85 plasma specimens were selected to meet our requirements for RNA integrity: a single peak of small RNA in the 4–40 nucleotide (nt) range (Figure 1S). The age range for the AD group is between 56 and 90, with an average age of 80; the NEC cohort is between 61 and 90, with an average of 72 (**Table 1**). Our cohort's cognitive ability, assessed by the MMSE, is as reported in the literature (Sharp and Gatz, 2011), i.e., lower scores are linked with fewer years of education in AD individuals (Figure 2S). Our recruitment program for normal control individuals is limited by the fact that many are in their 60 s. In order to achieve an agematched study design, we further selected from the AD and NEC groups such that the age range of both subcohorts lies between 76–90; the average age for the former is 82, and the latter 80 (**Table 1**).

To optimize the accuracy of quantitative PCR assays, we used two control steps: (a) quality control for the cDNA synthesis step with *C. elegans* Cel 39 spike-in; and (b) using miR-16 to standardize baseline level determination. **Figures 1A–D** shows similar levels of miR-34a and miR-34c with and without spike-in experiments in both AD and NEC plasma samples. These results suggest that the protocol established for miR-34a and miR-34c qPCR assays is stringent, providing optimal quality cDNA synthesis with minimal putative RT-RNA inhibitions. An additional standardized control is selecting appropriate microRNAs with no changes **Table 1 | Plasma specimens from the entire cohorts of Alzheimer's disease (AD) and normal elderly control (NEC) individuals, selected after RNA quality control, with age range, average age with standard deviation (SD), and sample size (N) for acceptable RNA in the entire cohort and age-matched cohorts.**

between AD and control group blood samples. We followed the manufacturer's recommendation, as well as reports by Kroh et al. (2010) and Gaughwin et al. (2011) to use miR-16 as the control for each individual qPCR assay. However, since miR-16 is an abundant microRNA associated with red blood cells, even minimal hemolysis can cause unreliable qPCR results, as shown by Müller et al. (2014) in cerebrospinal fluid (CSF) qPCR assays. In this context, we evaluated miR-16 levels in all samples used in our study, as shown in **Figures 1E,F**, and found that in both total and age-matched cohorts, levels of this microRNA are similar between the AD and NEC cohorts. Thus, miR-16 levels are not differentially regulated between AD and NEC plasma samples in our study, and can serve as a standard.

#### **EXPRESSION LEVELS OF miR-34a AND miR-34c IN PLASMA SAMPLES OF AD PATIENTS AND NORMAL ELDERLY CONTROLS (NEC)**

Our initial study of quantitative reverse transcription PCR (qPCR) analyses of transcript levels of miR-34a and -34c in plasma samples of AD and NEC was performed with the entire cohorts (**Table 1**). When miR-34c levels were analyzed, NEC individuals were distributed in a close cluster around the median levels of the box plots, while AD specimens were distributed over a wider scatter span, with significantly wider SD range (**Figures 2A,B**). Similar wide spreads of AD values while NEC samples are clustered were also observed with miR-34a levels (**Figures 2C,D**). **Table 2** shows the wide distribution and high SD indices for both microRNAs in the AD group. To address the concern that higher AD microRNA values could be due to the older age group used, we next selected from our cohorts NEC individuals age-matched to AD counterparts, as listed in **Table 1**.

**Figures 3A,B** shows box plots and distribution of individual values for miR-34c in the two age-matched subcohorts. Clearly, the majority of AD plasma specimens in the age-matched control study exhibit higher miR-34c levels than do normal controls; **Table 3** shows 1/deltaCt values in the 0.20–0.53 range for AD, and 0.05–0.22 for NEC plasma specimens. When levels of miR-34a were analyzed, however, the range of distribution is not differentially expressed between the AD and NEC groups, with more spread-out distribution for both groups, as well as significant overlapping ranges, by both box plot and scatter plots (**Figures 3C,D**; **Table 3**). Levels of miR-34b were evaluated in the same age-matched subcohorts of the AD and NEC groups in plasma; the results show no significant difference between the two groups (Figure 3S, Table 1S). The box plot presentation of inverted -Ct values from AD and NEC groups shows no change, indicating that the level of miR-34b does not change in the AD group compared to NEC.

Accuracy in differentiating AD (*n* = 25) and NEC (*n* = 27) individuals by miR-34c levels in plasma specimens was further tested by ROC curves (**Figure 4**). The area under the miR-34c curve is 0.99 (*p* < 0.0001), essentially 1.0, indicating perfect accuracy of the data; 95% confidence interval values fall in the range of 0.97–1.0. The ROC curve shows that miR-34c level is an excellent test for AD, with 94% accuracy, 92% sensitivity, and 96% specificity (**Table 4**). The area under the ROC curve for miR-34a is 0.81 (*p* = 0.0001), indicating a fairly good test; the 95% confidence interval lies between 0.69 and 0.93. The sensitivity is 84%, and specificity is 74% (**Figure 4**, **Table 4**). The area under the ROC curve, sensitivity, specificity and accuracy were calculated for a cut-off point chosen using the coordinates of ROC curves with balanced levels of high sensitivity and specificity.

#### **miR-34c LEVELS IN MODERATE AND MILD AD COMPARED WITH AGE-MATCHED NORMAL ELDERLY CONTROLS**

In our age-matched AD cohort, only two patients were evaluated by the MMSE in the severe stage, with scores in the range 4–9. **Figures 5A,B** show that when these two individuals' samples are excluded from our analysis, miR-34c levels in samples

**FIGURE 2 | Transcript levels of miR-34a and miR-34c in plasma samples of Alzheimer's disease (AD) patients and normal elderly controls (NEC). (A)** Comparison of plasma levels of miR-34c in the whole population of AD patients (*n* = 78) with the NEC cohort (*n* = 85). **(B)** Distributions of individuals' levels of miR-34c along the box plot. NEC individuals were distributed in a close cluster around the median levels of the box plots, whereas AD specimens were distributed over a wider range with higher standard deviation. **(C)** Box plot representation of comparison in plasma levels of miR-34a in the whole population of AD patients (*n* = 78) with the NEC cohort (*n* = 85). **(D)** Individual miR-34a values along the box plot are less wide spread in the AD group as compared to those for miR-34c and a tight cluster for NEC is also observed. The data are plotted as inverse -Ct derived from qPCR analysis.  Student's *t-*test was used to determine statistical significance; ∗∗*P* < 0.01.

**Table 2 | Plasma microRNA 1/deltaCt level ranges in Alzheimer disease (AD) patients and normal elderly controls (NEC) with standard deviation** *(SD)* **and median, for miR-34c and miR-34a in the whole cohorts.**

from moderate and mild stages, with MMSE scores in 10–20 or 21–24 ranges respectively, remain higher than NEC controls. **Table 5** shows that levels of miR-34c expression are not only distinct between the moderate and NEC groups, but also between the mild group and normal controls, with the former in the range of 0.21–0.38, while the latter shows 0.05–0.22 1/deltaCt values. Similar separation between mild AD patients and the normal control group is not observed for levels of miR-34a in this agematched cohort study, due to overlapping ranges between these two groups (**Table 5**).

We investigated further the relationship between MMSE and miR-34c levels, by calculating Pearson's correlation *r*-value of miR-34c levels and MMSE scores for moderate and mild AD and age-matched control cohorts. **Figure 6A** shows an *r*-value

**FIGURE 3 | Transcript levels of miR-34a and miR-34c in plasma samples of age-matched cohorts of Alzheimer's disease (AD) patients and normal elderly controls (NEC). (A)** Plasma level comparison of miR-34c in the age-matched cohort of AD patients (*n* = 25) with the NEC cohort (*n* = 27). AD plasma samples in the age-matched cohort showed higher miR-34c levels than controls. **(B)** Distribution of individuals' levels of miR-34c along the box plot; NEC individuals were distributed in a close cluster around the median of the box plots, while AD patients were distributed over a wider range with greater standard deviation. **(C)** Comparison of plasma levels of miR-34a in age-matched cohorts of AD patients (*n* = 25) with the NEC cohort (*n* = 27). miR-34a levels were distributed in a range that is not differentially expressed between AD and NEC groups, with more spread-out distribution for both groups, as well as significant overlapping ranges, by both box and scatter plots. Distributing individuals along the box plot, NEC were distributed in a close cluster around the median levels of the box plots, whereas AD specimens were distributed over a wider range with higher standard deviation. Data points represent Mini-Mental Status Examination (MMSE) score as follows, ( ) severe AD (4–9), ( ) moderate AD (10–20), ( ) mild AD (21–24), and ( ) NEC (25–30). **(D)** Representation of individual miR-34a levels along the box plot for the range of distribution between AD and NEC groups. The data are plotted as inverse -Ct derived from qPCR analysis. Student's *t-*test was used to determine statistical significance; ∗∗*P* < 0.01.

**Table 3 | Ranges of 1/deltaCt levels of plasma microRNAs in Alzheimer disease (AD) patients and normal elderly controls (NEC), with standard deviation** *(SD)* **and median, for miR-34c and miR-34a in age-matched subcohorts.**

of −0.72, suggesting a strong inverse correlation between the expression levels of miR-34c and the two cognitive assessment groups. This inverse correlation between MMSE scores and miR-34c levels, i.e., lower scores and higher levels, as shown for the three groups, was not observed for miR-34a expression, with an r coefficient score of −0.34 (**Figure 6B**). Likewise, when Pearson's correlation was computed for only the moderate and mild clinical groups without NEC, the *r*-value is very low. These results

**representing AD diagnostic tests by miR-34c and miR-34a in age-matched cohorts.** The Receiver Operation Characteristic (ROC) curve for miR-34c (**…..**) is an excellent test since the area under the curve is 0.99 (*p* < 0.0001), number of samples for AD = 25 and NEC = 27. The area under the Receiver Operation Characteristic (ROC) for miR-34a ( ) **----** is 0.81 (*p* = 0.0001) which is a good/fair test, number of samples for AD = 25 and NEC = 27. Sensitivity and specificity are reported based on a cut-off point chosen using the coordinates of Receiver Operation Characteristic (ROC) curves with balanced levels of high sensitivity and specificity. The diagonal line represents a reference line showing zero sensitivity and zero specificity ( ).

**Table 4 | Receiver Operating Characteristic (ROC) curve parameters for plasma miR-34c and -34a among age-matched subcohorts.**

suggests no relationship between MMSE scores and the levels of the two microRNA expression when comparing the moderate and mild groups between themselves (Figure 4S).

#### **INCREASED EXPRESSION OF miR-34c IN CELLULAR COMPONENTS OF AD BLOOD SAMPLES**

The observation of increased miR-34c expression in AD plasma prompted us to determine the level of expression of this microRNA in the cellular compartment, i.e., PBMCs of agematched cohorts of AD and NEC samples. Increased miR-34c levels were observed in PBMC samples of AD blood specimens, compared to normal controls (**Figure 7A**), with a similar increase for miR-34a in AD over normal controls (**Figure 7B**). However, as shown in **Table 6**, as observed in the plasma samples, there is scant range overlap for miR-34c levels between AD and agematched controls; this is not true for miR-34a levels between the two groups. Interestingly, the miR-34a levels assayed here as validation for our previous study (Schipper et al., 2007) show the

**FIGURE 5 | Differential expression of miR-34c in plasma from patients with mild and moderate severity of Alzheimer's disease (AD), compared with normal elderly controls (NEC) in age-matched cohorts.** AD patients were grouped by mild and moderate severity according to their Mini-Mental Status Examination (MMSE) scores. **(A)** Percentile distribution of samples within the various stages of AD and NEC, plotted against inverse -Ct for individuals of age-matched cohorts. The levels of miR-34c are higher in samples from moderate ( ) and mild ( ) clinical groups, with MMSE scores in the range of 10–20 or 21–24, respectively, than NEC ( ) controls in the range of 25–30. **(B)** Data points added to illustrate the distribution of individuals within each group of the age-matched cohorts. We observe NEC to be in a close cluster around the median, whereas AD patients are scattered in a wider range in both moderate and mild groups. Both the LSD test (higher type 1 Error) and the Scheffé test (higher stringency) were used to determine statistical significance. The statistical significance level for the LSD test is represented with the symbol ( ), and for Scheffé test is represented with the symbol (∗). *P* < 0.01, ∗∗*P* < 0.01.

**Table 5 | 1/deltaCt levels of plasma miR-34c and -34a by clinical category of mild and moderate Alzheimer's disease (AD) and in age-matched normal elderly controls (NEC): range with standard deviation (***SD***) and median.**

same range of fold changes, i.e., ∼3-fold increase in AD samples beyond normal controls (Figure 5S).

#### **FUNCTIONAL ANALYSIS OF THE REPRESSION OF TARGET PROTEINS BY OVEREXPRESSING miRs -34a AND -34c IN CULTURED CELLS**

We next investigated whether miR-34c represses the same targets as reported for its sister, miR-34a (He et al., 2007; Yamakuchi et al., 2008), by transfection studies in cultures of human embryonic kidney cells (HEK 293) overexpressing these microRNAs. Only cultures with >98% of cells showing green fluorescence positivity for the green fluorescence protein gene (GFP) were used in our functional assays. Our results with these transfection experiments show repression of Psen1, Bcl2, Sirt1, and Onecut2 by miR-34a ranging from ∼11% to 27%, with the latter being the most affected (**Figures 8A–C**, **Table 7**). A more pronounced impact is observed by miR-34c, with repression ranging from ∼16 to 40% (**Figures 8A,B,D**, **Table 7**). Co-transfection with both miRNAs induced further repression of all four target proteins, from ∼32 to 46% (**Figures 8A,B,E**, **Table 7**). In all analyses, repression levels were evaluated by comparing transfected and

**FIGURE 6 | Relationship between expression levels of miR-34c and miR-34a in plasma with Mini-Mental Status Examination (MMSE) scores in selected samples of age-matched groups. (A)** Plasma expression levels of miR-34c represented by inverse -Ct, plotted against corresponding MMSE scores. A high Pearson correlation coefficient value of −0.72 indicates a strong correlation between MMSE scores and 1/deltaCt values, *P* < 0.0001. **(B)** Expression levels of miR-34a in plasma of AD and NEC individuals represented by inverse -Ct, plotted against corresponding MMSE scores. A low Pearson correlation coefficient value of −0.34 indicates a weak correlation between the MMSE scores and 1/deltaCt values, *P* = 0.012. Data points of Mini-Mental Status Examination (MMSE) score are shown as follows: ( ) moderate AD (score 10–20), ( ) mild AD (score 21–24), and ( ) NEC (score 25–30).

scrambled control cultures, with repression of actin observed at ∼0%. These observations suggest that miR-34c is a stronger suppressor of target gene expression than miR-34a, and that Onecut2 is the most repressed among the target proteins.

#### **DISCUSSION**

Members of the microRNA-34 family are present in most tissues, including PBMC. Our previous study reported miR-34a as a lead **Table 6 | 1/deltaCt level ranges, with standard deviation (SD) and median, of microRNAs in PBMC from Alzheimer's disease (AD) patients and age-matched normal elderly controls (NEC), for miR-34c and miR-34a.**

microRNA from array profiling and validated by qPCR assays, showing a 2.5-fold increase in PBMC isolated from AD specimens (Schipper et al., 2007). The present study shows that this microRNA's sister, miR-34c, is even more prominently increased in both PBMC and plasma fractions of AD blood samples over age-matched NEC. Most importantly, the majority of miR-34c levels among mild AD patients, determined by mini-mental state examination (MMSE), are elevated beyond those of normal controls. In addition, inclusion of patients with moderate degrees of AD dementia with mild and normal counterparts in our study shows a relatively strong inverse relationship between MMSE scores and levels of this microRNA among the three groups. Levels of miR-34b, the bicistronic sister of miR-34c, are not significantly different between AD and NEC age-matched controls. Thus, our results reported here will serve as future leads to identify key microRNA abundance, such as miR-34c, as noninvasive indicators for plasma changes associated with AD.

Age-matched circulating blood microRNA studies like ours by design suffer several limitations, such as: (1) restriction of obtaining gene expression changes at a particular snap-shot time window; and (2) inability to link with changes of the same gene expression in individuals' brains. The first limitation cannot be addressed by small scale studies like ours, but rather in large consortium studies such as the Alzheimer's Disease Neuroimaging Initiative (ADNI) study of the National Institute on Aging, USA, with longitudinal blood sample collection linking plasma and CSF analysis study, and MRI imaging of brain changes in the same individuals. However, most of these studies are centered on the analysis of protein changes, with some focus on Tau and Aβ as biomarkers (O'Bryant et al., 2011; Toledo et al., 2011, 2013). Other studies such as Müller et al. (2014) have limited sample size because such samples are only available from a very few centers where longitudinal *ante mortem* blood or CSF samples are collected along with follow-up acquisition of autopsy brain specimens. Comparative studies between *ante-mortem* blood samples and *post mortem* autopsy will emerge in future studies, yielding results elucidating the relationships between plasma biomarkers and brain pathology structural changes, including but not limited to amyloid plaques and Tau-associated tangle formation.

To identify body fluid-associated microRNA biomarkers for neurodegeneration, standardization of the sample collection protocol and quality control of RNA isolation are two obvious quality control criteria in any study of this kind. Although we have established meticulous procedures for collecting blood samples and further processing to PBMC and plasma fractions, a significant portion, ∼20%, of the RNA fractions isolated from these

transfected cultures, cells transfected with either GFP vector alone or carrying a computer-generated scrambled sequence. **(A)** Western blot analysis of cell lysates probed for SIRT1, Onecut2, Presenilin-1, Bcl2, and

*Percent repression of target proteins in HEK 293 cells transfected with miR-34a, miR-34c, or both miRNAs. The repression levels shown here represent protein band intensities, after normalization against* β*-actin band intensities, when cultures transfected by microRNA are compared with those transfected by scrambled controls. As shown in Figure 8, the % change of* β*-actin levels between cultures transfected by the microRNAs of interest and by the scrambled controls is* ∼*0. Mean % repression with standard deviation (SD) of each miR for each protein is tabulated.*

samples were unacceptable because of poor RNA integrity. Even among the 70–80 samples with good RNA quality, we had to estimated using scrambled controls for comparison (panels **C–E**): transfection with miR-34a **(C)**; miR-34c **(D)**; miR-34a and miR-34c **(E)**. One-way ANOVA followed by LSD test was used to determine statistical significance; ∗*P* < 0.05, ∗∗*P* < 0.01.

narrow our study to 25 AD and 27 NEC as shown in **Table 1**, in order to satisfy age-matched criteria for our study. With samples from these two subcohorts, as in most neurodegenerative studies, before proceeding to qPCR assays of a particular microRNA of interest, we implemented quality control to insure the cDNA synthesis part of the assays by incorporating *Cel 39* spike-in in the step of RNA isolation.

Besides the above issues, selection of reference microRNAs for determination of microRNA levels of expression is crucial for studies of blood samples of neuronal disorders. We use miR-16 as our reference microRNA, following the report by Gaughwin et al. (2011), who used it as the reference microRNA to show that miR-34b is elevated in the plasma of Huntingdon mutation gene carriers, prior to disease manifestation. Interestingly, our study with this microRNA shows that miR-34b levels do not differ significantly in plasma specimens between AD patients and NEC. A recent study by Müller et al. (2014) shows that miR-16 and miR-146 are increased in AD in both hippocampus and cerebrospinal fluid (CSF). However, this report also suggests that miR-16, a major red blood cell microRNA (Kirschner et al., 2011, 2013; McDonald et al., 2011), may be contaminatedby hemolysis in CSF samples; even as little as 100μl of erythrocytes added skews the qPCR results (Müller et al., 2014). In addition, several other microRNAs reported so far as unchanged in AD tissue have been used as references to determine levels of microRNAs of interest (Geekiyanage et al., 2012; Sheinerman et al., 2012; Sheinerman and Umansky, 2013). Notwithstanding all these reports, no unifying approach of selecting appropriate reference controls has yet been reached. At present we are limited to the use of miR-16 as the reference, based on the rationale that we found no significant variance in it among our samples, as shown in **Figures 1E,F**.

The widespread variance in levels of both miR-34c and its sister, miR-34a in AD plasma compared with the tight NEC cluster range, shown in **Figures 1**–**4**, clearly suggests individual variability in the control of these microRNAs' expression. From our small age-matched cohorts, miR-34c levels may be a better candidate than miR-34a and miR-34b for future large-scale study as a blood-based biomarker, simply because its expression in most mild AD patients shows higher levels than most NEC. The ROC analyses performed for both miR-34c and miR-34a 1/deltaCt values in age-matched cohorts proved the former to be an excellent discriminant (AUC = 0.99), whereas the latter was merely fairly good (AUC = 0.81). However, these results suggest the need of further research with larger cohorts, to stringently validate our results of sensitivity using miR-34c as a diagnostic biomarker for plasma samples. Likewise, the inverse correlation, observed only when the moderate and mild groups are compared with their NEC between cognitive scoring and levels of expression of miR-34c in plasma also needs future studies with larger cohorts to define their true relationship. Nevertheless, our findings open the possibility that plasma microRNAs, along with other biomarkers, may be fertile ground for future research linking cognitive decline with changes of gene expression in blood samples.

The increased miR-34c in AD plasma may derive from PBMC, where miR-34c is also increased. As reported, microRNAs present in the circulating plasma may be found either in the encapsulated vesicles known as exosomes, or in lipid- or protein-associated free particulates (Smalheiser, 2007; Etheridge et al., 2011). Reports so far are inconclusive as to what proportion of plasma microRNAs are associated with the former vs. the latter components. Future comparative studies of PBMC, exosomal and free-form miR-34c will reveal the relationship among these three components of AD circulating blood specimens responsible for this microRNA's increased expression.

MicroRNA-34c, as reported for its sister, miR-34a (He et al., 2007; Yamakuchi et al., 2008) and as shown in **Figure 8**, represses Bcl2, SIRT1, Psen1, and Onecut2, all associated with cellular survival and oxidative defense signaling (Clotman et al., 2005; Goodall et al., 2013). This led us to suggest that increased miR-34c may be one of many factors contributing to an overall systemic weakening of stress defense and cell survival, as suggested in our model presented in **Figure 9**. Life-long cumulative oxidative stress induction of p53 up-regulation may activate the expression of this microRNA, with a steadily increasing trend during aging (Li et al., 2011b,c). In AD, this oxidative stress may be further enhanced, manifested in brain as well as perhaps system-wide, reflected in circulating blood by both PBMC and plasma (Maes et al., 2010; Goodall et al., 2013). Future culture experiments in PBMC isolated from AD patients compared with those from normal counterparts will allow us to investigate whether these cells with increased miR-34c levels are indeed prone to apoptotic

death, and whether plasma from the same samples stimulates stress and apoptotic signaling in neighboring cells.

In conclusion, data presented here indicate that levels of miR-34c significantly increase in plasma samples of sporadic AD. Future studies with larger age-matched cohorts will validate these results, and reveal whether this microRNA change is characteristic of sporadic AD as a biomarker criterion, and further comparative studies will elucidate whether it is a common biomarker shared among various neurodegenerative disorders, associated with the decline of oxidative defense and cell survival in neuronal dysfunction.

#### **DISCLOSURE STATEMENT**

Howard Chertkow is supported by operating grants from the Canadian Institutes for Health Research (CIHR) and the Fonds de la recherche en santé du Québec (FRSQ). Dr. Chertkow sits on an adjudication board for clinical trials for Bristol Myers Squibb, and has been a speaker and Advisory Board member for Pfizer Canada. Hyman M. Schipper is supported by operating grants from the Canadian Institutes for Health Research (CIHR), and has served as consultant to Osta Biotechnologies, Molecular Biometrics, Inc., TEVA Neurosciences, and Caprion Pharmaceuticals. Eugenia Wang is on entrepreneurial leave from the University of Louisville, with 51% of her effort committed to Advanced Genomic Technology, LLC, a start-up company in Louisville, Kentucky; her other 49% is at the University of Louisville, as the Gheens Endowed Chair on Aging and Professor of Biochemistry and Molecular Biology. Shephali Bhatnagar, Zongfei Yuan, Vikranth Shetty, Samantha Jenkins, Timothy Jones, and Eugenia Wang are employees of Advanced Genomic Technology.

#### **ACKNOWLEDGMENTS**

This work was supported by a Small Business Innovation Research (SBIR) grant (R44AG035410) from the National Institute on Aging of the U.S. National Institutes of Health to Advanced Genomic Technology, LLC. The authors thank Adrienne Liberman, Linda Eizenstat, Carmela Galindez, Alexandra Lyninger, Orchid Lin, Nathaniel Shelburne, Victor Whitehead, and ljlal Yazdani for technical assistance. We thank the staff of the JGH Memory Clinic for support and organization of sample collection, and Chris Hosein and Shelley Solomon for outstanding organization of the clinical collections; Mr. Alan N. Bloch proof-read the article.

#### **SUPPLEMENTARY MATERIAL**

The Supplementary Material for this article can be found online at: http://www.frontiersin.org/journal/10.3389/fnmol.2014. 00002/abstract

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Wechsler D. (2008). *Wechsler Memory Scale, 4th Edn*. San Antonio, TX: Pearson.

Yamakuchi, M., Ferlito, M., and Lowenstein, C. J. (2008). miR-34a repression of SIRT1 regulates apoptosis. *Proc. Natl. Acad. Sci. U.S.A.* 105, 13421–13426. doi: 10.1073/pnas.0801613105

Zovoilis, A., Agbemenyah, H. Y., Agis-Balboa, R. C., Stilling, R. M., Edbauer, D., Rao, P., et al. (2011). microRNA-34c is a novel target to treat dementias. *EMBO J.* 30, 4299–4308. doi: 10.1038/emboj.2011.327

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**Conflict of Interest Statement:** The authors, except Drs. Howard Chertkow and Hyman Schipper, were all employees of the Advanced Genomic Technology (AGT) in Louisville, Kentucky and the results presented in the paper were generated by the funding support of a Small Business Innovation Technology (SBIR) grant from the National Institutes of Health, USA to AGT.

*Received: 16 September 2013; accepted: 12 January 2014; published online: 04 February 2014.*

*Citation: Bhatnagar S, Chertkow H, Schipper HM, Yuan Z, Shetty V, Jenkins S, Jones T and Wang E (2014) Increased microRNA-34c abundance in Alzheimer's disease circulating blood plasma. Front. Mol. Neurosci. 7:2. doi: 10.3389/fnmol.2014.00002 This article was submitted to the journal Frontiers in Molecular Neuroscience.*

*Copyright © 2014 Bhatnagar, Chertkow, Schipper, Yuan, Shetty, Jenkins, Jones and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

# RISC in PD: the impact of microRNAs in Parkinson's disease cellular and molecular pathogenesis

#### *Sabrina M. Heman-Ackah1,2, Martina Hallegger 1, Mahendra S. Rao2 and Matthew J. A. Wood1 \**

*1 Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, UK*

*2 Center for Regenerative Medicine, US National Institutes of Health, Bethesda, MD, USA*

#### *Edited by:*

*Hermona Soreq, The Hebrew University of Jerusalem, Israel*

#### *Reviewed by:*

*Hermona Soreq, The Hebrew University of Jerusalem, Israel Darren J. Moore, Swiss Federal Institute of Technology, Switzerland*

#### *\*Correspondence:*

*Matthew J. A. Wood, Department of Physiology, Anatomy and Genetics, University of Oxford, Le Gros Clark Building, South Parks Road, Oxford OX1 3QX, UK e-mail: matthew.wood@ dpag.ox.ac.uk*

Parkinson's disease (PD) is a debilitating neurodegenerative disease characterized primarily by the selective death of dopaminergic (DA) neurons in the substantia nigra pars compacta of the midbrain. Although several genetic forms of PD have been identified, the precise molecular mechanisms underlying DA neuron loss in PD remain elusive. In recent years, microRNAs (miRNAs) have been recognized as potent post-transcriptional regulators of gene expression with fundamental roles in numerous biological processes. Although their role in PD pathogenesis is still a very active area of investigation, several seminal studies have contributed significantly to our understanding of the roles these small non-coding RNAs play in the disease process. Among these are studies which have demonstrated specific miRNAs that target and down-regulate the expression of PD-related genes as well as those demonstrating a reciprocal relationship in which PD-related genes act to regulate miRNA processing machinery. Concurrently, a wealth of knowledge has become available regarding the molecular mechanisms that unify the underlying etiology of genetic and sporadic PD pathogenesis, including dysregulated protein quality control by the ubiquitin-proteasome system and autophagy pathway, activation of programmed cell death, mitochondrial damage and aberrant DA neurodevelopment and maintenance. Following a discussion of the interactions between PD-related genes and miRNAs, this review highlights those studies which have elucidated the roles of these pathways in PD pathogenesis. We highlight the potential of miRNAs to serve a critical regulatory role in the implicated disease pathways, given their capacity to modulate the expression of entire families of related genes. Although few studies have directly linked miRNA regulation of these pathways to PD, a strong foundation for investigation has been laid and this area holds promise to reveal novel therapeutic targets for PD.

**Keywords: Parkinson's disease, microRNA (miRNA), ubiquitin-proteasome system, autophagy, apoptosis, mitochondria, dopaminergic neurons, iPS cells**

#### **INTRODUCTION**

#### **PARKINSON'S DISEASE**

PD is a progressive neurodegenerative disease which manifests as a debilitating movement disorder with late cognitive sequelae. Pathologically, PD is characterized by the selective loss of DA neurons in the midbrain substantia nigra pars compacta with four clinical hallmarks resulting from the loss of dopamine signaling in the striatum. These four hallmarks, which clinically define Parkinsonism, are pill-rolling tremor, cogwheel rigidity, bradykinesia/akinesia and postural instability (Savitt et al., 2006). With progression of the disease, the neurodegenerative process spreads to involve other brain regions. Most notably, the forebrain is commonly affected in late PD leading to cognitive decline and dementia (Jellinger, 2012). Despite pharmacologic therapy with dopamine replacement (Cotzias, 1968; Antonini and Cilia, 2009), and recent advances in surgical treatment with deep brain stimulation (Pereira and Aziz, 2006; Pereira et al., 2007; Farris and Giroux, 2011), there remains a void in understanding and inhibiting the underlying progressive neurodegeneration that defines PD. It has thus become a significant research focus of recent years to examine the molecular mediators underlying this process with the objective of translating this understanding to the development of new therapeutic approaches.

In recent years, researchers have identified genetic mutations which cause approximately 10% of PD cases (Klein and Westenberger, 2012). Investigations into the precise link between genetic mutation and disease, and the etiology of the 90% of sporadic PD cases, have led to the identification of molecular pathways that culminate in initial DA neuron injury and the resulting DA neuron death and progressive neurodegeneration. The primary pathways which have been implicated as mediators of the degenerative process are the protein quality control pathways, the ubiquitin-proteasome system (UPS) and autophagy pathway, as well as apoptosis, mitochondrial quality control, and DA differentiation and maintenance; the proposed role of each of these pathways in PD pathogenesis is described in detail below. Defects in these pathways may explain the underlying etiology of both genetic and sporadic PD pathogenesis. Thus, great interest has developed in understanding mechanisms of endogenous regulation of these pathways. Toward this end, the characterization of miRNA function in PD pathogenesis has become of particular interest and the potential of these molecules to serve as pathway modifiers for therapeutic intervention in PD is increasingly appreciated.

#### **miRNA BIOGENESIS AND FUNCTION**

miRNAs are endogenous regulators of gene expression. These small non-coding RNAs (ncRNAs) can be transcribed by RNA polymerase II (RNA Pol II) from two primary genomic loci: miRNA genes and intronic sequences. In the canonical biogenesis pathway (**Figure 1A**), transcription from miRNA genes yields pri-miRNAs which are processed in the nucleus by the Drosha/DGCR8 microprocessor complex to produce premiRNAs. The processed pre-miRNAs are then exported to the cytoplasm by Exportin-5, where they are further cleaved by the RNase III enzyme Dicer to produce a mature miRNA duplex. The mature guide strand is 20–22 nucleotides in length and associates with Argonaute proteins, AGO 1–4, to form a functional RNA-induced silencing complex (RISC). The anti-sense strand, denoted by miRNA∗, was previously thought to be degraded; recent evidence suggests that some of these may have biological activity. The mature miRNA is then responsible for aligning the RISC to target mRNA by binding at complementary seed sequences in the 3 UTR. This association of target mRNA with the miRNA-containing RISC most commonly results in downregulation of gene expression by translational repression and recruitment of protein complexes causing deadenylation and degradation of target mRNA. Conversely, miRNAs have also been shown to stabilize transcripts under certain cellular conditions (Melton et al., 2010).

The canonical pathway of miRNA biogenesis is also referred to as Drosha-dependent/Dicer-dependent miRNA biogenesis. In the much rarer, non-canonical Drosha-dependent/Dicerindependent miRNA biogenesis pathway, the exported premiRNA is processed by AGO2 in the cytoplasm to produce the mature guide strand (**Figure 1B**). miRNAs generated by another non-canonical pathway, termed Drosha-independent/Dicerdependent miRNA biogenesis, are referred to as mirtrons (**Figure 1C**). In this pathway, mirtrons are spliced from the intronic sequences of transcribed genes, forming a lariat structure which is de-branched to form the pre-miRNA hairpin structure. After export from the nucleus, mirtrons function similarly to miRNA generated via the canonical pathway. Further discussion of miRNA biogenesis pathways can be found in detailed reviews on this topic (Bartel, 2009; Ameres and Zamore, 2013).

Our understanding of the role ncRNAs play in development and disease has expanded rapidly over the last decade. Recent insights are providing evidence that other classes of ncRNAs may contribute to transcriptome alterations in PD. For a more detailed discussion on the roles of long ncRNAs and small vault RNAs in PD, the reader is referred to the following references (Minones-Moyano et al., 2013; Wu et al., 2013). miRNAs in particular have been identified as critical regulators of gene expression in a number of normal biological and pathophysiological processes. miRNA dysregulation has been convincingly linked to a number of neurodegenerative diseases. For a discussion of the role miRNAs play in Alzheimer's disease and Huntington's disease, the reader is referred to the enclosed references (Delay et al., 2012; Sinha et al., 2012). Recently discovered relationships between PD-related genes and miRNAs and consideration of miRNA regulation of cellular and molecular pathways that have been implicated in PD pathogenesis are explored further in this review.

#### **miRNA-BASED THERAPEUTICS**

A better understanding of the role miRNAs play in the cellular and molecular pathogenesis of PD will undoubtedly contribute to the development of novel miRNA-based therapies. It is therefore, imperative to make a number of important considerations in moving miRNAs from molecular targets to viable therapeutics. First, because of their ability to modulate the expression of families of related genes that participate in common cellular and molecular pathways, miRNAs hold great potential to restore balance to dysregulated pathways at the onset and very early stages of PD (Ouellet et al., 2006). However, the ability to apply a miRNAbased therapy within this critical time period is currently limited by an inability to accurately diagnose PD at these early stages (Akhtar and Stern, 2012). Thus, the detection of biomarkers, development of sensitive imaging techniques, and discovery of clinical criteria that can discern PD in these early stages will be critical for the development of miRNA-based therapeutics with disease-altering potential (Akhtar and Stern, 2012). Additionally, given the mechanism of action of RNAi-based therapies, it is important to consider off-target effects resulting from transcriptional and translational repression of unintended miRNA targets (Rao et al., 2009). In the development of miRNA-based therapies for PD, it is also necessary to consider the route of delivery to achieve therapeutic levels of the miRNA in the brain (Boudreau et al., 2011). Although targeted delivery of small RNAs to the brain has presented a formidable challenge in the past, recent evidence suggests that exosomes may be used to deliver exogenous cargo, including nucleic acids, to the brain, providing a novel means of overcoming this current limitation to the translation of miRNA-based therapies (Alvarez-Erviti et al., 2011). Finally, an important limitation to the testing of miRNA therapeutics is the lack of an animal model that recapitulates key features of PD pathology (Beal, 2010). Although animal models have been developed that display nigostriatal degeneration, an animal model that demonstrates Lewy body pathology, a key feature of human Parkinsonism, remains to be discovered. This underscores a potential difference between the cellular and molecular mechanisms of Parkinson's symptomatology in existing models compared to that in humans, presenting a challenge for the accurate detection and application of miRNAs that would modulate these pathways for therapeutic benefit. Despite these limitations, it has been demonstrated that an integrated analysis of PD mouse models, existing human cell culture models and novel human iPSC-derived DA neuron models can provide an accurate prediction of clinical efficacy for drug treatments of PD, indicating promise for utilizing such an integrated approach for testing miRNA-based therapies. Specifically, it has been shown that a subset of compounds which were found to have pharmacologic benefit in mouse models of PD had a protective effect in MPP+ treated SH-SY5Y cells, and further that a subset of those were

**FIGURE 1 | MiRNA biogenesis and function. (A)** The canonical miRNA biogenesis pathway is Drosha- and Dicer-dependent. It begins with RNA Pol II-mediated transcription of genomic loci containing miRNA genes. The primary transcript is referred to as pri-miRNA and like other RNA Pol II transcripts contains a 5 7-methylgunaosine cap and 3 poly-A tail. The pri-miRNA is processed in the nucleus by Drosha and DGCR8 to form pre-miRNA. The pre-miRNA is then exported into the cytoplasm by Exportin-5 where it is further processed by the RNase III enzyme, Dicer. Interaction with Dicer leads to hairpin cleavage and degradation of the anti-sense strand of the miRNA duplex, while the mature guide strand is complexed with members of the Argonaute family of proteins to form a functional RNA-induced silencing complex (RISC). **(B)** In a Drosha-dependent and Dicer-independent non-canonical pathway of miRNA biogenesis, the cytoplasmic pri-miRNA undergoes processing by AGO2, although the precise mechanism of miRNA maturation via this pathway remains unclear. **(C)** Another non-canonical pathway involving Drosha-independent/Dicer-dependent biogenesis generates mirtrons, transcribed from intronic sequences and obtained by splicing and lariat-debranching. Mature miRNA can act via three primary methods: (1) destabilization and cleavage of target mRNA, (2) translational repression, and (3) mRNA stabilization.

protective in MPP+-treated iPSC-derived TH+ neurons (Peng et al., 2013). Importantly, those compounds which were effective in both SH-SY5Y and iPSC-derived TH+ neurons had the greatest benefit when translated to patient care (Peng et al., 2013). Similar integrative approaches using non-human primate and human iPSC culture models have been successful in predicting miRNA-based therapeutic efficacy (Chan and Kocerha, 2012). Thus, this integrative approach provides a means of thoroughly testing miRNA-based therapeutics with a readout that can provide an indication of clinical effectiveness as never previously attainable.

#### **INTERACTION BETWEEN miRNA AND PD-RELATED GENES PD-RELATED GENES**

Of the 28 distinct chromosomal loci that have been convincingly linked to PD, only six have been demonstrated to cause heritable monogenic PD (Klein and Westenberger, 2012). **Table 1** lists these six genes, their mode of inheritance, and the cellular pathways currently thought to be affected. The following sections discuss studies which have identified miRNA interactions with these genes; these are summarized in **Figure 2**. No studies to date have identified direct miRNA interactions with PINK1 or ATP13A2.

#### **Table 1 | PD-related genes.**

*Mutations in these six genes have been demonstrated to cause inherited monogenic forms of PD. Several miRNAs have been identified to regulate particular genes in this list, including alpha synuclein and LRRK2. A miRNA-gene interaction has been described for DJ-1 and Parkin, however the mechanisms remain elusive. Each of these interactions is described in detail in the text. Of note, no miRNA have yet been described to interact with PINK1 or ATP13A2.*

#### **ALPHA SYNUCLEIN LEVELS ARE REGULATED BY miR-7 AND miR-153**