Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2014 May 1.
Published in final edited form as: J Cell Physiol. 2013 May;228(5):1070–1075. doi: 10.1002/jcp.24254

Cerebrospinal Fluid miRNA Profile in HIV-Encephalitis†

Marco Pacifici 1, Serena Delbue 4, Pasquale Ferrante 5, Duane Jeansonne 1, Ferdous Kadri 1, Steve Nelson 1,2, Cruz Velasco-Gonzalez 3, Jovanny Zabaleta 1, Francesca Peruzzi 1,*
PMCID: PMC3760673  NIHMSID: NIHMS430821  PMID: 23042033

Abstract

MicroRNAs are short non-coding RNAs that modulate gene expression by translational repression. Because of their high stability in intracellular as well as extracellular environments, miRNAs have recently emerged as important biomarkers in several human diseases. However, they have not been tested in the cerebrospinal fluid (CSF) of HIV-1 positive individuals. Here, we present results of a study aimed at determining the feasibility of detecting miRNAs in the CSF of HIV-infected individuals with and without encephalitis (HIVE). We also evaluated similarities and differences between CSF and brain tissue miRNAs in the same clinical setting. We utilized a high throughput approach of miRNA detection arrays and identified differentially expressed miRNAs in the frontal cortex of three cases each of HIV+, HIVE, and HIV− controls, and CSF of ten HIV-positive and ten HIV-negative individuals. For the CSF samples, the group of HIV+ individuals contained nine cases of HIV-Associated Neurological Disorders (HAND) and, among those, four had HIVE. All the HIV-negative samples had non-viral acute disseminate encephalomyelitis. A total of 66 miRNAs were found differentially regulated in HIV+ compared to HIV− groups. The greatest difference in miRNA expression was observed when four cases of HIVE were compared to five non-HIVE cases, previously normalized with the HIV-negative group. After statistical analyses, eleven miRNAs were fund significantly up-regulated in HIVE. Although more clinical samples should be examined, this work represents the first report of CSF miRNAs in HIV-infection and offers the basis for future investigation.

Keywords: miRNA, HIV, CSF

Introduction

HIV-associated neurological disorders (HAND) comprise cognitive, motor and behavioral impairments which affect a substantial number of HIV-1 infected individuals. While the introduction of the highly active antiretroviral therapy (HAART) has greatly reduced the incidence of HIV-associated dementia, the prevalence of HIV-associated minor cognitive and motor disorders has increased (reviewed in (Gannon et al., 2011)). Understanding the biology of HIV-1 infection in the brain and identifying markers for its pathological manifestations, including HIV-encephalitis, are of critical importance. Although several molecules in the CSF have been identified as putative indicators of neurocognitive impairments, the need for biomarkers persists (McGuire, 2009).

After over a decade of investigation of miRNAs, it is now clear that these non-coding RNA molecules serve a fundamental role in the regulation of gene expression; even though specific regulation and function of miRNAs are largely unknown. Similarly to mRNAs, some miRNAs show specific tissue distribution, such as miR-124 which is enriched in the central nervous system (CNS) (Mishima et al., 2007). Changes in the expression of cellular miRNAs have been associated with a variety of pathologic conditions (Montano, 2011), including neurodegenerative disorders (Eacker et al., 2009; Sonntag, 2010), and systematic high throughput approaches, such as miRNA profiling arrays, have lead to the identification of miRNA signatures in several types of cancer (Erkan et al., 2011; Gibcus et al., 2009; O’Hara et al., 2008; Pedranzini et al., 2010; Robertus et al., 2010; Shah et al., 2010). Finally, emerging evidence accumulated supporting the concept of establishing microRNAs expression profiles in body fluids as possible diagnostic and prognostic markers (Alevizos and Illei, 2010; Cho, 2010; De Smaele et al., 2010; Ferracin et al., 2010; Heneghan et al., 2010; Ju, 2010; Kosaka et al., 2010; Taft et al., 2009; Wittmann and Jack, 2010; Xie et al., 2010). Although the literature confirms a growing interest for miRNAs and their potential role in HIVE (Eletto et al., 2008; Noorbakhsh et al., 2010; Rom et al., 2010; Tatro et al., 2010; Yelamanchili et al., 2010), to our knowledge this is the first report of CSF miRNA profile.

We have utilized RT-PCR-based miRNA arrays to detect expression of 742 unique miRNAs in the CSF of HIV-1-positive individuals with HAND compared to HIV-1-negative individuals. After normalization, 66 miRNAs were considered for further analysis. Comparison between HIVE and HIV+ (non-HIVE) groups showed 47 up- and 7 down-regulated miRNAs in HIVE. Statistical analysis (ANOVA followed by Dunnet’s test) indicated significant differences (p<0.2) among the three groups for 11 miRNAs, with miR-182*, miR-362-5p, miR-720, and miR-937 having a p-value <0.05. Using a similar approach we have also obtained miRNA profiles from the frontal cortex of HIV+, HIVE, and HIV− clinical samples. Although the CSF specimens and frontal cortices were obtained from different individuals, our results show a surprisingly high number of miRNAs deregulated in both types of specimens.

Altogether, our study demonstrates feasibility in detecting CSF miRNAs, it provides a putative CSF miRNA signature for HIVE, and shows important correlation in differentially regulated miRNAs in the frontal cortex and CSF samples.

Materials and Methods

Clinical samples

CSF samples were collected over a period of five years from patients at the University of Milan and stored frozen at −80°C in aliquots until analysis. Formalin-fixed paraffin embedded (FFPE) samples from the frontal cortex were previously obtained from the Manhattan HIV Brain Bank (Dr. Susan Morgello, Mt. Sinai, New York).

RNA preparation and miRNA array

RNA was extracted from 0.5 ml of CSF using the mirVANA miRNA isolation kit (Ambion, Austin, TX) following the manufacturer’s instructions. Carrier RNA (MS2 RNA, Roche) was added at 1μg per sample prior to total RNA extraction (Andreasen et al., 2010). RNA was quantified using the NanoDrop spectrophotometer and 40 ng were retrotranscribed using the universal cDNA synthesis kit (Exiqon, Woburn, MA). Three RT reactions were performed for each sample. Each cDNA reaction was mixed with SYBR Green master mix (Exiqon) and dispensed into two 384 well plates containing 742 unique miRNAs and several controls (Exiqon microRNA ready to use PCR, human panels I and II). PCR was performed with a Roche LightCycler 480 System (Roche Applied Science, Indianapolis, IN) following Exiqon’s recommended settings.

For the FFPE samples, RNA was extracted using the high pure miRNA isolation kit (Roche) from two 10μm thick sections, 1cm × 1cm each.

Data processing and statistical analyses

Absolute quantification using 2nd derivative maximum was calculated with Roche Lightcycler 480 software. Exiqon arrays were performed in triplicate and qPCR data were analyzed in GenEx Professional 5 software (MultiD Analyses AB, Goteborg, Sweden). If a miRNA was not present in at least two out of three replicas it was set as non-expressed. Because of the low abundance of miRNAs in the CSF, we set a cut off of 39. If a miRNA had a cycle threshold Ct >39 for one probe, that reading was replaced with the average of the Ct for the other two probes, provided they were both below 39. Relative quantification was determined by the ΔCt method using miR-622 and miR-1266 as reference genes, as determined by GeNorm and Normfinder incorporated in the GenEx software. The control HIV-negative group was used as the calibrator between the HIVE and non-HIVE groups and the fold change was calculated using the formula 2−ΔΔCt. miRNAs with a fold change <0.5 were considered down-regulated, and miRNAs with a fold change >2 were set as up-regulated. The three groups, HIV−, HIVE, and HIV+ for the CSF experiment were compared using oneway ANOVA allowing for unequal variances, and followed by Dunnet tests. For this exploratory study a cut off for the p-value was set at 0.2.

For the analysis of brain tissue miRNAs, miR-650 and miR-1266 were used as reference genes. Due to the low number of samples analyzed (n=3 for each group), statistical analysis was not performed on the experiments related to the brain tissue samples.

Bioinformatics

Predicted gene targets for the eleven differentially regulated miRNAs were identified using miRWalk module (Dweep et al.), which allows the search of up to eight established target prediction programs. The list of genes predicted by three out of five databases was filtered for the brain tissue using DAVID functional annotation bioinformatics microarray analysis (Huang da et al., 2009a; Huang da et al., 2009b). Next, Gene Ontology (GO) annotations of predicted gene targets were determined using Genego integrated software (MetaCore).

Results

RNA was extracted from a total of 20 CSF samples, 10 HIV-negative and 10 HIV-positive, and subjected to miRNA arrays. The group of HIV-positive individuals consisted of nine cases of HAND and was further divided into HIV-encephalitis (HIVE) (n=4) and non-HIVE (n=5) cases. The HIV-negative group comprised all cases of acute disseminated encephalomyelitis of non-viral origin and none of the individuals in this group had cognitive impairments. Males and females were represented in both HIV− and HIV+ groups. The age of the subjects ranged from 36 to 66 years old, with an overall average of 52.7 (51.3 for the HIV+ and 54.1 for the HIV− group). Each sample was retrotranscribed in triplicates and the resulting cDNA was loaded into three sets of miRNA plates. The Exiqon miRNA ready plates contain preassigned 6 wells for 6 different genes which have stable expression levels over a wide range of sample types. Three of these are microRNAs which are often stably expressed, and the other three are small RNA reference genes. However, using Normfinder and GeNorm applications incorporated in the GEnEx analysis software, we found that in CSF the most uniformly expressed miRNAs were miR-622 and miR-1266; therefore, we have selected them as reference genes. The CSF samples were divided in three groups, HIV− (n=10), HIV+ (n=5) and HIVE (n=4) and the ratio HIVE/HIV+ was determined after normalization of HIVE and HIV+ with the control HIV− group. For comparison analyses, only miRNAs present in all the three groups were selected and a new list of 66 miRNAs was generated (Figure 1); while miRNAs expressed preferentially in only one or two groups were removed. In general, infection with HIV-1 results in an overall down-regulation of miRNAs (Figure 1, lane HIV+/HIV−; values < 0.5 highlighted in green), with only five up-regulated miRs (highlighted in red). In contrast, in the group of cases with encephalitis (lane indicated with HIVE/HIV−; values between 0.5 and 2) the majority of miRNAs showed unchanged expression levels and only three, miR-937, miR-598, and miR-595 were up-regulated. Finally, an opposite trend in miRNA expression was observed when HIVE and HIV+ groups were compared and most of miRNAs showed up-regulation, while only seven were down-regulated (Figure 1, lane indicated with HIVE/HIV+). Expression of 11 miRNAs, miR-502-5p, miR-877*, miR-127-5p, miR-543, miR-1247, miR-615-3p, miR-574-3p, miR-663, miR-197, miR-485-3p, and miR-766 remained unchanged across the three groups of samples (Figure 1, highlighted in yellow). The list of 66 miRNAs was further subjected to ANOVA analysis followed by the Dunnet’s test to determine statistically significant differences among the three groups. Eleven miRNAs, miR-1203, miR-1224-3p, miR-182*, miR-19b-2*, miR-204, miR-362-5p, miR-484, miR-720, miR-744*, miR-934, and miR-937 were found differentially expressed with a p< 0.2 (Figure 2). Within this group, miR-182*, miR-362-5p, miR-720, and mir-937 had the lowest p-value, p<0.05.

Figure 1.

Figure 1

Open in a new tab

Differentially regulated CSF miRNAs. MiRNAs were profiled in ten HIV+ and ten HIV− CSF samples. Nine cases in the HIV+ group had HAND and, among those, 4 had HIVE. After data processing, comparison between three groups, HIVE, HIV+ and HIV− was performed as follows. First the HIVE and HIV+ groups were normalized for the HIV− control group according to the ΔΔCt formula and then the fold change was calculated as the ratio HIVE/HIV+. Over-expressed miRNAs (fold change > 2) are indicated in red, under-expressed miRNAs in green (values < 0.5), and miRNAs whose expression was unchanged (values between 0.5 and 2.0) are highlighted in yellow.

Figure 2.

Figure 2

Open in a new tab

List of miRNAs with differential expression significant at the 0.2 level. Global column p-values are for overall tests on the 3 groups (HIVE, HIV+ and HIV−). Dunnet tests comparing each of HIVE and HIV+ to HIV− were carried out. A total of 66 miRNAs was subjected to these analyses.

We also evaluated miRNA profiles in archived brain tissues previously obtained from the Manhattan HIV Brain Bank. We analyzed three cases each of the following: HIV− control, HIV+, and HIVE. Comparison between groups was performed as described for the CSF study in which the fraction HIVE/HIV+ represents the ratio of the fold changes between the two groups HIVE and HIV+, calculated using the HIV− group as control. The results show 121 differentially regulated miRNAs (Figure 3). In general, and similar to the CSF miRNA profiles, HIV-infection resulted in overall down-regulation of miRNAs. In contrast, in the presence of encephalitis miRNA levels increased. This trend is clear when we compare HIVE versus HIV+ (ratio HIVE/HIV+) in which most of the miRNAs were up-regulated (Figures 1 and 3).

Figure 3.

Figure 3

Open in a new tab

List of differentially regulated miRNAs in archived brain tissue samples. Three cases each of HIVE, HIV+, and HIV− were analyzed and results show 121 differentially expressed miRNAs. Up-regulated and unchanged miRNAs are indicated in red and yellow, respectively. Note that in the lanes indicated as HIV+/HIV− and HIVE/HIV− miRNAs are all down-regulated.

Next, we compared CSF and frontal cortex miRNA profiles and the results are shown in Figure 4A. Thirty five of the 66 CSF miRNAs (53%) were also present in the list of differentially regulated miRNAs in the frontal cortex. Four miRNAs, miR-1296, miR-134, miR-16, and miR-495 had comparable expression changes for the ratio HIVE/HIV+ in both CSF and brain tissue, with miR-1296 being the most up-regulated. Differences in the expression level of six out of the 11 statistically significant CSF miRNAs were plotted in Figure 4B. Strikingly, miR-937 is more than 20 times up-regulated in the CSF compared to frontal cortex in HIVE cases.

Figure 4.

Figure 4

Open in a new tab

Comparison of CSF and brain tissue miRNA expression profiles. A) 35 of the 66 differentially expressed CSF miRNAs are also deregulated in the frontal cortex and are listed in alphabetical order. Values are expressed as indicated in Figure 1. Note that, for simplicity, although not in green, most of the values in column 1 (HIVE/HIV−) are down-regulated. B) Six of the 11 statistically significant CSF miRNAs were also present in the frontal cortex and their relative expression is shown in panel B.

Next, we investigated predicted gene targets for the 11 statistically significant miRNAs (Figure 2) in the context of maps, networks and diseases and the results are shown in Figure 5. The most represented genes appear to be related to the maps of cytoskeleton remodeling, cell adhesion and chemokines (Figure 5, upper table). Various aspects of neurogenesis including axonal guidance, notch signaling, synaptogenesis and transmission of nerve impulse were the most represented networks (Figure 5, middle table). Interestingly, the predicted gene targets for the combined miRNAs were associated with mental disorders such as mood, affective and depressive disorders, as highlighted in Figure 5 (lowest table).

Figure 5.

Figure 5

Open in a new tab

Annotation results from Gene Ontology analysis. Predicted gene targets for the eleven differentially regulated CSF miRNAs (miR-1203, miR-1224-3p, miR-182*, miR-19b-2*, miR-204, miR-362-5p, miR-484, miR-720, miR-744*, miR-934, and miR-937) were subjected to GO analysis. The three tables show the list of the most significant maps, networks and diseases, respectively.

In summary, our findings indicate an overall down-regulation of miRNAs in HIV-infected individuals in both CSF and frontal cortex. This trend, however, appears to be reversed in the presence of encephalitis, with miR-937 being the most up-regulated. While more clinical samples should be profiled to validate a miRNA signature in the CSF of individuals with HIV-1, with and without encephalitis, this work provides knowledge and important procedural aspects to investigate CSF miRNAs in the clinical setting of HIV-infection.

Discussion

HIV enters the CNS early during infection, frequently leading to cognitive impairments that can persist even after successful viral suppression (Heaton et al., 2010; Power et al., 2009; Woods et al., 2009). HIV-associated neurological disorders comprise a variety of cognitive and motor impairments that range from mild to severe and that are characterized by molecular events leading to neuronal injury, inflammation, and loss of neuroprotection. While several indicators of neuronal injury and immune activation have been identified in the CSF, they do not correlate with the severity and/or progression of HAND, and the need of diagnostic and/or prognostic markers is high priority (McGuire, 2009). MicroRNAs have emerged as critical regulators of gene expression in mammals. Several features of miRNAs, including their stability and tissue specificity, make them suitable for their detection and relative quantification in a variety of tissues and body fluids. In contrast to most mRNAs, detection of miRNAs in specimens such as formalin-fixed tissue and cerebrospinal fluid (CSF) or other body fluids is more accurate and reliable (Doleshal et al., 2008; Mitchell et al., 2008). However, there are some limitations that have been outlined in a recent review by Pritchard et al. (Pritchard et al., 2010). Accordingly, we found that recovery of the RNA for qPCR miRNA profiling was higher in the brain samples (FFPE) in comparison to CSF specimens. Differently from chip-based microarrays, which generally use micrograms of RNA, the advantage of using quantitative PCR panels is that they require nanogram amounts of RNA, allowing diagnostic analyses from low RNA recovery type of tissues, such as CSF and FFPE samples. Furthermore, the low RNA abundance could be, at least partially, overcome by pre-amplification cycles, for which kits have been recently developed and are commercially available. Although the global normalization approach is widely used in microarrays (Pradervand et al., 2009; Wylie et al., 2011), it couldn’t be applied to our system due to the low number of miRNAs that were represented in all samples (Mestdagh et al., 2009). Eight miRNAs had an average Ct< 37 and two of them, miR-622 and miR-1266, that showed uniform expression across all samples were chosen as reference genes. Of interest, expression levels of miR-1266 remained unchanged across the samples and in the brain tissue. MiR-937 was the most abundant miR in all CSF samples with an average Ct of 33.6, while in the frontal cortex miR-720 (average Ct=23) was the most expressed miRNA. Although the number of the analyzed brain samples was lower than CSF cases, we have observed significantly less variability across the samples in the brain tissues compared to the CSF. Despite of this difference in RNA samples a high number of miRNAs were found differentially regulated in both brain and CSF. Accordingly, 53% of differentially regulated miRNAs in the CSF were also present in the frontal cortex (Figure 4A).

Figure 1 shows a global downregulation of miRNA expression in the CSF of HIV+ individuals compared to HIV− controls (Figure 1, lane HIV+/HIV−). This trend is reversed in the presence of encephalitis (compare lanes identified as HIVE/HIV− and HIV+/HIV−). A similar general miRNA down-regulation was also observed in the frontal cortex as shown in Figure 3. Overall, these data may suggest impairment in the processing of miRNAs (synthesis and/or maturation). Indeed, we have some indication from gene expression arrays performed on the same brain tissue cases that levels of proteins involved in the miRNA biogenesis, such as Dicer and DCGR8, are down-regulated (data not shown). Among the most up-regulated miRNAs in the CSF of HIVE cases, miR-19b-2*, miR-937, and miR-362-5p had the largest fold change. Although only 11 CSF miRNAs showed statistically significant differences, several other miRNAs may be promising in that they show a large fold change, such as the up-regulated miR-595, miR-300, miR-598, miR-1296, miR-555, miR-222, and miR-105*, or the down-regulated miR-92b* and let-7d*.

Gene ontology analysis performed on the predicted gene targets for the eleven differentially regulated miRNAs revealed maps of genes associated with neurogenesis, cytoskeletal remodeling, chemokine signaling, and networks related to synaptogenesis and axonal guidance (Figure 5). Overall, bioinformatics analysis of CSF miRNA predicted gene targets may further provide additional information on the molecular events that lead to neuronal damage and/or persistent inflammation.

In summary, this is the first report detecting CSF miRNA in HIV patients and identifying a potential miRNA signature for HIVE. By demonstrating the possibility of detecting miRNAs in the CSF, this work offers both scientific and technical details for future longitudinal studies aimed to determine miRNA profiles in clinical settings that include antiretroviral therapies and/or addictive behaviors, such as alcohol and drug usage, often associated with HIV-infection.

Acknowledgments

This work was supported by the NIH grant MH 079751 and by the Stanley S Scott Cancer Center of the Louisiana Cancer Research Consortium (LCRC) to FP, and P60 AA009803-18 to SN.

Footnotes

†

This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as doi: [10.1002/jcp.24254]

The Authors declare no conflict of interests.

References

  1. Alevizos I, Illei GG. MicroRNAs as biomarkers in rheumatic diseases. Nat Rev Rheumatol. 2010;6(7):391–398. doi: 10.1038/nrrheum.2010.81. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Andreasen D, Fog JU, Biggs W, Salomon J, Dahslveen IK, Baker A, Mouritzen P. Improved microRNA quantification in total RNA from clinical samples. Methods. 2010;50(4):S6–9. doi: 10.1016/j.ymeth.2010.01.006. [DOI] [PubMed] [Google Scholar]
  3. Cho WC. MicroRNAs: potential biomarkers for cancer diagnosis, prognosis and targets for therapy. Int J Biochem Cell Biol. 2010;42(8):1273–1281. doi: 10.1016/j.biocel.2009.12.014. [DOI] [PubMed] [Google Scholar]
  4. De Smaele E, Ferretti E, Gulino A. MicroRNAs as biomarkers for CNS cancer and other disorders. Brain Res. 2010;1338:100–111. doi: 10.1016/j.brainres.2010.03.103. [DOI] [PubMed] [Google Scholar]
  5. Doleshal M, Magotra AA, Choudhury B, Cannon BD, Labourier E, Szafranska AE. Evaluation and validation of total RNA extraction methods for microRNA expression analyses in formalin-fixed, paraffin-embedded tissues. J Mol Diagn. 2008;10(3):203–211. doi: 10.2353/jmoldx.2008.070153. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Dweep H, Sticht C, Pandey P, Gretz N. miRWalk--database: prediction of possible miRNA binding sites by “walking” the genes of three genomes. J Biomed Inform. 44(5):839–847. doi: 10.1016/j.jbi.2011.05.002. [DOI] [PubMed] [Google Scholar]
  7. Eacker SM, Dawson TM, Dawson VL. Understanding microRNAs in neurodegeneration. Nat Rev Neurosci. 2009;10(12):837–841. doi: 10.1038/nrn2726. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Eletto D, Russo G, Passiatore G, Del Valle L, Giordano A, Khalili K, Gualco E, Peruzzi F. Inhibition of SNAP25 expression by HIV-1 Tat involves the activity of mir-128a. J Cell Physiol. 2008;216(3):764–770. doi: 10.1002/jcp.21452. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Erkan EP, Breakefield XO, Saydam O. miRNA signature of schwannomas: Possible role(s) of “tumor suppressor” miRNAs in benign tumors. Oncotarget. 2011:265–270. doi: 10.18632/oncotarget.251. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Ferracin M, Veronese A, Negrini M. Micromarkers: miRNAs in cancer diagnosis and prognosis. Expert Rev Mol Diagn. 2010;10(3):297–308. doi: 10.1586/erm.10.11. [DOI] [PubMed] [Google Scholar]
  11. Gannon P, Khan MZ, Kolson DL. Current understanding of HIV-associated neurocognitive disorders pathogenesis. Curr Opin Neurol. 2011 doi: 10.1097/WCO.0b013e32834695fb. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Gibcus JH, Tan LP, Harms G, Schakel RN, de Jong D, Blokzijl T, Moller P, Poppema S, Kroesen BJ, van den Berg A. Hodgkin lymphoma cell lines are characterized by a specific miRNA expression profile. Neoplasia. 2009;11(2):167–176. doi: 10.1593/neo.08980. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Heaton RK, Clifford DB, Franklin DR, Jr, Woods SP, Ake C, Vaida F, Ellis RJ, Letendre SL, Marcotte TD, Atkinson JH, Rivera-Mindt M, Vigil OR, Taylor MJ, Collier AC, Marra CM, Gelman BB, McArthur JC, Morgello S, Simpson DM, McCutchan JA, Abramson I, Gamst A, Fennema-Notestine C, Jernigan TL, Wong J, Grant I. HIV-associated neurocognitive disorders persist in the era of potent antiretroviral therapy: CHARTER Study. Neurology. 2010;75(23):2087–2096. doi: 10.1212/WNL.0b013e318200d727. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Heneghan HM, Miller N, Kerin MJ. MiRNAs as biomarkers and therapeutic targets in cancer. Curr Opin Pharmacol. 2010;10(5):543–550. doi: 10.1016/j.coph.2010.05.010. [DOI] [PubMed] [Google Scholar]
  15. Huang da W, Sherman BT, Lempicki RA. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 2009a;37(1):1–13. doi: 10.1093/nar/gkn923. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Huang da W, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009b;4(1):44–57. doi: 10.1038/nprot.2008.211. [DOI] [PubMed] [Google Scholar]
  17. Ju J. miRNAs as biomarkers in colorectal cancer diagnosis and prognosis. Bioanalysis. 2010;2(5):901–906. doi: 10.4155/bio.10.45. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Kosaka N, Iguchi H, Ochiya T. Circulating microRNA in body fluid: a new potential biomarker for cancer diagnosis and prognosis. Cancer Sci. 2010;101(10):2087–2092. doi: 10.1111/j.1349-7006.2010.01650.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. McGuire D. CSF biomarkers in HIV dementia: through a glass darkly. Neurology. 2009;73(23):1942–1944. doi: 10.1212/WNL.0b013e3181c51a92. [DOI] [PubMed] [Google Scholar]
  20. Mestdagh P, Van Vlierberghe P, De Weer A, Muth D, Westermann F, Speleman F, Vandesompele J. A novel and universal method for microRNA RT-qPCR data normalization. Genome Biol. 2009;10(6):R64. doi: 10.1186/gb-2009-10-6-r64. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Mishima T, Mizuguchi Y, Kawahigashi Y, Takizawa T. RT-PCR-based analysis of microRNA (miR-1 and -124) expression in mouse CNS. Brain Res. 2007;1131(1):37–43. doi: 10.1016/j.brainres.2006.11.035. [DOI] [PubMed] [Google Scholar]
  22. Mitchell PS, Parkin RK, Kroh EM, Fritz BR, Wyman SK, Pogosova-Agadjanyan EL, Peterson A, Noteboom J, O’Briant KC, Allen A, Lin DW, Urban N, Drescher CW, Knudsen BS, Stirewalt DL, Gentleman R, Vessella RL, Nelson PS, Martin DB, Tewari M. Circulating microRNAs as stable blood-based markers for cancer detection. Proc Natl Acad Sci U S A. 2008;105(30):10513–10518. doi: 10.1073/pnas.0804549105. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Montano M. MicroRNAs: miRRORS of health and disease. Transl Res. 2011;157(4):157–162. doi: 10.1016/j.trsl.2011.02.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Noorbakhsh F, Ramachandran R, Barsby N, Ellestad KK, LeBlanc A, Dickie P, Baker G, Hollenberg MD, Cohen EA, Power C. MicroRNA profiling reveals new aspects of HIV neurodegeneration: caspase-6 regulates astrocyte survival. FASEB J. 2010;24(6):1799–1812. doi: 10.1096/fj.09-147819. [DOI] [PubMed] [Google Scholar]
  25. O’Hara AJ, Vahrson W, Dittmer DP. Gene alteration and precursor and mature microRNA transcription changes contribute to the miRNA signature of primary effusion lymphoma. Blood. 2008;111(4):2347–2353. doi: 10.1182/blood-2007-08-104463. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Pedranzini L, Mottadelli F, Ronzoni S, Rossella F, Ferracin M, Magnani I, Roversi G, Colapietro P, Negrini M, Pelicci PG, Larizza L. Differential cytogenomics and miRNA signature of the Acute Myeloid Leukaemia Kasumi-1 cell line CD34+38- compartment. Leuk Res. 2010;34(10):1287–1295. doi: 10.1016/j.leukres.2010.02.012. [DOI] [PubMed] [Google Scholar]
  27. Power C, Boisse L, Rourke S, Gill MJ. NeuroAIDS: an evolving epidemic. Can J Neurol Sci. 2009;36(3):285–295. doi: 10.1017/s0317167100007009. [DOI] [PubMed] [Google Scholar]
  28. Pradervand S, Weber J, Thomas J, Bueno M, Wirapati P, Lefort K, Dotto GP, Harshman K. Impact of normalization on miRNA microarray expression profiling. RNA. 2009;15(3):493–501. doi: 10.1261/rna.1295509. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Pritchard CC, Cheng HH, Tewari M. MicroRNA profiling: approaches and considerations. Nat Rev Genet. 2010;13(5):358–369. doi: 10.1038/nrg3198. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Robertus JL, Kluiver J, Weggemans C, Harms G, Reijmers RM, Swart Y, Kok K, Rosati S, Schuuring E, van Imhoff G, Pals ST, Kluin P, van den Berg A. MiRNA profiling in B non-Hodgkin lymphoma: a MYC-related miRNA profile characterizes Burkitt lymphoma. Br J Haematol. 2010;149(6):896–899. doi: 10.1111/j.1365-2141.2010.08111.x. [DOI] [PubMed] [Google Scholar]
  31. Rom S, Rom I, Passiatore G, Pacifici M, Radhakrishnan S, Del Valle L, Pina-Oviedo S, Khalili K, Eletto D, Peruzzi F. CCL8/MCP-2 is a target for mir-146a in HIV-1-infected human microglial cells. FASEB J. 2010;24(7):2292–2300. doi: 10.1096/fj.09-143503. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Shah AA, Leidinger P, Blin N, Meese E. miRNA: small molecules as potential novel biomarkers in cancer. Curr Med Chem. 2010;17(36):4427–4432. doi: 10.2174/092986710794182980. [DOI] [PubMed] [Google Scholar]
  33. Sonntag KC. MicroRNAs and deregulated gene expression networks in neurodegeneration. Brain Res. 2010;1338:48–57. doi: 10.1016/j.brainres.2010.03.106. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Taft RJ, Pang KC, Mercer TR, Dinger M, Mattick JS. Non-coding RNAs: regulators of disease. J Pathol. 2009;220(2):126–139. doi: 10.1002/path.2638. [DOI] [PubMed] [Google Scholar]
  35. Tatro ET, Scott ER, Nguyen TB, Salaria S, Banerjee S, Moore DJ, Masliah E, Achim CL, Everall IP. Evidence for Alteration of Gene Regulatory Networks through MicroRNAs of the HIV-infected brain: novel analysis of retrospective cases. PLoS One. 2010;5(4):e10337. doi: 10.1371/journal.pone.0010337. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Wittmann J, Jack HM. Serum microRNAs as powerful cancer biomarkers. Biochim Biophys Acta. 2010;1806(2):200–207. doi: 10.1016/j.bbcan.2010.07.002. [DOI] [PubMed] [Google Scholar]
  37. Woods SP, Moore DJ, Weber E, Grant I. Cognitive neuropsychology of HIV-associated neurocognitive disorders. Neuropsychol Rev. 2009;19(2):152–168. doi: 10.1007/s11065-009-9102-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Wylie D, Shelton J, Choudhary A, Aai AT. A novel mean-centering method for normalizing microRNA expression from high-throughput RT-qPCR data. BMC Res Notes. 2011;4:555. doi: 10.1186/1756-0500-4-555. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Xie L, Qian X, Liu B. MicroRNAs: novel biomarkers for gastrointestinal carcinomas. Mol Cell Biochem. 2010;341(1–2):291–299. doi: 10.1007/s11010-010-0463-0. [DOI] [PubMed] [Google Scholar]
  40. Yelamanchili SV, Chaudhuri AD, Chen LN, Xiong H, Fox HS. MicroRNA-21 dysregulates the expression of MEF2C in neurons in monkey and human SIV/HIV neurological disease. Cell Death Dis. 2010;1(9):e77. doi: 10.1038/cddis.2010.56. [DOI] [PMC free article] [PubMed] [Google Scholar]

RESOURCES