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Opinion ARTICLE

Front. Neurol., 13 July 2015 | https://doi.org/10.3389/fneur.2015.00162

microRNA-based biomarkers and the diagnosis of Alzheimer’s disease

imageYuhai Zhao1,2, imageSurjyadipta Bhattacharjee1, imagePrerna Dua3, imagePeter N. Alexandrov4 and imageWalter J. Lukiw1,5,6*
  • 1LSU Neuroscience Center Louisiana State University Health Science Center, New Orleans, LA, USA
  • 2Department of Cell Biology and Anatomy, LSU Neuroscience Center Louisiana State University Health Science Center, New Orleans, LA, USA
  • 3Department of Health Information Management, Louisiana State University, Ruston, LA, USA
  • 4Russian Academy of Medical Sciences, Moscow, Russia
  • 5Department of Ophthalmology, LSU Neuroscience Center Louisiana State University Health Science Center, New Orleans, LA, USA
  • 6Department of Neurology, LSU Neuroscience Center Louisiana State University Health Science Center, New Orleans, LA, USA

Alzheimer’s disease (AD) is characterized as a complex, age-related neurological disorder of the human central nervous system (CNS) that involves the progressive mis-regulation of multiple biological pathways at multiple molecular, genetic, epigenetic, neurophysiological, cognitive, and behavioral levels. It has been about 8 years since the first reports of altered microRNA (miRNA) abundance and speciation: (i) in anatomical regions of the brain targeted by the AD process after post-mortem examination, (ii) in blood serum, and (iii) in cerebrospinal fluid (CSF) (13). Since then an in depth overview of the peer-reviewed literature has provided no general consensus of what miRNAs are up-or-down regulated in any tissue or biofluid compartment in thousands of AD patients. In this brief “Opinion” paper on “Biomarkers of Alzheimer’s disease: the present and the future,” we will highlight the extremely heterogeneous nature of miRNA expression in AD, based on very recent advances in the analysis of miRNA populations in various biofluid compartments compared to normally aging, neurologically normal controls. This work is based against a background of our laboratory’s 24 years of research experience into the structure and function of small, non-coding RNAs in the aging human CNS in health and in age-related neurological disease (4).

First, it is important to appreciate that all forms of dementia due to AD are broadly classified as either early onset (EOAD, under 65 years of age), or late onset (LOAD, over 65 years of age) (5, 6). About ~5% of all AD cases have a genetic component (see below) while the remaining ~95% of all AD cases are of a sporadic (idiopathic) nature or are of unknown origin (58). The extremely heterogeneous nature of AD pervades all molecular, genetic, neuropathological and behavioral, mnemonic, and cognitive levels, including the clinical presentation of the disease (615). For example, the key neuropathological markers of AD include: (i) the progressive deposition of amyloid-beta (Aβ) peptides into dense, insoluble pro-inflammatory senile plaques (SP); (ii) the accumulation of hyperphosphorylated tau into neurofibrillary tangles (NFT); (iii) synaptic atrophy, “pruning” and loss, neuronal degeneration and neuronal cell death; (iv) alterations in the innate-immune response; and (v) the progressive inflammatory neurodegeneration and anatomical targeting of only specific anatomical regions of the brain (115). These highly interactive characteristics collectively suggest the participation of multiple pathogenic pathways, and the involvement of multiple deficits in the expression of CNS genes (115). Accordingly, this culminates in a remarkably heterogeneous neuropathological scaffold for AD, with significant variations in disease onset, progression, severity of neuropathology, extent of behavioral and cognitive deficits, and memory loss (412). To cite one very recent example, a relatively large epidemiological study of AD patient data (N = 7815) (12) indicated significant heterogeneity in the first cognitive/behavioral symptomatic “indicator” experienced by AD patients (1316). In other recent studies, two laboratories have independently reported significant variation in the miRNA-34a-mediated triggering receptor expressed in myeloid/microglial cells-2 (TREM2) down-regulation in an African-American population that further underscores (i) the importance of investigating different ethnic populations for AD epigenetic risk; (ii) intrinsic variance and human biochemical and genetic individuality; and (iii) allelic heterogeneity and potentially diverse pathogenic contributory mechanisms to the AD process (sufficient TREM2 is important in the clearance of excessive Aβ peptides from the brain) (916). Related to these observations are studies that over the last 15 years have indicated that gene expression patterns at the messenger RNA (mRNA) level, Aβ peptide load, SP and NFT densities and localization, and familial and clinical histories further underscore AD heterogeneity (812, 1720). Indeed, there appears to be intrinsic limitations of useful AD biomarkers because just one biomarker cannot define the mechanism of AD, by nature are associative and/or correlative, and are unable to unequivocally prove disease causality (1317, 2123). For example current genome-wide association studies (GWAS), whole-exome and whole-genome sequencing have revealed mutations in excess of 20 genetic loci associated with AD risk (11, 19, 20, 24). Three main genes are involved in EOAD: amyloid precursor protein (APP), presenilin 1 (PSEN1), and presenilin 2 (PSEN2), while the apolipoprotein E (ApoE) E4 allele has been found to be a main risk factor for LOAD (1, 1719, 23). Additionally, recent studies have discovered other genes that might be peripherally involved in AD, including clusterin (CLU), complement receptor 1 (CR1), phosphatidylinositol binding clathrin assembly protein (PICALM), sortilin-related receptor (SORL1), complement factor H (CFH), the triggering receptor expressed on myeloid/microglial cells 2 (TREM2), and the cluster of differentiation 33 (CD33) gene loci; although not one single case of AD has yet been found to be associated with more than one of these aberrant genetic loci (11, 25). Indeed, most AD cases do not contain any of these mutant genetic “biomarkers” (11, 20, 2426). Further, the persistence of mutations in these genes from birth and throughout life, in contrast to the general development of AD in old age, suggests that multiple age-associated gene regulatory mechanisms must come into play to initiate and drive development and propagation of the AD process, and miRNAs are excellent candidates for these diverse age-related, developmental, and regulatory roles (15, 9, 22).

Regarding the rate and variability of cognitive decline in AD, one large recent study did not find evidence supporting a substantial role of the mini-mental status examination (MMSE) as a stand-alone single-administration test in the identification of mild cognitively impaired patients who eventually develop AD, suggesting the need for additional neuropsychological testing and comprehensive biomarker analysis (2123). Indeed, although AD is the most common form of senile dementia, it can often be challenging to distinguish this insidious and fatal disorder from other equally heterogeneous neurodegenerative disorders, such as frontal temporal dementia, human prion disease [including bovine spongiform encephalopathy (BSE; mad cow disease), Creutzfeldt–Jakob disease, Gerstmann–Sträussler–Scheinker syndrome, and other relatively rare human prion diseases], Huntington’s disease, Lewy Body dementia, Parkinson’s disease, cerebrovascular disease, or vascular (multiple infarct) dementia (1618, 2123). Indeed, the diagnostic accuracy of when brain-mediated cognitive deficits actually begin may require a dimensional rather than a categorical classification, and a lifespan rather than aging grouping, and it has been recently suggested that a multidimensional system-vulnerability approach rather than a simple “hypothetical biomarker” model of age-associated cognitive decline and dementia may be more useful diagnostically (12, 20). Put another way, AD might be classified not as a discrete disease entity but rather as a “neurological disconnection syndrome” (7, 8, 11, 15, 24). This “neurological disconnection syndrome” is more broadly defined as an abnormal condition characterized by an established group of variable neurological signs, symptoms, and molecular markers, including miRNA abundance and speciation, that individually possess only limited neuropathological and cognition/behavioral similarities from patient to patient (79, 1118, 2124).

Further to the concept of AD heterogeneity are the ideas that form the conceptual basis for “human biochemical and genetic individuality” (5, 9, 18). These include individual gene sequence variation, gene-based susceptibility to disease and heterogeneity in miRNA abundance and complexity, that may in part drive a general redundancy in gene expression in different human populations (5, 9, 16, 21, 22). Interestingly, these variations may directly impact the genetic evolution of the human species (4, 5, 1820, 2426). Much independently derived data support the concept that the genetics, epigenetics, and genome-wide regulatory networks of AD vary considerably among different human populations that possess different genetic and/or environmental backgrounds. Furthermore, despite the fact that genetic factors are inherited and fixed, non-genetic factors, such as (i) environmental or occupational exposures to pesticides, organic solvents, anesthetics, and/or food additives; (ii) pre-existing medical conditions such as cancer, cerebrovascular, and/or cardiovascular disease, depression, diabetes, dyslipidemia, hypertension, traumatic brain injury, older age, female gender, and ApoE status; and (iii) lifestyle factors such as alcohol and coffee consumption, salt, sugar, and cholesterol and fat intake, body mass index, cognitive activity, physical activity, and smoking, are life-style determined and these are known to impact the incidence, development and propagation of AD (1820, 2431). Interestingly, certain potentially pathogenic “pro-inflammatory miRNAs” of the host are significantly inducible by common microbial and environmental factors such as herpes simplex-1 virus (HSV-1) and naturally occurring elements of the biosphere (such as aluminum oxides that make up almost 9% of the earth’s crust) (3235).

To make another important point concerning the variable contribution of specific miRNAs to AD, we surveyed the most recently published papers on “miRNA biomarkers for AD” using the National Institutes of Health National Library of Medicine website MedLine (www.ncbi.nlm.nih.gov; using the keywords “Alzheimer’s disease,” “miRNA” and “2015”). The most recent findings of 15 independent labs further support the contention of extremely high miRNA heterogeneity in AD tissue and biofluids (3650). For example, the last 15 reports of diagnostic markers in AD CSF (36–39; involving miRNA-27a, miRNA-29a, miRNA-191, miRNA-384) and others, AD blood serum (38–46; involving miRNA-107, miRNA-125b, miRNA-128, miRNA-132, miRNA-191, miRNA-206, miRNA-384) and others; “humanized” AD cell models (47–50; involving miRNA-125b, miRNA-128, miRNA-138) and others, and several recent reviews (5155) provides no common or general consensus of any single miRNA that defines causality for the onset or duration of the AD process. To further complicate these findings, recent molecular-genetic studies have also shown that even when derived from homogenous source populations, such as pluripotent stem cells, individual cells from those populations exhibit significant differences in gene expression, protein abundance and phenotypic output; here specific families of miRNAs appear to have a deterministic role in reconfiguring the “pluripotency network” of individual cells with important downstream functional consequences (4749, 56, 57).

It is further important to point out exactly what an advanced analytical technique will tell us. For example, most AD researchers would agree that the production of Aβ42 peptides is involved in the AD process. Aβ42 peptides and fragments are generated by a variety of secretases (chiefly α-, β-, and γ-secretases), however, other secretase-like enzymes and enzyme modifiers appear to be involved (5, 8, 14, 25, 31, 58). While RNA-seq and other “next generation sequencing” (NGS) methods will tell us something about the levels of expression of these secretases they would give us no clue about the activity of these secretases in the brain, and their ability to generate Aβ42 or other AD-relevant peptides, which are affected by many other genetic, epigenetic, non-genetic, environmental, and host lifestyle factors. So it is unlikely that RNA-seq, NGS, or other “advanced sequencing methodologies” could give us the entire story of what is going on in AD, although most agree it would give us very valuable insight as to what is happening at the molecular-genetic level, and perhaps be of some value diagnostically.

Lastly, if high-density microarray- and advanced RNA-sequencing based profiles of AD brain or biofluid samples are any indication of AD variability then there are real and significant human population differences in AD onset, incidence, epidemiology, disease course and progression (9, 16, 21, 22, 25, 50, 57). It is unlikely that a single miRNA in the CSF, blood serum, urine, or any other biofluid compartments from multiple human populations will be predictive for AD at any stage of the disease. However, what might be particularly useful for significantly improved AD diagnostics would be a selective, high-density panel of a “pathogenic and neurodegeneration-associated miRNA family” that along with other gene expression-based biometrics could more accurately predict the onset of AD-type change. This highly interactive, “personalized medicine” approach – involving a comprehensive evaluation that scores multiple AD deficiencies including miRNA-, mRNA-, and protein-based gene expression alterations, AD-relevant DNA mutations, pro-inflammatory biomarkers (such as C-reactive protein or CRP), and Aβ40- and Aβ42-peptide load in the CSF and blood serum, combined with data from MRI- and PET-based brain imaging, and familial, clinical history, lifestyle, and other factors could be extremely useful in the improved diagnosis of AD susceptibility and development (5258). These highly integrated and multidimensional diagnostic approaches certainly lie within the grasp of current medical technologies – it will just be a matter of improved application, data acquisition and integration of clinical research and healthcare resources to frame a more accurate diagnostic portrait of the “alleged AD patient.” Indeed, an equally wide variety of individualistic prevention and “personalized” treatment strategies would be required to more effectively address such age-related neurological disorders, including the implementation of combinatorial and/or customized anti-miRNA strategies that have as yet not been considered.

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.

Acknowledgments

This work was presented in part at the Society for Neuroscience (SFN) Annual Meeting 15–19 November 2014, Washington, DC, USA and at the Association for Research in Vision and Ophthalmology (ARVO) Annual conference 3–7 May 2015 in Denver, CO, USA. Sincere thanks are extended to Drs. L. Carver, E. Head, W. Poon, H. LeBlanc, F. Culicchia, C. Eicken, and C. Hebel for short post-mortem interval (PMI) human brain and/or retinal tissues or extracts, miRNA array work and initial data interpretation, and to D. Guillot and A. I. Pogue for expert technical assistance. Thanks are also extended to the many neuropathologists, physicians, and researchers of Canada and the US, who have provided high quality, short PMI human CNS and retinal tissues or extracted total brain and retinal RNA for scientific study. Research on miRNA in the Lukiw laboratory involving the innate-immune response in AD, AMD, and in other forms of neurological or retinal disease, amyloidogenesis, and neuro-inflammation was supported through an unrestricted grant to the LSU Eye Center from Research to Prevent Blindness (RPB); the Louisiana Biotechnology Research Network (LBRN), and NIH grants NEI EY006311, NIA AG18031, and NIA AG038834.

Abbreviations

miRNA, microRNA.

References

1. Lukiw WJ. MiRNA speciation in fetal, adult and Alzheimer’s disease hippocampus. Neuroreport (2007) 18:297–300. doi: 10.1097/WNR.0b013e3280148e8b

PubMed Abstract | CrossRef Full Text | Google Scholar

2. Schipper HM, Maes OC, Chertkow HM, Wang E. MicroRNA expression in Alzheimer blood mononuclear cells. Gene Regul Syst Bio (2007) 1:263–74.

PubMed Abstract | Google Scholar

3. Cogswell JP, Ward J, Taylor IA, Waters M, Shi Y, Cannon B, et al. Identification of miRNA changes in Alzheimer’s disease brain and CSF yields putative biomarkers and insights into disease pathways. J Alzheimers Dis (2008) 14(1):27–41.

PubMed Abstract | Google Scholar

4. Lukiw WJ, Handley P, Wong L, Crapper McLachlan DR. BC200 RNA in normal human neocortex, non-Alzheimer dementia (NAD), and senile dementia of the Alzheimer type (AD). Neurochem Res (1992) 17:591–7. doi:10.1007/BF00968788

PubMed Abstract | CrossRef Full Text | Google Scholar

5. Jiang T, Yu JT, Tian Y, Tan L. Epidemiology and etiology of Alzheimer’s disease: from genetic to non-genetic factors. Curr Alzheimer Res (2013) 10:852–67. doi:10.2174/15672050113109990155

PubMed Abstract | CrossRef Full Text | Google Scholar

6. Guerreiro RJ, Gustafson DR, Hardy J. The genetic architecture of Alzheimer’s disease: beyond APP, PSENs and APOE. Neurobiol Aging (2012) 33:437–56. doi:10.1016/j.neurobiolaging.2010.03.025

PubMed Abstract | CrossRef Full Text | Google Scholar

7. Canobbio I, Abubaker AA, Visconte C, Torti M, Pula G. Role of amyloid peptides in vascular dysfunction and platelet dysregulation in Alzheimer’s disease. Front Cell Neurosci (2015) 9:65. doi:10.3389/fncel.2015.00065

PubMed Abstract | CrossRef Full Text | Google Scholar

8. Kim DH, Yeo SH, Park JM, Choi JY, Lee TH, Park SY, et al. Genetic markers for diagnosis and pathogenesis of Alzheimer’s disease. Gene (2014) 545:185–93. doi:10.1016/j.gene.2014.05.031

CrossRef Full Text | Google Scholar

9. Lukiw WJ. Variability in micro RNA (miRNA) abundance, speciation and complexity amongst different human populations and potential relevance to Alzheimer’s disease (AD). Front Cell Neurosci (2013) 7:133. doi:10.3389/fncel.2013.00133

CrossRef Full Text | Google Scholar

10. Sherva R, Tripodis Y, Bennett DA, Chibnik LB, Crane PK, de Jager PL, et al. Genome-wide association study of the rate of cognitive decline in Alzheimer’s disease. Alzheimers Dement (2014) 10:45–52. doi:10.1016/j.jalz.2013.01.008

PubMed Abstract | CrossRef Full Text | Google Scholar

11. Jin SC, Carrasquillo MM, Benitez BA, Skorupa T, Carrell D, Patel D, et al. TREM2 is associated with increased risk for Alzheimer’s disease in African Americans. Mol Neurodegener (2015) 10:19–26. doi:10.1186/s13024-015-0016-9

PubMed Abstract | CrossRef Full Text | Google Scholar

12. Barnes J, Dickerson BC, Frost C, Jiskoot LC, Wolk D, van der Flier WM. Alzheimer’s disease first symptoms are age dependent: evidence from the NACC dataset. Alzheimers Dement (2015). doi:10.1016/j.jalz.2014.12.007

PubMed Abstract | CrossRef Full Text | Google Scholar

13. Verhülsdonk S, Hellen F, Höft B, Supprian T, Lange-Asschenfeldt C. Attention and CERAD test performances in cognitively impaired elderly subjects. Acta Neurol Scand (2015) 131:364–71. doi:10.1111/ane.12346

PubMed Abstract | CrossRef Full Text | Google Scholar

14. Praticò D. Alzheimer’s disease and the quest for its biological measures. J Alzheimers Dis (2013) 33:S237–41. doi:10.3233/JAD-2012-129023

PubMed Abstract | CrossRef Full Text | Google Scholar

15. Wang Z, Wang J, Zhang H, Mchugh R, Sun X, Li K, et al. Interhemispheric functional and structural disconnection in Alzheimer’s disease: a combined resting-state fMRI and DTI Study. PLoS One (2015) 10(5):e0126310. doi:10.1371/journal.pone.0126310

PubMed Abstract | CrossRef Full Text | Google Scholar

16. Walhovd KB, Fjell AM, Espeseth T. Cognitive decline and brain pathology in aging-need for a dimensional, lifespan and systems vulnerability view. Scand J Psychol (2014) 55:244–54. doi:10.1111/sjop.12120

PubMed Abstract | CrossRef Full Text | Google Scholar

17. Kim MO, Geschwind MD. Clinical update of Jakob-Creutzfeldt disease. Curr Opin Neurol (2015) 28:302–10. doi:10.1097/WCO.0000000000000197

CrossRef Full Text | Google Scholar

18. Li JQ, Tan L, Wang HF, Tan MS, Tan L, Xu W, et al. Risk factors for predicting progression from mild cognitive impairment to Alzheimer’s disease: a systematic review and meta-analysis of cohort studies. J Neurol Neurosurg Psychiatry (2015). doi:10.1136/jnnp-2014-310095

PubMed Abstract | CrossRef Full Text | Google Scholar

19. Colangelo V, Schurr J, Ball MJ, Pelaez RP, Bazan NG, Lukiw WJ. Gene expression profiling of 12633 genes in Alzheimer hippocampal CA1. J Neurosci Res (2002) 70:462–73. doi:10.1002/jnr.10351

PubMed Abstract | CrossRef Full Text | Google Scholar

20. Loring JF, Wen X, Lee JM, Seilhamer J, Somogyi R. A gene expression profile of Alzheimer’s disease. DNA Cell Biol (2002) 20:683–95. doi:10.1089/10445490152717541

CrossRef Full Text | Google Scholar

21. Arevalo-Rodriguez I, Smailagic N, Roqué I, Figuls M, Ciapponi A, Sanchez-Perez E, et al. Mini-Mental State Examination (MMSE) for the detection of Alzheimer’s disease and other dementias in people with mild cognitive impairment (MCI). Cochrane Database Syst Rev (2015) 3:CD010783. doi:10.1002/14651858.CD010783.pub2

PubMed Abstract | CrossRef Full Text | Google Scholar

22. Blennow K, Dubois B, Fagan AM, Lewczuk P, de Leon MJ, Hampel H. Clinical utility of cerebrospinal fluid biomarkers in the diagnosis of early Alzheimer’s disease. Alzheimers Dement (2015) 11(1):58–69. doi:10.1016/j.jalz.2014.02.004

PubMed Abstract | CrossRef Full Text | Google Scholar

23. Chitravas N, Jung RS, Kofskey DM, Blevins JE, Gambetti P, Leigh RJ, et al. Treatable neurological disorders misdiagnosed as Creutzfeldt-Jakob disease. Ann Neurol (2011) 70(3):437–44. doi:10.1002/ana.22454

PubMed Abstract | CrossRef Full Text | Google Scholar

24. Counts SE, Alldred MJ, Che S, Ginsberg SD, Mufson EJ. Synaptic gene dysregulation within hippocampal CA1 pyramidal neurons in mild cognitive impairment. Neuropharmacology (2014) 79:172–9. doi:10.1016/j.neuropharm.2013.10.018

PubMed Abstract | CrossRef Full Text | Google Scholar

25. Chouraki V, Seshadri S. Genetics of Alzheimer’s disease. Adv Genet (2014) 87:245–94. doi:10.1016/B978-0-12-800149-3.00005-6

CrossRef Full Text | Google Scholar

26. Tan MS, Jiang T, Tan L, Yu JT. Genome-wide association studies in neurology. Ann Transl Med (2014) 2:124. doi:10.3978/j.issn.2305-5839.2014.11.12

PubMed Abstract | CrossRef Full Text | Google Scholar

27. Tai LM, Ghura S, Koster KP, Liakaite V, Maienschein-Cline M, Kanabar P, et al. APOE-modulated Aβ-induced neuroinflammation in Alzheimer’s disease. J Neurochem (2015) 133:465–88. doi:10.1111/jnc.13072

PubMed Abstract | CrossRef Full Text | Google Scholar

28. Yaghmoor F, Noorsaeed A, Alsaggaf S, Aljohani W, Scholtzova H, Boutajangout A, et al. The role of TREM2 in Alzheimer’s disease and other neurological disorders. J Alzheimers Dis Parkinsonism (2014) 4(5):160. doi:10.4172/2161-0460.1000160

PubMed Abstract | CrossRef Full Text | Google Scholar

29. Cheng XJ, Gao Y, Zhao YW, Cheng XD. Sodium chloride increases Aβ Levels by suppressing Aβ clearance in cultured cells. PLoS One (2015) 10:e0130432. doi:10.1371/journal.pone.0130432

PubMed Abstract | CrossRef Full Text | Google Scholar

30. Baumgart M, Snyder HM, Carrillo MC, Fazio S, Kim H, Johns H. Summary of the evidence on modifiable risk factors for cognitive decline and dementia: a population-based perspective. Alzheimers Dement (2015) 11:718–26. doi:10.1016/j.jalz.2015.05.016

PubMed Abstract | CrossRef Full Text | Google Scholar

31. Østergaard SD, Mukherjee S, Sharp SJ, Proitsi P, Lotta LA, Day F, et al. Associations between potentially modifiable risk factors and Alzheimer disease. PLoS Med (2015) 12:e1001841. doi:10.1371/journal.pmed.1001841

PubMed Abstract | CrossRef Full Text | Google Scholar

32. Bhela S, Mulik S, Reddy PB, Richardson RL, Gimenez F, Rajasagi NK, et al. Critical role of microRNA-155 in herpes simplex encephalitis. J Immunol (2014) 192:2734–43. doi:10.4049/jimmunol.1302326

PubMed Abstract | CrossRef Full Text | Google Scholar

33. Hill JM, Clement C, Zhao Y, Lukiw WJ. Induction of the pro-inflammatory NF-kB-sensitive miRNA-146a by human neurotrophic viruses. Front Microbiol (2015) 6:43. doi:10.3389/fmicb.2015.00043

CrossRef Full Text | Google Scholar

34. Alexandrov PN, Zhao Y, Jones BM, Bhattacharjee S, Lukiw WJ. Expression of the phagocytosis-essential protein TREM2 is down-regulated by an aluminum-induced miRNA-34a in a murine microglial cell line. J Inorg Biochem (2013) 128:267–9. doi:10.1016/j.jinorgbio.2013.05.010

PubMed Abstract | CrossRef Full Text | Google Scholar

35. Pogue AI, Percy ME, Cui JG, Li YY, Bhattacharjee S, Hill JM, et al. Up-regulation of NF-kB-sensitive miRNA-125b and miRNA-146a in metal sulfate-stressed human astroglial (HAG) primary cell cultures. J Inorg Biochem (2011) 105:1434–7. doi:10.1016/j.jinorgbio.2011.05.012

PubMed Abstract | CrossRef Full Text | Google Scholar

36. Müller M, Jäkel L, Bruinsma IB, Claassen JA, Kuiperij HB, Verbeek MM. microRNA-29a is a candidate biomarker for Alzheimer’s disease in cell-free cerebrospinal fluid. Mol Neurobiol (2015). doi:10.1007/s12035-015-9156-8

PubMed Abstract | CrossRef Full Text | Google Scholar

37. Sala Frigerio C, Lau P, Salta E, Tournoy J, Bossers K, Vandenberghe R, et al. Reduced expression of hsa-miR-27a-3p in CSF of patients with Alzheimer disease. Neurology (2013) 81:2103–6. doi:10.1212/01.wnl.0000437306.37850.22

PubMed Abstract | CrossRef Full Text | Google Scholar

38. Liu CG, Wang JL, Li L, Wang PC. MicroRNA-384 regulates both amyloid precursor protein and β-secretase expression and is a potential biomarker for Alzheimer’s disease. Int J Mol Med (2014) 34:160–6. doi:10.3892/ijmm.2014.1780

PubMed Abstract | CrossRef Full Text | Google Scholar

39. Nagpal N, Kulshreshtha R. miR-191: an emerging player in disease biology. Front Genet (2014) 5:99. doi:10.3389/fgene.2014.00099

PubMed Abstract | CrossRef Full Text | Google Scholar

40. Wang T, Chen K, Li H, Dong S, Su N, Liu Y, et al. The feasibility of utilizing plasma miRNA107 and BACE1 messenger RNA gene expression for clinical diagnosis of amnestic mild cognitive impairment. J Clin Psychiatry (2015) 76:135–41. doi:10.4088/JCP.13m08812

PubMed Abstract | CrossRef Full Text | Google Scholar

41. Xie B, Zhou H, Zhang R, Song M, Yu L, Wang L, et al. Serum miR-206 and miR-132 as potential circulating biomarkers for mild cognitive impairment. J Alzheimers Dis (2015) 45:721–31. doi:10.3233/JAD-142847

PubMed Abstract | CrossRef Full Text | Google Scholar

42. Tan L, Yu JT, Liu QY, Tan MS, Zhang W, Hu N, et al. Circulating miR-125b as a biomarker of Alzheimer’s disease. J Neurol Sci (2014) 336:52–6. doi:10.1016/j.jns.2013.10.002

PubMed Abstract | CrossRef Full Text | Google Scholar

43. Tiribuzi R, Crispoltoni L, Porcellati S, Di Lullo M, Florenzano F, Pirro M, et al. miR128 up-regulation correlates with impaired amyloid β(1-42) degradation in monocytes from patients with sporadic Alzheimer’s disease. Neurobiol Aging (2014) 35:345–56. doi:10.1016/j.neurobiolaging.2013.08.003

PubMed Abstract | CrossRef Full Text | Google Scholar

44. Leidinger P, Backes C, Deutscher S, Schmitt K, Mueller SC, Frese K, et al. A blood based 12-miRNA signature of Alzheimer disease patients. Genome Biol (2013) 14:R78. doi:10.1186/gb-2013-14-7-r78

PubMed Abstract | CrossRef Full Text | Google Scholar

45. Satoh J, Kino Y, Niida S. MicroRNA-seq data analysis pipeline to identify blood biomarkers for Alzheimer’s disease from public data. Biomark Insights (2015) 10:21–31. doi:10.4137/BMI.S25132

PubMed Abstract | CrossRef Full Text | Google Scholar

46. Burgos K, Malenica I, Metpally R, Courtright A, Rakela B, Beach T, et al. Profiles of extracellular miRNA in CSF and serum from patients with Alzheimer’s and Parkinson’s diseases correlate with disease status and features of pathology. PLoS One (2014) 9:e94839. doi:10.1371/journal.pone.0094839

PubMed Abstract | CrossRef Full Text | Google Scholar

47. Gstir R, Schafferer S, Scheideler M, Misslinger M, Griehl M, Daschil N, et al. Generation of a neurospecific microarray reveals novel differentially expressed noncoding RNAs in mouse models for neurodegenerative diseases. RNA (2014) 20:1929–43. doi:10.1261/rna.047225.114

PubMed Abstract | CrossRef Full Text | Google Scholar

48. Kumar RM, Cahan P, Shalek AK, Satija R, Daley Keyser AJ, Li H, et al. Deconstructing transcriptional heterogeneity in pluripotent stem cells. Nature (2014) 516:56–61. doi:10.1038/nature13920

CrossRef Full Text | Google Scholar

49. Patel AP, Tirosh I, Trombetta JJ, Shalek AK, Gillespie SM, Wakimoto H, et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science (2015) 344:1396–401. doi:10.1126/science.1254257

PubMed Abstract | CrossRef Full Text | Google Scholar

50. Danborg PB, Simonsen AH, Waldemar G, Heegaard NH. The potential of miRNAs as biofluid markers of neuro-degenerative disease – a systematic review. Biomarkers (2014) 19:259–68. doi:10.3109/1354750X.2014.904001

CrossRef Full Text | Google Scholar

51. Wang X, Tan L, Lu Y, Peng J, Zhu Y, Zhang Y, et al. MicroRNA-138 promotes tau phosphorylation by targeting retinoic acid receptor alpha. FEBS Lett (2015) 589:726–9. doi:10.1016/j.febslet.2015.02.001

PubMed Abstract | CrossRef Full Text | Google Scholar

52. Femminella GD, Ferrara N, Rengo G. The emerging role of microRNAs in Alzheimer’s disease. Front Physiol (2015) 6:40. doi:10.3389/fphys.2015.00040

CrossRef Full Text | Google Scholar

53. Cheng L, Quek CY, Sun X, Bellingham SA, Hill AF. The detection of microRNA associated with Alzheimer’s disease in biological fluids using next-generation sequencing technologies. Front Genet (2013) 4:150. doi:10.3389/fgene.2013.00150

PubMed Abstract | CrossRef Full Text | Google Scholar

54. Koh W, Pan W, Gawad C, Fan HC, Kerchner GA, Wyss-Coray T, et al. Noninvasive in vivo monitoring of tissue-specific global gene expression in humans. Proc Natl Acad Sci U S A (2014) 111:7361–6. doi:10.1073/pnas.1405528111

PubMed Abstract | CrossRef Full Text | Google Scholar

55. Grasso M, Piscopo P, Confaloni A, Denti MA. Circulating miRNAs as biomarkers for neurodegenerative disorders. Molecules (2014) 19:6891–910. doi:10.3390/molecules19056891

PubMed Abstract | CrossRef Full Text | Google Scholar

56. Liu ZP. Reverse engineering of genome-wide gene regulatory networks from gene expression data. Curr Genomics (2015) 16:3–22. doi:10.2174/1389202915666141110210634

CrossRef Full Text | Google Scholar

57. Li Y, Zhang Z. Computational Biology in microRNA. Wiley Interdiscip Rev RNA (2015) 6(4):435–52. doi:10.1002/wrna.1286

PubMed Abstract | CrossRef Full Text | Google Scholar

58. Michaud M, Balardy L, Moulis G, Gaudin C, Peyrot C, Vellas B, et al. Proinflammatory cytokines, aging, and age-related diseases. J Am Med Dir Assoc (2013) 14:877–82. doi:10.1016/j.jamda.2013.05.009

PubMed Abstract | CrossRef Full Text | Google Scholar

Keywords: aging, Alzheimer’s disease, diagnostic panel, heterogeneity, human biochemical individuality, inflammation, microRNA, prion disease

Citation: Zhao Y, Bhattacharjee S, Dua P, Alexandrov PN and Lukiw WJ (2015) microRNA-based biomarkers and the diagnosis of Alzheimer’s disease. Front. Neurol. 6:162. doi: 10.3389/fneur.2015.00162

Received: 25 May 2015; Accepted: 29 June 2015;
Published: 13 July 2015

Edited by:

Charlotte Elisabeth Teunissen, VU University Medical Center Amsterdam, Netherlands

Reviewed by:

Cees Oudejans, VU University Medical Center Amsterdam, Netherlands
Argonde Corien Van Harten, VU University Medical Center Amsterdam, Netherlands

Copyright: © 2015 Zhao, Bhattacharjee, Dua, Alexandrov and Lukiw. 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.

*Correspondence: Walter J. Lukiw, wlukiw@lsuhsc.edu