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BDNF Modulation by microRNAs: An Update on the Experimental Evidence

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02 April 2024

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02 April 2024

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Abstract
Abstract: MicroRNAs can interfere with a protein function by suppressing their messenger RNA translation or the synthesis of its related factors. The function of brain-derived neurotrophic factor (BDNF) is essential to the proper formation and function of the nervous system and is seen to be regulated by many microRNAs. However, understanding how microRNAs influence BDNF actions within cells requires a wider comprehension of their integrative regulatory mechanisms. Aim: In this literature review, we have synthesized the evidence of microRNA regulation on BDNF in cells and tissues, and provided an analytical discussion about direct and indirect mechanisms that appeared involved in BDNF regulation by microRNAs. Methods: Searches were conducted on PubMed.gov using the terms “BDNF” AND “MicroRNA” and “brain-derived neurotrophic factor” AND “MicroRNA”, updated on September 1st, 2023. Papers without open access were requested from the authors. One hundred and seventy-one papers were included for review and discussion. Results and Discussion: The local regulation of BDNF by microRNAs involves a complex interaction between a series of microRNAs with target proteins that can either inhibit or enhance BDNF expression, at the core of cell metabolism. Therefore, understanding this homeostatic balance provides resources for the future development of vector-delivery-based therapies for the neuroprotective effects of BDNF.
Keywords: 
BDNF
;  
microRNA
;  
regenerative medicine
;  
neuroprotection
Subject: 
Biology and Life Sciences  -   Neuroscience and Neurology

Introduction

MicroRNAs are a class of non-coding RNAs which do not code for proteins but carry out biological function by regulating cell proteome at the translational level. They can be expressed within the activation of a gene promoter or their own promoters (Lai, 2002; Smallridge, 2001), and play a regulatory role in protein synthesis by targeting and degrading RNA transcripts containing compatible nucleotide sequences (Gulyaeva & Kushlinskiy, 2016; Ha & Kim, 2014; Y. Lee et al., 2004; O’Brien et al., 2018; Shukla et al., 2011). MicroRNAs are seen to participate in the functional regulation at the distant synaptic sites in neuronal cells, and to modulate inflammatory mechanisms that lead to neurological diseases (Chen et al., 2024; Kaurani, 2024).
As a main expressed neurotrophin, the brain-derived neurotrophic factor (BDNF) plays essential roles in the development and maintenance of neural tissues (Labrador-Velandia et al., 2019; Trzaska et al., 2009). Signaling of the mature form of BDNF via tropomyosin receptor kinase (Trk) B, participates in neuronal survival, dendritogenesis, synaptogenesis, axon growth and synaptic function; meanwhile, release pro-BDNF isoform can bind with low affinity to p75 neurotrophin receptor (p75NTR) and lead to apoptosis. So that, a tight regulation of BDNF activity is necessary for the proper functioning of the central nervous system (CNS) (Bouron et al., 2006; de Assis & Hoffman, 2022; Ibarra et al., 2022).
Analyses in silico estimate hundreds of microRNAs as possible regulators of BDNF. However, with a 10–20% variability detected in predicted regulatory relationships between genes and microRNAs in human RefSeq data set, the effective regulation of BDNF mRNA transcripts by microRNAs in biological systems is much smaller (Rajewsky, 2006). In addition, the microRNA affinity for multiple targets and microRNA-microRNA interactions in a cell milieu influence on their regulation and cannot be predicted by computational logarithms. As the studies typically address only one or a few number of microRNAs in the experiments, it remains a challenge to design pre-clinical studies based on computational predictions (Lai, 2002; S. C. Li et al., 2010).
Regarding the above mentioned, and that understanding how microRNAs effectively regulate BDNF actions provide basis for the development of potential therapies against neurodegenerative conditions; we have collected all the available data on the post-transcriptional regulation of BDNF by microRNAs evidenced in experimental studies, and provided a synthesis of the regulatory mechanisms currently demonstrated.

Methods

In order to retire all the scientific publications possibly reporting data from the analysis of microRNAs and BDNF expression in a same biological system, a systematic search was conducted on PubMed.gov using the following combination of terms: [“BDNF” AND “MicroRNA”] OR [“brain-derived neurotrophic factor” AND “MicroRNA”]. All the available publications were retrieved and screened by abstract. Papers published without open-access were requested from the authors via email or ResearchGate. Studies containing data from BDNF and microRNA analyses in vivo or in vitro were considered for inclusion. The studies reporting data of microRNAs that did not influence on BDNF regulation, Reviews, articles not written in English, high throughput profiles and computational predictor studies, as well as those not made available by the authors were excluded from discussion during full text assessment. The last search update, performed in September 1th 2023, launched 314 papers published from 2006 to 2023 indexed in PubMed (see Figure 1). The studies selection was performed using the software Mendeley 1.19.8.
Two-hundred and ninety-seven articles were sought for retrieval according to the inclusion criteria of containing data from BDNF and microRNA analyses, after duplicates removal. Two hundred and sixty-one papers were assessed by full text. A total of 172 were found to include the analyses of BDNF and diverse microRNAs and were included in the qualitative synthesis, after exclusion criteria (Figure 1). A list of the studies and microRNAs involved in BDNF regulation was displayed in Table 1.

Results

Discussion

BDNF by microRNAs

A great number of microRNAs are able to target and influence BDNF activity (Figure 2). The post-transcriptional regulation of BDNF can influence on BDNF synthesis and activity in a non-specific manner throughout tissues. Moreover, in addition to the ability to target and degrade the transcripts of BDNF mRNA in a ‘direct regulation’, microRNAs can affect the activity of BDNF (either positively or negatively) via regulation of other factors, here referred as ‘indirect regulation’. Great part of research identifying BDNF-target microRNAs regard to investigations on the oncogenicity of BDNF/TrkB signal transduction in tumor cell growth and metastasis (Y. Li et al., 2023; Ni & Zhang, 2020). A list of microRNAs found to degrade BDNF mRNA transcripts in oncology research follows: miR-10a, miR-22, miR-204, miR-107, miR-382, miR-496, miR-497, miR-584, miR-744, miR-26a-1 and miR-26a-2 subtypes (Caputo et al., 2011; Cheng et al., 2018; B. Gao et al., 2017; L. Gao et al., 2018; Imam et al., 2012; J. D. Jiang et al., 2019; R. Li et al., 2018; Wu Li et al., 2015; Long et al., 2016; R. Ma et al., 2019; J. Y. Peng et al., 2016; D. Song et al., 2017; Y. Song et al., 2019; Z. Sun et al., 2019; P. Wang et al., 2017; Xia et al., 2016; Xiang et al., 2016; A. J. Xu et al., 2017; Yan et al., 2015; Xiao yu Zhang et al., 2018). The evidences that miR-206 is able to suppress BDNF synthesis in diverse tissues such as the cardiac muscles, the skeletal muscles and the endothelial tissue elucidate a role for microRNAs in a tissue-tissue communication, although their actions might be locally regulated (Guan et al., 2021; S. T. Lee et al., 2012; Mu et al., 2015; D. Peng et al., 2022; Solomon et al., 2019; W. Sun et al., 2017; Tapocik et al., 2014; Tian et al., 2014; W. Tu et al., 2022; M. Wang et al., 2018; Xie et al., 2017; Xing et al., 2018; X. Yang et al., 2014; H. Zhao et al., 2019).
Some microRNAs can be are exported from cells by membrane-derived vesicles, lipoproteins, and other ribonucleoprotein complexes and travel through the blood stream reaching recipient cells in distant tissues (Boon & Vickers, 2013), providing a communication between disparate cell types and diverse biological mechanisms and homeostatic pathways. Our data collection reports a number of microRNAs whose circulating levels are increased in inflammatory conditions and that demonstrably suppress BDNF synthesis in the CNS, namely: miR-1, miR-128, miR-182-5p, miR-195-5p and miR-451a (F. Fang et al., 2022; Jian Gao et al., 2022; Giordano et al., 2020; J. C. Ma et al., 2015; Misiorek et al., 2020). Among those circulating microRNAs which suppress BDNF synthesis, some were found involved in the physiopathology of neuropsychiatric disorders and disease such as anxiety/depression – miR-182, miR-206-3p, miR-1-3p, MiR-16, miR-124, miR-432 and miR-182 (Bai et al., 2012; Ding et al., 2022; Y. Fang et al., 2018; Y. J. Li et al., 2013; Miao et al., 2018; Y. X. Sun et al., 2013; Xiaonan Zhang et al., 2021; Z. Zhang et al., 2022), Schizophrenia - miR-16, miR-195 and miR-30a-5p (Asadi et al., 2022; Mellios et al., 2008, 2009; Pan et al., 2021), Parkinson’s disease - miR-494-3p (C. Deng et al., 2020) and Dementia - miR-10a, miR-34a-5p, miR-204 and miR-613 (Cui et al., 2017; Ge et al., 2018; Giannotti et al., 2014; Lambert et al., 2003; Wei Li et al., 2016; Ji Chao Ma et al., 2018; Ryan et al., 2013; B. W. Wu et al., 2018). Together, those findings suggest a mechanistic crosstalk driven by microRNAs between inflammation in peripheral systems and neurodegenerative processes.
MicroRNAs play crucial roles in immunoinflammatory reactions. In normal conditions, the CNS parenchyma is not exposed to peripheral immune cells or robust inflammatory responses and microglia and astrocytes remain quiescent. However, upon stress, astrocytes and microglia transiently activate and produce chemokines and cytokines, and other small molecule messengers (prostaglandins, nitric oxide, reactive oxygen species - ROS) which contribute to the inflammatory response and subsequent restoration of CNS homeostasis (Jingdan Zhang et al., 2023). The study by (Kynast et al., 2013) identified that miR-124 is constitutively expressed in neuron of the dorsal horn in spinal cord, where its elevation is associated with a decrease in BDNF levels. While a decrease in miR-124 levels lead elevation in MeCP2 and BDNF expression levels. From a different perspective, the studies by (Duclot & Kabbaj, 2017; Wenqian Yang et al., 2019) demonstrated that miR-124 is able to attenuate an acute increase in pro-inflammatory factors in the CNS, by suppressing the early growth response 1 (EGR1) and preventing a decline in BDNF expression. Conversely, (Yu et al., 2022) showed that BDNF administration increased the expression levels of miR-3168, and suppressed the secretion of interleukin (IL)-1β, TNF-α, and IL-6 in the activated macrophage.
Another mechanism by which microRNAs indirectly modulate BDNF synthesis in inflammatory conditions involve the Let-7 miRNA family (Roush & Slack, 2008), which include let-7a, let-7b, let-7c, let-7d, let-7e, let-7f, let-7g, let-7i, miR-98 and miR-202 (Y. Ma et al., 2021). Dysregulation of let-7 leads to a less differentiated cellular state and cell-based diseases such as cancer. Cho and colleagues (2015) investigation in neural tissue reported that let-7a levels increase in microglia following the accumulation of ROS and pro-inflammatory cytokines. The data indicated that let-7a participates in reducing nitrite production while increasing the levels of inducible NO synthase and IL-6. Anti-inflammatory events that accompanied an upregulation in BDNF expression levels. Alternatively, (Nguyen et al., 2018) detected that miR-let-7i suppresses the synthesis of progesterone receptor membrane component 1, reducing progesterone-inducible release of BDNF by astrocytes. Such reduction has a negative effect on neuronal tissue recovery. These findings show that Let-7 members might exert specific roles that positively or negatively affect BDNF function in CNS parenchyma.
Although the regulation of a protein function by microRNAs mostly always depend on their nucleotide sequence to target mRNA transcripts present in a same micro environment; in silico predictions of BDNF target microRNAs are not always confirmed in biological systems. Meanwhile, some experiments have pointed out that microRNA targeting of BDNF mRNA is selectively guided by their prime untranslated region (3′-UTR) (Caputo et al., 2011; Shrestha et al., 2019; Varendi et al., 2014; Jun Zhang et al., 2018). Having noted the presence of two variants of 3’ UTR regions in the mRNA transcripts of BDNF, which exert an influence on their cellular trafficking/localization (Lekk et al., 2023), the mechanistic regulation of BDNF by microRNA within a cell might as well occurs in a local specificity manner, a least in cells that express the two BDNF mRNA 3’ UTR isoforms.

Neuroplasticity and BDNF Regulation by microRNAs

The expression of BDNF is present in progenitor cells from the early embryonic phase and in neural tissue throughout the whole lifespan. It participation in essential processes such as dendritogenesis, axonal innervation and synaptogenesis, neuronal growth and survival guaranties the maintenance and proper functioning of the neuronal tissue (Barde et al., 1987; Citri & Malenka, 2008; Robinson, 1996).
A growing number of microRNAs have been identified as direct regulators of BDNF in the neural tissue. Here, we list some of the microRNAs that target and degrade BDNF mRNA transcripts and modulate BDNF actions in processes such as neuronal cell growth, differentiation and proliferation: miR-1, miR-1b, miR-1-3p, miR-10a, miR-10b, miR-10a-5p, miR-15a, miR-16, miR-26a, miR-34a, miR-103, miR-125b, miR-125b-5p, miR-30a-5p, miR-34a-5p, miR-140, miR-139-5p, miR-155, miR-191, miR-181, miR-186, miR-191, miR-191-5p, miR-191a-5p, miR-204-5p, miR-206, miR-210, miR-210-3p miR-211, miR-216a-5p, miR-219, miR-636-3p, miR-365, miR-375, miR-551b-5p, miR-937, miR-497 and the miR-497a subtypes (Aili et al., 2016; Angelucci et al., 2011; Burckhardt et al., 2019; Croce et al., 2013; Darcq et al., 2015; L. Deng et al., 2022; Duan et al., 2018; Ehinger et al., 2021; Fu et al., 2017; Lin Gao et al., 2020; J. J. Hu et al., 2020; X. M. Hu et al., 2016; Huan et al., 2021; Hung et al., 2019; Hutchison et al., 2013; Y. Jiang & Zhu, 2015; Ke et al., 2021; Hongxia Li et al., 2021; Xiaojie Li et al., 2021; Xiu-juan Li, 2018; S. P. Liang et al., 2019; B. Lin et al., 2021; H. Liu et al., 2020; X. Liu et al., 2020; Zhen Liu et al., 2015; Lu et al., 2018; Lv et al., 2018; Miao et al., 2019; Mohammadipoor-Ghasemabad et al., 2019; Nagpal et al., 2013; Neumann et al., 2015, 2016; Niu et al., 2021; Panta et al., 2019; Scheen et al., 2015; B. Su et al., 2022; Tang et al., 2021; Z. Tu et al., 2017; F. Wang et al., 2020; Li Wang et al., 2020; Linxiao Wang et al., 2022; P. Wang et al., 2017; G. Wu et al., 2020; Y. Wu et al., 2021; H. Xu et al., 2020; W. Xu et al., 2022; Y. Xu et al., 2021; S. Yi et al., 2016; Yongguang et al., 2022; Zeng et al., 2016; Zhai et al., 2022; Jing Zhang et al., 2014; Jun Zhang et al., 2018; K. Zhang et al., 2017; T. Zhang et al., 2020; Xiao yu Zhang et al., 2018; P. Zhao et al., 2021; X. Zhao et al., 2019).
Some studies showed that Sonic hedgehog (Shh) is able to relief the suppression exerted by miR-206 on BDNF synthesis, which led to an enhancement in BDNF-TrkB signaling during the processes of differentiation and innervation in muscle cells (Miura et al., 2012; Radzikinas et al., 2011). Shh is a key signaling molecule in the embryonic morphogenesis and organization of nervous system. Its signaling via receptor Patched mediated Smoothened receptor complex is putative to the development of neural tube; whereas, abnormal activation of Shh signaling implicates in various types of cancers (Odent et al., 1999). This indirect and positive effect of Shh on BDNF activity seem to be involved in a complex and phasic destabilization of cell homeostasis during differentiation in mesenchymal cells.
BDNF binding to TrkB receptors at neuronal cells surface lead to the dimerization and transphosphorylation of a critical regulator of actin dynamics in the axons and dendrites named LIM Domain Kinase 1 (LIMK1). This occurs independently of TrkB kinase activity. The LIMK1 mRNA transcript is a target for miR-134 in the axon and dendrite cell compartments, and is able to annul BDNF/TrkB-induced protein synthesis during the synaptic activity, whenever TrkB activation is not sufficient to surpass miR-134 suppression on LIMK1. This suggest that miR-134 actively participates in competitive synapses formation; and establishes a role for this microRNA in the fine tune regulation of neuroplasticity processes (Dong et al., 2012; Jun Gao et al., 2010; Han et al., 2011; Kim et al., 2019; Kumari et al., 2016; M. Li et al., 2015; Schratt et al., 2006).
Several microRNAs were found to target different components of BDNF-TrkB signaling intracellular cascades, consequently decreasing the activity of cAMP response element binding (CREB) protein, leading to a decrease in BDNF gene expression. The investigation by (Thomas et al., 2017) identified that miR-137 regulates the levels of various proteins within the PI3K-Akt-mTOR pathway in neurons, namely: p55g, PTEN, Akt2, GSK3b, mTOR, and rictor. And this negatively affects BDNF- induced dendritic outgrowth. In addition, the miR-221, miR-383 and miR-199a-5p were shown to suppress the synthesis of Wnt2, which is a glycoprotein with essential roles in the embryonic development and dendrite development (Wayman et al., 2006). The neuronal activity enhances CREB-dependent transcription of Wnt2, which in turn, stimulates dendritic arborization. Both Wnt2 and BDNF are CREB-responsive genes and so Wn2t suppression results in a decrease in BDNF expression possibly via a Wnt2/CREB/BDNF axis (Lian et al., 2018; S. Liu et al., 2021; Zheng Liu et al., 2021). Additionally, some microRNAs were reported as negatively correlated with the levels of BDNF in studies, i.e. miR-183/96 (Hongyang Li et al., 2015; C. R. Lin et al., 2014), miR-134 (Huang et al., 2017; Shen et al., 2018, 2019), and miR-182-5p (F. Fang et al., 2022; C. Li et al., 2022).

Cell Metabolism and BDNF Regulation by microRNAs

The post-transcriptional regulation of proteins elicits compensatory mechanisms to maintain transcriptional activity of essential proteins involved in cell energy homeostasis. The integrative regulation of a number of proteins in the core of cell metabolism homeostasis affects BDNF gene expression by various manners, including it self-regulation via autocrine and/or paracrine TrkB signaling. BDNF/TrkB activation leads to the activation of several small G proteins in addition to the pathways regulated by mitogen-activated protein kinase (MAPK), PI 3-kinase (PI-3K), and phospholipase-Cγ (PLCγ) (Numakawa et al., 2010). Meanwhile, as miR-101 suppresses MAPK phosphatases 1 (which dephosphorylates p38, JNK and ERK), it has a positive effect on ERK phosphorylation and the downstream activation of BDNF expression in cortical neurons (Y. Zhao et al., 2017).
The activity of AMP-activated protein kinase (AMPK) and CREB represent the axis of cell energy metabolism. A compensatory increase in CREB activity following a decrease in the concentrations of BDNF and methyl CpG binding protein 2 (MeCP2) was evidenced in the brain of 132/212 KO mice (Hernandez-Rapp et al., 2015; Klein et al., 2007). While MeCP2 is a nuclear protein that may function as both a transcriptional activator or repressor, it works as a stabilizer of BDNF expression patterns and cell homeostasis (Buist et al., 2021; Pejhan et al., 2020; Vuu et al., 2023). Another compensatory effect is seen for BDNF in dendritogenesis when inhibition of miR-15a, and the consequent relief of BDNF supression, can rescue dendritic maturation deficits in MeCP2-deficient neurons (Y. Gao et al., 2015). Further, upregulation in BDNF gene expression accompanies an increase in the expression of miR-132/212 cluster; both of which target and suppress MeCP2 mRNA translation. Suppression of miR-132 and miR-212 on MeCP2 relieves its repression on BDNF expression. By this manner, the expression of BDNF and miR-132 and miR-212 represent a self-regulatory homeostatic mechanism that involves the nuclear protein MeCP2 at the core of cell metabolism (Chen-Plotkin et al., 2012; Im et al., 2010; Jimenez-Gonzalez et al., 2016; Kawashima et al., 2010; Klein et al., 2007; Y. Liang et al., 2016; Marler et al., 2014; Mendoza-Viveros et al., 2017; M. Su et al., 2015; Wibrand et al., 2012; L. T. Yi et al., 2014).
The enzymatic activity of the histone deacetylase Sirtuin 1 (SIRT1) in the nicotinamide adenine dinucleotide (NAD)-dependent deacetylation of histones is crucial to protect cells from oxidative stressors. SIRT1 activates the expression of mitochondrial DNA genes related to mitochondrial biogenesis, ATP generation and cell proliferation. It was detected in experiments that SIRT1 is able to inhibit miR-134 expression by directly binding to its inhibitory elements. Whereas, SIRT1 deficiency and high levels of miR-134 result in a downregulation of CREB and BDNF expression, and a negative effect on neuronal survival/plasticity. Another indirect mechanism by which miR-134 negatively affects BDNF function in the core of cell metabolism (Jun Gao et al., 2010; Huang et al., 2015, 2017; Shao et al., 2015; Shen et al., 2019). Finally, the study by (Oikawa et al., 2015) showed that the guanine nucleotide binding protein alpha inhibitor 1 (GNAI1), an adenylate cyclase inhibitor which regulates the ATP conversion to cAMP, is a target of miR-124. In physiological conditions, suppression of GNAI1 by miR-124 increases in cAMP activity and leads to an upregulation of BDNF expression via cAMP/PKA/CREB pathway. Indeed, alterations in cell metabolism and microRNA environment reflect on the regulation of BDNF.
MiR-124 suppression on BDNF activity negatively influence on neuronal plasticity in various brain regions such as hippocampus and striatum (Bahi, 2016, 2017; Bahi et al., 2014; Bahi & Dreyer, 2013; Chandrasekar & Dreyer, 2009). More recently, (Wei Yang et al., 2020) identified that the miR-124 targets CREB mRNA, consequently downregulating BDNF expression, and alters BDNF function via targeting to various gene transcripts downstream TrkB signaling, e.g. PI3K, Akt3 and Ras (Kang et al., 2022). Likewise, miR-124 negatively influence on BDNF signal transduction via suppression of glucocorticoid receptors (S. S. Wang et al., 2017; L. T. Yi et al., 2018), known to enhance TrkB signaling pathways (de Assis & Gasanov, 2019). From another perspective, by testing different exercise intensities, (Mojtahedi et al., 2013) showed that miR-124 levels increase with the intensity, and this increase is amplified in strenuous intensity. BDNF and TrkB also increased but not in strenuous the intensity exercise. The findings indicate a threshold beyond which changes metabolic demands evoke an acute rise in miR-124 levels and suppression.
Amongst the indirect effects seen for microRNA on BDNF, (B. Jiang et al., 2016) study registered that miR-9 upregulates BDNF expression in retinal ganglion cells by suppressing the restrictive silencer factor/RE1-silencing transcription factor (REST), a transcription repressor whose suppression is required for neuronal cell differentiation. Similarly, miR-29c has a positive effect on BDNF expression levels by targeting DNA methyltransferase 3 (G. Yang et al., 2015). The miR-705 was also found in a positive correlation with BDNF levels in ischemic injured brain (Ji et al., 2017). Further, BDNF administration increases miR-214 expression during embryonic stem cells differentiation into endothelial cells (Descamps et al., 2018); and to promote vascular endothelial growth factor-C- dependent lymph angiogenesis by suppressing miR-624-3p in human chondrosarcoma cells (C. Y. Lin et al., 2017).

Final Considerations

Regulation of BDNF by microRNAs involves a dynamic regulation of basic proteins that integrate core mechanisms in neuronal cell homeostasis. This complexity represents a relevant limitation in research towards development of therapeutic strategies. Nevertheless, based on the collection of data, using multiple microRNAs that cooperatively influence on BDNF function might be a prospective strategy for future studies addressing vector-delivery based treatments.

Funding

No funding declared.

Conflicts of Interest

No conflict of interest declared.

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  208. Zhao, Y., Wang, S., Chu, Z., Dang, Y., Zhu, J., & Su, X. (2017). MicroRNA-101 in the ventrolateral orbital cortex (VLO) modulates depressive-like behaviors in rats and targets dual-specificity phosphatase 1 (DUSP1). Brain Research, 1669, 55–62. [CrossRef]
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Figure 1. Search flow chart.
Figure 1. Search flow chart.
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Figure 2. BDNF regulation by microRNAs. Black lines; positive (arrows) and negative (cut lines) regulation predictions confirmed in tissue samples, Red lines: positive (arrows) and negative (cut lines) associations between microRNA and BDNF levels.
Figure 2. BDNF regulation by microRNAs. Black lines; positive (arrows) and negative (cut lines) regulation predictions confirmed in tissue samples, Red lines: positive (arrows) and negative (cut lines) associations between microRNA and BDNF levels.
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Table 1. MicroRNAs that participate in BDNF regulation.
Table 1. MicroRNAs that participate in BDNF regulation.
1 Schratt, G. M., et al. (2006). https://doi.org/10.1038/nature04367 miR-134
2 Klein, M. E., et al. (2007).
https://doi.org/10.1038/nn2010		 miR132
3 Mellios, N., et al. (2008).
https://doi.org/10.1093/hmg/ddn201		 miR-30a-5p miR-195
4 Mellios, N., et al. (2009). https://doi.org/10.1016/j.biopsych.2008.11.019 miR-195
5 Chandrasekar, V., & Dreyer, J. L. (2009). https://doi.org/10.1016/j.mcn.2009.08.009 miR-124
6 Im, H.-I., et al. (2010).
https://doi.org/10.1038/nn.2615		 MeCP2
7 Gao J. amd Wang, et al. (2010). https://doi.org/10.1038/NATURE09271 miR-134
8 Kawashima, H., et al. (2010). https://doi.org/10.1016/j.neuroscience.2009.11.057 miR-132
9 Han, L., et al. (2011).
https://doi.org/10.1186/1756-6606-4-40		 miR-134
10 Radzikinas, K., et al. (2011). https://doi.org/10.1523/JNEUROSCI.2745-11.2011 miR-206
11 Angelucci, F., et al. (2011).
https://doi.org/10.1159/000322528		 miR30a-5p
12 Caputo, V., et al. (2011). https://doi.org/10.1371/journal.pone.0028656 miR-26a1/2 miR-26b
13 Bai, M., et al. (2012). https://doi.org/10.1371/journal.pone.0046921 miR-16
14 Lee, S. T., et al. (2012).
https://doi.org/10.1002/ana.23588		 miR-260
15 Imam, J. S., et al. (2012). https://doi.org/10.1371/journal.pone.0052397 miR-204
16 Chen-Plotkin, A. S., et al. (2012). https://doi.org/10.1523/JNEUROSCI.0521-12.2012 miR-132
17 Wibrand, K., et al. (2012). https://doi.org/10.1371/journal.pone.0041688 miR-132
18 Miura, P., et al. (2012).
https://doi.org/10.1111/j.1471-4159.2011.07583.x		 miR-206
19 Bahi, A., & Dreyer, J.-L. (2013). https://doi.org/10.1111/ejn.12228 miR124a
20 Hutchison, E. R., et al. (2013). https://doi.org/10.1002/glia.22483 McCP2
21 SUN, Y.-X., et al. (2013).
https://doi.org/10.3892/or.2013.2731		 miR-16
22 Li, Y.-J., et al. (2013). https://doi.org/10.1371/journal.pone.0063648 miR-182
23 Kynast, K. L., et al. (2013). https://doi.org/10.1016/j.pain.2012.11.010 miRNA-124a
24 Croce, N., et al. (2013).
https://doi.org/10.1007/s11010-013-1567-0		 NPY
25 Ryan, K. M., et al. (2013). https://doi.org/10.1016/j.neulet.2013.05.035 miR-212
26 Nagpal, N., et al. (2013).
https://doi.org/10.1093/carcin/bgt107		 miR-191
27 Mojtahedi, S., et al. (2013).
https://doi.org/10.1002/cbin.10022		 miR-124
28 Tapocik, J. D., et al. (2014). https://doi.org/10.1523/JNEUROSCI.0445-14.2014 miR-206
29 Lin, C. R., et al. (2014).
https://doi.org/10.1111/ejn.12522		 miR-183
30 Tian, N., et al. (2014).
https://doi.org/10.1007/s12264-013-1419-7		 miR-206
31 Bahi, A., et al. (2014). https://doi.org/10.1016/j.psyneuen.2014.04.009 miR124a
32 Marler, K. J., et al. (2014). https://doi.org/10.1523/JNEUROSCI.1910-13.2014 miRNA-132
33 Yang, X., et al. (2014).
https://doi.org/10.1007/s12017-014-8312-z		 miR-206
34 Yi, L. T., et al. (2014).
https://doi.org/10.1503/jpn.130169		 miR-132
35 Giannotti, G., et al. (2014). https://doi.org/10.1017/S1461145713001454 miR124 miR132
36 Varendi, K., et al. (2014).
https://doi.org/10.1007/s00018-014-1628-x		 miR-1/206 miR-10
37 Zhang, J., et al. (2014). https://doi.org/10.3760/cma.j.issn.0366-6999.20131683 miR- 34a
38 Cho, K. J., et al. (2015). https://doi.org/10.1016/j.mcn.2015.07.004 miR-Let-7a
39 Li, H., et al. (2015).
https://doi.org/10.3892/mmr.2015.3736 		miR-183/96/182
40 Ma, J. C., et al. (2015). https://doi.org/10.1016/j.neuroscience.2015.04.061 miR-1
41 Yang, G., et al. (2015).
https://doi.org/10.3892/mmr.2015.3531		 miR-29c
42 Neumann, E., et al. (2015).
https://doi.org/10.1186/s12990-015-0045-y		 miR-1
43 Li, W., et al. (2015).
https://doi.org/10.1155/2015/302653		 miR-22
44 Xiang, L., et al. (2015). https://doi.org/10.1016/j.brainres.2015.06.046 miR-132 miR-132
45 Yan, H., et al. (2015). https://doi.org/10.1097/IGC.0000000000000456 miR-204
46 Mu, Y., et al. (2015).
https://doi.org/10.3892/mmr.2015.4456		 miR-206-3p
47 Li, M., et al. (2015).
https://doi.org/10.1002/path.4484		 miRNA-134 miR-132
48 Liu, Z., et al. (2015).
https://doi.org/10.1159/000430356		 miR-937
49 Su, M., et al. (2015).
https://doi.org/10.3892/mmr.2015.4104		 MeCP2
50 Hernandez-Rapp, J., et al. (2015). https://doi.org/10.1016/j.bbr.2015.03.032 miR-132/212
51 Huang, W., et al. (2015).
https://doi.org/10.1007/s12031-015-0500-2		 miR-134
52 Oikawa, H., et al. (2015). http://doi.org/10.1016/j.neuint.2015.10.010 miR-124
53 Jiang, Y., & Zhu, J. (2015). http://www.ncbi.nlm.nih.gov/pubmed/25755749 miR-10B
54 Shao, Y., et al. (2015).
https://doi.org/10.1007/s12031-015-0522-9		 miR-134
55 Gao, Y., et al. (2015).
https://doi.org/10.1002/stem.1950		 miR-15a+
56 Darcq, E., et al. (2015).
https://doi.org/10.1038/mp.2014.120		 miR-30a-5p
57 Long, J., et al. (2016).
https://doi.org/10.1007/s13277-015-4427-6		 MiR-15a-5p
58 Hu, X. M., et al. (2016). https://doi.org/10.1177/1744806916666283 miR-219
59 Li, Y., et al. (2016). https://doi.org/10.1016/j.pnpbp.2015.09.004 miR-182
60 Peng, J. Y., et al. (2016). https://doi.org/10.1016/j.domaniend.2015.09.005 miR-10b
61 Yi, S., et al. (2016).
https://doi.org/10.1038/srep29121		 miR-1
62 Bahi, A. (2016).
https://doi.org/10.1016/j.bbr.2016.05.033		 miR124a
63 Xia, H., Li, Y., & Lv, X. (2016). https://doi.org/10.3892/ijo.2016.3628 miR-107
64 Neumann, E., et al. (2016). https://doi.org/10.1016/j.mcn.2016.06.003 miR-1
65 Kumari, A., et al. (2016). https://doi.org/10.1016/j.physbeh.2016.02.032 miRNA-132 and miRNA-134
66 Liang, Y., et al. (2016). https://doi.org/10.1177/1744806916641679 miRNA-212/132
67 Jiang, B., et al. (2016). https://doi.org/10.3892/mmr.2016.5810 miR-9
68 Hang, P., et al. (2016)
https://doi.org/10.7150/ijbs.15071		 miRNA-195
69 Li, W., et al. (2016).
https://doi.org/10.5582/bst.2016.01127		 miR-613
70 Jimenez-Gonzalez, A., et al. (2016). https://doi.org/10.1016/j.bbagen.2016.03.001 miR-212 and miR-132
71 Aili, A., Chen, Y., & Zhang, H. (2016). https://doi.org/10.3892/mmr.2015.4506 miR-10b
72 Zeng, L. L., et al. (2016).
https://doi.org/10.1111/cns.12589		 MiR-210
73 Xiang, L., et al. (2016). https://doi.org/10.1016/j.brainres.2016.02.045 MiR-204
74 Cui, M., et al. (2017). https://doi.org/10.1016/j.bbadis.2017.06.021 miR-34a-5p
75 Thomas, K. T., et al. (2017). https://doi.org/10.1016/j.celrep.2017.06.038 miR-137
76 Mendoza-Viveros, L., et al. (2017). https://doi.org/10.1016/j.celrep.2017.03.057 miR-132/212
77 Xie, B., et al. (2017).
https://doi.org/10.3233/JAD-160468		 miR-206
78 Wang, P., et al. (2017). https://doi.org/10.18632/oncotarget.13747 miR-497
79 Gao, B., et al. (2017).
https://doi.org/10.1002/jgm.2932		 miR-107
80 Tu, Z., et al. (2017). https://doi.org/10.1016/j.biopha.2017.05.016 miR-140
81 Song, D., ett al. (2017).
https://doi.org/10.3892/mmr.2017.7396		 miR-382
82 Lin, C. Y., et al. (2017).
https://doi.org/10.1038/cddis.2017.354		 miR-624-3p
83 Zhao, Y., et al. (2017). https://doi.org/10.1016/j.brainres.2017.05.020 miR-101
84 Zhang, K., et al. (2017).
https://doi.org/10.1042/BSR20170755		 miR-211
85 Bahi, A. (2017).
https://doi.org/10.1016/j.bbr.2017.03.010		 miR124a
86 Xu, A. J., et al. (2017). https://doi.org/10.3892/mmr.2017.7167 miR-744
87 Sun, W., et al. (2017). https://doi.org/10.1016/j.neulet.2016.12.047 miR-206
88 Wang, S. S., et al. (2017). https://doi.org/10.1016/j.pnpbp.2017.07.024 miR-124
89 (Wang et al., 2017)
https://doi.org/10.3892/mmr.2017.8282
miR-103
90 Huang, W., et al. (2017).
https://doi.org/10.1007/s12031-017-0907-z		 MiR-134
91 Ji, M., et al. (2017).
https://doi.org/10.3892/mmr.2017.7626		 miR-705
92 Fu et al., (2017)
http://dx.doi.org/10.1038/s41598-017-01261-x		 miR-125b-5p
93 Lian, N., et al. (2018). https://doi.org/10.1080/15384101.2018.1556060 miR-221
94 Duan, W., et al. (2018). https://doi.org/10.3892/ijmm.2018.3711 miR-155
95 Yi, L. T., et al. (2018). https://doi.org/10.1177/0269881118758304 miR-124
96 Nguyen, T., et al. (2018). https://doi.org/10.1073/pnas.1803384115 let-7i
97 Cheng, F., et al. (2018). https://doi.org/10.1016/j.micpath.2018.04.060 miR-107
98 Fang, Y., et al. (2018). https://doi.org/10.1016/j.jad.2017.11.090 miR-124
99 Zhang, S., et al. (2018). https://doi.org/10.1016/j.neulet.2017.10.014 miR-210-3p
100 Xing, Q., et al. (2018).
https://doi.org/10.1055/a-0658-2095		 miR-206
101 Li, X. J. (2018).
https://doi.org/10.1177/1479164117749382		 miR-497
102 Lu, Y., et al. (2018).
https://doi.org/10.1007/s10571-018-0599-0		 miR-155
103 Miao, Z., et al. (2018).
https://doi.org/10.1007/s12035-016-0378-1		 miR-206-3p
104 Descamps, B., et al. (2018). https://doi.org/10.1161/ATVBAHA.118.311400 miR-214
105 Lv, M., Yet al. (2018).
https://doi.org/10.1002/mnfr.201800621		 miR-26a miR-125b miR-132
106 Ge, Q. Di, et al. (2018). https://doi.org/10.1016/j.etap.2018.08.011 miR-132 miR-204
107 Shen, J., et al. (2018). https://doi.org/10.1016/j.bbr.2018.04.050 miR-134
108 Wu, B. W., et al. (2018).
https://doi.org/10.1002/jcp.26328		 miR-10a
109 Ma, J. C., et al. (2018).
https://doi.org/10.1159/000494657		 miR-1
110 Zhang, X. yu, et al. (2018). https://doi.org/10.1016/j.lfs.2018.09.002 miR-10a
111 Gao, L., et al. (2018). https://doi.org/10.4149/neo_2018_161128N594 MiR-1-3p
112 Zhang, J., et al. (2018).
https://doi.org/10.1007/s11064-018-2475-1		 miR-322
113 Jiang, J. D., et al. (2019).
https://doi.org/10.1002/iub.1911		 miR-107
114 Yang, W., et al. (2019).
https://doi.org/10.1002/jcp.28862		 miR-124
115 Shrestha, S., et al. (2019).
https://doi.org/10.1002/2211-5463.12581		 miRNA-206
116 Miao, Z., et al. (2019). https://doi.org/10.1016/j.neuropharm.2019.03.032 miR-30a
117 Song, Y., et al. (2019). https://doi.org/10.3892/mmr.2019.10424 miR-584
118 Sun, Z., et al. (2019).
https://doi.org/10.1002/jcp.26928		 miR-497
119 Mohammadipoor-Ghasemabad, L., et al. (2019). https://doi.org/10.1016/j.neuroscience.2019.06.037 miR-191a
120 Hung, Y. Y., et al. (2019). https://doi.org/10.3390/cells8091021 miR-204-5p
121 Li, B. B., et al. (2019). https://doi.org/10.1016/j.chemosphere.2019.05.064 miR-7
122 Ma, R.,et al. (2019).
https://doi.org/10.1016/j.bbrc.2019.08.046		 miR-496
123 Shen, J., et al. (2019).
https://doi.org/10.1016/j.jad.2019.01.031		 miR-134
124 Solomon, M. G., et al. (2019). https://doi.org/10.1016/j.neuroscience.2019.02.012 miR-206
125 Zhao, X., et al. (2019). https://doi.org/10.1016/j.ejphar.2018.11.035 miR-375
126 Panta, A., et al. (2019). https://doi.org/10.1016/j.bbi.2019.01.003 miR-363-3p
127 Zhao, H., et al. (2019). https://doi.org/10.1016/j.neuroscience.2019.07.051 miR-206
128 Xie, W., et al. (2020).
https://doi.org/10.1007/s11064-020-03013-2		 miR-185
129 Deng, C., et al. (2020).
https://doi.org/10.1007/s11064-019-02910-5		 miR-494-3p
130 Hu, J. J., et al. (2020).
https://doi.org/10.1002/kjm2.12136		 miR-15a
131 Liu, X., et al. (2020). https://doi.org/10.1080/15384101.2019.1710916 MiR-192-5p
132 Zhang, T., et al. (2020). https://doi.org/10.1016/j.neulet.2019.134562 miR-10a-5p
133 Yang, W., et al. (2020). https://doi.org/10.2174/1567202617666200319141755 miR-124
134 Wu, G., et al. (2020). https://doi.org/10.1016/j.yexcr.2020.111937 miR-129-5p
135 Liu, H., et al. (2020).
https://doi.org/10.14670/HH-18-266		 miR-204-5p
136 Misiorek, J. O., et al. (2020).
https://doi.org/10.1007/s12035-020-01899-1		 miR-10a-5p
137 Wang, F., et al. (2020). https://doi.org/10.3892/mmr.2020.11065 mir-210
138 Wang, L., et al. (2020).
https://doi.org/10.1007/s11010-020-03726-6		 miR-10b-5p
139 Gao, L., et al. (2020).
https://doi.org/10.1093/JNEN/NLAA069		 miR-103-3p
140 Giordano, M., et al. (2020). https://doi.org/10.3390/ijms21207615 miR-195-5p
141 Xu, H., et al. (2020).
https://doi.org/10.12659/MSM.920855		 miR-216a-5p
142 Pejhan et al., (2020)
https://doi.org/10.3389/fcell.2020.00763		 miR-132
143 Lin, B., et al. (2021).
https://doi.org/10.18632/aging.103640		 miR-155
144 Niu, Y., et al. (2021).
https://doi.org/10.1186/s10020-020-00258-z		 miR-186
145 Zhang, X., et al. (2021). https://doi.org/10.1016/j.bbr.2020.113087 miR-432
146 Huan, Z., et al. (2021).
https://doi.org/10.1007/s11064-021-03234-z		 miR-155
147 Zhao, P., et al. (2021).
https://doi.org/10.1007/s11255-021-02853-3		 miR-365
148 Ke, X., et al. (2021).
https://doi.org/10.1159/000515750		 miR-10b-5p
149 Xu, Y., et al. (2021). https://doi.org/10.1080/21655979.2021.1918991 miR-191-5p
150 Pan, S., et al. (2021).
https://doi.org/10.1038/s41398-021-01240-x		 MiR-195
151 Li, H., et al. (2021). https://doi.org/10.1080/15376516.2021.1886211 miR-191
152 Tang, Y., et al. (2021). https://doi.org/10.1371/journal.pone.0257280 miR-155
153 Li, X., et al. (2021).
https://doi.org/10.5114/fn.2021.105132		 miR-1b
154 Peng, D., et al. (2022).
https://doi.org/10.7150/thno.70951		 miR-206-3p
155 Wu, Y., et al. (2021).
https://doi.org/10.4103/0028-3886.333459		 miR-191-5p
156 Guan, W., et al. (2021). https://doi.org/10.1016/j.phrs.2021.105932 miR-206-3p
157 Zhang, Q., et al. (2021). https://doi.org/10.3389/fendo.2021.637384 miR-103a-3p miR-10a-5p
158 Ehinger, Y., et al. (2021).
https://doi.org/10.1111/adb.12890		 miR30a-5p miR-195-5p miR191-5p miR206-3p
159 Fang, F., et al. (2022).
https://doi.org/10.1186/s13019-022-01802-0		 miR-182-5p
160 Su, B., et al. (2022). https://doi.org/10.1080/21655979.2022.2059937 miR-139-5p
161 Yongguang, L., et al. (2022).
https://doi.org/10.1186/s12906-021-03483-z		 miR-497
162 Tu, W., et al. (2022).
https://doi.org/10.1155/2022/1489841		 miR-206-3p
163 Gao, J., et al. (2022). https://doi.org/10.1080/21655979.2022.2067293 miR-155-5p
164 Zhai, Y., et al. (2022).
https://doi.org/10.1002/kjm2.12486		 miR-210
165 Yu, H. C., et al. (2022).
https://doi.org/10.3390/ijms23010570		 miR-3168
166 Kang, E. M., et al. (2022).
https://doi.org/10.1111/cns.13845		 miR- 124-3p
167 Li, C., et al. (2022).
https://doi.org/10.3892/mmr.2021.12577		 mir-182-5p
168 Deng, L., et al. (2022).
https://doi.org/10.1186/s13018-022-03315-x		 miR-210-3p
169 Ding, J., Jiang, C., Yang, L., & Wang, X. (2022). https://doi.org/10.14715/CMB/2022.68.1.10 miR-1-3p
170 Ma, L., et al. (2022).
https://doi.org/10.1038/s41398-022-02192-6		 miR-132-5p
171 Asadi, M. R., et al. (2022).
https://doi.org/10.1186/s12888-022-04442-9		 miR-16
172 Wang, L., et al. (2022). https://doi.org/10.1016/j.brainresbull.2022.03.002 miR-551b-5p
173 Zhang, Z., et al. (2022). https://doi.org/10.1016/j.bbrc.2022.05.038 miR-382 miR-182
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