A Systematic Review of Extracellular Vesicle-Derived Piwi-Interacting RNA in Human Body Fluid and Its Role in Disease Progression

Abstract

The state of host cells is reflected in the cargo carried by their extracellular vesicles (EVs). This makes EV a potential source of biomarkers for human diseases. Piwi-interacting RNA (piRNA) regulates gene expression through epigenetic regulation and post-transcriptional gene silencing. Thus, piRNA profiling in EVs derived from human clinical samples could identify markers that characterize disease stages and unveil their roles in disease pathology. This review aimed to report the expression profiles of EV-derived piRNA (EV-piRNA) in various human samples, as well as their role in each pathology. A systematic review was conducted to collate the findings of human EV-piRNA from original research articles published in indexed scientific journals up to February 16, 2022. Article searches were performed in PubMed, Web of Science, and Scopus databases, using a combination of keywords, including “EV” and “piRNA.” A total of 775 nonredundant original articles were identified. After subjecting articles to inclusion and exclusion criteria, 34 articles were accepted for this review. The piRNA expression levels among the small RNA profiles of human-derived EVs range from 0.09% to 43.84%, with the lowest expression level reported in urine-derived EVs and the highest percentage in plasma-derived EVs. Differentially expressed EV-piRNAs have been identified in patients with specific disease conditions compared to their counterparts (healthy control), suggesting an association between piRNA and progression in various diseases. Seven articles identified piRNA putative target genes and/or the pathway enrichment of piRNA target genes, and one study demonstrated a direct role of piRNA candidates in disease pathology. In conclusion, EV-piRNA has been isolated successfully from various human body fluids. EV-piRNA is a new research niche in human disease pathology. The expression profiles of EV-piRNA in various tissue types and disease conditions remain largely unexplored. Furthermore, there is currently a lack of guidelines on piRNA bioinformatics analysis, which could lead to inconsistent results and thus hinder the progression of piRNA discoveries. Finally, the lack of published scientific evidence on the role of EV-piRNA supports the need for future research to focus on the functional analysis of EV-piRNA as part of the route in piRNA discoveries.

Impact statement

This systematic review provides a detailed analysis of the extracellular vesicle-derived Piwi-interacting RNA (EV-piRNA) in various human samples that include the methods used for isolation and characterization of the EVs, sequencing platforms used, and bioinformatics analysis findings of the EV-piRNA expression profiles, as well as the distinct EV-piRNAs identified in various body fluids/clinical conditions. A summary of the EV-piRNAs derived from different samples is listed in this review, which can be used as a reference for future studies on the role of EV-piRNA in human disease.

Introduction

Extracellular vesicles (EVs) are membrane-bound vesicles (30–10,000 nm) that are released from cells into body fluid, such as blood, urine, and synovial fluid. They can be grouped into three subtypes, including exosomes (30–200 nm), microvesicles (100–1000 nm), and apoptotic bodies (>1000 nm), based on their size ranges and biogenesis pathways. Despite numerous studies on exosomes, distinguishing exosomes from other EV subtypes, such as microvesicles, due to overlapping size ranges and protein markers, remains challenging.

Exosomes arise by clathrin-dependent endocytosis, in which the cytosolic components of the donor cell are enclosed in a membrane-bound multivesicular body of late endosomes, and eventually fuse with the plasma membrane.¹ The vesicles are then released into the extracellular compartment through a process called budding. Identification of exosomes/EVs can utilize their biogenesis pathway. Membrane proteins and endosomal sorting cells required for transport (ESCRT)-related proteins are typically found on their surface or enclosed within exosomes, including tetraspanin family proteins (CD63, CD9, and CD81), Flotillin-1, and TSG101.² According to the International Society for Extracellular Vesicles (ISEV) guideline, EV will be used as the generic term in this review.³

The in vitro and in vivo preclinical analyses of EVs' ability to transport cellular contents and regulate gene expression in their targeted cells have generated great interest in human disease studies. EVs carry various types of cargo, including proteins and nucleic acids, that reflect the nature/phenotype of donor cells, as well as their physiological and pathological state. Many RNA subtypes have been identified in EVs, including messenger RNA (mRNA), microRNA (miRNA), long noncoding RNA (lncRNA), transfer RNA (tRNA), small nuclear RNA (snRNA), as well as Piwi-interacting RNA (piRNA).⁴ This underpins their ability to serve as a biomarker source for human disease. EVs can also mediate intercellular communication as they activate the signaling cascades in target cells upon binding to the transmembrane receptor. Moreover, EVs can fuse into recipient cells and release their cargo contents into the cytoplasmic compartment.⁵

piRNA is a small noncoding RNA that can mediate gene silencing. It is 26–31 nucleotides in length, with 2′-O-methylation at the 3′ end. piRNA acts through interaction with Piwi protein subfamily of the ARGONAUTE protein family.⁶ The vast majority of piRNAs are produced by specific genomic loci called piRNA clusters. piRNA clusters can be divided into two main groups: the uni-strand clusters, which are a widely distributed type, or dual-strand clusters, which are mainly detected in Drosophila germ cells.

Although mRNA has been the RNA subtype most frequently interrogated in studies of cellular behavior and pathophysiological conditions in vitro⁷ or in vivo,⁸ piRNA has also been investigated in cancer and other human diseases. Indeed, differentially expressed (DE) piRNAs have been detected between patients and controls,⁹ suggesting that piRNA might play a role in disease progression. Early data indicated the functions of piRNAs to include transposon silencing and gene integrity maintenance in germline cells. More recently, piRNAs have been detected in both germline and somatic cells in many animal phyla, from insects and fish to mammals.¹⁰ It is becoming apparent that piRNAs not only serve as transposon silencers but also they play many crucial roles in regulating gene expression. piRNA can mediate gene silencing in both the transcriptional and post-transcriptional levels, by interacting with DNA methyltransferases or histone-modifying enzymes or directly binding to complementary sequences in mRNAs, respectively.¹¹

To investigate if piRNA is expressed and stable in human body fluid-derived EVs, we have conducted a systematic review using original articles that characterized and analyzed the EV-derived piRNA (EV-piRNA) extracted from clinical body fluid samples. This systematic review aimed to interrogate the expression profiles of EV-piRNA in various human body fluids, as well as their potential role in each clinical condition.

Methods

This systematic review queried three literature databases, namely PubMed, Web of Science (WoS), and Scopus, for original articles published up to February 16, 2022 (protocol summarized in Fig. 1). The search terms used for exhaustive searches against the three databases were: “exosome OR exosomes OR extracellular vesicle OR extracellular vesicles OR exosomal OR EVs OR EV OR small EVs OR small EV OR small extracellular vesicle OR small extracellular vesicles” AND “piRNA OR piRNAs OR piwi-interacting RNA OR piwi-interacting RNAs OR piwiRNA OR piwiRNAs OR piwi RNA OR piwi RNAs OR PIWI-interacting RNA OR PIWI-interacting RNAs OR piwi interacting RNA OR piwi interacting RNAs.” The articles identified by the searches were compiled and subjected to an initial screening based on their title and/or abstract. A total of 775 nonredundant original articles were identified.

FIG. 1.

Flow chart for the literature search. PRISMA workflow for the systemic selection process for articles included in this systematic review. PRISMA, Preferred Reporting Items for Reviews and Meta-Analyses.

Inclusion criteria were original research articles related to EV-piRNA derived from human clinical samples and published in English, whereas exclusion criteria were nonoriginal articles (i.e., review articles) and other document types, such as book chapters and meeting reports. In addition, articles that were not published in the English language, non-EV-piRNA relevant, reported on nonhuman species, reported on piRNAs isolated from in vitro cell culture supernatant-derived EVs, and reported solely on in silico analysis of EV-piRNA, were excluded.

Based on these inclusion and exclusion criteria, a total of 34 articles were eligible for this systematic review. A full list of the selected articles is shown in Table 1. The trend for research in this area is increasing trend (Fig. 2); no EV-piRNA publications related to human clinical samples were reported in 2015 and 2017.

FIG. 2.

Trend of publication on human biofluid-derived EV-piRNA from the year 2013 to 2022. Review queried conducted up to February 16, 2022, for the year 2022. EV, extracellular vesicle; EV-piRNA, EV-derived piRNA; piRNA, Piwi-interacting RNA.

Table 1.

Full List of Original Articles Reviewed (n = 34) and the Pipeline/Software and Piwi-Interacting RNA Database Used for Piwi-Interacting RNA Identification

Year	Article title	Author(s)	Journal details	piRNA identification/prediction		References
Year	Article title	Author(s)	Journal details	Pipeline or software used	piRNA database used	References
2013	Characterization of human plasma-derived exosomal RNAs by deep sequencing	Huang et al	BMC Genomics, Vol. 14, Issue 1, Page 319	Bowtie	GRCh (NCBI ftp site—Release 103)	²⁵
2013	Small RNA transcriptomes of two types of exosomes in human whole saliva determined by next generation sequencing	Ogawa et al	Biological & Pharmaceutical Bulletin, Vol. 36, Issue 1, Pages 66–75	Seqmap	GRCh (hg18), piRNABank	²⁶
2014	Exosomes in human semen carry a distinctive repertoire of small noncoding RNAs with potential regulatory functions	Vojtech et al	Nucleic Acids Research, Vol. 42 Issue 11, Pages 7290–7304	Bowtie2	piRNABank	¹⁵
2016	Plasma extracellular RNA profiles in healthy and cancer patients	Yuan et al	Scientific Reports, Vol. 6, Page 19413	eRNA (v1.2)—Bowtie	piwiRNABank	²⁷
2018	Small noncoding RNA profiling in human biofluids and surrogate tissues from healthy individuals: description of the diverse and most represented species	Ferrero et al	Oncotarget, Vol. 9, Issue 3, Pages 3097–3111	Bowtie2	GRCh38 (hg38)	²⁸
2018	Characterization and selective incorporation of small noncoding RNAs in nonsmall cell lung cancer extracellular vesicles	Li et al	Cell and Bioscience, Vol. 8, Issue 1, Page 2	Burrows-Wheeler Aligner software	piRNABank	²⁹
2018	Exosomal piRNA sequencing reveals differences between heart failure and healthy patients	Yang et al	European Review for Medical and Pharmacological Sciences, Vol. 22, Issue 22, Pages 7952–7961	Bowtie2; piano	Human genome	³⁰
2019	piRNA-823 delivered by multiple myeloma-derived extracellular vesicles promoted tumorigenesis through re-educating endothelial cells in the tumor environment	Li et al	Oncogene, Vol. 38, Issue 26, Pages 5227–5238	—	—	¹⁷
2019	Noninvasive approach for evaluation of pulmonary hypertension using extracellular vesicle-associated small noncoding RNA	Lipps et al	Biomolecules, Vol. 9, Issue 11, Page 666	Not stated	miRanda, human transcriptome (hg38)	³¹
2019	A combined miRNA-piRNA signature to detect Alzheimer's disease	Jain et al	Translational Psychiatry, Vol. 9, Issue 1, Page 250	Customized in-house software pipeline	Not stated	³²
2019	Small RNA-sequence analysis of plasma-derived extracellular vesicle miRNAs in smokers and patients with chronic obstructive pulmonary disease as circulating biomarkers	Sundar et al	Journal of Extracellular Vesicles, Vol. 8, Issue 1, Page 1684816	exceRpt small RNA-seq pipeline (v4.6.2)	piRNABank	³³
2019	Noncoding RNAs serve as diagnosis and prognosis biomarkers for hepatocellular carcinoma	Tan et al	Clinical Chemistry, Vol. 65, Issue 7, Pages 905–915	Bowtie2	piwiRNABank	³⁴
2020	Small RNA sequencing of extracellular vesicles identifies circulating miRNAs related to inflammation and oxidative stress in HIV patients	Chettimada et al	BMC Immunology, Vol. 21, Issue 1, Page 57	exceRpt small RNA-seq pipeline (v4.6.2)	piRNABank	³⁵
2020	Circulating exosomal small RNAs are promising noninvasive diagnostic biomarkers for gastric cancer	Ge et al	Journal of Cellular and Molecular Medicine, Vol. 24, Issue 24, Pages 14502–14513	Bowtie2	piRNABank	³⁶
2020	Exosomal piRNA profiling revealed unique circulating piRNA signatures of cholangiocarcinoma and gallbladder carcinoma	Gu et al.	Acta Biochimica et Biophysica Sinica (Shanghai), Vol. 52, Issue 5, Pages 475–484	Bowtie; piano	piRBase	³⁷
2020	Deep sequencing of small RNAs from neurosurgical extracellular vesicles substantiates miR-486-3p as a circulating biomarker that distinguishes glioblastoma from lower-grade astrocytoma patients	Hallal et al	International Journal of Molecular Sciences, Vol. 21, Issue 14, Page 4954	Bowtie	Human miRBase mature database (GRCh38)	³⁸
2020	The expression of small RNAs in exosomes of follicular fluid altered in human polycystic ovarian syndrome	Hu et al	PeerJ, Vol. 2020, Issue 2, Page e8640	Not stated	GRCh37 (hg19)	³⁹
2020	RNA cargos in extracellular vesicles derived from blood serum in pancreas-associated conditions	Kumar et al	Scientific Reports, Vol. 10, Issue 1, Page 2800	Bowtie2	GRCh37 (hg19)	⁴⁰
2020	Comparison of methods and characterization of small RNAs from plasma extracellular vesicles of HIV/HCV coinfected patients	Martínez-González et al	Scientific Reports, Vol. 10, Issue 1, Page 11140	Oasis 2 software	piRNABank	⁴¹
2020	Small noncoding RNA profiling in plasma extracellular vesicles of bladder cancer patients by next-generation sequencing: expression levels of miR-126-3p and piR-5936 increase with higher histologic grades	Sabo et al.	Cancers, Vol. 12, Issue 6, Page 1507	Not stated	piRBase	⁴²
2020	Exosomal microRNAs are novel circulating biomarkers in cigarette, waterpipe smokers, E-cigarette users, and dual smokers	Singh et al	BMC Medical Genomics, Vol. 13, Issue 1, Page 128	exceRpt small RNA-seq pipeline	piRNABank	⁴³
2021	Outcome prediction of microdissection testicular sperm extraction based on extracellular vesicle piRNAs	Chen et al	Journal of Assisted Reproduction and Genetics, Vol. 38, Issue 6, Pages 1429–1439	Not stated	piRNABank	⁴⁴
2021	Screening plasma exosomal RNAs as diagnostic markers for cervical cancer: An analysis of patients who underwent primary chemoradiotherapy	Cho et al	Biomolecules, Vol. 11, Issue 11, Page 1691	Not stated	GRCh, RNACentral	¹⁴
2021	Small nucleolar RNAs in plasma extracellular vesicles and their discriminatory power as diagnostic biomarkers of Alzheimer's disease	Fitz et al	Neurobiology of Disease, Vol. 159, Page 105481	exceRpt small RNA-seq pipeline	piRNABank	⁴⁵
2021	Decreased piRNAs in infertile semen are related to downregulation of sperm MitoPLD expression	Hong et al	Frontiers in Endocrinology (Lausanne), Vol. 12, Page 696121	Not stated	piRNABank	⁴⁶
2021	Distinct extracellular RNA profiles in different plasma components	Jia et al	Frontiers in Genetics, Vol. 12, Page 564780	DNASTAR Lasergene 15.3	piRNA Quest	⁴⁷
2021	PIWI-interacting RNA sequencing profiles in maternal plasma-derived exosomes reveal novel noninvasive prenatal biomarkers for the early diagnosis of nonsyndromic cleft lip and palate	Jia et al	EBioMedicine, Vol. 65, Page 103253	Not stated	piRNABank	⁴⁸
2021	Molecular signature of extracellular vesicular small noncoding RNAs derived from cerebrospinal fluid of leptomeningeal metastasis patients: functional implication of miR-21 and other small RNAs in cancer malignancy	Lee et al	Cancers, Vol. 13, Issue 2, Page 209	Not stated	Rfam	⁴⁹
2021	PIWI-interacting RNAs are aberrantly expressed and may serve as novel biomarkers for diagnosis of lung adenocarcinoma	Li et al	Thoracic Cancer, Vol. 12, Issue 18, Pages 2468–2477	CLC genomics_workbench 5.5	piRbank	⁵⁰
2021	Cargo small noncoding RNAs of extracellular vesicles isolated from uterine fluid associated with endometrial receptivity and implantation success	Li et al	Fertility and Sterility, Vol. 115, Issue 5, Pages 1327–1336	Not stated	piRBase	⁵¹
2021	Longitudinal saliva omics responses to immune perturbation: a case study	Mias et al	Scientific Reports, Vol. 11, Issue 1, Page 710	exceRpt pipeline	piRNABank	⁵²
2021	Identification of piRNA targets in urinary extracellular vesicles for the diagnosis of prostate cancer	Peng et al	Diagnostics (Basel), Vol. 11, Issue 10, Page 1828	Bowtie; piano	piRNABank	⁵³
2022	A universal catalytic hairpin assembly system for direct plasma biopsy of exosomal PIWI-interacting RNAs and microRNAs	Zhang et al	Analytica Chimica Acta, Vol. 1192, Page 339382	—	—	⁵⁴
2022	Exosomal and plasma noncoding RNA signature associated with urinary albumin excretion in hypertension	Riffo-Campos et al	International Journal of Molecular Sciences, Vol. 23, Issue 2, Page 823	STAR v2.7.3a	piRNA database Homo sapiens hg38 annotation file v1.7.6	⁵⁵

GRCh Genome Reference Consortium Human genome build; piRNABank (http://pirnabank.ibab.ac.in/request.html); piano (http://ento.njau.edu.cn/Piano.html), piwiRNABank (http://piwiRNAbank.ibab.ac.in), piRBase (www.regulatoryrna.org/database/piRNA), RNACentral (https://www.rnacentral.org/or https://www.rnacentral.org/expert-database/pirbase), piRNA Quest (http://bicresources.jcbose.ac.in/zhumur/pirnaquest), Rfam (https://rfam.xfam.org), and piRbank (https://www.ncbi.nlm.nih.gov/assembly/GCF_000511935.2).

piRNA, Piwi-interacting RNA.

In addition, piRNA sequence and the putative target genes of the reported EV-piRNAs, either highly expressed in the biofluids or significantly DE in a particular clinical condition, were retrieved from piRNABank (http://pirnabank.ibab.ac.in), piRBase (http://bigdata.ibp.ac.cn/piRBase), and RNAcentral (https://rnacentral.org).

Results

Clinical conditions and sources of EV-piRNA

Subjects diagnosed with defined clinical conditions were recruited together with their matched controls. piRNA identification and analysis were performed on the DE small RNA profiles that could potentially characterize disease pathological conditions (Supplementary Data S1). The frequency of each clinical condition category investigated is summarized in Figure 3a. From this review, cancer is the most studied human disease for EV-piRNA identification, which accounts for 41% (14/34) of the articles reviewed. Pulmonary/cardiovascular disease (n = 5), reproductive disorder (n = 4), and neurological disorder (n = 3) have been widely studied for their EV-piRNA. The aim of these studies was to identify potential piRNA candidates to serve as diagnostic biomarkers or therapeutic targets. No studies have yet reported on aging-related degenerative noncommunicable diseases such as osteoarthritis (OA) and other musculoskeletal disorders.

FIG. 3.

Clinical conditions and source of EVs where EV-piRNA was identified or analyzed. (A) Type of clinical conditions for all the articles included in this systematic review (n = 34); (B) Biofluid types where EV is derived.^a CSF, cerebrospinal fluid; CUSA, Cavitron Ultrasonic Surgical Aspirator.^aOne article has derived EV-piRNA from more than one biofluid type.

EV-piRNAs have been identified from various human body fluids, including plasma, serum, seminal plasma, cerebrospinal fluid, saliva, urine, follicular fluid, glioma Cavitron Ultrasonic Surgical Aspirator (CUSA), and uterine fluid. Upon EV extraction, total RNA was further extracted for genomic analysis followed by bioinformatics analysis (Fig. 3b and Supplementary Data S1). Plasma is the most common source of EV-piRNA for piRNA discovery and identification, accounting for 49% of all sample types (17/35), followed by serum, reported in a total of six articles.

EV isolation method

EVs can be isolated using different methods as summarized in Figure 4 and Table 2 (Supplementary Data S2). The most commonly used approach in the articles reviewed for this study and performed by 40% were centrifugation-based methods (16/40). These include ultracentrifugation, differential ultracentrifugation, and density gradient ultracentrifugation. A further 35% of studies (14/40) performed polymer-based precipitation.

FIG. 4.

EV isolation methods categorized based on the principle of each method^a: ultracentrifugation, polymer-precipitation-based, column chromatography, ultrafiltration, EV phase, membrane affinity-based spin column, and combination of two methods. ^aA total of three articles used more than one method to isolate EV, and one article did not report on the isolation method used.

Table 2.

Extracellular Vesicle Isolation Methods Reported in the Reviewed Articles (n = 34)

EV isolation methods^a	Number of article (percentage)
Centrifugation	16 (40)
Ultracentrifugation	8
Sequential ultracentrifugation	3
Density gradient ultracentrifugation	2
Differential ultracentrifugation	2
Differential centrifugation	1
Polymer-based precipitation	14 (35)
ExoQuick™/Exosome Precipitation Solution	7
miRCURY Exosome Cell/Urine/CSF Kit	2
Total Exosome Isolation Reagent/Kit	2
ExoQuick TC	1
PEG based protocols (PEG6 and PEG10)	1
Ribo™ Exosome Isolation Reagent	1
Column chromatography	3 (8)
Plasma/Exosome Serum and Free-Circulating RNA Isolation Mini Kit	1
Plasma/Serum Exosome Purification Mini Kit	1
Size exclusion chromatography (Exo-Spin)	1
Precipitation + column purification	2 (5)
ExoQuick Ultra	1
PureExo Exosome Isolation Kit	1
(Ultra)filtration	2 (5)
Amicon^® Ultra-15 Centrifugal Filter Unit with an Ultracel-3 membrane of 3 kDa	1
Gel filtration	1
EV phase	1 (3)
Exo2D™	1
Membrane affinity-based spin column	1 (3)
exoRNeasy Serum/Plasma Kit	1
Ultracentrifugation + spin column	1 (3)
Ultracentrifugation + ExoEasy Kit	1

One article did not report on the isolation method used, and a total of three articles used more than one method to isolate EV.

CSF, cerebrospinal fluid; EV, extracellular vesicle; PEG, polyethylene glycol.

Ultracentrifugation is a commonly used method for EV isolation, as it can separate the vesicles from other materials, such as cellular debris and macromolecular proteins while retaining the vesicular RNA components. However, ultracentrifugation is a time-consuming and heavily instrument-dependent technique. In addition, it cannot separate vesicle subtypes with overlapping sizes, which could result in low purity. In contrast, density gradient ultracentrifugation has a greater separation efficiency and thus results in higher purity as different EVs present different densities regardless of their size, reflecting their cellular origin.¹²

Another widely used method for isolating EVs is the polymer-based precipitation method. In this method, the fluidic samples are first mixed with a polymeric reagent, which reacts and forms a polymeric web to capture the vesicles according to size.¹³ A precipitating reagent is then applied to pellet the EVs at low centrifugal speed. As this method largely depends on the size, it can isolate vesicles with great uniformity in size; however, it can also include contaminants, such as lipoprotein that are within the same size range. An example of this method is the ExoQuick™ Ultra Kit which combines the precipitation-based method with a purification column that increases the yield and purity of EV isolated.

Column chromatography is an EV isolation principle applied in Norgen's Plasma/Serum Exosome and Free-Circulating RNA Isolation Mini Kit. In this technique, the sample is loaded onto a Mini Filter spin column with a silicon carbide resin separation matrix. This method can facilitate the subsequent isolation of vesicular total RNA, as no centrifugal force is applied that could potentially damage the EVs. Similarly, Qiagen's exoRNeasy Serum/Plasma Kit also allows the isolation of vesicular RNA in the subsequent steps, as it makes use of the membrane affinity-based spin column to capture EVs from sample fluid without subjecting to high centrifugation speed.

Ultrafiltration membranes are also used for EV isolation, in which the isolation is dependant on the pore size of the membrane. As the EVs are concentrated or loaded onto the membrane, this can lead to EV loss because of poor recovery from the membrane. In addition, the need to apply additional force to push the sample fluid through the filter membrane could potentially damage the EVs.¹³ Another less commonly used technique is Exo2D™, an EV isolation kit based on the surface property of EVs. In this technique, biofluid-containing EVs are mixed with an aqueous two-phase buffer, in which the EVs can be isolated to one of the phases through the interaction between EVs' surface and each phase. The principle of this method is called “EV phase” and has been suggested to yield EVs with higher purity than ultracentrifugation and ExoQuick.¹⁴

EV characterization

The ISEV has established a guideline outlining the minimal information for studies of EVs (MISEV2018).³ The recommendation is that three aspects of the isolated EV should be characterized: (1) quantification, for example, measuring the particle number and EV diameter using nanoparticle tracking analysis (NTA) or flow cytometry, (2) protein composition, for example, western blotting (WB) or flow cytometry can be performed to determine the presence of EVs and assess their purity, and (3) single vesicle analysis, for example, visualization techniques, including transmission electron microscopy (TEM), to observe EV morphology.

Regarding the protein markers, the ISEV recommended that researchers use at least one marker from each of the three main categories to characterize EVs. The three main categories are (1) Transmembrane protein, which can demonstrate the lipid-bilayer structure specific to each EV subtype, such as tetraspanins (CD9, CD63, and CD81); (2) Cytosolic protein, to determine that the isolated vesicles enclose cellular contents, including TSG101, ALIX, Flotillins, and heat shock proteins; and (3) Proteins that are major components of non-EV structures (negative marker), to assess the purity, such as lipoproteins APOA1/2. Other protein markers coexpressed in the intracellular compartments (e.g., mitochondria and endoplasmic reticulum) can also serve as negative markers for EV, such as calnexin and GM130.

Our systematic review noted that before the publication of MISEV2018 guideline, only one article reported the characterization of EVs which fulfilled the criteria recommended in MISEV2018 guidelines¹⁵; most of the studies reported did not perform EV characterization in all three aspects. In most studies, the markers used to assess protein composition fit only one or two categories. After publication of MISEV2018 guidelines, there was an increase of 12% in published articles that followed the MISEV2018 characterization criteria (Fig. 5a). Most of the studies performed NTA, TEM, and WB to characterize the isolated EVs. Other methods include flow cytometry and mass spectrometry (Fig. 5b). Of note, most of the studies failed MISEV2018 guideline compliance from their lack of negative markers used in EV characterization.

FIG. 5.

EVs were characterized based on their expression markers and/or size. (A) Percentage of articles fulfilled the MISEV2018 guidelines on EV characterization. (B) Various methods used for EV characterization among 27 articles. (C) Protein markers used to characterize EVs in 18 articles. (D) EV diameter revealed by NTA and TEM. FACS, fluorescence-activated cell sorting; LC-MS/MS, liquid chromatography–mass spectrometry; MISEV, minimal information for studies of EVs; NTA, nanoparticle tracking analysis; TEM, transmission electron microscopy; WB, Western blotting.

Apart from quantification with NTA and single EV imaging with TEM, most studies performed WB to characterize the isolated EV (Supplementary Data S3). The protein markers widely used in WB for EV characterization are (1) external markers: CD9, CD63, and CD81, (2) internal markers: TSG101, ALIX, and Flotillins, and (3) negative markers: Calnexin, GM130, and Apolipoproteins (Fig. 5c). Moreover, the NTA and TEM results suggested that the diameter of EVs mostly ranged from 30 to 200 nm, corresponding to the exosome subtype, with a lower number of EVs larger than 200 nm; indicating the isolation of various EV subtypes (Fig. 5d and Supplementary Data S3).

Next-generation sequencing platforms

Most of the studies performed transcriptome sequencing using the Illumina next-generation sequencing (NGS) platform, with distinct systems, such as HiSeq2500 and NextSeq500, apart from four studies which used the BGISEQ-500 platform (Fig. 6a and Supplementary Data S4.1). The distinct features of commonly used Illumina sequencing platforms are summarized in Table 3. Two studies did not either use or document any sequencing platform; these studies used the uniCHA system and/or performed quantitative real-time polymerase chain reaction to assess the expression levels of piRNA of interest.

FIG. 6.

NGS and bioinformatics analysis on EV-piRNA. (A) Percentage of NGS platform utilized for small RNA sequencing among 32 articles^a; (B) piRNA databases utilized for raw sequencing data alignment and annotation in a total of 31 articles.^b ^aOne article reported more than one sequencing platform. ^bFive articles utilized more than one database/software to annotate piRNA, whereas one article did not report on the database used. NGS, next-generation sequencing.

Table 3.

Comparison Between Four Types of Illumina Next-Generation Sequencing Platforms

Features	HiSeq2500	NextSeq500	MiSeq	NovaSeq6000
Run time	7 h to 6 days	12–29 h	4–56 h	13–44 h
Maximum output	1 Tb	120 Gb	15 Gb	6 Tb
Maximum reads per run	4 Billion	400 Million	25 Million	20 Billion
Maximum read length	2 × 250 bp	2 × 150 bp	2 × 300 bp	2 × 250 bp
Key applications	Large WGS	Small WGS, targeted gene sequencing, and transcriptome sequencing	Small genome sequencing, targeted gene sequencing	Large-scale WGS

WGS, whole genome sequencing.

Illumina NGS platform uses a fluorescence-based paradigm for reading the bases in a nucleotide sequence, and all sequence reads generated during a single experiment have the same lengths. Moreover, it allows paired-end sequencing (read from both ends of a fragment). In contrast, the BGISEQ-500 sequencing platform is featured with combinatorial Probe-Anchor Synthesis (cPAS) and DNA Nanoballs (DNB) technology. The DNA sample is anchored on the DNB, and amplification is initiated once the fluorescent probe is incorporated onto the DNA sample by cPAS. BGISEQ-500 has been demonstrated to provide a comparative performance in gene quantification with the Illumina HiSeq4000 sequencing platform.¹⁶

Bioinformatics analysis of piRNA expression profiles

Upon sequencing, the raw data have to be filtered and aligned to the reference genome and database to annotate for the corresponding small RNA type. For piRNA identification, the most widely reported database is piRNABank (Fig. 6b and Table 1). Other databases, including piRBase, human genome reference database (GRCh), and piRNA Quest, are also available (Supplementary Data S4.1).

A total of 14 articles have reported on the expression levels of piRNA among EV small RNA profile (Supplementary Data S4.1). The results showed that EV-piRNA expression level ranges from 0.09% to 43.84% of the EV small RNAs, with the lowest expression levels reported in urine-derived EVs and the highest percentage in plasma-derived EVs.

Among all sample types, seminal plasma has been reported with the highest quantity of EV-piRNAs, with 2344 piRNAs detected; the next most abundant source were serum samples which reported 533 piRNAs. A total of 195 piRNAs commonly expressed among at least two sample types (Supplementary Data S4.2). Serum-derived EV and glioma CUSA-derived EV reported the highest number of commonly expressed EV-piRNA, with 43 commonly expressed EV-piRNAs, followed by plasma-derived EV and seminal plasma-derived EV, with 23 commonly expressed EV-piRNAs.

Furthermore, DE EV-piRNA profiles in subjects diagnosed with clinical conditions of interest compared to their matched control were revealed (Supplementary Data S4.1). This suggests that EV-piRNA can potentially serve as a diagnostic biomarker and might play a role in disease progression. However, not all studies identifying EV-piRNA conducted a downstream analysis to explore the potential role of piRNA in each disease pathology (Supplementary Data S4.1). Among these, five studies investigated the putative target genes of piRNA candidates by utilizing software or databases, such as miRanda database and RNAhybrid algorithm. In addition, several studies performed Gene Ontology (GO) enrichment, Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis, or Ingenuity Pathway Analysis (IPA) to explore the piRNA involving pathways (based on the predicted piRNA target genes) (Supplementary Data S4.1).

Only one study reported a direct role of piRNA candidates in promoting disease pathology. Li et al explored the potential of multiple myeloma (MM) cell-derived EVs in delivering piRNA-823 to endothelial cells.¹⁷ The treatment of MM-derived EVs carrying piRNA-823 has increased cell proliferation and attenuated apoptosis of endothelial cells, suggesting a role of piRNA-823 in promoting tumorigenesis.

In addition, to providing a reference for future studies on the role of EV-piRNA in human diseases, putative target genes of the reported EV-piRNAs, either highly expressed EV-piRNAs or significant DE EV-piRNAs in each article, were retrieved from piRNA databases and summarized (Table 4).

Table 4.

Putative Target Genes(s) of the Reported Extracellular Vesicle-Derived Piwi-Interacting RNAs in each Article (n = 31).

Category	Clinical condition	Source of EV	Highly expressed piRNA	DE piRNA	Sequence	Putative target gene(s)	Article
Healthy subject	Healthy subject	Saliva	piR-hsa-39980		AAGAACATATGTGGCCACATTACC	METTL21A, CREB1
			piR-hsa-48209	Not applicable	AGTTGATGATTTGGGCCCTGAAGTT	DHX15	²⁵
			piR-hsa-52207		TGGGAATGATGATTTCACAGACTAGAG	SNHG32
			piR-hsa-38581		TTCCCTGAGAAAAATGGACCTTTGGCTT	PRPH2
	Healthy subject	Plasma	piR-hsa-43137	Not applicable	TCAGCTCAGGAAGGAACCCAGAAGCAAGT	HMCN1	²⁸
Cancer	Various cancer types	Plasma	hsa_piR_000765		AGCAUUGGUGGUUCAGUGGUAGAAUUCUCGC	TERF2
			hsa_piR_020326	Not applicable	GGCAUUGGUGGUUCAGUGGUAGAAUUCUCGC	TERF2	²⁷
			hsa_piR_004153		UCCCUGGUGGUCUAGUGGUUAGGAUUCGGCAC	LEKR1
	Multiple myeloma	Serum	piRNA-823 (piR-hsa-823)	Not applicable	ACCTGCTAGTTGCAGATGTTTGCCACTGGA	LINC01801 (lncRNA)	¹⁷
	Hepatocellular carcinoma	Plasma	Not reported	piR-hsa-23821	CTCAGTGATGCAATCTCTGTGTGGTTCTGAAA	TSR1, SNORD91A	³³
	Bladder cancer	Plasma	Not reported	piR-hsa-5936	TCCCTGGTGGTCTAGTGGTTAGGATA	-	⁴²
	Gastric cancer	Serum	Not reported	hsa_piR_001918	UCAAACUCCUGACCUCAUGAUCCGCCUGCCU	TNFRSF8, PAPPA2, CFH, EPHB2, GPN2, AHDC1, EPB41	³⁶
				hsa_piR_002433	UCACUUCCCAGACGGAGUGGCUGCCG	LINC00624, LINC00598, F11 (lncRNA), HHAT, ECE1, FAF1, NRG3, PFKM, MSH2, TRPC4AP, SH3TC2, PLXNA4, ZNF727
				hsa_piR_002528	UCAGAGUGAACAGGCAACCUACAAAAUGG	OLFM3, COL11A1
				hsa_piR_004918	UCGAACUCCUGACCUCAGGUGAUCCGCCUGC	PGD, SLC16A4, MTOR, MAGI3, SYT6, NPPA
				hsa_piR_019308	GAGACCAGCCUGACCAACAUGGUGGAACCC	VWF, DNM3, CHST15, PLCE1, CLMP, GCN1
				hsa_piR_016945	CGGCUAGCUCAGUCGGUAGAGCAUGAGACU	CNOT6
				hsa_piR_018569	UUGGUGGUUCAGUGGUAGAAUUCUCGCCU	BANK1
	Biliary tract cancers	Plasma	Not reported	piR-hsa-23209	CCCCCCACTGCTAAATTTGACTGGCTA	-
				nov-piR-2660989 (piR-hsa-2660989)	TAATTGCTCGACACTATCAAATGGAATC	SLC25A36
				nov-piR-10506469^a	-	-
				nov-piR-10822895^a	-	-
				nov-piR-18044111^a	-	-	³⁷
				nov-piR-20548188^a	-	-
				nov-piR-17802142^a	-	-
				nov-piR-12355115^a	-	-
				nov-piR-4262304 (piR-hsa-4262304)	TTATGCTCTGTCACCTCTTGAATG	IL18R1
				nov-piR-5114107 (piR-hsa-5114107)	AAGATGGACTGGGAAGAATTGAAAAC	CCNJL
	Pancreatic ductal adenocarcinoma (PDAC), Intraductal papillary mucinous neoplasms (IPMN)	Serum	Not reported	piR-hsa-30690	TACTAGTTAAAGGTCAAGACCACTGC	ZHF862
				piR-hsa-52959	CGCGGCGGGGCGCGGGACATGTGGCGTACGGAAG	lncRNAs	⁴⁰
				piR-hsa-53108	AAACCTGTTCTGGACAAGCTATGACACCA	LRP1
	Cervical cancer	Plasma	Not reported	URS00001553DC (piR-hsa-32376)	TGCTATGGAAATCTGCTTACCTCTG	-	¹⁴
				URS000020C970 (piR-hsa-32377)	TGTTGTGGAACAAGGGACCATAAAGGTGC	-
				URS0000387552 (piR-hsa-26682)	GAGAAAGCTCACAAGAACTGCTAACTCACA	-
				URS00004F243F (piR-hsa-26683)	GAGAAAGCTCACAAGAACTGCTAACTCACC	-
	Various cancer types	Plasma	Not reported	piR-hsa-33949	AGAGCAGACTGTTGGGCCAACAGGAGAAGCTCCC	RTL6
				piR-hsa-55706	GTTGACCTGTTCAGGAGTTTCCAGCA	AJAP1	⁴⁷
				piR-hsa-34249	TATTGGCTCCAGTTAGGCAAACAGAA	UPB1
	Leptomeningeal Metastasis	CSF	Not reported	piR-hsa-36340	GTATGGAAATCGACACTCATAAAAT	SOX5	⁴⁹
				piR-hsa-33415	TTATAGGTATTTTTGGTAGGTTCTTGAGA	-
	Lung adenocarcinoma	Serum	Not reported	piR-hsa-26925	GAGGCGGGCATGACACAGCAAGACGAGAAG	MT-RNR2	⁵⁰
				piR-hsa-5444	TCCCAGACGGGGCAGCTGGCCGGGCGGA	ENST00000539652 (lncRNA)
	Prostate cancer	Urine	Not reported	hsa_piR_002468	UCAGAAGAUUCCAGGUUCGACUCCUGGC	ZNF322
				novel_pir158533 (piR-hsa-158533)	ACATAAATCAGTGGAAAGGTACATAGAACT	PCDHGA, PCDHGB	⁵³
				novel_pir349843 (piR-hsa-349843)	TAATTTATAAGGAAAGGAGGTTTAGTCGGC	CDH4
				novel_pir382289 (piR-hsa-382289)	AAAAGCTGGGTTGAGAGGGGGAAT	-
	Breast cancer	Plasma	Not reported	piR-hsa-651	ACCAAACCCATTCTCCCATCAGTGCTGC	NIPA1	⁵⁴
Pulmonary/Cardio-vascular disease	Heart failure	Serum	Not reported	hsa_piR_006426	UGAACAUGGGUCAGUCGGUCCUGAGA	EPN4	³⁰
				hsa_piR_020009	GCUAAACCUAGCCCCAAACCCACUCCA	-
	Chronic thromboembolic pulmonary hypertension (CTEPH)	Serum	Not reported	DQ570956 (hsa_piR_000765)	AGCAUUGGUGGUUCAGUGGUAGAAUUCUCGC	TERF2
				DQ593039 (hsa_piR_016735)	CCGCCUGGGAAUACCGGGUGCUGUAGGCUUA	ENST00000642310 (lncRNA)	³¹
				DQ593431 (hsa_piR_016984)	CGUGCUGGGCCCAUAACCCAGAGGUCGAUGGA	TNRC6C, BTN2A2, H2BC15
	Chronic obstructive pulmonary disease	Plasma	Not reported	hsa_piR_012753	UGGAUAUGAUGACUGAUUACCUGAGA	DIS3L2
				hsa_piR_016735	CCGCCUGGGAAUACCGGGUGCUGUAGGCUUA	ENST00000642310 (lncRNA)	³³
				hsa_piR_020813	GUUCACUGAUGAGAGCAUUGUUCUGAGCCA	NKAIN1
	Smokers	Plasma	Not reported	hsa_piR_000552	ACUGUGUGCUGAUUGUCACGUUCUGAUU	-
				hsa_piR_004153	UCCCUGGUGGUCUAGUGGUUAGGAUUCGGCAC	LEKR1
				hsa_piR_014620	UGGUCGUGGUUGUAGUCCGUGCGAGAA	FAM172A	⁴³
				hsa_piR_019825	GCAUUGGUGGUUCAGUGGUAGAAUUCUCAC	SNAP47
				hsa_piR_020450	GGGAGAUGAAGAGGACAGUGACUGAGAGAC	RPL7A, SURF1
	Hypertension	Plasma	Not reported	piR-hsa-32157	TGCAGTGTGGAACACAATGAACTGAAC	CCAR1, SNORD98	⁵⁵
		Urine		piR-hsa-33056	AGAATTTTTAGAGAGAGAGAATATGCACC	NR2F2
Reproductive disorder	Polycystic ovarian syndrome	Follicular fluid	Not reported	pir-35414 (piR-hsa-27623)	GCCCGGNTAGCTCAGTCGGTAGAGCATGAGAC	tRNA
				pir-36441 (piR-hsa-28590)	GTAGTCGTGGCCGAGTGGTTAAGGCTATGGA	tRNA	³⁹
				pir-43772 (piR-hsa-5938)	TCCCTGGTGGTCTAGTGGTTAGGATTCGGCAC	PLCH1
				pir-57942 (piR-hsa-21126)	TGTAGTCGTGGCCGAGTGGTTAAGGC	tRNA
	Obstructive azoospermia	Seminal plasma	Not reported	pir-61927 (piR-hsa-26047)	TTTCTTGTTGACTCTACTCTGACTTAGGGCA	LINC01517, LINC00837	⁴⁴
	Male infertility	Seminal plasma	Not reported	piR-hsa-10292	TGACTTTAGGGCTGGGTGGGCCTTGGGGG	SINE
				piR-hsa-10293	TGACTTTAGGGCTGGGTGGGCCTTGGGGGT	SINE	⁴⁵
				piR-hsa-18597	TGGGAACGAAAAGACACTCGTGGAGGCGTC	DNM1P47
	Endometrial receptivity and implantation success	Uterine fluid	Not reported	piR-hsa-456	AAGGCAGTGTATTGCTAGCGGCTGTTAT	-	⁵¹
Neurological disorder	Alzheimer's disease	CSF	hsa_piR_017936		UUCUGGGUCGGGGUUUCGUACGUAGCA	lncRNAs
			hsa_piR_020381	Not applicable	GGCGGGAGUAACUAUGACUCUCUUAAGGUA	LYST, GDPD4	³²
			hsa_piR_020365		GGCCGUGAUCGUAUAGUGGUUAGUACUCUG	-
			hsa_piR_019675		GCAAUAACAGGUCUGUGAUGCCCUUAGA	FOXN3
	Glioblastoma	Glioma CUSA	Not reported	hsa_piR_004153	UCCCUGGUGGUCUAGUGGUUAGGAUUCGGCAC	LEKR1
				hsa_piR_016658	CCCCCCACUGCUAAAUUUGACUGGCUA	-
				hsa_piR_016659	CCCCCCACUGCUAAAUUUGACUGGUU	GDA
				hsa_piR_016677	CCCCUGGUGGUCUAGUGGUUAGGAUUCGGC	-	³⁸
				hsa_piR_020365	GGCCGUGAUCGUAUAGUGGUUAGUACUCUG	-
				hsa_piR_020829	GUUUCCGUAGUGUAGUGGUCAUCACGUUCGCC	CNOT6
	Alzheimer's disease	Plasma	Not reported	hsa_piR_000552	ACUGUGUGCUGAUUGUCACGUUCUGAUU	-	⁴⁵
				hsa_piR_020450	GGGAGAUGAAGAGGACAGUGACUGAGAGAC	RPL7A, SURF1
Immunological disorder	Immune response upon vaccination	Saliva	hsa_piR_000421	Not applicable	ACCGGCUGGUGACCUGAAGUAAAGGAU	-	⁵²
			hsa_piR_000999		AGGGAAGCCACGAGGGACUGUGCCAUGA	SLC8A1 (lncRNA)
Infectious disease	HIV	Plasma	hsa_piR_018780	Not applicable	UUUCUGUGUGGAAUUUGAAUAUCUGAAA	SOX9 (lncRNA)	³⁵
	HIV/HCV	Plasma	hsa_piR_022690		UAGAUGAAAAUCUUUUCCUCUGGGUGUU	FBLIM1
			hsa_piR_017388		UUCAAGAUGAGAUUUGAGUGGGGACACAGC	ARHGEF10L, EIF4G3, H3-3A, HIVEP3, CFAP57, AGBL4, AK5
			hsa_piR_016796	Not applicable	CCUCGAACUCCUGACCUCAGGUGAUCCACC	FMN2, FAM89A, MAGI3, NOTCH2NLC, ZFYVE9, MROH7	⁴¹
			hsa_piR_019068		GAAUGCAGCCCAAAGCGGGUGGUAAACU	DLG2
			hsa_piR_022017		UACCAUCUUGGCUCACUGCAACCUCCGCCU	CPNE8, NRXN3, CELF6, IL12RB1, FRMD3, PIK3R3
Developmental disorder	Nonsyndromic cleft lip and palate	Plasma	Not reported	hsa_piR_009228	CAGUUGAACAUGGGUCAGUCGGUCCU	EPN2
				hsa_piR_016659	CCCCCCACUGCUAAAUUUGACUGGUU	GDA	⁴⁸
				hsa_piR_020496	GGGGGAUUAGCUCAAAUGGUAGAGCGCUCG	ABHD1, CSPP1

Not found in piRNA database.

CUSA, Cavitron Ultrasonic Surgical Aspirator; DE, differentially expressed; lncRNA, long noncoding RNA; tRNA, transfer RNA; SINE, short interspersed nuclear element.

In Table 4, five piRNA candidates were reported to be highly expressed in healthy subjects, of which four piRNAs were from saliva-derived EV and one piRNA from plasma-derived EV. These piRNAs were not detected in other EV samples derived from any particular disease condition. Furthermore, putative target genes of these piRNA were analyzed, in which PRPH2 and HMCN1 were reported to be involved in vision, METTL21A and DHX15 were involved in mRNA transcription and splicing, and CREB1 as a transcription factor involved in various signaling pathways, such as inflammation. Hence, these piRNAs can potentially serve as a negative biomarker or a therapeutic target in human disease.

Of note, some of the novel piRNAs reported through piano are undetectable in current piRNA databases, including piRNABank and piRBase.

Discussion

This systematic review examined 34 articles that reported on human body fluid-derived EV-piRNA. A detailed analysis of the EV-piRNA was conducted, in which the isolation and characterization methods of the EV, sequencing platform, and bioinformatics analysis findings of the EV-piRNA in each article were reported here. This review can serve as a reference for future studies on the analysis workflow of EV-piRNA.

Due to the ability to transport cellular contents and regulate gene transcripts in the targeted cells, EVs have emerged as a source for biomarker discovery, as well as an important element in cell-free therapy. This review revealed that piRNA can be detected in EVs, with the expression levels ranging from 0.09% to 43.84% of the EV small RNAs. Moreover, most studies reported high percentages of unannotated sequencing reads. This suggests that there might be still unidentified piRNAs contributing to the low number of piRNA detected. To address this problem, three studies reported on the use of piano software, in revealing novel piRNA. However, some of the novel piRNAs reported through piano are undetectable in current piRNA databases, including piRNABank and piRBase. Hence, the identity of these piRNAs remains putative.

In addition, by analyzing EV-piRNAs reported in each study, a total of 195 EV-piRNAs were found to be commonly expressed among different sample types. However, it is important to note that different piRNA databases were used for small RNA annotation among each article. As the nomenclature of the piRNA is not consistent among databases, this could lead to redundancy in piRNA reported, and thus, it cannot be concluded yet that the piRNA candidates identified from each EV source are tissue specific.

Furthermore, it is noted that only one study has demonstrated a direct correlation between EV-piRNAs and cellular behaviours, such as cell proliferation and cell death, in MM. As such, the exact role of EV-piRNA in other disease conditions is still unclear. In addition, the EV-piRNA expression profile also remains unexplored in various sample types of different disease conditions, that is, osteoarthritic synovial fluid.

OA is a noncommunicable degenerative disease that occurs in the knee joint and is characterized by low-grade systemic inflammation and cartilage damage. Mesenchymal stem cells (MSCs) have been proposed as a feasible cell source for cartilage repair, as they can differentiate into chondrocytes¹⁸ and promote the synthesis of cartilage matrix components, such as type II collagen and proteoglycans.

The role of noncoding RNAs, such as miRNA and lncRNA, has been investigated in OA, with a large number of candidates being implicated in the pathological changes of OA, including chondrocyte apoptosis and extracellular matrix degradation.¹⁹ In addition, a few piRNA candidates have also been reported to be involved in inflammation in OA,²⁰ as well as chondrogenesis of MSCs.²¹ However, there is a scarcity of downstream analysis of the functional role of these EV-piRNAs. Thus, together with the results of the systematic review, this confirms the need for future studies to unravel the expression profile and mechanism of EV-piRNA in different sample types, to fully explore the potential of EV-piRNA as a novel therapeutic intervention in human diseases.

In vivo and in vitro studies are useful approaches to demonstrate the functional role of EV-piRNA. piRNA functions together with PIWI proteins to mediate gene silencing, through complementary base pairing with its mRNA target, or through epigenetic regulation. Hence, to study the functional role of EV-piRNA in animal models, homologous gene and protein function, as well as the mechanism of function of piRNA/PIWI complex among species, need to be understood. Previous studies have reported on the highly conserved structure of PIWI proteins among zebrafish, mice, and humans,²² as well as similar piRNA biogenesis pathway and mechanism of function of piRNA/PIWI complex in gene regulation among Drosophila, murine, and human.²³ In addition, comparative genomic studies have revealed gene homology between humans and mice.²⁴ Hence, these findings suggest the potential application of animal models in understanding the potential of EV-piRNA as an RNA-based drug before translation into clinical studies.

piRNA is still an emerging field in human disease; however, it is apparent that the current bioinformatics analysis database and software for piRNA and the experimental analysis of piRNA functional role are limited and often lacking entirely. Of particular concern is that the piRNA nomenclature in different piRNA databases is inconsistent, suggesting an urgent need for a harmonized piRNA nomenclature to be established. Alternatively, researchers should annotate the sequencing data on more than one piRNA database, to acquire a more complete piRNA identification and expression profile analysis, as well as to screen for redundancy across the different databases. It is important to improve the bioinformatics analysis platform, not only for a harmonized annotated database but also for a standardized guidelines for putative piRNA target gene prediction software/pipeline, to accelerate the study of EV-piRNA functional role in human disease.

Despite this systematic review reporting the current limitations in EV-piRNA studies regarding the bioinformatics analysis platform of piRNA, most of the studies partially or completely fulfilled the ISEV guideline in EV characterization. In addition, the sequencing platforms used are mostly Illumina sequencers, which provide consistency and quality in the sequencing reads. Thus, this review regards it as confirmed that EV-piRNA can be derived from various biofluid samples with confidence.

Conclusion

Our systematic review has demonstrated that piRNA is stably expressed in EV derived from various human body fluids, and DE piRNAs are also reported between subjects with specific clinical conditions and healthy controls. It is crucial to investigate the role of EV-piRNAs in human disease pathology, which could lead to the discovery of a diagnostic biomarker or the development of a novel therapeutic intervention.

Footnotes

Authors' Contributions

T.-X.G. has conducted article search, data collection, and article writing. S.-L.T., M.M.R., S.-H.T., and T.K. supervised and contributed to the article writing.

Disclosure Statement

Authors declare no conflict of interest.

Funding Information

This study is part of the Fundamental Research Grant Scheme (FRGS) project funded by the Ministry of Higher Education (reference number: FP039-2017A and FRGS/1/2017/STG05/UM/02/9).

Supplementary Material

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