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As an essential part of central dogma, RNA delivers genetic and regulatory information and reflects cellular states. Based on high-throughput sequencing technologies, cumulating data show that various RNA molecules are able to serve as biomarkers for the diagnosis and prognosis of various diseases, for instance, cancer. In particular, detectable in various bio-fluids, such as serum, saliva and urine, extracellular RNAs (exRNAs) are emerging as non-invasive biomarkers for earlier cancer diagnosis, tumor progression monitor, and prediction of therapy response. In this review, we summarize the latest studies on various types of RNA biomarkers, especially extracellular RNAs, in cancer diagnosis and prognosis, and illustrate several well-known RNA biomarkers of clinical utility. In addition, we describe and discuss general procedures and issues in investigating exRNA biomarkers, and perspectives on utility of exRNAs in precision medicine. Introduction Biomarkers are defined as measurable alterations in biological substance that associate with normal or abnormal conditions. In the past decades, various types of biomarkers have assisted diagnosis and prognosis of diseases in clinical trials ,.
In the field of oncology, biomarkers generally possess three types of clinical relevance: diagnostic values, prognostic values, and predictive values. The diagnostic values include early detection of diseases, determination of tumor origins, and classification of cancer subtypes. The prognostic values include prediction of disease outcomes and risk assessment independent of treatments. The predictive values contain the prediction of responses to treatments, etc.
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Sensitive and specific biomarkers in many clinical trials are essential to precision medicine in that they enable the determination of clinical outcomes in a relatively earlier stage. Biomarkers also serve as potential targets for drug design. Moreover, integration of biomarker data using bioinformatics methods would enhance our understanding of biological pathways and regulatory mechanisms associated with diseases. In this review, we will summarize latest studies on various of RNA biomarkers, especially extracellular RNA (exRNA) biomarkers, in cancer. In addition, we will describe biogenesis and clinical relevance of exRNA, and related bioinformatics methods and databases.
Comparison of Different Types of Biomarkers RNAs serve not only as transmitters of genetic information, but also subjects of transcriptional and post-transcriptional regulation ,. Although RNAs are unstable in alkaline conditions, they are easy to detect and quantify at very low abundance.
Compared with protein biomarkers, RNA biomarkers have more sensitivity and specificity. PCR enables traces of RNA sequences to be amplified and thus captured specifically with high sensitivity. Moreover, the cost of RNA biomarker is much lower than protein biomarker because detecting each protein requires a specific antibody. Compared with DNA biomarkers, RNA biomarkers have the advantage of providing dynamic insights into cellular states and regulatory processes than DNA biomarkers. Besides, RNA has multiple copies in a cell, which delivers more information than DNA. Moreover, some RNAs with specific structures, such as circular RNA, have the potential to exist stably in plasma and/or serum ,. Recently, next-generation sequencing technology facilitates the quantified measurements of RNA expression levels at whole genome level.
Increasing depth of RNA sequencing also enables the detection of novel transcripts, such as lowly expressed noncoding RNAs, and subtle variations in expression with greater accuracy ,. In summary, large scale expression profiles of RNAs provide both genetic and dynamic regulatory information, and thus can work as accurate and direct markers of cellular state. Different Types of RNA Biomarkers in Cancer The high-throughput sequencing technologies have enabled the detection of protein–coding RNAs (i.e., mRNAs) and different types of non-coding RNAs (e.g., small nuclear RNA, micro RNA, small nucleolar RNA, etc.) in human at transcriptome level. Of particular note is that there are lots of novel non-coding RNAs discovered recently. With many international collaborated projects conducted (e.g., The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC)) and vast data in cancer accumulated, the number of studies on cancer associated RNA biomarkers has been increasing quickly (A).
Various types of RNAs were typically used as biomarkers in cancer (B). Studies and examples of RNA biomarkers in cancer. ( A) Numbers of publications on RNA biomarker in cancer (The data were collected using keywords of “RNA biomarker” and “cancer” browsed in NCBI PubMed); ( B) Typical types of RNAs used as biomarkers in cancer; ( C) Gene expression pattern of PAM50 genes, calculated from published TCGA breast cancer data (BRCA). The patients were classified into five subtypes (Basal, HER2, LumB, LumA and Normal-like) based on PAM50 genes’ expression profile; ( D) Kaplan-Meier analysis for different subtypes in the TCGA BRCA cohort. Subtypes were classified using the PAM50 panel.
P-value was determined based on log-rank test. The first well-studied type of RNA as biomarker is mRNA. Differential expression of specific genes would positively or negatively correlate with disease pathology.
So far, multi-gene expression profiles have been used as biomarker for clinical outcome in many cancer studies. For instance, PAM50, a 50-gene panel, has been successfully applied to the classification of breast cancer.
Here, we have used the PAM50 panel to reanalyze TCGA breast cancer data to show its power of classification and prognosis in breast cancer (C,D). Similarly, another expression panel of 31 mRNAs related to cell cycle progression was used as prognosis marker to predict metastasis, recurrence and risk of prostate cancer. In addition to mRNAs, lots of functionally important RNAs that do not encode proteins have been discovered recently. Many of them can also be used as biomarkers. For instance, microRNAs (miRNAs) are small and evolutionary conserved non-coding RNAs that usually involve in RNA silencing and other types of post-transcriptional regulations. Some miRNAs play pivotal roles in cell proliferation, differentiation and apoptosis, and thus function as oncogenes or tumor suppressors. Expression profile of miRNAs has been reported to successfully classify poorly differentiated tumor types.
In addition, low expression of miR-21 was shown to indicate low hazard rate for pancreatic ductal adenocarcinoma patients after adjuvant therapy. Moreover, miR-21 was reported as a potential therapy target. Biomarker Name RNA Type Cancer Type Up/Down Value Reference PAM50 mRNA Breast cancer - Diagnosis/Prognosis Cell Cycle Progression mRNA panel mRNA Prostate cancer Up Prognosis PCA3 lncRNA Prostate cancer Up Diagnosis HULC lncRNA Pancreatic cancer Up Prognosis HOTAIR lncRNA Nasopharyngeal cancer Up Prognosis miR-21 miRNA Pancreatic cancer Down Clinical outcome prediction, potential therapy target piR-651 piwiRNA Lymphoma Down Prognosis SNORD33, SNORD66, SNORD76 snoRNA Non-small-cell lung cancer Up Diagnosis Hsacirc002059 circRNA Gastric cancer Down Diagnosis. Piwi-interacting RNA (piRNA) is a novel type of small non-coding RNA that interacts with Piwi subclass Argonaute proteins, which participate in transposon silencing via DNA methylation. PiRNAs have been shown related to cell proliferation and invasion. Low expression of a piRNA, piR-651, was found to be associated with short survival time for lymphoma patients, which could serve as an prognostic marker. Small nucleolar RNA (snoRNA) is a type of non-coding RNA discovered in nucleolar and regulate ribosome maturation and function.
Studies also show that some snoRNAs are involved in alternative splicing and gene silencing ,. Furthermore, expression profile of a snoRNA panel can be used to detect early non-small-cell lung cancer. In addition to small RNAs, long noncoding RNAs (lncRNAs) could also serve as biomarkers. Accumulating evidence has emerged to show their presence and function, although the classification and characterization of lncRNAs is still rather premature. For instance, a well-known lncRNA, HOTAIR, was reported to be correlated with tumorigenesis, tumor progression, metastasis and patient survival. Therefore, HOTAIR has a potential to be a promising biomarker ,.
In addition to linear RNA molecules described above, a specific type of non-coding RNA, circular RNA (circRNA), is generated from pre-mRNA with a back splice mechanism, which connects the 3’ end and 5’ end of a transcript’s precursor to form a circle. The circular structure makes circRNA more resistant to exonucleases than other types of RNA molecules ,. Its hypothetical function involves downregulation of miRNAs by sequestering complementary miRNAs like a sponge.
As an example related to cancer, Hsacirc002059 is significantly downregulated in gastric tumor tissues compared to normal tissues, and correlated with tumor metastasis. Furthermore, regulatory alterations such as alternative splicing can be revealed by RNA-seq using specific bioinformatics analyses. Distorted alternative splicing produces dysfunctional isoforms that may have detrimental consequences. Isoform ratios for alternatively-spliced genes can be estimated from RNA-seq results.
Aberrant splicing events have been reported to be associated with survival of cancer patients. Another important event that assist clinical trials is gene fusion.
Fusion genes result from chromosomal aberrations and are usually absent in normal tissues. Presence and abundance of chimeric RNA transcripts generated from fusion genes in tumor samples can effectively classify cancer subtypes and identify unstable chromosome regions. For instance, gene fusion of TMPRSS2 to ERG leads to lower survival rate, making it a potential marker for prognosis and stratification of cancer. Biogenesis and categories of extracellular RNAs (exRNAs). Biogenesis of Extracellular vesicles (EVs) released by tumor cells. EVs include exosomes, micro-vesicles, oncosomes, and apoptotic bodies.
Apoptotic tumor cells release apoptotic bodies, while normal or active tumor cells release exosomes, micro-vesicles, and oncosomes. Exosomes are endocytic membrane-derived vesicles released by the fusion of the multivesicular bodies (MVBs) with the cell membrane. However, the cell membrane directly outwards buds to the extracellular milieu and forms the micro-vesicle. Besides, oncosomes are large EVs formed through the budding of the tumor cell membrane. EVs deliver a variety of DNA, protein, and RNA species including miRNA, piwiRNA, lncRNA, mRNA, tRNA, snoRNA, circRNA, etc. Studies on exRNA have a long history, although major progresses were made in the last decade.
In 1971, exRNA was found in bio-fluids, which laid the basis for the hypothesis that exRNA could play important role in cell-cell communication. ExRNAs as signaling molecules in the regulatory circuitry have been detected in both plants and animals. Surprisingly, exRNAs detected in plasma has unexpected abundance despite of the relative high level of RNase in blood.
Later, studies on exRNA profiles have extended the knowledgebase of exRNA types. In 2016, Freedman et al. First performed RNA sequencing using ion proton system for plasma of 40 individuals. They identified several classes of the 1192 exRNAs including miRNA, piwiRNA and snRNA. They then performed RT-qPCR on additional two thousand individuals for the top 500 expressed exRNAs. This study is so far the largest profiling of plasma extracellular miRNA species and other small RNAs from a large population. While there are many different types of RNAs, miRNA is the most abundant type of exRNAs.
The abundance of miRNA in recipient cells can be altered by miRNAs transferred from vesicles, which will lead to the downregulation of several mRNAs inside the cells. Biogenesis of exRNAs To survive degradation, exRNAs are hypothesized to be stabilized by protein or lipid complexes, such as proteolipid, lipoprotein or other RNA-binding proteins, and packed in vesicle structures ,. They may be released as a result of cell death, and packed into apoptotic bodies or as communicators. Extracellular vesicles (EVs) are nanomeric cell-released vesicles carrying DNAs, RNAs, and proteins which function in intercellular communication. EVs have been divided into several classes including exosomes, oncosomes, micro-vesicles, and apoptotic bodies, according to size, morphology and origin ,. They can travel to nearby or distant tissues, captured by target cells and transmit genetic and regulatory information from their origins to targets. Analyses of EVs and their RNA contents will be useful since the concentration and characteristics of RNAs reflects their cellular origins and diffusing conditions.
There are several different mechanisms in the process of transferring content into the recipient cells from vesicles. For instance, exosomal membrane proteins could associate with and activate receptors of recipient cell.
In some situations, these proteins are cleaved by proteases before targeting. Then the membranes of vesicles fuse with the recipient cells. They can also transmit their cargo to targets via endocytosis.
Many studies focus on the influence of exRNAs on the recipient cells. Studies have shown that exosomal shuttle RNAs in the EVs can be delivered into the recipient cells, and translated into proteins. Vesicles transmitted among normal cells are the basis for many important biological events and communications between cells, which may shed light on clinical treatments. ExRNAs provide the great promise in molecular diagnostics, but at present the understandings of their regulatory mechanisms are still limited. The mechanisms of exRNA release, uptake, regulation and function on recipient cells need further investigation.
Clinical Relevance of exRNAs in Cancer At present, exRNAs found in the blood of cancer patients has encouraged more and more studies. Actually, both normal cells and tumor cells can secrete vesicles. Using deep sequencing methods, altered expression of exRNAs has been found in different cancers which can be of potential clinical relevance. More vesicles are secreted from tumor cells than from normal cells and work as helpers for cancer progression. ExRNAs in the vesicles play key roles in the intercellular communication and influence the phenotype of the recipient cells by targeting specific genes.
For example, hepatocellular carcinoma cells (HCC) can secret miRNAs and lncRNAs via EVs to adjacent cells that alter local environment, which potentially enhance the local spread and multifocal growth of tumor ,. Tumor cells can also release exosomes that assist organ-specific metastasis by transforming the distant tissues into ideal microenvironments for the early survival of disseminating tumor cells called pre-metastatic niche. For instance, U1 snRNAs in exosomes may serve as possible ligands of Toll-like receptor 3 (TLR3), which further trigger the formation of pre-metastatic niche. It has also been shown that tumor exosomes, which contain a variety of proteins, RNAs, and DNAs, could decrease the immune ability of T cells in preparation for metastasis. ExRNAs’ potential to be therapeutic targets for cancer therapy has become a hot research topic of exRNA studies. It was proved that short interfering RNAs (siRNAs) can downregulate the EVs release in tumor microenvironment, and thus enhance the tumor suppression. SiRNA delivery system has been performed in phase 1 clinical trial.
For example, Khvalevsky et al. Succeeded in delivering siRNA to mutated KRAS oncogene and found that this local prolonged siRNA delivery system suppressed the growth of human pancreatic tumor cells. Ozpolat et al.
Reported the feasibility and stability of liposomal nanoparticles as means for the siRNAs’ transporting to tumor cells. Therefore, EVs containing siRNAs may become therapeutic tools targeting tumor cells in the future. Moreover, extracellular miRNAs in EVs may also be used in therapy, considering their inhibiting or suppressing properties in cancer growth.
For example, Nishimura et al. Proved that the EphA2-targeting siRNA and the tumor suppressor miR-520d-3p could target oncogenic pathways and repress ovarian cancer growth. Extracellular RNA Biomarkers ExRNAs have promising potential as diagnostic and prognostic biomarkers, because exRNAs are easy to detect and provide non-invasive molecular diagnosis techniques. Samples acquired from blood, saliva and other cell-free fluids do not require direct operations on tissues. Currently, blood is the most widely used bio-fluids in exRNA biomarker development. So far, a large amount of experimental data and potential biomarkers have been accumulated and reported ,.
Previous studies have verified the potential of exRNAs as biomarkers in certain diseases, especially in several types of cancer. For instance, exRNAs can aid the diagnosis and classification of cancer patients when the solid tumor tissue is not available. Prostate cancer is a common type of cancer in the male reproductive system. Some tumor-derived exRNAs are present in the blood of prostate cancer patients with remarkable stability. For instance, upregulated telomerase reverse transcriptase (hTERT) mRNA have been discovered with similar expression behaviors in peripheral blood and tumor tissues in prostate cancer patients, and is associated with tumor size and malignancy. In addition, miR-141 was found to be expressed in various epithelial cancers, showing strong differential expression between serum of prostate cancer patients and healthy controls. Biomarkers for cancers in reproductive systems can also be found in urine.
For instance, PCA3, a lncRNA exclusively expressed in prostate, can be detected with significant abundance in prostate cancer patients’ urine ,. Cancers that occur in the digestive system include liver, gastric, pancreatic and esophageal cancers, etc.
A study of serum exosomal RNAs in liver cancer showed that several miRNAs are differentially expressed between hepatocellular carcinoma and chronic hepatitis. Examples of piRNA in peripheral blood of gastric cancer patients are associated with occurrence, sub-type and metastasis status of tumor. In addition, for these types of cancer, saliva is also shown to be a promising source of biomarker discovery.
Saliva RNAs have been found to associate with parotid gland, esophageal, pancreatic and oral squamous cell cancer ,. Jae Hoon Bahn et al. Described the landscape of several types of exRNAs in human saliva, including miRNA, piRNA and circular RNA, providing a comprehensive extracellular non-coding RNA database in human saliva for further biomarker discovery. Glioblastoma is a common and highly aggressive cancer in the nervous system. Cerebrospinal fluid (CSF) circulates in the ventricular system of human brain.
It is a promising source to study brain’s RNA expression profile. A couple of miRNAs, such as miR-10b and miR-21, have been found to be enriched in CSF for glioblastoma patients and patients having brain metastasis from breast and lung cancer. For instance, Akers et al. Used the RT-PCR to quantitatively assess the miRNAs in the EVs of the glioblastoma and non-oncologic patients’ cerebrospinal fluid. They found that the miR-21 was significantly increased in gliblastoma patients.
Furthermore, they have discriminated glioblastoma patients from the non-oncologic patients using miR-21’s expression level, based on a relatively small patient cohort (twenty-nine). In the respiratory system, non-small-cell lung cancer (NSCLC) accounts for the majority of lung cancer incidences. A 4-miRNA signature facilitates the early detection of NSCLC. SnoRNAs overexpressed in NSCLC tissues show high expression in plasma as well. Identification of Novel Extracellular RNA Biomarkers Many more exRNAs continue to be found as potential biomarkers.
For instance, as important components of splicing machinery, U2 snRNAs’ fragments were found in blood, showing altered abundance in mice when implanted with several human cancer types ,. Circular RNAs (CircRNAs) were found to be stably existed in exosomes and differentially expressed between cancer and normal serum, making a potential source of biomarkers as well. Discovery of novel RNA biomarkers in cell-free fluids requires preparation of RNA samples and libraries, data generation with quantified methods, and correlation with diagnostic or prognostic properties using bioinformatics analysis. Experimental and analytical procedures for the identification of novel RNA biomarkers. First, tissues and/or bio-fluids are collected from both patients and health individuals. Then RT-qPCR, small RNA-seq or other RNA sequencing methods are performed on isolated and purified RNA samples. RT-qPCR procedure includes cDNA synthesis by reverse transcription from total RNAs and qPCR reactions with the synthesized cDNA templates.
RNA-seq procedure includes library preparation, in which RNA transcripts are fragmentized and transcribed into cDNAs, and high-throughput sequencing. Then, RNA-seq data are processed with a pipeline that includes mapping of reads to the reference genome, assembly of transcriptome from mapped reads, calculating each transcript’s expression abundance (i.e., FPKM, fragments per kilobase of transcript, per million fragments sequenced), and differential expression analysis. Differential expression of selected RNA biomarkers can then be validated by RT-qPCR results in different sample groups. Finally, advanced bioinformatics and statistical analyses will integrate clinical data with expression profiles to obtain the biomarkers’ correlations with diagnostic or prognostic properties. In contrast to tissue collection, most body fluid samples can be collected less invasively, without direct operation on tissues. For example, plasma and serum of both healthy controls and cancer individuals could be collected though venipuncture and separator tubes ,.
Then RNAs can be isolated using certain RNA isolation kits that best meet the experimental requirements. Meanwhile, flow cytometry and dynamic light scattering could be used for the assessment of RNA quantity ,.
After the isolation of RNA samples, several methods can be used to obtain quantified expression profile data. RT-qRCR procedure includes cDNA synthesis by reverse transcription from total RNAs and qPCR reactions with the synthesized cDNA templates. High throughput sequencing such as RNA-seq is performed on the purified RNA samples after library preparation. In preparation of RNA-seq libraries, RNA transcripts are fragmentized and reverse transcribed into cDNAs. The collected quantification data would then go through bioinformatics and statistical analysis. RNA-seq data are processed with a pipeline that includes mapping of reads to the reference genome, assembly of transcriptome from mapped reads and differential expression analysis. Using regression algorithms, features in the expression profile data across samples could be selected and correlated with clinical features such as existence and subtype of diseases, tumor recurrence, normal and tumor tissues, usage of treatments, and patient survival, varying with the purpose of the study.
Since experiment process has significant influence on the results, it is necessary to ensure the consistency of the experimental and analytical procedures in the different sample types. In addition, considering the fluctuation of RNA abundance in bio-fluids and difference of total reads generated between experiments, data normalization is an essential part for the following advanced analyses.
Furthermore, differential expression of selected RNA biomarkers should be validated by RT-qPCR. A database of the Extracellular RNA Communication Consortium (ERCC) including the small RNA sequencing and RT-qPCR-derived exRNA profiles. Many of the biomarker databases are disease-centered. For instance, the Human MicroRNA Disease Database (HMDD) is an experiment-supported database of human miRNA-disease associations with experimental evidences from genetics, epigenetics, circulating miRNAs and miRNA-target interactions. Osteosarcoma Database contains osteosarcoma-associated protein-coding genes and miRNAs by literature search and manual annotation, providing a platform for evaluating potential miRNAs as osteosarcoma biomarkers.
Colon Rectal Cancer Gene (CoReCG) is a resource for factual colon-rectal carcinoma related genes and relating mechanisms, as well as information about differentially expressed, mutated, and polymorphic genes involved in distinct cancer stages. Bladder Cancer Biomarker Evaluation Tool (BC-BET) provides an online platform for evaluating diagnostic and prognostic gene expression biomarkers integrating curated gene expression data from publicly available patient cohorts. It enables users to estimate the association between gene expression and the presence, grade, stage and predicted outcome of tumor. A database of disease-related biomarkers uses a dictionary-based Named Entity Recognition system to curate a dataset of biomarkers with minimized false positive ratio. Some other biomarker databases are more comprehensive than the above disease-centered databases. For instance, MIRUMIR includes publicly available miRNA datasets annotated with patients’ survival information. It can be used to predict whether a given miRNA is a potential robust biomarker for survival of cancer patients.
Biomarker Database (BMDB) is a database constructed by the United States National Cancer Institute’s (NCI) Early Detection Research Network (EDRN). Based on the curation of the currently available biomarker data and raw results, EDRN team developed a common information model for cancer biomarker research, normalized and screened the data before combined into an integrated knowledge system including gene, protein, genetic, genomic, epigenetic and proteomic biomarkers classified by organs. In addition, several databases specifically designed for exosomal and extracellular biomarkers have been developed.
For instance, exRNA Atlas collects the latest information on various exRNA studies, including exRNA profiling data derived from small RNA sequencing and RT-qPCR, standardized exRNA protocols, and many other useful tools and technologies. A miRNA database, miRandola, is an extracellular circulating miRNA database, which is useful for studying biological function of the predicted extracellular miRNA biomarkers. ExoCarta stores various published and unpublished information of exosomal studies about exosomal proteins, RNAs and lipids ,. Future Perspectives and Challenges Since the identification of exRNAs in various human bio-fluids, an increasing number of studies have positioned exRNA as a new type of non-invasive biomarker with numerous clinical potential. Due to the important roles of exRNAs in biological processes and promising potentials in molecular diagnosis, a number of exRNA projects have been funded by National Institutes of Health to advance the technologies of exRNA identification from different types of bio-fluid.
The Extracellular RNA Communication Consortium (ERCC) was organized in 2012 and supported by the American National Institutes of Health (NIH) Common Fund. ERCC aims to investigate the mechanism of exRNA biogenesis, delivery and function; to define a reference catalogue of exRNA in normal individual body fluids; to develop the clinical utility of exRNA as biomarkers of disease or therapeutic molecules. Compared to previous researches on the discovery and feasibility of exRNA before 2015 revealing the potential use of exRNA, recent studies focus more on exploring the usage of extracellular RNAs as biomarkers. In the future, systematic identification of novel exRNA biomarkers will need to be further explored, although a few exRNA biomarkers have been discovered individually. Considering the variety of exRNA species, though most studies focused on profiling miRNA outside cells, other exRNA species such as piwiRNA, circRNA and lncRNA may also serve as alternatives in clinical utility.
Currently, there are only limited mature exRNA biomarkers that could guide clinical decision making. Large cohorts with matched clinical information, including survival time, disease recurrence, response for drug usage or other information are urgently needed in the identification of novel exRNA biomarkers.
Sufficient clinical cohorts are also required to validate the performance of biomarkers for early-diagnosis, prognosis and drug usage. Moreover, the mechanisms of exRNA biogenesis and regulation are still unclear. A better understanding of pathways, interactomes and regulatory networks of exRNAs would serve as guidance for biomarker screening and drug design. With the advancements in researches on relating mechanisms, more biomarkers with greater predictive and explanatory power could be identified for different types of cancer from various sources, which will in return facilitate the understanding of mechanisms. It is also possible to target exRNAs as cancer therapeutic methods.
The secretion and circulation of extracellular vesicle that contain regulatory RNAs can be blocked to prevent cancer from progressing and metastasis. In addition, extracellular vesicles could be used as a transmitter of specific regulatory elements into target cells, inhibiting the development of tumor. Some regulatory RNAs that play roles in pivotal processes in tumor development could be repressed or sequestered to lower their abundance and inhibit their functions. Further applications require more comprehensive understanding of the biogenesis of exRNA and extracellular vesicles, as well as regulatory roles of different types of non-coding RNAs. Many challenges exist in the studies of exRNAs. For instance, exRNAs’ abundance in different human body fluids is distinct. For instance, using high-throughput RT-PCR, Shah R.
Illustrated that miRNAs isolated from simultaneous whole blood and plasma in 2391 individuals had different expression levels. The divergent miRNA levels indicate that exRNAs obtained from consistent human sources are required when designing the experimental procedure for biomarker investigation in future. Because RNA is easy to be degraded by RNAase and the abundance of exRNA is relatively low, the extraction, purification and protection of exRNAs from body fluids are essential for further high-throughput sequencing and bioinformatics analyses.
RNA isolation kits, RNA-seq library preparation, PCR methodology and even the gel size selection would affect the results of the RNA quantitative measurement and the RNA species detection. Another issue is that data normalization in the exRNA quantification may also introduce technical bias. Therefore, standardized methods may lead to reasonable comparison between different studies. Furthermore, due to the relative low exRNA abundance and noisy background, retrieving useful information from the fragmented raw reads being sequenced is a challenging problem for both experiment and bioinformatics. Large scale sample size, sequencing technology with substantial depth, improved data mining method (e.g., machine learning method), standard bioinformatics tools and pipelines are the potential key points to provide solutions. In summary, a fine-tuned and standardized pipeline, starting from exRNA isolation procedures, low abundance RNA amplification and sequencing to the advanced bioinformatics analysis methods with high efficiency, sensitivity and specificity, would play an essential role in the exRNA biomarker development.
Kehui Xie 1,2†, Yong Deng 1,2†, 3, 1,2, Guangbo Kang 1,2, Liang Bai 1,2 and 1,2. 1Department of Biochemical Engineering, School of Chemical Engineering and Technology, Tianjin University, Tianjin, China. 2Key Laboratory of Systems Bioengineering, Ministry of Education, Tianjin University, Tianjin, China.
3School of Chemistry and Chemical Engineering, Qinghai University for Nationalities, Xining, China High-throughput amplicon sequencing technology has been widely used in soil microbiome studies. Here, we estimated the bias of amplicon sequencing data affected by DNA extraction methods in a saline soil, and a non-saline normal soil was used as a control. Compared with the normal soil, several unique points were observed in the saline soil. The soil washing pretreatment can improve not only DNA quantity and quality but also microbial diversities in the saline soil; therefore, we recommend the soil washing pretreatment for saline soils especially hypersaline soils that cannot be achieved with detectable DNA amounts without the pretreatment. Also, evenness indices were more easily affected by DNA extraction methods than richness indices in the saline soil.
Moreover, proportions of Gram-positive bacteria had significant positive correlations with the achieved microbial diversities within replicates of the saline soil. Though DNA extraction methods can bias the microbial diversity or community and relative abundances of some phyla/classes can vary by a factor of more than five, soil types were still the most important factor of the whole community.
We confirmed good comparability in the whole community, but more attention should be paid when concentrating on an exact diversity value or the exact relative abundance of a certain taxon. Our study can provide references for the DNA extraction from saline and non-saline soils and comparing sequencing data across studies who may employ different DNA extraction methods. Introduction The fast-growing high-throughput sequencing technology has considerably changed our understanding of microbial communities in all kinds of environments on Earth (;,). Compared with shotgun metagenomics, the amplicon sequencing was much more cost-effective and more widely used , but additional PCR amplification may bias microbiome studies. Other procedures, such as DNA extraction, library preparation, and downstream bioinformatic analysis, may also cause biases (;; ).
Two recent reports have confirmed that DNA extraction had more effects than other procedures in human fecal microbiome studies (; ). Lots of microorganisms distributed in extremely complex and diverse soil communities (; ). Humus, contaminants, salts, and many other substances made DNA extraction a crucial and challenging procedure in soil metagenomic studies (; ). Soil DNA extraction methods can be divided into direct methods and indirect methods with the former being more widely used (;; ). Moreover, DNA solutions achieved with manual methods were often contaminated by the humus, which would interfere with further PCR amplification and sequencing (; ). Therefore, DNA purification steps were also needed for crude DNA from manual methods.
Previous reports have estimated the biases associated with different DNA extraction methods in various kinds of soil or sediment microbiomes (;;;;;;; ). Few studies have evaluated the bias in saline soil or saline sediment microbiomes (;;; ). Saline or hypersaline ecosystems, such as salt lakes, playas, and salt salterns, are globally distributed (; ). Lots of salt and low biomass often exist in soils or sediments from these ecosystems (; ). Enough DNA can be difficult to achieve from saline soils or saline sediments (; ).
Therefore, more studies need doing to optimize DNA extraction methods for saline soils or saline sediments. The widely used DNA extraction methods, which also included many commercial kits, usually contained bead-beating lysis steps before further DNA extraction (; ). Previous studies (;;; ) have reported that higher DNA yield and quality were achieved with the bead beating step than other physical steps, such as freezing and thawing, microwave heating, and liquid nitrogen grinding. However, some studies have confirmed that high DNA yield or quality did not correspond to high microbial diversities (; ). Several recent reports (;; ) only employed methods including the bead-beating step, ignoring other physical lysis steps when estimating the biases of high-throughput amplicon sequencing data. The microbial diversity and community biases caused only by different physical lysis methods need to be clarified. In the present study, we estimated biases associated with different DNA extraction methods in saline and non-saline soil microbiomes, and microbiome biases associated only with different physical lysis steps were also estimated.
The estimated amplicon sequencing biases include raw data, qualified data, alpha diversities, beta diversities, microbial community compositions, phylogenetic analyses, and predictive functional compositions. The quality and quantity of achieved DNA were estimated, too.
Based on these estimates, we also made several suggestions for soil DNA extraction methods. Materials and Methods Soil Sample Collection Two saline soil samples (SS1, SS2; Table ) were collected in spring 2017 at a depth of 0–10 cm in Binhai New Area, a coastal area in Tianjin, China.
They were typical thalassohaline soils. The non-saline normal soil was collected in summer 2016 at a depth of 0–10 cm in Water Park, Tianjin. The sampling method was same as before : subsamples at four vertices of a one-meter square were mixed together into a representative sample. Roots, plants debris, and stones were removed from soils. Sampling locations and altitudes were recorded with a GPS locator (Table ). Collected soils were stored into sterile plastic bags and transported to the laboratory in an ice box.
For each soil sample, a part of soil was stored at 4°C for physical and chemical analyses, the others were stored at -80°C for the DNA extraction. Physical and Chemical Determinations Before physical and chemical determinations, soils were air-dried and filtered through a 2-mm sieve. Soil pH and electrical conductivity (EC) were measured in slurries with soil/water (w/w) ratio 1:2.5 and 1:5, respectively. Water contents were determined by drying fresh soils at 105°C to a constant mass.
Total organic carbon (TOC) was detected with the potassium dichromate heating oxidation method. The Kjeldahl method was used to detect total nitrogen (TN) contents. Phosphorus and potassium contents were determined with an inductively coupled plasma optical emission spectrometry (700 series; Agilent technologies, United States). All measures were conducted in duplicate, then took the means (Table ). DNA Extraction DNA extraction methods in the present study were summarized in Table. Zhou’s method and ISO 11063 method (; ) were two commonly used manual DNA extraction methods from soils. We also selected the modified Zhou’s method , because it was modified for extracting DNA from seafloor sediments and the sediments were saline same as thalassohaline soils in the present study.
We designed experiments according to our purposes: (a) different physical lysis steps (liquid nitrogen grinding, freezing and thawing, and bead beating) following the same method (Zhou’s method in the present study); (b) the same physical lysis step (bead beating) following different methods (Table ). The original Zhou’s method was used for a large amount of soil (5 g); we scaled down the original soil weight to 0.3 g and corresponding solutions to fit into 2 ml centrifuge tubes. The liquid nitrogen grinding step was applied by grinding fresh soil (about 0.5 g) in liquid nitrogen with a mortar and a pestle for 5 min.
The mortar and pestle were firstly washed with 75% ethanol, then sterilized at 121°C for 20 min in an autoclave. The freezing and thawing step was three cycles of freezing at -80°C for 10 min and thawing at 65°C for 10 min after mixing soil with the extraction buffer. The bead beating step was applied by mixing soil with equal weight of 0.4–0.6 mm-diameter glass beads and two 4 mm-diameter glass beads; then add the extraction buffer, vortex blend at 2800 rpm for 5 min, and homogenize in a tissuelyser (Tissuelyser II; QIAGEN, Germany) at 30 Hz for 30 s for three cycles. The bead beating step was the same across different following methods. Moreover, we used PowerSoil commercial kit (Mo Bio Laboratories, Carlsbad, CA, United States) following manufacturer’s instructions with the alternative protocol for low-biomass soils as a control. Each extraction method was conducted in triplicate for both the saline soil and the normal soil. To overcome salt interferences, we tested effects of the soil washing pretreatment with phosphate-buffered saline (PBS) in saline soils: washing 3 g saline soil with 30 ml PBS in a 50 ml centrifuge tube, gently vortex blending for 15 min, and centrifuging at 8000 g for 10 min.
For hypersaline soils, the amount of PBS can be increased. Then the PowerSoil kit was used for further DNA extraction. Except the saline soil SS1 for further sequencing, we also tested another saline soil SS2 with much higher salinity (Table ). The quality and quantity of DNA were considerably improved after soil washing (Supplementary Table ). The soil after washing froze so quickly in liquid nitrogen that the grinding cannot be applied. Therefore, except Zhou’s method with liquid nitrogen grinding step (ZL), the saline soil was all pretreated with PBS washing within other methods in the present study.
All crude DNA solutions achieved with manual methods were further purified with PowerClean DNA clean-up kit (Mo Bio Laboratories, Carlsbad, CA, United States); DNA solutions achieved with PowerSoil kit needed no further purification. Then, quality and quantity of purified DNA were measuring with an ultraviolet (UV) spectrophotometer (Q5000; Quawell, United States); DNA fragment sizes were measured by the electrophoresis on 1% agarose gels. Analysis of Amplicon Sequencing Data The V4 regions of prokaryotic 16S rRNA genes were amplified with primers 515F and 806R (; ) in triplicate.
The primers were fused with a barcode and an Illumina adaptor. Triplicate PCR products were pooled and sequenced in the Illumina HiSeq2500 platform by Novogene (Beijing, China), generating 250 bp paired-end reads.
Generated reads were demultiplexed based on the barcode of each sample, and barcodes and primers were removed from reads. Raw tags were then generated by merging paired-end reads of each sample with FLASH software. Raw tags were qualified with the script splitlibrariesfastq.py in QIIME 1.9.1 : (a) truncate at the first base call when existing three or more consecutive low-quality base calls with Phred quality scores lower than 20; (b) remove tags with a low percentage of consecutive high-quality base calls (lower than 75%). We performed both de novo and reference-based chimera detections with the script identifychimericseqs.py in QIIME against the RDP database through UCHIME algorithm. Effective tags were obtained.
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Then, effective tags were clustered together to OTUs with ≥97% similarity by UPARSE algorithm. The most abundant sequence in each OTU was picked out as a representative. Moreover, all representative sequences were assigned with RDP classifier against the Greengenes database in QIIME. A phylogenetic tree, which was used for further UniFrac distance calculation, was constructed with FasTree after aligning against the Greengenes core set with PyNAST in QIIME.
Prokaryotic Diversity and Functional Prediction Further alpha and beta diversity analyses were conducted after normalizing all samples with 64500 sequences per sample. For alpha diversity indices, richness indices (observed OTUs, Chao1 estimators), Pielou evenness index, Shannon and Simpson diversity indices, and Good’s coverage values were calculated in QIIME. For the beta diversity, principal coordinate analysis (PCoA) was conducted with the weighted Unifrac distance in QIIME.
Venn diagrams were drawn with VennDiagram package in R (3.31) and OTUs that had total sequences in each group more than two were included. Heat map was conducted with Pheatmap package in R. PerMANOVA was conducted with PAST software (version 3.16) , which was based on the Bray-Curtis distance at the OTU level. The linear discriminant analysis (LDA) effect size (LEfSe) was carried out to discover biomarker taxa in each method with the threshold LDA value of two (Figure ). The mean nearest taxon distance (MNTD) was used to estimate phylogenetic clustering extents of taxa in a sample, and the net relatedness index (NTI) was standardized MNTD measures of taxa in a sample. Low MNTD and high NTI values indicate strong phylogenetic clustering extent of taxa.
MNTD was calculated based on an OTU table and a corresponding phylogenetic tree with Picante package in R. The OTU table was generated through UPARSE algorithm with ≥15 sequences in each OTU. The closed-reference biom table was generated with the script pickclosedreferenceotus.py in QIIME against the Greengenes database (version 13-5) for PICRUSt functional predictions. We conducted the prediction based on the KEGG Orthology database. The weighted nearest sequenced taxon index (NSTI) was also calculated for characterizing the predictive accuracy: increasing NSTI values means decreasing accuracies. The closed-reference biom table was also used for predictions of Gram-positive bacteria proportions in soil microbiomes using BugBase software with default parameters. Data Availability Well-assembled raw tags were stored at NCBI SRA (Sequence Read Archive) database with the BioProject accession number SRP125719.
BioSample accession numbers of all replicate were listed in Supplementary Table. Results Soil Physical and Chemical Parameters The saline soil differed greatly from the normal soil in physical and chemical parameters, especially electrical conductivity (Table ). The salinity of two saline soil samples (EC 16 dS/m) was much higher than that of the non-saline normal soil (EC = 0.32 dS/m).
Except salinity, the saline soil SS1 used for further sequencing had similar pH with the normal soil NS, but TOC and WC were much lower in the saline soil than the normal soil (Table ). DNA Quality and Quantity The sizes of most DNA fragments after purification were more than 15 kb, and the DNA band intensity of PowerSoil kit was clearly higher than other methods (Supplementary Figure ). Significantly ( P 0.05) among six different methods in both the two soils. Moreover, boxes of SZL were clearly lower than those of other methods (Figure ); significantly lower Shannon index ( P = 0.05, Mann–Whitney), Pielou index ( P = 0.03), and Simpson index ( P = 0.02) were observed within Zhou’s method with liquid nitrogen grinding step (SZL) than other methods in the saline soil (Supplementary Table ).
Venn diagrams showed that biases existed among different methods in the same soil at the OTU level (Figure ). Shared OTU percentages were summarized in the Supplementary Table.
Every method had its unique OTUs with a mean of 11.59% ± 3.33% (s.d.) in the normal soil and 15.01% ± 6.83% in the saline soil (Supplementary Table ). Also, percentages of unique OTUs were clearly low within SZL, NIB, and SIB groups compared with other groups in both the two soils. The sum of OTU percentages shared by three or four methods were 76.25% ± 3.94% in the normal soil and 73.25% ± 7.99% in the saline soil. Moreover, the IB method achieved clearly lower OTU sum (NIB: 5092) than other methods in the normal soil; except the IB method (SIB: 2968), the ZL method also achieved clearly lower OTU sum (SZL: 2814) than other methods in the saline soil (Supplementary Table ). Prokaryotic Community Compositions The prokaryotic community compositions in soils of our present study were similar to previous reports (; ). Most phyla/classes were significantly different between the two different soils but not among six different methods in the same soil, and the prokaryotic community composition in the normal soil clearly differed from that in the saline soil at the phylum/class level (Figure ).
Prokaryotic community compositions of all replicates were also showed (Supplementary Figure ). Moreover, perMANOVA at OTU level further confirmed significant community difference between two different soils no matter which method was employed but not among six different methods in the same soil (Table ). To further verify variations of each phylum/class in different methods, we calculated and drew boxplots of top six most abundant phyla including four proteobacterial classes (Supplementary Figure ). All nine phyla/classes differed clearly between the two different soils. For the nine phyla/classes, though only Actinobacteria and Gemmatimonadetes in the normal soil and Gemmatimonadetes in the saline soil were significantly different among different methods within the same soil ( P. Except ISO 11063 method with bead beating in the saline soil (SIB), each method has several indicator taxa with LDA value higher than two even four according to the LEfSe analysis (Figure ).
Zhou’s method with bead beating (ZB) was detected with different indicator taxa compared with different methods in the same soil sample (Figure ). The family Actinosynnemataceae was detected within NZB group in both Figures, suggesting Actinosynnemataceae may be an indicator taxon of the ZB method in the normal soil. Gram-Positive Bacteria Proportions We observed significant differences of Gram-positive bacteria between the saline soil (0.032 ± 0.012) and the normal soil (0.184 ± 0.051) ( P. Phylogenetic Analysis Significantly lower MNTD ( P = 0.002, Wilcoxon signed) and significantly higher NTI values ( P. Discussion In the present study, we employed several direct DNA extraction methods to optimize extraction methods for saline soils and estimated soil microbiome biases associated with different DNA extraction methods. The saline soil SS1 for further sequencing were saline and alkaline; the saline soil has much higher salinity, lower TOC and lower WC than the normal soil, associated with previous reports (; ). Because of high salinity and low biomass in saline soils, DNA was difficult to extract from saline soils or sediments (;; ).
The soil washing pretreatment can substantially improve DNA quantity and quality from contaminated or high organic sediments (; ). Though saline soils were uncontaminated and organic contents in them were not high, huge amounts of salts in saline especially hypersaline soils may greatly change the original state of DNA extraction buffers. Therefore, we applied the soil washing pretreatment with PBS to prevent salt interferences. The quality and quantity of DNA increased clearly after PBS washing (Supplementary Table ).
For most hypersaline soil samples in our previous study, detectable DNA amount even cannot be achieved without PBS washing pretreatment. In this study, the soil washing pretreatment was applied within all DNA extraction methods except ZL method in the saline soil. Previous reports have observed that more DNA was achieved with manual methods than the commercial kit (; ).
However, crude DNA achieved with manual methods cannot be directly PCR-amplified for further sequencing (; ), and crude DNA yields were often overestimated based on UV measurements. Therefore, we employed additional purification procedures. We observed the purified DNA quantity achieved with manual methods was significantly lower than PowerSoil kit in both the two soils, suggesting the low recovery rate of additional purification procedures (; ). Moreover, purified DNA solutions achieved with five manual methods has higher OD 260/OD 230 ratios and lower OD 260/OD 280 ratios than PowerSoil kit, agreed with a previous report.
That suggested manual methods did better in clearing away the humus, and the commercial kit was more efficient at reducing protein contaminations (; ). The DNA quantity was significantly different between the two soils, corroborating different DNA quantity achieved from different soil types (; ). The DNA quantity was also different among different DNA extraction methods, being consistent with previous reports (; ), though the difference was unsignificant among five manual methods in the present study. Accorded with previous reports (; ), numbers of raw tags and effective tags were unsignificantly different between the two soils or among six different DNA extraction methods, suggesting that the amplification efficiency was independent of soil type, DNA quality and quantity. However, we observed significant effective ratio (effective tags/raw tags) and GC% differences between the two soils but not among different methods; that also agreed with previous reports (; ). The results indicated that DNA extraction methods have no effect on the formation of low-quality or chimera reads, and the chimera formation or GC% depended on soil types and their associated microbial community. All alpha diversity indices were significantly different between the two soils, and the microbial diversity was much lower in the saline soil than the normal soil (Figure, Supplementary Figure, and Supplementary Table ), being consistent with previous reports (; ).
Also, except Pielou evenness index and Simpson diversity index in the saline soil, other alpha diversity indices were all unsignificantly different among six DNA extraction methods in the same soil. That indicated evenness indices may be more easily affected by DNA extraction methods than richness indices in the saline soil.
That was probably due to low evenness in the saline soil compared with the normal soil (Supplementary Table and Supplementary Figure ), the variation of a single species caused by DNA extraction methods had more effects on the whole community. Previous reports have also observed no significant difference of microbial diversities between two extraction methods and the microbial diversities were unrelated with achieved DNA quantities. The ISO 11063 method has less unique OTUs and lower OTU sum than other methods according to Venn diagrams (Figure and Supplementary Table ); the Gram-positive bacteria proportion in NIB group was also relatively low in the normal soil (Supplementary Figure ). Previous studies have reported that the ISO 11063 method underestimated the rRNA gene abundance in soil microbiomes (; ). Compared with other two methods (ZB, MB), the ISO 11063 method lacked the enzyme lysis step (Table ). We inferred this factor mainly caused the low effectiveness of ISO 11063 method.
Therefore, the physical lysis step should be combined with the enzyme lysis step. We observed no significant alpha diversity difference caused only by physical lysis methods in the normal soil (Figure ), though less Gram-positive bacteria were achieved with the freezing and thawing method. Also, the high proportion of Gram-positive bacteria within NZL group (Supplementary Figures, ) suggested the liquid nitrogen grinding step was effective for Gram-positive bacteria in the normal soil, but lower alpha diversity indices (Figure and Supplementary Table ), less unique OTUs (Figure and Supplementary Table ), and lower proportion of Gram-positive bacteria (Supplementary Figure ) were observed within SZL group than other methods in the saline soil. Because it is the only method with no soil washing pretreatment, we referred that the soil washing pretreatment can increase not only DNA quality and quantity but also the achieved microbial diversity in saline soils. Though alpha diversity indices were significantly low within SZL group, the total relative abundance of halotolerant or halophilic bacteria was high within the group, suggesting some halotolerant or halophilic bacteria may lyse during the soil washing pretreatment. By summarizing our present and previous studies , we recommend soil washing pretreatment should be applied for saline soils, and the pretreatment was especially crucial for hypersaline soils that cannot be achieved with detectable DNA amounts without the pretreatment. High proportion of halophilic microorganisms can still be achieved after PBS washing within hypersaline soils in our previous study.
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Gram-positive bacteria were harder to lyse than Gram-negative bacteria. The DNA extraction efficiency was based on lysis extents of Gram-positive bacteria in soils (; ). Therefore, we predicted proportions of Gram-positive and Gram-negative bacteria in each sample (Supplementary Figures, ). We observed no significant correlation ( P 0.1) between Gram-positive bacteria proportions and alpha diversity indices in the normal soil, but significant positive correlations were observed between Gram-positive bacteria proportions and observed OTU number ( r = 0.76, P. Reviewed by:, Japan Agency for Marine-Earth Science and Technology, Japan, Tianjin Institute of Industrial Biotechnology (CAS), China Copyright © 2018 Xie, Deng, Zhang, Wang, Kang, Bai and Huang.
This is an open-access article distributed under the terms of the. The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) 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: He Huang, †These authors have contributed equally to this work.