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      Screening and Identification of Six Serum microRNAs as Novel Potential Combination Biomarkers for Pulmonary Tuberculosis Diagnosis

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          Abstract

          Background

          It is very difficult to prevent pulmonary tuberculosis (TB) due to the lack of specific and diagnostic markers, which could lead to a high incidence of pulmonary TB. We screened the differentially expressed serum microRNAs (miRNAs) as potential biomarkers for the diagnosis of pulmonary TB.

          Methods

          In this study, serum miRNAs were screened using the Solexa sequencing method as the potential biomarkers for the diagnosis of pulmonary TB. The stem-loop quantitative reverse-transcription polymerase chain reaction (qRT-PCR) assay was used to verify differentially expressed serum miRNAs. The receiver operating characteristic (ROC) curve and logistic regression model were used to analyze the sensitivity and specificity of the single miRNA and a combination of miRNAs for diagnosis, respectively. Using the predicted target genes, we constructed the regulatory networks of miRNAs and genes that were related to pulmonary TB.

          Results

          The Solexa sequencing data showed that 91 serum miRNAs were differentially expressed in pulmonary TB patients, compared to healthy controls. Following qRT-PCR confirmation, six serum miRNAs (hsa-miR-378, hsa-miR-483-5p, hsa-miR-22, hsa-miR-29c, hsa-miR-101 and hsa-miR-320b) showed significant difference among pulmonary TB patients, healthy controls ( P<0.001) and differential diagnosis groups (including patients with pneumonia, lung cancer and chronic obstructive pulmonary disease) ( P<0.05). The logistic regression analysis of a combination of six serum miRNAs revealed that the sensitivity and the specificity of TB diagnosis were 95.0% and 91.8% respectively. The miRNAs-gene regulatory networks revealed that several miRNAs may regulate some target genes involved in immune pathways and participate in the pathogenesis of pulmonary TB.

          Conclusion

          Our study suggests that a combination of six serum miRNAs have great potential to serve as non-invasive biomarkers of pulmonary TB.

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          Most cited references17

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          Integration of biological networks and gene expression data using Cytoscape.

          Cytoscape is a free software package for visualizing, modeling and analyzing molecular and genetic interaction networks. This protocol explains how to use Cytoscape to analyze the results of mRNA expression profiling, and other functional genomics and proteomics experiments, in the context of an interaction network obtained for genes of interest. Five major steps are described: (i) obtaining a gene or protein network, (ii) displaying the network using layout algorithms, (iii) integrating with gene expression and other functional attributes, (iv) identifying putative complexes and functional modules and (v) identifying enriched Gene Ontology annotations in the network. These steps provide a broad sample of the types of analyses performed by Cytoscape.
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            Large-scale sequencing reveals 21U-RNAs and additional microRNAs and endogenous siRNAs in C. elegans.

            We sequenced approximately 400,000 small RNAs from Caenorhabditis elegans. Another 18 microRNA (miRNA) genes were identified, thereby extending to 112 our tally of confidently identified miRNA genes in C. elegans. Also observed were thousands of endogenous siRNAs generated by RNA-directed RNA polymerases acting preferentially on transcripts associated with spermatogenesis and transposons. In addition, a third class of nematode small RNAs, called 21U-RNAs, was discovered. 21U-RNAs are precisely 21 nucleotides long, begin with a uridine 5'-monophosphate but are diverse in their remaining 20 nucleotides, and appear modified at their 3'-terminal ribose. 21U-RNAs originate from more than 5700 genomic loci dispersed in two broad regions of chromosome IV-primarily between protein-coding genes or within their introns. These loci share a large upstream motif that enables accurate prediction of additional 21U-RNAs. The motif is conserved in other nematodes, presumably because of its importance for producing these diverse, autonomously expressed, small RNAs (dasRNAs).
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              Identification of microRNAs and other small regulatory RNAs using cDNA library sequencing.

              Distinct classes of small RNAs, 20-32 nucleotides long, play important regulatory roles for diverse cellular processes. It is therefore important to identify and quantify small RNAs as a function of development, tissue and cell type, in normal and disease states. Here we describe methods to prepare cDNA libraries from pools of small RNAs isolated from organisms, tissues or cells. These methods enable the identification of new members or new classes of small RNAs, and they are also suitable to obtain miRNA expression profiles based on clone count frequencies. This protocol includes the use of new deep sequencing methods (454/Roche and Solexa) to facilitate the characterization of diverse sequence pools of small RNAs.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2013
                5 December 2013
                : 8
                : 12
                : e81076
                Affiliations
                [1 ]Institute of Cell Biology, Zhejiang University, Hangzhou, China
                [2 ]State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
                [3 ]Department of Radiology, Taizhou Hospital, Taizhou, China
                [4 ]Department of Respiratory Medicine, Sixth of People’s Hospital of Shaoxing zhejiang, Shaoxing, China
                [5 ]Department of Respiratory Medicine, Tongde Hospital of Zhejiang Province, Hangzhou, China
                Sanjay Gandhi Medical Institute, India
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: XZ JCL. Performed the experiments: XZ ZLC. Analyzed the data: XZ JG TTJ CW JYL. Contributed reagents/materials/analysis tools: XZ SFF YYL DDX JXW ZJL LLW XYY ZPP. Wrote the paper: XZ YQQ JCL.

                Article
                PONE-D-13-29159
                10.1371/journal.pone.0081076
                3857778
                24349033
                375a6b4d-d5ec-4d17-b8ff-f61299aef9e0
                Copyright @ 2013

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 15 July 2013
                : 8 October 2013
                Page count
                Pages: 11
                Funding
                This work was supported by grants from National Special Sci-Tech Projects (No. 2012ZX10005001-006), National Natural Science Foundation of China (No. 81072724, No.81273882) and National Science Technology Major Project of the Ministry of Science and Technology: New Drug Creation of National 12th Five-Year Plan (No. 2011ZX09302-003-03). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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