scholarly journals Research Article Differential expression of microRNAs in a hyperoxia-induced rat bronchopulmonary dysplasia model revealed by deep sequencing

2021 ◽  
Vol 20 (2) ◽  
Author(s):  
D. Li ◽  
H. Cheng ◽  
L. Chen ◽  
B. Wu
Keyword(s):  
Differential Expression ◽  
Bronchopulmonary Dysplasia ◽  
Deep Sequencing ◽  
Research Article

Differential expression of long non-coding RNAs in hyperoxia-induced bronchopulmonary dysplasia

2016 ◽  
Vol 34 (5) ◽  
pp. 299-309 ◽  
Author(s):  
Tian-Ping Bao ◽  
Rong Wu ◽  
Huai-Ping Cheng ◽  
Xian-Wei Cui ◽  
Zhao-Fang Tian
Keyword(s):  
Differential Expression ◽  
Bronchopulmonary Dysplasia ◽  
Non Coding Rnas

An effective differential expression analysis of deep-sequencing data based on the Poisson log-normal model

2015 ◽  
Vol 13 (02) ◽  
pp. 1550001 ◽  
Author(s):  
Jun Wu ◽  
Xiaodong Zhao ◽  
Zongli Lin ◽  
Zhifeng Shao
Keyword(s):  
Parameter Estimation ◽  
Differential Expression ◽  
Expression Analysis ◽  
Deep Sequencing ◽  
Differential Expression Analysis ◽  
Biomedical Science ◽  
Sequencing Data ◽  
Discrimination Ability ◽  
Deep Sequencing Data ◽  
Log Normal

Tremendous amount of deep-sequencing data has unprecedentedly improved our understanding in biomedical science by digital sequence reads. To mine useful information from such data, a proper distribution for modeling all range of the count data and accurate parameter estimation are required. In this paper, we propose a method, called "DEPln," for differential expression analysis based on the Poisson log-normal (PLN) distribution with an accurate parameter estimation strategy, which aims to overcome the inconvenience in the mathematical analysis of the traditional PLN distribution. The performance of our proposed method is validated by both synthetic and real data. Experimental results indicate that our method outperforms the traditional methods in terms of the discrimination ability and results in a good tradeoff between the recall rate and the precision. Thus, our work provides a new approach for gene expression analysis and has strong potential in deep-sequencing based research.

scholarly journals Deep Sequencing of Guinea Pig (Cavia Porcellus) Lung Small RNAs Reveals Differential Expression of microRNAs between Non-infected and BCG-Infected Lungs

2021 ◽  
Author(s):  
Xiaoqi jing ◽  
Biqiong Jiang ◽  
Long Cheng ◽  
Yong Li
Keyword(s):  
Immune Responses ◽  
Guinea Pig ◽  
Differential Expression ◽  
Deep Sequencing ◽  
Target Genes ◽  
Transforming Growth Factor ◽  
Expression Profiles ◽  
Mitogen Activated Protein Kinase ◽  
Public Health Burden ◽  
Small Noncoding Rnas

Abstract Background: Pulmonary tuberculosis (TB) caused by Mycobacterium tuberculosis (Mtb) infection remains a major public health burden worldwide. It has been well documented that a group of small noncoding RNAs, microRNAs (miRNAs) are involved in the development and pathogenesis many diseases, including the TB. Guinea pigs are considered as one of the best animal models for biomedical research in TB, the potential roles of miRNAs in the innate immune regulation of guinea pig lung against Mtb infection are not well understood. Methods: In this study, we investigated the differential expression of miRNA profiles between the un-infected lungs and Mycobacterium bovis bacillus Calmette-Guérin (BCG)-infected lungs of guinea pigs via deep sequencing and bioinformatics analysis. Results: A total of 2508 miRNAs were identified, among them 1187 were conserved miRNAs and 56 were novel miRNAs in the uninfected lungs, and 1202 were identified as conserved miRNAs and 63 were novel miRNAs in the BCG-infected lungs. Interestingly, comparison analysis further identified 902 co-expressed miRNAs and 585 distinct miRNAs between these two groups. Of the 15 most abundantly conserved miRNAs in guinea pig lungs, which belong to 7 miRNA families, including miR23, miR29, miR145, miR320, miR378, miR451, and miR423. 13 of these 15 most abundant miRNAs were significantly downregulated and 2 of them were significantly upregulated in the BCG-infected lungs. Individually, miRNA Let-7f-5p, let-7f, let-7-5p and let-7b-5p were the most abundant in both profiles of the non-infected and BCG-infected guinea pig lungs. The predicted target genes of specific miRNAs found in guinea pig lungs were involved in regulation signaling pathways related to immune responses, including Toll-like receptors (TLRs), nuclear factor (NF)-kappa B, Wnt, mitogen-activated protein kinase (MAPK), and transforming growth factor (TGF)-beta signaling, as well as related to autophagy signaling mTOR and apoptotic molecule p53. Conclusions: These data of comprehensive analysis of miRNA transcriptome demonstrated the differential expression profiles of miRNAs during M. tuberculosis infection of guinea pig lungs. These results could facilitate the future exploitation of the roles of miRNAs in regulation of immune responses to M. tuberculosis infection using the guinea pig model.

scholarly journals Integrative Deep Sequencing of the Mouse Lung Transcriptome Reveals Differential Expression of Diverse Classes of Small RNAs in Response to Respiratory Virus Infection

mBio ◽  
2011 ◽  
Vol 2 (6) ◽  
Author(s):  
Xinxia Peng ◽  
Lisa Gralinski ◽  
Martin T. Ferris ◽  
Matthew B. Frieman ◽  
Matthew J. Thomas ◽  
...  
Keyword(s):  
Influenza Virus ◽  
Virus Infection ◽  
Differential Expression ◽  
Deep Sequencing ◽  
Small Rnas ◽  
Host Response ◽  
Mouse Strains ◽  
Sequencing Analysis ◽  
Virus Infections ◽  
Protein Coding

ABSTRACT We previously reported widespread differential expression of long non-protein-coding RNAs (ncRNAs) in response to virus infection. Here, we expanded the study through small RNA transcriptome sequencing analysis of the host response to both severe acute respiratory syndrome coronavirus (SARS-CoV) and influenza virus infections across four founder mouse strains of the Collaborative Cross, a recombinant inbred mouse resource for mapping complex traits. We observed differential expression of over 200 small RNAs of diverse classes during infection. A majority of identified microRNAs (miRNAs) showed divergent changes in expression across mouse strains with respect to SARS-CoV and influenza virus infections and responded differently to a highly pathogenic reconstructed 1918 virus compared to a minimally pathogenic seasonal influenza virus isolate. Novel insights into miRNA expression changes, including the association with pathogenic outcomes and large differences between in vivo and in vitro experimental systems, were further elucidated by a survey of selected miRNAs across diverse virus infections. The small RNAs identified also included many non-miRNA small RNAs, such as small nucleolar RNAs (snoRNAs), in addition to nonannotated small RNAs. An integrative sequencing analysis of both small RNAs and long transcripts from the same samples showed that the results revealing differential expression of miRNAs during infection were largely due to transcriptional regulation and that the predicted miRNA-mRNA network could modulate global host responses to virus infection in a combinatorial fashion. These findings represent the first integrated sequencing analysis of the response of host small RNAs to virus infection and show that small RNAs are an integrated component of complex networks involved in regulating the host response to infection. IMPORTANCE Most studies examining the host transcriptional response to infection focus only on protein-coding genes. However, mammalian genomes transcribe many short and long non-protein-coding RNAs (ncRNAs). With the advent of deep-sequencing technologies, systematic transcriptome analysis of the host response, including analysis of ncRNAs of different sizes, is now possible. Using this approach, we recently discovered widespread differential expression of host long (>200 nucleotide [nt]) ncRNAs in response to virus infection. Here, the samples described in the previous report were again used, but we sequenced another fraction of the transcriptome to study very short (about 20 to 30 nt) ncRNAs. We demonstrated that virus infection also altered expression of many short ncRNAs of diverse classes. Putting the results of the two studies together, we show that small RNAs may also play an important role in regulating the host response to virus infection.

scholarly journals Differential expression of miRNAs and their target genes in senescing leaves and siliques: insights from deep sequencing of small RNAs and cleaved target RNAs

2014 ◽  
Vol 38 (1) ◽  
pp. 188-200 ◽  
Author(s):  
SHAWN R. THATCHER ◽  
SHAUL BURD ◽  
CHRISTOPHER WRIGHT ◽  
AMNON LERS ◽  
PAMELA J. GREEN
Keyword(s):  
Differential Expression ◽  
Deep Sequencing ◽  
Small Rnas ◽  
Target Genes

scholarly journals Deep sequencing reveals differential expression of microRNAs in favorable versus unfavorable neuroblastoma

2010 ◽  
Vol 38 (17) ◽  
pp. 5919-5928 ◽  
Author(s):  
Johannes H. Schulte ◽  
Tobias Marschall ◽  
Marcel Martin ◽  
Philipp Rosenstiel ◽  
Pieter Mestdagh ◽  
...  
Keyword(s):  
Differential Expression ◽  
Deep Sequencing

scholarly journals An Empirical Evaluation of normalization Methods for MicroRNA Arrays in a Liposarcoma Study

2013 ◽  
Vol 12 ◽  
pp. CIN.S11384 ◽  
Author(s):  
Li-Xuan Qin ◽  
Tom Tuschl ◽  
Samuel Singer
Keyword(s):  
Differential Expression ◽  
Deep Sequencing ◽  
Expression Profiles ◽  
Microrna Expression ◽  
Quantile Normalization ◽  
Tissue Samples ◽  
Expression Arrays ◽  
Normalization Methods ◽  
Array Normalization ◽  
Disease Relevance

Background Methods for array normalization, such as median and quantile normalization, were developed for mRNA expression arrays. These methods assume few or symmetric differential expression of genes on the array. However, these assumptions are not necessarily appropriate for microRNA expression arrays because they consist of only a few hundred genes and a reasonable fraction of them are anticipated to have disease relevance. Methods We collected microRNA expression profiles for human tissue samples from a liposarcoma study using the Agilent microRNA arrays. For a subset of the samples, we also profiled their microRNA expression using deep sequencing. We empirically evaluated methods for normalization of microRNA arrays using deep sequencing data derived from the same tissue samples as the benchmark. Results: In this study, we demonstrated array effects in microRNA arrays using data from a liposarcoma study. We found moderately high correlation between Agilent data and sequence data on the same tumors, with the Pearson correlation coefficients ranging from 0.6 to 0.9. Array normalization resulted in some improvement in the accuracy of the differential expression analysis. However, even with normalization, there is still a significant number of false positive and false negative microRNAs, many of which are expressed at moderate to high levels. Conclusions Our study demonstrated the need to develop more efficient normalization methods for microRNA arrays to further improve the detection of genes with disease relevance. Until better methods are developed, an existing normalization method such as quantile normalization should be applied when analyzing microRNA array data.

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