scholarly journals Limits in the detection of m6A changes using MeRIP/m6A-seq

2019 ◽  
Author(s):  
Alexa B.R. McIntyre ◽  
Nandan S. Gokhale ◽  
Leandro Cerchietti ◽  
Samie R. Jaffrey ◽  
Stacy M. Horner ◽  
...  
Keyword(s):  
Gene Expression ◽  
Statistical Methods ◽  
Common Method ◽  
Published Data ◽  
Data Sets ◽  
Cell Type ◽  
Peak Overlap ◽  
Cellular Mrnas ◽  
Rna Immunoprecipitation

AbstractMany cellular mRNAs contain the modified base m6A, and recent studies have suggested that various stimuli can lead to changes in m6A. The most common method to map m6A and to predict changes in m6A between conditions is methylated RNA immunoprecipitation sequencing (MeRIP-seq), through which methylated regions are detected as peaks in transcript coverage from immunoprecipitated RNA relative to input RNA. Here, we generated replicate controls and reanalyzed published MeRIP-seq data to estimate reproducibility across experiments. We found that m6A peak overlap in mRNAs varies from ∼30 to 60% between studies, even in the same cell type. We then assessed statistical methods to detect changes in m6A peaks as distinct from changes in gene expression. However, from these published data sets, we detected few changes under most conditions and were unable to detect consistent changes across studies of similar stimuli. Overall, our work identifies limits to MeRIP-seq reproducibility in the detection both of peaks and of peak changes and proposes improved approaches for analysis of peak changes.

Non-Overlapping and Non–Cell-Type–Specific Gene Expression Signatures Predict Lung Cancer Survival

2008 ◽  
Vol 26 (6) ◽  
pp. 877-883 ◽  
Author(s):  
Zhifu Sun ◽  
Dennis A. Wigle ◽  
Ping Yang
Keyword(s):  
Gene Expression ◽  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Gene Signature ◽  
Data Sets ◽  
Cell Type ◽  
Validation Data ◽  
Adjusted Risk ◽  
Gene Expression Signatures

Purpose Gene expression profiling for outcome prediction of non–small-cell lung cancer (NSCLC) remains clouded by heterogeneous and unvalidated results. This study applied multivariate approaches to identify and evaluate value-added gene expression signatures in two types of NSCLC. Materials and Methods Two NSCLC oligonucleotide microarray data sets of adenocarcinoma and squamous cell carcinoma were used as training sets to select prognostic genes independent of conventional predictors. The top 50 genes from each set were used to predict the outcomes of two independent validation data sets of 84 and 91 NSCLC cases. Results Adenocarcinomas with the 50-gene signature from adenocarcinoma in both validation data sets had a 2.4-fold (95% CI, 1.3 to 4.4 and 1.0 to 5.8) increased mortality after adjustment for conventional predictors. Squamous cell carcinoma with this high-risk signature had an adjusted risk of 1.1 (95% CI, 0.4 to 3.2) in one data set and 2.5 (95% CI, 1.1 to 5.8) in another consisting of stage I tumors. Adenocarcinoma with the 50-gene signature from squamous cell carcinoma had an elevated risk of 3.5 (95% CI, 1.4 to 9.0) after adjustment for conventional predictors. Squamous cell carcinoma with this high risk signature had an adjusted risk of 1.8 (95% CI, 0.7 to 4.6). Despite the little overlap in individual genes, the two gene signatures had significant functional connectedness in molecular pathways. Conclusion Two non-overlapping but functionally related gene expression signatures provide consistently improved survival prediction for NSCLC regardless of histologic cell type. Multiple sets of genes may exist for NSCLC with predictive value, but ones with independent predictive value beyond clinical predictors will be required for clinical translation.

scholarly journals Spermatogonial Type 3 Deiodinase Regulates Thyroid Hormone Target Genes in Developing Testicular Somatic Cells

Endocrinology ◽  
2019 ◽  
Vol 160 (12) ◽  
pp. 2929-2945
Author(s):  
M Elena Martinez ◽  
Christine W Lary ◽  
Aldona A Karaczyn ◽  
Michael D Griswold ◽  
Arturo Hernandez
Keyword(s):  
Gene Expression ◽  
Thyroid Hormone ◽  
Target Genes ◽  
Somatic Cells ◽  
Published Data ◽  
Data Sets ◽  
Testicular Cells ◽  
Differentiation And Proliferation ◽  
Neonatal Testis ◽  
Type 3

Abstract Premature overexposure to thyroid hormone causes profound effects on testis growth, spermatogenesis, and male fertility. We used genetic mouse models of type 3 deiodinase (DIO3) deficiency to determine the genetic programs affected by premature thyroid hormone action and to define the role of DIO3 in regulating thyroid hormone economy in testicular cells. Gene expression profiling in the neonatal testis of DIO3-deficient mice identified 5699 differentially expressed genes. Upregulated and downregulated genes were, respectively, involved according to DAVID analysis with cell differentiation and proliferation. They included anti-Müllerian hormone and genes involved in the formation of the blood–testis barrier, which are specific to Sertoli cells (SCs). They also included steroidogenic genes, which are specific to Leydig cells. Comparison with published data sets of genes enriched in SCs and spermatogonia, and responsive to retinoic acid (RA), identified a subset of genes that were regulated similarly by RA and thyroid hormone. This subset of genes showed an expression bias, as they were downregulated when enriched in spermatogonia and upregulated when enriched in SCs. Furthermore, using a genetic approach, we found that DIO3 is not expressed in SCs, but spermatogonia-specific inactivation of DIO3 led to impaired testis growth, reduced SC number, decreased cell proliferation and, especially during neonatal development, altered gene expression specific to somatic cells. These findings indicate that spermatogonial DIO3 protects testicular cells from untimely thyroid hormone signaling and demonstrate a mechanism of cross-talk between somatic and germ cells in the neonatal testis that involves the regulation of thyroid hormone availability and action.

Estrogen-Regulated Genes Predict Survival in Hormone Receptor–Positive Breast Cancers

2006 ◽  
Vol 24 (11) ◽  
pp. 1656-1664 ◽  
Author(s):  
Daniel S. Oh ◽  
Melissa A. Troester ◽  
Jerry Usary ◽  
Zhiyuan Hu ◽  
Xiaping He ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Published Data ◽  
Data Sets ◽  
Breast Cancers ◽  
Outcome Predictor ◽  
Training Set ◽  
Hormone Receptor Positive ◽  
Prognosis Group ◽  
Mcf 7

Purpose The prognosis of a patient with estrogen receptor (ER) and/or progesterone receptor (PR) –positive breast cancer can be highly variable. Therefore, we developed a gene expression–based outcome predictor for ER+ and/or PR+ (ie, luminal) breast cancer patients using biologic differences among these tumors. Materials and Methods The ER+ MCF-7 breast cancer cell line was treated with 17β-estradiol to identify estrogen-regulated genes. These genes were used to develop an outcome predictor on a training set of 65 luminal epithelial primary breast carcinomas. The outcome predictor was then validated on three independent published data sets. Results The estrogen-induced gene set identified in MCF-7 cells was used to hierarchically cluster a 65 tumor training set into two groups, which showed significant differences in survival (P = .0004). Supervised analyses identified 822 genes that optimally defined these two groups, with the poor-prognosis group IIE showing high expression of cell proliferation and antiapoptosis genes. The good prognosis group IE showed high expression of estrogen- and GATA3-regulated genes. Mean expression profiles (ie, centroids) created for each group were applied to ER+ and/or PR+ tumors from three published data sets. For all data sets, Kaplan-Meier survival analyses showed significant differences in relapse-free and overall survival between group IE and IIE tumors. Multivariate Cox analysis of the largest test data set showed that this predictor added significant prognostic information independent of standard clinical predictors and other gene expression–based predictors. Conclusion This study provides new biologic information concerning differences within hormone receptor–positive breast cancers and a means of predicting long-term outcomes in tamoxifen-treated patients.

Gene-family profiling: a normalization-free real-time RT-PCR approach with increased physiological resolution

2010 ◽  
Vol 42 (1) ◽  
pp. 1-4 ◽  
Author(s):  
Stian Ellefsen ◽  
Kåre-Olav Stensløkken
Keyword(s):  
Gene Expression ◽  
Gene Family ◽  
Real Time ◽  
Published Data ◽  
Data Sets ◽  
Rt Pcr ◽  
Experimental Systems ◽  
As Species ◽  
Physiological Relevance ◽  
Reference Gene Expression

Here we present gene-family profiling, an approach for improved real-time RT-PCR analyses. It is based on recently published data, and we argue that it bring solutions to two major problems. First, it is normalization-free and therefore unbiased by variation in normalization agents such as reference gene expression. This strengthens data validity and also increases data resolution, reducing coefficients of variation by ∼48% in our data sets. Second, it includes all members of a particular gene family, treating individual genes as constituting fractions of collective gene-family expression rather than as unrelated entities. Because different family members typically fulfill similar, but complementary roles, this increases the physiological relevance. Gene-family profiling is particularly useful for evaluation of cellular adaptations to physiological challenges and for comparison of properties between different experimental systems such as species, tissues or tissue regions. In addition, it seems suitable for analyses of inherent patterns of gene expression in singular biological samples. In our opinion, the approach is valuable for both research and diagnostic purposes, and should be readily applicable for many studies of gene expression. Its value is likely to increase as science continues to unravel gene function.

scholarly journals Maximizing ecological and evolutionary insight from bisulfite sequencing data sets

2016 ◽  
Author(s):  
Amanda J. Lea ◽  
Tauras P. Vilgalys ◽  
Paul A.P. Durst ◽  
Jenny Tung
Keyword(s):  
Dna Methylation ◽  
Population Structure ◽  
Effect Size ◽  
Evolutionary Biology ◽  
Bisulfite Sequencing ◽  
Published Data ◽  
Data Sets ◽  
Cell Type ◽  
Sequencing Data ◽  
Bisulfite Sequencing Data

AbstractThe role of DNA methylation in development, divergence, and the response to environmental stimuli is of substantial interest in ecology and evolutionary biology. Measuring genome-wide DNA methylation is increasingly feasible using sodium bisulfite sequencing. Here, we analyze simulated and published data sets to demonstrate how effect size, kinship/population structure, taxonomic differences, and cell type heterogeneity influence the power to detect differential methylation in bisulfite sequencing data sets. Our results reveal that the effect sizes typical of evolutionary and ecological studies are modest, and will thus require data sets larger than those currently in common use. Additionally, our findings emphasize that statistical approaches that ignore the properties of bisulfite sequencing data (e.g., its count-based nature) or key sources of variance in natural populations (e.g., population structure or cell type heterogeneity) often produce false negatives or false positives, thus leading to incorrect biological conclusions. Finally, we provide recommendations for handling common issues that arise in bisulfite sequencing analyses and a freely available R Shiny application for simulating and performing power analyses on bisulfite sequencing data. This app, available at www.tung-lab.org/protocols-and-software.html, allows users to explore the effects of sequencing depth, sample size, population structure, and expected effect size, tailored to their own system.

Analysis of Transcriptomic Similarity between Osteosarcoma Cell Lines and Primary Tumors

Oncology ◽  
2020 ◽  
Vol 98 (11) ◽  
pp. 814-816
Author(s):  
Sai Batchu ◽  
Justin Lee Gold
Keyword(s):  
Gene Expression ◽  
Gene Ontology ◽  
Cell Lines ◽  
Disease Process ◽  
Published Data ◽  
Data Sets ◽  
Osteosarcoma Cell ◽  
Gene Ontology Analysis ◽  
Primary Tumors

<b><i>Background:</i></b> Osteosarcoma (OS) cell lines are commonly used to mimic tumors for in vitro experiments. The present study explores the resemblance of OS cell lines to OS primary tumors in regard to gene expression. <b><i>Methods:</i></b> Transcriptomic data were retrieved from published data sets for 18 primary tumor samples and 13 commonly used OS cell lines. Tumor purity was accounted for when correlating tumor and cell line gene expression. Differentially expressed genes between tumors and cell lines were discovered and gene ontology analysis was performed. <b><i>Results:</i></b> Certain commonly used cell lines, including NY, NOS1, and U2OS, display less resemblance to OS tumors than do other cell lines. For genes overexpressed in tumors, and consequently underexpressed in cell lines, gene ontology analysis enriched pathways related to cell-cell adhesion and stimulus detection. <b><i>Conclusion:</i></b> The pathways dysregulated between cell lines and tumors have been implicated in OS pathogenesis. Therefore, the findings suggest that the transcriptome of OS cell lines may not be completely representative of OS primary tumors’ gene expression and the disease process.

scholarly journals Blood Genomics Identifies Three Subtypes of Systemic Lupus Erythematosus: “IFN-High,” “NE-High,” and “Mixed”

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Mintian Cui ◽  
Taotao Li ◽  
Xinwei Yan ◽  
Chao Wang ◽  
Qi Shen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Systemic Lupus Erythematosus ◽  
Molecular Mechanism ◽  
Molecular Diagnosis ◽  
Lupus Erythematosus ◽  
Molecular Mechanisms ◽  
Published Data ◽  
Data Sets ◽  
Systemic Lupus ◽  
Drug Benefits

Purpose. Systemic lupus erythematosus (SLE) is a systemic and multifactorial autoimmune disease, and its diverse clinical manifestations affect molecular diagnosis and drug benefits. Our study was aimed at defining the SLE subtypes based on blood transcriptome data, analyzing functional patterns, and elucidating drug benefits. Methods. Three data sets were used in this paper that were collected from the Gene Expression Omnibus (GEO) database, which contained two published data sets of pediatric and adult SLE patients (GSE65391, GSE49454) and public longitudinal data (GSE72754) from a cohort of SLE patients treated with IFN-α Kinoid (IFN-K). Based on disease activity scores and gene expression data, we defined a global SLE signature and merged three clustering algorithms to develop a single-sample subtype classifier (SSC). Systematic analysis of coexpression networks based on modules revealed the molecular mechanism for each subtype. Results. We identified 92 genes as a signature of the SLE subtypes and three intrinsic subsets (“IFN-high,” “NE-high,” and “mixed”), which varied in disease severity. We speculated that IFN-high might be due to the overproduction of interferons (IFNs) caused by viral infection, leading to the formation of autoantibodies. NE-high might primarily result from bacterial and fungal infections that stimulated neutrophils (NE) to produce neutrophil extracellular traps (NETs) and induced individual autoimmune responses. The mixed type contained both of these molecular mechanisms and showed an intrinsic connection. Conclusions. Our research results indicated that identifying the molecular mechanism associated with different SLE subtypes would benefit the molecular diagnosis and stratified therapy. Moreover, repositioning of IFN-K based on subtypes also revealed an improved therapeutic effect, providing a new direction for disease treatment and drug development.

scholarly journals BRAin INteractive Sequencing Analysis Tool (BRAIN-SAT); facilitating interactive transcriptome analyses (http://brainsat.eu/)

2019 ◽  
Author(s):  
M.L. Dubbelaar ◽  
M.L. Brummer ◽  
M. Meijer ◽  
B.J.L. Eggen ◽  
H.W.G.M. Boddeke
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Expression Analysis ◽  
New Technologies ◽  
Easy Access ◽  
Published Data ◽  
Data Sets ◽  
Analysis Tool ◽  
Sequencing Analysis ◽  
Quantitative Expression

AbstractOver the last decade, a large number of glia transcriptome studies has been published. New technologies and platforms have been developed to allow access and interrogation of the published data. The increase in large transcriptomic data sets allows for innovative in silico analyses to address biological questions. Here we present BRAIN-SAT, the follow-up of our previous database GOAD, with several new features available on an interactive platform that enables access to recent, high quality bulk and single cell RNA-Seq data. The combination of several functions including gene searches, differential and quantitative expression analysis and a single cell expression analysis feature enables the exploration of published data sets at different levels. These different functionalities can be used for researchers and research companies in the neuroscience field to evaluate and visualize gene expression levels in a set of relevant publications. Here, we present a new platform with easy access to published gene expression studies for data exploration and gene of interest searches.

scholarly journals dtangle: accurate and fast cell-type deconvolution

2018 ◽  
Author(s):  
Gregory J. Hunt ◽  
Saskia Freytag ◽  
Melanie Bahlo ◽  
Johann A. Gagnon-Bartsch
Keyword(s):  
Gene Expression ◽  
Differential Expression Analysis ◽  
Disease Status ◽  
Data Sets ◽  
Cell Type ◽  
Link Type ◽  
Cell Type Composition ◽  
Type Composition ◽  
Human Immune Response ◽  
Gene Expression Studies

AbstractMotivationUnderstanding cell type composition is important to understanding many biological processes. Furthermore, in gene expression studies cell type composition can confound differential expression analysis (DEA). To aid understanding cell type composition, methods of estimating (deconvolving) cell type proportions from gene expression data have been developed.ResultsWe propose dtangle, a new cell-type deconvolution method. dtangle works on a range of DNA microarray and bulk RNA-seq platforms. It estimates cell-type proportions using publicly available, often cross-platform, reference data. To comprehensively evaluate dtangle, we assemble ten benchmark data sets. Here, dtangle is competitive with published deconvolution methods, is robust to selection of tuning parameters and is quicker than other methods. As a case study, we investigate the human immune response to Lyme disease. dtangle’s estimates reveal a temporal trend consistent with previous findings and are important covariates for DEA across disease status.Availabilitydtangle is on CRAN (cran.r-project.org/package=dtangle) or github (dtangle.github.io)[email protected]

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