Calling genotypes from public RNA-sequencing data enables identification of genetic variants that affect gene-expression levels

Background. Necrotizing enterocolitis (NEC) is one of the most serious gastrointestinal disease-causing high morbidity and mortality in premature infants. However, the underlying mechanism of the pathogenesis of NEC is still not fully understood. Methods. RNA sequencing of intestinal specimens from 9 NEC and 5 controls was employed to quantify the gene expression levels. RNA sequencing was employed to quantify the gene expression levels. DESeq2 tool was used to identify the differentially expressed genes. The biological function, pathways, transcription factors, and immune cells dysregulated in NEC were characterized by gene set enrichment analysis. Results. In the present study, we analyzed RNA sequencing data of NECs and controls and revealed that immune-related pathways were highly activated, while some cellular responses to external stimuli-related pathways were inactivated in NEC. Moreover, B cells, macrophages M1, and plasma cells were identified as the major cell types involved in NEC. Furthermore, we also found that inflammation-related transcription factor genes, such as STAT1, STAT2, and IRF2, were significantly activated in NEC, further suggesting that these TFs might play critical roles in NEC pathogenesis. In addition, NEC samples exhibited heterogeneity to some extent. Interestingly, two subgroups in the NEC samples were identified by hierarchical clustering analysis. Notably, B cells, T cells, Th1, and Tregs involved in adaptive immune were predicted to highly infiltrate into subgroup I, while subgroup II was significantly infiltrated by neutrophils. The heterogeneity of immune cells in NEC indicated that both innate and adaptive immunes might induce NEC-related inflammatory response. Conclusions. In summary, we systematically analyzed inflammation-related genes, signaling pathways, and immune cells to characterize the NEC pathogenesis and samples, which greatly improved our understanding of the roles of inflammatory responses in NEC.

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Enhancer variants associated with Alzheimer’s disease affect gene expression via chromatin looping

BMC Medical Genomics ◽

10.1186/s12920-019-0574-8 ◽

2019 ◽

Vol 12 (1) ◽

Cited By ~ 7

Author(s):

Masataka Kikuchi ◽

Norikazu Hara ◽

Mai Hasegawa ◽

Akinori Miyashita ◽

Ryozo Kuwano ◽

...

Keyword(s):

Gene Expression ◽

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Regulatory Elements ◽

Genome Wide Association Studies ◽

Expression Levels ◽

Chromatin Loops ◽

Coding Regions ◽

Affect Gene Expression ◽

Gene Expression Levels

Abstract Background Genome-wide association studies (GWASs) have identified single-nucleotide polymorphisms (SNPs) that may be genetic factors underlying Alzheimer’s disease (AD). However, how these AD-associated SNPs (AD SNPs) contribute to the pathogenesis of this disease is poorly understood because most of them are located in non-coding regions, such as introns and intergenic regions. Previous studies reported that some disease-associated SNPs affect regulatory elements including enhancers. We hypothesized that non-coding AD SNPs are located in enhancers and affect gene expression levels via chromatin loops. Methods To characterize AD SNPs within non-coding regions, we extracted 406 AD SNPs with GWAS p-values of less than 1.00 × 10− 6 from the GWAS catalog database. Of these, we selected 392 SNPs within non-coding regions. Next, we checked whether those non-coding AD SNPs were located in enhancers that typically regulate gene expression levels using publicly available data for enhancers that were predicted in 127 human tissues or cell types. We sought expression quantitative trait locus (eQTL) genes affected by non-coding AD SNPs within enhancers because enhancers are regulatory elements that influence the gene expression levels. To elucidate how the non-coding AD SNPs within enhancers affect the gene expression levels, we identified chromatin-chromatin interactions by Hi-C experiments. Results We report the following findings: (1) nearly 30% of non-coding AD SNPs are located in enhancers; (2) eQTL genes affected by non-coding AD SNPs within enhancers are associated with amyloid beta clearance, synaptic transmission, and immune responses; (3) 95% of the AD SNPs located in enhancers co-localize with their eQTL genes in topologically associating domains suggesting that regulation may occur through chromatin higher-order structures; (4) rs1476679 spatially contacts the promoters of eQTL genes via CTCF-CTCF interactions; (5) the effect of other AD SNPs such as rs7364180 is likely to be, at least in part, indirect through regulation of transcription factors that in turn regulate AD associated genes. Conclusion Our results suggest that non-coding AD SNPs may affect the function of enhancers thereby influencing the expression levels of surrounding or distant genes via chromatin loops. This result may explain how some non-coding AD SNPs contribute to AD pathogenesis.

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Genetic Variants Associated mRNA Stability in Lung

10.21203/rs.3.rs-770241/v1 ◽

2021 ◽

Author(s):

Jian-Rong Li ◽

Mabel Tang ◽

Yafang Li ◽

Christopher I Amos ◽

Chao Cheng

Keyword(s):

Gene Expression ◽

Mrna Stability ◽

Genetic Variants ◽

Molecular Mechanisms ◽

Rna Binding ◽

Tissue Expression ◽

Rna Seq ◽

Genetic Traits ◽

Expression Levels ◽

Gene Expression Levels

Abstract Background: Expression quantitative trait loci (eQTLs) analyses have been widely used to identify genetic variants associated with gene expression levels to understand what molecular mechanisms underlie genetic traits. The resultant eQTLs might affect the expression of associated genes through transcriptional or post-transcriptional regulation. In this study, we attempt to distinguish these two types of regulation by identifying genetic variants associated with mRNA stability of genes (stQTLs).Results: Here, we presented a computational framework that take the advantage of recently developed methods to infer the mRNA stability of genes based on RNA-seq data and performed association analysis to identify stQTLs. Using the Genotype-Tissue Expression (GTEx) lung RNA-Seq data, we identified a total of 142,801 stQTLs for 3,942 genes and 186,132 eQTLs for 4,751 genes from 15,122,700 genetic variants for 13,476 genes, respectively. Interesting, our results indicated that stQTLs were enriched in the CDS and 3’UTR regions, while eQTLs are enriched in the CDS, 3’UTR, 5’UTR, and upstream regions. We also found that stQTLs are more likely than eQTLs to overlap with RNA binding protein (RBP) and microRNA (miRNA) binding sites. Our analyses demonstrate that simultaneous identification of stQTLs and eQTLs can provide more mechanistic insight on the association between genetic variants and gene expression levels.

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Genetic variants in R-Spondin/RNF43 complex and gene expression levels to predict efficacy of cetuximab (cet) in patients (pts) with metastatic colorectal cancer (mCRC): Data from the FIRE-3 phase III trial.

Journal of Clinical Oncology ◽

10.1200/jco.2020.38.4_suppl.190 ◽

2020 ◽

Vol 38 (4_suppl) ◽

pp. 190-190

Author(s):

Francesca Battaglin ◽

Yi Xiao ◽

Joshua Millstein ◽

Andreas Seeber ◽

Hiroyuki Arai ◽

...

Keyword(s):

Gene Expression ◽

Genetic Variants ◽

Multivariable Analysis ◽

Colorectal Carcinogenesis ◽

Phase Iii ◽

First Line ◽

Tissue Samples ◽

Expression Levels ◽

The Impact ◽

Gene Expression Levels

190 Background: Wnt signaling deregulation is a primary driver of colorectal carcinogenesis. RNF43 is a key suppressor of Wnt activation while R-Spodin inhibits RNF43 activity. RNF43 mutations are associated with the serrated neoplasia pathway, BRAF mutation and MSI. We hypothesized that genetic variants in the R-Spodin/RNF43 complex and corresponding genes expression levels may predict cetuximab efficacy in mCRC pts. Methods: Genomic DNA from blood samples of pts enrolled in the randomized FIRE-3 trial was genotyped through the OncoArray, a custom array manufactured by Illumina. The impact on outcome of 17 functional SNPs within RNF43/ ZNRF3, LGR4/5 and RSPO1/2/3 was analyzed in 129 pts treated with first-line FOLFIRI/cet and 107 pts treated with FOLFIRI/bevacizumab (bev). Gene expression levels were measured from tumor tissue samples from 102 pts in the cet arm by HTG EdgeSeq Oncology Biomarker Panel. False discovery rate (FDR) for gene expression analysis was computed using the Benjamini-Hochberg approach (significant Q < 0.1). Results: In the cet cohort, pts with the C/C genotype of ZNRF3 rs132531 had significantly shorter overall survival compared to any T allele carriers (mOS: 20.3 vs 52 mo) in both univariable (HR 3.61, 95% CI 1.65-7.88, P < .001) and multivariable analysis (adjusted P = .01). Conversely, RSPO1 rs4652964 any G allele carriers showed increased tumor response (TR) rates compared to the A/A genotype (83 vs 66 %, P = .04). These associations were not observed in bev arm. Lower gene expression levels of RNF43 were associated with shorter PFS in pts with right-sided tumors receiving FOLFIRI/cet ( P = .006, Q < 0.1). RSPO1 expression levels were also associated with TR in the same subgroup (70 vs 10% in high vs low; P = .001, Q < .05). RNF43 expression was associated with TR in pts with left-sided tumors (82% in high vs 58% in low, P = .014, Q = 0.1). Conclusions: Our results provide the first evidence that germline polymorphisms and tumor gene expression levels of RNF43/ ZNRF3 and RSPO1 may have a predictive value in mCRC pts receiving first-line cetuximab-based treatment and contribute to modulate anti-EGFRs activity.

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RNA sequencing reveals two major classes of gene expression levels in metazoan cells

Molecular Systems Biology ◽

10.1038/msb.2011.28 ◽

2011 ◽

Vol 7 (1) ◽

pp. 497 ◽

Cited By ~ 188

Author(s):

Daniel Hebenstreit ◽

Miaoqing Fang ◽

Muxin Gu ◽

Varodom Charoensawan ◽

Alexander van Oudenaarden ◽

...

Keyword(s):

Gene Expression ◽

Rna Sequencing ◽

Expression Levels ◽

Gene Expression Levels

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Association of the Adipokines Chemerin, Apelin, Vaspin and Omentin and Their Functional Genetic Variants with Rheumatoid Arthritis

Journal of Personalized Medicine ◽

10.3390/jpm11100976 ◽

2021 ◽

Vol 11 (10) ◽

pp. 976

Author(s):

Alaa S. Wahba ◽

Maha E. Ibrahim ◽

Dina M. Abo-elmatty ◽

Eman T. Mehanna

Keyword(s):

Rheumatoid Arthritis ◽

Gene Expression ◽

Genetic Variants ◽

Serum Levels ◽

Expression Levels ◽

Protein Levels ◽

Egyptian Patients ◽

Gene Expression Levels ◽

Increased Susceptibility

Adipokines were shown to exert crucial roles in rheumatic diseases. This study aimed to assess the role of chemerin, apelin, vaspin, and omentin adipokines and their genetic variants rs17173608, rs2235306, rs2236242, and rs2274907, respectively, in rheumatoid arthritis (RA) pathogenesis in Egyptian patients. A total of 150 RA patients and 150 healthy individuals were recruited. Blood samples were collected and used for genotyping. Serum was separated and used for expression analysis by quantitative PCR, and various biochemical markers determination by ELISA. Serum protein levels of chemerin and vaspin, as well as their gene expression levels were higher, while those of apelin and omentin were lower in RA patients and were associated with most of RA clinical and laboratory characteristics. G allele of chemerin rs17173608, T allele of vaspin rs2236242, and T allele of omentin rs2274907 were more frequent in RA patients. Serum levels and gene expression levels of chemerin in GG genotype carriers and vaspin in TT genotype group were significantly higher, while those of omentin in TT genotype carriers were significantly lower than RA patients with other genotypes. There was no association between apelin rs2235306 and RA. Chemerin rs17173608, vaspin rs2236242, and omentin rs2274907 polymorphisms were associated with increased susceptibility to RA.

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An Efficient and Flexible Method for Deconvoluting Bulk RNA-Seq Data with Single-Cell RNA-Seq Data

Cells ◽

10.3390/cells8101161 ◽

2019 ◽

Vol 8 (10) ◽

pp. 1161 ◽

Cited By ~ 2

Author(s):

Xifang Sun ◽

Shiquan Sun ◽

Sheng Yang

Keyword(s):

Gene Expression ◽

Single Cell ◽

Rna Sequencing ◽

Specific Gene ◽

Rna Seq ◽

Cell Type ◽

Disease Etiology ◽

Expression Levels ◽

Cell Type Specific ◽

Gene Expression Levels

Estimating cell type compositions for complex diseases is an important step to investigate the cellular heterogeneity for understanding disease etiology and potentially facilitate early disease diagnosis and prevention. Here, we developed a computationally statistical method, referring to Multi-Omics Matrix Factorization (MOMF), to estimate the cell-type compositions of bulk RNA sequencing (RNA-seq) data by leveraging cell type-specific gene expression levels from single-cell RNA sequencing (scRNA-seq) data. MOMF not only directly models the count nature of gene expression data, but also effectively accounts for the uncertainty of cell type-specific mean gene expression levels. We demonstrate the benefits of MOMF through three real data applications, i.e., Glioblastomas (GBM), colorectal cancer (CRC) and type II diabetes (T2D) studies. MOMF is able to accurately estimate disease-related cell type proportions, i.e., oligodendrocyte progenitor cells and macrophage cells, which are strongly associated with the survival of GBM and CRC, respectively.

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scTSSR: gene expression recovery for single-cell RNA sequencing using two-side sparse self-representation

Bioinformatics ◽

10.1093/bioinformatics/btaa108 ◽

2020 ◽

Vol 36 (10) ◽

pp. 3131-3138

Author(s):

Ke Jin ◽

Le Ou-Yang ◽

Xing-Ming Zhao ◽

Hong Yan ◽

Xiao-Fei Zhang

Keyword(s):

Gene Expression ◽

Single Cell ◽

Rna Sequencing ◽

Expression Patterns ◽

Differential Expression Analysis ◽

Supplementary Information ◽

Expression Levels ◽

Single Cell Rna Sequencing ◽

Downstream Analysis ◽

Gene Expression Levels

Abstract Motivation Single-cell RNA sequencing (scRNA-seq) methods make it possible to reveal gene expression patterns at single-cell resolution. Due to technical defects, dropout events in scRNA-seq will add noise to the gene-cell expression matrix and hinder downstream analysis. Therefore, it is important for recovering the true gene expression levels before carrying out downstream analysis. Results In this article, we develop an imputation method, called scTSSR, to recover gene expression for scRNA-seq. Unlike most existing methods that impute dropout events by borrowing information across only genes or cells, scTSSR simultaneously leverages information from both similar genes and similar cells using a two-side sparse self-representation model. We demonstrate that scTSSR can effectively capture the Gini coefficients of genes and gene-to-gene correlations observed in single-molecule RNA fluorescence in situ hybridization (smRNA FISH). Down-sampling experiments indicate that scTSSR performs better than existing methods in recovering the true gene expression levels. We also show that scTSSR has a competitive performance in differential expression analysis, cell clustering and cell trajectory inference. Availability and implementation The R package is available at https://github.com/Zhangxf-ccnu/scTSSR. Supplementary information Supplementary data are available at Bioinformatics online.

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Accurate Estimation of Gene Expression Levels from DGE Sequencing Data

Bioinformatics Research and Applications - Lecture Notes in Computer Science ◽

10.1007/978-3-642-21260-4_37 ◽

2011 ◽

pp. 392-403 ◽

Cited By ~ 2

Author(s):

Marius Nicolae ◽

Ion Măndoiu

Keyword(s):

Gene Expression ◽

Accurate Estimation ◽

Sequencing Data ◽

Expression Levels ◽

Gene Expression Levels

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Rare Genetic Variants Underlie Outlying levels of DNA Methylation and Gene-Expression

10.1101/2020.02.19.950659 ◽

2020 ◽

Author(s):

V. Kartik Chundru ◽

Riccardo E. Marioni ◽

James G. D. Pendergast ◽

Tian Lin ◽

Allan J. Beveridge ◽

...

Keyword(s):

Gene Expression ◽

Dna Methylation ◽

Phenotypic Variation ◽

Genetic Variants ◽

Rare Variants ◽

Effect Sizes ◽

Expression Levels ◽

Low Level ◽

Rare Genetic Variants ◽

Gene Expression Levels

AbstractTesting the effect of rare variants on phenotypic variation is difficult due to the need for extremely large cohorts to identify associated variants given expected effect sizes. An alternative approach is to investigate the effect of rare genetic variants on low-level genomic traits, such as gene expression or DNA methylation (DNAm), as effect sizes are expected to be larger for low-level compared to higher-order complex traits. Here, we investigate DNAm in healthy ageing populations - the Lothian Birth cohorts of 1921 and 1936 and identify both transient and stable outlying DNAm levels across the genome. We find an enrichment of rare genetic variants within 1kb of DNAm sites in individuals with stable outlying DNAm, implying genetic control of this extreme variation. Using a family-based cohort, the Brisbane Systems Genetics Study, we observed increased sharing of DNAm outliers among more closely related individuals, consistent with these outliers being driven by rare genetic variation. We demonstrated that outlying DNAm levels have a functional consequence on gene expression levels, with extreme levels of DNAm being associated with gene expression levels towards the tails of the population distribution. Overall, this study demonstrates the role of rare variants in the phenotypic variation of low-level genomic traits, and the effect of extreme levels of DNAm on gene expression.

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