Effect Of Gene Expression Levels Research Articles

In silico design of CMV promoter binding oligonucleotides and their impact on inhibition of gene expression in Chinese hamster ovary cells

Modulation of expression levels of endogenous or recombinant genes can be of great interest for diverse applications, such as the study of genotype-phenotype relationships for a gene of interest, or fine-tuning of transcription to determine physiologically relevant effects of gene expression levels. During the last decades, several synthetic biology tools were established to control gene expression in mammalian cells such as Chinese hamster ovary (CHO) cells, one of the most important cell systems for basic research as well as the production of biopharmaceuticals. Here we describe the use of triplex forming oligos (TFOs), short RNA or ssDNA molecules that can bind to the major grove of their target duplex with great specificity, to control transgene expression in CHO cells. For proof of concept, a panel of TFOs with a size of 10–20 nts were designed with the help of the on-line tool Triplexator targeting the viral cytomegalovirus (CMV) promoter/enhancer region controlling the downstream reporter gene hCD4. The effect of TFOs was tested as ssDNA oligos pre-annealed to the promoter/enhancer region in vitro as well as upon endogenous transcription of the TFO as an RNA molecule binding to their target duplex in vivo. Results showed that not only binding of the TFO, but the exact location of triplex formation within the promoter/enhancer is paramount for transcription inhibition. After relieving a binding conflict by introducing a point mutation within the CMV promoter, longer TFOs (26–30 nts) could be designed and analysed. Selected TFOs achieved a reduction in recombinant hCD4 expression of up to 85% in CHO-K1 cells.

Low MICA Gene Expression Confers an Increased Risk of Graves' Disease: A Mendelian Randomization Study.

Background: Expression of natural killer group 2 member D (NKG2D) ligand (NKG2DL) plays a major role as a "danger signal" on stressed cells to promote removal of the latter by NKG2D-expressing cytotoxic lymphocytes. NKG2DL expression has been found in peripheral immune cells as well, such as in macrophages; however, the effect of this expression is yet to be determined. Methods: We determined instrumental variables (IVs; R2 <0.01 in linkage disequilibrium), explaining the major variance in major histocompatibility complex class I chain-related protein A (MICA) and B (MICB) gene expression levels from the expression-quantitative trait locus (eQTL) of NKG2DLs based on the RNA-seq analysis of peripheral blood mononuclear cells (PBMCs) from 381 Japanese. Simultaneously, the target outcomes were filtered by PheWAS from 58 health risks, using a community-based cohort study composed of 44,739 Japanese residents. Finally, we estimated the causal effect of gene expression levels on the outcomes using the Mendelian randomization approach. Results: We determined nine and four IVs, explaining 87.6% and 33.0% of MICA and MICB gene expression levels, respectively. In the association test, we identified 10 or 13 significant outcomes associated with the MICA or MICB eQTLs, respectively, as well as the causal effect of MICA expression on Graves' disease (GD) (p = 4.2 × 10-3; odds ratio per 1 S.D. difference in the expression: 0.983 [confidence interval: 0.971-0.995]), using the weighted median estimator, without significant pleiotropy (p > 0.05), and the results were consistent across the sensitivity analyses. Conclusions: Our study provide novel evidence associating NKG2DL expression with GD, an autoimmune thyroiditis; direction of the effect indicated the immunoregulatory role of MICA expression in PBMCs, suggesting the importance of further functional assays in inflammatory diseases.

Improved methods for RNAseq-based alternative splicing analysis

The robust detection of disease-associated splice events from RNAseq data is challenging due to the potential confounding effect of gene expression levels and the often limited number of patients with relevant RNAseq data. Here we present a novel statistical approach to splicing outlier detection and differential splicing analysis. Our approach tests for differences in the percentages of sequence reads representing local splice events. We describe a software package called Bisbee which can predict the protein-level effect of splice alterations, a key feature lacking in many other splicing analysis resources. We leverage Bisbee’s prediction of protein level effects as a benchmark of its capabilities using matched sets of RNAseq and mass spectrometry data from normal tissues. Bisbee exhibits improved sensitivity and specificity over existing approaches and can be used to identify tissue-specific splice variants whose protein-level expression can be confirmed by mass spectrometry. We also applied Bisbee to assess evidence for a pathogenic splicing variant contributing to a rare disease and to identify tumor-specific splice isoforms associated with an oncogenic mutation. Bisbee was able to rediscover previously validated results in both of these cases and also identify common tumor-associated splice isoforms replicated in two independent melanoma datasets.

Biosynthesis of poly‐γ‐glutamic acid in Escherichia coli by heterologous expression of pgsBCAE operon from Bacillus

Poly-γ-glutamic acid (γ-PGA) is an excellent water-soluble biosynthesis material. To confirm the rate-limiting steps of γ-PGA biosynthesis pathway, we introduced a heterologous Bacillus strain pathway and employed an enzyme-modulated dismemberment strategy in Escherichia coli. In this study, we heterologously introduced the γ-PGA biosynthesis pathway of two laboratory-preserved strains-Bacillus amyloliquefaciens FZB42 and Bacillus subtilis 168 into E. coli, and compared their γ-PGA production levels. Next,by changing the plasmid copy numbers and supplying sodium glutamate, we explored the effects of gene expression levels and concentrations of the substrate l-glutamic acid on γ-PGA production. We finally employed a two-plasmid induction system using an enzyme-modulated dismemberment of pgsBCAE operon to confirm the rate-limiting genes of the γ-PGA biosynthesis pathway. Through heterologously over-expressing the genes of the γ-PGA biosynthesis pathway and exploring gene expression levels, we produced 0·77gl-1 γ-PGA in strain RSF-EBCAE(BS). We also confirmed that the rate-limiting genes of the γ-PGA biosynthesis pathway were pgsB and pgsC. This work is beneficial to increase γ-PGA production and study the mechanism of γ-PGA biosynthesis enzymes.

Synaptic and brain-expressed gene sets relate to the shared genetic risk across five psychiatric disorders.

Mounting evidence shows genetic overlap between multiple psychiatric disorders. However, the biological underpinnings of shared risk for psychiatric disorders are not yet fully uncovered. The identification of underlying biological mechanisms is crucial for the progress in the treatment of these disorders. We applied gene-set analysis including 7372 gene sets, and 53 tissue-type specific gene-expression profiles to identify sets of genes that are involved in the etiology of multiple psychiatric disorders. We included genome-wide meta-association data of the five psychiatric disorders schizophrenia, bipolar disorder, major depressive disorder, autism spectrum disorder, and attention-deficit/hyperactivity disorder. The total dataset contained 159 219 cases and 262 481 controls. We identified 19 gene sets that were significantly associated with the five psychiatric disorders combined, of which we excluded five sets because their associations were likely driven by schizophrenia only. Conditional analyses showed independent effects of several gene sets that in particular relate to the synapse. In addition, we found independent effects of gene expression levels in the cerebellum and frontal cortex. We obtained novel evidence for shared biological mechanisms that act across psychiatric disorders and we showed that several gene sets that have been related to individual disorders play a role in a broader range of psychiatric disorders.

DupRadar: a Bioconductor package for the assessment of PCR artifacts in RNA-Seq data.

BackgroundPCR clonal artefacts originating from NGS library preparation can affect both genomic as well as RNA-Seq applications when protocols are pushed to their limits. In RNA-Seq however the artifactual reads are not easy to tell apart from normal read duplication due to natural over-sequencing of highly expressed genes. Especially when working with little input material or single cells assessing the fraction of duplicate reads is an important quality control step for NGS data sets. Up to now there are only tools to calculate the global duplication rates that do not take into account the effect of gene expression levels which leaves them of limited use for RNA-Seq data.ResultsHere we present the tool dupRadar, which provides an easy means to distinguish the fraction of reads originating in natural duplication due to high expression from the fraction induced by artefacts. dupRadar assesses the fraction of duplicate reads per gene dependent on the expression level. Apart from the Bioconductor package dupRadar we provide shell scripts for easy integration into processing pipelines.ConclusionsThe Bioconductor package dupRadar offers straight-forward methods to assess RNA-Seq datasets for quality issues with PCR duplicates. It is aimed towards simple integration into standard analysis pipelines as a default QC metric that is especially useful for low-input and single cell RNA-Seq data sets.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1276-2) contains supplementary material, which is available to authorized users.

Massively Parallel Interrogation of the Effects of Gene Expression Levels on Fitness

Data of gene expression levels across individuals, cell types, and disease states is expanding, yet our understanding of how expression levels impact phenotype is limited. Here, we present a massively parallel system for assaying the effect of gene expression levels on fitness in Saccharomyces cerevisiae by systematically altering the expression level of ∼100 genes at ∼100 distinct levels spanning a 500-fold range at high resolution. We show that the relationship between expression levels and growth is gene and environment specific and provides information on the function, stoichiometry, and interactions of genes. Wild-type expression levels in some conditions are not optimal for growth, and genes whose fitness is greatly affected by small changes in expression level tend to exhibit lower cell-to-cell variability in expression. Our study addresses a fundamental gap in understanding the functional significance of gene expression regulation and offers a framework for evaluating the phenotypic effects of expression variation.

Tuning genetic control through promoter engineering

Gene function is typically evaluated by sampling the continuum of gene expression at only a few discrete points corresponding to gene knockout or overexpression. We argue that this characterization is incomplete and present a library of engineered promoters of varying strengths obtained through mutagenesis of a constitutive promoter. A multifaceted characterization of the library, especially at the single-cell level to ensure homogeneity, permitted quantitative assessment correlating the effect of gene expression levels to improved growth and product formation phenotypes in Escherichia coli. Integration of these promoters into the chromosome can allow for a quantitative accurate assessment of genetic control. To this end, we used the characterized library of promoters to assess the impact of phosphoenolpyruvate carboxylase levels on growth yield and deoxy-xylulose-P synthase levels on lycopene production. The multifaceted characterization of promoter strength enabled identification of optimal expression levels for ppc and dxs, which maximized the desired phenotype. Additionally, in a strain preengineered to produce lycopene, the response to deoxy-xylulose-P synthase levels was linear at all levels tested, indicative of a rate-limiting step, unlike the parental strain, which exhibited an optimum expression level, illustrating that optimal gene expression levels are variable and dependent on the genetic background of the strain. This promoter library concept is illustrated as being generalizable to eukaryotic organisms (Saccharomyces cerevisiae) and thus constitutes an integral platform for functional genomics, synthetic biology, and metabolic engineering endeavors.