scholarly journals Genome-Wide and Experimental Resolution of Relative Translation Elongation Speed at Individual Gene Level in Human Cells

PLoS Genetics ◽  
2016 ◽  
Vol 12 (2) ◽  
pp. e1005901 ◽  
Author(s):  
Xinlei Lian ◽  
Jiahui Guo ◽  
Wei Gu ◽  
Yizhi Cui ◽  
Jiayong Zhong ◽  
...  
Keyword(s):  
Human Cells ◽  
Translation Elongation ◽  
Individual Gene ◽  
Genome Wide ◽  
Gene Level ◽  
Experimental Resolution

scholarly journals Humans and other commonly used model organisms are resistant to cycloheximide-mediated biases in ribosome profiling experiments

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Puneet Sharma ◽  
Jie Wu ◽  
Benedikt S. Nilges ◽  
Sebastian A. Leidel
Keyword(s):  
Human Cells ◽  
Ribosome Profiling ◽  
Model Organisms ◽  
Treatment Conditions ◽  
Genome Wide ◽  
Optimized Protocol ◽  
Gene Level ◽  
Translation Inhibitor ◽  
Species Specific ◽  
Conformational Restrictions

AbstractRibosome profiling measures genome-wide translation dynamics at sub-codon resolution. Cycloheximide (CHX), a widely used translation inhibitor to arrest ribosomes in these experiments, has been shown to induce biases in yeast, questioning its use. However, whether such biases are present in datasets of other organisms including humans is unknown. Here we compare different CHX-treatment conditions in human cells and yeast in parallel experiments using an optimized protocol. We find that human ribosomes are not susceptible to conformational restrictions by CHX, nor does it distort gene-level measurements of ribosome occupancy, measured decoding speed or the translational ramp. Furthermore, CHX-induced codon-specific biases on ribosome occupancy are not detectable in human cells or other model organisms. This shows that reported biases of CHX are species-specific and that CHX does not affect the outcome of ribosome profiling experiments in most settings. Our findings provide a solid framework to conduct and analyze ribosome profiling experiments.

scholarly journals Identification of anti-norovirus genes in mouse and human cells using genome-wide CRISPR activation screening

2018 ◽  
Author(s):  
Robert C. Orchard ◽  
Meagan E. Sullender ◽  
Bria F. Dunlap ◽  
Dale R. Balce ◽  
John G. Doench ◽  
...  
Keyword(s):  
Antiviral Activity ◽  
World Wide ◽  
Human Cells ◽  
Gene Promoters ◽  
Murine Norovirus ◽  
Individual Gene ◽  
Genome Wide ◽  
Host Genes ◽  
Crispr Activation

AbstractNoroviruses (NoVs) are a leading cause of gastroenteritis world-wide, yet host factors that restrict NoV replication are not well understood. Here, we use a CRISPR activation (CRISPRa) genome-wide screening to identify host genes that can inhibit murine norovirus (MNoV) replication in either mouse or human cells. Our screens identified with high confidence 57 genes that can inhibit MNoV infection when overexpressed. A significant number of these genes are in interferon and immune regulation signaling networks, but surprising, the majority of the genes identified are not associated with innate or adaptive immunity nor with any antiviral activity. Confirmatory studies of eight of the genes in validate the initial screening data. Mechanistic studies on TRIM7 demonstrated a conserved role of the molecule in mouse and human cells in restricting MNoV in a step of infection after viral entry. Furthermore, we demonstrate that two isoforms of TRIM7 have differential antiviral activity. Taken together these data provide a resource for understanding norovirus biology and demonstrate a robust methodology for identifying new antiviral molecules across cell types and species.Author SummaryNorovirus is one of the leading causes of foodborne illness world-wide. Despite its prevalence, our understanding of norovirus biology is limited due to the difficulty in growing human norovirus in vitro and a lack of an animal model. Murine norovirus (MNoV) is a model norovirus system because MNoV replicates robustly in cell culture and in mice. To identify host genes that can restrict norovirus replication when overexpressed we performed genome-wide CRISPR activation (CRISPRa) screens to induce gene overexpression at the native locus through recruitment of transcriptional activators to individual gene promoters. We found 57 genes could block murine norovirus replication in either mouse or human cells. Several of these genes are associated with classical immune signaling pathways, while many of the molecules we identified have not been previously associated with antiviral activity. Our data is a resource for those studying norovirus and we provide a robust approach to identify novel antiviral genes.

scholarly journals Identification of Antinorovirus Genes in Human Cells Using Genome-Wide CRISPR Activation Screening

2018 ◽  
Vol 93 (1) ◽  
Author(s):  
Robert C. Orchard ◽  
Meagan E. Sullender ◽  
Bria F. Dunlap ◽  
Dale R. Balce ◽  
John G. Doench ◽  
...  
Keyword(s):  
Antiviral Activity ◽  
Viral Entry ◽  
Human Cells ◽  
Gene Promoters ◽  
Murine Norovirus ◽  
Individual Gene ◽  
Genome Wide ◽  
Host Genes ◽  
Crispr Activation

ABSTRACT Noroviruses (NoVs) are a leading cause of gastroenteritis worldwide, yet host factors that restrict NoV replication are not well understood. Here, we use a CRISPR activation genome-wide screening to identify host genes that can inhibit murine norovirus (MNoV) replication in human cells. Our screens identified with high confidence 49 genes that can inhibit MNoV infection when overexpressed. A significant number of these genes are in interferon and immune regulation signaling networks, but surprisingly, the majority of the genes identified are neither associated with innate or adaptive immunity nor associated with any antiviral activity. Confirmatory studies of eight of the genes validate the initial screening data. Mechanistic studies on TRIM7 demonstrated a conserved role of the molecule in mouse and human cells in restricting MNoV in a step of infection after viral entry. Furthermore, we demonstrate that two isoforms of TRIM7 have differential antiviral activity. Taken together, these data provide a resource for understanding norovirus biology and demonstrate a robust methodology for identifying new antiviral molecules. IMPORTANCE Norovirus is one of the leading causes of food-borne illness worldwide. Despite its prevalence, our understanding of norovirus biology is limited due to the difficulty in growing human norovirus in vitro and a lack of an animal model. Murine norovirus (MNoV) is a model norovirus system because MNoV replicates robustly in cell culture and in mice. To identify host genes that can restrict norovirus replication when overexpressed, we performed genome-wide CRISPR activation screens to induce gene overexpression at the native locus through recruitment of transcriptional activators to individual gene promoters. We found 49 genes that could block murine norovirus replication in human cells. Several of these genes are associated with classical immune signaling pathways, while many of the molecules we identified have not been previously associated with antiviral activity. Our data are a resource for those studying noroviruses, and we provide a robust approach to identify novel antiviral genes.

scholarly journals MethylationToActivity: a deep-learning framework that reveals promoter activity landscapes from DNA methylomes in individual tumors

2020 ◽  
Author(s):  
Justin Williams ◽  
Beisi Xu ◽  
Daniel Putnam ◽  
Andrew Thrasher ◽  
Chunliang Li ◽  
...  
Keyword(s):  
Promoter Activity ◽  
Malignant Neoplasms ◽  
Clinical Value ◽  
Individual Gene ◽  
Learning Framework ◽  
Genome Wide ◽  
Gene Level ◽  
Biological Impacts ◽  
The Individual ◽  
Methylation Patterns

AbstractAlthough genome-wide DNA methylomes have demonstrated their clinical value as reliable biomarkers for tumor detection, subtyping, and classification, their direct biological impacts at the individual gene level remain elusive. Here we present MethylationToActivity (M2A), a machine learning framework that uses convolutional neural networks to infer promoter activities (H3K4me3 and H3K27ac enrichment) from DNA methylation patterns for individual genes. Using publicly available datasets in real-world test scenarios, we demonstrate that M2A is highly accurate and robust in revealing promoter activity landscapes in various pediatric and adult cancers, including both solid and hematologic malignant neoplasms.

scholarly journals MethylationToActivity: a deep-learning framework that reveals promoter activity landscapes from DNA methylomes in individual tumors

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Justin Williams ◽  
Beisi Xu ◽  
Daniel Putnam ◽  
Andrew Thrasher ◽  
Chunliang Li ◽  
...  
Keyword(s):  
Promoter Activity ◽  
Malignant Neoplasms ◽  
Clinical Value ◽  
Individual Gene ◽  
Learning Framework ◽  
Genome Wide ◽  
Gene Level ◽  
Biological Impacts ◽  
The Individual ◽  
Methylation Patterns

AbstractAlthough genome-wide DNA methylomes have demonstrated their clinical value as reliable biomarkers for tumor detection, subtyping, and classification, their direct biological impacts at the individual gene level remain elusive. Here we present MethylationToActivity (M2A), a machine learning framework that uses convolutional neural networks to infer promoter activities based on H3K4me3 and H3K27ac enrichment, from DNA methylation patterns for individual genes. Using publicly available datasets in real-world test scenarios, we demonstrate that M2A is highly accurate and robust in revealing promoter activity landscapes in various pediatric and adult cancers, including both solid and hematologic malignant neoplasms.

scholarly journals Coex-Rank: An approach incorporating co-expression information for combined analysis of microarray data

2012 ◽  
Vol 9 (1) ◽  
pp. 32-43 ◽  
Author(s):  
Jinlu Cai ◽  
Henry L. Keen ◽  
Curt D. Sigmund ◽  
Thomas L. Casavant
Keyword(s):  
Microarray Data ◽  
Statistical Power ◽  
Meta Analysis ◽  
Rank Aggregation ◽  
Microarray Data Analysis ◽  
Combined Approach ◽  
Linear Modeling ◽  
Modeling Process ◽  
Genome Wide ◽  
Gene Level

Summary Microarrays have been widely used to study differential gene expression at the genomic level. They can also provide genome-wide co-expression information. Biologically related datasets from independent studies are publicly available, which requires robust combined approaches for integration and validation. Previously, meta-analysis has been adopted to solve this problem.As an alternative to meta-analysis, for microarray data with high similarity in biological experimental design, a more direct combined approach is possible. Gene-level normalization across datasets is motivated by the different scale and distribution of data due to separate origins. However, there has been limited discussion about this point in the past. Here we describe a combined approach for microarray analysis, including gene-level normalization and Coex-Rank approach. After normalization, a linear modeling process is used to identify lists of differentially expressed genes. The Coex-Rank approach incorporates co-expression information into a rank-aggregation procedure. We applied this computational approach to our data, which illustrated an improvement in statistical power and a complementary advantage of the Coex-Rank approach from a biological perspective.Our combined approach for microarray data analysis (Coex-rank) is based on normalization, which is naturally driven. The Coex-rank process not only takes advantage of merging the power of multiple methods regarding normalization but also assists in the discovery of functional clusters of genes.

scholarly journals Timing Polymerase Pausing with TV-PRO-seq

2018 ◽  
Author(s):  
Jie Zhang ◽  
Massimo Cavallaro ◽  
Daniel Hebenstreit
Keyword(s):  
High Resolution ◽  
Human Cells ◽  
Rrna Genes ◽  
Dna Motifs ◽  
Single Base ◽  
Chromatin States ◽  
Genome Wide ◽  
A Genome ◽  
Single Base Resolution ◽  
Polymerase Pausing

Transcription of many genes in metazoans is subject to polymerase pausing, which corresponds to the transient arrest of transcriptionally engaged polymerase. It occurs mainly at promoter proximal regions and is not well understood. In particular, a genome-wide measurement of pausing times at high resolution has been lacking.We present here an extension of PRO-seq, time variant PRO-seq (TV-PRO-seq), that allowed us to estimate genome-wide pausing times at single base resolution. Its application to human cells reveals that promoter proximal pausing is surprisingly short compared to other regions and displays an intricate pattern. We also find precisely conserved pausing profiles at tRNA and rRNA genes and identified DNA motifs associated with pausing time. Finally, we show how chromatin states reflect differences in pausing times.

scholarly journals Technical considerations when designing a gene expression panel for renal transplant diagnosis

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
F. Toulza ◽  
K. Dominy ◽  
T. Cook ◽  
J. Galliford ◽  
J. Beadle ◽  
...  
Keyword(s):  
Gene Expression ◽  
Renal Transplant ◽  
Biological Significance ◽  
Tissue Sampling ◽  
Individual Gene ◽  
Antibody Mediated Rejection ◽  
Gene Level ◽  
Diagnostic Panel ◽  
The Individual ◽  
Gene Panels

Abstract Gene expression analysis is emerging as a new diagnostic tool in transplant pathology, in particular for the diagnosis of antibody-mediated rejection. Diagnostic gene expression panels are defined on the basis of their pathophysiological relevance, but also need to be tested for their robustness across different preservatives and analysis platforms. The aim of this study is the investigate the effect of tissue sampling and preservation on candidate genes included in a renal transplant diagnostic panel. Using the NanoString platform, we compared the expression of 219 genes in 51 samples, split for formalin-fixation and paraffin-embedding (FFPE) and RNAlater preservation (RNAlater). We found that overall, gene expression significantly correlated between FFPE and RNAlater samples. However, at the individual gene level, 46 of the 219 genes did not correlate across the 51 matched FFPE and RNAlater samples. Comparing gene expression results using NanoString and qRT-PCR for 18 genes in the same pool of RNA (RNAlater), we found a significant correlation in 17/18 genes. Our study indicates that, in samples from the same routine diagnostic renal transplant biopsy procedure split for FFPE and RNAlater, 21% of 219 genes of potential biological significance do not correlate in expression. Whether this is due to fixatives or tissue sampling, selection of gene panels for routine diagnosis should take this information into consideration.

scholarly journals Read trimming is not required for mapping and quantification of RNA-seq reads at the gene level

2020 ◽  
Vol 2 (3) ◽  
Author(s):  
Yang Liao ◽  
Wei Shi
Keyword(s):  
Data Analysis ◽  
Pearson Correlation ◽  
Rna Seq ◽  
Genome Wide ◽  
Gene Level ◽  
Sequencing Quality ◽  
Total Data ◽  
Order Of Magnitude ◽  
Gene Expression Quantification ◽  
The Impact

Abstract RNA sequencing (RNA-seq) is currently the standard method for genome-wide expression profiling. RNA-seq reads often need to be mapped to a reference genome before read counts can be produced for genes. Read trimming methods have been developed to assist read mapping by removing adapter sequences and low-sequencing-quality bases. It is however unclear what is the impact of read trimming on the quantification of RNA-seq data, an important task in RNA-seq data analysis. In this study, we used a benchmark RNA-seq dataset and simulation data to assess the impact of read trimming on mapping and quantification of RNA-seq reads. We found that adapter sequences can be effectively removed by read aligner via ’soft-clipping’ and that many low-sequencing-quality bases, which would be removed by read trimming tools, were rescued by the aligner. Accuracy of gene expression quantification from using untrimmed reads was found to be comparable to or slightly better than that from using trimmed reads, based on Pearson correlation with reverse transcriptase-polymerase chain reaction data and simulation truth. Total data analysis time was reduced by up to an order of magnitude when read trimming was not performed. Our study suggests that read trimming is a redundant process in the quantification of RNA-seq expression data.

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