An expression-based variant impact phenotyping protocol to predict the impact of gene variants in cell lines.
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ABSTRACT:
SUBMITTER: Thornton AM
PROVIDER: S-EPMC9449654 | biostudies-literature | 2022 Sep
REPOSITORIES: biostudies-literature
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Project description:While advancements in genome sequencing have identified millions of somatic mutations in cancer, their functional impact is poorly understood. We previously developed the expression-based variant impact phenotyping (eVIP) method to use gene expression data to characterize the function of gene variants. The eVIP method uses a decision tree-based algorithm to predict the functional impact of somatic variants by comparing gene expression signatures induced by introduction of wild-type versus mutant cDNAs in cell lines. The method distinguishes between variants that are gain-of-function, loss-of-function, change-of-function, or neutral. We present eVIP2, software that allows for pathway analysis (eVIP Pathways) and usage with RNA-seq data. To demonstrate the eVIP2 software and approach, we characterized two recurrent frameshift variants in RNF43, a negative regulator of Wnt signaling, frequently mutated in colorectal, gastric and endometrial cancer. RNF43 WT, RNF43 R117fs, RNF43 G659fs, or GFP control cDNA were overexpressed in HEK293T cells. Analysis with eVIP2 predicted that the frameshift at position 117 was a loss-of-function mutation, as expected. The second frameshift at position 659, was, surprisingly, predicted to be a gain-of-function mutation. Additional eVIP Pathways analysis of RNF43 G659fs predicted 10 pathways to be significantly altered, including TNF alpha via NFKB signaling, KRAS signaling, and hypoxia. To validate these predictions, we performed reporter assays and found that all eVIP2 impactful pathways tested in the assay were activated by expression of RNF43 G659fs, but not by expression of RNF43 WT, supporting that RNF43 G659fs is a gain-of-function mutation and its effect on the identified pathways. The eVIP2 method is an important step towards overcoming the current challenge of variant interpretation in the implementation of precision medicine. eVIP2 is available at https://github.com/BrooksLabUCSC/eVIP2
2019-12-13 | GSE141963 | GEO
Project description:While advancements in genome sequencing have identified millions of somatic mutations in cancer, their functional impact is poorly understood. We previously developed the expression-based variant impact phenotyping (eVIP) method to use gene expression data to characterize the function of gene variants. The eVIP method uses a decision tree-based algorithm to predict the functional impact of somatic variants by comparing gene expression signatures induced by introduction of wild-type (WT) versus mutant cDNAs in cell lines. The method distinguishes between variants that are gain-of-function, loss-of-function, change-of-function, or neutral. We present eVIP2, software that allows for pathway analysis (eVIP Pathways) and usage with RNA-seq data. To demonstrate the eVIP2 software and approach, we characterized two recurrent frameshift variants in RNF43, a negative regulator of Wnt signaling, frequently mutated in colorectal, gastric, and endometrial cancer. RNF43 WT, RNF43 R117fs, RNF43 G659fs, or GFP control cDNA were overexpressed in HEK293T cells. Analysis with eVIP2 predicted that the frameshift at position 117 was a loss-of-function mutation, as expected. The second frameshift at position 659 has been previously described as a passenger mutation that maintains the RNF43 WT function as a negative regulator of Wnt. Surprisingly, eVIP2 predicted G659fs to be a change-of-function mutation. Additional eVIP Pathways analysis of RNF43 G659fs predicted 10 pathways to be significantly altered, including TNF-α via NFκB signaling, KRAS signaling, and hypoxia, highlighting the benefit of a more comprehensive approach when determining the impact of gene variant function. To validate these predictions, we performed reporter assays and found that each pathway activated by expression of RNF43 G659fs, but not expression of RNF43 WT, was identified as impacted by eVIP2, supporting that RNF43 G659fs is a change-of-function mutation and its effect on the identified pathways. Pathway activation was further validated by Western blot analysis. Lastly, we show primary colon adenocarcinoma patient samples with R117fs and G659fs variants have transcriptional profiles similar to BRAF missense mutations with activated RAS/MAPK signaling, consistent with KRAS signaling pathways being GOF in both variants. The eVIP2 method is an important step towards overcoming the current challenge of variant interpretation in the implementation of precision medicine. eVIP2 is available at https://github.com/BrooksLabUCSC/eVIP2.
| S-EPMC8281988 | biostudies-literature
Project description:eVIP2: Expression-based variant impact phenotyping to predict the function of gene variants
| PRJNA595401 | ENA
Project description:BackgroundStatins are widely prescribed to lower plasma low-density lipoprotein cholesterol levels. Though statins reduce cardiovascular disease risk overall, statin efficacy varies, and some people experience adverse side effects while on statin treatment. Statins also have pleiotropic effects not directly related to their cholesterol-lowering properties, but the mechanisms are not well understood. To identify potential genetic modulators of clinical statin response, we looked for genetic variants associated with statin-induced changes in gene expression (differential eQTLs or deQTLs) in lymphoblastoid cell lines (LCLs) derived from participants of the Cholesterol and Pharmacogenetics (CAP) 40 mg/day 6-week simvastatin clinical trial. We exposed CAP LCLs to 2 μM simvastatin or control buffer for 24 h and performed polyA-selected, strand-specific RNA-seq. Statin-induced changes in gene expression from 259 European ancestry or 153 African American ancestry LCLs were adjusted for potential confounders prior to association with genotyped and imputed genetic variants within 1 Mb of each gene's transcription start site.ResultsFrom the deQTL meta-analysis of the two ancestral populations, we identified significant cis-deQTLs for 15 genes (TBC1D4, MDGA1, CHI3L2, OAS1, GATM, ASNSD1, GLUL, TDRD12, PPIP5K2, OAS3, SERPINB1, ANKDD1A, DTD1, CYFIP2, and GSDME), eight of which were significant in at least one of the ancestry subsets alone. We also conducted eQTL analyses of the endogenous (control-treated), statin-treated, and average of endogenous and statin-treated LCL gene expression levels. We identified eQTLs for approximately 6000 genes in each of the three (endogenous, statin-treated, and average) eQTL meta-analyses, with smaller numbers identified in the ancestral subsets alone.ConclusionsSeveral of the genes in which we identified deQTLs have functions in human health and disease, such as defense from viruses, glucose regulation, and response to chemotherapy drugs. This suggests that DNA variation may play a role in statin effects on various health outcomes. These findings could prove useful to future studies aiming to assess benefit versus risk of statin treatment using individual genetic profiles.
| S-EPMC7430882 | biostudies-literature
Project description:RNA samples from human tissues (brain and liver) and cell lines (K562 and HL60) were used to assess RNA fragmentation, RNA fractionation, cDNA synthesis, and single versus multiple tag counting. Technical replicates and different cDNA synthesis protocols were compared. Though protocols employing polyA RNA selection generate the highest number of non-ribosomal reads and the most precise measurements for coding transcripts, such protocols were found to detect only a fraction of the non-ribosomal RNA in human cells. PolyA RNA excludes thousands of annotated and even more unannotated transcripts, resulting in an incomplete view of the transcriptome. Ribosomal-depleted RNA provides a more cost-effective method for generating complete transcriptome coverage. Expression measurements using single tag counting provided advantages for assessing gene expression and for detecting short RNAs relative to multi-read protocols. Detection of short RNAs was also hampered by RNA fragmentation.
2011-03-24 | GSE28123 | GEO
Project description:Transcription factors (TFs) regulate gene expression by binding to specific binding sites (TFBSs) in gene promoters. TFBS motifs may contain one or more variable positions. Although the prevailing assumption is that nucleotide variants at such positions are functionally equivalent, there is increasing evidence that such variants play a role in regulation of gene expression. In this article, we propose a method for studying the relationship between the expression of target genes and nucleotide variants in TFBS motifs at a genome-wide scale in Saccharomyces cerevisiae, especially the combinatorial effects of variants at two positions. Our analysis shows that nucleotide variations in more than one-third of variable positions and in 20% of dependent position pairs are highly correlated to gene expression. We define such positions as 'functional'. However, some positions are only functional as dependent pairs, but not individually. In addition, a significant proportion of the functional positions have been well conserved across all yeast-related species studied. We also find that some positions require the presence of co-occurring TFs, while others maintain their functionality in the absence of a co-occurring TF. Our analysis supports the importance of nucleotide variants at variable positions of TFBSs in gene regulation.
| S-EPMC2790881 | biostudies-literature
Project description:RNA samples from human tissues (brain and liver) and cell lines (K562 and HL60) were used to assess RNA fragmentation, RNA fractionation, cDNA synthesis, and single versus multiple tag counting. Technical replicates and different cDNA synthesis protocols were compared. Though protocols employing polyA RNA selection generate the highest number of non-ribosomal reads and the most precise measurements for coding transcripts, such protocols were found to detect only a fraction of the non-ribosomal RNA in human cells. PolyA RNA excludes thousands of annotated and even more unannotated transcripts, resulting in an incomplete view of the transcriptome. Ribosomal-depleted RNA provides a more cost-effective method for generating complete transcriptome coverage. Expression measurements using single tag counting provided advantages for assessing gene expression and for detecting short RNAs relative to multi-read protocols. Detection of short RNAs was also hampered by RNA fragmentation. 69 different sequencing channels from a HeliScope Genetic Analysis System and 2 channels from an Illumina machine are included. Some samples were sequenced on multiple channels and some samples include technical replicates.
2011-03-24 | E-GEOD-28123 | biostudies-arrayexpress
Project description:BackgroundMost disease-associated variants identified by genome-wide association studies (GWAS) exist in noncoding regions. In spite of the common agreement that such variants may disrupt biological functions of their hosting regulatory elements, it remains a great challenge to characterize the risk of a genetic variant within the implicated genome sequence. Therefore, it is essential to develop an effective computational model that is not only capable of predicting the potential risk of a genetic variant but also valid in interpreting how the function of the genome is affected with the occurrence of the variant.ResultsWe developed a method named kmerForest that used a random forest classifier with k-mer counts to predict accessible chromatin regions purely based on DNA sequences. We demonstrated that our method outperforms existing methods in distinguishing known accessible chromatin regions from random genomic sequences. Furthermore, the performance of our method can further be improved with the incorporation of sequence conservation features. Based on this model, we assessed importance of the k-mer features by a series of permutation experiments, and we characterized the risk of a single nucleotide polymorphism (SNP) on the function of the genome using the difference between the importance of the k-mer features affected by the occurrence of the SNP. We conducted a series of experiments and showed that our model can well discriminate between pathogenic and normal SNPs. Particularly, our model correctly prioritized SNPs that are proved to be enriched for the binding sites of FOXA1 in breast cancer cell lines from previous studies.ConclusionsWe presented a novel method to interpret functional genetic variants purely base on DNA sequences. The proposed k-mer based score offers an effective means of measuring the impact of SNPs on the function of the genome, and thus shedding light on the identification of genetic risk factors underlying complex traits and diseases.
| S-EPMC5374684 | biostudies-literature
Project description:RNA Seq provides unparalleled levels of information about the transcriptome including precise expression levels over a wide dynamic range. It is essential to understand how technical variation impacts the quality and interpretability of results, how potential errors could be introduced by the protocol, how the source of RNA affects transcript detection, and how all of these variations can impact the conclusions drawn. Multiple human RNA samples were used to assess RNA fragmentation, RNA fractionation, cDNA synthesis, and single versus multiple tag counting. Though protocols employing polyA RNA selection generate the highest number of non-ribosomal reads and the most precise measurements for coding transcripts, such protocols were found to detect only a fraction of the non-ribosomal RNA in human cells. PolyA RNA excludes thousands of annotated and even more unannotated transcripts, resulting in an incomplete view of the transcriptome. Ribosomal-depleted RNA provides a more cost-effective method for generating complete transcriptome coverage. Expression measurements using single tag counting provided advantages for assessing gene expression and for detecting short RNAs relative to multi-read protocols. Detection of short RNAs was also hampered by RNA fragmentation. Thus, this work will help researchers choose from among a range of options when analyzing gene expression, each with its own advantages and disadvantages.
| S-EPMC3089619 | biostudies-literature
Project description:BackgroundSmartphone apps that capture surveys and sensors are increasingly being leveraged to collect data on clinical conditions. In mental health, this data could be used to personalize psychiatric support offered by apps so that they are more effective and engaging. Yet today, few mental health apps offer this type of support, often because of challenges associated with accurately predicting users' actual future mental health.ObjectiveIn this protocol, we present a study design to explore engagement with mental health apps in college students, using the Technology Acceptance Model as a theoretical framework, and assess the accuracy of predicting mental health changes using digital phenotyping data.MethodsThere are two main goals of this study. First, we present a logistic regression model fit on data from a prior study on college students and prospectively test this model on a new student cohort to assess its accuracy. Second, we will provide users with data-driven activity suggestions every 4 days to determine whether this type of personalization will increase engagement or attitudes toward the app compared to those receiving no personalized recommendations.ResultsThe study was completed in the spring of 2022, and the manuscript is currently in review at JMIR Publications.ConclusionsThis is one of the first digital phenotyping algorithms to be prospectively validated. Overall, our results will inform the potential of digital phenotyping data to serve as tailoring data in adaptive interventions and to increase rates of engagement.International registered report identifier (irrid)PRR1-10.2196/37954.
| S-EPMC9748794 | biostudies-literature