Analysis of allelic differential expression in the human genome using allele-specific serial analysis of gene expression tags

Genome ◽  
2011 ◽  
Vol 54 (2) ◽  
pp. 120-127 ◽  
Author(s):  
Daniel Onofre Vidal ◽  
Jorge Estefano S. de Souza ◽  
Lilian Campos Pires ◽  
Cibele Masotti ◽  
Anna Christina Matos Salim ◽  
...  

Recent reports have demonstrated that a significant proportion of human genes display allelic differential expression (ADE). ADE is associated with phenotypic variability and may contribute to complex genetic diseases. Here, we present a computational analysis of ADE using allele-specific serial analysis of gene expression (SAGE) tags representing 1295 human genes. We identified 472 genes for which unequal representation (>3-fold) of allele-specific SAGE tags was observed in at least one SAGE library, suggesting the occurrence of ADE. For 235 out of these 472 genes, the difference in the expression level between both allele-specific SAGE tags was statistically significant (p < 0.05). Eleven candidate genes were then subjected to experimental validation and ADE was confirmed for 8 out of these 11 genes. Our results suggest that at least 25% of the human genes display ADE and that allele-specific SAGE tags can be efficiently used for the identification of such genes.


2020 ◽  
Vol 10 (4) ◽  
pp. 158 ◽  
Author(s):  
Angel Pey

Establishing accurate and large-scale genotype–phenotype correlations and predictions of individual response to pharmacological treatments are two of the holy grails of Personalized Medicine. These tasks are challenging and require an integrated knowledge of the complex processes that regulate gene expression and, ultimately, protein functionality in vivo, the effects of mutations/polymorphisms and the different sources of interindividual phenotypic variability. A remarkable example of our advances in these challenging tasks is the highly polymorphic CYP2D6 gene, which encodes a cytochrome P450 enzyme involved in the metabolization of many of the most marketed drugs (including SARS-Cov-2 therapies such as hydroxychloroquine). Since the introduction of simple activity scores (AS) over 10 years ago, its ability to establish genotype–phenotype correlations on the drug metabolizing capacity of this enzyme in human population has provided lessons that will help to improve this type of score for this, and likely many other human genes and proteins. Multidisciplinary research emerges as the best approach to incorporate additional concepts to refine and improve such functional/activity scores for the CYP2D6 gene, as well as for many other human genes associated with simple and complex genetic diseases.



2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Aziz M. Mezlini ◽  
Sudeshna Das ◽  
Anna Goldenberg

AbstractMost two-group statistical tests find broad patterns such as overall shifts in mean, median, or variance. These tests may not have enough power to detect effects in a small subset of samples, e.g., a drug that works well only on a few patients. We developed a novel statistical test targeting such effects relevant for clinical trials, biomarker discovery, feature selection, etc. We focused on finding meaningful associations in complex genetic diseases in gene expression, miRNA expression, and DNA methylation. Our test outperforms traditional statistical tests in simulated and experimental data and detects potentially disease-relevant genes with heterogeneous effects.



Genetics ◽  
1978 ◽  
Vol 90 (2) ◽  
pp. 323-330
Author(s):  
Drew Schwartz

ABSTRACT The effect of ethylene on the induction of alcohol dehydrogenase-2 in seedling roots of maize is reported. Allele-specific differences are observed in the response to the hormone. Hormonal treatment acts to eliminate the difference in gene expression that is characteristic of the alleles in untreated roots.



2017 ◽  
Author(s):  
Hyun Jae Lee ◽  
Michael Walther ◽  
Athina Georgiadou ◽  
Davis Nwakanma ◽  
Lindsay B. Stewart ◽  
...  

AbstractThe pathogenesis of severe Plasmodium falciparum malaria is incompletely understood. Since the pathogenic stage of the parasite is restricted to blood, dual RNA-sequencing of host and parasite transcripts in blood can reveal their interactions at a systemic scale. Here we identify human and parasite gene expression associated with severe disease features in Gambian children. Differences in parasite load explained up to 99% of differential expression of human genes but only a third of the differential expression of parasite genes. Co-expression analyses showed a remarkable co-regulation of host and parasite genes controlling translation, and host granulopoiesis genes uniquely co-regulated and differentially expressed in severe malaria. Our results indicate that high parasite load is the proximal stimulus for severe P. falciparum malaria, that there is an unappreciated role for many parasite genes in determining virulence, and hint at a molecular arms-race between host and parasite to synthesise protein products.



2004 ◽  
Vol 16 (2) ◽  
pp. 184-193 ◽  
Author(s):  
Tomi Pastinen ◽  
Robert Sladek ◽  
Scott Gurd ◽  
Alya’a Sammak ◽  
Bing Ge ◽  
...  

The identification of human sequence polymorphisms that regulate gene expression is key to understanding human genetic diseases. We report a survey of human genes that demonstrate allelic differences in gene expression, reflecting the presence of putative allele-specific cis-acting factors of either genetic or epigenetic nature. The expression of allelic transcripts in heterozygous samples is assessed directly by relative quantitation of intragenic marker alleles in messenger or heteronuclear RNA derived from cells or tissues. This survey used 193 single-nucleotide polymorphisms (SNPs) from 129 genes expressed in lymphoblastoid cell lines, to identify 23 genes (18%) with common allele-specific transcripts whose expression deviated from the expected equimolar ratio. A subset of these deviations, or “allelic imbalances,” can be observed in multiple samples derived from reference CEPH (“Centre d’Etude du Polymorphisme Humain”) pedigrees and demonstrate a spectrum of patterns of transmission, including cosegregation of allelic skewing across generations compatible with Mendelian inheritance as well as random monoallelic expression for three genes ( IL1A, HTR2A, and FGB). Additional studies for BTN3A2 provide evidence of SNPs and haplotypes in complete linkage disequilibrium with high- and low-expressing transcripts. The pipeline described herein offers tools for efficient identification and characterization of allelic expression allowing identification of regulatory sequence variants as well as epigenetic variation affecting human gene expression.



2020 ◽  
Author(s):  
Aziz M. Mezlini ◽  
Sudeshna Das ◽  
Anna Goldenberg

AbstractMost two-group statistical tests are implicitly looking for a broad pattern such as an overall shift in mean, median or variance between the two groups. Therefore, they operate best in settings where the effect of interest is uniformly affecting everyone in one group versus the other. In real-world applications, there are many scenarios where the effect of interest is heterogeneous. For example, a drug that works very well on only a proportion of patients and is equivalent to a placebo on the remaining patients, or a disease associated gene expression dysregulation that only occurs in a proportion of cases whereas the remaining cases have expression levels indistinguishable from the controls for the considered gene. In these examples with heterogeneous effect, we believe that using classical two-group statistical tests may not be the most powerful way to detect the signal. In this paper, we developed a statistical test targeting heterogeneous effects and demonstrated its power in a controlled simulation setting compared to existing methods. We focused on the problem of finding meaningful associations in complex genetic diseases using omics data such as gene expression, miRNA expression, and DNA methylation. In simulated and real data, we showed that our test is complementary to the traditionally used statistical tests and is able to detect disease-relevant genes with heterogeneous effects which would not be detectable with previous approaches.



2018 ◽  
Author(s):  
Saman Amini ◽  
Annika Jacobsen ◽  
Olga Ivanova ◽  
Philip Lijnzaad ◽  
Jaap Heringa ◽  
...  

AbstractGenetic interactions, a phenomenon whereby combinations of mutations lead to unexpected effects, reflect how cellular processes are wired and play an important role in complex genetic diseases. Understanding the molecular basis of genetic interactions is crucial for deciphering pathway organization as well as understanding the relationship between genetic variation and disease. Several putative molecular mechanisms have been linked to different genetic interaction types. However, differences in genetic interaction patterns and their underlying mechanisms have not yet been compared systematically between different functional gene classes. Here, differences in the occurrence and types of genetic interactions are compared for two classes, gene-specific transcription factors (GSTFs) and signaling genes (kinases and phosphatases). Genome-wide gene expression data for 63 single and double deletion mutants in baker’s yeast reveals that the two most common genetic interaction patterns are buffering and inversion. Buffering is typically associated with redundancy and is well understood. In inversion, genes show opposite behavior in the double mutant compared to the corresponding single mutants. The underlying mechanism is poorly understood. Although both classes show buffering and inversion patterns, the prevalence of inversion is much stronger in GSTFs. To decipher potential mechanisms, a Petri Net modeling approach was employed, where genes are represented as nodes and relationships between genes as edges. This allowed over 9 million possible three and four node models to be exhaustively enumerated. The models show that a quantitative difference in interaction strength is a strict requirement for obtaining inversion. In addition, this difference is frequently accompanied with a second gene that shows buffering. Taken together, these results provide a mechanistic explanation for inversion. Furthermore, the ability of transcription factors to differentially regulate expression of their targets provides a likely explanation why inversion is more prevalent for GSTFs compared to kinases and phosphatases.Author SummaryThe relationship between genotype and phenotype is one of the major challenges in biology. While many previous studies have identified genes involved in complex genetic diseases, there is still a gap between genotype and phenotype. One of the difficulties in filling this gap has been attributed to genetic interactions. Large-scale studies have revealed that genetic interactions are widespread in model organisms such as baker’s yeast. Several molecular mechanisms have been proposed for different genetic interaction types. However, differences in occurrence and underlying molecular mechanism of genetic interactions have not yet been compared between gene classes of different function. Here, we compared genetic interaction patterns identified using gene expression profiling for two classes of genes: gene specific transcription factors and signaling related genes. We modelled all possible molecular networks to unravel putative molecular differences underlying different genetic interaction patterns. Our study proposes a new mechanistic explanation for a certain genetic interaction pattern that is more strongly associated with transcription factors compared to signaling related genes. Overall, our findings and the computational methodologies implemented here can be valuable for understanding the molecular mechanisms underlying genetic interactions.



2012 ◽  
Vol 2012 ◽  
pp. 1-6 ◽  
Author(s):  
Kenji Inoue ◽  
Tatsuhiko Kodama ◽  
Hiroyuki Daida

Numerous studies have recently examined the role of pentraxin 3 (PTX3) in clinical situations. The pentraxin family includes C-reactive protein (CRP); however, unlike CRP, PTX3 is expressed predominantly in atherosclerotic lesions that involve macrophages, neutrophils, dendritic cells, or smooth muscle cells. Interestingly, PTX3 gene expression in human endothelial cells is suppressed to a greater extent by pitavastatin than the expression of 6,000 other human genes that have been examined, suggesting that PTX3 may be a novel biomarker for inflammatory cardiovascular disease. The expression and involvement of PTX3 in cardiovascular diseases are discussed in this paper, along with the characteristics of PTX3 that make it a suitable biomarker; namely, that the physiological concentration is known and it is independent of other risk factors. The results discussed in this paper suggest that further investigations into the potential novel use of PTX3 as a biomarker for inflammatory cardiovascular disease should be undertaken.



2021 ◽  
Vol 11 (10) ◽  
pp. 4589
Author(s):  
Ivan Duvnjak ◽  
Domagoj Damjanović ◽  
Marko Bartolac ◽  
Ana Skender

The main principle of vibration-based damage detection in structures is to interpret the changes in dynamic properties of the structure as indicators of damage. In this study, the mode shape damage index (MSDI) method was used to identify discrete damages in plate-like structures. This damage index is based on the difference between modified modal displacements in the undamaged and damaged state of the structure. In order to assess the advantages and limitations of the proposed algorithm, we performed experimental modal analysis on a reinforced concrete (RC) plate under 10 different damage cases. The MSDI values were calculated through considering single and/or multiple damage locations, different levels of damage, and boundary conditions. The experimental results confirmed that the MSDI method can be used to detect the existence of damage, identify single and/or multiple damage locations, and estimate damage severity in the case of single discrete damage.



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