scholarly journals In Silico Analysis of the Gene Expression Patterns between Aldosterone-Producing Adenoma and Nonfunctional Adrenocortical Adenoma

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Yongfa Dai ◽  
Jing Li ◽  
Hong Wen ◽  
Jie Liu ◽  
Jianling Li
Keyword(s):  
Gene Expression ◽  
Expression Pattern ◽  
Primary Aldosteronism ◽  
Expression Patterns ◽  
Gene Expression Pattern ◽  
Gene Expression Patterns ◽  
Pathophysiological Mechanism ◽  
Adrenocortical Adenoma ◽  
Hub Genes ◽  
Aldosterone Producing Adenoma

Primary aldosteronism is the most common form of secondary hypertension, and aldosteronoma makes up a significant proportion of primary aldosteronism cases. Aldosteronoma is also called aldosterone-producing adenoma (APA). Although there have been many studies about APA, the pathogenesis of this disease is not yet fully understood. In this study, we aimed to find out the difference of gene expression patterns between APA and nonfunctional adrenocortical adenoma (NFAA) using a weighted gene coexpression network (WGCNA) and differentially expressed gene (DEG) analysis; only the genes that meet the corresponding standards of both methods were defined as real hub genes and then used for further analysis. Twenty-nine real hub genes were found out, most of which were enriched in the phospholipid metabolic process. WISP2, S100A10, SSTR5-AS1, SLC29A1, APOC1, and SLITRK4 are six real hub genes with the same gene expression pattern between the combined and validation datasets, three of which indirectly or directly participate in lipid metabolism including WISP2, S100A10, and APOC1. According to the gene expression pattern of DEGs, we speculated five candidate drugs with potential therapeutic value for APA, one of which is cycloheximide, an inhibitor for phospholipid biosynthesis. All the evidence suggests that phospholipid metabolism may be an important pathophysiological mechanism for APA. Our study provides a new perspective regarding the pathophysiological mechanism of APA and offers some small molecules that may possibly be effective drugs against APA.

Biological Image Analysis via Matrix Approximation

2011 ◽  
pp. 166-170
Author(s):  
Jieping Ye ◽  
Ravi Janardan ◽  
Sudhir Kumar
Keyword(s):  
Gene Expression ◽  
Expression Pattern ◽  
Expression Patterns ◽  
Gene Expression Pattern ◽  
Controlled Vocabulary ◽  
Gene Expression Patterns ◽  
Image Content ◽  
Gene Expressions ◽  
Microarray Gene Expression ◽  
Individual Gene

Understanding the roles of genes and their interactions is one of the central challenges in genome research. One popular approach is based on the analysis of microarray gene expression data (Golub et al., 1999; White, et al., 1999; Oshlack et al., 2007). By their very nature, these data often do not capture spatial patterns of individual gene expressions, which is accomplished by direct visualization of the presence or absence of gene products (mRNA or protein) (e.g., Tomancak et al., 2002; Christiansen et al., 2006). For instance, the gene expression pattern images of a Drosophila melanogaster embryo capture the spatial and temporal distribution of gene expression patterns at a given developmental stage (Bownes, 1975; Tsai et al., 1998; Myasnikova et al., 2002; Harmon et al., 2007). The identification of genes showing spatial overlaps in their expression patterns is fundamentally important to formulating and testing gene interaction hypotheses (Kumar et al., 2002; Tomancak et al., 2002; Gurunathan et al., 2004; Peng & Myers, 2004; Pan et al., 2006). Recent high-throughput experiments of Drosophila have produced over fifty thousand images (http://www. fruitfly.org/cgi-bin/ex/insitu.pl). It is thus desirable to design efficient computational approaches that can automatically retrieve images with overlapping expression patterns. There are two primary ways of accomplishing this task. In one approach, gene expression patterns are described using a controlled vocabulary, and images containing overlapping patterns are found based on the similarity of textual annotations. In the second approach, the most similar expression patterns are identified by a direct comparison of image content, emulating the visual inspection carried out by biologists [(Kumar et al., 2002); see also www.flyexpress.net]. The direct comparison of image content is expected to be complementary to, and more powerful than, the controlled vocabulary approach, because it is unlikely that all attributes of an expression pattern can be completely captured via textual descriptions. Hence, to facilitate the efficient and widespread use of such datasets, there is a significant need for sophisticated, high-performance, informatics-based solutions for the analysis of large collections of biological images.

scholarly journals BEST: A Novel Computational Approach for Comparing Gene Expression Patterns From Early Stages of Drosophila melanogaster Development

Genetics ◽  
2002 ◽  
Vol 162 (4) ◽  
pp. 2037-2047
Author(s):  
Sudhir Kumar ◽  
Karthik Jayaraman ◽  
Sethuraman Panchanathan ◽  
Rajalakshmi Gurunathan ◽  
Ana Marti-Subirana ◽  
...  
Keyword(s):  
Gene Expression ◽  
Developmental Biology ◽  
Drosophila Melanogaster ◽  
Expression Pattern ◽  
Early Stage ◽  
Expression Patterns ◽  
Gene Expression Pattern ◽  
Computational Approach ◽  
Gene Expression Patterns ◽  
Early Stages

Abstract Embryonic gene expression patterns are an indispensable part of modern developmental biology. Currently, investigators must visually inspect numerous images containing embryonic expression patterns to identify spatially similar patterns for inferring potential genetic interactions. The lack of a computational approach to identify pattern similarities is an impediment to advancement in developmental biology research because of the rapidly increasing amount of available embryonic gene expression data. Therefore, we have developed computational approaches to automate the comparison of gene expression patterns contained in images of early stage Drosophila melanogaster embryos (prior to the beginning of germ-band elongation); similarities and differences in gene expression patterns in these early stages have extensive developmental effects. Here we describe a basic expression search tool (BEST) to retrieve best matching expression patterns for a given query expression pattern and a computational device for gene interaction inference using gene expression pattern images and information on the associated genotypes and probes. Analysis of a prototype collection of Drosophila gene expression pattern images is presented to demonstrate the utility of these methods in identifying biologically meaningful matches and inferring gene interactions by direct image content analysis. In particular, the use of BEST searches for gene expression patterns is akin to that of BLAST searches for finding similar sequences. These computational developmental biology methodologies are likely to make the great wealth of embryonic gene expression pattern data easily accessible and to accelerate the discovery of developmental networks.

AML with Translocation T(8;16) Shows Unique Cytomorphological, Cytogenetic, Molecular, and Prognostic Features and Therefore Qualifies as An Own Entity According to WHO Criteria.

Blood ◽  
2008 ◽  
Vol 112 (11) ◽  
pp. 1197-1197
Author(s):  
Alexander Kohlmann ◽  
Martin Dugas ◽  
Hans-Ulrich Klein ◽  
Christian Ruckert ◽  
Wolfgang Kern ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Pattern ◽  
De Novo ◽  
Expression Patterns ◽  
Gene Expression Pattern ◽  
Gene Expression Patterns ◽  
Specific Gene ◽  
Differentiation Potential ◽  
Who Criteria ◽  
Prognostic Features

Abstract Balanced chromosomal rearrangements define distinct biological subsets in acute myeloid leukemia (AML). It is recognized that recurrent balanced aberrations, such as t(15;17), t(8;21), inv(16), and 11q23/MLL translocations, show a close correlation to cytomorphology and also harbor specific gene expression signatures. We here present a cohort of 13 AML cases with t(8;16)(p11;p13). This translocation is rare with only 13 cases (6 males, 7 females) diagnosed from our overall cohort of 6124 cases of AML over recent years, and is more frequently found in therapy-related AML than in de novo AML (7/438 t-AML, and 6/5686 de novo, p=0.00001). Prognosis was poor with median overall survival of 4.7 months. Five patients deceased within the first month after diagnosis. AML with t(8;16) is characterized by striking cytomorphologic features: In all 13 cases the positivity for myeloperoxidase (MPO) on bone marrow smears was >30% (median: 85%) and intriguingly, in parallel also >40% (median: 88%) of blast cells stained strongly positive for non-specific esterase (NSE) in the same cell, suggesting that AML with t(8;16) arise from a very early stem cell with both myeloid and monoblastic differentiation potential. Therefore, AML with t(8;16) cases can not be classified according to standard FAB categories. Morphologically we also detected erythrophagocytosis in 7/13 cases, a specific feature in AML with t(8;16) that was previously described. With respect to cytogenetics, 6/13 patients had t(8;16)(p11;p13) as sole abnormality. 7/13 patients demonstrated additional non-recurrent abnormalities, 4 cases with single additional aberrations, and 3 cases with two or more additional aberrations. Molecular analyses detected the MYST3- CREBBP fusion transcript in all cases tested (12/12). We then compared gene expression patterns in 7 cases of AML with t(8;16) to: (i) AML FAB subtypes M1 and M4/5 with strong MPO or NSE with normal karyotype and to (ii) distinct AML subtypes with balanced chromosomal aberrations according to WHO classification. In a first series using Affymetrix HG-U133A+B microarrays 4 cases of AML with t(8;16) were compared to FAB M1 (n=46), M4 (n=41), M5a (n=9), and M5b (n=16). Hierarchical clustering and principal component analyses revealed that AML with t(8;16) were intercalating rather with FAB subtypes M4 and M5b and did not cluster near to FAB M1, although strong positivity for MPO was seen in all t(8;16) cases. Thus, monocytic characteristics influence the gene expression pattern stronger than myeloid features. When further compared to AML WHO subtypes t(15;17) (n=43), t(8;21) (n=43), inv(16) (n=49), and 11q23/MLL (n=50), AML with t(8;16) samples were repeatedly grouped in the vicinity of the 11q23/MLL cases. This can be explained by a similar expression of genes such as EAF2, HOXA9, HOXA10, PRKCD, or HNMT. Yet, in a subsequent pairwise comparison AML with t(8;16) could also be clearly discriminated from 11q23/MLL with differentially expressed genes including CAPRIN1, RAN, SMARCD2, LRRC41, or H2BFS, higher expressed in AML with t(8;16) and SOCS2, PRAME, RUNX3, or TPT1, lower expressed in AML with t(8;16), respectively. Moreover, the respective FAB-type or WHO-type signatures were validated on a separate cohort of patients (n=3 AML with t(8;16); n=107 other AML subtypes as above), all prospectively analyzed with the successor HG-U133 Plus 2.0 microarray. Again, in direct comparison to FAB-type or WHO-type cases, dominant and unique gene expression patterns were seen for AML with t(8;16), confirming the molecular distinctiveness of this rare AML entity. Using a classification algorithm we were able to correctly predict all AML with t(8;16) cases by their gene expression pattern. This accuracy was observed not only for both FAB-type and WHO-type signatures, but also correctly classified the cases across the different patient cohorts and microarray designs. In conclusion, AML with t(8;16) is a specific subtype of AML with very poor prognosis that often presents as treatment-related AML and with particular characteristics not only in morphology and clinical profile, but also on a molecular level. Due to these unique features, it qualifies as a specific recurrent entity according to WHO criteria.

Recruited vs. nonrecruited molecular signatures of brown, “brite,” and white adipose tissues

2012 ◽  
Vol 302 (1) ◽  
pp. E19-E31 ◽  
Author(s):  
Tomas B. Waldén ◽  
Ida R. Hansen ◽  
James A. Timmons ◽  
Barbara Cannon ◽  
Jan Nedergaard
Keyword(s):  
Gene Expression ◽  
Expression Pattern ◽  
Expression Patterns ◽  
Gene Expression Pattern ◽  
Gene Expression Patterns ◽  
Culture Studies ◽  
Gene Markers ◽  
Different Types ◽  
Regulatory Functions

Mainly from cell culture studies, a series of genes that have been suggested to be characteristic of different types of adipocytes have been identified. Here we have examined gene expression patterns in nine defined adipose depots: interscapular BAT, cervical BAT, axillary BAT, mediastinic BAT, cardiac WAT, inguinal WAT, retroperitoneal WAT, mesenteric WAT, and epididymal WAT. We found that each depot displayed a distinct gene expression fingerprint but that three major types of depots were identifiable: the brown, the brite, and the white. Although differences in gene expression pattern were generally quantitative, some gene markers showed, even in vivo, remarkable depot specificities: Zic1 for the classical BAT depots, Hoxc9 for the brite depots, Hoxc8 for the brite and white in contrast to the brown, and Tcf21 for the white depots. The effect of physiologically induced recruitment of thermogenic function (cold acclimation) on the expression pattern of the genes was quantified; in general, the depot pattern dominated over the recruitment effects. The significance of the gene expression patterns for classifying the depots and for understanding the developmental background of the depots is discussed, as are the possible regulatory functions of the genes.

scholarly journals Verticillium wilt resistant and susceptible olive cultivars express a very different basal set of genes in roots

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Jorge A. Ramírez-Tejero ◽  
Jaime Jiménez-Ruiz ◽  
Alicia Serrano ◽  
Angjelina Belaj ◽  
Lorenzo León ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Pattern ◽  
Growth And Development ◽  
Expression Patterns ◽  
Gene Expression Pattern ◽  
Expression Level ◽  
Resistant Cultivars ◽  
Pathogen Invasion ◽  
Wide Range ◽  
Olive Cultivars

Abstract Background Olive orchards are threatened by a wide range of pathogens. Of these, Verticillium dahliae has been in the spotlight for its high incidence, the difficulty to control it and the few cultivars that has increased tolerance to the pathogen. Disease resistance not only depends on detection of pathogen invasion and induction of responses by the plant, but also on barriers to avoid the invasion and active resistance mechanisms constitutively expressed in the absence of the pathogen. In a previous work we found that two healthy non-infected plants from cultivars that differ in V. dahliae resistance such as ‘Frantoio’ (resistant) and ‘Picual’ (susceptible) had a different root morphology and gene expression pattern. In this work, we have addressed the issue of basal differences in the roots between Resistant and Susceptible cultivars. Results The gene expression pattern of roots from 29 olive cultivars with different degree of resistance/susceptibility to V. dahliae was analyzed by RNA-Seq. However, only the Highly Resistant and Extremely Susceptible cultivars showed significant differences in gene expression among various groups of cultivars. A set of 421 genes showing an inverse differential expression level between the Highly Resistant to Extremely Susceptible cultivars was found and analyzed. The main differences involved higher expression of a series of transcription factors and genes involved in processes of molecules importation to nucleus, plant defense genes and lower expression of root growth and development genes in Highly Resistant cultivars, while a reverse pattern in Moderately Susceptible and more pronounced in Extremely Susceptible cultivars were observed. Conclusion According to the different gene expression patterns, it seems that the roots of the Extremely Susceptible cultivars focus more on growth and development, while some other functions, such as defense against pathogens, have a higher expression level in roots of Highly Resistant cultivars. Therefore, it seems that there are constitutive differences in the roots between Resistant and Susceptible cultivars, and that susceptible roots seem to provide a more suitable environment for the pathogen than the resistant ones.

scholarly journals Unique Gene Expression Patterns in Human T-cell Lines Generated from Multiple Sclerosis Patients by Stimulation with a Synthetic MOG Peptide

2005 ◽  
Vol 12 (3) ◽  
pp. 203-209 ◽  
Author(s):  
Mathilda Mandel ◽  
Michael Gurevich ◽  
Gad Lavie ◽  
Irun R. Cohen ◽  
Anat Achiron
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
T Cell ◽  
Cell Lines ◽  
Healthy Subjects ◽  
Expression Patterns ◽  
Gene Expression Pattern ◽  
Gene Expression Patterns ◽  
T Cell Lines ◽  
Myelin Antigens

Multiple sclerosis (MS) is an autoimmune disease where T-cells activated against myelin antigens are involved in myelin destruction. Yet, healthy subjects also harbor T-cells responsive to myelin antigens, suggesting that MS patient-derived autoimmune T-cells might bear functional differences from T-cells derived from healthy individuals. We addressed this issue by analyzing gene expression patterns of myelin oligodendrocytic glycoprotein (MOG) responsive T-cell lines generated from MS patients and healthy subjects. We identified 150 transcripts that were differentially expressed between MS patients and healthy controls. The most informative 43 genes exhibited >1.5-fold change in expression level. Eighteen genes were up-regulated including BCL2, lifeguard, IGFBP3 and VEGF. Twenty five genes were down-regulated, including apoptotic activators like TNF and heat shock protein genes. This gene expression pattern was unique to MOG specific T-cell lines and was not expressed in T-cell lines reactive to tetanus toxin (TTX). Our results indicate that activation in MS that promotes T-cell survival and expansion, has its own state and that the unique gene expression pattern that characterize autoreactive T-cells in MS represent a constellation of factors in which the chronicity, timing and accumulation of damage make the difference between health and disease.

Acute Myeloid Leukemia with Translocation t(8;16) Demonstrates Specific Cytomorphological, Cytogenetic, and Gene Expression Characteristics and Can Clearly Be Discriminated from Other AML with Balanced Translocations.

Blood ◽  
2004 ◽  
Vol 104 (11) ◽  
pp. 2897-2897
Author(s):  
Torsten Haferlach ◽  
Helmut Loeffler ◽  
Alexander Kohlmann ◽  
Martin Dugas ◽  
Wolfgang Hiddemann ◽  
...  
Keyword(s):  
Gene Expression ◽  
Who Classification ◽  
De Novo ◽  
Expression Patterns ◽  
Gene Expression Pattern ◽  
Support Vector ◽  
Gene Expression Patterns ◽  
Down Regulation ◽  
Specific Expression ◽  
Balanced Translocations

Abstract Balanced chromosomal rearrangements leading to fusion genes on the molecular level define distinct biological subsets in AML. The four balanced rearrangements (t(15;17), t(8;21), inv(16), and 11q23/MLL) show a close correlation to cytomorphology and gene expression patterns. We here focused on seven AML with t(8;16)(p11;p13). This translocation is rare (7/3515 cases in own cohort). It is more frequently found in therapy-related AML than in de novo AML (3/258 t-AML, and 4/3287 de novo, p=0.0003). Cytomorphologically, AML with t(8;16) is characterized by striking features: In all 7 cases the positivity for myeloperoxidase on bone marrow smears was >70% and intriguingly, in parallel >80% of blast cells stained strongly positive for non-specific esterase (NSE) in all cases. Thus, these cases can not be classified according to FAB categories. These data suggest that AML-t(8;16) arise from a very early stem cell with both myeloid and monoblastic potential. Furthermore, we detected erythrophagocytosis in 6/7 cases that was described as specific feature in AML with t(8;16). Four pts. had chromosomal aberrations in addition to t(8;16), 3 of these were t-AML all showing aberrations of 7q. Survival was poor with 0, 1, 1, 2, 20 and 18+ (after alloBMT) mo., one lost to follow-up, respectively. We then analyzed gene expression patterns in 4 cases (Affymetrix U133A+B). First we compared t(8;16) AML with 46 AML FAB M1, 41 M4, 9 M5a, and 16 M5b, all with normal karyotype. Hierachical clustering and principal component analyses (PCA) revealed that t(8;16) AML were intercalating with FAB M4 and M5b and did not cluster near to M1. Thus, monocytic characteristics influence the gene expression pattern stronger than myeloid. Next we compared the t(8;16) AML with the 4 other balanced subtypes according to the WHO classification (t(15;17): 43; t(8;21): 40; inv(16): 49; 11q23/MLL-rearrangements: 50). Using support vector machines the overall accuracy for correct subgroup assignment was 97.3% (10-fold CV), and 96.8% (2/3 training and 1/3 test set, 100 runs). In PCA and hierarchical cluster analysis the t(8;16) were grouped in the vicinity of the 11q23 cases. However, in a pairwise comparison these two subgroups could be discriminated with an accuracy of 94.4% (10-fold CV). Genes with a specific expression in AML-t(8;16) were further investigated in pathway analyses (Ingenuity). 15 of the top 100 genes associated with AML-t(8;16) were involved in the CMYC-pathway with up regulation of BCOR, COXB5, CDK10, FLI1, HNRPA2B1, NSEP1, PDIP38, RAD50, SUPT5H, TLR2 and USP33, and down regulation of ERG, GATA2, NCOR2 and RPS20. CEBP beta, known to play a role in myelomonocytic differentiation, was also up-regulated in t(8;16)-AML. Ten additional genes out of the 100 top differentially expressed genes were also involved in this pathway with up-regulation of DDB2, HIST1H3D, NSAP1, PTPNS1, RAN, USP4, TRIM8, ZNF278 and down regulation of KIT and MBD2. In conclusion, AML with t(8;16) is a specific subtype of AML with unique characteristics in morphology and gene expression patterns. It is more frequently found in t-AML, outcome is inferior in comparison to other AML with balanced translocations. Due to its unique features, it is a candidate for inclusion into the WHO classification as a specific entity.

Follistatin alters myostatin gene expression in C2C12 muscle cells

2004 ◽  
Vol 52 (2) ◽  
pp. 135-141 ◽  
Author(s):  
H. Kocams¸ ◽  
N. Gulmez ◽  
S. Aslan ◽  
M. Nazlı
Keyword(s):  
Gene Expression ◽  
Expression Pattern ◽  
Time Course ◽  
Expression Patterns ◽  
Mrna Level ◽  
Gene Expression Pattern ◽  
Muscle Cells ◽  
Treated Group ◽  
Myostatin Gene ◽  
Significant Difference

The objective of the present study was to determine the effects of follistatin addition on myostatin and follistatin gene expression patterns in C2C12 muscle cells. C2C12 cells were administered with 100 ng/ml recombinant human (rh) follistatin in Dulbecco's modified Eagle medium (DMEM) containing 10% fetal bovine serum (FBS), 4 mM glutamine and antibiotics daily for three days. Rh follistatin was not added in the control wells. Follistatin and myostatin gene cDNAs were synthesised by reverse transcriptase polymerase chain reaction (RT-PCR).The time course of follistatin gene expression pattern was similar in both the control and the follistatin-treated group. Myostatin mRNA level significantly increased in the follistatin-treated group after 24 h of culture (Fig. 3, P < 0.01). Amounts then sharply decreased (Fig. 3, P < 0.01) at 48 h of culture, whereas there was no significant difference between the control and the follistatin-treated group at 72 h of culture. Our results demonstrated that myostatin and follistatin mRNA were expressed in C2C12 cells and rh follistatin changed the myostatin expression pattern.

scholarly journals An Actinidia chinensis (kiwifruit) eFP browser and network analysis of transcription factors

2020 ◽  
Author(s):  
Lara Brian ◽  
Ben Warren ◽  
Peter McAtee ◽  
Jessica Rodrigues ◽  
Niels Nieuwenhuizen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Network Analysis ◽  
Fruit Development ◽  
Transcriptional Control ◽  
Expression Patterns ◽  
Gene Expression Patterns ◽  
Actinidia Chinensis ◽  
Knox Gene ◽  
Hub Genes ◽  
Flower And Fruit Development

Abstract BackgroundTranscriptomic studies combined with a well annotated genome have laid the foundations for new understanding of molecular processes. Tools which visualise gene expression patterns have further added to these resources. The manual annotation of the Actinidia chinensis (kiwifruit) genome has resulted in a high quality set of 33,044 genes. Here we investigate gene expression patterns in diverse tissues, visualised in an Electronic Fluorescent Pictograph (eFP) browser, to study the relationship of transcription factor (TF) expression using network analysis. ResultsSixty-one samples covering diverse tissues at different developmental time points were selected for RNAseq analysis and an eFP browser was generated to visualise this dataset. 2,839 TFs representing 57 different classes were identified and named. Network analysis of the TF expression patterns separated TFs into 14 different modules. Two modules consisting of 237 TFs were correlated with floral bud and flower development, a further two modules containing 160 TFs were associated with fruit development and maturation. A single module of 480 TFs was associated with ethylene-induced fruit ripening. Three “hub” genes correlated with flower and fruit development consisted of a HAF-like gene central to gynoecium development, an ERF and a DOF gene. Maturing and ripening hub genes included a KNOX gene that was associated with seed maturation, and a GRAS-like TF.ConclusionsThis study provides an insight into the complexity of the transcriptional control of flower and fruit development, as well as providing a new resource to the plant community. The eFP browser is provided in an accessible format that allows researchers to download and work internally.

scholarly journals Transvection regulates the sex-biased expression of a fly X-linked gene

Science ◽  
2021 ◽  
Vol 371 (6527) ◽  
pp. 396-400 ◽  
Author(s):  
Charalampos Chrysovalantis Galouzis ◽  
Benjamin Prud’homme
Keyword(s):  
Gene Expression ◽  
Sexual Dimorphism ◽  
Sex Determination ◽  
Expression Pattern ◽  
X Chromosome ◽  
Expression Patterns ◽  
Gene Expression Patterns ◽  
Regulatory Interaction ◽  
Linked Gene ◽  
Homologous Chromosomes

Sexual dimorphism in animals results from sex-biased gene expression patterns. These patterns are controlled by genetic sex determination hierarchies that establish the sex of an individual. Here we show that the male-biased wing expression pattern of the Drosophila biarmipes gene yellow, located on the X chromosome, is independent of the fly sex determination hierarchy. Instead, we find that a regulatory interaction between yellow alleles on homologous chromosomes (a process known as transvection) silences the activity of a yellow enhancer functioning in the wing. Therefore, this enhancer can be active in males (XY) but not in females (XX). This transvection-dependent enhancer silencing requires the yellow intron and the chromatin architecture protein Mod(mdg4). Our results suggest that transvection can contribute more generally to the sex-biased expression of X-linked genes.

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