scholarly journals Sex-specific gene expression in the BXD mouse liver

2010 ◽  
Vol 42 (3) ◽  
pp. 456-468 ◽  
Author(s):  
Daniel M. Gatti ◽  
Ni Zhao ◽  
Elissa J. Chesler ◽  
Blair U. Bradford ◽  
Andrey A. Shabalin ◽  
...  

Differences in clinical phenotypes between the sexes are well documented and have their roots in differential gene expression. While sex has a major effect on gene expression, transcription is also influenced by complex interactions between individual genetic variation and environmental stimuli. In this study, we sought to understand how genetic variation affects sex-related differences in liver gene expression by performing genetic mapping of genomewide liver mRNA expression data in a genetically defined population of naive male and female mice from C57BL/6J, DBA/2J, B6D2F1, and 37 C57BL/6J × DBA/2J (BXD) recombinant inbred strains. As expected, we found that many genes important to xenobiotic metabolism and other important pathways exhibit sexually dimorphic expression. We also performed gene expression quantitative trait locus mapping in this panel and report that the most significant loci that appear to regulate a larger number of genes than expected by chance are largely sex independent. Importantly, we found that the degree of correlation within gene expression networks differs substantially between the sexes. Finally, we compare our results to a recently released human liver gene expression data set and report on important similarities in sexually dimorphic liver gene expression between mouse and human. This study enhances our understanding of sex differences at the genome level and between species, as well as increasing our knowledge of the molecular underpinnings of sex differences in responses to xenobiotics.

2007 ◽  
Vol 31 (1) ◽  
pp. 63-74 ◽  
Author(s):  
Karl H. Clodfelter ◽  
Gregory D. Miles ◽  
Valerie Wauthier ◽  
Minita G. Holloway ◽  
Xiaohua Zhang ◽  
...  

Sexual dimorphism in mammalian liver impacts genes affecting hepatic physiology, including inflammatory responses, diseased states, and the metabolism of steroids and foreign compounds. Liver sex specificity is dictated by sex differences in pituitary growth hormone (GH) secretion, with the transcription factor signal transducer and activator of transcription (STAT)5b required for intracellular signaling initiated by the pulsatile male plasma GH profile. STAT5a, a minor liver STAT5 form >90% identical to STAT5b, also responds to sexually dimorphic plasma GH stimulation but is unable to compensate for the loss of STAT5b and the associated loss of sex-specific liver gene expression. A large-scale gene expression study was conducted using 23,574-feature oligonucleotide microarrays and livers of male and female mice, both wild-type and Stat5a-inactivated mice, to elucidate any dependence of liver gene expression on STAT5a. Significant sex differences in expression were found for 2,482 mouse genes, 1,045 showing higher expression in males and 1,437 showing higher expression in females. In contrast to the widespread effects of the loss of STAT5b, STAT5a deficiency had a limited but well-defined impact on liver sex specificity, with 219 of 1,437 female-predominant genes (15%) specifically decreased in expression in STAT5a-deficient female liver. Analysis of liver RNAs from wild-type mice representing three mixed or outbred strains identified 1,028 sexually dimorphic genes across the strains, including 393 female-predominant genes, of which 89 (23%) required STAT5a for normal expression in female liver. These findings highlight the importance of STAT5a for regulation of sex-specific gene expression specifically in female liver, in striking contrast to STAT5b, whose major effects are restricted to male liver.


2007 ◽  
Vol 05 (02a) ◽  
pp. 251-279 ◽  
Author(s):  
WENYUAN LI ◽  
YANXIONG PENG ◽  
HUNG-CHUNG HUANG ◽  
YING LIU

In most real-world gene expression data sets, there are often multiple sample classes with ordinals, which are categorized into the normal or diseased type. The traditional feature or attribute selection methods consider multiple classes equally without paying attention to the up/down regulation across the normal and diseased types of classes, while the specific gene selection methods particularly consider the differential expressions across the normal and diseased, but ignore the existence of multiple classes. In this paper, to improve the biomarker discovery, we propose to make the best use of these two aspects: the differential expressions (that can be viewed as the domain knowledge of gene expression data) and the multiple classes (that can be viewed as a kind of data set characteristic). Therefore, we simultaneously take into account these two aspects by employing the 1-rank generalized matrix approximations (GMA). Our results show that GMA cannot only improve the accuracy of classifying the samples, but also provide a visualization method to effectively analyze the gene expression data on both genes and samples. Based on the mechanism of matrix approximation, we further propose an algorithm, CBiomarker, to discover compact biomarker by reducing the redundancy.


Blood ◽  
2007 ◽  
Vol 110 (11) ◽  
pp. 266-266
Author(s):  
Joshua H. Wong ◽  
Robert E. Levy ◽  
Jonathan Dukes ◽  
Sara A. Mason ◽  
Brandon Sos ◽  
...  

Abstract Clinical reports suggest significant sex differences in risk for thrombosis-related diseases such as myocardial infarction, stroke, and venous thromboembolism. However, little is known about mechanism for such differences. There is a well-described sexual dimorphism in liver protein synthesis that is growth hormone (GH) dependent. GH secretion from the pituitary is itself highly sexually dimorphic with males (M) secreting in a pulsatile (P) and females (F) a continuous (C) fashion. These patterns induce M- and F-specific signatures of liver gene expression. In the past, we and others have observed significant sex differences in murine thrombosis models. Given that most coagulation proteases and inhibitors are synthesized or modified in the liver, we aimed to test whether sex-specific GH secretion patterns contribute to the observed sex differences in thrombosis. We measured whole blood clotting times (WCT), thrombosis susceptibility in the thromboplastin-mediated pulmonary embolism (PE) model, and hemostasis in the tail bleeding time (BT) model in M and F control (WT) and GH-deficient “little” (LIT) mice. We observed that WT Fs had longer WCTs (mean time 61.38 vs. 56.72 sec) and were significantly protected in the PE model (median survival 232.5 vs 165 sec) as compared to M. There were no differences in the BT model across all experiments. Interestingly, F and M LIT animals both had significantly prolonged WCTs (67.56 and 67.30 sec, respectively) and were substantially protected in the PE model (median survival 900 and 1200 sec) as compared to WT. Next, LIT animals were injected twice daily with GH to simulate the P pattern of GH secretion (LIT+). This resulted in a significant shortening of the F and M WCTs back to WT M levels (53.16 and 50.97 sec). A group of F WT animals were also injected with M pattern GH (WT+). This too resulted in significant shortening of the F WCTs (54.10 sec). To explore for possible mechanisms underlying these differences, we measured activity of coagulation factors II, V, VII, VIII, IX, X, and XI. The average of all factor activity levels was significantly higher in WT M vs F (100 vs. 81.99%), significantly lower and in both M and F LIT (60.85 and 57.97%), and increased to WT M levels in M and F LIT+ animals (106.6 and 99%). To determine whether these changes were mediated by changes in liver gene expression, we measured a panel of 30 coagulation protease and inhibitor genes in liver and vascular tissue by Taqman®. Surprisingly, we found no significant differences in coagulation factor expression, but found that expression of TFPI was significantly increased in F vs M WT vasculature (9431 vs. 7678 gene copy number (GCN)). Expression was increased in M and F LIT animals (10350 and 11710 GCN) and fell to below WT levels in M and F LIT+ animals (4534 and 4194 GCN). These results indicate that sex differences in thrombosis in mice are at least in part mediated by sex differences in GH secretion with F mice relatively protected as compared to M. M and F GH-deficient LIT mice are similarly protected as compared to WT M. Repletion of GH in a P pattern reverts M and F LIT and F WT mice to WT M levels. Finally, P GH secretion may promote increased thrombosis through inhibition of TFPI in the vasculature. This represents a novel mechanism underlying these sex-differences in thrombosis mediated by sexually dimorphic GH secretion and its effect on regulation of TFPI in the vasculature.


2000 ◽  
Vol 3 (1) ◽  
pp. 9-15 ◽  
Author(s):  
PETER J. WOOLF ◽  
YIXIN WANG

Woolf, Peter J., and Yixin Wang. A fuzzy logic approach to analyzing gene expression data. Physiol Genomics 3: 9–15, 2000.—We have developed a novel algorithm for analyzing gene expression data. This algorithm uses fuzzy logic to transform expression values into qualitative descriptors that can be evaluated by using a set of heuristic rules. In our tests we designed a model to find triplets of activators, repressors, and targets in a yeast gene expression data set. For the conditions tested, the predictions made by the algorithm agree well with experimental data in the literature. The algorithm can also assist in determining the function of uncharacterized proteins and is able to detect a substantially larger number of transcription factors than could be found at random. This technology extends current techniques such as clustering in that it allows the user to generate a connected network of genes using only expression data.


2021 ◽  
Author(s):  
Richard R Green ◽  
Renee C Ireton ◽  
Martin Ferris ◽  
Kathleen Muenzen ◽  
David R Crosslin ◽  
...  

To understand the role of genetic variation in SARS and Influenza infections we developed CCFEA, a shiny visualization tool using public RNAseq data from the collaborative cross (CC) founder strains (A/J, C57BL/6J, 129s1/SvImJ, NOD/ShILtJ, NZO/HILtJ, CAST/EiJ, PWK/PhJ, and WSB/EiJ). Individual gene expression data is displayed across founders, viral infections and days post infection.


2019 ◽  
Vol 36 (3) ◽  
pp. 782-788 ◽  
Author(s):  
Jiebiao Wang ◽  
Bernie Devlin ◽  
Kathryn Roeder

Abstract Motivation Patterns of gene expression, quantified at the level of tissue or cells, can inform on etiology of disease. There are now rich resources for tissue-level (bulk) gene expression data, which have been collected from thousands of subjects, and resources involving single-cell RNA-sequencing (scRNA-seq) data are expanding rapidly. The latter yields cell type information, although the data can be noisy and typically are derived from a small number of subjects. Results Complementing these approaches, we develop a method to estimate subject- and cell-type-specific (CTS) gene expression from tissue using an empirical Bayes method that borrows information across multiple measurements of the same tissue per subject (e.g. multiple regions of the brain). Analyzing expression data from multiple brain regions from the Genotype-Tissue Expression project (GTEx) reveals CTS expression, which then permits downstream analyses, such as identification of CTS expression Quantitative Trait Loci (eQTL). Availability and implementation We implement this method as an R package MIND, hosted on https://github.com/randel/MIND. Supplementary information Supplementary data are available at Bioinformatics online.


2007 ◽  
Vol 1 (S1) ◽  
Author(s):  
Alfonso Buil ◽  
Alexandre Perera-Lluna ◽  
Ramon Souto ◽  
Juan M Peralta ◽  
Laura Almasy ◽  
...  

BMC Genomics ◽  
2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Bryan W. Penning ◽  
Tânia M. Shiga ◽  
John F. Klimek ◽  
Philip J. SanMiguel ◽  
Jacob Shreve ◽  
...  

Abstract Background The cellular machinery for cell wall synthesis and metabolism is encoded by members of large multi-gene families. Maize is both a genetic model for grass species and a potential source of lignocellulosic biomass from crop residues. Genetic improvement of maize for its utility as a bioenergy feedstock depends on identification of the specific gene family members expressed during secondary wall development in stems. Results High-throughput sequencing of transcripts expressed in developing rind tissues of stem internodes provided a comprehensive inventory of cell wall-related genes in maize (Zea mays, cultivar B73). Of 1239 of these genes, 854 were expressed among the internodes at ≥95 reads per 20 M, and 693 of them at ≥500 reads per 20 M. Grasses have cell wall compositions distinct from non-commelinid species; only one-quarter of maize cell wall-related genes expressed in stems were putatively orthologous with those of the eudicot Arabidopsis. Using a slope-metric algorithm, five distinct patterns for sub-sets of co-expressed genes were defined across a time course of stem development. For the subset of genes associated with secondary wall formation, fifteen sequence motifs were found in promoter regions. The same members of gene families were often expressed in two maize inbreds, B73 and Mo17, but levels of gene expression between them varied, with 30% of all genes exhibiting at least a 5-fold difference at any stage. Although presence-absence and copy-number variation might account for much of these differences, fold-changes of expression of a CADa and a FLA11 gene were attributed to polymorphisms in promoter response elements. Conclusions Large genetic variation in maize as a species precludes the extrapolation of cell wall-related gene expression networks even from one common inbred line to another. Elucidation of genotype-specific expression patterns and their regulatory controls will be needed for association panels of inbreds and landraces to fully exploit genetic variation in maize and other bioenergy grass species.


2007 ◽  
Vol 5 ◽  
pp. 117693510700500
Author(s):  
K-A. Do ◽  
G.J. McLachlan ◽  
R. Bean ◽  
S. Wen

Researchers are frequently faced with the analysis of microarray data of a relatively large number of genes using a small number of tissue samples. We examine the application of two statistical methods for clustering such microarray expression data: EMMIX-GENE and GeneClust. EMMIX-GENE is a mixture-model based clustering approach, designed primarily to cluster tissue samples on the basis of the genes. GeneClust is an implementation of the gene shaving methodology, motivated by research to identify distinct sets of genes for which variation in expression could be related to a biological property of the tissue samples. We illustrate the use of these two methods in the analysis of Affymetrix oligonucleotide arrays of well-known data sets from colon tissue samples with and without tumors, and of tumor tissue samples from patients with leukemia. Although the two approaches have been developed from different perspectives, the results demonstrate a clear correspondence between gene clusters produced by GeneClust and EMMIX-GENE for the colon tissue data. It is demonstrated, for the case of ribosomal proteins and smooth muscle genes in the colon data set, that both methods can classify genes into co-regulated families. It is further demonstrated that tissue types (tumor and normal) can be separated on the basis of subtle distributed patterns of genes. Application to the leukemia tissue data produces a division of tissues corresponding closely to the external classification, acute myeloid meukemia (AML) and acute lymphoblastic leukemia (ALL), for both methods. In addition, we also identify genes specific for the subgroup of ALL-Tcell samples. Overall, we find that the gene shaving method produces gene clusters at great speed; allows variable cluster sizes and can incorporate partial or full supervision; and finds clusters of genes in which the gene expression varies greatly over the tissue samples while maintaining a high level of coherence between the gene expression profiles. The intent of the EMMIX-GENE method is to cluster the tissue samples. It performs a filtering step that results in a subset of relevant genes, followed by gene clustering, and then tissue clustering, and is favorable in its accuracy of ranking the clusters produced.


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