Altered levels of histone deacetylase OsHDT1 affect differential gene expression patterns in hybrid rice

Hybrids between different inbred varieties display novel patterns of gene expression resulted from parental variation in allelic nucleotide sequences. To study the function of chromatin regulators in hybrid gene expression, the histone deacetylase gene OsHDT1 whose expression displayed a circadian rhythm was over-expressed or inactivated by RNAi in an elite rice parent. Increased OsHDT1 expression did not affect plant growth in the parent but led to early flowering in the hybrid. Nonadditive up-regulation of key flowering time genes was found to be related to flowering time of the hybrid. Over-expression of OsHDT1 repressed the nonadditive expression of the key flowering repressors in the hybrid (i.e. OsGI and Hd1) inducing early flowering. Analysis of histone acetylation suggested that OsHDT1 over-expression might promote deacetylation on OsGI and Hd1 chromatin during the peak expression phase. High throughput differential gene expression analysis revealed that altered OsHDT1 levels affected nonadditive expression of many genes in the hybrid. These data demonstrate that nonadditive gene expression was involved in flowering time control in the hybrid rice and that OsHDT1 level was important for nonadditive or differential expression of many genes including the flowering time genes, suggesting that OsHDT1 may be involved in epigenetic control of parental genome interaction for differential gene expression.     

Tumor-specific gene expression patterns with gene expression profiles

Gene expression profiles of 14 common tumors and their counterpart normal tissues were analyzed with machine learning methods to address the problem of selection of tumor-specific genes and analysis of their differential expressions in tumor tissues. First, a variation of the Relief algorithm, "RFE_Relief algorithm" was proposed to learn the relations between genes and tissue types. Then, a support vector machine was employed to find the gene subset with the best classification performance for distinguishing cancerous tissues and their counterparts. After tissue-specific genes were removed, cross validation experiments were employed to demonstrate the common deregulated expressions of the selected gene in tumor tissues. The results indicate the existence of a specific expression fingerprint of these genes that is shared in different tumor tissues, and the hallmarks of the expression patterns of these genes in cancerous tissues are summarized at the end of this paper.     

Tumor-specific gene expression patterns with gene expression profiles

Gene expression profiles of 14 common tumors and their counterpart normal tissues were analyzed with machine learning methods to address the problem of selection of tumor-specific genes and analysis of their differential expressions in tumor tissues. First, a variation of the Relief algorithm, “RFE_Relief algorithm” was proposed to learn the relations between genes and tissue types. Then, a support vector machine was employed to find the gene subset with the best classification performance for distinguishing cancerous tissues and their counterparts. After tissue-specific genes were removed, cross validation experiments were employed to demonstrate the common deregulated expressions of the selected gene in tumor tissues. The results indicate the existence of a specific expression fingerprint of these genes that is shared in different tumor tissues, and the hallmarks of the expression patterns of these genes in cancerous tissues are summarized at the end of this paper.     

Clustering gene expression patterns.

Recent advances in biotechnology allow researchers to measure expression levels for thousands of genes simultaneously, across different conditions and over time. Analysis of data produced by such experiments offers potential insight into gene function and regulatory mechanisms. A key step in the analysis of gene expression data is the detection of groups of genes that manifest similar expression patterns. The corresponding algorithmic problem is to cluster multicondition gene expression patterns. In this paper we describe a novel clustering algorithm that was developed for analysis of gene expression data. We define an appropriate stochastic error model on the input, and prove that under the conditions of the model, the algorithm recovers the cluster structure with high probability. The running time of the algorithm on an n-gene dataset is O[n2[log(n)]c]. We also present a practical heuristic based on the same algorithmic ideas. The heuristic was implemented and its performance is demonstrated on simulated data and on real gene expression data, with very promising results.     

Altered gene expression patterns of innate and adaptive immunity pathways in transgenic rainbow trout harboring Cecropin P1 transgene

Background

We have recently developed several homozygous families of transgenic rainbow trout harbouring cecropin P1 transgene. These fish exhibit resistance characteristic to infection by Aeromonas salmonicida and infectious hematopoietic necrosis virus (IHNV). In our earlier studies we have reported that treatment of a rainbow trout macrophage cell line (RTS11) with a linear cationic α-helical antimicrobial peptide (e.g., cecropin B) resulted in elevated levels of expression of two pro-inflammatory relevant genes (e.g., IL-1β and COX-2). Therefore, we hypothesized that in addition to the direct antimicrobial activity of cecropin P1 in the disease resistant transgenic rainbow trout, this antimicrobial peptide may also affect the expression of immune relevant genes in the host. To confirm this hypothesis, we launched a study to determine the global gene expression profiles in three immune competent organs of cecropin P1 transgenic rainbow trout by using a 44k salmonid microarray.

Results

From the microarray data, a total of 2480 genes in the spleen, 3022 in the kidney, and 2102 in the liver were determined as differentially expressed genes (DEGs) in the cecropin P1 transgenic rainbow trout when compared to the non-transgenics. There were 478 DEGs in common among three tissues. Enrichment analyses conducted by two different bioinformatics tools revealed a tissue specific profile of functional pathway perturbation. Many of them were directly related to innate immune system such as phagocytosis, lysosomal processing, complement activation, antigen processing/presentation, and leukocyte migration. Perturbation of other biological functions that might contribute indirectly to host immunity was also observed.

Conclusions

The gene product of cecropin P1 transgene produced in the disease resistant transgenic rainbow trout not only can kill the pathogens directly but also exert multifaceted immunomodulatory properties to boost host immunity. The identified genes involved in different pathways related to immune function are valuable indicators associated with enhanced host immunity. These genes may serve as markers for selective breeding of rainbow trout or other aquaculture important fish species bearing traits of disease resistance.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-887) contains supplementary material, which is available to authorized users.     

Altered gene expression in lymphoblastoid cell lines after subculture

Lymphoblastoid cell lines (LCLs) are nearly immortalized B lymphocytes that are used as long-lasting supply of human cells for studies on gene expression analyses. However, studies on the stability of the cellular features of LCLs are scarce. To address this issue, we measured gene expression in LCLs with different passage numbers and observed that gene expression substantially changed within 10 passages. In particular, the expression of glyceraldehyde-3-phosphate dehydrogenase (GAPDH), a well-known housekeeping gene, varied considerably during subculture; thus, the use GAPDH as an internal control may be unsuitable. In conclusion, this study highlights the need for exercising caution during determination of gene expression in LCLs.     

Root organization and gene expression patterns

New tools of microscopy, molecular biology, and genetics aremaking it possible for biologists to study roots with new vigour.Such investigations have enabled plant biologists to noticethe symmetry, pattern, and simplicity of root structures sothat there is now an exciting rebirth in the study of roots.The literature of root biology, development and structure isvast. In this short review we will concentrate on describingour notion of how roots are organized structurally, and thendiscuss what is known about tissue- and zone-specific gene expressionin roots. Key words: Root apex, root anatomy, root development, gene expression     

Altered cardiac tissue gene expression during acute hypoxic exposure

Summary Anoxia has been shown to induce the expression of one or more stress proteins in mammalian cells and tissues. A less severe form of oxygen depletion, hypoxic hypoxia, occurs in response to hypobaric decompression which simulates high altitude conditions. Under these conditions mouse hearts accumulate mRNAs for at least two polypeptides at substantially elevated levels. The molecular weights of these proteins, 85 kDa and 95 kDa, are similar to those reported for other mammalian stress proteins or glucose-regulated proteins. Time course experiments suggest that mRNAs for these species increase continuously for up to 16 hours of treatment, while mRNA for 71 kDa and 79 kDa polypeptides are elevated early in the treatment, but later decrease to control values. Total heart mRNA template activity is also increased by the hypobaric treatment. These results demonstrate that mouse cardiac tissue is capable of mounting a cellular stress-like response when exposed to moderately stressful conditions. It also provides a model for studying the direct effects of acute hypoxic stress on cellular gene expression, and its relationship to physiological adaptation.     

Altered patterns of gene expression in Arabidopsis elicited by cauliflower mosaic virus (CaMV) infection and by a CaMV gene VI transgene

Cauliflower mosaic virus (CaMV) gene VI protein (P6) is an important determinant of symptom expression. Differential display polymerase chain reaction (PCR) was used to identify changes in gene expression in Arabidopsis elicited by a P6 transgene that causes a symptomatic phenotype. We used slot blot hybridization to measure the abundance of mRNAs complementary to 66 candidate PCR products in transgenic, CaMV-infected, and uninfected Arabidopsis plants. CaMV-infected and P6 transgenic plants showed broadly similar changes in abundance of mRNA species. In P6 transgenic plants we detected 18 PCR products that showed unambiguous changes in abundance plus another 15 that showed more limited changes (approximately twofold). CaMV-infected plants showed 17 unambiguous and 13 limited changes. Down-regulated species include those encoding a novel, phenol-like sulfotransferase, and a glycine-rich, RNA-binding protein. Up-regulated species included ones encoding an myb protein, glycine-rich and stress-inducible proteins, and a member of a previously unreported gene family. CaMV infection causes alterations in expression of many Arabidopsis genes. Transgene-mediated expression of P6 mimics virus infection in its effect on host gene expression, providing a potential mechanism for this process.