Gene expression disruptions of organism versus organ in Drosophila species hybrids.

Hybrid dysfunctions, such as sterility, may result in part from disruptions in the regulation of gene expression. Studies of hybrids within the Drosophila simulans clade have reported genes expressed above or below the expression observed in their parent species, and such misexpression is associated with male sterility in multigenerational backcross hybrids. However, these studies often examined whole bodies rather than testes or had limited replication using less-sensitive but global techniques. Here, we use a new RNA isolation technique to re-examine hybrid gene expression disruptions in both testes and whole bodies from single Drosophila males by real-time quantitative RT-PCR. We find two early-spermatogenesis transcripts are underexpressed in hybrid whole-bodies but not in assays of testes alone, while two late-spermatogenesis transcripts seem to be underexpressed in both whole-bodies and testes alone. Although the number of transcripts surveyed is limited, these results provide some support for a previous hypothesis that the spermatogenesis pathway in these sterile hybrids may be disrupted sometime after the expression of the early meiotic arrest genes.

Intergenic and genic sequence lengths have opposite relationships with respect to gene expression.

Eukaryotic genomes are mostly composed of noncoding DNA whose role is still poorly understood. Studies in several organisms have shown correlations between the length of the intergenic and genic sequences of a gene and the expression of its corresponding mRNA transcript. Some studies have found a positive relationship between intergenic sequence length and expression diversity between tissues, and concluded that genes under greater regulatory control require more regulatory information in their intergenic sequences. Other reports found a negative relationship between expression level and gene length and the interpretation was that there is selection pressure for highly expressed genes to remain small. However, a correlation between gene sequence length and expression diversity, opposite to that observed for intergenic sequences, has also been reported, and to date there is no testable explanation for this observation. To shed light on these varied and sometimes conflicting results, we performed a thorough study of the relationships between sequence length and gene expression using cell-type (tissue) specific microarray data in Arabidopsis thaliana. We measured median gene expression across tissues (expression level), expression variability between tissues (expression pattern uniformity), and expression variability between replicates (expression noise). We found that intergenic (upstream and downstream) and genic (coding and noncoding) sequences have generally opposite relationships with respect to expression, whether it is tissue variability, median, or expression noise. To explain these results we propose a model, in which the lengths of the intergenic and genic sequences have opposite effects on the ability of the transcribed region of the gene to be epigenetically regulated for differential expression. These findings could shed light on the role and influence of noncoding sequences on gene expression.

Diagnosis of partial body radiation exposure in mice using peripheral blood gene expression profiles.

In the event of a terrorist-mediated attack in the United States using radiological or improvised nuclear weapons, it is expected that hundreds of thousands of people could be exposed to life-threatening levels of ionizing radiation. We have recently shown that genome-wide expression analysis of the peripheral blood (PB) can generate gene expression profiles that can predict radiation exposure and distinguish the dose level of exposure following total body irradiation (TBI). However, in the event a radiation-mass casualty scenario, many victims will have heterogeneous exposure due to partial shielding and it is unknown whether PB gene expression profiles would be useful in predicting the status of partially irradiated individuals. Here, we identified gene expression profiles in the PB that were characteristic of anterior hemibody-, posterior hemibody- and single limb-irradiation at 0.5 Gy, 2 Gy and 10 Gy in C57Bl6 mice. These PB signatures predicted the radiation status of partially irradiated mice with a high level of accuracy (range 79-100%) compared to non-irradiated mice. Interestingly, PB signatures of partial body irradiation were poorly predictive of radiation status by site of injury (range 16-43%), suggesting that the PB molecular response to partial body irradiation was anatomic site specific. Importantly, PB gene signatures generated from TBI-treated mice failed completely to predict the radiation status of partially irradiated animals or non-irradiated controls. These data demonstrate that partial body irradiation, even to a single limb, generates a characteristic PB signature of radiation injury and thus may necessitate the use of multiple signatures, both partial body and total body, to accurately assess the status of an individual exposed to radiation.

Bayesian inference of the number of factors in gene-expression analysis: application to human virus challenge studies.

BACKGROUND: Nonparametric Bayesian techniques have been developed recently to extend the sophistication of factor models, allowing one to infer the number of appropriate factors from the observed data. We consider such techniques for sparse factor analysis, with application to gene-expression data from three virus challenge studies. Particular attention is placed on employing the Beta Process (BP), the Indian Buffet Process (IBP), and related sparseness-promoting techniques to infer a proper number of factors. The posterior density function on the model parameters is computed using Gibbs sampling and variational Bayesian (VB) analysis. RESULTS: Time-evolving gene-expression data are considered for respiratory syncytial virus (RSV), Rhino virus, and influenza, using blood samples from healthy human subjects. These data were acquired in three challenge studies, each executed after receiving institutional review board (IRB) approval from Duke University. Comparisons are made between several alternative means of per-forming nonparametric factor analysis on these data, with comparisons as well to sparse-PCA and Penalized Matrix Decomposition (PMD), closely related non-Bayesian approaches. CONCLUSIONS: Applying the Beta Process to the factor scores, or to the singular values of a pseudo-SVD construction, the proposed algorithms infer the number of factors in gene-expression data. For real data the "true" number of factors is unknown; in our simulations we consider a range of noise variances, and the proposed Bayesian models inferred the number of factors accurately relative to other methods in the literature, such as sparse-PCA and PMD. We have also identified a "pan-viral" factor of importance for each of the three viruses considered in this study. We have identified a set of genes associated with this pan-viral factor, of interest for early detection of such viruses based upon the host response, as quantified via gene-expression data.

Radioactive in situ hybridization for detecting diverse gene expression patterns in tissue.

Knowing the timing, level, cellular localization, and cell type that a gene is expressed in contributes to our understanding of the function of the gene. Each of these features can be accomplished with in situ hybridization to mRNAs within cells. Here we present a radioactive in situ hybridization method modified from Clayton et al. (1988)(1) that has been working successfully in our lab for many years, especially for adult vertebrate brains(2-5). The long complementary RNA (cRNA) probes to the target sequence allows for detection of low abundance transcripts(6,7). Incorporation of radioactive nucleotides into the cRNA probes allows for further detection sensitivity of low abundance transcripts and quantitative analyses, either by light sensitive x-ray film or emulsion coated over the tissue. These detection methods provide a long-term record of target gene expression. Compared with non-radioactive probe methods, such as DIG-labeling, the radioactive probe hybridization method does not require multiple amplification steps using HRP-antibodies and/or TSA kit to detect low abundance transcripts. Therefore, this method provides a linear relation between signal intensity and targeted mRNA amounts for quantitative analysis. It allows processing 100-200 slides simultaneously. It works well for different developmental stages of embryos. Most developmental studies of gene expression use whole embryos and non-radioactive approaches(8,9), in part because embryonic tissue is more fragile than adult tissue, with less cohesion between cells, making it difficult to see boundaries between cell populations with tissue sections. In contrast, our radioactive approach, due to the larger range of sensitivity, is able to obtain higher contrast in resolution of gene expression between tissue regions, making it easier to see boundaries between populations. Using this method, researchers could reveal the possible significance of a newly identified gene, and further predict the function of the gene of interest.

A noisy linear map underlies oscillations in cell size and gene expression in bacteria.

During bacterial growth, a cell approximately doubles in size before division, after which it splits into two daughter cells. This process is subjected to the inherent perturbations of cellular noise and thus requires regulation for cell-size homeostasis. The mechanisms underlying the control and dynamics of cell size remain poorly understood owing to the difficulty in sizing individual bacteria over long periods of time in a high-throughput manner. Here we measure and analyse long-term, single-cell growth and division across different Escherichia coli strains and growth conditions. We show that a subset of cells in a population exhibit transient oscillations in cell size with periods that stretch across several (more than ten) generations. Our analysis reveals that a simple law governing cell-size control-a noisy linear map-explains the origins of these cell-size oscillations across all strains. This noisy linear map implements a negative feedback on cell-size control: a cell with a larger initial size tends to divide earlier, whereas one with a smaller initial size tends to divide later. Combining simulations of cell growth and division with experimental data, we demonstrate that this noisy linear map generates transient oscillations, not just in cell size, but also in constitutive gene expression. Our work provides new insights into the dynamics of bacterial cell-size regulation with implications for the physiological processes involved.

Wnt Protein Signaling Reduces Nuclear Acetyl-CoA Levels to Suppress Gene Expression during Osteoblast Differentiation.

Developmental signals in metazoans play critical roles in inducing cell differentiation from multipotent progenitors. The existing paradigm posits that the signals operate directly through their downstream transcription factors to activate expression of cell type-specific genes, which are the hallmark of cell identity. We have investigated the mechanism through which Wnt signaling induces osteoblast differentiation in an osteoblast-adipocyte bipotent progenitor cell line. Unexpectedly, Wnt3a acutely suppresses the expression of a large number of genes while inducing osteoblast differentiation. The suppressed genes include Pparg and Cebpa, which encode adipocyte-specifying transcription factors and suppression of which is sufficient to induce osteoblast differentiation. The large scale gene suppression induced by Wnt3a corresponds to a global decrease in histone acetylation, an epigenetic modification that is associated with gene activation. Mechanistically, Wnt3a does not alter histone acetyltransferase or deacetylase activities but, rather, decreases the level of acetyl-CoA in the nucleus. The Wnt-induced decrease in histone acetylation is independent of β-catenin signaling but, rather, correlates with suppression of glucose metabolism in the tricarboxylic acid cycle. Functionally, preventing histone deacetylation by increasing nucleocytoplasmic acetyl-CoA levels impairs Wnt3a-induced osteoblast differentiation. Thus, Wnt signaling induces osteoblast differentiation in part through histone deacetylation and epigenetic suppression of an alternative cell fate.

Characterization of autonomous thyroid adenoma: metabolism, gene expression, and pathology.

Fifty-one in vivo characterized autonomous single adenomas have been studied for functional parameters in vitro, for gene and protein expression and for pathology, and have been systematically compared to the corresponding extratumoral quiescent tissue. The adenomas were characterized by a high level of iodide trapping that corresponds to a high level of Na+ /iodide symporter gene expression, a high thyroperoxidase mRNA and protein content, and a low H2O2 generation. This explains the iodide metabolism characteristics demonstrated before, ie, the main cause of the "hot" character of the adenomas is their increased iodide transport. The adenomas spontaneously secreted higher amounts of thyroid hormone than the quiescent tissue and in agreement with previous in vivo data, this secretion could be further enhanced by thyrotropin (TSH). Inositol uptake was also increased but there was no spontaneous increase of the generation of inositol phosphates and this metabolism could be further activated by TSH. These positive responses to TSH are in agreement with the properties of TSH-stimulated thyroid cells in vitro and in vivo. They are compatible with the characteristics of mutated TSH receptors whose constitutive activation accounts for the majority of autonomous thyroid adenomas in Europe. The number of cycling cells, as evaluated by MIB-1 immunolabeling was low but increased in comparison with the corresponding quiescent tissue or normal tissue. The cycling cells are observed mainly at the periphery; there was very little apoptosis. Both findings account for the slow growth of these established adenomas. On the other hand, by thyroperoxidase immunohistochemistry, the whole lesion appeared hyperfunctional, which demonstrates a dissociation of mitogenic and functional stimulations. Thyroglobulin, TSH receptor, and E-cadherin mRNA accumulations were not modified in a consistent way, which confirms the near-constitutive expression of the corresponding genes in normal differentiated tissue. On the contrary, early immediate genes expressions (c-myc, NGF1B, egr 1, genes of the fos and jun families) were decreased. This may be explained by the proliferative heterogeneity of the lesion and the previously described short, biphasic expression of these genes when induced by mitogenic agents. All the characteristics of the autonomous adenomas can therefore be explained by the effect of the known activating mutations of genes coding for proteins of the TSH cyclic adenosine monophosphate (cAMP) cascade, all cells being functionally activated while only those at the periphery multiply. The reason of this heterogeneity is unknown.

Study of gene expression in thyrotropin-stimulated thyroid cells by cDNA expression array: ID3 transcription modulating factor as an early response protein and tumor marker in thyroid carcinomas.

Induction of cell proliferation by mitogen or growth factor stimulation leads to the specific induction or repression of a large number of genes. To identify genes differentially regulated by the cAMP-dependent transduction pathway, which is poorly characterized so far, we used the cDNA expression array technology. Hybridizations of Atlas human cDNA expression arrays with (32)P-labeled cDNA probes derived from control or thyrotropin (TSH)-stimulated dog thyrocytes in primary culture generated expression profiles of hundreds of genes simultaneously. Among the genes that displayed modified expression, we selected the transcription factor ID3, whose expression was increased by a cAMP-dependent stimulus. ID3 overexpression after TSH stimulation was first verified by Northern blotting analysis, and its mRNA regulation was then investigated in response to a variety of agents acting on thyrocyte proliferation and/or differentiation. We show that: (1) ID3 mRNA induction was stronger after stimulation of the cAMP cascade, but was not restricted to this signaling pathway, as phorbol myristate ester (TPA) and insulin also stimulated mRNA accumulation; (2) in contrast, powerful mitogens for thyroid cells, epidermal growth factor and hepatocyte growth factor, did not significantly modify ID3 mRNA levels; (3) ID3 protein levels closely parallelled mRNA levels, as revealed by immunofluorescence experiments showing a nuclear signal regulated by TSH; (4) in papillary thyroid carcinomas, ID3 mRNA was downregulated. Our results suggest that ID3 expression might be more related to the differentiating process induced by TSH than to the proliferative action of this hormone.

Immediate early gene expression in dog thyrocytes in response to growth, proliferation, and differentiation stimuli.

In dog thyroid cells, insulin or IGF-1 induces cell growth and is required for the mitogenic action of TSH through cyclic AMP, of EGF, and of phorbol esters. HGF per se stimulates cell proliferation and is thus the only full mitogenic agent. TSH and cAMP enhance, whereas EGF phorbol esters and HGF repress differentiation expression. In this study, we have investigated for each factor and regulatory cascade of the intermediate step of immediate early gene induction, that is, c-myc, c-jun, jun D, jun B, c-fos, fos B, fra-1, fra-2, and egr1; fra-1 and fra-2 expressions were very low. TSH or forskolin increased the levels of c-myc, jun B, jun D, c-fos, and fos B while decreasing those of c-jun and egr1. Phorbol myristate ester stimulated the expression of all the genes. EGF and HGF stimulated the expression of all the genes except jun D and for EGF fos B. All these effects were obtained in the presence and in the absence of insulin, which shows that insulin is not necessary for the effects of the mitogens on immediate early gene expression. The definition of the repertoire of early immediate genes inductible by the various growth cascades provides a framework for the analysis of gene expression in tumors. (1) Insulin was able to induce all the protooncogenes investigated except fos B. This suggests that fos B could be the factor missing for insulin to induce mitogenesis. (2) No characteristic pattern of immediate early gene expression has been observed for insulin, which induces cell hypertrophy and is permissive for the action of the other growth factors. These effects are therefore not accounted for by a specific immediate early gene expression. On the other hand, insulin clearly enhances the effects of TSH, phorbol ester, and EGF on c-myc, junB, and c-fos expression. This suggests that the effect of insulin on mitogenesis might result from quantitative differences in the transcription complexes formed. (3) c-myc, c-fos, and jun B mRNA induction by all stimulating agents, whether inducing cell hypertrophy, or growth and dedifferentiation, or growth and differentiation, suggests that, although these expressions are not sufficient, they may be necessary for the various growth responses of thyroid cells. (4) The inhibition of c-jun and egr1 mRNA expression, and the marked induction of jun D mRNA appear to be specific features of the TSH cAMP pathway. They might be related to its differentiating action. (5) fos B, which is induced by TSH, forskolin, phorbol ester, and HGF but not by insulin, could be involved in the mitogenic action of the former factors.

Event models for tumor classification with SAGE gene expression data

Serial Analysis of Gene Expression (SAGE) is a relatively new method for monitoring gene expression levels and is expected to contribute significantly to the progress in cancer treatment by enabling a precise and early diagnosis. A promising application of SAGE gene expression data is classification of tumors. In this paper, we build three event models (the multivariate Bernoulli model, the multinomial model and the normalized multinomial model) for SAGE data classification. Both binary classification and multicategory classification are investigated. Experiments on two SAGE datasets show that the multivariate Bernoulli model performs well with small feature sizes, but the multinomial performs better at large feature sizes, while the normalized multinomial performs well with medium feature sizes. The multinomial achieves the highest overall accuracy.