Previous research demonstrated the use of evolutionary computation for the discovery of transcription factor binding sites (TFBS) in promoter regions upstream of coexpressed genes. However, it remained unclear whether or not composite TFBS elements, commonly found in higher organisms where two or more TFBSs form functional complexes, could also be identified by using this approach. Here, we present an important refinement of our previous algorithm and test the identification of composite elements using NFAT/AP-1 as an example. We demonstrate that by using appropriate existing parameters such as window size, novel-scoring methods such as central bonusing and methods of self-adaptation to automatically adjust the variation operators during the evolutionary search, TFBSs of different sizes and complexity can be identified as top solutions. Some of these solutions have known experimental relationships with NFAT/AP-1. We also indicate that even after properly tuning the model parameters, the choice of the appropriate window size has a significant effect on algorithm performance. We believe that this improved algorithm will greatly augment TFBS discovery.

The nuclear hormone receptors liver X receptor alpha (LXRalpha) and LXRbeta function as physiological receptors for oxidized cholesterol metabolites (oxysterols) and regulate several aspects of cholesterol and lipid metabolism. Seladin-1 was originally identified as a gene whose expression was down-regulated in regions of the brain associated with Alzheimer's disease. Seladin-1 has been demonstrated to be neuroprotective and was later characterized as 3beta-hydroxysterol-Delta24 reductase (DHCR24), a key enzyme in the cholesterologenic pathway. Seladin-1 has also been shown to regulate lipid raft formation. In a whole genome screen for direct LXRalpha target genes, we identified an LXRalpha occupancy site within the second intron of the Seladin-1/DHCR24 gene. We characterized a novel LXR response element within the second intron of this gene that is able to confer LXR-specific ligand responsiveness to reporter gene in both HepG2 and human embryonic kidney 293 cells. Furthermore, we found that Seladin-1/DHCR24 gene expression is significantly decreased in skin isolated from LXRbeta-null mice. Our data suggest that Seladin-1/DHCR24 is an LXR target gene and that LXR may regulate lipid raft formation.

Cholesterol is required for normal cellular and physiological function, yet dysregulation of cholesterol metabolism is associated with diseases such as atherosclerosis. Cholesterol biosynthesis is regulated by end product negative feedback inhibition where the levels of sterols and oxysterols regulate the expression of cholesterologenic enzymes. Sterol regulatory element-binding protein-2 is responsive to both sterols and oxysterols and has been shown to mediate the transcriptional response of the cholesterologenic enzymes to these lipids. Here, we show that the nuclear hormone receptor for oxysterols, the liver X receptor α (LXRα), regulates cholesterol biosynthesis by directly silencing the expression of two key cholesterologenic enzymes (lanosterol 14α-demethylase (CYP51A1), and squalene synthase (farnesyl diphosphate farnesyl transferase 1)) via novel negative LXR DNA response elements (nLXREs) located in each of these genes. Examination of the CYP51A1 gene revealed that both the SRE and nLXRE are required for normal oxysterol-dependent repression of this gene. Thus, these data suggest that LXRα plays an important role in the regulation of cholesterol biosynthesis.

Background: The liver X receptor (LXR), a transcription factor that forms a heterodimer with the retinoid X receptor, plays a key role in the transcriptional regulation of many important genes implicated in prevalent metabolic diseases. In spite of numerous studies, a complete list of LXR direct target genes remains elusive. To complement experimental approaches, computational prediction can be used to help build such a list because all LXR target genes are expected to carry the response elements (LXREs) in their promoter or enhancer regions. In practice, however, such a prediction has been hampered by the inaccuracies of currently available predictive models of LXREs. We report on a novel computational application for the highly accurate prediction of LXREs in DNA sequences.Methods: We first conducted a comprehensive review of experimentally determined LXR target genes and collected all known LXREs. Subsequently, all such sites were classified using various computational methods based on sequence similarity to identify multiple subtypes. A library of Hidden Markov Models (LXRE.HMM) was developed to represent all subtypes and to enable the promoter scanning of LXR target genes.Results and conclusion: Our model outperformed the widely used LXRE model in MatInspector in identifying the LXREs for all known LXR direct target genes at the experimentally verified positions. As a result, this new approach will make the genomewide prediction of LXR target genes feasible.

Type I human hepatic 3alpha-hydroxysteroid dehydrogenase (AKR1C4) plays a significant role in bile acid biosynthesis, steroid hormone metabolism, and xenobiotic metabolism. Utilization of a hidden Markov model for predictive modeling of nuclear hormone receptor response elements coupled with chromatin immunoprecipitation/microarray technology revealed a putative binding site in the AKR1C4 promoter for the nuclear hormone receptor known as liver X receptor alpha, (LXRalpha [NR1H3]), which is the physiological receptor for oxidized cholesterol metabolites. The putative LXRalpha response element (LXRE), identified by chromatin immunoprecipitation, was approximately 1.5 kilobase pairs upstream of the transcription start site. LXRalpha was shown to bind specifically to this LXRE and mediate transcriptional activation of the AKR1C4 gene, leading to increased AKR1C4 protein expression. These data suggest that LXRalpha may modulate the bile acid biosynthetic pathway at a unique site downstream of CYP7A1 and may also modulate the metabolism of steroid hormones and certain xenobiotics.

Abstract Background Uterine fibroids or leiomyoma are a common benign smooth muscle tumor. The tumor growth is well known to be estrogen-dependent. However, the molecular mechanisms of its estrogen-dependency is not well understood. Methods Differentially expressed genes in human uterine fibroids were either retrieved from published papers or from our own statistical analysis of downloaded array data. Probes for the same genes on different Affymetrix chips were mapped based on probe comparison information provided by Affymetrix. Genes identified by two or three array studies were submitted for ortholog analysis. Human and rat ortholog genes were identified by using ortholog gene databases, HomoloGene and TOGA and were confirmed by synteny analysis with MultiContigView tool in the Ensembl genome browser. Results By integrated analysis of three recently published DNA microarray studies with human tissue, thirty-eight genes were found to be differentially expressed in the same direction in fibroid compared to adjacent uterine myometrium by at least two research groups. Among these genes, twelve with rat orthologs were identified as estrogen-regulated from our array study investigating uterine expression in ovariectomized rats treated with estrogen. Functional and pathway analyses of the twelve genes suggested multiple molecular mechanisms for estrogen-dependent cell survival and tumor growth. Firstly, estrogen increased expression of the anti-apoptotic PCP4 gene and suppressed the expression of growth inhibitory receptors PTGER3 and TGFBR2. Secondly, estrogen may antagonize PPARγ signaling, thought to inhibit fibroid growth and survival, at two points in the PPAR pathway: 1) through increased ANXA1 gene expression which can inhibit phospholipase A2 activity and in turn decrease arachidonic acid synthesis, and 2) by decreasing L-PGDS expression which would reduce synthesis of PGJ2, an endogenous ligand for PPARγ. Lastly, estrogen affects retinoic acid (RA) synthesis and mobilization by regulating expression of CRABP2 and ALDH1A1. RA has been shown to play a significant role in the development of uterine fibroids in an animal model. Conclusion Integrated analysis of multiple array datasets revealed twelve human and rat ortholog genes that were differentially expressed in human uterine fibroids and transcriptionally responsive to estrogen in the rat uterus. Functional and pathway analysis of these genes suggest multiple potential molecular mechanisms for the poorly understood estrogen-dependent growth of uterine fibroids. Fully understanding the exact molecular interactions among these gene products requires further study to validate their roles in uterine fibroids. This work provides new avenues of study which could influence the future direction of therapeutic intervention for the disease.

Transcription factors are key regulatory elements that control gene expression. The TRANSFAC database represents the largest repository for experimentally derived transcription factor binding sites (TFBS). Understanding TFBS, which are typically conserved during evolution, helps us identify genomic regions related to human health and disease, and regions that might be predictive of patient outcomes. Here we present a statistical analysis of all TFBS in the TRANSFAC database. Our analysis suggests that current definition of TFBS core regions in TRANSFAC should be re-examined so as to capture a more precise notion of "cores." We offer insight into more appropriate definitions of TFBS consensus sequences and core regions. These revised definitions provide a better understanding of the nature of transcription factor-DNA binding and assist with developing algorithms for de novo TFBS discovery as well as finding novel variants of known TFBS.

BackgroundUterine fibroids or leiomyoma are a common benign smooth muscle tumor. The tumor growth is well known to be estrogen-dependent. However, the molecular mechanisms of its estrogen-dependency is not well understood.MethodsDifferentially expressed genes in human uterine fibroids were either retrieved from published papers or from our own statistical analysis of downloaded array data. Probes for the same genes on different Affymetrix chips were mapped based on probe comparison information provided by Affymetrix. Genes identified by two or three array studies were submitted for ortholog analysis. Human and rat ortholog genes were identified by using ortholog gene databases, HomoloGene and TOGA and were confirmed by synteny analysis with MultiContigView tool in the Ensembl genome browser.ResultsBy integrated analysis of three recently published DNA microarray studies with human tissue, thirty-eight genes were found to be differentially expressed in the same direction in fibroid compared to adjacent uterine myometrium by at least two research groups. Among these genes, twelve with rat orthologs were identified as estrogen-regulated from our array study investigating uterine expression in ovariectomized rats treated with estrogen. Functional and pathway analyses of the twelve genes suggested multiple molecular mechanisms for estrogen-dependent cell survival and tumor growth. Firstly, estrogen increased expression of the anti-apoptotic PCP4 gene and suppressed the expression of growth inhibitory receptors PTGER3 and TGFBR2. Secondly, estrogen may antagonize PPARγ signaling, thought to inhibit fibroid growth and survival, at two points in the PPAR pathway: 1) through increased ANXA1 gene expression which can inhibit phospholipase A2 activity and in turn decrease arachidonic acid synthesis, and 2) by decreasing L-PGDS expression which would reduce synthesis of PGJ2, an endogenous ligand for PPARγ. Lastly, estrogen affects retinoic acid (RA) synthesis and mobilization by regulating expression of CRABP2 and ALDH1A1. RA has been shown to play a significant role in the development of uterine fibroids in an animal model.ConclusionIntegrated analysis of multiple array datasets revealed twelve human and rat ortholog genes that were differentially expressed in human uterine fibroids and transcriptionally responsive to estrogen in the rat uterus. Functional and pathway analysis of these genes suggest multiple potential molecular mechanisms for the poorly understood estrogen-dependent growth of uterine fibroids. Fully understanding the exact molecular interactions among these gene products requires further study to validate their roles in uterine fibroids. This work provides new avenues of study which could influence the future direction of therapeutic intervention for the disease.

The increasingly extensive usage of microarray technologies to quantify gene expression in research calls for rigorous methods for assessing the quality and reproducibility of microarray data. Typical sources of variation in microarray data include biological, mechanical (from instruments), experimental (sample handling), and random noise. A high-quality microarray experiment is expected to have smaller non-biological variation compared to biological ("real") variation. However, outlier genes and samples, if exist, enlarge the variation of all sources and reduce the power of statistical analyses. Therefore, detection of outlier genes and microarray chips (to which the samples are hybridized) is an important step towards revealing true biological signals. We present here a simple non-parametric method to simultaneously detect outlier genes and chips. This method is capable of detecting outlier genes as well as outlier chips/samples in replicate experiments. Our method performs well with computer-simulated microarray data as well as real experimental data.

The human 5-HT1E receptor gene was cloned more than a decade ago. Little is known about its function, and there have been no reports of its existence in the genome of small laboratory animals. In this study, attempts to clone the 5-HT1E gene from the rat and mouse were unsuccessful. In fact, a search of the mouse genome database revealed that the 5-HT1E receptor gene is missing from the mouse genome. However, the 5-HT1E gene was cloned from guinea pig genomic DNA and was characterized. The guinea pig 5-HT1E receptor gene encodes a protein of 365 amino acids. It shares 88% (nucleic acid) and 95% (amino acid) homology with the human receptor. The guinea pig 5-HT1E receptor showed similar pharmacology to the human 5-HT1E receptor in radioligand binding assays. Serotonin (5-hydroxytryptamine, 5-HT) dose-dependently stimulated [35S]GTPγS binding to the guinea pig 5-HT1E receptor with an EC50 of 13.6±1.92 nM, similar to that of the human 5-HT1E receptor (13.7±1.78 nM). Activation of the guinea pig 5-HT1E receptor was also achieved by ergonovine, α-methyl-5-HT, 1-naphthylpiperazine, methysergide, tryptamine, and 1-(2,5-dimethoxy-4-iodophenyl)-2-aminopropane (DOI). Methiothepin exhibited antagonist activity. Quantitative real-time polymerase chain reaction (qRT-PCR) analysis showed that 5-HT1E mRNA was present in the guinea pig brain with the greatest abundance in the hippocampus, followed by the olfactory bulb. Lower levels were detected in the cortex, thalamus, pons, hypothalamus, midbrain, striatum, and cerebellum. Our current study marks the first identification of the 5-HT1E receptor gene in a commonly used laboratory animal species. This finding should allow the elucidation of the receptor's role(s) in the complex coordination of central serotonergic effects.

In recent years research has focused on the development of specific, targeted drugs to treat cancer. One approach has been to block intracellular signaling proteins, such as protein kinase C alpha (PKC-α). To help support the rationale for clinical studies of a PKC-α-targeted therapy in breast and ovarian cancers, we reviewed publications studying PKC-α expression in these tumors. Since these investigations were mostly performed in cell lines, we supplemented this review with some preliminary findings from studies examining PKC-α expression in tumor tissue biopsies obtained from patients with breast and ovarian cancer. Based on the reviewed publications using representative cell lines and our preliminary findings on tumor tissue of patients with breast cancer, we infer that PKC-α levels may especially be increased in breast cancer patients with low or negative estrogen receptor (ER) levels. Thus, clinical studies determining efficacy of selective or specific inhibitors of PKC-α should include determination of ER status in order to help answer whether blocking PKC-α in patients with low or absent ER can result in clinical benefit.

Transcription factors are key regulatory elements that control gene expression. Recognition of transcription factor binding site (TFBS) motifs in the upstream region of coexpressed genes is therefore critical towards a true understanding of the regulations of gene expression. The task of discovering eukaryotic TFBSs remains a challenging problem. Here, we demonstrate that evolutionary computation can be used to search for TFBSs in upstream regions of genes known to be coexpressed. Evolutionary computation was used to search for TFBSs of genes regulated by octamer-binding factor and nuclear factor kappa B. The discovered binding sites included experimentally determined known binding motifs as well as lists of putative, previously unknown TFBSs. We believe that this method to search nucleotide sequence information efficiently for similar motifs will be useful for discovering TFBSs that affect gene regulation.

Current treatments of non–small-cell lung cancer (NSCLC) are inadequate and new therapies are being developed that target specific cellular signaling proteins associated with tumor growth. One potential target is protein kinase C (PKC)–α, a signaling molecule with an important role in cell regulation and proliferation. The present study examines the expression levels of PKC-α in NSCLC to better understand the distribution of PKC- α in NSCLC. We analyzed tumor specimens from an independent tumor tissue bank to determine PKC-α protein and messenger RNA gene expression in NSCLC. In addition, we used publicly available gene expression array data to further understand PKC-α–associated gene expression profiles in NSCLC. We found that PKC-a is highly expressed in ≤ 20% of patients with NSCLC. We also found that PKC-α was preferentially expressed in adenocarcinoma compared with squamous cell carcinoma of the lung.

A typical immunoglobulin (Ig) molecule is composed of four polypeptide chains: two identical heavy (H) chains and two identical light (L) chains. This tetrameric structure is conserved in almost all jawed vertebrate species. However, it has been discovered that camels and llamas (family: Camelidae) possess a type of dimeric Ig that consists of two H chains only. These H chains do not associate with L chains, and they do not have the first constant region (CH1), which is present in the conventional Ig. In spite of these changes, the dimeric Ig maintains the normal immune function. To understand the evolution of the dimeric Ig, we studied the phylogenetic relationships of the variable region (V(H)H) genes of the dimeric Ig from Camelidae and those (V(H)) of the conventional Ig from mammals. The results showed that the V(H)H genes form a monophyletic cluster within one of the mammalian V(H) groups, group C. We examined the type of selective force in complementarity-determining regions (CDRs) and framework regions (FRs) by comparing the rate of synonymous (dS) and nonsynonymous (dN) substitutions. We found that the results obtained from V(H)H genes were similar to those from V(H) genes in that CDRs showed an excess of dN over dS (indicating positive selection), whereas the reverse was true for FRs (purifying selection). However, when the extent of positive selection or purifying selection was investigated at each codon site, three major differences between V(H)H and V(H) genes were found. That is, very different types of selective force were observed between V(H)H and V(H) genes (1) at the sites that contact the L chain in the conventional Ig, (2) at the sites that interact with the CH1 region in the conventional Ig, and (3) in the H1 loop. Our findings suggest that adaptive evolution has occurred in the functionally important sites of the V(H)H genes to maintain the normal immune function in the dimeric Ig.

The emergence in Camelidae species of functional antibodies devoid of light chains (referred to as heavy-chain antibodies or HCAbs) is an intriguing evolutionary event. Homodimeric HCAbs have also been documented in spotted ratfish ( Cos5-Abs) and nurse shark (NAR). To reveal the evolutionary history of HCAbs, we evaluated the phylogenetic and phenotypic relationships among HCAbs and conventional antibodies across taxa and confirmed the current viewpoint that different groups of HCAbs have evolved independently in the three lineages. At least, in the camelids, HCAbs are not the result of resuscitation of dormant genes. They are derived from the conventional antibodies within the Camelidae lineage, and are apparently the outcome of more recent adaptive changes occurring in the compartment of heteromeric antibodies. The shared structural properties of HCAbs across taxa are therefore explained by convergent evolution due to similar constraints related to the absence of pairing to the light chain. It appears that innovative evolutionary changes in Camelidae have led to a new level of antigen binding repertoire diversification and have allowed acquisition of novel antigen-receptor properties.

The diversity of T-cell receptors is generated primarily by the variable-region gene families, each of which is composed of a large number of member genes. The entire genomic sequence of the variable region (VB) of the T-cell receptor beta chain from humans and mice has become available. To understand the evolutionary dynamics of the VB gene family, we conducted a phylogenetic analysis of all VB genes from humans and mice, as well as a detailed analysis of internal DNA duplications in the human genomic VB region. The phylogenetic tree obtained shows that human and mouse VB genes intermingle extensively rather than forming two separate clusters and that many gene duplications occurred both before and after the divergence between primates and rodents. Analyzing the genomic maps of transposable elements (e.g., LINEs and SINEs) and relic VB genes in the VB gene region, we present evidence that a 20-kb VB region duplicated tandemly four times in the human lineage during the last 32 Myr, and 6 out of the 15 VB genes in this region have become nonfunctional during this period. Our results show that the VB gene family is subject to evolution by a birth-and-death process rather than to concerted evolution.