Basically, gene expression data represents a snapshot of gene expression activity for a given set of samples and defined experimental conditions. Single gene expression measurements do not contain sufficient information to infer regulatory contexts; only more comprehensive experiments, such as time-course experiments, allow to derive more detailed or causal models. During the last years, several directions for integrated gene expression data analysis have been tackled. Bayesian networks have been used to model complex stochastic processes and thus to describe interactions between genes ( Friedman et al. , 2000 ). The Microarray Experiment Functional Integration Technology (MEFIT) is a scalable Bayesian framework that can be applied on large compendia of microarray datasets and predicts functional relationships within the context of specific biological processes ( Huttenhower et al. , 2006 ). Finer structured interactions between genes, such as causality, mediation, activation, and inhibition can also be discovered from bayesian networks ( Pe’er et al. , 2001 ). Integration of a large number of gene expression experiments can be used for detection of modules; that is, gene sets that participate in specific biological processes, their regulators and the conditions under which regulation occurs ( Segal et al. , 2003a , b , 2004 ). Second order expressionanalysis ( Zhou et al. , 2005 ) integrates data from different platforms by extracting expression patterns from each data set and then analyzing patterns across multiple data sets. Thus, genes of the same function yet without coexpression patterns can be identified, and transcription factor activities can be quantified.
ing relative expression values.
Primer sequences for ITIH2 expressionanalysis were: for- ward 5'-ACC AGG TCT CCA CTC CAT TG-3'; reverse 5'- ATC CTG CAA GTC GTC CAT CT-3' (230 bp product size) and for the reference gene GAPDH: 5'-GAA GGT GAA GGT CGG AGT CA-3'; reverse 5'-TGG ACT CCA CGA CGT ACT CA-3' (108 bp product size). The cycling conditions were set up to an initial denaturation at 95°C for 15 min, fol- lowed by 40 cycles with denaturation at 95°C for 20 s, annealing at 60°C for 20 s and elongation at 72°C for 30 s. To verify the specificity of the PCR products, melting curve analyses were performed. The relative gene expres- sion levels were standardized to the expression level of a normal breast tissue sample that contained approximately 50% of epithelial cells (tumors generally contained >70% of tumor cells). To ensure experiment accuracy, all reac- tions were performed in triplicates.
After definition of the human genome, it has become an important task to perform research on up till now undefined open reading frames. The goal represents the clarification of the primary structure and function of the novel proteins. By comparative expressionanalysis of normal versus tumour tissue, we could observe over expression of C20orf116 in multiple tumour tissues. Encouraged by these findings we decided to clone and characterize the non-defined transcript and translation product. Multiple tissue Northern blot analysis showed expression of the 1.35 kb transcript coding for 314 amino acids in liver, placenta and heart with less quantity in kidney and lung as well as in various regions of the central nervous system. After production of the recombinant protein in E. coli and insect cells, polyclonal and monoclonal antibodies against the C20orf116 gene product were generated. This enabled us to demonstrate the protein expression in liver, kidney, pancreas, thyroid and brain.
function of the identified genes. Although they had to be confirmed as differentially expressed, a first general look on the main pathways involved evidenced genes which encode for proteins associated to degradation, transport and folding of proteins, to plant development and morphogenesis, to cell signalling and to stress/pathogens response. The picture resulting from the sequence analysis reflects deep changes in the general plant metabolism as observed in previous studies ((Kartte and Seemüller, 1991; Lepka et al., 1999; Musetti et al., 2000; Carginale et al., 2004; Christensen et al., 2005; Musetti et al., 2005). Nevertheless, the involvement of the genes individuated in the differential expressionanalysis in the plant response after „Ca. P. mali‟ infection had to be validated through specific studies on their expression level. Because of the high number of genes to be analysed the work was focused on those sequences that:
Results: We developed the ideal software package, which serves as a web applica- tion for interactive and reproducible RNA-seq analysis, while producing a wealth of vis- ualizations to facilitate data interpretation. ideal is implemented in R using the Shiny framework, and is fully integrated with the existing core structures of the Bioconductor project. Users can perform the essential steps of the differential expressionanalysis workflow in an assisted way, and generate a broad spectrum of publication-ready outputs, including diagnostic and summary visualizations in each module, all the way down to functional analysis. ideal also offers the possibility to seamlessly generate a full HTML report for storing and sharing results together with code for reproducibility. Conclusion: ideal is distributed as an R package in the Bioconductor project ( http://bioco nduct or.org/packa ges/ideal / ), and provides a solution for perform- ing interactive and reproducible analyses of summarized RNA-seq expression data, empowering researchers with many different profiles (life scientists, clinicians, but also experienced bioinformaticians) to make the ideal use of the data at hand.
Calibration experiments constitute an investment. It is thus interesting to consider how measurements under suboptimal conditions affect the quality of biologically relevant observations. For this, we consider two comple- mentary aspects: Firstly, the quality measure Eq. (3) was chosen to reflect how reliably differential gene expres- sion can be identified for a particular protocol. Conse- quently, under suboptimal conditions fewer differentially expressed genes are expected to pass significance tests under the corresponding ANOVA model for Eq. (2). To quantify this in the context of a typical application, we use the number of genes n sig that could be identified as differentially expressed by FSPMA , a standard gene expressionanalysis tool using a balanced ANOVA model  for p-value calculation. Raw p-values were obtained from vsn normalized  expression values and converted to Benjamini-Hochberg corrected false discovery rates (FDRs). Gene counts reported refer to an FDR cut-off of q < 0.01. The corresponding FSPMA gene lists are provided in the online supplement.
the c-MYC-TFII-II complex inhibits the transcription initiation (Ananda L. Roy, 1993). Another study demonstrated inhibition of the transcriptional regulator Yin-Yang-1 (YY1), a transcription factor that regulates the transcription of many genes, including the oncogenes c-FOS and c-MYC. Depending on the context, YY1 acts as a transcriptional repressor, a transcriptional activator, or a transcriptional initiator. In this study the authors showed that in vitro, c-MYC inhibits both the repressor and the activator functions of YY1, which suggests that c-MYC functions by modulating the activity of YY1 (A. Shrivastava, 1993). There are also several studies implicating c-MYC in gene expression by itself, without the necessity to interact with another transcription factor. For example, a study showed that c-MYC activates the expression of telomerase by inducing expression of its catalytic subunit, telomerase reverse transcriptase (TERT). Telomerase is a ribonucleoprotein complex expressed in proliferating and transformed cells, in which it preserves chromosome integrity by maintaining telomere length. In this study, the authors identified numerous transcriptionally active c-MYC-binding sites located in the TERT promoter and, through luciferase assays they were able to show regulation of the telomerase activity by transcriptional activation of TERT induced by c-MYC. In addition, they were able to demonstrate that TERT expression was directly induced by transcription factor c-MYC (Kou-Juey Wu, 1999). Given all these evidences, c-Myc may regulate Rbfox1 expression either interacting with proteins other than MAX or acting as a gene expression regulator by itself.
delberg, Germany) [41-43], containing spotted tumor cDNAs and corresponding normal tissue from the same patient .
The Cancer Profiling Array (CPA) consisted of 511 dots with 494 cDNAs synthesized from various human tumors and corresponding normal tissue specimens, i.e. 241 tumor and 241 matched normal tissue specimens as well as 12 cDNAs from metastases corresponding to 12 of the tumor/normal pairs. The following 241 matched tumor/ normal tissue cDNA pairs and 12 matching metastases were included on the CPA: 50 breast cancer/50 normal/ three matching metastases, 42 uterine cancer/42 normal/ two matching metastases, 35 colon cancer/35 normal/ four matching metastases, 27 gastric cancer/27 normal stomach, 14 ovarian cancer/14 normal/two matching metastases, one cervical cancer/one normal, 21 lung can- cer/21 normal, 20 renal cancer/20 normal, 18 rectal can- cer/18 normal/one matching metastase, two small intestine cancer/two normal, six thyroid cancer/six nor- mal, four prostate cancer/four normal, one pancreatic cancer/one normal. Each cDNA pair was independently normalized based on the expression of four housekeeping genes (ubiquitin, 23 kDa highly basic protein, β-actin and glutamate dehydrogenase) and immobilized in separate dots. Patient age, histological type, disease stage, tumor size, node status, and presence or absence of metastases for each specimen is supplied with the product and can be obtained from the manufacturer upon request.
Increasing evidence indicates the pathogenetic relevance of regulatory genomic motifs for variability in the manifestation of brain disorders. In this context, cis- regulatory effects of single nucleotide polymorphisms (SNPs) on gene expression can contribute to changing transcript levels of excitability-relevant molecules and episodic seizure manifestation in epilepsy. Biopsy specimens of patients undergoing epilepsy surgery for seizure relief provide unique insights into the impact of pro- moter SNPs on corresponding mRNA expression. Here, we have scrutinized whether two linked regulatory SNPs (rs2744575; 4779C > G and rs4646830; 4854C > G) located in the aldehyde dehydrogenase 5a1 (succinic semialdehyde dehydrogenase;
Die Untersuchung der differenziellen Genexpression sowohl der ITIH-Familie als auch von AMBP und dem Reaktionspartner TNFAIP6 in 13 verschiedenen humanen Tumorentitäten (Karzinome von Mamma, Endometrium, Ovar, Cervix, Magen, Dünndarm, Colon, Rektum, Lunge, Schilddrüse, Prostata, Niere und Pankreas) erfolgte mittels RNA-Expressionsanalysen (an einem Multiple Tissue Northern Blot, MTN), cDNA-Dot-Blot-Expressionsanalysen (an einem sog. Cancer Profiling Array, CPA, für 241 Paare von Tumor- und Normalgewebe derselben Patienten), semiquantitativer RT-PCR (an Gewebeproben von Brust- krebspatientinnen) sowie immunhistochemischer Färbungen (im Tissue Micro Array für 185 invasive duktale Mammakarzinome). Statistisch wurde der Zu- sammenhang zwischen den Ergebnissen der quantitativen immunhistoche- mischen Auswertung für die Expression von ITIH2 und den verfügbaren klinisch-pathologischen Daten in Kreuztafeln (zweiseitiger exakter Test nach Fisher) und Überlebensanalysen (Verfahren nach Kaplan-Meier) untersucht.
Many L. plantarum strains isolated from different environments are known to produce bacteriocin, often more than one (Ben Omar et al., 2008; Knoll et al., 2008; Rojo-Bezares et al., 2008; Settani et al., 2008; Diep et al., 2009; Sáenz et al., 2009). Bacteriocin production in this species may partly contribute to its success in colonizing a wide variety of niches such as fermenting wine and olives, fermented cheeses, vegetables and sausages, as well as the human saliva and gastrointestinal tract (Ehrmann, 2000; Holo et al., 2001; Maldonado et al., 2003; Ben Omar et al., 2008; Knoll et al., 2008; Rojo-Bezares et al., 2008; Trmcić et al., 2008; Müller et al., 2009; Diep et al., 2009; Sáenz et al., 2009). The genetic determinants for bacteriocins in most investigated L. plantarum strains, such as C11 (Diep et al., 1996), LMG 2379 (Holo et al., 2001), NC8 (Maldonado et al., 2003), J23 (Rojo-Bezares et al., 2008) and J51 (Navarro et al., 2008) are generally chromosomally encoded, and are organized in gene clusters. This was also true for the strains BFE 5092 and strain PCS20 in this study. The bacteriocin genes in both strains were chromosomally located, but PFGE analysis followed by hybridisation with a plnEF probe, showed that the genes occurred on different regions of the respective chromosomes. Indeed, this confirms results by Molenaar et al. (2005) who explored genome diversity of L. plantarum strains using microarrays and showed that regions encoding plantaricin biosynthesis varied between strains.
t(6;12)(q23;p13) is one of the translocations that involves ETV6 and a previously unknown gene. The fusion partner was named STL (six twelve leukemia) gene by Suto et al. in 1997. The translocation t(6;12)(q23;p13) was first discovered in a precursor B-cell ALL cell line (SUB-B2), which was established from the leukemic cells of a 5-year-old boy with common ALL (LQ Zhang, 1993). To date, this translocation is unique. But two cases exist with the similar breakpoint t(6;12)(q21;p13) discovered in children with pre-B ALL (Y Hayashi, 1990). Fluorescence in situ hybridization (FISH) analysis with specific probes for 12p was necessary to discover t(6;12)(q23;p13) in the SUB-B2 cell line. Still it is possible that these cases are not really different from the cases with t(6;12)(q21;p13) discovered by Hayashi et al.
Since their introduction in 1995, DNA microarrays have become a mature gene expres- sion analysis technology for the analysis of gene sets, which were assembled based on prior selection (Sheena et al. 1995). The microarray technology is based on DNA probes (oligonucleotides) with sequences complementary to genes of interest, which are synthesized or cross-linked to a solid surface. The oligonucleotides are hybridized with fluorescent-labeled cDNAs or RNAs, which are generated from transcript samples (Phimister 1999). A few microarray platforms have been published for genome-wide ex- pression analysis in maize, including the 57k and 46k maize oligonucleotide arrays based on long-oligonucleotides (~70 nt) from the Maize Oligonucleotide Array Project (Gardiner et al. 2005). These two microarray platforms have served for the expressionanalysis in various studies until these days (see Appendix Table 1), although gene ex- pression analyses in most recent studies are performed by RNA high throughput se- quencing. The analysis of microarray experiments is highly reliant on the quality of the design of the oligonucleotide probes and the integrity of the annotation. The latter did, in case of the maize oligonucleotide arrays, not include information from the B73 reference genome and thus required a re-annotation to improve gene expression analyses and allow for the identification of putative target effects of sRNAs.
Expression data (RACE) suite is a collection of bioin- formatics web tools designed for the analysis of DNA microarray data. RACE performs probe-level data pre- processing, extensive quality checks, data visualiza- tion and data normalization for Affymetrix GeneChips. In addition, it offers differential expressionanalysis on normalized expression levels from any array platform. RACE estimates the false discovery rates of lists of potentially regulated genes and provides a Gene Ontology-term analysis tool for GeneChip data to support the biological interpretation and annotation of results. The analysis is fully automated but can be customized by flexible parameter settings. To offer a convenient starting point for subsequent analyses, and to provide maximum transparency, the R scripts used to generate the results can be downloaded along with the output files. RACE is freely available for use at http://race.unil.ch.
These results were confirmed for various cases: for data from actual biological measure- ments as well as for artificial data generated in a controlled way for a DAG-based Gaussian causal model. We studied small and larger DAGs, as well as completely connected and diluted ones. The general result also stays the same independently of whether one compares the esti- mated weight parameters directly, uses thresholding to find correct estimates, or performs an ROC analysis of the estimated nonzero weights. Also when restricting the analysis to just the prediction of the orderings, the triplet approach turns out to be much more efficient than the pair approach.
Chloroplast gene expression is predominantly regulated at the posttranscriptional levels of mRNA stability and translation efficiency. The expression of psbA, an important photosynthesis-related chloroplast gene, has been revealed to be regulated via its 5’- untranslated region (UTR). Some cis-acting elements within this 5’UTR and the correlated trans-acting factors have been defined in Chlamydomonas. However, no in vivo evidence with respect to the cis-acting elements of the psbA 5’UTR has been so far achieved in higher plants such as tobacco. To attempt this, we generated a series of mutants of the tobacco psbA 5’UTR by base alterations and sequence deletions, with special regard to the stem-loop structure and the putative target sites for ribosome association and binding of nuclear regulatory factors. In addition, a versatile plastid transformation vector pKCZ with an insertion site in the inverted repeat region of the plastid genome was constructed. In all constructs, the psbA 5’UTR (Wt or modified) was used as the 5’ leader of the reporter gene uidA under control of the same promoter, Prrn, the promoter of the rRNA operon. Through biolistic DNA delivery to tobacco chloroplasts, transplastomic plants were obtained. DNA and RNA analyses of these transplastomic plants demonstrated that the transgenes aadA and uidA had been correctly integrated into the plastome at the insertion site, and transcribed in discrete sizes. Quantitative assays were also done to determine the proportion of intact transplastome, the uidA mRNA level, Gus activity, and uidA translation efficiency. The main results are the following:
2.3. Facial Expression Recognition 21 geometric features extracted across video clip from facial points and specific re- gions associated with the 3-D face model of each subject. These points are initially annotated or detected on the neutral state image and tracked over the remain- ing sequence. Spatio-temporal features of image sequence are utilized as well for the expression recognition, e.g. Valstar et al.  exploit the motion history in- side the face image to infer the facial expression via Sparse Network of Winnows (SNoW) and a standard k Nearest Neighbour (kNN) classifier. Zhu et al.  use Hidden Markov Model (HMM) along with moment invariants to do facial expression recognition. By modeling the temporal behavior of the facial expres- sions via dynamic based network, Zhang et al.  identify the expression from spatio-temporal information. They employ IR illuminations and Kalman filtering to assist the facial point detection and tracking. Baltrusaitis et al.  suggest a dynamic system with three levels of inference on progressively longer time scale to understand the human mental states from facial expressions and upper-body gestures, where they employ both Dynamic Bayesian Network (DBN) and HMM. Lorincz et al.  exploit time-series kernels to analyze the spatio-temporal pro- cess of the facial points, where the point movements in 3D space are classified with kernels derived from time-warping similarity measures. Many other approaches exploit the dynamics of the facial points within an image sequence to recognize the depicted facial expression, assuming the dynamics start from the neutral state [162, 183, 96, 124, 130]. Texture dynamics are used as well for the expression recog- nition as could be seen in [86, 182, 168].
indicating a widespread induction of gene expression. To determine the function of these DE genes subsets, gene descriptions and GOE analysis were investigated. Consistent with the highest numbers of DEG detected at 4 DPI, most GO terms reported in Table 17 belong to this dataset, with terms related to metabolic and biosynthetic processes being the most prevalent. Interestingly, all datasets had enriched terms related to oxidoreductase activities (GO:001649) confirmed also by the upregulation of BdiBd21- 3.4G0171000, coding for a multicopper oxidase, in all setups, and BdiBd21- 3.1G0233800 and BdiBd21-3.1G0233900, coding for a peroxidase, in the 4 DPI leaves datasets. Peroxidases are commonly associated with plant responses to stress and specifically fungal infection, as they are involved in a variety of processes including the synthesis of compounds toxic for pathogens (phenols such as tanins and melanins), ROS removal, lignin biosynthesis and induction of defense responses by stimulating intracellular Ca 2+ signaling (Kawano, 2003). Another gene upregulated in all three setups
(A) Phylogram based on an alignment of the amino acid sequences from CDJ3–5 and their homologues, all lacking the N-terminal extensions from their J-domains. Sequences used were from the following organisms: Oryza sativa (Os1–4; Genbank ® accession numbers NP_001056124, NP_001044143, AAS72346 and NP_001054247 respectively), Arabidopsis thaliana (AtDjC17 and AtDjC18; Genbank ® accession numbers NP_197715 and NP_565982 respectively), Vitis vinifera (Vv1–3; Genbank ® accession numbers XP_002281976, CAN73797 and XP_002278893 respectively), Picea sitchensis (Ps1–3; Genbank ® accession numbers ABK21719, ABK24669 and assembly of ESTs DR538561 and ES860441 respectively), Physcomitrella patens [Pp1–3; Genbank ® accession number EDQ75158 (complemented with ESTs with Genbank ® accession numbers FC338026 and FC448519), EDQ53967, and EDQ72847 respectively], Ostreococcus tauri (Ot1; Genbank ® accession number CAL50030) and Chlamydomonas reinhardtii (CrCDJ3–5; Genbank ® accession numbers GQ421467, GQ421468 and EDP07097 respectively). Phylogenetic analysis was conducted using version 4 of the MEGA program  on the basis of alignments made by version 1.8 of CLUSTALW. The scale bar indicates 0.1 substitutions per site. (B) Alignment of amino acid sequences of CDJ3–5 homologues. Sequences were limited to one representative for the CDJ3/4 and CDJ5 clades from angiosperms, gymnosperms, moss and algae from the same sources as in (A). The sequence of Synechocystis sp. PCC 6803 bacterial type ferredoxin (Genbank ® accession number BAA10759) is also shown. Amino acids highlighted in black are conserved in all ten proteins; those highlighted in grey are conserved in at least eight proteins. Italicized sequences represent chloroplast transit peptides as predicted by TargetP  or ChloroP  programs, with the bold underlined residue corresponding to the first amino acid of the mature protein. No prediction was obtained for Pp2. Asterisks indicate cysteine residues involved in [4Fe–4S] cluster binding , and boxed regions represent patches enriched in aromatic and charged residues. Alignments were determined using CLUSTALW and the GeneDoc program was used for presentation.
Abstract A subset of uterine leiomyomas (UL) shows chromosomal rearrangements of the region 12q14~q15, leading to an overexpression of the high-mobility group protein A2 gene (HMGA2). Recent studies identiﬁed microRNAs of the let-7 family as post-transcriptional regulators of HMGA2. Intragenic chromosomal breakpoints might cause truncated HMGA2 transcripts lacking part of the 3’ UTR. The corresponding loss of let-7 complementary sites (LCS) located in the 3’ UTR would therefore stabilize HMGA2 mRNA. The aim of this study was to check UL with rear- rangements of the chromosomal region 12q14~15 for truncated HMGA2 transcripts by real-time reverse-transcription polymerase chain reaction. In 8/13 leiomyomas with aberrations of chromo- somal region 12q15, the results showed the presence of the complete 3’ UTR with all LCS. A differ- ential expression with highly reduced 3’ untranslated region levels was found in 5/13 myomas. In two of these, full-length transcripts were almost undetectable. Truncated transcripts were apparently predominant in roughly one-third of UL with chromosomal rearrangements affecting the HMGA2 locus, where they lead to a higher stability of its transcripts and subsequently contribute to the over- expression of the protein. The assay used is also generally suited to detect submicroscopic alter- ations leading to truncated transcripts of HMGA2. Ó 2010 Elsevier Inc. All rights reserved.