Xylella fastidiosa gene expression analysis by DNA microarrays

Xylella fastidiosa genome sequencing has generated valuable data by identifying genes acting either on metabolic pathways or in associated pathogenicity and virulence. Based on available information on these genes, new strategies for studying their expression patterns, such as microarray technology, were employed. A total of 2,600 primer pairs were synthesized and then used to generate fragments using the PCR technique. The arrays were hybridized against cDNAs labeled during reverse transcription reactions and which were obtained from bacteria grown under two different conditions (liquid XDM2 and liquid BCYE). All data were statistically analyzed to verify which genes were differentially expressed. In addition to exploring conditions for X. fastidiosa genome-wide transcriptome analysis, the present work observed the differential expression of several classes of genes (energy, protein, amino acid and nucleotide metabolism, transport, degradation of substances, toxins and hypothetical proteins, among others). The understanding of expressed genes in these two different media will be useful in comprehending the metabolic characteristics of X. fastidiosa, and in evaluating how important certain genes are for the functioning and survival of these bacteria in plants.

LATENT RANK CHANGE DETECTION FOR ANALYSIS OF SPLICE-JUNCTION MICROARRAYS WITH NONLINEAR EFFECTS

Alternative splicing of gene transcripts greatly expands the functional capacity of the genome, and certain splice isoforms may indicate specific disease states such as cancer. Splice junction microarrays interrogate thousands of splice junctions, but data analysis is difficult and error prone because of the increased complexity compared to differential gene expression analysis. We present Rank Change Detection (RCD) as a method to identify differential splicing events based upon a straightforward probabilistic model comparing the over-or underrepresentation of two or more competing isoforms. RCD has advantages over commonly used methods because it is robust to false positive errors due to nonlinear trends in microarray measurements. Further, RCD does not depend on prior knowledge of splice isoforms, yet it takes advantage of the inherent structure of mutually exclusive junctions, and it is conceptually generalizable to other types of splicing arrays or RNA-Seq. RCD specifically identifies the biologically important cases when a splice junction becomes more or less prevalent compared to other mutually exclusive junctions. The example data is from different cell lines of glioblastoma tumors assayed with Agilent microarrays.

Effect of the immunosuppressants on hepatocyte proliferation and apoptosis in a young animal model of liver regeneration: An immunohistochemical study using tissue microarrays

Hepatocyte proliferation and apoptosis (programmed cell death) occur during the liver parenchyma regeneration and the liver size modeling is mainly controlled by hepatocyte apoptosis. The purpose of the present study was to verify the influence of immunosuppressant drugs on these phenomena by utilizing tissue microarray techniques. Thirty-six weaning rats (age 21-23 days, weight 30-50 g) were divided into six groups: control, sham, hepatectomy, hepatectomy plus solumedrol, hepatectomy plus CsA, and hepatectomy plus Tac. The animals were killed one day after hepatectomy, and the remnant livers were weighed and harvested for tissue microarray sections. Liver cell proliferation was evaluated by staining for PCNA and apoptosis was detected by the TUNEL method. It was verified that CsA promoted a decrease in the liver weight, Tac and CsA decreased the proliferation index of hepatocytes, and glucocorticoid had no significant effects. The apoptosis index was not altered by hepatectomy or immunosuppressants. Our data indicate that, in the growing rat, CsA and Tac have negative effects on hepatocyte proliferation and have no effect on the hepatocyte apoptosis.

Characterization of the products of aniline peroxydisulfate oligo/polymerization in media with different pH by resonance Raman spectroscopy at 413.1 and 1064 nm excitation wavelengths

The pH-structure correlation of the products of aniline peroxydisulfate reaction was mainly investigated by resonance Raman spectroscopy. The reactions of aniline and ammonium peroxydisulfate were carried out in aqueous solutions of initial pH ranging from 4.9 to 13.2 and monomer/oxidant molar ratio of 4/1. For an initial pH of 4.9, the spectroscopic techniques showed that the emeraldine salt form of polyaniline (PANI-ES) is the main product, corroborating that the usual head-to-tail coupling mechanism is taking place. The resonance Raman spectra at 1064 nm exciting wavelength were useful to detect the emeraldine salt as a minor product for reactions at an initial pH of 5.3-11.5. The Raman spectra of the main product of the reaction at initial pH of 13.2 excited at 1064 and 413.1 nm showed new spectral features consistent with 1,4-Michael-type adducts of aniline monomers and 1,4-benzoquinone-monoimine unit. These compounds and their products of hydrolysis/oxidation are the predominant species for the reaction media of initial pH from 5.3 to 13.2. In order to get PANI with different nanoscale morphologies, a pH value of more than 0 or 1 was used in the aniline polymerization. The spectroscopic data obtained in this work reveal that head-to-tail coupling does not occur when aniline reacts at media pH higher than about 5. It is suggested that chemical structures of the products of aniline oxidation by an unusual mechanism are the driving force for the development of assorted morphologies. Copyright (C) 2011 John Wiley & Sons, Ltd.

Production of DNA microarray and expression analysis of genes from Xylella fastidiosa in different culture media

DNA Microarray was developed to monitor the expression of many genes from Xylella fastidiosa, allowing the side by-side comparison of two situations in a single experiment. The experiments were performed using X. fastidiosa cells grown in two culture media: BCYE and XDM2. The primers were synthesized, spotted onto glass slides and the array was hybridized against fluorescently labeled cDNAs. The emitted signals were quantified, normalized and the data were statistically analyzed to verify the differentially expressed genes. According to the data, 104 genes were differentially expressed in XDM2 and 30 genes in BCYE media. The present study showed that DNA microarray technique efficiently differentiate the expressed genes under different conditions.

Synthesis and structural characterization of block and random low molecular weight copolymers composed of L-lactic acid and isosorbide succinate moieties

Isosorbide succinate moieties were incorporated into poly(L-lactide) (PLLA) backbone in order to obtain a new class of biodegradable polymer with enhanced properties. This paper describes the synthesis and characterization of four types of low molecular weight copolymers. Copolymer I was obtained from monomer mixtures of L-lactide, isosorbide, and succinic anhydride; II from oligo(L-lactide) (PLLA), isosorbide, and succinic anhydride; III from oligo(isosorbide succinate) (PIS) and L-lactide; and IV from transesterification reactions between PLLA and PIS. MALDI-TOFMS and 13C-NMR analyses gave evidence that co-oligomerization was successfully attained in all cases. The data suggested that the product I is a random co-oligomer and the products II-IV are block co-oligomers.

Chicken skeletal muscle-associated macroarray for gene discovery

Macro- and microarrays are well-established technologies to determine gene functions through repeated measurements of transcript abundance. We constructed a chicken skeletal muscle-associated array based on a muscle-specific EST database, which was used to generate a tissue expression dataset of similar to 4500 chicken genes across 5 adult tissues (skeletal muscle, heart, liver, brain, and skin). Only a small number of ESTs were sufficiently well characterized by BLAST searches to determine their probable cellular functions. Evidence of a particular tissue-characteristic expression can be considered an indication that the transcript is likely to be functionally significant. The skeletal muscle macroarray platform was first used to search for evidence of tissue-specific expression, focusing on the biological function of genes/transcripts, since gene expression profiles generated across tissues were found to be reliable and consistent. Hierarchical clustering analysis revealed consistent clustering among genes assigned to 'developmental growth', such as the ontology genes and germ layers. Accuracy of the expression data was supported by comparing information from known transcripts and tissue from which the transcript was derived with macroarray data. Hybridization assays resulted in consistent tissue expression profile, which will be useful to dissect tissue-regulatory networks and to predict functions of novel genes identified after extensive sequencing of the genomes of model organisms. Screening our skeletal-muscle platform using 5 chicken adult tissues allowed us identifying 43 'tissue-specific' transcripts, and 112 co-expressed uncharacterized transcripts with 62 putative motifs. This platform also represents an important tool for functional investigation of novel genes; to determine expression pattern according to developmental stages; to evaluate differences in muscular growth potential between chicken lines, and to identify tissue-specific genes.

Sugarcane genes associated with sucrose content

Background -: Sucrose content is a highly desirable trait in sugarcane as the worldwide demand for cost-effective biofuels surges. Sugarcane cultivars differ in their capacity to accumulate sucrose and breeding programs routinely perform crosses to identify genotypes able to produce more sucrose. Sucrose content in the mature internodes reach around 20% of the culms dry weight. Genotypes in the populations reflect their genetic program and may display contrasting growth, development, and physiology, all of which affect carbohydrate metabolism. Few studies have profiled gene expression related to sugarcane's sugar content. The identification of signal transduction components and transcription factors that might regulate sugar accumulation is highly desirable if we are to improve this characteristic of sugarcane plants. Results -: We have evaluated thirty genotypes that have different Brix (sugar) levels and identified genes differentially expressed in internodes using cDNA microarrays. These genes were compared to existing gene expression data for sugarcane plants subjected to diverse stress and hormone treatments. The comparisons revealed a strong overlap between the drought and sucrose-content datasets and a limited overlap with ABA signaling. Genes associated with sucrose content were extensively validated by qRT-PCR, which highlighted several protein kinases and transcription factors that are likely to be regulators of sucrose accumulation. The data also indicate that aquaporins, as well as lignin biosynthesis and cell wall metabolism genes, are strongly related to sucrose accumulation. Moreover, sucrose-associated genes were shown to be directly responsive to short term sucrose stimuli, confirming their role in sugar-related pathways. Conclusion -: Gene expression analysis of sugarcane populations contrasting for sucrose content indicated a possible overlap with drought and cell wall metabolism processes and suggested signaling and transcriptional regulators to be used as molecular markers in breeding programs. Transgenic research is necessary to further clarify the role of the genes and define targets useful for sugarcane improvement programs based on transgenic plants.

A reliable measure of similarity based on dependency for short time series: an application to gene expression networks

Background: Microarray techniques have become an important tool to the investigation of genetic relationships and the assignment of different phenotypes. Since microarrays are still very expensive, most of the experiments are performed with small samples. This paper introduces a method to quantify dependency between data series composed of few sample points. The method is used to construct gene co-expression subnetworks of highly significant edges. Results: The results shown here are for an adapted subset of a Saccharomyces cerevisiae gene expression data set with low temporal resolution and poor statistics. The method reveals common transcription factors with a high confidence level and allows the construction of subnetworks with high biological relevance that reveals characteristic features of the processes driving the organism adaptations to specific environmental conditions. Conclusion: Our method allows a reliable and sophisticated analysis of microarray data even under severe constraints. The utilization of systems biology improves the biologists ability to elucidate the mechanisms underlying celular processes and to formulate new hypotheses.

Increased Levels of NOTCH1, NF-kappa B, and Other Interconnected Transcription Factors Characterize Primitive Sets of Hematopoietic Stem Cells

As previously shown, higher levels of NOTCH1 and increased NF-kappa B signaling is a distinctive feature of the more primitive umbilical cord blood (UCB) CD34+ hematopoietic stem cells (HSCs), as compared to bone marrow ( BM). Differences between BM and UCB cell composition also account for this finding. The CD133 marker defines a more primitive cell subset among CD34+ HSC with a proposed hemangioblast potential. To further evaluate the molecular basis related to the more primitive characteristics of UCB and CD133+ HSC, immunomagnetically purified human CD34+ and CD133+ cells from BM and UCB were used on gene expression microarrays studies. UCB CD34+ cells contained a significantly higher proportion of CD133+ cells than BM (70% and 40%, respectively). Cluster analysis showed that BM CD133+ cells grouped with the UCB cells ( CD133+ and CD34+) rather than to BM CD34+ cells. Compared with CD34+ cells, CD133+ had a higher expression of many transcription factors (TFs). Promoter analysis on all these TF genes revealed a significantly higher frequency ( than expected by chance) of NF-kappa B-binding sites (BS), including potentially novel NF-kappa B targets such as RUNX1, GATA3, and USF1. Selected transcripts of TF related to primitive hematopoiesis and self-renewal, such as RUNX1, GATA3, USF1, TAL1, HOXA9, HOXB4, NOTCH1, RELB, and NFKB2 were evaluated by real-time PCR and were all significantly positively correlated. Taken together, our data indicate the existence of an interconnected transcriptional network characterized by higher levels of NOTCH1, NF-kappa B, and other important TFs on more primitive HSC sets.

Identification of the Schistosoma mansoni TNF-Alpha Receptor Gene and the Effect of Human TNF-Alpha on the Parasite Gene Expression Profile

Background: Schistosoma mansoni is the major causative agent of schistosomiasis. The parasite takes advantage of host signals to complete its development in the human body. Tumor necrosis factor-alpha (TNF-alpha) is a human cytokine involved in skin inflammatory responses, and although its effect on the adult parasite's metabolism and egg-laying process has been previously described, a comprehensive assessment of the TNF-alpha pathway and its downstream molecular effects is lacking. Methodology/Principal Findings: In the present work we describe a possible TNF-alpha receptor (TNFR) homolog gene in S. mansoni (SmTNFR). SmTNFR encodes a complete receptor sequence composed of 599 amino acids, and contains four cysteine-rich domains as described for TNFR members. Real-time RT-PCR experiments revealed that SmTNFR highest expression level is in cercariae, 3.5 (+/- 0.7) times higher than in adult worms. Downstream members of the known human TNF-alpha pathway were identified by an in silico analysis, revealing a possible TNF-alpha signaling pathway in the parasite. In order to simulate parasite's exposure to human cytokine during penetration of the skin, schistosomula were exposed to human TNF-alpha just 3 h after cercariae-to-schistosomula in vitro transformation, and large-scale gene expression measurements were performed with microarrays. A total of 548 genes with significantly altered expression were detected, when compared to control parasites. In addition, treatment of adult worms with TNF-alpha caused a significantly altered expression of 1857 genes. Interestingly, the set of genes altered in adults is different from that of schistosomula, with 58 genes in common, representing 3% of altered genes in adults and 11% in 3 h-old early schistosomula. Conclusions/Significance: We describe the possible molecular elements and targets involved in human TNF-alpha effect on S. mansoni, highlighting the mechanism by which recently transformed schistosomula may sense and respond to this host mediator at the site of cercarial penetration into the skin.

Gene Expression Complex Networks: Synthesis, Identification, and Analysis

Thanks to recent advances in molecular biology, allied to an ever increasing amount of experimental data, the functional state of thousands of genes can now be extracted simultaneously by using methods such as cDNA microarrays and RNA-Seq. Particularly important related investigations are the modeling and identification of gene regulatory networks from expression data sets. Such a knowledge is fundamental for many applications, such as disease treatment, therapeutic intervention strategies and drugs design, as well as for planning high-throughput new experiments. Methods have been developed for gene networks modeling and identification from expression profiles. However, an important open problem regards how to validate such approaches and its results. This work presents an objective approach for validation of gene network modeling and identification which comprises the following three main aspects: (1) Artificial Gene Networks (AGNs) model generation through theoretical models of complex networks, which is used to simulate temporal expression data; (2) a computational method for gene network identification from the simulated data, which is founded on a feature selection approach where a target gene is fixed and the expression profile is observed for all other genes in order to identify a relevant subset of predictors; and (3) validation of the identified AGN-based network through comparison with the original network. The proposed framework allows several types of AGNs to be generated and used in order to simulate temporal expression data. The results of the network identification method can then be compared to the original network in order to estimate its properties and accuracy. Some of the most important theoretical models of complex networks have been assessed: the uniformly-random Erdos-Renyi (ER), the small-world Watts-Strogatz (WS), the scale-free Barabasi-Albert (BA), and geographical networks (GG). The experimental results indicate that the inference method was sensitive to average degree k variation, decreasing its network recovery rate with the increase of k. The signal size was important for the inference method to get better accuracy in the network identification rate, presenting very good results with small expression profiles. However, the adopted inference method was not sensible to recognize distinct structures of interaction among genes, presenting a similar behavior when applied to different network topologies. In summary, the proposed framework, though simple, was adequate for the validation of the inferred networks by identifying some properties of the evaluated method, which can be extended to other inference methods.

The impact of measurement errors in the identification of regulatory networks

Background: There are several studies in the literature depicting measurement error in gene expression data and also, several others about regulatory network models. However, only a little fraction describes a combination of measurement error in mathematical regulatory networks and shows how to identify these networks under different rates of noise. Results: This article investigates the effects of measurement error on the estimation of the parameters in regulatory networks. Simulation studies indicate that, in both time series (dependent) and non-time series (independent) data, the measurement error strongly affects the estimated parameters of the regulatory network models, biasing them as predicted by the theory. Moreover, when testing the parameters of the regulatory network models, p-values computed by ignoring the measurement error are not reliable, since the rate of false positives are not controlled under the null hypothesis. In order to overcome these problems, we present an improved version of the Ordinary Least Square estimator in independent (regression models) and dependent (autoregressive models) data when the variables are subject to noises. Moreover, measurement error estimation procedures for microarrays are also described. Simulation results also show that both corrected methods perform better than the standard ones (i.e., ignoring measurement error). The proposed methodologies are illustrated using microarray data from lung cancer patients and mouse liver time series data. Conclusions: Measurement error dangerously affects the identification of regulatory network models, thus, they must be reduced or taken into account in order to avoid erroneous conclusions. This could be one of the reasons for high biological false positive rates identified in actual regulatory network models.

KeaA, a Dictyostelium kelch-domain protein that regulates the response to stress and development

Background: The protein kinase YakA is responsible for the growth arrest and induction of developmental processes that occur upon starvation of Dictyostelium cells. yakA-cells are aggregation deficient, have a faster cell cycle and are hypersensitive to oxidative and nitrosoative stress. With the aim of isolating members of the YakA pathway, suppressors of the death induced by nitrosoative stress in the yakA-cells were identified. One of the suppressor mutations occurred in keaA, a gene identical to DG1106 and similar to Keap1 from mice and the Kelch protein from Drosophila, among others that contain Kelch domains. Results: A mutation in keaA suppresses the hypersensitivity to oxidative and nitrosoative stresses but not the faster growth phenotype of yakA-cells. The growth profile of keaA deficient cells indicates that this gene is necessary for growth. keaA deficient cells are more resistant to nitrosoative and oxidative stress and keaA is necessary for the production and detection of cAMP. A morphological analysis of keaA deficient cells during multicellular development indicated that, although the mutant is not absolutely deficient in aggregation, cells do not efficiently participate in the process. Gene expression analysis using cDNA microarrays of wild-type and keaA deficient cells indicated a role for KeaA in the regulation of the cell cycle and pre-starvation responses. Conclusions: KeaA is required for cAMP signaling following stress. Our studies indicate a role for kelch proteins in the signaling that regulates the cell cycle and development in response to changes in the environmental conditions.

Differential gene expression between the biotrophic-like and saprotrophic mycelia of the witches` broom pathogen Moniliophthora perniciosa

Moniliophthora perniciosa is a hemibiotrophic fungus that causes witches` broom disease (WBD) in cacao. Marked dimorphism characterizes this fungus, showing a monokaryotic or biotrophic phase that causes disease symptoms and a later dikaryotic or saprotrophic phase. A combined strategy of DNA microarray, expressed sequence tag, and real-time reverse-transcriptase polymerase chain reaction analyses was employed to analyze differences between these two fungal stages in vitro. In all, 1,131 putative genes were hybridized with cDNA from different phases, resulting in 189 differentially expressed genes, and 4,595 reads were clusterized, producing 1,534 unigenes. The analysis of these genes, which represent approximately 21% of the total genes, indicates that the biotrophic-like phase undergoes carbon and nitrogen catabollite repression that correlates to the expression of phytopathogenicity genes. Moreover, downregulation of mitochondrial oxidative phosphorylation and the presence of a putative ngr1 of Saccharomyces cerevisiae could help explain its lower growth rate. In contrast, the saprotrophic mycelium expresses genes related to the metabolism of hexoses, ammonia, and oxidative phosphorylation, which could explain its faster growth. Antifungal toxins were upregulated and could prevent the colonization by competing fungi. This work significantly contributes to our understanding of the molecular mechanisms of WBD and, to our knowledge, is the first to analyze differential gene expression of the different phases of a hemibiotrophic fungus.