Functional genomic and molecular analyses of transcripts related to glycerol synthesis and trafficking in rainbow smelt (Osmerus mordax) using cold temperature-induced models of glycerol production

The rainbow smelt (Osmerus mordax) is an anadromous teleost that produces type II antifreeze protein (AFP) and accumulates modest urea and high glycerol levels in plasma and tissues as adaptive cryoprotectant mechanisms in sub-zero temperatures. It is known that glyceroneogenesis occurs in liver via a branch in glycolysis and gluconeogenesis and is activated by low temperature; however, the precise mechanisms of glycerol synthesis and trafficking in smelt remain to be elucidated. The objective of this thesis was to provide further insight using functional genomic techniques [e.g. suppression subtractive hybridization (SSH) cDNA library construction, microarray analyses] and molecular analyses [e.g. cloning, quantitative reverse transcription - polymerase chain reaction (QPCR)]. Novel molecular mechanisms related to glyceroneogenesis were deciphered by comparing the transcript expression profiles of glycerol (cold temperature) and non-glycerol (warm temperature) accumulating hepatocytes (Chapter 2) and livers from intact smelt (Chapter 3). Briefly, glycerol synthesis can be initiated from both amino acids and carbohydrate; however carbohydrate appears to be the preferred source when it is readily available. In glycerol accumulating hepatocytes, levels of the hepatic glucose transporter (GLUT2) plummeted and transcript levels of a suite of genes (PEPCK, MDH2, AAT2, GDH and AQP9) associated with the mobilization of amino acids to fuel glycerol synthesis were all transiently higher. In contrast, in glycerol accumulating livers from intact smelt, glycerol synthesis was primarily fuelled by glycogen degradation with higher PGM and PFK (glycolysis) transcript levels. Whether initiated from amino acids or carbohydrate, there were common metabolic underpinnings. Increased PDK2 (an inhibitor of PDH) transcript levels would direct pyruvate derived from amino acids and / or DHAP derived from G6P to glycerol as opposed to oxidation via the citric acid cycle. Robust LIPL (triglyceride catabolism) transcript levels would provide free fatty acids that could be oxidized to fuel ATP synthesis. Increased cGPDH (glyceroneogenesis) transcript levels were not required for increased glycerol production, suggesting that regulation is more likely by post-translational modification. Finally, levels of a transcript potentially encoding glycerol-3-phosphatase, an enzyme not yet characterized in any vertebrate species, were transiently higher. These comparisons also led to the novel discoveries that increased G6Pase (glucose synthesis) and increased GS (glutamine synthesis) transcript levels were part of the low temperature response in smelt. Glucose may provide increased colligative protection against freezing; whereas glutamine could serve to store nitrogen released from amino acid catabolism in a non-toxic form and / or be used to synthesize urea via purine synthesis-uricolysis. Novel key aspects of cryoprotectant osmolyte (glycerol and urea) trafficking were elucidated by cloning and characterizing three aquaglyceroporin (GLP)-encoding genes from smelt at the gene and cDNA levels in Chapter 4. GLPs are integral membrane proteins that facilitate passive movement of water, glycerol and urea across cellular membranes. The highlight was the discovery that AQP10ba transcript levels always increase in posterior kidney only at low temperature. This AQP10b gene paralogue may have evolved to aid in the reabsorption of urea from the proximal tubule. This research has contributed significantly to a general understanding of the cold adaptation response in smelt, and more specifically to the development of a working scenario for the mechanisms involved in glycerol synthesis and trafficking in this species.

Functional characterisation of Schistosoma japonicum acetylcholinesterase

BACKGROUND: Acetylcholinesterase (AChE) is an important metabolic enzyme of schistosomes present in the musculature and on the surface of the blood stage where it has been implicated in the modulation of glucose scavenging from mammalian host blood. As both a target for the antischistosomal drug metrifonate and as a potential vaccine candidate, AChE has been characterised in the schistosome species Schistosoma mansoni, S. haematobium and S. bovis, but not in S. japonicum. Recently, using a schistosome protein microarray, a predicted S. japonicum acetylcholinesterase precursor was significantly targeted by protective IgG1 immune responses in S. haematobium-exposed individuals that had acquired drug-induced resistance to schistosomiasis after praziquantel treatment.

RESULTS: We report the full-length cDNA sequence and describe phylogenetic and molecular structural analysis to facilitate understanding of the biological function of AChE (SjAChE) in S. japonicum. The protein has high sequence identity (88 %) with the AChEs in S. mansoni, S. haematobium and S. bovis and has 25 % sequence similarity with human AChE, suggestive of a highly specialised role for the enzyme in both parasite and host. We immunolocalized SjAChE and demonstrated its presence on the surface of adult worms and schistosomula, as well as its lower expression in parenchymal regions. The relatively abundance of AChE activity (90 %) present on the surface of adult S. japonicum when compared with that reported in other schistosomes suggests SjAChE may be a more effective drug or immunological target against this species. We also demonstrate that the classical inhibitor of AChE, BW285c51, inhibited AChE activity in tegumental extracts of paired worms, single males and single females by 59, 22 and 50 %, respectively, after 24 h incubation with 200 μM BW284c51.

CONCLUSIONS: These results build on previous studies in other schistosome species indicating major differences in the enzyme between parasite and mammalian host, and provide further support for the design of an anti-schistosome intervention targeting AChE.

Insights into the multistep transformation of MGUS to myeloma using microarray expression analysis.

To define specific pathways important in the multistep transformation process of normal plasma cells (PCs) to monoclonal gammopathy of uncertain significance (MGUS) and multiple myeloma (MM), we have applied microarray analysis to PCs from 5 healthy donors (N), 7 patients with MGUS, and 24 patients with newly diagnosed MM. Unsupervised hierarchical clustering using 125 genes with a large variation across all samples defined 2 groups: N and MGUS/MM. Supervised analysis identified 263 genes differentially expressed between N and MGUS and 380 genes differentially expressed between N and MM, 197 of which were also differentially regulated between N and MGUS. Only 74 genes were differentially expressed between MGUS and MM samples, indicating that the differences between MGUS and MM are smaller than those between N and MM or N and MGUS. Differentially expressed genes included oncogenes/tumor-suppressor genes (LAF4, RB1, and disabled homolog 2), cell-signaling genes (RAS family members, B-cell signaling and NF-kappaB genes), DNA-binding and transcription-factor genes (XBP1, zinc finger proteins, forkhead box, and ring finger proteins), and developmental genes (WNT and SHH pathways). Understanding the molecular pathogenesis of MM by gene expression profiling has demonstrated sequential genetic changes from N to malignant PCs and highlighted important pathways involved in the transformation of MGUS to MM.

Specific serology for emerging human coronaviruses by protein microarray

We present a serological assay for the specific detection of IgM and IgG antibodies against the emerging human coronavirus hCoV-EMC and the SARS-CoV based on protein microarray technology. The assay uses the S1 receptor-binding subunit of the spike protein of hCoV-EMC and SARS-CoV as antigens. The assay has been validated extensively using putative cross-reacting sera of patient cohorts exposed to the four common hCoVs and sera from convalescent patients infected with hCoV-EMC or SARS-CoV.

EMMA 2--a MAGE-compliant system for the collaborative analysis and integration of microarray data

Background: Understanding transcriptional regulation by genome-wide microarray studies can contribute to unravel complex relationships between genes. Attempts to standardize the annotation of microarray data include the Minimum Information About a Microarray Experiment (MIAME) recommendations, the MAGE-ML format for data interchange, and the use of controlled vocabularies or ontologies. The existing software systems for microarray data analysis implement the mentioned standards only partially and are often hard to use and extend. Integration of genomic annotation data and other sources of external knowledge using open standards is therefore a key requirement for future integrated analysis systems. Results: The EMMA 2 software has been designed to resolve shortcomings with respect to full MAGE-ML and ontology support and makes use of modern data integration techniques. We present a software system that features comprehensive data analysis functions for spotted arrays, and for the most common synthesized oligo arrays such as Agilent, Affymetrix and NimbleGen. The system is based on the full MAGE object model. Analysis functionality is based on R and Bioconductor packages and can make use of a compute cluster for distributed services. Conclusion: Our model-driven approach for automatically implementing a full MAGE object model provides high flexibility and compatibility. Data integration via SOAP-based web-services is advantageous in a distributed client-server environment as the collaborative analysis of microarray data is gaining more and more relevance in international research consortia. The adequacy of the EMMA 2 software design and implementation has been proven by its application in many distributed functional genomics projects. Its scalability makes the current architecture suited for extensions towards future transcriptomics methods based on high-throughput sequencing approaches which have much higher computational requirements than microarrays.

Microarrays as a functional approach to the transcriptome

Knowing a cell’s transcriptome is a fundamental requisite in order to analyze its response to the environment. Microarrays have supposed a revolution on this field as they are able to yield an overview of gene expression at any environmental condition on a genome-wide scale. This technique consists in the hybridisation of a nucleic acid sample, previously marked, with a probe (which might be made up of cDNA, oligonucleotides or PCR products) anchored to a solid surface (made of glass, plastic, silicon...) giving as a result a dot grid which reveals, after image analysis, which genes are being expressed. Nevertheless, this only can be achieved if information on the species genome has been generated. Different kinds of expression microarrays exist attending to the probe’s nature and the method used in its synthesis. In this poster two of these will be treated: Spotted Microarrays, for which the probe is synthesised prior to its fixation to the array and allow the analysis of two targets simultaneously. They can be easily customized, but lack high reproducibility and sensitivity. Oligonucleotide Microarrays, which are characterized by the direct printing of the probe on the array. In this case the probes consist on, invariably, oligonucleotides that are complementary to a small fraction of the gene it is representing at the microarray. Their application is somewhat restricted. This fact, however, makes them more reproducible. Currently, the approach towards the transcriptome studies from the Next Generation Sequencing technologies offers a large volume of information in a short amount of time needing less previous information on the target organism than that needed by microarrays, but their expensive price limits their use. The versatility of the latter, together with their reduced costs in comparison to other techniques, makes them an interesting resource in applications that may need less complexity.

ArrayMining: a modular web-application for microarray analysis combining ensemble and consensus methods with cross-study normalization

Background: Statistical analysis of DNA microarray data provides a valuable diagnostic tool for the investigation of genetic components of diseases. To take advantage of the multitude of available data sets and analysis methods, it is desirable to combine both different algorithms and data from different studies. Applying ensemble learning, consensus clustering and cross-study normalization methods for this purpose in an almost fully automated process and linking different analysis modules together under a single interface would simplify many microarray analysis tasks. Results: We present ArrayMining.net, a web-application for microarray analysis that provides easy access to a wide choice of feature selection, clustering, prediction, gene set analysis and cross-study normalization methods. In contrast to other microarray-related web-tools, multiple algorithms and data sets for an analysis task can be combined using ensemble feature selection, ensemble prediction, consensus clustering and cross-platform data integration. By interlinking different analysis tools in a modular fashion, new exploratory routes become available, e.g. ensemble sample classification using features obtained from a gene set analysis and data from multiple studies. The analysis is further simplified by automatic parameter selection mechanisms and linkage to web tools and databases for functional annotation and literature mining. Conclusion: ArrayMining.net is a free web-application for microarray analysis combining a broad choice of algorithms based on ensemble and consensus methods, using automatic parameter selection and integration with annotation databases.

mRNA-Seq and microarray development for the Grooved carpet shell clam, Ruditapes decussatus: a functional approach to unravel host -parasite interaction

Background The Grooved Carpet shell clam Ruditapes decussatus is the autochthonous European clam and the most appreciated from a gastronomic and economic point of view. The production is in decline due to several factors such as Perkinsiosis and habitat invasion and competition by the introduced exotic species, the manila clam Ruditapes philippinarum. After we sequenced R. decussatus transcriptome we have designed an oligo microarray capable of contributing to provide some clues on molecular response of the clam to Perkinsiosis. Results A database consisting of 41,119 unique transcripts was constructed, of which 12,479 (30.3%) were annotated by similarity. An oligo-DNA microarray platform was then designed and applied to profile gene expression in R. decussatus heavily infected by Perkinsus olseni. Functional annotation of differentially expressed genes between those two conditionswas performed by gene set enrichment analysis. As expected, microarrays unveil genes related with stress/infectious agents such as hydrolases, proteases and others. The extensive role of innate immune system was also analyzed and effect of parasitosis upon expression of important molecules such as lectins reviewed. Conclusions This study represents a first attempt to characterize Ruditapes decussatus transcriptome, an important marine resource for the European aquaculture. The trancriptome sequencing and consequent annotation will increase the available tools and resources for this specie, introducing the possibility of high throughput experiments such as microarrays analysis. In this specific case microarray approach was used to unveil some important aspects of host-parasite interaction between the Carpet shell clam and Perkinsus, two non-model species, highlighting some genes associated with this interaction. Ample information was obtained to identify biological processes significantly enriched among differentially expressed genes in Perkinsus infected versus non-infected gills. An overview on the genes related with the immune system on R. decussatus transcriptome is also reported.

Utility of a panviral microarray for detection of swine respiratory viruses in clinical samples

Several factors have recently converged, elevating the need for highly parallel diagnostic platforms that have the ability to detect many known, novel, and emerging pathogenic agents simultaneously. Panviral DNA microarrays represent the most robust approach for massively parallel viral surveillance and detection. The Virochip is a panviral DNA microarray that is capable of detecting all known viruses, as well as novel viruses related to known viral families, in a single assay and has been used to successfully identify known and novel viral agents in clinical human specimens. However, the usefulness and the sensitivity of the Virochip platform have not been tested on a set of clinical veterinary specimens with the high degree of genetic variance that is frequently observed with swine virus field isolates. In this report, we investigate the utility and sensitivity of the Virochip to positively detect swine viruses in both cell culture-derived samples and clinical swine samples. The Virochip successfully detected porcine reproductive and respiratory syndrome virus (PRRSV) in serum containing 6.10 × 10(2) viral copies per microliter and influenza A virus in lung lavage fluid containing 2.08 × 10(6) viral copies per microliter. The Virochip also successfully detected porcine circovirus type 2 (PCV2) in serum containing 2.50 × 10(8) viral copies per microliter and porcine respiratory coronavirus (PRCV) in turbinate tissue homogenate. Collectively, the data in this report demonstrate that the Virochip can successfully detect pathogenic viruses frequently found in swine in a variety of solid and liquid specimens, such as turbinate tissue homogenate and lung lavage fluid, as well as antemortem samples, such as serum.

Analysis of the genome content of Lactococcus garvieae by genomic interspecies microarray hybridization

BACKGROUND Lactococcus garvieae is a bacterial pathogen that affects different animal species in addition to humans. Despite the widespread distribution and emerging clinical significance of L. garvieae in both veterinary and human medicine, there is almost a complete lack of knowledge about the genetic content of this microorganism. In the present study, the genomic content of L. garvieae CECT 4531 was analysed using bioinformatics tools and microarray-based comparative genomic hybridization (CGH) experiments. Lactococcus lactis subsp. lactis IL1403 and Streptococcus pneumoniae TIGR4 were used as reference microorganisms. RESULTS The combination and integration of in silico analyses and in vitro CGH experiments, performed in comparison with the reference microorganisms, allowed establishment of an inter-species hybridization framework with a detection threshold based on a sequence similarity of >or= 70%. With this threshold value, 267 genes were identified as having an analogue in L. garvieae, most of which (n = 258) have been documented for the first time in this pathogen. Most of the genes are related to ribosomal, sugar metabolism or energy conversion systems. Some of the identified genes, such as als and mycA, could be involved in the pathogenesis of L. garvieae infections. CONCLUSIONS In this study, we identified 267 genes that were potentially present in L. garvieae CECT 4531. Some of the identified genes could be involved in the pathogenesis of L. garvieae infections. These results provide the first insight into the genome content of L. garvieae.

Discovery of stable and variable differences in the Mycobacterium avium subsp. paratuberculosis type I, II, and III genomes by pan-genome microarray analysis

Mycobacterium avium subsp. paratuberculosis is an important animal pathogen widely disseminated in the environment that has also been associated with Crohn's disease in humans. Three M. avium subsp. paratuberculosis genomotypes are recognized, but genomic differences have not been fully described. To further investigate these potential differences, a 60-mer oligonucleotide microarray (designated the MAPAC array), based on the combined genomes of M. avium subsp. paratuberculosis (strain K-10) and Mycobacterium avium subsp. hominissuis (strain 104), was designed and validated. By use of a test panel of defined M. avium subsp. paratuberculosis strains, the MAPAC array was able to identify a set of large sequence polymorphisms (LSPs) diagnostic for each of the three major M. avium subsp. paratuberculosis types. M. avium subsp. paratuberculosis type II strains contained a smaller genomic complement than M. avium subsp. paratuberculosis type I and M. avium subsp. paratuberculosis type III genomotypes, which included a set of genomic regions also found in M. avium subsp. hominissuis 104. Specific PCRs for genes within LSPs that differentiated M. avium subsp. paratuberculosis types were devised and shown to accurately screen a panel (n = 78) of M. avium subsp. paratuberculosis strains. Analysis of insertion/deletion region INDEL12 showed deletion events causing a reduction in the complement of mycobacterial cell entry genes in M. avium subsp. paratuberculosis type II strains and significantly altering the coding of a major immunologic protein (MPT64) associated with persistence and granuloma formation. Analysis of MAPAC data also identified signal variations in several genomic regions, termed variable genomic islands (vGIs), suggestive of transient duplication/deletion events. vGIs contained significantly low GC% and were immediately flanked by insertion sequences, integrases, or short inverted repeat sequences. Quantitative PCR demonstrated that variation in vGI signals could be associated with colony growth rate and morphology.

Lung cancer prediction from microarray data by gene expression programming

Lung cancer is a leading cause of cancer-related death worldwide. The early diagnosis of cancer has demonstrated to be greatly helpful for curing the disease effectively. Microarray technology provides a promising approach of exploiting gene profiles for cancer diagnosis. In this study, the authors propose a gene expression programming (GEP)-based model to predict lung cancer from microarray data. The authors use two gene selection methods to extract the significant lung cancer related genes, and accordingly propose different GEP-based prediction models. Prediction performance evaluations and comparisons between the authors' GEP models and three representative machine learning methods, support vector machine, multi-layer perceptron and radial basis function neural network, were conducted thoroughly on real microarray lung cancer datasets. Reliability was assessed by the cross-data set validation. The experimental results show that the GEP model using fewer feature genes outperformed other models in terms of accuracy, sensitivity, specificity and area under the receiver operating characteristic curve. It is concluded that GEP model is a better solution to lung cancer prediction problems.