The cDNA cloning and mRNA expression of heat shock protein 70 gene in the haemocytes of bay scallop (Argopecten irradians, Lamarck 1819) responding to bacteria challenge and naphthalin stress

Heat shock protein 70 (HSP70) is an important member of the heat shock protein superfamily, and it plays a key role in the process of protecting cells, facilitating the folding of nascent peptides and responding to stress. The cDNA of bay scallop Argopecten irradians HSP70 (designated AIHSP70) was cloned by the techniques of homological cloning and rapid amplification of cDNA end (RACE). The full length of AIHSP70 cDNA was 2651 bp in length, having a 5' untranslated region (UTR) of 96 bp, a 3' UTR of 575 bp, and an open reading frame (ORF) of 1980 bp encoding a polypeptide of 659 amino acids with an estimated molecular mass of 71.80 kDa and an estimated isoelectric point of 5.26. BLAST analysis revealed that the AIHSP70 gene shared high identity with other known HSP70 genes. Three classical HSP signature motifs were detected in AIHSP70 by InterPro, analysis. 3-D structural prediction of AIHSP70 showed that its N terminal ATPase activity domain and,C terminal substrate-binding domain shared high similarity with that in human heat shock protein 70. The results indicated that the AIHSP70 was a member of the heat shock protein 70 family. A semi-quantitive RT-PCR method was used to analyse the expression of AIHSP70 gene after the treatment of naphthalin which is one kind of polycyclic aromatic hydrocarbon (PAH) and the challenge of bacteria. mRNA expression of AIHSP70 in scallop was up-regulated significantly after the stimulation of naphthalin and increased with increasing naphthalin concentration. A clearly time-dependent expression pattern of AIHSP70 was observed after the scallops were infected by Vibrio anguillarum, and the mRNA expression reached a maximum level at 8 h and lasted to 16 h, and then dropped progressively. The results indicated that AIHSP70 could play an important role in mediating the environmental stress and immune response in scallop. (c) 2006 Elsevier Ltd. All rights reserved.

cDNA cloning and overexpression of acidic ribosomal phosphoprotein P1 gene (RPLP1) from the Giant Panda

RPLP1 is one of acidic ribosomal phosphoproteins encoded by RPLP1 gene, which plays an important role in the elongation step of protein synthesis. The cDNA of RPLP1 was cloned successfully for the first time from the Giant Panda (Ailuropoda melanoleuca) using RT-PCR technology, which was also sequenced, analyzed preliminarily and expressed in E. coli. The cDNA fragment cloned is 449bp in size, containing an open reading frame of 344bp encoding 114 amino acids. Alignment analysis indicated that the nucleotide sequence and the deduced amino acid sequence are highly conserved to other five species studied, including Homo sapiens, Mus musculus, Rattus norvegicus, Bos Taurus and Sus scrofa. The homologies for nucleotide sequences of Giant Panda PPLP1 to that of these species are 92.4%, 89.8%, 89.0%, 91.3% and 87.5%, while the homologies for amino acid sequences are 96.5%, 94.7%, 95.6%, 96.5% and 88.6%. Topology prediction showed there are three Casein kinase II phosphorylation sites and two N-myristoylation sites in the RPLP1 protein of the Giant Panda (Ailuropoda melanoleuca). The RPLP1 gene was overexpressed in E. coli and the result indicated that RPLP1 fusion with the N-terminally His-tagged form gave rise to the accumulation of an expected 18kDa polypeptide, which was in accordance with the predicted protein and could also be used to purify the protein and study its function.

Predicting gene function in Saccharomyces cerevisiae

Clare, A. and King R.D. (2003) Predicting gene function in Saccharomyces cerevisiae. 2nd European Conference on Computational Biology (ECCB '03). (published as a journal supplement in Bioinformatics 19: ii42-ii49)

Integration of relational and hierarchical network information for protein function prediction

BACKGROUND:In the current climate of high-throughput computational biology, the inference of a protein's function from related measurements, such as protein-protein interaction relations, has become a canonical task. Most existing technologies pursue this task as a classification problem, on a term-by-term basis, for each term in a database, such as the Gene Ontology (GO) database, a popular rigorous vocabulary for biological functions. However, ontology structures are essentially hierarchies, with certain top to bottom annotation rules which protein function predictions should in principle follow. Currently, the most common approach to imposing these hierarchical constraints on network-based classifiers is through the use of transitive closure to predictions.RESULTS:We propose a probabilistic framework to integrate information in relational data, in the form of a protein-protein interaction network, and a hierarchically structured database of terms, in the form of the GO database, for the purpose of protein function prediction. At the heart of our framework is a factorization of local neighborhood information in the protein-protein interaction network across successive ancestral terms in the GO hierarchy. We introduce a classifier within this framework, with computationally efficient implementation, that produces GO-term predictions that naturally obey a hierarchical 'true-path' consistency from root to leaves, without the need for further post-processing.CONCLUSION:A cross-validation study, using data from the yeast Saccharomyces cerevisiae, shows our method offers substantial improvements over both standard 'guilt-by-association' (i.e., Nearest-Neighbor) and more refined Markov random field methods, whether in their original form or when post-processed to artificially impose 'true-path' consistency. Further analysis of the results indicates that these improvements are associated with increased predictive capabilities (i.e., increased positive predictive value), and that this increase is consistent uniformly with GO-term depth. Additional in silico validation on a collection of new annotations recently added to GO confirms the advantages suggested by the cross-validation study. Taken as a whole, our results show that a hierarchical approach to network-based protein function prediction, that exploits the ontological structure of protein annotation databases in a principled manner, can offer substantial advantages over the successive application of 'flat' network-based methods.

Prediction and prevention of venous thrombosis in pregnancy

Venous thromboembolism (VTE) remains the leading cause of maternal mortality. Reports identified further research is required in obese and women post caesarean section (CS). Risk factors for VTE during pregnancy are periodically absent indicating the need for a simple and effective screening tool for pregnancy. Perturbation of the uteroplacental haemostasis has been implicated in placenta mediated pregnancy complications. This thesis had 4 main aims: 1) To investigate anticoagulant effects following a fixed thromboprophylaxis dose in healthy women post elective CS. 2) To evaluate the calibrated automated thrombogram (CAT) assay as a potential predictive tool for thrombosis in pregnancy. 3) To compare the anticoagulant effects of fixed versus weight adjusted thromboprophylaxis dose in morbidly obese pregnant women. 4) To investigate the LMWH effects on human haemostatic gene and antigen expression in placentae and plasma from the uteroplacental , maternal and fetal circulation. Tissue factor pathway inhibitor (TFPI), thrombin antithrombin (TAT), CAT and anti-Xa levels were analysed. Real-time PCR and ELISA were used to quantify mRNA and protein expression of TFPI and TF in placental tissue. In women post CS, anti-Xa levels do not reflect the full anticoagulant effects of LMWH. LMWH thromboprophylaxis in this healthy cohort of patients appears to have a sustained effect in reducing excess thrombin production post elective CS. The results of this study suggest that predicting VTE in pregnant women using CAT assay is not possible at present time. The prothrombotic state in pregnant morbidly obese women was substantially attenuated by weight adjusted but not at fixed LMWH doses. LMWH may be effective in reducing in- vivo thrombin production in the uteroplacental circulation of thrombophilic women. All these results collectively suggest that at appropriate dosage, LMWH is effective in attenuating excess thrombin generation, in low risk pregnant women post caesarean section or moderate to high risk pregnant women who are morbidly obese or tested positive for thrombophilia. The results of the studies provided data to inform evidence-based practice to improve the outcome for pregnant women at risk of thrombosis.

An integrated approach to the prediction of chemotherapeutic response in patients with breast cancer.

BACKGROUND: A major challenge in oncology is the selection of the most effective chemotherapeutic agents for individual patients, while the administration of ineffective chemotherapy increases mortality and decreases quality of life in cancer patients. This emphasizes the need to evaluate every patient's probability of responding to each chemotherapeutic agent and limiting the agents used to those most likely to be effective. METHODS AND RESULTS: Using gene expression data on the NCI-60 and corresponding drug sensitivity, mRNA and microRNA profiles were developed representing sensitivity to individual chemotherapeutic agents. The mRNA signatures were tested in an independent cohort of 133 breast cancer patients treated with the TFAC (paclitaxel, 5-fluorouracil, adriamycin, and cyclophosphamide) chemotherapy regimen. To further dissect the biology of resistance, we applied signatures of oncogenic pathway activation and performed hierarchical clustering. We then used mRNA signatures of chemotherapy sensitivity to identify alternative therapeutics for patients resistant to TFAC. Profiles from mRNA and microRNA expression data represent distinct biologic mechanisms of resistance to common cytotoxic agents. The individual mRNA signatures were validated in an independent dataset of breast tumors (P = 0.002, NPV = 82%). When the accuracy of the signatures was analyzed based on molecular variables, the predictive ability was found to be greater in basal-like than non basal-like patients (P = 0.03 and P = 0.06). Samples from patients with co-activated Myc and E2F represented the cohort with the lowest percentage (8%) of responders. Using mRNA signatures of sensitivity to other cytotoxic agents, we predict that TFAC non-responders are more likely to be sensitive to docetaxel (P = 0.04), representing a viable alternative therapy. CONCLUSIONS: Our results suggest that the optimal strategy for chemotherapy sensitivity prediction integrates molecular variables such as ER and HER2 status with corresponding microRNA and mRNA expression profiles. Importantly, we also present evidence to support the concept that analysis of molecular variables can present a rational strategy to identifying alternative therapeutic opportunities.

Gene expression signatures of radiation response are specific, durable and accurate in mice and humans.

BACKGROUND: Previous work has demonstrated the potential for peripheral blood (PB) gene expression profiling for the detection of disease or environmental exposures. METHODS AND FINDINGS: We have sought to determine the impact of several variables on the PB gene expression profile of an environmental exposure, ionizing radiation, and to determine the specificity of the PB signature of radiation versus other genotoxic stresses. Neither genotype differences nor the time of PB sampling caused any lessening of the accuracy of PB signatures to predict radiation exposure, but sex difference did influence the accuracy of the prediction of radiation exposure at the lowest level (50 cGy). A PB signature of sepsis was also generated and both the PB signature of radiation and the PB signature of sepsis were found to be 100% specific at distinguishing irradiated from septic animals. We also identified human PB signatures of radiation exposure and chemotherapy treatment which distinguished irradiated patients and chemotherapy-treated individuals within a heterogeneous population with accuracies of 90% and 81%, respectively. CONCLUSIONS: We conclude that PB gene expression profiles can be identified in mice and humans that are accurate in predicting medical conditions, are specific to each condition and remain highly accurate over time.

Prediction and Verification of miRNA Expression in Human and Rat Retinas.

PURPOSE: MicroRNAs (miRNAs) play a global role in regulating gene expression and have important tissue-specific functions. Little is known about their role in the retina. The purpose of this study was to establish the retinal expression of those miRNAs predicted to target genes involved in vision. METHODS: miRNAs potentially targeting important "retinal" genes, as defined by expression pattern and implication in disease, were predicted using a published algorithm (TargetScan; Envisioneering Medical Technologies, St. Louis, MO). The presence of candidate miRNAs in human and rat retinal RNA was assessed by RT-PCR. cDNA levels for each miRNA were determined by quantitative PCR. The ability to discriminate between miRNAs varying by a single nucleotide was assessed. The activity of miR-124 and miR-29 against predicted target sites in Rdh10 and Impdh1 was tested by cotransfection of miRNA mimics and luciferase reporter plasmids. RESULTS: Sixty-seven miRNAs were predicted to target one or more of the 320 retinal genes listed herein. All 11 candidate miRNAs tested were expressed in the retina, including miR-7, miR-124, miR135a, and miR135b. Relative levels of individual miRNAs were similar between rats and humans. The Rdh10 3'UTR, which contains a predicted miR-124 target site, mediated the inhibition of luciferase activity by miR-124 mimics in cell culture. CONCLUSIONS: Many miRNAs likely to regulate genes important for retinal function are present in the retina. Conservation of miRNA retinal expression patterns from rats to humans supports evidence from other tissues that disruption of miRNAs is a likely cause of a range of visual abnormalities.

Prediction of microRNAs affecting mRNA expression during retinal development.

Background: MicroRNAs (miRNAs) are small RNA molecules (similar to 22 nucleotides) which have been shown to play an important role both in development and in maintenance of adult tissue. Conditional inactivation of miRNAs in the eye causes loss of visual function and progressive retinal degeneration. In addition to inhibiting translation, miRNAs can mediate degradation of targeted mRNAs. We have previously shown that candidate miRNAs affecting transcript levels in a tissue can be deduced from mRNA microarray expression profiles. The purpose of this study was to predict miRNAs which affect mRNA levels in developing and adult retinal tissue and to confirm their expression.

Results: Microarray expression data from ciliary epithelial retinal stem cells (CE-RSCs), developing and adult mouse retina were generated or downloaded from public repositories. Analysis of gene expression profiles detected the effects of multiple miRNAs in CE-RSCs and retina. The expression of 20 selected miRNAs was confirmed by RT-PCR and the cellular distribution of representative candidates analyzed by in situ hybridization. The expression levels of miRNAs correlated with the significance of their predicted effects upon mRNA expression. Highly expressed miRNAs included miR-124, miR-125a, miR-125b, miR-204 and miR-9. Over-expression of three miRNAs with significant predicted effects upon global mRNA levels resulted in a decrease in mRNA expression of five out of six individual predicted target genes assayed.

Conclusions: This study has detected the effect of miRNAs upon mRNA expression in immature and adult retinal tissue and cells. The validity of these observations is supported by the experimental confirmation of candidate miRNA expression and the regulation of predicted target genes following miRNA over-expression. Identified miRNAs are likely to be important in retinal development and function. Misregulation of these miRNAs might contribute to retinal degeneration and disease. Conversely, manipulation of their expression could potentially be used as a therapeutic tool in the future.

Complement Component 3: an assessment of association with AMD and analysis of gene-gene and gene-environment interactions in a Northern Irish cohort

Purpose: A non-synonymous single nucleotide polymorphism ( SNP) in complement component 3 has been shown to increase the risk of age-related macular degeneration (AMD). We assess its effect on AMD risk in a Northern Irish sample, test for gene-gene and gene-environment interaction, and review a risk prediction model.

Gene expression profiling in MDS and AML: potential and future avenues

Today, the classification systems for myelodysplastic syndromes (MDS) and acute myeloid leukemia (AML) already incorporate cytogenetic and molecular genetic aberrations in an attempt to better reflect disease biology. However, in many MDS/AML patients no genetic aberrations have been identified yet, and even within some cytogenetically well-defined subclasses there is considerable clinical heterogeneity. Recent advances in genomics technologies such as gene expression profiling (GEP) provide powerful tools to further characterize myeloid malignancies at the molecular level, with the goal to refine the MDS/AML classification system, incorporating as yet unknown molecular genetic and epigenetic pathomechanisms, which are likely reflected by aberrant gene expression patterns. In this study, we provide a comprehensive review on how GEP has contributed to a refined molecular taxonomy of MDS and AML with regard to diagnosis, prediction of clinical outcome, discovery of novel subclasses and identification of novel therapeutic targets and novel drugs. As many challenges remain ahead, we discuss the pitfalls of this technology and its potential including future integrative studies with other genomics technologies, which will continue to improve our understanding of malignant transformation in myeloid malignancies and thereby contribute to individualized risk-adapted treatment strategies for MDS and AML patients. Leukemia (2011) 25, 909-920; doi:10.1038/leu.2011.48; published online 29 March 2011

Identification of Gene Expression-Based Prognostic Markers in the Hematopoietic Stem Cells of Patients With Myelodysplastic Syndromes

The diagnosis of patients with myelodysplastic syndromes (MDS) is largely dependent on morphologic examination of bone marrow aspirates. Several criteria that form the basis of the classifications and scoring systems most commonly used in clinical practice are affected by operator-dependent variation. To identify standardized molecular markers that would allow prediction of prognosis, we have used gene expression profiling (GEP) data on CD34+ cells from patients with MDS to determine the relationship between gene expression levels and prognosis.

Model selection for prognostic time-to-event gene signature discovery with applications in early breast cancer data

Model selection between competing models is a key consideration in the discovery of prognostic multigene signatures. The use of appropriate statistical performance measures as well as verification of biological significance of the signatures is imperative to maximise the chance of external validation of the generated signatures. Current approaches in time-to-event studies often use only a single measure of performance in model selection, such as logrank test p-values, or dichotomise the follow-up times at some phase of the study to facilitate signature discovery. In this study we improve the prognostic signature discovery process through the application of the multivariate partial Cox model combined with the concordance index, hazard ratio of predictions, independence from available clinical covariates and biological enrichment as measures of signature performance. The proposed framework was applied to discover prognostic multigene signatures from early breast cancer data. The partial Cox model combined with the multiple performance measures were used in both guiding the selection of the optimal panel of prognostic genes and prediction of risk within cross validation without dichotomising the follow-up times at any stage. The signatures were successfully externally cross validated in independent breast cancer datasets, yielding a hazard ratio of 2.55 [1.44, 4.51] for the top ranking signature.

Mechanisms of reduced susceptibility and genotypic prediction of antibiotic resistance in Prevotella isolated from cystic fibrosis (CF) and non-CF patients

OBJECTIVES: To investigate mechanisms of reduced susceptibility to commonly used antibiotics in Prevotella cultured from patients with cystic fibrosis (CF), patients with invasive infection and healthy control subjects and to determine whether genotype can be used to predict phenotypic resistance.

METHODS: The susceptibility of 157 Prevotella isolates to seven antibiotics was compared, with detection of resistance genes (cfxA-type gene, ermF and tetQ), mutations within the CfxA-type β-lactamase and expression of efflux pumps.

RESULTS: Prevotella isolates positive for a cfxA-type gene had higher MICs of amoxicillin and ceftazidime compared with isolates negative for this gene (P < 0.001). A mutation within the CfxA-type β-lactamase (Y239D) was associated with ceftazidime resistance (P = 0.011). The UK CF isolates were 5.3-fold, 2.7-fold and 5.7-fold more likely to harbour ermF compared with the US CF, UK invasive and UK healthy control isolates, respectively. Higher concentrations of azithromycin (P < 0.001) and clindamycin (P < 0.001) were also required to inhibit the growth of the ermF-positive isolates compared with ermF-negative isolates. Furthermore, tetQ-positive Prevotella isolates had higher MICs of tetracycline (P = 0.001) and doxycycline (P < 0.001) compared with tetQ-negative isolates. Prevotella spp. were also shown, for the first time, to express resistance nodulation division (RND)-type efflux pumps.

CONCLUSIONS: This study has demonstrated that Prevotella isolated from various sources harbour a common pool of resistance genes and possess RND-type efflux pumps, which may contribute to tetracycline resistance. The findings indicate that antibiotic resistance is common in Prevotella spp., but the genotypic traits investigated do not reflect phenotypic antibiotic resistance in every instance.

Discrete wavelet transform de-noising in eukaryotic gene splicing

This paper compares the most common digital signal processing methods of exon prediction in eukaryotes, and also proposes a technique for noise suppression in exon prediction. The specimen used here which has relevance in medical research, has been taken from the public genomic database - GenBank.Here exon prediction has been done using the digital signal processing methods viz. binary method, EIIP (electron-ion interaction psuedopotential) method and filter methods. Under filter method two filter designs, and two approaches using these two designs have been tried. The discrete wavelet transform has been used for de-noising of the exon plots.Results of exon prediction based on the methods mentioned above, which give values closest to the ones found in the NCBI database are given here. The exon plot de-noised using discrete wavelet transform is also given.Alterations to the proven methods as done by the authors, improves performance of exon prediction algorithms. Also it has been proven that the discrete wavelet transform is an effective tool for de-noising which can be used with exon prediction algorithms