Integrated RNA extraction and RT-PCR for semi-quantitative gene expression studies on a microfluidic device

This paper describes the development of a microfluidic methodology, using RNA extraction and reverse transcription PCR, for investigating expression levels of cytochrome P450 genes. Cytochrome P450 enzymes are involved in the metabolism of xenobiotics, including many commonly prescribed drugs, therefore information on their expression is useful in both pharmaceutical and clinical settings. RNA extraction, from rat liver tissue or primary rat hepatocytes, was performed using a silica-based solid-phase extraction technique. Following elution of the purified RNA, amplification of target sequences for the housekeeping gene, glyceraldehyde-3-phosphate dehydrogenase (GAPDH) and the cytochrome P450 gene CYP1A2, was carried out using a one-step reverse transcription PCR. Once the microfluidic methodology had been optimized, analysis of control and 3-methylcholanthrene-induced primary rat hepatocytes were used to evaluate the system. As expected, GAPDH was consistently expressed, whereas CYP1A2 levels were found to be raised in the drug-treated samples. The proposed system offers an initial platform for development of both rapid throughput analyzers for pharmaceutical drug screening and point-of-care diagnostic tests to aid provision of drug regimens, which can be tailor-made to the individual patient.

Breast cancer prognosis risk estimation using integrated gene expression and clinical data

Novel prognostic markers are needed so newly diagnosed breast cancer patients do not undergo any unnecessary therapy. Various microarray gene expression datasets based studies have generated gene signatures to predict the prognosis outcomes, while ignoring the large amount of information contained in established clinical markers. Nevertheless, small sample sizes in individual microarray datasets remain a bottleneck in generating robust gene signatures that show limited predictive power. The aim of this study is to achieve high classification accuracy for the good prognosis group and then achieve high classification accuracy for the poor prognosis group.

Sub-lethal UV-C radiation induces callose, hydrogen peroxide and defence-related gene expression in Arabidopsis thaliana

Exposure of plants to UV-C irradiation induces gene expression and cellular responses that are commonly associated with wounding and pathogen defence, and in some cases can lead to increased resistance against pathogen infection. We examined, at a physiological, molecular and biochemical level, the effects of and responses to, sub-lethal UV-C exposure on Arabidopsis plants when irradiated with increasing dosages of UV-C radiation. Following UV-C exposure plants had reduced leaf areas over time, with the severity of reduction increasing with dosage. Severe morphological changes that included leaf glazing, bronzing and curling were found to occur in plants treated with the 1000 J·m(-2) dosage. Extensive damage to the mesophyll was observed, and cell death occurred in both a dosage- and time-dependent manner. Analysis of H2 O2 activity and the pathogen defence marker genes PR1 and PDF1.2 demonstrated induction of these defence-related responses at each UV-C dosage tested. Interestingly, in response to UV-C irradiation the production of callose (β-1,3-glucan) was identified at all dosages examined. Together, these results show plant responses to UV-C irradiation at much lower doses than have previously been reported, and that there is potential for the use of UV-C as an inducer of plant defence.

Fatty acid-specific alterations in leptin, PPARα, and CPT-1 gene expression in the rainbow trout

It is known that fatty acids (FA) regulate lipid metabolism by modulating the expression of numerous genes. In order to gain a better understanding of the effect of individual FA on lipid metabolism related genes in rainbow trout (Oncorhynchus mykiss), an in vitro time-course study was implemented where twelve individual FA (butyric 4:0; caprylic 8:0; palmitic (PAM) 16:0; stearic (STA) 18:0; palmitoleic16:1n-7; oleic 18:1n-9; 11-cis-eicosenoic 20:1n-9; linoleic (LNA) 18:2n-6; α-linolenic (ALA) 18:3n-3; eicosapentenoic (EPA) 20:5n-3; docosahexaenoic (DHA) 22:6n-3; arachidonic (ARA) 20:4n-6) were incubated in rainbow trout liver slices. The effect of FA administration over time was evaluated on the expression of leptin, PPARα and CPT-1 (lipid oxidative related genes). Leptin mRNA expression was down regulated by saturated fatty acids (SFA) and LNA, and was up regulated by monounsaturated fatty acids (MUFA) and long chain PUFA, whilst STA and ALA had no effect. PPARα and CPT-1mRNA expression were up regulated by SFA, MUFA, ALA, ARA and DHA; and down regulated by LNA and EPA. These results suggest that there are individual and specific FA induced modifications of leptin, PPARα and CPT-1 gene expression in rainbow trout, and it is envisaged that such results may provide highly valuable information for future practical applications in fish nutrition.

Postexercise muscle cooling enhances gene expression of PGC-1α

This study aimed to investigate the influence of localized muscle cooling on postexercise vascular, metabolic, and mitochondrial-related gene expression.

IPA: Integrated predictive gene signature from gene expression based breast cancer patient samples

Background: Novel predictive markers are needed to accurately diagnose the breast cancer patients so they do not need to undergo any unnecessary aggressive therapies. Various gene expression studies based predictive gene signatureshave generated in the recent past to predict the binary estrogen-receptor subclass or to predict the therapy response subclass. However, the existing algorithms comes with many limitations, including low predictive performances over multiple cohorts of patients and non-significant or limited biological roles associated with thepredictive gene signatures. Therefore, the aim of this study is to develop novel predictive markers with improved performances.Methods: We propose a novel prediction algorithm called IPA to construct a predictive gene signature for performing multiple prediction tasks of predicting estrogen-receptor based binary subclass and predicting chemotherapy response (neoadjuvantly) based binary subclass. The constructed gene signature with considering multiple classification techniques was used to evaluate the algorithm performance on multiple cohorts of breast cancer patients.Results: The evaluation on multiple validation cohorts demonstrated that proposed algorithm achieved stable and high performance to perform prediction tasks, with consideration given to any classification techniques. We show that the predictive gene signature of our proposed algorithm reflects the mechanisms underlying the estrogen-receptors or response to therapy with significant greater biological interpretations, compared with the other existing algorithm.

Hidden Markov models for cancer classification using gene expression profiles

This paper introduces an approach to cancer classification through gene expression profiles by designing supervised learning hidden Markov models (HMMs). Gene expression of each tumor type is modelled by an HMM, which maximizes the likelihood of the data. Prominent discriminant genes are selected by a novel method based on a modification of the analytic hierarchy process (AHP). Unlike conventional AHP, the modified AHP allows to process quantitative factors that are ranking outcomes of individual gene selection methods including t-test, entropy, receiver operating characteristic curve, Wilcoxon test and signal to noise ratio. The modified AHP aggregates ranking results of individual gene selection methods to form stable and robust gene subsets. Experimental results demonstrate the performance dominance of the HMM approach against six comparable classifiers. Results also show that gene subsets generated by modified AHP lead to greater accuracy and stability compared to competing gene selection methods, i.e. information gain, symmetrical uncertainty, Bhattacharyya distance, and ReliefF. The modified AHP improves the classification performance not only of the HMM but also of all other classifiers. Accordingly, the proposed combination between the modified AHP and HMM is a powerful tool for cancer classification and useful as a real clinical decision support system for medical practitioners.

Cytokine expression and secretion by skeletal muscle cells: regulatory mechanisms and exercise effects

Cytokines are important mediators of various aspects of health and disease, including appetite, glucose and lipid metabolism, insulin sensitivity, skeletal muscle hypertrophy and atrophy. Over the past decade or so, considerable attention has focused on the potential for regular exercise to counteract a range of disease states by modulating cytokine production. Exercise stimulates moderate to large increases in the circulating concentrations of interleukin (IL)-6, IL-8, IL- 10, IL-1 receptor antagonist, granulocyte-colony stimulating factor, and smaller increases in tumor necrosis factor-α, monocyte chemotactic protein-1, IL-1β, brain-derived neurotrophic factor, IL-12p35/p40 and IL-15. Although many of these cytokines are also expressed in skeletal muscle, not all are released from skeletal muscle into the circulation during exercise. Conversely, some cytokines that are present in the circulation are not expressed in skeletal muscle after exercise. The reasons for these discrepant cytokine responses to exercise are unclear. In this review, we address these uncertainties by summarizing the capacity of skeletal muscle cells to produce cytokines, analyzing other potential cellular sources of circulating cytokines during exercise, and discussing the soluble factors and intracellular signaling pathways that regulate cytokine synthesis (e.g., RNA-binding proteins, microRNAs, suppressor of cytokine signaling proteins, soluble receptors).

Nematicidal activity of Solanum nigrum and S. sisymbriifolium extracts against the root-lesion nematode Pratylenchus goodeyi and its effects on infection and gene expression

The control of Pratylenchus goodeyi a common nematode parasite of banana crop in Madeira Island can benefit from searching for natural nematicides through plants extracts. With this aim we submitted Solanum nigrum and S. sisymbriifolium dried plants to a sequential extraction in the solvent sequence of dichloromethane, acetone, ethanol and water, and to na aqueous extraction of the fresh and dried plants. Analyses with the extracts at several concentrations were used to assess mobility and mortality on P. goodeyi. Results showed that the water extract and aqueous extracts from both plants at a concentration of 10 mg/mL affected nematode mobility and caused mortality but the acetone extract from S. nigrum was the most efficient, causing 100% mortality whereas dichloromethane had no effect on P. goodeyi. Determination of the lipophilic and phenolic compounds present in the two most effective Solanum extracts (acetone and water) and in dichloromethane extract revealed that some of these compounds had nematicidal activity. S. nigrum acetone extract (10 mg/mL) was used to find out the nematicidal potential following the effect at gene expression level and nematode behaviour. Genes coding for calreticulin and beta-1,4- endoglucanase related to parasitism and translocon-associated protein putatively connected to stress were obtained and its relative expression assessed in nematodes exposed to the extract. Results revealed that expression of Pg-CRT decreased showing to influence the infection, Pg-ENG remained steady and Pg-TRAPδ was induced over time exposure. Biological assays showed that P. goodeyi mobility and ability to infect the banana roots were affected as a decrease in the number of nematodes that reached the roots was obtained with the increased exposure time to the extract being implicated in the infection success. The information obtained from this thesis showed that S. nigrum has potential to be used for the development of a new control strategy against plant-parasitic nematodes.