Kothandaraman Narasimhan

Enhancement of Plant-Microbe Interactions Using a Rhizosphere Metabolomics-Driven Approach and Its Application in the Removal of Polychlorinated Biphenyls

Persistent organic pollutants, such as polychlorinated biphenyls (PCBs), are a global problem. We demonstrate enhanced depletion of PCBs using root-associated microbes, which can use plant secondary metabolites, such as phenylpropanoids. Using a “rhizosphere metabolomics” approach, we show that phenylpropanoids constitute 84% of the secondary metabolites exuded from Arabidopsis roots. Phenylpropanoid-utilizing microbes are more competitive and are able to grow at least 100-fold better than their auxotrophic mutants on roots of plants that are able to synthesize or overproduce phenylpropanoids, such as flavonoids. Better colonization of the phenylpropanoid-utilizing strain in a gnotobiotic system on the roots of flavonoid-producing plants leads to almost 90% removal of PCBs in a 28-d period. Our work complements previous approaches to engineer soil microbial populations based on opines produced by transgenic plants and used by microbes carrying opine metabolism genes. The current approach based on plant natural products can be applied to contaminated soils with pre-existing vegetation. This strategy is also likely to be applicable to improving the competitive abilities of biocontrol and biofertilization strains.

Metabolomics and its role in understanding cellular responses in plants

A natural shift is taking place in the approaches being adopted by plant scientists in response to the accessibility of systems-based technology platforms. Metabolomics is one such field, which involves a comprehensive non-biased analysis of metabolites in a given cell at a specific time. This review briefly introduces the emerging field and a range of analytical techniques that are most useful in metabolomics when combined with computational approaches in data analyses. Using cases from Arabidopsis and other selected plant systems, this review highlights how information can be integrated from metabolomics and other functional genomics platforms to obtain a global picture of plant cellular responses. We discuss how metabolomics is enabling large-scale and parallel interrogation of cell states under different stages of development and defined environmental conditions to uncover novel interactions among various pathways. Finally, we discuss selected applications of metabolomics.

Application of porous membrane protected micro-solid-phase-extraction combined with gas chromatography-mass spectrometry for the determination of estrogens in ovarian cyst fluid samples.

A cost effective and environmentally friendly extraction technique using porous membrane protected micro-solid phase extraction (μ-SPE) is described for the extraction of estrogens in cyst fluid samples obtained from cancer patients. A sorbent (ethylsilane (C2) modified silica) (20 mg) was packed in a porous polypropylene envelope (2 cm×1.5 cm) whose edges were heat sealed to secure the contents. The μ-SPE device was conditioned with acetone and placed in a stirred (1:5) diluted cyst fluid sample solution (10 mL) to extract estrogens for 60 min. After extraction, the analytes were desorbed and simultaneously derivatized with a 5:1 mixture of acetone and N,O-bis(trimethylsilyl)-trifluoroacetamide. The extract (2 μL) was analyzed by gas chromatography-mass spectrometry. Various extraction, desorption and derivatization conditions were optimized for μ-SPE. With this simple technique, low limits of detection of between 9 and 22 ng L(-1) and linear range from the detection limits up to 50 μg L(-1) were achieved. The optimized method was used to extract estrogens from cyst fluid samples obtained from patients with malignant and benign ovarian tumors.

Database for exploration of functional context of genes implicated in ovarian cancer

Ovarian cancer (OC) is becoming the most common gynecological cancer in developed countries and the most lethal gynecological malignancy. It is also the fifth leading cause of all cancer-related deaths in women. The identification of diagnostic biomarkers and development of early detection techniques for OC largely depends on the understanding of the complex functionality and regulation of genes involved in this disease. Unfortunately, information about these OC genes is scattered throughout the literature and various databases making extraction of relevant functional information a complex task. To reduce this problem, we have developed a database dedicated to OC genes to support exploration of functional characterization and analysis of biological processes related to OC. The database contains general information about OC genes, enriched with the results of transcription regulation sequence analysis and with relevant text mining to provide insights into associations of the OC genes with other genes, metabolites, pathways and nuclear proteins. Overall, it enables exploration of relevant information for OC genes from multiple angles, making it a unique resource for OC and will serve as a useful complement to the existing public resources for those interested in OC genetics. Access is free for academic and non-profit users and database can be accessed at http://apps.sanbi.ac.za/ddoc/.

Proteomic technologies for prenatal diagnostics: advances and challenges ahead

Proteomics-based identification of biomarkers for fetal abnormalities in maternal plasma, amniotic fluid and reproductive fluids has made significant progress in the past 5 years. This is attributed mainly to advances in various technology platforms associated with mass spectrometry-based techniques. As these techniques are highly sensitive and require only small quantities of body fluids, it is hoped that they will pave the way for the development of effective noninvasive approaches, without subjecting the developing fetus to the same degree of harm as current invasive procedures (e.g., amniocentesis). It is possible that these developments will include same-day analyses, thereby permitting rapid intervention when necessary. To date, a host of body fluids, such as maternal serum and plasma, amniotic fluid, cervical fluid, vaginal fluid, urine, saliva or fetal material, such as placental trophoblast, fetal membranes or cord blood, have been used successfully in the quest to develop markers for a number of pregnancy-related pathologies. In the current review update we focus on the emergence of proteomics as a major platform technology in studying various types of fetal conditions and developing markers for pregnancy-related disorders, such fetal aneuploidy, preterm birth, preeclampsia, intra-amniotic infection and fetal stress. Should the development of these markers be successful, then it is to be envisaged that proteomic approaches will become standard of care for a number of disease conditions associated with feto–maternal health.

Application of microwave-assisted micro-solid-phase extraction for determination of parabens in human ovarian cancer tissues

Parabens (alkyl esters of p-hydroxybenzoic acid) are widely used as preservatives in food, cosmetics and pharmaceutical products. However, weak estrogenicity of some parabens has been reported in several studies, which provided the impetus for this work. Here, a simple and efficient analytical method for quantifying parabens in cancer tissues has been developed. This technique involves the simultaneous use of microwave-assisted solvent extraction (MASE) and micro-solid phase extraction (μ-SPE), in tandem with high performance liquid chromatography (HPLC/UV) analysis for the determination of parabens. The pollutants studied included four parabens (methyl, ethyl, propyl and butyl parabens). Optimization of the experimental parameters for MASE and μ-SPE was performed. Good relative standard deviation (%RSD) ranged from 0.09 to 2.81% and high enrichment factors (27-314) were obtained. Coefficients of determination (r(2)) up to 0.9962 were obtained across a concentration range of 5.0-200ngg(-1). The method detection limits for parabens ranged from 0.005 to 0.0244ngg(-1). The procedure was initially tested on prawn samples to demonstrate its feasibility on a complex biological matrix. Preliminary studies on human ovarian cancer (OC) tissues showed presence of parabens. Higher levels of parabens were detected in malignant ovarian tumor tissues compared to benign tumor tissue samples.

Aurora-A expression, hormone receptor status and clinical outcome in hormone related cancers

Aims: We investigated the correlation between protein expression of Aurora‐A with hormone receptor expression and clinicopathological parameters in ovarian, breast and prostate cancer. Methods: Subcellular expression of Aurora‐A, and androgen receptor (AR), oestrogen receptor (ER) and progesterone receptor (PR) expression, were examined by immunohistochemistry in human tissue microarrays of the three cancer types and by Western blot in cancer cell lines and selected patient tissues. Results: Subgroups of all three cancer types exhibited both nuclear and cytoplasmic expression of Aurora‐A. Nuclear presence of Aurora‐A was observed in ER positive and negative breast cancer cell lines and tissues. Eighteen of the 126 (14%) tumour tissues that showed nuclear expression of Aurora‐A were strongly associated with ER and PR positive breast tumours (p = 0.001). Cytoplasmic expression of AR and Aurora‐A was strongly associated in prostate cancer tissues (45% versus 0, p = 0.015). Ovarian tumours (n = 45) with Aurora‐A nuclear expression had decreased patient survival (mean survival, 29.5 versus 106.7 months; p < 0.0005) and showed a significant association with recurrence‐free survival (mean survival 19.7 versus 95.9 months; p = 0.002). Conclusion: Association between nuclear Aurora‐A with hormone receptors in breast cancer and with poor clinical outcome in ovarian cancer suggests the significance of active Aurora‐A in disease initiation and progression.

Ovarian cancer proteomics: Many technologies one goal.

The last decade has seen major changes in the technologies used to identify markers for diagnosing cancer. This review focuses on recent developments on the evolving field of biomarker discovery, and validation techniques using proteomics platforms for ovarian cancer. It is possible now to diagnose various disease conditions using microliter quantities of body fluids. Currently the major developments were made in three distinct areas: (i) protein profiling, (ii) high‐throughput validation techniques, and (iii) solid and liquid phase protein microarray platforms for analyzing candidate markers across subclasses and stages of cancers. The recent addition to the long list of technologies is metabolomics using metabolite profiling and informatics‐based filtering of information for biomarker discovery of ovarian cancer. Emerging technologies need to address ways to eliminate the limitations posed by the complex dynamic nature of body fluids as well as ways to enrich low‐abundance tumor markers if they were to become a successful biomarker discovery tool. These new technologies hold significant promise in identifying more robust markers for ovarian cancer. Since the prevalence of this disease in the population is low, the test must have a high specificity.

In Silico discovery of transcription factors as potential diagnostic biomarkers of ovarian cancer

BackgroundOur study focuses on identifying potential biomarkers for diagnosis and early detection of ovarian cancer (OC) through the study of transcription regulation of genes affected by estrogen hormone.ResultsThe results are based on a set of 323 experimentally validated OC-associated genes compiled from several databases, and their subset controlled by estrogen. For these two gene sets we computationally determined transcription factors (TFs) that putatively regulate transcription initiation. We ranked these TFs based on the number of genes they are likely to control. In this way, we selected 17 top-ranked TFs as potential key regulators and thus possible biomarkers for a set of 323 OC-associated genes. For 77 estrogen controlled genes from this set we identified three unique TFs as potential biomarkers.ConclusionsWe introduced a new methodology to identify potential diagnostic biomarkers for OC. This report is the first bioinformatics study that explores multiple transcriptional regulators of OC-associated genes as potential diagnostic biomarkers in connection with estrogen responsiveness. We show that 64% of TF biomarkers identified in our study are validated based on real-time data from microarray expression studies. As an illustration, our method could identify CP2 that in combination with CA125 has been reported to be sensitive in diagnosing ovarian tumors.

Maternal serum protein profile and immune response protein subunits as markers for non-invasive prenatal diagnosis of trisomy 21, 18, and 13

To use proteomics to identify and characterize proteins in maternal serum from patients at high‐risk for fetal trisomy 21, trisomy 18, and trisomy 13 on the basis of ultrasound and maternal serum triple tests.