Bharat B. Chattoo, Ph.D.
Professor, Faculty of Science M.S.University of Baroda, India
Biology of plant infection by Magnaporthe oryzae
The rice blast disease caused by the ascomycetous fungus Magnaporthe oryzae is a major constraint in rice production. Rice-M.oryzae is also emerging as a good model patho-system to investigate how the fungus invades and propagates within the host. Identification and characterisation of genes critical for fungal pathogenesis provides opportunities to explore their use as possible targets for development of strategies for combating fungal infection and to better understand the complex process of host-pathogen interaction.
We have used insertional mutagenesis and RNAi based approaches to identify pathogenesis related genes in this fungus. A large number of mutants were isolated using Agrobacterium tumefaciens mediated transformation (ATMT). Characterisation of several interesting mutants is in progress. We have identified a novel gene, MGA1, required for the development of appressoria. The mutant mga1 is unable to infect and is impaired in glycogen and lipid mobilization required for appressorium development. The glycerol content in the mycelia of the mutant was significantly lower as compared to wild type and it was unable to tolerate hyperosmotic stress. A novel ABC transporter was identified in this fungus. The abc4 mutant did not form functional appressoria, was non-pathogenic and showed increased sensitivity to certain antifungal molecules implying the role of ABC4 in multidrug resistance (MDR). Another mutant MoSUMO (MGG_05737) was isolated using a Split Marker technique; the mutant showed defects in growth, germination and infection. Immuno-fluorescence microscopy revealed that MoSumo is localized to septa in mycelia and nucleus as well as septa in spores. Two Dimensional Gel Electrophoresis showed differences in patterns of protein expression between Wild Type B157 and MoΔSumo mutant. We also isolated and charaterised mutants in MoALR2 (MGG_08843) and MoMNR2 (MGG_09884). Our results indicate that both MoALR2 and MoMNR2 are Mg2+ transporters, and the reduction in the levels of CorA transporters caused defects in surface hydrophobicity, cell wall stress tolerance, sporulation, appressorium formation and infection are mediated through changes in the key signaling cascades in the knock-down transformants. (Work supported by the Department of Biotechnology, Government of India)
Lalitha Subramanian, Ph.D.
Chief Scientific Officer & VP, Services at Scienomics, USA
Nanoscale Simulations – Tackling Form and Formulation Challenges in Drug Development and Drug Delivery
Lalitha Subramanian, Dora Spyriouni, Andreas Bick, Sabine Schweizer, and Xenophon Krokidis Scienomics
The discovery of a compound which is potent in activity against a target is a major milestone in Pharmaceutical and Biotech industry. However, a potent compound is only effective as a therapeutic agent when it can be administered such that the optimal quantity is transported to the site of action at an optimal rate. The active pharmaceutical ingredient (API) has to be tested for its physicochemical properties before the appropriate dosage form and formulation can be designed. Some of the commonly evaluated parameters are crystal forms and polymorphs, solubility, dissolution behavior, stability, partition coefficient, water sorption behavior, surface properties, particle size and shape, etc. Pharmaceutical development teams face the challenge of quickly and efficiently determining a number of properties with small quantities of the expensive candidate compounds. Recently the trend has been to screen these properties as early as possible and often the candidate compounds are not available in sufficient quantities. Increasingly, these teams are leveraging nanoscale simulations similar to those employed by drug discovery teams for several decades. Nanoscale simulations are used to predict the behavior using very little experimental data and only if this is promising further experiments are done. Another aspect where nanoscale simulations are being used in drug development and drug delivery is to get insights into the behavior of the system so that process failures can be remediated and formulation performance can be improved. Thus, the predictive screening and the in-depth understanding leads to experimental efficiency resulting in far-reaching business impacts.
With specific examples, this talk will focus on the different types of nanoscale simulations used to predict properties of the API in excipients and also provide insight into system behavior as a function of shelf life, temperature, mechanical stress, etc.
Srisairam Achuthan, Ph.D.
Senior Scientific Programmer, Research Informatics Division, Department of Information Sciences, City of Hope, CA, USA
Applying Machine learning for Automated Identification of Patient Cohorts
Srisairam Achuthan, Mike Chang, Ajay Shah, Joyce Niland
Patient cohorts for a clinical study are typically identified based on specific selection criteria. In most cases considerable time and effort are spent in finding the most relevant criteria that could potentially lead to a successful study. For complex diseases, this process can be more difficult and error prone since relevant features may not be easily identifiable. Additionally, the information captured in clinical notes is in non-coded text format. Our goal is to discover patterns within the coded and non-coded fields and thereby reveal complex relationships between clinical characteristics across different patients that would be difficult to accomplish manually. Towards this, we have applied machine learning techniques such as artificial neural networks and decision trees to determine patients sharing similar characteristics from available medical records. For this proof of concept study, we used coded and non-coded (i.e., clinical notes) patient data from a clinical database. Coded clinical information such as diagnoses, labs, medications and demographics recorded within the database were pooled together with non-coded information from clinical notes including, smoking status, life style (active / inactive) status derived from clinical notes. The non-coded textual information was identified and interpreted using a Natural Language Processing (NLP) tool I2E from Linguamatics.
![Delegate Talk: A Mobile Phone Application for Daily Physical Activity Monitoring in Chronic Obstructive Pulmonary Disease @ Amriteshwari Hall | Vallikavu | Kerala | India](http://www.amritabioquest.org/conference/2013/wp-content/uploads/sites/2/2015/02/biomed.jpg)
H S M Kort, J-W J Lammers, S N W Vorrink, T Troosters
Introduction
Chronic Obstructive Pulmonary Disease (COPD) is a disabling airway disease with variable extrapulmonary effects that may contribute to disease severity in individual patients (Rabe et al. 2007). The world health organization predicts that COPD will become the third leading cause of death worldwide by 2030. Patients with COPD demonstrate reduced levels of spontaneous daily physical activity (DPA) compared with healthy controls (Pitta et al. 2005). This results in a higher risk of hospital admission and shorter survival (Pitta et al. 2006). Pulmonary rehabilitation can help to improve the DPA level, however, obtained benefits decline after 1–2 years (Foglio et al. 2007).
Purpose
In order to maintain DPA in COPD patients after rehabilitation, we developed a mobile phone application. This application measures DPA as steps per day, measured by the accelerometer of the smartphone, and shows the information to the patient via the display of the mobile phone. A physiotherapist can monitor the patient via a secure website where DPA measurements are visible for all patients. Here, DPA goals can be adjusted and text messages sent.
Method
Three pilot studies were performed with healthy students and COPD patients to test the application for usability, user friendliness and reliability with questionnaires and focus groups. Subjects also wore a validated accelerometer. For the Randomized Controlled Trial (RCT) 140 COPD patients will be recruited in Dutch physiotherapy practises. They will be randomised in an intervention group that receives the smartphone for 6 months and a control group. Measurements include lungfunction, dyspnea, and exercise capacity and are held at 0, 3, 6 and 12 months.
Results and Discussion
The application was found to be useful, easy to learn and use. Subjects had no problems with health care professionals seeing information on their physical activity performance. They do find it important to be able to determine who can see the information. Correlations between the accelerometer and the measurements on DPA of the smartphone for steps per hour were 0.69 and 0.70 for pilot studies 1 (students) and 2 (COPD patients) respectively. The version of the application in pilot study 3 contained an error, which made correlations with the accelerometer unusable. The RCT study is now being executed.
![Delegate Talk: Inflammation Induced Epigenetic Changes in Endothelial Cells: Role in Vascular Insulin Resistance @ Acharya Hall | Vallikavu | Kerala | India](http://www.amritabioquest.org/conference/2013/wp-content/uploads/sites/2/2015/02/gtm.jpg)
Aswath Balakrishnan, Kapaettu Satyamoorthy and Manjunath B Joshi
Introduction
Insulin resistance is a hall mark of metabolic disorders such as diabetes. Reduced insulin response in vasculature leads to disruption of IR/Akt/eNOS signaling pathway resulting in vasoconstriction and subsequently to cardiovascular diseases. Recent studies have demonstrated that inflammatory regulator interleukin-6 (IL-6), as one of the potential mediators that can link chronic inflammation with insulin resistance. Accumulating evidences suggest a significant role of epigenetic mechanisms such as DNA methylation in progression of metabolic disorders. Hence the present study aimed to understand the role of epigenetic mechanisms involved during IL-6 induced vascular insulin resistance and its consequences in cardiovascular diseases.
Materials and Methods
Human umbilical vein endothelial cells (HUVEC) and Human dermal microvascular endothelial cells (HDMEC) were used for this study. Endothelial cells were treated in presence or absence of IL-6 (20ng/ml) for 36 hours and followed by insulin (100nM) stimulation for 15 minutes. Using immunoblotting, cell lysates were stained for phosphor- and total Akt levels to measure insulin resistance. To investigate changes in DNA methylation, cells were treated with or without neutrophil conditioned medium (NCM) as a physiological source of inflammation or IL-6 (at various concentrations) for 36 hours. Genomic DNA was processed for HPLC analysis for methyl cytosine content and cell lysates were analyzed for DNMT1 (DNA (cytosine-5)-methyltransferase 1) and DNMT3A (DNA (cytosine-5)-methyltransferase 3A) levels using immunoblotting.
Results
Endothelial cells stimulated with insulin exhibited an increase in phosphorylation of Aktser 473 in serum free conditions but such insulin response was not observed in cells treated with IL-6, suggesting chronic exposure of endothelial cells to IL-6 leads to insulin resistance. HPLC analysis for global DNA methylation resulted in decreased levels of 5-methyl cytosine in cells treated with pro-inflammatory molecules (both by NCM and IL-6) as compared to untreated controls. Subsequently, analysis in cells treated with IL-6 showed a significant decrease in DNMT1 levels but not in DNMT3A. Other pro-inflammatory marker such as TNF-α did not exhibit such changes.
Conclusion
Our study suggests: a) Chronic treatment of endothelial cells with IL-6 results in insulin resistance b) Neutrophil conditioned medium and IL-6 decreases methyl cytosine levels c) DNMT1 but not DNMT3a levels are reduced in cells treated with IL-6.