Aug
13
Tue
2013
Plenary Address: Making sense of pathogen sensors of Innate Immunity: Utility of their ligands as antiviral agens and adjuvants for vaccines. @ Acharya Hall
Aug 13 @ 9:17 am – 9:55 am

SuryaprakashSuryaprakash Sambhara, DVM, Ph.D
Chief, Immunology Section, Influenza Division, CDC, Atlanta, USA


Making sense of pathogen sensors of Innate Immunity: Utility of their ligands as antiviral agents and adjuvants for vaccines.

Currently used antiviral agents act by inhibiting viral entry, replication, or release of viral progeny.  However, recent emergence of drug-resistant viruses has become a major public health concern as it is limiting our ability to prevent and treat viral diseases.  Furthermore, very few antiviral agents with novel modes of action are currently in development.  It is well established that the innate immune system is the first line of defense against invading pathogens.  The recognition of diverse pathogen-associated molecular patterns (PAMPs) is accomplished by several classes of pattern recognition receptors (PRRs) and the ligand/receptor interactions trigger an effective innate antiviral response.  In the past several years, remarkable progress has been made towards understanding both the structural and functional nature of PAMPs and PRRs.  As a result of their indispensable role in virus infection, these ligands have become potential pharmacological agents against viral infections.  Since their pathways of action are evolutionarily conserved, the likelihood of viruses developing resistance to PRR activation is diminished.  I will discuss the recent developments investigating the potential utility of the ligands of innate immune receptors as antiviral agents and molecular adjuvants for vaccines.

Suryaprakash (1) Suryaprakash (4) Suryaprakash-Nagaraja

Invited Talk: Applying Machine learning for Automated Identification of Patient Cohorts @ Sathyam Hall
Aug 13 @ 2:40 pm – 3:05 pm

SriSairamSrisairam 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: Inflammation Induced Epigenetic Changes in Endothelial Cells: Role in Vascular Insulin Resistance @ Acharya Hall
Aug 13 @ 6:39 pm – 6:49 pm
Delegate Talk: Inflammation Induced Epigenetic Changes in Endothelial Cells: Role in Vascular Insulin Resistance @ Acharya Hall | Vallikavu | Kerala | India

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.