S. Ramaswamy, Ph.D.
CEO of c-CAMP, Dean, inStem, NCBS, Bangalore, India
Discovery, engineering and applications of Blue Fish Protein with Red Fluorescence
Swagatha Ghosh, Chi-Li Yu, Daniel Ferraro, Sai Sudha, Wayne Schaefer, David T Gibson and S. Ramaswamy
Fluorescent proteins and their applications have revolutionized our understanding of biology significantly. In spite of several years since the discovery of the classic GFP, proteins of this class are used as the standard flag bearers. We have recently discovered a protein from the fish Sanders vitrius that shows interesting fluorescent properties – including a 280 nm stoke shift and infrared emission. The crystal structure of the wild type protein shows that it is a tetramer. We have engineered mutations to make a monomer with very similar fluorescent properties. We have used this protein for tissue imaging as well as for in cell-fluorescence successfully
K. Satyamoorthy, Ph.D.
Director, Life Sciences Centre, Manipal University, India
Epigenetic Changes due to DNA Methylation in Human Epithelial Tumors
Extensive global hypomethylation in the genome and hypermthylation of selective tumor specific suppressor genes appears to be a hallmark of human cancers. Data suggests that hypermethylation of promoter region in genes is more closely related to subsequent gene expression; contrary to gene-body DNA methylation. The intricate balance between these two may contribute to the progressive process of development, differentiation and carcinogenesis. Epigenetic changes encompass, apart from DNA methylation, chromatin modifications through post-translational changes in histones and control by miRNAs. At the genome level, effects from these are compounded by copy number variations (CNVs) which may ultimately influence protein functions. From clinical perspective, changes in DNA methylation occur very early which are reversible and are influenced by environmental factors. Therefore, these can be potential resource for identifying therapeutic targets as well as biomarkers for early screening of cancer. Our current efforts in profiling genome wide DNA methylation changes in oral, cervical and breast cancers through DNA methylation microarray analysis has revealed number of alterations critical for survival, progression and metastatic behavior of tumors. Bioinformatics and functional analysis revealed several key regulatory molecules controlled by DNA methylation and suggests that DNA methylation changes in several CpG islands appear to co-segregate in the regions of miRNAs as well as in the CNVs. We have validated the signatures for methylation of CpG islands through bisufite sequencing for essential genes in clinical samples and have undertaken transcriptional and functional analysis in tumor cell lines. These results will be presented.
Aditya Murthy, Ph.D.
Associate Professor, Centre For Neuroscience, Indian Institute of Science, Bangalore, India
Since Karl Lashley’s seminal work on the formulation of serial order, numerous models assume simultaneous representation of competitive elements of a sequence, to account for serial order effects in different types of behavior like typing, speech, etc. Such models follow two basic assumptions: (1) more than one plan representation can be simultaneously active in a planning layer; (2) the most active plan is chosen in another layer called the competitive choice layer. Using the oculomotor system I will describe behavioral and neurophysiological experiments that tests the two critical predictions of such queuing models, providing evidence that basal ganglia in monkeys and humans instantiate a form of queuing that transforms parallel movement representations into more serial representations, allowing for the expression of sequential saccadic eye movements.
Michelle Hermiston, MD, Ph.D.
Assistant Professor, Department of Pediatrics University of California San Francisco, USA
Interrogating Signaling Networks at the Single Cell Level In Primary Human Patient Samples
Multiparameter phosphoflow cytometry is a highly sensitive proteomic approach that enables monitoring of biochemical perturbations at the single cell level. By combining antisera to cell surface markers and key intracellular proteins, perturbations in signaling networks, cell survival and apoptosis mediators, cell cycle regulators, and/or modulators of other cellular processes can be analyzed in a highly reproducible and sensitive manner in the basal state and in response to stimulation or drug treatment. Advantages of this approach include the ability to identify the biochemical consequences of genetic and/or epigenetic changes in small numbers of cells, to map potential interplay between various signaling networks simultaneously in a single cell, and to interrogate potential mechanisms of drug resistance or response in a primary patient sample. Application of this technology to patients with acute lymphoblastic leukemia or the autoimmune disease systemic lupus erythematosus (SLE) will be discussed.
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.