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.
K. P. Mohanakumar, Ph.D.
Chief Scientist, Cell Biology & Physiology Division, Indian Institute of Chemical Biology, Kolkata
Neuroprotective and neurodestructive effects of Ayurvedic drug constituents: Parkinson’s disease
The present study reports the good and the bad entities in an Indian traditional medicine used for treating Parkinson’s disease (PD). A prospective clinical trial on the effectiveness of Ayurvedic medication in a population of PD patients revealed significant benefits, which has been attributed to L-DOPA present in the herbs [1]. Later studies revealed better benefits with one of the herbs alone, compared to pure L-DOPA in a clinical trial conducted in UK [2], and in several studies conducted on animal models of PD in independent laboratories world over [3-5]. We have adapted strategies to segregate molecules from the herb, and then carefully removed L-DOPA contained therein, and tested each of these sub-fractions for anti-PD activity in 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine, rotenone and 6-hydroxydopamine -induced parkinsonian animal models, and transgenic mitochondrial cybrids. We report here two classes of molecules contained in the herb, one of which possessed severe pro-parkinsonian (phenolic amine derivatives) and the other having excellent anti-parkinsonian potential (substituted tetrahydroisoquinoline derivatives). The former has been shown to cause severe dopamine depletion in the striatum of rodents, when administered acutely or chronically. It also caused significant behavioral aberrations, leading to anxiety and depression [6]. The latter class of molecules administered in PD animal model [7], caused reversal of behavioral dysfunctions and significant attenuation of striatal dopamine loss. These effects were comparable or better than the effects of the anti-PD drugs, selegiline or L-DOPA. The mechanism of action of the molecule has been found to be novel, at the postsynaptic receptor signaling level, as well as cellular α-synuclein oligomerization and specifically at mitochondria. The molecule helped in maintaining mitochondrial ETC complex activity and stabilized cellular respiration, and mitochondrial fusion-fission machinery with specific effect on the dynamin related protein 1. Although there existed significant medical benefits that could be derived to patients due to the synergistic actions of several molecules present in a traditional preparation, accumulated data in our hands suggest complicated mechanisms of actions of Ayurvedic medication. Our results also provide great hope for extracting, synthesizing and optimizing the activity of anti-parkinsonian molecules present in traditional Ayurvedic herbs, and for designing novel drugs with novel mechanisms of action.
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- Katzenschlager R, Evans A, Manson A, Patsalos PN, Ratnaraj N, Watt H, Timmermann L, Van der Giessen R, Lees AJ. J Neurol Neurosurg Psychiatry.75, 1672-7, 2004.
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- Kasture S, Pontis S, Pinna A, Schintu N, Spina L, Longoni R, Simola N, Ballero M, Morelli M. Neurotox Res. 15, 111-22, 2009.
- Lieu CA, Kunselman AR, Manyam BV, Venkiteswaran K, Subramanian T. Parkinsonism Relat Disord.16, 458-65, 2010.
- T Sengupta and KP Mohanakumar, Neurochem Int. 57, 637-46, 2010.
- T Sengupta, J Vinayagam, N Nagashayana, B Gowda, P Jaisankar and KP Mohanakumar, Neurochem Res 36, 177-86, 2011
Karmeshu, Ph.D.
Dean & Professor, School of Computer & Systems Sciences & School of Computational & Integrative Sciences, Jawaharlal Nehru University, India.
Interspike Interval Distribution of Neuronal Model with distributed delay: Emergence of unimodal, bimodal and Power law
The study of interspike interval distribution of spiking neurons is a key issue in the field of computational neuroscience. A wide range of spiking patterns display unimodal, bimodal ISI patterns including power law behavior. A challenging problem is to understand the biophysical mechanism which can generate the empirically observed patterns. A neuronal model with distributed delay (NMDD) is proposed and is formulated as an integro-stochastic differential equation which corresponds to a non-markovian process. The widely studied IF and LIF models become special cases of this model. The NMDD brings out some interesting features when excitatory rates are close to inhibitory rates rendering the drift close to zero. It is interesting that NMDD model with gamma type memory kernel can also account for bimodal ISI pattern. The mean delay of the memory kernels plays a significant role in bringing out the transition from unimodal to bimodal ISI distribution. It is interesting to note that when a collection of neurons group together and fire together, the ISI distribution exhibits power law.
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.