Biotechnology Research at Amrita Vishwa Vidyapeetham

BACKGROUND: Dysregulation of miRNAs is associated with the development of non-small cell lung cancer (NSCLC). It is imperative to study the dysregulation of miRNAs by cigarette smoke which will affect their targets, either leading to the overexpression of oncoproteins or downregulation of tumor suppressor proteins.

OBJECTIVE AND METHODS: In this study, we carried out miRNA sequencing and SILAC-based proteomic analysis of H358 cells chronically exposed to cigarette smoke condensate. Using bioinformatics analysis, we mapped the dysregulated miRNAs to differentially expressed target proteins identified in our data. Gene ontology-based enrichment and pathway analysis was performed using the deregulated targets to study the role of cigarette smoke-mediated miRNA dysregulation in NSCLC cell line.

RESULTS: miRNA sequencing resulted in the identification of 208 miRNAs, of which 6 miRNAs were found to be significantly dysregulated (2 fold, Log Base 2; p-value ≤ 0.05) in H358-Smoke cells. Proteomic analysis of the smoke exposed cells compared to the untreated parental cells resulted in the quantification of 2,610 proteins, of which 690 proteins were found to be differentially expressed (fold change ≥ 2). Gene ontology based analysis of target proteins revealed enrichment of proteins driving metabolism and a decrease in expression of proteins associated with immune response in the cells exposed to cigarette smoke. Pathway study using Ingenuity Pathway Analysis (IPA) revealed activation of NRF2-mediated oxidative stress response and actin-cytoskeleton signaling, and repression of protein kinase A signaling in H358-Smoke cells. We also identified 5 novel miRNAs in H358-Smoke cells using unassigned reads of small RNA-Seq dataset.

CONCLUSION: In summary, this study indicates that chronic exposure to cigarette smoke leads to widespread dysregulation of miRNAs and their targets, resulting in signaling aberrations in NSCLC cell line. The miRNAs and their targets identified in the study need to be further investigated to explore their role as potential therapeutic targets and/or molecular markers in NSCLC especially in smokers.

Background: The cerebellar granular layer input stage of cerebellum receives information from tactile and sensory regions of the body. The somatosensory activity in the cerebellar granular layer corresponds to sensory and tactile input has been observed by recording Local Field Potential (LFP) from the Crus-IIa regions of cerebellum in brain slices and in anesthetized animals. Purpose: In this paper, a detailed biophysical model of Wistar rat cerebellum granular layer network model and LFP modelling schemas were used to simulate circuit’s evoked response. Methods: Point Source Approximation and Line Source Approximation were used to reconstruct the network LFP. The LFP mechanism in in vitro was validated in network model and generated the in vivo LFP using the same mechanism. Results: The network simulations distinctly displayed the Trigeminal and Cortical (TC) wave components generated by 2 independent bursts implicating the generation of TC waves by 2 independent granule neuron populations. Induced plasticity was simulated to estimate granule neuron activation related population responses. As a prediction, cerebellar dysfunction (ataxia) was also studied using the model. Dysfunction at individual neurons in the network was affected by the population response. Conclusion: Our present study utilizes available knowledge on known mechanisms in a single cell and associates network function to population responses.

Internet-enabled technologies for robotics education are gaining importance as online platforms facilitating and promoting skill training. Understanding the use and design of robotics is now introduced at university undergraduate levels, but in developing economies establishing usable hardware and software platforms face several challenges like cost, equipment etc. Remote labs help providing alternatives to some of the challenges. We developed an online laboratory for bioinspired robotics using a low-cost 6 degree-of-freedom robotic articulator with a neuro-inspired controller. Cerebellum-inspired neural network algorithm approximates forward and inverse kinematics for movement coordination. With over 210000 registered users, the remote lab has been perceived as an interactive online learning tool and a practice platform. Direct feedback from 60 students and 100 university teachers indicated that the remote laboratory motivated self-organized learning and was useful as teaching material to aid robotics skill education.