Biotechnology Research at Amrita Vishwa Vidyapeetham

Nontuberculous mycobacterial (NTM) species present a major challenge for global health with serious clinical manifestations ranging from pulmonary to skin infections. Multiomics research and its applications toward clinical microbial proteogenomics offer veritable potentials in this context. For example, the Mycobacterium abscessus, a highly pathogenic NTM, causes bronchopulmonary infection and chronic pulmonary disease. The rough variant of the M. abscessus UC22 strain is extremely virulent and causes lung upper lobe fibrocavitary disease. Although several whole-genome next-generation sequencing studies have characterized the genes in the smooth variant of M. abscessus, a reference genome sequence for the rough variant was generated only recently and calls for further clinical applications. We carried out whole-genome sequencing and proteomic analysis for a clinical isolate of M. abscessus UC22 strain obtained from a pulmonary tuberculosis patient. We identified 5506 single-nucleotide variations (SNVs), 63 insertions, and 76 deletions compared with the reference genome. Using a high-resolution LC-MS/MS-based approach (liquid chromatography tandem mass spectrometry), we obtained protein coding evidence for 3601 proteins, representing 71% of the total predicted genes in this genome. Application of proteogenomic approach further revealed seven novel protein-coding genes and enabled refinement of six computationally derived gene models. We also identified 30 variant peptides corresponding to 16 SNVs known to be associated with drug resistance. These new observations offer promise for clinical applications of microbial proteogenomics and next-generation sequencing, and provide a resource for future global health applications for NTM species.

To understand brain activity relating neurons to circuits to learning and behavior, we explored a bottom-up computational reconstruction of population signals arising from cerebellum granular layer. As a first implementation, using bio-realistic computational models of cerebellum granule cell, in vivo spike train patterns were computed and then translated into functional Magnetic Resonance Imaging, Blood Oxygen-Level Dependent (BOLD) signals. The BOLD response was generated from averaged activity arising from center-surround organization modeled by using excitatory-inhibitory ratios related to experimental data. The averaged responses were converted to BOLD signals using the balloon and modified Windkessel models. Although both models generated BOLD responses corresponding to neural activity, the temporal mismatch was attributed to the response by the delayed compliance parameter in the Windkessel model. The modeling suggests that experimental variability observed in the cerebellar micro-zones could be related to compliance chances, activation patterns and number of neurons. Although detailed neuro-vasculature information was not modeled, the advantage in this methodology is that cerebellar cortex may allow seemingly linear transformations of underlying spiking that could be then used to validate network reconstructions.