Research summary

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Read: JOYI - Journey of a Young Investigator@IndiaBioscience

IRINS: Indian Research Information Network System : https://tezu.irins.org/profile/116569

Main research areas

The focus area of my research has been ‘evolutionary systems biology’. Biological systems are highly complex, composed of densely interconnected elements, arranged in a hierarchical manner from the subcellular molecular levels (microscopic) to the whole organism and ecosystem level (macroscopic). Evolution is a complex process of cumulative changes over generations, which introduces diversity at every level of biological organisation, including species, individual organisms and molecules such as DNA and proteins. Integrative multidisciplinary approach is the best strategy for understanding such organisational and functional complexities present in biological systems. I have been using such integrative approaches to generate mechanistic and evolutionary understanding of genotype-phenotype relationships in multiple model organisms at multiple levels. 

Key words: Computational systems biology, Bioinformatics, Structural systems biology, Evolutionary systems biology, Next Generation Sequencing (NGS), Big data in biology and medicine, Biological networks

Current research areas:

• Systems medicine  

  • Next Generation Sequencing and Cancer Genomics

  • Stress interactions in plants

  • Role of non-coding mutations in genome evolution

Past research: 

1.    M.Sc. thesis (Evolutionary relationships among sequence-structure and function in distally related protein superfamilies): During my M.Sc. thesis (2004-2006), I worked in the group of Prof. Ramanathan Sowdhamini at the National Centre for Biological Sciences (NCBS) in Bangalore. My project was to understand the evolutionary relationships among protein sequence-structure and function.  During the course of evolution, protein domains undergo diverse modifications in sequence and structure to achieve versatility in function. Different molecular events, such as point mutations, gene duplications, gene fusions etc., are seen to contribute extensively to this diversity. Length differences between domains are introduced through insertions and deletions (indels) into pre-existing protein domains. Members of a given protein superfamily thus have a common evolutionary origin, with low but detectable sequence similarity. I have manually assessed the functional and structural advantages of length variations amongst homologous members of 64 length-deviant domain superfamilies from the SCOP (Structural Classification of Proteins) database.  Based on the initial findings of the manual inspection, an algorithm ‘CUSP’ was developed, to identify indel regions in protein domain superfamilies, in an automated manner. Further, the method was used to characterize the length, structural type and biochemical features of indels in related protein domains .  Significance: Indels of different lengths, even within a single protein domain superfamily could have structural and functional consequences that drive their selection. Systematic identification of such indels using benchmarked algorithm CUSP has significantly helped the researchers for remote homology detection, and computational modelling of protein structures.

2.    Post-M.Sc. research works

2.1.  Geno-Cluster: India's first indigenously developed bioinformatics software package: After completion of my M.Sc. degree (2006), I worked at Jalaja Technologies Pvt. Ltd. in Bangalore. I worked there as a part of the development team for the India's first Bioinformatics software package ‘Geno-Cluster’. This project was carried out under the ‘New Millennium Indian Technology Leadership Initiative (NMITLI)’ program of the Council of Scientific and Industrial Research (CSIR), Govt. of India; having technical collaboration with the Institute of Genomics and Integrative Biology (IGIB). I was part of the package called ‘PROTEOME CALKULATOR’. This algorithm provided a rapid method to compare all the proteins (proteome) of a species with proteomes of other species using a peptide library based machine learning and statistical biology approach. Significance:I have contributed to India's first indigenously developed bioinformatics software package, which could help to design new drug molecules and vaccines.

 2.2. Developing a novel active site alignment method in protein 3D structures: With a ‘Junior Research Fellowship’ (JRF), from the Department of Biotechnology (DBT), Government of India, I have joined the Bioinformatics Centre, at the University of Pune in 2006. I worked in the group of Prof. Indira Ghosh, on a project for developing a novel active site alignment method in protein 3D structures, in the finest atomistic levels.  Significance: The developed computational method has contributed towards the enhancement of efficient target selection, during computer aided drug discovery.

 2.3.  Analysing protein folds using protein contact networks : In July 2007, I have joined the Mathematical Modelling and Computational Biology Group, at the Centre for Cellular and Molecular Biology (CCMB), Hyderabad, headed by Prof. Somdatta Sinha. During this tenure, I have developed a simple mathematical approach for comparing protein three-dimensional (3D) structures using two-dimensional (2D) coarse-grained graph theoretical models, called ‘Protein Contact Networks’. I have shown that the native state protein structures can, be modelled, as coarse-grained networks of amino acid residues as ‘nodes’ and the inter-residue interactions as ‘links’. Irrespective of the size and type of the protein domains, the conserved contact patterns representing a typical protein fold could easily be identified using the network representation of protein structures and their 2D contact maps. Significance: This computationally simple, but efficient methodology has been widely used by the community to infer comparative evolutionary dynamics of conserved amino acid contact patterns, and folding kinetics of protein 3D domains. A recent review has mentioned our approach ‘Protein contact networks’ as an emerging paradigm in chemistry . 

3.   PhD works (Integrative systems approaches to study plant stress responses) I have joined the Norwegian University of Science & Technology, Norway as a PhD fellow in August 2009. I was part of the ‘ERA-NET MultiStress’ project, which was collaboratively carried out by seven universities from five different countries in Europe. The main aim of the project was to generate a ‘systems level understanding of multiple stress responses and adaptations in phenotypically diverged group of plant ecotypes’. This was the first of its kind large scale study in the world to use 5 single stresses and 6 combinations of stresses on 10 phenotypically distinct ecotypes, and 15 single or double knock out mutants of the model plant Arabidopsis thaliana, in a single homogeneous experiment [3]. As the core bioinformatics data analyst of this mega scale project, I have analysed the generated multi-omics datasets with integrative computational approaches, to generate following significant outcomes-  Significance: During the co-occurrence of multiple abiotic and biotic stresses in plants, the molecular responses to combined stresses could not be predicted from the single stress response signatures. Prominent intraspecific natural variations in stress response patterns among Arabidopsis thaliana ecotypes were identified, indicating local climate adaptation in plants via extensive transcriptional reprogramming. Such intraspecific natural variation has also stimulated a discussion within the community, regarding the selection of model plant ecotypes for genetic perturbation experiments in plant science. I have delineated the common and unique molecular stress response signatures in plants during an insect attack and a bacterial infection. Together with that, I have developed a knowledgebase for exploring the role of microRNA during plant defence response. An initial framework for systems level modelling of stress response mechanism in plants during multiple biotic and abiotic stresses was developed. Two application cases were demonstrated to translate knowledge from the lab into the field, for improving crop productivity and abiotic stress tolerance using integrative systems biology approach in rapeseed (Brassica napus) and in strawberry (Fragaria sp.).

4. Post-doctoral research works

4.1. Regulatory basis of local climate adaptation and genome evolution in plants: Upon completion of my PhD, I have received an opportunity to join as a post-doctorate researcher in the Cell, Molecular Biology and Genomics Group at the Norwegian University of Science & Technology (2013). I have also received a short-term grant from the European Cooperation in Science and Technology (COST) to visit Comparative & Integrative Genomics Lab, Department of Plant Systems Biology, University of Ghent, Belgium (2015). Using in-house multi-dimensional omics data, data from the Arabidopsis 1001 genome project, I have taken up a project to decipher the structural and functional architecture of the complex gene regulatory circuits in plants, which help them to face stressful environmental conditions.   Significance: I have generated an integrative computational model to explain the adaptive plasticity and intraspecific natural variations in plants, responding to the environmental stresses as an outcome of differentially evolved gene regulatory networks. I have identified the differential utilization of the gene regulatory network topology (transcription factor to target gene interaction), by stress-specific regulators and multifunctional regulators. The multifunctional regulators maintain the core stress response processes while the transient regulators confer the specificity to certain conditions.

4.2. Systems medicine approach for understanding therapy resistance and tumour evolution in human gliomas: 

At the German Cancer Research Center (DKFZ) in Heidelberg, I have worked as a part of the International Cancer Genome Consortium (ICGC: https://icgc.org/). I am analysing a large multi-omics dataset (genome, exome, transcritome, methylome), and clinical records from glioblastoma multiform (GBM) patient cohort. There is still no curative therapy available for malignant brain tumors, including GBM.  GBM carries a universally dismal prognosis in children and adults with median survival times still below 12 months from diagnosis. Clinically, two types of glioblastomas are distinguished, i.e. primary glioblastomas that arise de novo with a short clinical history, and secondary glioblastomas that develop by progression from a preexisting lower grade glioma. The knowledge regarding glioma progression and resistance to therapy is still insufficient for targeted therapeutic interference. I have been carried out a genome scale integrative analysis to identify the underlying molecular mechanism of therapy resistance and tumour evolution in GBM patients. The main datasets for this analysis is provided by the three German International Cancer Genome Consortium (ICGC) projects as well as the Heidelberg Center for Personalized Oncology (HIPO) projects. Significance:   As an initial finding, we have identified key mutational signatures (common and patient specific) in primary and secondary glioblastoma cohort. These findings are directly sent to the patient care clinic (National Center for Tumor Diseases, Heidelberg, Germany), for developing efficient personalised therapies. The entire clinical trial is a state of the art interdisciplinary consortium, composed of researchers with expertise in genomics, computational and systems biology, mouse genetics, neuropathology and clinical neuro-oncology.  Apart from contributing to the translational aspects, I am extending my analysis further to develop a mathematical model for understanding clonal evolution in primary and secondary glioblastomas.