Sandip Paul

Associate Professor

Centre for Health Science & Technology

Email: sandipp@jisiasr.org, websandip@gmail.com

Postdoctoral Fellow, Department of Microbiology, University of Washington, Seattle, USA

PhD : Jadavpur University, Kolkata, India

M.Sc : Biophysics, Kalyani University, Kalyani, India

Professional Engagements:

2021 – Present: Associate Professor, JIS Institute of Advanced Studies and Research Kolkata

2016 – 2021: Ramanujan Fellow, CSIR-IICB, Kolkata, India

2011 – 2016: Senior Fellow, Dept. of Microbiology, University of Washington, Seattle, USA

Member, Indian National Young Academy of Sciences (INYAS), INSA, New Delhi

Research Topics:

Human microbiome in health and disease; Microbial community structure and their evolution; Multi-omics data mining and methods development; Comparative genomics and microbial adaptation

Research Focus:

  1. Human microbiome in health and disease

The human body is inhabited by abundant microorganisms, including bacteria, archaea, viruses, fungi, protozoa etc. collectively known as human microbiota. The advent of a new sequencing era like high-throughput DNA sequencing and metagenomic studies have explored the highly diverse and dynamic nature of the microbial communities and their crucial role in human health and in disease.

We have several collaborations with experimental and clinical laboratories in order to analyse the human microbiome data at different taxonomic levels as well as functional potentials between diverse microbial communities for understanding human health and diseases that are associated with the microbiota.

  1. Multi-omics data mining and methods development

Recent advances in multi-omics (metagenomics, metatranscriptomics, metaproteomics, metabolomics and viriomics) technologies created new avenues to explore the dynamic relationships between molecular events and clinical outcomes using quantitative methods. However, the extraordinary range and complexity of these omics data have faced critical computational bottlenecks requiring emerging concepts and methods. We work in collaboration with our data science department in order to develop new computational framework to integrate big data mining algorithms with high-performance computing strategies for revealing the complex relationships between multi-omics patterns and phenotypic outcomes. This will enhance the integration of different data types and the interpretation of results from multiple aspects.

  1. Microbial community structure and their evolution

The concept of Phylosymbiosis is an emerging topic in the field of host-microbiome interaction. This describes the host-associated similarities in the microbial communities that can even define the phylogeny of their hosts. Although the phylosymbiosis has been described across several animal and plant systems, we lack in the understanding of the various evolutionary mechanisms shaping the host specific microbial community structure. The goal of this project is to explore the relative contributions of various evolutionary processes shaping the host-associated microbial community leading to phylosymbiosis and thus further better understanding of the ecology and evolution of host–microbe interactions.

  1. Comparative genomics and genome adaptation

The genome divergence is a dynamic evolutionary process of ecological and genetic differentiation. A considerable portion of the genetic diversity is likely to be considered as regulators of adaptive features associated with particular habitat. One powerful approach to unraveling the molecular mechanisms of adaptation is the comparative genomics study with large number of complete genome sequences. In this regard, the pan-genome approach, has been found to be valuable in the comparative analysis for a set of genomes.

We employ the comparative pan-genomic study coupled with evolutionary analyses as a framework to investigate the genome diversification associated with various life-style/pathogenicity. This framework is very useful in deciphering the contingent contributions of evolutionary processes along with the selective pressures that can potentially shape the genomic and metabolic diversification.

Selected Journal Articles :

  1. Lee et al.; Structural specificities of cell surface β-glucan polysaccharides determine commensal yeast mediated immuno-modulatory activities. 2021, Nature Communications (In Press)
  2. Banerjee R., Chaudhari N.M., Lahiri A., Gautam A., Bhowmik D., Dutta C., Chattopadhyay S., Huson D.H., Paul S.; Interplay of various evolutionary modes in genome diversification and adaptive evolution of the family Sulfolobaceae. 2021,  Front. Microbiol.
  3. Chaudhari N. M., Gautam A., Gupta V. K., Dutta C., Paul S.; PanGFR-HM: a dynamic web resource for pan-genomic and functional profiling of human microbiome with comparative features. 2018, Front. Microbiol., 9, 30349509, 10.3389/fmicb.2018.02322
  4. Gupta V. K., Paul S., Dutta C.; Geography, ethnicity or subsistence-specific variations in human microbiome composition and diversity. 2017, Front. Microbiol., 8, 28690602, 10.3389/fmicb.2017.01162
  5. Kisiela D. I., Radey M., Paul S., Porter S., Polukhina K., Tchesnokova V., Shevchenko S., Chan D., Aziz M., Johnson T. J., Price L. B., Johnson J. R., Sokurenko E. V. ; Inactivation of transcriptional regulators during within-household evolution of Escherichia coli. 2017, J Bacteriol, 199, 28439032, 10.1128/JB.00036-17
  6. Paul S., Sokurenko E. V., Chattopadhyay S.; Corrected Genome Annotations Reveal Gene Loss and Antibiotic Resistance as Drivers in the Fitness Evolution of Salmonellaenteric Serovar Typhimurium. 2016 , J Bacteriol, 198, 3152-3161, 27621280, 10.1128/JB.00545-16
  7. Paul S., , Minnick M. F., Chattopadhyay S.; Mutation-Driven Divergence and Convergence Indicate Adaptive Evolution of the Intracellular Human-Restricted Pathogen, Bartonella bacilliformis. 2016, PLoS Negl Trop Dis, 10, 27167125, 10.1371/journal.pntd.0004712
  8. Paul S., Bhardwaj A., Bag S. K., Sokurenko E. V., Chattopadhyay S.; PanCoreGen – profiling, detecting, annotating protein-coding genes in microbial genomes. 2015, Genomics, 106, 367-372, 26456591, 10.1016/j.ygeno.2015.10.001
  9. Chattopadhyay S., Paul S., Dykhuizen D. E., Sokurenko E. V. ; Tracking recent adaptive evolution in microbial species using TimeZone. 2013, Nature Protocols, 8, 652-665, 23471110, 10.1038/nprot.2013.031
  10. Kisiela D. I., Chattopadhyay S., Tchesnokova V., Paul S., Weissman S. J., Medenica I., Clegg S., Sokuernko E. V.; Evolutionary analysis points to divergent physiological roles of type 1 fimbriae in Salmonella and E. Coli. 2013, mBio, 4, e00625-12, 23462115, 10.1128/mBio.00625-12
  11. Paul S., Million-Weaver S., Chattopadhyay S., Sokurenko E. V., Merrikh H.; Accelerated gene evolution through replication–transcription conflicts. 2013, Nature, 495, 512-515, 23538833, 10.1038/nature11989.
  12. Chattopadhyay S., Taub F., Paul S., Weissman S.J., Sokurenko E. V.; Microbial variome database: point mutations, adaptive or not, in bacterial core genomes. 2013, Mol Biol Evol, 30, 1465-1470, 23493258, 10.1093/molbev/mst048
  13. Paul S., Linardopoulou E., Billig M., Tchesnokova V., Price L., Johnson J., Chattopadhyay S., Sokurenko E. V.; Role of homologous recombination in adaptive diversification of extra-intestinal Escherichia coli. 2013, J Bacteriol, 195, 231-242, 23123908, 10.1128/JB.01524-12
  14. Chattopadhyay S., Paul S., Kisiela D. I., Linardopoulou E., Sokurenko E. V. ; Convergent molecular evolution of genomic cores in Salmonella enterica and Escherichia coli. 2012, J Bacteriol, 194, 5002-5011, 22797756, 10.1128/JB.00552-12
  15. Paul S., Bag S.K., Das S., Harvill E.T., Dutta C.; Molecular signature of hypersaline adaptation: insights from genome and proteome composition of halophilic prokaryotes. 2008, Genome Biology, 9, R70, 18397532, 10.1186/gb-2008-9-4-r70

For the complete list of publications, please follow this link: https://scholar.google.com/citations?user=4_p529wAAAAJ&hl=en&authuser=1

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