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Jishnu Das Ph.D.

  • Assistant Professor, Department of Immunology
  • Assistant Professor, Computational & Systems Biology
  • Core Member, Center for Systems Immunology
  • PMI Graduate Faculty

    Education & Training

  • Postdoc – Ragon Institute of MGH, MIT & Harvard and MIT BE (2016-2019)
  • Ph.D. – Cornell University (2010-2016)
  • BTech - Indian Institute of Technology, Kanpur (2006-2010)
Representative Publications

1. Das J et al “Mining for humoral correlates of HIV control and latent reservoir size". PLoS Pathogens 16(10): e1008868

2. Suscovich T*, Fallon J*, Das J* et al, “Mapping functional humoral correlates of protection against malaria challenge following RTS,S/AS01 vaccination”. Science Translational Medicine 12(553): eabb4757. *=co-first

3. Lu L*, Das J*, et al (2020), “Antibody Fc-glycosylation discriminates latent and active tuberculosis”. Journal of Infectious Diseases jiz643. *=co-first

4. Bing X, Bunea F, Royer M, Das J^ “Latent model-based clustering for biological discovery”. iScience (Cell Press) 2019; 14:125-135 ^=corresponding author

5. Fragoza R*, Das J* et al, “Extensive disruption of protein interactions by genetic variants across the allele frequency spectrum in human populations.” Nature Communications 2019; 10(1):4141 *=co-first

6. Ackerman M, Das J et al, “Route of immunization defines multiple mechanisms of vaccine-mediated protection against SIV.” Nature Medicine 2018; 24(10): 1590-1598.

7. Sadanand S*, Das J* et al, “Temporal variation in HIV-specific IgG subclass antibodies during acute infection differentiates spontaneous controllers from chronic progressors.” AIDS 2018; 32(4):443-450 *=co-first

8. Goetghebuer T*, Smolen K*, Adler C*, Das J* et al, “Initiation of Antiretroviral Therapy Before Pregnancy Reduces the Risk of Infection-related Hospitalization in Human Immunodeficiency Virus-exposed Uninfected Infants Born in a High-income Country.” Clinical Infectious Diseases 2018; 68(7):1193-1203 *=co-first

9. Vo T*, Das J* et al, “A Proteome-wide Fission Yeast Interactome Reveals Network Evolution Principles from Yeasts to Human.” Cell 2016; 164(1-2):310-323 *=co-first

10. Wei X*, Das J* et al, “A massively parallel pipeline to clone DNA variants and examine molecular phenotypes of human disease mutations.” PLoS Genetics 2014; 10(12): e1004819 *=co-first

11. Das J et al, “Elucidating common structural features of human pathogenic variations using large-scale atomic-resolution protein networks.” Human Mutation 2014; 35(5): 585-593.

12. Das J et al, “Cross-species protein interactome mapping reveals species-specific wiring of stress response pathways.” Science Signaling 2013; 6(276):ra38.

13. Das J et al, “HINT: High-quality protein interactomes and their applications in understanding human disease.” BMC Systems Biology 2012; 6:92

14. Das J et al, “Genome-scale analysis of interaction dynamics reveals organization of biological networks.” Bioinformatics 2012; 28(14):1873-1878

15. Wang X*, Wei X*, Thijssen B, Das J* et al, “Three-dimensional reconstruction of protein networks provides insight into human genetic disease.” Nature Biotechnology 2012; 30(2): 159-164 *=co-first

Research Interests

Our research focuses on the development and use of novel systems approaches to analyze high-dimensional immunological datasets, and elucidate mechanisms of immune regulation and dysregulation. Our previous work has utilized systems approaches to analyze Mendelian mutations in the context of three-dimensional protein-protein interaction networks, to understand molecular mechanisms of corresponding disorders. We have also developed network analyses frameworks to characterize the evolutionary dynamics of these protein networks. Another dimension of our past work has been the use of statistical methods for the analyses of high-dimensional data and machine-learning approaches to elucidate correlates of natural and vaccine-mediated immunity in HIV, tuberculosis and malaria.

We are currently working on using network systems and functional genomic approaches to perform multi-scale integration of genomic and epigenomic datasets with biological networks to identify molecular phenotypes underlying these immunological disorders. We also use statistical and machine-learning techniques to integrate multi-omic datasets (genomic, transcriptomic, proteomic, metabolomic and antibody-omic) and elucidate molecular mechanisms of immune regulation and dysregulation. We currently have several federally funded projects focusing on infectious diseases as well as autoimmune and alloimmune disorders. We were fortunate to recently receive a NIAID New Innovators DP2 Award. Our lab ( is highly interdisciplinary and provides a dynamic training environment.