About

Charles DeLisi is an American Biomedical scientist who has made mathematical, computational, and experimental contributions to various areas of cell and systems biology including protein and DNA structure and function, gene regulation, cancer genetics, and immunology. His most recent publications have focused on climate change. He received a BA in History from the City College of New York and a PhD in Physics from NYU, followed by post-doctoral research in biophysical chemistry at Yale. DeLisi has held numerous prominent positions, including staff scientist at Los Alamos National Laboratory, senior investigator at NIH, and Founding Head of the Section on Theoretical Immunology at NCI, NIH. He was also the Director of the Department of Energy's Health and Environmental Research Programs and contributed to the development of one of the earliest databases of nucleic acid and protein sequences and structures. In 1986, he and colleagues proposed the concept of a reference human genome, which he successfully translated into policy. DeLisi served as Professor and Chair of the Department of Biomathematical Science at Mount Sinai School of Medicine, and as Dean of Boston University’s College of Engineering. He proposed and obtained funding for the nation's first graduate program in Bioinformatics and Systems Biology, which he chaired for over a decade. He was appointed the first Arthur Metcalf Professor of Science and Engineering at Boston University until his retirement in 2024. His honors include the Presidential Citizens Medal and the Informa Clinical and Research Excellence Lifetime Achievement award.

Research topics

• Biology
• Mathematics
• Geography
• Medicine
• Virology
• Philosophy
• Art history
• Demography
• Internal medicine
• Statistics
• Econometrics
• Art
• Ecology
• Environmental ethics

Selected publications

• The Amphipathic Helix as a Structural Feature Involved in T-Cell Recognition

  2024-12-04 · 2 citations

  book-chapter

  The mammalian immune response to an invasion by a virus, bacteria, protozoa, or other foreign agent involves two distinct, but interdependent mechanisms referred to as humoral response and cellular response. In the humoral response, B cells release antibodies that attach to distinct binding sites on the proteins of the foreign agent, thus enabling phagocytic cells or complement to attach to and eliminate the foreign agent. A central character of antibody binding is that antibodies bind to foreign proteins while the proteins are in their functional, three-dimensional conformation. In contrast, in the cellular immune response, foreign protein is degraded within the host cells; certain fragments of foreign protein are then bound to major histocompatibility (MHC) proteins, and the MHC-fragment complexes migrate to the surface of the host cell where they stimulate a T cell response. The fragment of foreign protein in association with the MHC molecule on the cell surface is crucial for the T cell activation. The host cell that degrades the foreign protein and presents an MHC-fragment complex on its surface is referred to as an antigen-presenting cell (APC); and the foreign protein fragment, typically 8 to 14 consecutive amino acids in the protein chain, is referred to as an antigenic site. A molecule on the T cell that recognizes the MHC fragment complex on the antigen-presenting cell is called the T cell receptor. Although the three-dimensional structure of the T cell receptor is not known, its amino acid sequence shows many similarities with an antibody molecule.

  PublisherDOI

• An Agrigenomics Trifecta: Greenhouse Gas Drawdown, Food Security, and New Drugs

  Cold Spring Harbor Perspectives in Biology · 2023-12-18 · 1 citations

  reviewOpen access1st authorCorresponding

  An abundance of data, including decades of greenhouse gas (GHG) emission rates, atmospheric concentrations, and global average temperatures, is sufficient to allow a strictly empirical evaluation of the U.S. plan for controlling GHGs.This article presents an analysis, based solely on such data, that shows that the difference between atmospheric GHG levels that will be reached if current trends continue, and levels that would be achieved if the goals of the plan are met-even with worldwide implementation-is inconsequential.Further, the expected globally averaged temperature differences are well within measurement error.The results lend additional support to the argument that any mitigation strategy must include drawdown of atmospheric GHGs.Equally important, a particular drawdown strategy, agrigenomics, offers the opportunity for a revolutionary trifecta: climate change mitigation, food security, and medical advances. EMISSION REDUCTION ALONE WILL NOT CONTROL GLOBAL WARMING Current PolicyRecent estimates indicate that SIRA might reduce GHG emissions to a level that is 32%-40% below 2005 levels by the end of the decade (Jenkins et al. 2022).

  PublisherOA PDFDOI

• Supplementary Figure A6-1 from High Expression of Lymphocyte-Associated Genes in Node-Negative HER2+ Breast Cancers Correlates with Lower Recurrence Rates

  2023-03-30

  preprintOpen access

  Supplementary Figure A6-1 from High Expression of Lymphocyte-Associated Genes in Node-Negative HER2+ Breast Cancers Correlates with Lower Recurrence Rates

  PublisherOA PDFDOI

• Supplementary Figure A6-2 from High Expression of Lymphocyte-Associated Genes in Node-Negative HER2+ Breast Cancers Correlates with Lower Recurrence Rates

  2023-03-30

  preprintOpen access

  Supplementary Figure A6-2 from High Expression of Lymphocyte-Associated Genes in Node-Negative HER2+ Breast Cancers Correlates with Lower Recurrence Rates

  PublisherOA PDFDOI

• Inferring microbial co-occurrence networks from amplicon data: a systematic evaluation

  mSystems · 2023-06-20 · 21 citations

  articleOpen access

  Microbes commonly organize into communities consisting of hundreds of species involved in complex interactions with each other. 16S ribosomal RNA (16S rRNA) amplicon profiling provides snapshots that reveal the phylogenies and abundance profiles of these microbial communities. These snapshots, when collected from multiple samples, can reveal the co-occurrence of microbes, providing a glimpse into the network of associations in these communities. However, the inference of networks from 16S data involves numerous steps, each requiring specific tools and parameter choices. Moreover, the extent to which these steps affect the final network is still unclear. In this study, we perform a meticulous analysis of each step of a pipeline that can convert 16S sequencing data into a network of microbial associations. Through this process, we map how different choices of algorithms and parameters affect the co-occurrence network and identify the steps that contribute substantially to the variance. We further determine the tools and parameters that generate robust co-occurrence networks and develop consensus network algorithms based on benchmarks with mock and synthetic data sets. The Microbial Co-occurrence Network Explorer, or MiCoNE (available at https://github.com/segrelab/MiCoNE) follows these default tools and parameters and can help explore the outcome of these combinations of choices on the inferred networks. We envisage that this pipeline could be used for integrating multiple data sets and generating comparative analyses and consensus networks that can guide our understanding of microbial community assembly in different biomes. IMPORTANCE Mapping the interrelationships between different species in a microbial community is important for understanding and controlling their structure and function. The surge in the high-throughput sequencing of microbial communities has led to the creation of thousands of data sets containing information about microbial abundances. These abundances can be transformed into co-occurrence networks, providing a glimpse into the associations within microbiomes. However, processing these data sets to obtain co-occurrence information relies on several complex steps, each of which involves numerous choices of tools and corresponding parameters. These multiple options pose questions about the robustness and uniqueness of the inferred networks. In this study, we address this workflow and provide a systematic analysis of how these choices of tools affect the final network and guidelines on appropriate tool selection for a particular data set. We also develop a consensus network algorithm that helps generate more robust co-occurrence networks based on benchmark synthetic data sets.

  PublisherDOI

• Supplementary Table 3b from High Expression of Lymphocyte-Associated Genes in Node-Negative HER2+ Breast Cancers Correlates with Lower Recurrence Rates

  2023-03-30

  supplementary-materialsOpen access

  Supplementary Table 3b from High Expression of Lymphocyte-Associated Genes in Node-Negative HER2+ Breast Cancers Correlates with Lower Recurrence Rates

  PublisherDOI

• Supplementary Table 3a from High Expression of Lymphocyte-Associated Genes in Node-Negative HER2+ Breast Cancers Correlates with Lower Recurrence Rates

  2023-03-30

  supplementary-materialsOpen access

  Supplementary Table 3a from High Expression of Lymphocyte-Associated Genes in Node-Negative HER2+ Breast Cancers Correlates with Lower Recurrence Rates

  PublisherDOI

• Supplementary Figure A2-1 from High Expression of Lymphocyte-Associated Genes in Node-Negative HER2+ Breast Cancers Correlates with Lower Recurrence Rates

  2023-03-30

  preprintOpen access

  Supplementary Figure A2-1 from High Expression of Lymphocyte-Associated Genes in Node-Negative HER2+ Breast Cancers Correlates with Lower Recurrence Rates

  PublisherDOI

• Data from High Expression of Lymphocyte-Associated Genes in Node-Negative HER2+ Breast Cancers Correlates with Lower Recurrence Rates

  2023-03-30

  preprintOpen access

  <div>Abstract<p>Gene expression analysis has identified biologically relevant subclasses of breast cancer. However, most classification schemes do not robustly cluster all HER2+ breast cancers, in part due to limitations and bias of clustering techniques used. In this article, we propose an alternative approach that first separates the HER2+ tumors using a gene amplification signal for Her2/neu amplicon genes and then applies consensus ensemble clustering separately to the HER2+ and HER2− clusters to look for further substructure. We applied this procedure to a microarray data set of 286 early-stage breast cancers treated only with surgery and radiation and identified two basal and four luminal subtypes in the HER2− tumors, as well as two novel and robust HER2+ subtypes. HER2+ subtypes had median distant metastasis-free survival of 99 months [95% confidence interval (95% CI), 83–118 months] and 33 months (95% CI, 11–54 months), respectively, and recurrence rates of 11% and 58%, respectively. The low recurrence subtype had a strong relative overexpression of lymphocyte-associated genes and was also associated with a prominent lymphocytic infiltration on histologic analysis. These data suggest that early-stage HER2+ cancers associated with lymphocytic infiltration are a biologically distinct subtype with an improved natural history. [Cancer Res 2007;67(22):10669–76]</p></div>

  PublisherDOI

• Supplementary Figure A2-1 from High Expression of Lymphocyte-Associated Genes in Node-Negative HER2+ Breast Cancers Correlates with Lower Recurrence Rates

  2023-03-30

  preprintOpen access

  Supplementary Figure A2-1 from High Expression of Lymphocyte-Associated Genes in Node-Negative HER2+ Breast Cancers Correlates with Lower Recurrence Rates

  PublisherOA PDFDOI

Recent grants

• NIH Grant R01GM080625

  NIH · $1.2M · 2011

• NIH Grant R21CA135882

  NIH · $447k · 2011

• Visant-Predictome: A System for Integration, Mining, Visualization and Analysis

  NIH · $3.0M · 2007–2017

• CRI: Acquisition of a Linux Cluster for Bioinformatics Research and Education

  NSF · $325k · 2007–2009

• NIH Grant R01AI030535

  NIH · $1.4M · 1996

Frequent coauthors

• Gyan Bhanot

  Rutgers, The State University of New Jersey

  79 shared

• Gabriela Alexe

  Dana-Farber Cancer Institute

  47 shared

• Zhenjun Hu

  40 shared

• James Cornette

  Iowa State University

  37 shared

• Gul S. Dalgin

  27 shared

• Shridar Ganesan

  Rutgers Cancer Institute of New Jersey

  27 shared

• Jay A. Berzofsky

  National Institutes of Health

  26 shared

• Pablo Tamayo

  26 shared

Awards & honors

• Presidential Citizens Medal (President Clinton)
• Informa Clinical and Research Excellence Lifetime Achievemen…