About

Sushmita Roy is a Professor in the Department of Biostatistics and Medical Informatics and the Department of Computer Science at the University of Wisconsin, Madison. She is also affiliated with the Wisconsin Institute for Discovery (WID). The page lists her as a current member of the research community, but does not provide specific details about her research focus, background, or key contributions.

Research topics

• Genetics
• Biology
• Computer Science
• Computational biology
• Data science
• Evolutionary biology
• Neuroscience
• Cell biology
• Botany

Selected publications

• Defining the role of β-cell IRE1α/XBP1 pathway and its gene regulatory network components in non-obese diabetic mice

  Nature Communications · 2025-11-26 · 3 citations

  articleOpen access

  The unfolded protein response sensor, IRE1α, acts through its regulated IRE1α-dependent decay (RIDD) activity or transcription factor XBP1 to determine cell fate and survival. While blunting RIDD activity prevents diabetes in type 1 diabetes preclinical model non-obese diabetic mice, β-cell-specific function of XBP1 at different stages of disease remains unknown. Here we show that deletion of Xbp1 in β-cells (Xbp1β-/-) of non-obese diabetic mice before insulitis is protective against diabetes. Histological and transcriptomic analyses indicate that following a transient loss of maturity, β-cells of Xbp1β-/- mice exhibit reduced insulitis, apoptosis, and antigenicity phenocopying Ire1αβ-/- mice with no changes in RIDD activity. Comparative transcriptome and regulatory network analyses reveal a largely shared component between the Ire1αβ-/- and Xbp1β-/- mice as well as network components unique to Xbp1β-/-, indicative of IRE1α-independent roles of XBP1. Our findings define the role of β-cell IRE1α/XBP1 and identify previously unrecognized regulatory networks and nodes of this pathway. Modulating the unfolded protein response (UPR) can induce protective dedifferentiation of β-cells in non-obese diabetic mice. Here, the authors show that β-cell deletion of UPR transcription factor XBP1 is protective against diabetes in these mice and characterize the relevant regulatory networks.

  PublisherOA PDFDOI

• Uncovering Functional Gene Regulatory Networks in Bulk and Single-Cell Data through Robust Transcription Factor Activity Estimation and Model-Guided Experimental Validation

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-06-13 · 2 citations

  preprintOpen accessSenior authorCorresponding

  Reconstructing genome-scale gene regulatory networks (GRNs) remains a difficult problem in systems biology, and many experimental and computational methods have been developed to address this problem. Recent computational methods have aimed to more accurately model GRNs by estimating the hidden Transcription Factor Activity (TFA) from prior knowledge of TF target regulatory connections, encoded as an input directed graph, to relax the assumption that mRNA level of the regulator correlates with the protein activity of the regulator. However, the noise in the prior knowledge can adversely affect the estimated TFA levels and the quality of the downstream inferred GRNs. Here, we present a new approach, MERLIN+P+TFA, that uses prior knowledge-guided sparsity regularization to robustly and accurately estimate TFA and downstream GRNs. We apply our method to simulated and real expression data in yeast and mammalian systems and show improved quality of inferred GRNs for both bulk and single-cell datasets. Regularized TFA offers benefits to a variety of other GRN inference algorithms, including those that have traditionally been used with expression alone, in both bulk and scRNA-seq settings. We used the inferred GRN to prioritize key regulators for the mouse Embryonic Stem Cell (mESC) state and validate 58 regulators experimentally. We identified both known and novel regulators of the mESC state and further validate the targets of 4 known and novel regulators. Our validation experiments suggest that computationally inferred networks can capture functional targets of TFs with higher precision than estimated in current benchmarks, however, it is important to generate context-specific gold standards.

  PublisherOA PDFDOI

• Prevalence of Atherogenic Lipoprotein Phenotype among the Obese Medical Students in Bangladesh

  International Journal of Pharmaceutical and Bio-Medical Science · 2025-02-26

  articleOpen access

  Background: Medical students are expected to be conscious about nutrition and healthy active life styles. So, study of obesity & atherogenic lipoproteins among medical students may reflect the prevalence of this cardiovascular risk factor in our population. Objective: The aim of this study was to assess the prevalence of atherogenic lipoprotein phenotype among the obese medical students in Bangladesh. Methodology: This descriptive type of Cross-sectional study was carried out at the Department of Biochemistry, Sylhet M.A.G Osmani medical College from july 2018 to June 2019. 100 obese medical students were the study population. Random sampling was done according to availability of the subjects. Data were collected through interviewing of the subjects. The collected data were entered into the computer and analyzed by using SPSS (version 20.1) Result: Majority of the obese subjects (59%) were female. ‘Atherogenic Lipoprotein Phenotype’ components (increased TG, decreased HDL & predominance of small dense LDL) were-- 63%, 54% & 40% respectively. Prevalence of ‘atherogenic lipoprotein phenotype’ was found in 16% in total obese students. In male and female obese students, prevalence of ‘athrorgenic lipoprotein phenotype’ was 14.63% & 16.95% respectively. In male obese students, mean value of TG, HDL & sd LDL (small dense LDL) were-- 177.37, 37.51 and 1.052 respectively. In obese female students, mean value of TG, HDL & sd LDL were-- 163.32, 38.64 and 1.085 respectively. There were no significant differences of any parameter between obese male and female students. Conclusion: It may be concluded that, our young generation should be aware of atherogenicity due to considerable increased prevalence of obesity & atherogenic lipoprotein phenotype in medical students.

  PublisherOA PDFDOI

• Examining the dynamics of three-dimensional genome organization with multitask matrix factorization

  Genome Research · 2025-03-20 · 1 citations

  articleOpen accessSenior author

  Three-dimensional (3D) genome organization, which determines how the DNA is packaged inside the nucleus, has emerged as a key component of the gene regulation machinery. High-throughput chromosome conformation data sets, such as Hi-C, have become available across multiple conditions and time points, offering a unique opportunity to examine changes in 3D genome organization and link them to phenotypic changes in normal and disease processes. However, systematic detection of higher-order structural changes across multiple Hi-C data sets remains a major challenge. Existing computational methods either do not model higher-order structural units or cannot model dynamics across more than two conditions of interest. We address these limitations with tree-guided integrated factorization (TGIF), a generalizable multitask nonnegative matrix factorization (NMF) approach that can be applied to time series or hierarchically related biological conditions. TGIF can identify large-scale changes at the compartment or subcompartment levels, as well as local changes at boundaries of topologically associated domains (TADs). Based on benchmarking in simulated and real Hi-C data, TGIF boundaries are more accurate and reproducible across differential levels of noise and sources of technical artifacts, and are more enriched in CTCF. Application to three multisample mammalian data sets shows that TGIF can detect differential regions at compartment, subcompartment, and boundary levels that are associated with significant changes in regulatory signals and gene expression enriched in tissue-specific processes. Finally, we leverage TGIF boundaries to prioritize sequence variants for multiple phenotypes from the NHGRI GWAS catalog. Taken together, TGIF is a flexible tool to examine 3D genome organization dynamics across disease and developmental processes.

  PublisherOA PDFDOI

• Multi-species analysis of inflammatory response elements reveals ancient and lineage-specific contributions of transposable elements to NF-kB binding

  Genome Research · 2025-06-06 · 3 citations

  articleOpen access

  Transposable elements (TEs) provide a source of transcription factor (TF) binding sites that can rewire gene regulatory networks. NF-kB is an evolutionarily conserved TF complex primarily involved in innate immunity and inflammation. The extent to which TEs have contributed to NF-kB responses during mammalian evolution is not well established. Here, we perform a multi-species analysis of TEs bound by the NF-kB subunit RELA in response to the proinflammatory cytokine TNF. By comparing RELA ChIP-seq data from TNF-stimulated primary aortic endothelial cells isolated from human, mouse, and cow, we find that 55 TE subfamilies are associated with RELA-bound regions, many of which reside near TNF-responsive genes. A prominent example of lineage-specific contribution of transposons comes from the bovine SINE subfamilies Bov-tA1/2/3 which collectively contributed over 14,000 RELA-bound regions in cow. By comparing RELA binding data across species, we also find several examples of RELA motif-bearing TEs that colonized the genome prior to the divergence of the three species and contributed to species-specific RELA binding. For example, we find human RELA-bound MER81 instances are enriched for the interferon gamma pathway and demonstrate that one RELA-bound MER81 element can control the TNF-induced expression of interferon gamma receptor 2 ( IFNGR2 ). Using ancestral reconstructions, we find that RELA containing MER81 instances rapidly decayed during early primate evolution (>50 million years ago [MYA]) before stabilizing since the separation of Old World monkeys (<50 MYA). Taken together, our results suggest ancient and lineage-specific transposon subfamilies contributed to mammalian NF-kB regulatory networks.

  PublisherOA PDFDOI

• Étude internationale en vie réelle sur les séquences d’immunothérapie combinée dans le mélanome métastatique

  Annales de Dermatologie et de Vénéréologie - FMC · 2025-11-13

  article
  PublisherDOI

• The single-cell transcriptome program of nodule development cellular lineages in Medicago truncatula

  Cell Reports · 2024-02-01 · 37 citations

  articleOpen access

  Legumes establish a symbiotic relationship with nitrogen-fixing rhizobia by developing nodules. Nodules are modified lateral roots that undergo changes in their cellular development in response to bacteria, but the transcriptional reprogramming that occurs in these root cells remains largely uncharacterized. Here, we describe the cell-type-specific transcriptome response of Medicago truncatula roots to rhizobia during early nodule development in the wild-type genotype Jemalong A17, complemented with a hypernodulating mutant (sunn-4) to expand the cell population responding to infection and subsequent biological inferences. The analysis identifies epidermal root hair and stele sub-cell types associated with a symbiotic response to infection and regulation of nodule proliferation. Trajectory inference shows cortex-derived cell lineages differentiating to form the nodule primordia and, posteriorly, its meristem, while modulating the regulation of phytohormone-related genes. Gene regulatory analysis of the cell transcriptomes identifies new regulators of nodulation, including STYLISH 4, for which the function is validated.

  PublisherOA PDFDOI

• Student’s Perspective Towards Online Teaching-Learning Activity

  Journal of Enam Medical College · 2024-09-22

  articleOpen access

  Background: Online learning is a form of education that takes place over the Internet. With the advancement of information and communication technologies, the acceptance of online education is increasing. It is an alternative to traditional classroom learning and involves using primarily the internet and one or more other technologies. Objective: To identify the advantages and disadvantages of online learning from the student’s perspective. Materials and Methods: Data were collected from 256 MBBS students of Enam Medical College with a predesigned questionnaire by purposive sampling to assess the student’s perception towards the online teaching-learning modalities, its advantages, limitations and recommendations how the online teaching and learning process can be made more effective. Results: The findings of the study revealed that most of the students are self-educated and lack of institutional knowledge regarding ICT tools and online learning process. The study result showed several advantages including; it is flexible regarding the time and place, sharing of information is easy and cost effective. Whereas, lack of face-to-face interaction, less effective in practice-based learning, disturbance of physical health, lack in student feedback, reliable internet connection, lack in computer skills and limited infrastructure at work place are identified as challenges. The students have also given their opinion regarding the possible steps and strategies to improve the online learning more effective. Conclusion: As with most teaching methods, online learning also has its own set of positives and negatives. Several initiatives like; more communication between students and teachers, use of more suitable technology, supporting the struggling students and creating a sense of comfort can make it more effective. J Enam Med Col 2022; 12(1): 17−23

  PublisherOA PDFDOI

• Human iPSC-derived Committed Cardiac Progenitors Generate Cardiac Tissue Grafts in a Swine Ischemic Cardiomyopathy Model without Triggering Ventricular Arrhythmias

  bioRxiv (Cold Spring Harbor Laboratory) · 2024-02-17 · 6 citations

  preprintOpen access

  BACKGROUND: The adult human heart following a large myocardial infarction is unable to regenerate heart muscle and instead forms scar with the risk of progressive heart failure. Large animal studies have shown that intramyocardial injection of human pluripotent stem cell-derived cardiomyocytes (hPSC-CMs) following a myocardial infarction result in cell grafts but also ventricular arrhythmias. We hypothesized that intramyocardial injection of committed cardiac progenitor cells (CCPs) derived from iPSCs, combined with cardiac fibroblast-derived extracellular matrix (cECM) to enhance cell retention will: i) form cardiomyocyte containing functional grafts, ii) be free of ventricular arrhythmias and iii) restore left ventricular contractility in a post-myocardial infarction (MI) cardiomyopathy swine model. METHODS: hiPSCs were differentiated using bioreactors and small molecules to produce a population of committed cardiac progenitor cells (CCPs). MI was created using a coronary artery balloon occlusion and reperfusion model in Yucatan mini pigs. Four weeks later, epicardial needle injections of CCPs+cECM were performed in a small initial feasibility cohort, and then transendocardial injections (TEI) of CCPs+cECM, CCPs alone, cECM alone or vehicle control into the peri-infarct region in a larger randomized cohort. A 4-drug immunosuppression regimen was administered to prevent rejection of human CCPs. Arrhythmias were evaluated using implanted event recorders. Magnetic resonance imaging (MRI) and invasive pressure volume assessment were used to evaluate left ventricular anatomic and functional performance, including viability. Detailed histology was performed on the heart to detect human grafts. RESULTS: A scalable biomanufacturing protocol was developed generating CCPs which can efficiently differentiate to cardiomyocytes or endothelial cells in vitro. Intramyocardial delivery of CCPs to post-MI porcine hearts resulted in engraftment and differentiation of CCPs to form ventricular cardiomyocyte rich grafts. There was no significant difference in cardiac MRI-based measured cardiac volumes or function between control, CCP and CCP+cECM groups; however, dobutamine stimulated functional reserve was improved in CCP and CCP+cECM groups. TEI delivery of CCPs with or without cECM did not result in tumors or trigger ventricular arrhythmias. CONCLUSIONS: CCPs are a promising cell source for post-MI heart repair using clinically relevant TEI with a low risk of engraftment ventricular arrhythmias.

  PublisherOA PDFDOI

• Current and future directions in network biology

  Bioinformatics Advances · 2024-01-01 · 92 citations

  editorialOpen access

  Summary: Network biology is an interdisciplinary field bridging computational and biological sciences that has proved pivotal in advancing the understanding of cellular functions and diseases across biological systems and scales. Although the field has been around for two decades, it remains nascent. It has witnessed rapid evolution, accompanied by emerging challenges. These stem from various factors, notably the growing complexity and volume of data together with the increased diversity of data types describing different tiers of biological organization. We discuss prevailing research directions in network biology, focusing on molecular/cellular networks but also on other biological network types such as biomedical knowledge graphs, patient similarity networks, brain networks, and social/contact networks relevant to disease spread. In more detail, we highlight areas of inference and comparison of biological networks, multimodal data integration and heterogeneous networks, higher-order network analysis, machine learning on networks, and network-based personalized medicine. Following the overview of recent breakthroughs across these five areas, we offer a perspective on future directions of network biology. Additionally, we discuss scientific communities, educational initiatives, and the importance of fostering diversity within the field. This article establishes a roadmap for an immediate and long-term vision for network biology. Availability and implementation: Not applicable.

  PublisherDOI

Recent grants

• CAREER: Comparative Network Biology to Study the Evolution of Regulatory Networks

  NSF · $507k · 2014–2020

• Computational Inference of Regulatory Network Dynamics on Cell Lineages

  NIH · $1.5M · 2016–2023

• Computational approaches for comparative regulatory genomics to decipher long-range gene regulation

  NIH · $1.4M · 2018–2024

Frequent coauthors

• Alireza Fotuhi Siahpirani

  University of Tehran

  110 shared

• Shilu Zhang

  University of Science and Technology of China

  74 shared

• Deborah Chasman

  University of Wisconsin–Madison

  68 shared

• Sara Knaack

  Wisconsin Institutes for Discovery

  66 shared

• Sunnie Grace McCalla

  University of Wisconsin–Madison

  60 shared

• Junha Shin

  52 shared

• Rupa Sridharan

  University of Wisconsin–Madison

  47 shared

• Saptarshi Pyne

  Wisconsin Institutes for Discovery

  38 shared

Labs

• Roy LabPI

Education

• Ph.D., Biostatistics

  University of Wisconsin-Madison

  2005

• M.S., Biostatistics

  University of Wisconsin-Madison

  2001

• B.S., Statistics

  University of Calcutta

  1998