Biology subjects

Jhunjhunwala, S.

Publications and source records attributed to Jhunjhunwala, S..

At least 19 recordsLinked to original sources

bioRxiv2026

Systemic neoantigen-specific T cells reveal central determinants of PD-(L)1 blockade efficacy

The contribution of neoantigen-specific T cells to PD-(L)1 efficacy has largely been inferred from tumor mutational burden. We functionally profiled circulating T cell responses against 7,038 predicted HLA-I-restricted and 21,453 HLA-II-restricted neopeptides in 27 patients with advanced non-small cell lung cancer treated with anti-PD-(L)1. CD4 responses were frequent and correlated with neoantigen availability but not clinical benefit. In contrast, the magnitude and breadth of neoantigen-specific CD8 T cell responses were associated with clinical benefit, progression-free and overall survival, independently of tumor mutational burden. Patients mounting coordinated CD4 and CD8 responses experienced improved progression-free survival. Tumors from CD8 responders displayed immune signatures indicative of both T cell priming and effector functions. Circulating neoantigen-specific CD8 T cells recognized endogenously processed antigens, trafficked to tumors, and selectively expanded under therapy while retaining CD28, CD226, and CXCR3 expression. These findings identify coordinated, functionally engaged neoantigen-specific T cell responses as central determinants of PD-(L)1 efficacy.

cancer biology↗

bioRxiv2025

Phagosome Maturation in Macrophages is Enhanced by p38α MAPK Signaling

In macrophages, it is generally assumed that without active escape mechanisms, all phagocytosed cargo eventually reaches lysosomes. Yet, the influence of specific ligands, like lipopolysaccharide (LPS), on phagosome maturation is unclear. Here, using sterile, non-immunogenic particles as model cargo phagocytosed by macrophages, we show that in the absence of an external signal, less than half the phagosomes fuse with lysosomes. By quantifying phagosome maturation in terms of lysosomal localization and mapping lumenal pH dynamics, we find that early events triggered by LPS-induced signaling enhance both cargo delivery to lysosomes and phagosome acidification rates. We demonstrate that stress-activated p38 alpha mitogen-activated protein kinase (p38 MAPK) is important for this signaling event. Other signals known to activate p38 MAPK such as flagellin, IgG and albumin also enhance lysosomal delivery of phagocytosed cargo. Our work indicates that the maturation of phagosomes in macrophages is enhanced by the presence of specific ligands on the cargo, which activate cell surface receptors that signal via p38 MAPK.

cell biology↗

bioRxiv2025

Chronic Inflammation Induced Immature Neutrophils Drive Immunopathology During Subsequent Inflammatory Events

Individuals with underlying chronic inflammatory conditions are prone to increased morbidity when posed with an additional inflammatory challenge such as an injury or infection, but why this occurs remains unclear. Herein, we address this question in mouse models by demonstrating that chronic inflammation results in a pronounced expansion of circulating immature neutrophils that exhibit dysregulated effector functions as determined by single cell RNA sequencing and ex vivo functional assays. We show that these immature neutrophils are associated with and contribute to increased immunopathology in response to a new inflammatory challenge. Blocking the migration or function of these immature neutrophils through the administration of therapeutic antibodies or small molecules, profoundly lowers the immunopathology caused by the inflammatory challenge. Together, these studies establish a causal link between immature neutrophils and increased immunopathology, while also providing insights into new therapeutic strategies for treating individuals with chronic inflammatory ailments.

immunology↗

bioRxiv2024

Ex Vivo Delivery of mRNA to Immune Cells Via a Non-Endosomal Route Obviates the Need for Nucleoside Modification

Base modification and the use of lipid nanoparticles (LNPs) are thought to be essential for efficient in vivo delivery and expression of mRNA. However, for ex vivo immune cell engineering, the need for either of the two is unclear. Previous reports have suggested that nucleic acids may be efficiently delivered to immune cells ex vivo, through a non-endosomal delivery route, but the need for base modification has not been determined. Herein, we demonstrate that when a non-endosomal delivery method is used, unmodified mRNA performs equally well to the commonly used base-modified mRNA, including the N1 methyl pseudo-uridine modification, in terms of protein expression and inflammatory response in cells. However, if an endosomal delivery route is used, then N1 methyl pseudo-uridine modification is necessary for high expression and low inflammatory response, as demonstrated by others as well. Overall, we show that non-endosomal mRNA delivery renders nucleoside modifications non-essential, and that unmodified mRNA combined with non-endosomal delivery route may be used for efficient ex vivo mRNA-based engineering of immune cells.

bioengineering↗

bioRxiv2024

Immune cell death on silicone surfaces drives biofilm formation

Bacterial biofilms on indwelling medical devices is a major driver of healthcare-associated infection despite significant advances in antifouling surface engineering, suggesting that laboratory antibacterial performance does not fully capture the determinants of clinical colonization. Here we show that adherent immune cells constitute a previously underappreciated conditioning layer that promotes biofilm formation on otherwise antifouling biomaterials. Pre-exposure of clinically used substrates to macrophages, monocytes, neutrophils or human peripheral blood cells markedly increased Staphylococcus aureus and Escherichia coli adhesion and aggregation. These studies also reveal that immune cells promote biofilms even after cell death with cellular debris acting as a conditioning agent. We demonstrate that reactive-oxygen-species amplification by incorporating bismuth telluride into a silicone composite converts adherent immune cells from passive conditioning agents into active bactericidal effectors. We note that this antimicrobial composite confers durable antibacterial protection across early, delayed and late infection time points in a murine implantation model. Together, these findings introduce a class of immune-coupled antibacterial materials as an alternative to the current antifouling paradigm.

bioengineering↗

bioRxiv2024

Pep2Vec: An Interpretable Model for Peptide-MHC Presentation Prediction and Contaminant Identification in Ligandome Datasets

As personalized cancer vaccines advance, precise modeling of antigen presentation by MHC class I and II is crucial. High-quality training data is essential for clinical models. Existing deep learning models focus on prediction performance but lack interpretability. We introduce Pep2Vec, a modular, transformer-based model trained on MHC I and II ligandome data, transforming input sequences into interpretable vectors. This approach integrates source protein features and elucidates the source of its performance gains, revealing regions that correlate with gene expression and protein-protein interactions. Pep2Vecs peptide latent space shows relationships between peptides of varying MHC class, allotype, lengths, and submotifs. This enables identifying four major contaminant types, constituting 5.0% of our data. Pep2Vec enhances MHC presentation prediction, achieving higher average precision on our presentation test set and immunogenicity datasets than existing models, and reducing contaminant-like peptide recommendations. Pep2Vec addresses a critical need for the development of more precise and effective applications of peptide MHC models, such as for cancer vaccines and antibody deimmunization.

immunology↗

bioRxiv2024

Crosstalk between plasma membrane and Staphylococcus α-hemolysin during oligomerization

The infectious microbe Staphylococcus aureus releases an array of cytotoxic pore-forming toxins (PFTs) that severely damage the cell membrane during bacterial infection. However, the interaction interfaces between the host cell and toxin were merely explored. Herein, we monitored the active oligomeric states facilitated membrane disruption processes such as lysis, and protrusion in the plasma membrane and lipid membrane. Furthermore, necrosis was triggered in the neutrophil-like cells upon synergistic binding and oligomerization of the monomeric -HL. Additionally, we solved RBC membrane stabilized structure of different conformational states of this {beta}-PFT using a single-particle cryo-EM. We further confirmed that internal membrane fluidity was the deterministic factor associated with the formation of intermediate pre-pores, heptameric pore-like, and complete pore species. Together, this is the first study to unveil the structure-function analysis of pre-pore to pore transition of any small {beta}-PFT during its crosstalk with the cell. HighlightsO_LI-HL promotes necrosis in HL60 cells and lysis of shorter lipid bilayer region. C_LIO_LICryo-EM of small PFT in the cellular environment. C_LIO_LIStructural characterization of heptameric pore, pore-like, and pre-pore complex in the presence of RBCs. C_LIO_LIBilayer phase behavior (Ld/Lo) governs different conformational and geometrical variants of -HL. C_LI

biophysics↗

bioRxiv2024

Multistability and predominant double-positive states in a four node mutually repressive network: a case study of Th1/Th2/Th17/T-reg differentiation

Elucidating the emergent dynamics of complex regulatory networks enabling cellular differentiation is crucial to understand embryonic development and suggest strategies for synthetic circuit design. A well-studied network motif often driving cellular decisions is a toggle switch - a set of two mutually inhibitory lineage-specific transcription factors A and B. A toggle switch often enables two possible mutually exclusive states - (high A, low B) and (low A, high B) - from a common progenitor cell. However, the dynamics of networks enabling differentiation of more than two cell types from a progenitor cell is not well-studied. Here, we investigate the dynamics of four master regulators A, B, C and D inhibiting each other, thus forming a toggle tetrahedron. Our simulations show that a toggle tetrahedron predominantly allows for co-existence of six double positive or hybrid states where two of the nodes are expressed relatively high as compared to the remaining two - (high A, high B, low C, low D), (high A, low B, high C, low D), (high A, low B, low C, high D), (low A, high B, high C, low D), (low A, low B, high C, high D) and (low A, high B, low C, high D). Stochastic simulations showed state-switching among these phenotypes, indicating phenotypic plasticity. Finally, we apply our results to understand the differentiation of naive CD4+ T cells into Th1, Th2, Th17 and Treg subsets, suggesting Th1/Th2/Th17/Treg decision-making to be a two-step process. Our results reveal multistable dynamics and establish the stable co-existence of hybrid cell-states, offering a potential explanation for simultaneous differentiation of multipotent naive CD4+ T cells.

systems biology↗

bioRxiv2023

Graph-pMHC: Graph Neural Network Approach to MHC Class II Peptide Presentation and Antibody Immunogenicity

Antigen presentation on MHC Class II (pMHCII presentation) plays an essential role in the adaptive immune response to extracellular pathogens and cancerous cells. But it can also reduce the efficacy of large-molecule drugs by triggering an anti-drug response. Significant progress has been made in pMHCII presentation modeling due to the collection of large-scale pMHC mass spectrometry datasets (ligandomes) and advances in deep machine learning. Here, we develop graph-pMHC, a graph neural network approach to predict pMHCII presentation. We derive adjacency matrices for pMHCII using Alphafold2-multimer, and address the peptide-MHC binding groove alignment problem with a simple graph enumeration strategy. We demonstrate that graph-pMHC dramatically outperforms methods with suboptimal inductive biases, such as the multilayer-perceptron-based NetMHCIIan-4.0 (+22.84% average precision). Finally, we create an antibody drug immunogenicity dataset from clinical trial data, and develop a method for measuring anti-antibody immunogenicity risk using pMHCII presentation models. In comparison with BioPhis Sapiens score, a deep learning based measure of the humanness of an antibody drug, our strategy achieves a 7.14% ROC AUC improvement in predicting antibody drug immunogenicity.

immunology↗

bioRxiv2022

HLApollo: A superior transformer model for pan-allelic peptide-MHC-I presentation prediction, with diverse negative coverage, deconvolution and protein language features.

Antigen presentation on MHC class I (MHC-I) is key to the adaptive immune response to cancerous cells. Computational prediction of peptide presentation by MHC-I has enabled individualized cancer immunotherapies. Here, we introduce HLApollo, a transformer-based approach with end-to-end modeling of MHC-I sequence, deconvolution, and flanking sequences. To achieve this, we develop a novel training strategy, negative set switching, which greatly reduces overfitting to falsely presumed negatives that are necessarily found in presentation datasets. HLApollo shows a meaningful improvement compared to recent MHC-I models on peptide presentation (20.19% average precision (AP)) and immunogenicity (4.1% AP). As expected, adding gene expression boosts the performance of HLApollo. More interestingly, we show that introduction of features from a protein language model, ESM 1b, remarkably recoups much of the benefits of gene expression in absence of true expression measurements. Finally, we demonstrate excellent pan-allelic generalization, and introduce a framework for estimating the expected accuracy of HLApollo for untrained alleles. This guides the use of HLApollo in a clinical setting, where rare alleles may be observed in some subjects, particularly for underrepresented minorities.

immunology↗

bioRxiv2021

Emergent dynamics of a three-node regulatory network explain phenotypic switching and heterogeneity during helper T-cell differentiation

Naive helper (CD4+) T-cells can differentiate into distinct functional subsets including Th1, Th2, and Th17 phenotypes. Each of these phenotypes has a master regulator - T-bet (Th1), GATA3 (Th2) and ROR{gamma}T (Th17) - that inhibits the other two master regulators. Such mutual repression among them at a transcriptional level can enable multistability, giving rise to six experimentally observed phenotypes - Th1, Th2, Th17, hybrid Th/Th2, hybrid Th2/Th17 and hybrid Th1/Th17. However, the dynamics of switching among these phenotypes, particularly in the case of epigenetic influence, remains unclear. Here, through mathematical modeling, we investigated the coupled transcription-epigenetic dynamics in a three-node mutually repressing network to elucidate how epigenetic changes mediated by any master regulator can influence the transition rates among different cellular phenotypes. We show that the degree of plasticity exhibited by one phenotype depends on relative strength and duration of mutual epigenetic repression mediated among the master regulators in a three-node network. Further, our model predictions can offer putative mechanisms underlying relatively higher plasticity of Th17 phenotype as observed in vitro and in vivo. Together, our modeling framework characterizes phenotypic plasticity and heterogeneity as an outcome of emergent dynamics of a three-node regulatory network, such as the one mediated by T-bet/GATA3/ROR{gamma}T.

systems biology↗

bioRxiv2021

Particle Uptake Driven Phagocytosis in Macrophages and Neutrophils Enhances Bacterial Clearance

Humans are exposed to numerous synthetic foreign particulates in the form of environmental pollutants and diagnostic or therapeutic agents. Specialized immune cells (phagocytes) clear these particulates by phagocytosing and attempting to degrade them. The process of recognition and internalization of the particulates may trigger changes in the function of phagocytes. Some of these changes, especially the ability of a particle-loaded phagocyte to take up and neutralize pathogens, remains poorly studied. Herein, we demonstrate that the uptake of non-stimulatory cargo-free particles enhances the phagocytic ability of monocytes, macrophages and neutrophils. The enhancement in phagocytic ability was independent of particle properties, such as size or the base material constituting the particle. Additionally, we show that the increased phagocytosis was not a result of cellular activation or cellular heterogeneity but was driven by changes in cell membrane fluidity and cellular compliance. A consequence of the enhanced phagocytic activity was that particulate-laden immune cells neutralize E. coli faster in culture. Moreover, when administered in mice as a prophylactic, particulates enable faster clearance of E. coli and S. epidermidis. Together, we demonstrate that the process of uptake induces cellular changes that favor additional phagocytic events. This study provides insights into using non-stimulatory cargo-free particles to engineer immune cell functions for applications involving faster clearance of phagocytosable particulates.

immunology↗

bioRxiv2021

Phagocytic Uptake of Particles by Immune cells Under Flow Conditions

Particles injected intravenously are thought to be cleared by macrophages residing in the liver and spleen, but they also encounter circulating immune cells. It remains to be established if the circulating cells can take up particles while flowing, and if the uptake capacity is similar under static and flow conditions. Here, we use an in vitro peristaltic pump setup that mimics pulsatile blood flow to determine if immune cells take up particles under constant fluidic flow. We use polystyrene particles of varying sizes as the model of a polymeric particle for these studies. Our results show that the immune cells do phagocytose under flow conditions. We demonstrate that cell lines representing myeloid cells, primary human neutrophils and monocytes take up sub-micrometer-sized particles at similar or better rates under flow compared to static conditions. Experiments with whole human blood show that even under the crowding effects of red blood cells, neutrophils and monocytes take up particles while flowing. Together, these data suggest that circulating immune cells are likely to phagocytose intravenously injected particulates, which has implications for design of particles to evade or target these cells.

bioengineering↗

bioRxiv2021

Sterile Inflammation Alters Neutrophil Kinetics in Mice

Neutrophils play a crucial role in establishing inflammation in response to an infection or injury, but their production rates, as well as blood and tissue residence times, remain poorly characterized under these conditions. Herein, using a biomaterial implant model to establish inflammation followed by in vivo tracking of newly formed neutrophils, we determine neutrophil kinetics under inflammatory conditions. To obtain quantifiable information from our experimental observations, we develop an ordinary differential equation-based mathematical model to extract kinetic parameters. Our data show that in the presence of inflammation resulting in emergency granulopoiesis-like conditions, neutrophil maturation time in the bone marrow and half-life in the blood reduces by about 40%, compared to non-inflammatory conditions. Additionally, neutrophil residence time at the inflammatory site increases by two-fold. Together, these data improve our understanding of neutrophil kinetics under inflammatory conditions, which could pave the way for therapies that focus on modulating in vivo neutrophil dynamics.

immunology↗

bioRxiv2020

Immuno-Phenotyping of High-Grade Glioma Infiltrating Immune Cells Reveals Grade Specific Differences in Cells of Myeloid Origin

Gliomas are heavily infiltrated with immune cells of myeloid origin. Past studies have shown that high-grade gliomas have a higher proportion of alternatively activated and suppressive myeloid cells when compared to low-grade gliomas, which correlate with poor prognosis. However, the differences in immune cell phenotypes within high-grade gliomas (between grade III and IV) are relatively less explored, and a correlation of phenotypic characteristics between immune cells in the blood and high grade tumors has not been performed. Additionally, myeloid cells of granulocytic origin present in gliomas remain poorly characterized. Herein, we address these questions through phenotypic characterizations of monocytes and neutrophils present in blood and tumors of individuals with glioblastoma (GBM, grade IV) or grade III gliomas. Our data show that CD163 expressing M2 monocytes are present in greater proportions in GBM tissue when compared to grade III glioma tissue. In addition, we observe that neutrophils are highly heterogeneous among individuals with glioma, and a greater proportion of granulocytic myeloid-derived suppressor cells are present in grade III gliomas when compared to GBM. Finally, we show that the expression levels of CD86 and CD63 showed a high correlation between blood and tumor, and suggest that these may be used as possible markers for prognosis.

immunology↗

bioRxiv2020

Multistability in cellular differentiation enabled by a network of three mutually repressing master regulators

Identifying the design principles of complex regulatory networks driving cellular decision-making remains essential to decode embryonic development as well as enhance cellular reprogramming. A well-studied network motif involved in cellular decision-making is a toggle switch - a set of two opposing transcription factors A and B, each of which is a master regulator of a specific cell-fate and can inhibit the activity of the other. A toggle switch can lead to two possible states - (high A, low B) and (low A, high B), and drives the either-or choice between these two cell-fates for a common progenitor cell. However, the principles of coupled toggle switches remains unclear. Here, we investigate the dynamics of three master regulators A, B and C inhibiting each other, thus forming three coupled toggle switches to form a toggle triad. Our simulations show that this toggle triad can lead to co-existence of cells into three differentiated single positive phenotypes - (high A, low B, low C), (low A, high B, low C), and (low A, low B, high C). Moreover, the hybrid or double positive phenotypes - (high A, high B, low C), (low A, high B, high C) and (high A, low B, high C) - can co-exist together with single positive phenotypes. Including self-activation loops on A, B and C can increase the frequency of double positive states. Finally, we apply our results to understand the cellular decision-making in terms of differentiation of naive CD4+ T cells into Th1, Th2 and Th17 states, where hybrid Th1/Th2 and hybrid Th1/Th17 cells have been reported in addition to the Th1, Th2 and Th17 ones. Our results offer novel insights into the design principles of a multistable network topology and provides a framework for synthetic biology to design tristable systems.

systems biology↗

bioRxiv2020

Mechanistic modeling of the SARS-CoV-2 and immune system interplay unravels design principles for diverse clinicopathological outcomes

The disease caused by SARS-CoV-2 is a global pandemic that threatens to bring long-term changes worldwide. Approximately 80% of infected patients are asymptomatic or have mild symptoms such as fever or cough, while rest of the patients have varying degrees of severity of symptoms, with 3-4% mortality rate. Severe symptoms such as pneumonia and Acute Respiratory Distress Syndrome can be caused by tissue damage mostly due to aggravated and unresolved innate and adaptive immune response, often resulting from a cytokine storm. However, the mechanistic underpinnings of such responses remain elusive, with an incomplete understanding of how an intricate interplay among infected cells and cells of innate and adaptive immune system can lead to such diverse clinicopathological outcomes. Here, we use a dynamical systems approach to dissect the emergent nonlinear intra-host dynamics among virally infected cells, the immune response to it and the consequent immunopathology. By mechanistic analysis of cell-cell interactions, we have identified key parameters affecting the diverse clinical phenotypes associated with COVID-19. This minimalistic yet rigorous model can explain the various phenotypes observed across the clinical spectrum of COVID-19, various co-morbidity risk factors such as age and obesity, and the effect of antiviral drugs on different phenotypes. It also reveals how a fine-tuned balance of infected cell killing and resolution of inflammation can lead to infection clearance, while disruptions can drive different severe phenotypes. These results will help further the case of rational selection of drug combinations that can effectively balance viral clearance and minimize tissue damage simultaneously. Significance StatementThe SARS-CoV-2 pandemic has already infected millions of people, and thousands of lives have been lost to it. The pandemic has already tested the limits of our public healthcare systems with a wide spectrum of clinicopathological symptoms and outcomes. The mechanistic underpinnings of the resultant immunopathology caused by the viral infection still remains to be elucidated. Here we propose a minimalistic but rigorous description of the interactions of the virus infected cells and the core components of the immune system that can potentially explain such diversity in the observed clinical outcomes. Our proposed framework could enable a platform to determine the efficacy of various treatment combinations and can contributes a conceptual understanding of dynamics of disease pathogenesis in SARS-CoV-2 infections.

systems biology↗

bioRxiv2020

In silico tools for accurate HLA and KIR inference from clinical sequencing data empower immunogenetics on individual-patient and population scales

Immunogenetic variation in humans is important in research, clinical diagnosis and increasingly a target for therapeutic intervention. Two highly polymorphic loci play critical roles, namely the human leukocyte antigen (HLA) system, which is the human version of the major histocompatibility complex (MHC), and the Killer-cell immunoglobulin-like receptors (KIR) that are relevant for responses of Natural Killer (NK) cells and some subsets of T cells. Their accurate classification has typically required the use of dedicated biological specimens and a combination of in vitro and in silico efforts. Increased availability of next generation sequencing data has led to the development of ancillary computational solutions. Here, we report an evaluation of recently published algorithms to computationally infer complex immunogenetic variation in the form of HLA alleles and KIR haplotypes from whole-genome or whole-exome sequencing data. For both HLA allele and KIR gene typing, we identified tools that yielded >97% overall accuracy for 4-digit HLA types, and >99% overall accuracy for KIR gene presence, suggesting the readiness of in silico solutions for use in clinical and high-throughput research settings.

bioinformatics↗