Biology subjects

Das, J.

Publications and source records attributed to Das, J..

At least 37 records · Page 2Linked to original sources

bioRxiv2025

Characterizing spatial functional microniches with SpaceTravLR

The advent of spatial omics has revolutionized our understanding of tissue biology; however, these technologies remain largely descriptive and do not capture how changes in gene regulation propagate across spatial neighborhoods. While in-silico perturbation methods and foundation models aim to model the impact of genetic perturbations, these methods are limited to single-cell approaches that lack spatial resolution. Other studies can delineate morphological domains based on transcriptional similarity, but not spatial functional microniches. We address this major unmet need by developing SpaceTravLR (Spatially perturbing Transcription factors, Ligands and Receptors), a novel interpretable machine learning approach that generalizes across tissues and species, uncovering spatial features linked to functional outcomes, thereby capturing functional microniches with spatial resolution. SpaceTravLR infers how single or combinatorial genetic perturbations rewire signals across the tissue neighborhood, by propagating effects through underlying spatially resolved molecular networks, thereby modeling how perturbations can reshape both the targeted cell and its surrounding neighborhood. SpaceTravLR defines novel spatial microniches across a range of tissues at different scales of organization (niches, neighborhoods and tissues), disease and developmental contexts. SpaceTravLRs perturbation predictions are made solely from spatial omics data and closely align with experimental validation or known outcomes based on mechanistic studies. Critically, our approach enables the generation of mechanistic hypotheses underlying identified niches. We show SpaceTravLR discovered a novel mechanism for Ccr4 that drives the spatial location of a pathogenic population of allergen-specific T helper 2 (Th2) cells as they develop in the lymph node, which was experimentally validated in a murine model. Overall, SpaceTravLR provides a novel interpretable and experimentally validated framework for uncovering how genes act individually and combinatorially through cell-intrinsic and cell-extrinsic circuits to shape spatial tissue organization and function.

systems biology↗

bioRxiv2025

Pathogenic mutations in Clathrin Heavy Chain associated with intellectual disability impair synaptic architecture and learning in Drosophila

Clathrin-mediated endocytosis is essential for neural development and function. Recent studies have linked de novo mutations in the clathrin heavy chain (Chc) gene to a range of neurodevelopmental disorders. In this study we have modelled two pathogenic mutations: L1047P and W1108R in Drosophila melanogaster and examined their effects on vesicle dynamics, ligand uptake, neuronal development and memory formation. Our data shows that expression of these mutant forms of Chc result in reduced survival and defective learning when expressed ubiquitously or exclusively in neurons. Our analysis also reveals that these mutations have the ability to disrupt vesicle dynamics and reduce ligand uptake in cells. Although we do not see a defect in neuronal morphology and function at the larval neuro-muscular junction, we see an increase in the number of Dlg-negative boutons, and a significant reduction in Spectrin expression, indicative of disruptions in the process of synapse maturation. Overall, this study provides mechanistic insights into the cellular and molecular basis of Chc-related neurodevelopmental disorders.

cell biology↗

bioRxiv2025

A unified network systems approach uncovers a core novel program underlying T follicular helper cell differentiation

T follicular helper (Tfh) cells are central to the adaptive immune response and exhibit remarkable functional diversity and plasticity. The complex nature of Tfh cell populations, inconsistent findings across experimental systems and potential differences across species have fueled ongoing debate regarding core regulatory pathways that govern Tfh differentiation. Many studies have experimentally investigated individual proteins and circuits involved in Tfh differentiation in limited contexts, each providing only a partial understanding of the process. To address this, we adopted a novel multi-scale network systems approach that incorporates both regulatory and protein-protein interactions. Our approach integrates diverse data types, captures regulation across multiple levels of immune system organization, and recapitulates known drivers. Further, we discover a core Tfh gene set that is conserved across tissue types and disease contexts, and is consistent across data modalities - bulk, single-cell and spatial. While components of this set have been individually reported, a novel aspect of our work lies in the discovery, characterization, and connectivity of this core signature using a single unbiased approach. Using this method, we also uncover a novel function of IL-12, a molecule with reported conflicting functions, in the regulation of Tfh differentiation. Notably, we find that, in both humans and mice, IL-12 is permissive for the differentiation of Tfh precursors, but blocks subsequent differentiation into GC Tfh cells. Overall, this work elucidates novel networks with unexplored roles in governing Tfh cell differentiation across species and tissues, paving the way for novel -therapeutic interventions.

immunology↗

bioRxiv2025

Immune and non-immune cell fencing of tumor cells is a widespread and functionally relevant spatial pattern in solid cancers

Solid tumors are characterized by a spatially heterogeneous mixture of cancer cells, immune cells and other non-tumor cells. Recent characterization of the heterogeneity at the single cell level has revealed spatial patterns of different cell types often lacking a simple geometric structure associated with cancer progression. Here we investigated the occurrence of physical fencing of tumor cells by specific immune and non-immune phenotypes in the tumor microenvironment (TME) and the association of these clusters to cancer progression in a wide range solid tumors formed in different organs. We analyzed published patient response and imaging mass cytometry (IMC) datasets from tumor microarrays obtained from tumor tissues in triple-negative breast cancer (279 patients), lung cancer (416 patients), melanoma (30 patients), colorectal cancer (9 patients), glioma (185 patients), and head and neck cancer (139 patients) to characterize the presence of fencing clusters of various cell types and their association with differing patient outcomes. Devising and employing simple mechanistic and stochastic spatially-resolved computational models we quantify the dependence of the pro- and anti-tumor roles of a fencing cluster on the size and the lifetime of the cluster, as well as the chemokine gradient in the local environment. We unveiled that spatial patterns of immune cells, especially through fencing tumor boundary, affects tumor progression and treatment responsiveness to immunotherapy.

cancer biology↗

bioRxiv2025

Inhibiting ezrin triggers formin-mediated actin remodelling reducing cellular mechano-protection

Ezrin plays a crucial role in linking the cortical actin cytoskeleton to the plasma membrane in animal cells. Inactivated by small molecule ezrin inhibitors (EzrInh), ezrin is known to regulate cellular functions like adhesion and motility. However, whether ezrin plays any significant role in protecting cells against mechanical stresses is less explored. We use hypo-osmotic shock (HS) to challenge cells and study the mechano-protective role of ezrin. Inhibition of ezrin phosphorylation led to increased cell rupture rates on hypo-shock, with a greater impact at later time points ([~] 10 min). The time scales match with that of enhanced tension contrast at edges at the basal plane. We also observed a lowered relative retraction rate at protrusions created by hypo-shock and a less contractile mid-plane cortex - both indicate the role of a weakening of the cortex by EzrInh in compromising membrane integrity on hypo-shock. Concomitantly, EzrInh also increased stress fibres (SFs), traction forces and cell spreading clearly indicative of additional cellular pre-stress. Utilizing formin inhibitor (SMIFH2), we showed that formin-mediated actin polymerization was critical for the SF response to EzrInh while it also prevented the subsequent EzrInh-treatment from enhancing cell rupture rates. Together EzrInh reduced mechano-protection in cells by tilting of balance of actins organization from the cortex to SF enhancing formin-mediated SF assembly. HighlightsO_LIHypo-osmotically shocked CHO cells rupture more on ezrin inhibition C_LIO_LIEzrin inhibition weakens contractility of cortical actin but enhances stress fibre numbers at the base C_LIO_LIHypo-osmotic shock creates basal protrusions that are also retracted. C_LIO_LIRetractive potential in cells is weakened by ezrin inhibition C_LIO_LIFormin inhibitor prevents net increase in stress fibres by inhibition of ezrin C_LIO_LIOn pre-treatment with formin inhibitor, subsequent inhibition of ezrin doesnot render cells more vulnerable to hypo-osmotic shock C_LI

biophysics↗

bioRxiv2025

Expanding the DNA Motif Lexicon of the Transcriptional Regulatory Code

Transcriptional regulatory sequences in metazoans contain intricate combinations of transcription factor (TF) motifs. Stereospecific arrangements of simple motifs constitute composite elements (CEs) that enhance DNA-protein interaction specificity and enable combinatorial regulatory logic. Despite their importance, CEs remain underexplored. We advance CE discovery and functional characterization by developing an integrated framework that combines computational prediction, experimental testing and deep learning. The extended TF motif catalog comprises both synergistic and counteracting CEs, which are supported by evidence of TF binding in vivo and in vitro. A deep learning model GRACE trained on customized massively parallel reporter assays learns the lexicon of CEs at single-nucleotide resolution. Comparative analysis with a neural network model trained on chromatin accessibility demonstrates striking convergence and distinctions within the expanded regulatory lexicon, enabling joint predictions of motif contributions and the impact of variants on chromatin structure and transcriptional activity in diverse cellular contexts.

genomics↗

bioRxiv2025

Loss of B cell tolerance at the T2/T3a B cell transition is a convergent pathogenic mechanism in common variable immunodeficiency

Many patients with common variable immunodeficiency (CVID), including those with CTLA4 deficiency, NFKB1 variants and activated PI3K-delta syndrome (APDS), develop autoimmunity that is refractory to treatment. Despite this shared clinical phenotype, a unifying mechanism for the breakdown of B cell tolerance across monogenic forms of CVID has not been established. Here, we demonstrate that patients with loss-of-function NFKB1 variants, like those with CTLA4 variants and APDS, exhibit dysregulated CD4+ T cell expansion, accumulation of transitional B cells, and a relative lack of follicular B cells. In patients with monogenic CVID and clinical autoimmunity, we observed a relative expansion of transitional and activated naive (aN: CD21loCD11chi) B cells in peripheral blood accompanied by a marked increase in the frequency of VH4-34 expressing autoreactive 9G4+ B cells, which expanded between T2 and T3a transitional B cell stages. Single-cell transcriptomic and B cell receptor analysis further revealed a marked expansion of activated T1/2, T3 and extrafollicular activated naive and double negative (DN: IgD-CD27-) B cell subsets in APDS patients. Notably, one B cell subset appeared exclusively in the APDS disease state, characterized by high oxidative phosphorylation in transitional B cells, specifically. In APDS patients, we also observed a clonal expansion of specific extrafollicular class-switched DN B cells, which were clonally derived from activated transitional B cells. DN B cells were also identified in APDS lung tissue, consistent with the contribution of activated, extrafollicularly-derived B cells to tissue inflammation. Together, these findings suggest that in many patients with CVID and autoimmune features, premature activation of autoreactive transitional T2 and T3a B cells induces the survival and expansion, rather than the tolerization and elimination, of self-reactive B cells. This process leads to extrafollicular expansion of autoreactive B cells capable of tissue infiltration. One Sentence SummaryLoss of transitional B cell tolerance and extrafollicular expansion of autoreactive B cells drive autoimmunity in monogenic causes of CVID.

immunology↗

bioRxiv2025

Tumor-Educated-Platelets interact with Breast Cancer-Stem-Cells via P-selectin- PSGL1 and ensure stemness and metastasis through WNT-β-Catenin-VEGF-VEGFR2 intra-cellular signaling: Therapeutic modulation by aspirin

BackgroundProtagonistic role of platelets promote capillary infiltration of tumors for distant metastasis along with immunosurveillance. Despite existing reports highlighting role of platelets in tumorigenesis, its impact on breast cancer stem cells (BCSCs) remain underexplored. Our first ever report on murine and human system, accentuate that, tumor educated platelets (TEPs) of luminal A and TNBC subtypes are distinct from healthy counterparts, collaborating with BCSCs to generate sub-variants that elevate tumor aggressiveness. MethodsImpact of TEPs on BCSCs was evaluated from primary breast tumor and blood samples of luminal A/TNBC patients along with EC/4T1 murine breast tumor models and MCF-7/MDA-MB-231 cell lines. For downstream assays, TEPs were co-cultured with breast tumor samples or cell lines, followed by magnetic sorting of lin-CD44+CD24- BCSCs. TEP induced alterations of BCSCs were evaluated from 3D tumorsphere, colony formation, transwell migration, scratch-wound healing, matrigel invasion, in-vitro tube formation assays. Fluorescence-confocal microscopy, RT-PCR, flow-cytometry, western-blotting was utilized to decipher the role of genes and protein involved in stemness, metastasis along with the transcription factors in the downstream signaling cascade, followed by verifications by RNAi. ResultsTEPs have elevated expression of P-selectin and interacts with BCSCs via P-selectin and PSGL1 on BCSCs surface. Treatment with aspirin had restorative impact on P-selectin level, converting TEPs from active to resting platelet (RP) state. Under TEPs influence, BCSCs were tumorigenic, clonogenic, multidrug resistant, invasive with numerous invadopodia and remained skewed towards mesenchymal phenotype. Administration of RP reduced TEP associated BCSC virulence both in-vivo and in-vitro. P-selectin-PSGL1 interaction results in binding of WNT to FRIZZLED followed by stabilization and nuclear translocation of {beta}-Catenin. Nuclear {beta}-Catenin promotes stemness-EMT (Epithelial to mesenchymal transition)- metastasis, along with stimulation of autocrine VEGF-VEGFR2 cascade. Inhibition of WNT and VEGFR2 by RNAi confirmed the critical role of this axis in regulating TEPs influence on BCSCs. ConclusionThese insights into TEPs-BCSC interplay, acknowledges TEPs, as-well-as unveils novel receptor-ligand signaling cascade between TEPs and BCSCs, that could be a beneficial therapeutic strategy to target cancer metastasis. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=122 SRC="FIGDIR/small/657784v1_ufig1.gif" ALT="Figure 1"> View larger version (50K): org.highwire.dtl.DTLVardef@11db5d0org.highwire.dtl.DTLVardef@18b7270org.highwire.dtl.DTLVardef@64c2borg.highwire.dtl.DTLVardef@172d68f_HPS_FORMAT_FIGEXP M_FIG C_FIG Cellular interaction between TEPs P-selectin and BCSCs PSGL1 to regulate stemness, EMT, metastasis and angiogenesis via WNT-{beta}-Catenin-VEGF-VEGFR2 cascade: Modulation by pharmacological inhibitor and RNAi.

cancer biology↗

bioRxiv2025

Molecular Basis of Ionic Suppression of ZAP-70 Dependent T Cell Receptor Activation

Ionic imbalance in the tumor microenvironment alters the tumor-infiltrating T lymphocyte function. High extracellular K+ suppresses T cell function by negatively regulating T cell receptor (TCR) signaling. In contrast, elevated extracellular Na+ enhances T cell effector function by boosting the phosphorylation of TCR signaling modules. Here, we presented a mechanism explaining how the two monovalent cations differently regulate TCR function. At rest, high intracellular K+ uncouples allosteric recruitment of ZAP-70, a key signaling module, to the TCR complex. The formation of antigen TCR complex induces K+ efflux, causing spontaneous recruitment of ZAP-70 to the TCR. Increasing extracellular K+ perturbs K+ efflux and slows ZAP-70 recruitment to the TCR complex, even upon antigen binding. This leads to defects in T cell development and arthritis-like symptoms in juvenile mice. We conclude that K+ dynamics is integral to T cell ligand discrimination and fundamental to turning off the signaling during T cell quiescence.

immunology↗

bioRxiv2025

Neoantigens and Stochastic Fluctuations Regulate T Cell Proliferation in Primary and Metastatic Malignant Brain Tumors

Brain cancer is one of the most aggressive forms of cancer in the central nervous system occurring as primary or metastatic tumors. Sequencing of resected tissues from glioblastoma (GBM) and brain metastases (BrMET) reveals high heterogeneity in neoantigens and T cell receptor (TCR) repertoires. Our analysis of published sequencing data in different spatial regions of tumors GBM and BrMET patients show the presence of T cell clones of sizes with a heavy right-tailed distribution spanning several orders of magnitude (e.g., 1 - 1000 cells) with a few (<10) large clone sizes and many small clones. We investigated how neoantigens in the tumor microenvironment (TME) drive T cell expansion in GBM and BrMET by developing a mechanistic mathematical model based on the interaction of T cells and the neoantigens that incorporates their stochastic proliferation in the immunosuppressive environment and trained it to predict the emergence of T cell clones in different spatial regions. The model accurately predicts the distribution of observed T cell clone sizes and reveals that the strength of interaction between TCR and neoantigen-MHC complex and stochastic T cell proliferation crucially regulates T cell expansion in the TME. It also suggests higher rate of T cell proliferation BrMET compared to GBM. An extended version of the model predicts the ability of individual neoantigens to generate T cell clones in the periphery in patients receiving personalized neoantigen vaccines. Our model may facilitate the discovery of improved peptide combinations in neoantigen vaccine studies. Significance StatementNeoantigen-driven T cell responses are key to immune defense against solid tumors. Multi-region sequencing of brain tumors reveals spatial heterogeneity in neoantigens and T cell repertoires. To understand whether neoantigen-driven T cell expansion underlies the TCR repertoire heterogeneities, we developed a stochastic, mechanistic model of T cell proliferation using published TCR and neoantigen data from primary and metastatic brain tumors. The model accurately predicts clone size distributions, showing faster T cell proliferation in metastases and stronger responses to clonal (shared) neoantigens than to private (region-specific) ones. The model is extended to describe T cell clonal expansion in the periphery in response to neoantigen vaccine in glioblastoma patients. This framework may help design optimal peptide combinations in neoantigen vaccine development.

cancer biology↗

bioRxiv2025

Cell cycle-coupled transcriptional network orchestrates human B cell fate bifurcation

Bifurcation of activated human B cells into plasmablast (PB) and germinal center precursor (preGC) fates underlies protective and autoimmune antibody responses, yet gene regulatory networks (GRNs) governing the alternative trajectories remain poorly defined. Using temporal single-cell multiomics, we assembled state-specific human B cell GRNs spanning four scales: transcription factor (TF)-to-fate, TF-to-gene, cis-regulatory element (CRE)-to-gene and nucleotide-to-CRE. Applying the framework to in vitro differentiated and tonsil B cells revealed concordant regulatory architectures and impacts of in silico TF perturbations. CRISPR perturbations validated many TF-to-fate and TF-to-gene predictions and revealed a mutually repressive BATF-IRF4/PRDM1 network module. The GRNs were used to predict and interpret effects of autoimmune disease variants, uncovering partitioning of disease risk at distinct TF motifs and B cell states. Predicted functional variants were independently supported by chromatin and expression QTLs and reporter assays. A web application enables exploration of B cell regulatory determinants of vaccine responses and autoimmune diseases.

immunology↗

bioRxiv2025

Comprehensive Atomic-Scale 3D Viral-Host Protein Interactomes Enable Dissection of Key Mechanisms and Evolutionary Processes Underlying Viral Pathogenesis

Viral-human protein interactions are critical for viral replication and modulation of the host immune response. Structural modeling of these interactions is vital for developing effective antiviral therapies and vaccines. However, 99% of experimentally determined binary host-viral interactions currently lack structural information. We aimed to address this gap by leveraging computational protein structure prediction methods. Using extensive benchmarking, we found AlphaFold to be the most accurate structure prediction model for host-pathogen protein interactions. We then predicted the structures of 11,666 binary protein interactions across 33 viral families and created the most comprehensive atomic-scale 3D viral-host protein interactomes till date (https://3d-viralhuman.yulab.org). By integrating these interactomes with genetic variation data, we identified population-specific signatures of selection on variants coding for interfaces of viral-human interactions. We also found that viral interaction interfaces were less conserved than non-interface regions, a striking trend that is opposite to what is observed for host interfaces, suggesting different evolutionary pressures. Systematic analyses of interface sharing between host and viral proteins binding to the same host protein revealed mutation rate-dependent differences in interface mimicry. Similar mutation rate-dependent differences were seen in the interface sharing between viral proteins binding to a host protein. We also found that the patterns of E6 protein binding to KPNA2 differed between high- and low-risk oncogenic human papillomaviruses (HPVs), and clustering based on these binding patterns allowed the classification of HPVs with unknown oncogenic risk. Our interface mimicry analyses also unveiled a novel mechanism by which herpes simplex virus-1 UL37 suppresses the antiviral immune response through disruption of the TRAF6-MAVS signalosome interaction. Overall, our comprehensive 3D viral interactomes provide a resource at unprecedented scale and resolution that will enable researchers to explore how variation and signatures of selection influence viral interactions and disease progression. This tool also facilitates the identification of conserved and unique interaction patterns across viruses, empowering researchers to generate testable hypotheses and ultimately accelerate the discovery of novel therapeutic targets and intervention strategies.

systems biology↗

bioRxiv2025

Spatial statistics of submicron size clusters of activating and inhibitory Natural Killer cell receptors in the resting state regulate early time signal discrimination

Natural Killer (NK) cells are lymphocytes of the innate immunity and sense healthy or diseased target cells with activating and inhibitory NK cell receptor (NKR) molecules expressed on the cell surface. The protection provided by NK cells against viral infections and tumors critically depends on their ability to distinguish healthy cells from diseased target cells that express 100- fold more activating ligands. NK cell signaling and activation depend on integrating opposing signals initiated by activating and inhibitory NKRs interacting with the cognate ligands expressed on target cells. A wide range of imaging experiments have demonstrated aggregation of both activating and inhibitory NKRs in the plasma membrane on submicron scales in resting NK cells. How do these submicron size NKR clusters formed in the resting state affect signal discrimination? Using in silico mechanistic signaling modeling with information theory and published superresolution imaging data for two well-studied human NKRs, activating NKG2D and inhibitory KIR2DL1, we show that early time signal discrimination by NK cells depends on the spatial statistics of these clusters. When NKG2D and KIR2DL1 clusters are disjoint in the resting state, these clusters help NK cells to discriminate between target cells expressing low and high doses of the activating cognate ligand, whereas, when the NKR clusters fully overlap the NK cells are unable to distinguish between healthy and diseased target cells. Therefore, the spatial statistics of submicron scale clusters of activating and inhibitory NKRs at the resting state provides an additional layer of control for signal discrimination in NK cells. SignificanceSignal integration of opposing signals initiated by activating and inhibitory NK cell receptors (NKRs) in a noisy environment determine an NK cells response to healthy and diseased target cells. Superresolution microscopy imaging revealed aggregation of NKRs in submicron scales in resting NK cells. Using computational modeling, information theory, and published imaging data, we show when these clusters of the opposing NKRs are disjoint, the NK cells can separate healthy from diseased target cells but fail to do so when the clusters overlap. Thus, spatial statistics of submicron-sized NKR clusters in the resting state provide a lever for distinguishing self from non-self. The results suggest spatial organization of receptors in the resting state in may modulate signal discrimination in immune cells.

immunology↗

bioRxiv2025

Cracking the code of co-authorship networks geo-temporally using interpretable machine learning

An exponential growth in the scientific literature necessitates the development of highly scalable computational tools that can effectively analyze and distill insights from complex, interconnected research landscapes. We introduce Distributed, Interpretable, and Scalable computing for Co-authorship Networks (DISCo-Net), a robust and scalable tool engineered to curate and examine large-scale co-authorship networks by harnessing the power of distributed computing and advanced relational database queries. We use DISCo-Net to analyze co-authorship networks derived from millions of papers in the life sciences and physical sciences over more than two decades. Using a range of deep learning approaches, we surprisingly found that pre-trained zero-shot embeddings from a sentence transformer better captured global co-authorship relationships than a complex graphical attention transformer. Even more surprisingly, a simple interpretable Term Frequency-Inverse Document Frequency (TF-IDF) model performed as well as the Bidirectional Encoder Representations from Transformers (BERT) model. Through topic modeling on TF-IDF document descriptors, we identified nine major research areas prevalent globally over the past 24 years and captured topic-specific shifting trends in scientific output. Our study draws an innovative parallel between collaborative research networks and genomic regulatory structures, applying genomics data analysis methodologies to uncover patterns in global scientific collaboration. This approach reveals interpretable alignments between research interests and human developmental stages, while also identifying emerging influential players in the global research landscape. The findings highlight potential far-reaching consequences of current funding challenges, particularly in the U.S., and offer actionable insights for optimizing resource allocation and fostering innovation in an interconnected global scientific community.

scientific communication and education↗

bioRxiv2025

Natural Killer Cell Receptor Signaling and Activation Depend on Cell Cycle Stages

Receptor signaling in Natural Killer (NK) cells leads to post-translational modification (e.g., phosphorylation) of sub-cellular signaling proteins within minutes of receptor stimulation that eventually give rise to diverse effector functions including cell proliferation. Recent single-cell mass cytometry (i.e., CyTOF) experiments in macrophages showed variations of abundances of phosphorylated signaling proteins across cell cycle states indicating a dependence of cell signaling kinetics on an order of magnitude slower kinetics (~ several hours) of cell cycle transitions. We investigated cell cycle dependence of NKG2D signaling kinetics in NK cells by CyTOF measurements performed on IL-2-treated NKG2D-stimulated primary human CD56dim NK cells. The CyTOF experiments revealed monotonic or semi-monotonic increases of the average protein abundances of the majority of signaling proteins such as pCrkL, pPLC{gamma}2, and pErk, and the degranulation marker protein CD107a with progressing cell cycle states at specific time points post-NKG2D stimulation; however, several proteins such as pVav1, pS6, and pAkt, and early activation marker protein CD69 also showed non-monotonic variations in the average abundances with progressing cell cycle states. We used minimal mathematical and computational models coupling signaling and cell cycle processes to show that non-monotonic variations in the signaling protein abundances with progressing cell cycle stages are likely to arise in situations where protein synthesis and degradation and signaling kinetics are actively regulated by cell cycle processes.

immunology↗

bioRxiv2025

Future flooding tolerant rice germplasm: resilience afforded beyond Sub1A gene

Developing high-yielding, flood-tolerant rice varieties is essential for enhancing productivity and livelihoods in flood-prone ecologies. We explored genetic avenues beyond the well-known SUB1A gene to improve flood resilience in rice. We screened a collection of 6,274 elite genotypes from IRRIs germplasm repository for submergence and stagnant flooding tolerance over multiple seasons and years. This rigorous screening identified 89 outstanding elite genotypes, among which thirty-seven exhibited high submergence tolerance, surpassing the survival rate of SUB1A introgression genotypes by 40-50%. Thirty-five genotypes showed significant tolerance to stagnant flooding, and 17 demonstrated dual tolerance capabilities, highlighting their adaptability to varying flood conditions. The genotypes identified have a broader genetic diversity and harbor 86 key QTLs and genes related to traits such as grain quality, grain yield, herbicide resistance, and various biotic and abiotic traits, highlighting the richness of the identified elite collection. Besides germplasm, we introduce an innovative breeding approach called Transition from Trait to Environment (TTE). TTE leverages a parental pool of high-performing genotypes with complete submergence tolerance to drive population improvement and enable genomic selection in the flood breeding program. Our approach of TTE achieved a remarkable 65% increase in genetic gain for submergence tolerance, with the resulting fixed breeding genotypes demonstrating exceptional performance in flood-prone environments of India and Bangladesh. The elite genotypes identified herein represent invaluable genetic resources for the global rice research community. By adopting the TTE approach, which is trait agonistic, we establish a robust framework for developing more resilient genotypes using advanced breeding tools. Plain Language SummaryTo address climate challenges, an urgent focus is necessary to identify and develop flood-tolerant rice varieties, particularly for flood-prone ecosystems across Asia and Africa. We screened 6,274 elite genotypes from IRRIs germplasm and identified 89 promising lines with improved tolerance to submergence and stagnant flooding. Among these, 37 demonstrated 40-50% greater submergence tolerance than SUB1A introgression lines, 35 exhibited stagnant flooding tolerance, and 17 showed dual tolerance. These genotypes contain 86 key QTLs and genes associated with yield, grain quality, and biotic and abiotic tolerance traits. A new breeding strategy, the Transition from Trait to Environment (TTE) approach, was developed. We achieved a genetic gain of 65% for submergence tolerance in rice using this method. The newly identified germplasm provides invaluable genetic resources for the global rice research community to develop flood-tolerant rice genotypes. Core ideas The SUB1A gene, enabling rice to survive underwater for 14 days, marked a significant breakthrough. We have identified elite genotypes with submergence tolerance significantly surpassing the SUB1A gene-mediated tolerance. The diverse elite genotypes identified harbor 86 key genes and QTLs that affect various traits positively. Developed a unique breeding strategy for implementing population improvement in challenging environments. The new breeding strategy demonstrated a genetic gain of 65% for submergence tolerance.

plant biology↗

bioRxiv2025

CDKN2ALow cancer cells outcompete macrophages for microenvironmental zinc to drive immunotherapy resistance

Anti-PD1 therapies are primarily thought to rely on functional T cell responses; yet tumors with limited T cell infiltration can still benefit, suggesting alternative mechanisms contribute to therapeutic efficacy. Indeed, we found that myeloid-rich, T cell-poor tumor models respond to anti-Pd1, and this is dependent on a cancer cell-macrophage crosstalk mediated by cancer cell Cdkn2a expression. Mechanistically, we found that cancer cells with decreased Cdkn2a expression (Cdkn2aLow), which occurs in [~]50% of all human cancers, reorganize zinc compartmentalization by upregulating the zinc importer Slc39a9 at the plasma membrane. Increased cancer cell plasma membrane Slc39a9 leads to intracellular zinc accumulation in cancer cells and depletion of zinc in the tumor microenvironment (TME), resulting in zinc-starved tumor-associated macrophages (TAMs) with reduced phagocytic activity. Restoring zinc availability in TAMs--via dietary supplementation or Slc39a9 knockdown in cancer cells--reprograms TAMs to a pro-phagocytic state and sensitizes Cdkn2aLow tumors to anti-Pd1 therapy. Remarkably, Slc39a9 knockdown tumors respond to anti-Pd1 in Rag1-/- mice, and co-injection of zinc-replete macrophages is sufficient to drive an anti-Pd1 response in immunodeficient mice, demonstrating the T cell-independent nature of this response. Clinically, TAMs from CDKN2ALow cancer patients show reduced zinc and phagocytosis gene signatures. Moreover, patients with lower circulating zinc levels have significantly worse time-to-event outcomes than those with higher levels. Together, these findings uncover a previously unrecognized mechanism by which Cdkn2aLow cancer cells outcompete TAMs for zinc, impairing their function and limiting anti-Pd1 efficacy. They also provide evidence that macrophages alone, without T cells, can enhance anti-PD1 response through zinc-mediated reprogramming of phagocytosis.

cancer biology↗

bioRxiv2025

An Entropy-Based Approach to Model Selection with Application to Single-Cell Time-Stamped Snapshot Data

Recent single-cell experiments that measure copy numbers of over 40 proteins in individual cells at different time points [time-stamped snapshot (TSS) data] exhibit cell-to-cell variability. Because the same cells cannot be tracked over time, TSS data provide key information about the time-evolution of protein abundances that could yield mechanisms that underlie signaling kinetics. We recently developed a generalized method of moments (GMM) based approach that estimates parameters of mechanistic models using TSS data. However, when multiple mechanistic models potentially explain the same TSS data, selecting the best model (i.e., model selection) is often challenging. Popular approaches like Kullback-Leibler divergence and Akaikes Information Criterion are difficult to implement because the distribution that gave rise to the "noisy" data is only known numerically and approximately. To perform model selection in this situation, we introduce an entropy-based approach that incorporates our GMM based parameter estimation and commonly used estimators in kernel density estimation. Using simulated TSS data, we show that our approach can select the "ground truth" from a set of competing mechanistic models. Furthermore, we use a bootstrap procedure to compute model selection probabilities, which can be useful when measuring the relative support of a candidate model.

biophysics↗