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bioRxiv2024

Differential expression of oncogenic lncRNAs NEAT1 and MALAT1 in 2D monolayer vs. 3D tumoroid culture and its implications in cancer progression

Long non-coding RNAs (lncRNAs) have emerged as crucial regulators of cellular processes, overturning their previous classification as "junk" DNA. Their involvement in various cellular functions makes them potential therapeutic targets in a range of diseases, particularly in tumorigenesis and cancer. Among these lncRNAs, MALAT1 (Metastasis-Associated Lung Adenocarcinoma Transcript 1) and NEAT1 (Nuclear-Enriched Abundant Transcript 1) are prominent oncogenic lncRNAs with complex regulatory roles across multiple cancer types. Although the roles of these lncRNAs in cancer have been extensively studied, the majority of these investigations have been conducted in 2D culture systems. While 2D cell cultures are well-established and widely accepted models in cell biology, they lack the physiological complexity of 3D tumor architecture. In contrast, 3D cultures, where cells grow as three-dimensional clusters, better mimic in vivo conditions, making them essential for understanding tumor microenvironment (TME) dynamics. Despite the importance of 3D models, there is a lack of literature exploring lncRNA expression in 2D monolayers versus 3D cultures across different cancer types. This study examines the expression and function of the lncRNAs NEAT1 and MALAT1 in 3D tumoroids compared to 2D monolayer cultures, aiming to bridge the gap between in vitro models and the complex in vivo tumor microenvironment. We addressed this gap by quantifying the expression differences of NEAT1 and MALAT1 using qRT-PCR in breast cancer, liver cancer, cervical cancer, and glioblastoma (GBM) cells grown in both 2D and 3D cultures. Significant variations in NEAT1 and MALAT1 expression were observed between 2D and 3D cultures across these cancer types, signifying the need to study lncRNAs in 3D micro-environments. Furthermore, we established correlations between NEAT1 expression and cancer traits such as stemness, invasion, glucose transporter expression, and epithelial-mesenchymal transition (EMT) in GBM 3D tumoroids compared to 2D monolayers. Using siRNA to downregulate NEAT1 in GBM tumoroids, we demonstrated that reducing NEAT1 expression to the levels comparable to 2D cultures led to a decrease in the expression of mRNA markers associated with stemness, invasion, glucose transporters, and EMT. Additionally, siRNA-mediated downregulation of NEAT1 in 3D tumoroids directly impacted cancer properties, as validated by phenotypic assays, where reduced proliferation, migration, and invasion were observed in GBM (comparing 3D to 2D models). Therefore, our work provides new insights into the expression of two key oncogenic lncRNAs in 3D microenvironments of various cancers and lays the foundation for integrating lncRNAs as important molecular players within 3D culture systems, offering a better understanding of the in vivo complexity of the TME.

cancer biology↗

bioRxiv2020

BABEL enables cross-modality translation between multi-omic profiles at single-cell resolution

Simultaneous profiling of multi-omic modalities within a single cell is a grand challenge for single-cell biology. While there have been impressive technical innovations demonstrating feasibility - for example generating paired measurements of scRNA-seq and scATAC-seq - wide-spread application of joint profiling is challenging due to the experimental complexity, noise, and cost. Here we introduce BABEL, a deep learning method that translates between the transcriptome and chromatin profiles of a single cell. Leveraging a novel interoperable neural network model, BABEL can generate scRNA-seq directly from a cells scATAC-seq, and vice versa. This makes it possible to computationally synthesize paired multi-omic measurements when only one modality is experimentally available. Across several paired scRNA-seq and scATAC-seq datasets in human and mouse, we validate that BABEL accurately translates between these modalities for individual cells. BABEL also generalizes well to new biological contexts not seen during training. For example, starting from scATAC-seq of patient derived basal cell carcinoma (BCC), BABEL generated scRNA-seq that enabled fine-grained classification of complex cell states, despite having never seen BCC data. These predictions are comparable to analyses of the experimental BCC scRNA-seq data. We further show that BABEL can incorporate additional single-cell data modalities, such as CITE-seq, thus enabling translation across chromatin, RNA, and protein. BABEL offers a powerful approach for data exploration and hypothesis generation.

bioinformatics↗

bioRxiv2021

Machine Translation between paired Single Cell Multi Omics Data

BackgroundSingle-cell multi-omics technologies allow the profiling of different data modalities from the same cell. However, while isolated modalities only capture one view of the total information of a biological cell, an integrative analysis capturing the different modalities is challenging. In response, bioinformatics and machine learning methodologies have been developed for multi-omics single-cell analysis. Nevertheless, it is unclear if current tools can address the dual aspect of modality integration and prediction across modalities without requiring extensive parameter finetuning. ResultsWe designed LIBRA, a Neural Network based framework, to learn a translation between paired multi-omics profiles such that a shared latent space is constructed. LIBRA is a state-of-the-art tool when evaluating the ability to increase cell-type (clustering) resolution in the latent space. When assessing the predictive power across data modalities, LIBRA outperforms existing tools. Finally, considering the importance of hyperparameters, we implemented an adaptative-tuning strategy, labelled aLIBRA, in the LIBRA package. As expected, adaptive parameter optimization significantly boosts the performance of learning predictive models from paired datasets. Additionally, aLIBRA provides parameter combinations balancing the integrative and predictive tasks. ConclusionsLIBRA is a versatile tool, uniquely targeting both integration and prediction tasks of Single-cell multi-omics data. LIBRA is a data-driven robust platform that includes an adaptive learning scheme. Furthermore, LIBRA is freely available as R and Python libraries (https://github.com/TranslationalBioinformaticsUnit/LIBRA).

bioinformatics↗

bioRxiv2021

Naive and in vitro-activated primary mouse CD8+ T cells retain in vivo immune responsiveness after electroporation-based CRISPR/Cas9 genetic engineering

CRISPR/Cas9 technology has revolutionized genetic engineering of primary cells. Although its use is gaining momentum in studies on CD8+ T cell biology, it remains elusive to what extent CRISPR/Cas9 affects in vivo function of CD8+ T cells. Here, we optimized nucleofection-based CRISPR/Cas9 genetic engineering of naive and in vitro-activated primary mouse CD8+ T cells and tested their in vivo immune responses. Nucleofection of naive CD8+ T cells preserved their in vivo antiviral immune responsiveness to an extent that is indistinguishable from non-nucleofected cells, whereas in vitro activation of CD8+ T cells prior to nucleofection led to slightly impaired expansion/survival. Of note, different target proteins displayed distinct decay rates after gene editing. This is in stark contrast to a comparable period of time required to complete gene inactivation. Thus, for optimal experimental design, it is crucial to determine the kinetics of the loss of target gene product to adapt incubation period after gene editing. In sum, nucleofection-based CRISPR/Cas9 genome editing achieves efficient and rapid generation of mutant CD8+ T cells without imposing detrimental constraints on their in vivo functions.

immunology↗

bioRxiv2021

Cell cycle stage classification using phase imaging with computational specificity

Traditional methods for cell cycle stage classification rely heavily on fluorescence microscopy to monitor nuclear dynamics. These methods inevitably face the typical phototoxicity and photobleaching limitations of fluorescence imaging. Here, we present a cell cycle detection workflow using the principle of phase imaging with computational specificity (PICS). The proposed method uses neural networks to extract cell cycle-dependent features from quantitative phase imaging (QPI) measurements directly. Our results indicate that this approach attains very good accuracy in classifying live cells into G1, S, and G2/M stages, respectively. We also demonstrate that the proposed method can be applied to study single-cell dynamics within the cell cycle as well as cell population distribution across different stages of the cell cycle. We envision that the proposed method can become a nondestructive tool to analyze cell cycle progression in fields ranging from cell biology to biopharma applications. TeaserWe present a non-destructive, high-throughput method for cell cycle detection combining label-free imaging and deep learning.

bioengineering↗

bioRxiv2024

PP2A catalytic subunit alpha is critically required for CD8+ T cell homeostasis and anti-bacterial responses

While the functions of tyrosine phosphatases in T cell biology have been extensively studied, our knowledge on the contribution of serine/threonine phosphatases in T cells remains poor. Protein phosphatase 2A (PP2A) is one of the most abundantly expressed serine/threonine phosphatases. It is important in thymocyte development and CD4+ T cell differentiation. Utilizing a genetic model in which its catalytic subunit alpha isoform (PP2A C) is deleted in T cells, we investigated its contribution to CD8+ T cell homeostasis and effector functions. Our results demonstrate that T cell intrinsic PP2A C is critically required for CD8+ T cell homeostasis in secondary lymphoid organs and intestinal mucosal site. Importantly, PP2A C deficient CD8+ T cells exhibit reduced proliferation and survival. CD8+ T cell anti-bacterial response is strictly dependent on PP2A C. Expression of Bcl2 transgene rescues CD8+ T cell homeostasis in spleens, but not in intestinal mucosal site, nor does it restore the defective anti-bacterial responses. Finally, proteomics and phosphoproteomics analyses reveal potential targets dependent on PP2A C, including mTORC1 and AKT. Thus, PP2A C is a key modulator of CD8+ T cell homeostasis and effector functions.

immunology↗

bioRxiv2025

A Set of Novel Far-Red Fluorescent Proteins for Temporal Domain Multiplexing and Super-Resolution Imaging

The diverse spectral and photochemical properties of fluorescent proteins enable a variety of imaging applications in cell biology, ranging from cellular and organelle labeling to super-resolution microscopy and multiplexed live cell imaging. Here, we report a set of novel far-red fluorescent proteins, named mfRFP, mfRFP-A, and mCardinal-A, which are characterized by similar fluorescence spectra with excitation/emission at [~]600/660 nm while exhibiting distinct photobleaching rates. Differences in photostability allowed us to perform per-pixel unmixing of the three far-red FPs imaged simultaneously by employing a recently introduced temporal domain multiplexing approach. We demonstrated the application of the temporal domain multiplexing approach with different combinations of far-red fluorescent proteins possessing nearly identical emission spectra by acquiring BrainBow-like images of cellular populations and distinguishing subcellular structures in mammalian cells using a single imaging channel without applying any hardware modifications to the conventional microscope. Unlike previous temporal domain multiplying approaches employing photophysical properties of fluorescent proteins, the current approach is a wide range of microscopy modalities, including 3D imaging with a spinning disk and point scanning confocal microscopy. The most photostable fluorescent protein in the set, mfRFP, was further benchmarked against spectrally similar FPs and applied for super-resolution imaging of structural proteins in mammalian cells and for neuroimaging of model organisms, including mice, zebrafish, and C. elegans.

biochemistry↗

bioRxiv2026

Distinct sensing of BCAAs by mTOR and c-Myc governs T cell proliferation, independent of catabolism

The branched chain amino acids (BCAAs: leucine, isoleucine, valine) are essential amino acids that function as catabolic substrates and signaling molecules via mTORC1. While individual BCAAs have unique roles in organismal and cellular physiology, the mechanisms underlying their individual effects remain poorly understood. We demonstrate that the three BCAAs have distinct roles in T cell biology. We find that isoleucine and valine are necessary and sufficient for quiescence exit and cell division, whereas leucine is dispensable. Mechanistically, these effects are independent of their diverging catabolic fates and instead due to differential sensing of leucine and isoleucine/valine by mTOR and c-Myc. While isoleucine and valine are necessary and sufficient for c-Myc expression, mTORC1 leucine-sensing represses c-Myc and proliferation during BCAA restriction. Together, we find that the discrete sensing of the BCAAs uncouples two major anabolic regulators, mTORC1 and c-Myc, in cell growth. This provides mechanistic insight into the distinct roles of the BCAAs in cell physiology, highlighting divergent BCAA sensing rather than catabolism, and offering a new lens to appreciate their impact on immunity and pathophysiology.

immunology↗

bioRxiv2020

Uncovering Transcriptional Dark Matter via Gene Annotation Independent Single-Cell RNA Sequencing Analysis

Single-cell RNA sequencing (scRNA-seq) enables the study of cell biology with high resolution. scRNA-seq expression analyses rely on the availability of a high quality annotation of genes in the genome. Yet, as we show here with scRNA-seq experiments and analyses spanning human, mouse, chicken, mole rat, lemur and sea urchin, gene annotations often fail to cover the full transcriptome of every cell type at every stage of development, in particular for organisms that are not routinely studied. To overcome this hurdle, we created a scRNA-seq analysis routine that recovers biologically relevant information beyond the scope of the best available gene annotation. This is achieved by performing single-cell expression analysis on any region in the genome for which transcriptional products are detected. Our routine identifies transcriptionally active regions (TARs) using a hidden Markov model, generates a matrix of expression levels for all TARs across all cells in a dataset, performs single-cell TAR expression analysis to identify TARs that are biologically significant, and then annotates biologically significant TARs using gene homology analysis. This procedure leverages single-cell expression analyses as a filter to direct annotation efforts to biologically significant transcripts in complex tissues and thereby uncovers biology to which scRNA-seq would otherwise be in the dark.

bioinformatics↗

bioRxiv2026

Cell-level integration of gene expression and B cell receptor data captures complementary B cell information

Single-cell B cell analyses commonly define relationships among cells using gene expression (GEX), although paired B cell receptor (BCR) sequences provide complementary information about receptor features, differentiation, and clonal relationships. We evaluated multiple approaches for integrating paired GEX and BCR data into cell-level representations and compared their ability to capture complementary aspects of B cell biology. We compared BCR representations based on language-model embeddings and engineered sequence features across multiple integration strategies. Spaces dominated by BCR language-model embeddings captured broad sequence and clonal information but could distort cell type relationships, as assessed using independent cell surface protein measurements. In contrast, concatenating GEX with selected BCR features (isotype, somatic hypermutation frequency, and CDRH3 length) incorporated receptor information while retaining transcriptional and surface-protein-associated structure. The resulting space reduced discordance between GEX-based cell type annotations and BCR characteristics and revealed B-cell substructure supported by both transcriptional and receptor-derived features. Thus, integrating GEX and BCR data captures complementary biological information and provides a richer foundation for single-cell B cell analysis.

bioinformatics↗

bioRxiv2026

Combined Loss of VHL and TSC1 Drives Clear Cell Renal Cell Carcinoma with Metabolic and Redox Reprogramming that Creates an NRF2 Dependency

Cancer genomics studies have implicated combined loss of VHL and TSC1 as sufficient to initiate clear cell renal cell carcinoma (ccRCC) in humans. Here, we engineered a mouse model with kidney-specific deletion of Vhl and Tsc1. VhlF/FTsc1F/FKsp-Cre+ mice developed multifocal renal cell carcinomas starting at 7 weeks of age with 100% penetrance, and the resulting Vhl-/-Tsc1-/- tumors recapitulated the histopathology of human VHL-/-TSC1-/- ccRCC. Integrated transcriptomic and metabolomic analyses of 4-week-old preneoplastic renal cortices deficient for Vhl and Tsc1 demonstrated heightened HIF and mTORC1 signaling and unexpectedly revealed activation of an antioxidant response regulated by NRF2 (NFE2L2). Biochemical, cell biological, multi-omic, and xenograft studies identified an NRF2 dependency in human VHL-/-TSC1-/- ccRCC cells. Altogether, we report a mouse model that recapitulates human VHL-/-TSC1-/- ccRCC, delineate metabolic and redox reprogramming driven by unrestrained HIF and mTORC1 activation in the renal cortex, and identify NRF2 as a potential therapeutic vulnerability.

cancer biology↗

bioRxiv2024

Epstein-Barr virus reactivation induces divergent abortive, reprogrammed, and host shutoff states by lytic progression

Viral infection leads to heterogeneous cellular outcomes ranging from refractory to abortive and fully productive states. Single cell transcriptomics enables a high resolution view of these distinct post-infection states. Here, we have interrogated the host-pathogen dynamics following reactivation of Epstein-Barr virus (EBV). While benign in most people, EBV is responsible for infectious mononucleosis, up to 2% of human cancers, and is a trigger for the development of multiple sclerosis. Following latency establishment in B cells, EBV reactivates and is shed in saliva to enable infection of new hosts. Beyond its importance for transmission, the lytic cycle is also implicated in EBV-associated oncogenesis. Conversely, induction of lytic reactivation in latent EBV-positive tumors presents a novel therapeutic opportunity. Therefore, defining the dynamics and heterogeneity of EBV lytic reactivation is a high priority to better understand pathogenesis and therapeutic potential. In this study, we applied single-cell techniques to analyze diverse fate trajectories during lytic reactivation in two B cell models. Consistent with prior work, we find that cell cycle and MYC expression correlate with cells refractory to lytic reactivation. We further found that lytic induction yields a continuum from abortive to complete reactivation. Abortive lytic cells upregulate NF{kappa}B and IRF3 pathway target genes, while cells that proceed through the full lytic cycle exhibit unexpected expression of genes associated with cellular reprogramming. Distinct subpopulations of lytic cells further displayed variable profiles for transcripts known to escape virus-mediated host shutoff. These data reveal previously unknown and promiscuous outcomes of lytic reactivation with broad implications for viral replication and EBV-associated oncogenesis. AUTHOR SUMMARY / SIGNIFICANCEViral infections profoundly alter host cell biological programming in ways that potentiate disease. Epstein-Barr virus (EBV) is a particularly prevalent human pathogen associated with diverse cancers and several autoimmune disorders. EBV predominantly establishes latent infection in B cells and can promote B cell malignancies through functions of well-characterized latent oncoproteins. Aspects of the viral lytic cycle also clearly contribute to EBV-associated diseases, although pathologic roles of lytic reactivation are incompletely understood. Here we use single-cell techniques to examine cellular responses to EBV lytic reactivation in multiple B cell models. Consistent with prior studies, reactivation from latency is incomplete (abortive) in some cells and successful in others. Abortive and full lytic trajectories exhibit distinct biological responses that each may promote pathogenesis and reinforce bimodal latent-lytic control. Intriguingly, a portion on cells that proceed through the lytic cycle exhibits unexpected and striking expression of genes associated with cellular reprogramming, pluripotency, and self-renewal. Collectively, this study provides a valuable resource to understand diverse host-virus dynamics and fates during viral reactivation and identifies multiple modes of EBV lytic pathogenesis to investigate in future research.

microbiology↗

bioRxiv2022

Exploiting the Nonlinear Structure of the Antithetic Integral Controller to Enhance Dynamic Performance

The design of biomolecular feedback controllers has been identified as an important goal across a broad range of biological applications spanning synthetic biology, cell therapy, metabolic engineering, etc. This originates from the need to regulate various cellular processes in a robust and timely fashion. Recently, antithetic integral controllers found their way into synthetic biology due to the Robust Perfect Adaptation (RPA) property they endow -- the biological analogue of robust steady-state tracking. The antithetic integral motif hinges on a sequestration reaction between two molecules that annihilates their function. Here, we demonstrate that the complex resulting from the nonlinear sequestration reaction can be leveraged as an inhibitor to enhance the dynamic performance while maintaining the RPA property. We establish that this additional inhibition by the sequestration complex gives rise to a filtered Proportional-Integral (PI) controller thus offering more flexibility in shaping the dynamic response and reducing cell-to-cell variability. Furthermore, we explore the effect of various biological inhibitory mechanisms on the overall performance. The various analyses in the paper are carried out using analytical tools and are supported by numerical simulations. Finally, an experimental validation is performed using the cyberloop -- a hybrid platform where the controller is implemented in silico to control a genetic circuit in vivo.

synthetic biology↗

bioRxiv2025

Pseudo-time trajectory of single-cell lipidomics: Suggestion for experimental setup and computational analysis

Cellular heterogeneity is a fundamental facet of cell biology, influencing cellular signaling, metabolism, and gene regulation. Its accurate quantification requires measurements at the single-cell level. Most high-throughput single-cell technologies provide only a snapshot of cellular heterogeneity at a specific time point because the measurement is destructive. This limits our current ability to understand the dynamics of cellular behavior and quantify cell-specific parameters. We propose an experimental setup combined with a model-based analysis framework, enabling the extraction of longitudinal data from a single destructive measurement. Although broadly applicable, we focus on lipid metabolism, a domain where obtaining longitudinal single-cell data has remained elusive due to technical constraints. Our method leverages multiple labels whose measurements are linked to a shared dynamic. This allows the estimation of cell-specific parameters and the quantification of heterogeneity. This framework establishes a foundation for future investigations, providing a roadmap toward a deeper understanding of dynamic cellular processes.

systems biology↗

bioRxiv2020

Optical Tweezer Stretching of Miniature Coarse-Grained Red Blood Cells

Due to the high computational cost of full-cell coarse-grained molecular dynamics modelling, being able to simulate "miniature" cells that effectively represent their full-sized counterparts would be highly advantageous. To accurately represent the morphological and elastic properties of a human red blood cell in silico, such a model is employed utilising the molecular dynamics package LAMMPS. The scale invariance of the model is first tested qualitatively by following the shape evolution of red blood cells of various diameters, then quantitatively by evaluating the membrane shear modulus from simulations of optical tweezer-style stretching. Cells of physical diameter of at least 0.5{micro}m were able to form the characteristic biconcave shape of human red blood cells, though smaller cells instead equilibrated to bowl-shaped stomatocytes. A positive correlation was found between the cell size and both magnitude of deformation from optical tweezer stretching and scaled shear modulus, indicating a lack of scale invariance in the models elastic response. However, the stable morphology and measured shear modulus of the 0.5 - 1.0{micro}m diameter cells are deemed close enough to past in vitro studies on human red blood cells for them to still offer valuable use in making simplified predictions of whole-cell mechanics. SIGNIFICANCEThe study tests the invariance of a coarse-grained molecular dynamics red blood cell (RBC) model to system scale, asking whether it is qualitatively and quantitatively viable to perform whole-cell simulations in "miniature". Simulating cells at a reduced scale greatly improves computational speed, making possible computational experiments that would otherwise be too computationally demanding. This facilitates the simulation of larger systems, both in number of whole-cells, and cells of greater structural complexity than the RBC. More generally, the accurate and efficient modelling of biological cells allows computational experimentation of real-world systems that would be very challenging or impossible to perform in vitro. Therefore, miniature-cell modelling could help both direct development in whole-cell modelling, and also developments in more widespread bio-physical studies.

biophysics↗

bioRxiv2021

Molecular basis for Ras suppressor-1 recruitment to focal adhesions and stabilization of consensus adhesome complex

Ras suppressor-1 (Rsu-1) is a leucine-rich repeat (LRR)-containing protein that is crucial for regulating fundamental cell adhesion processes and tumor development. Rsu-1 interacts with a zinc-finger type multi LIM domain-containing adaptor protein PINCH-1 involved in the integrin-mediated consensus adhesome but not with highly homologous isoform PINCH-2. However, the structural basis for such specific interaction and regulatory mechanism remains unclear. Here, we determined the crystal structures of Rsu-1 and its complex with the PINCH-1 LIM4-5 domains. Rsu-1 displays an arc-shaped solenoid architecture with eight LRRs shielded by the N- and C-terminal capping modules. We show that a large conserved concave surface of the Rsu-1 LRR domain recognizes the PINCH-1 LIM5 domain, and that the C-terminal non-LIM region of PINCH-2 but not PINCH-1 sterically disfavors the Rsu-1 binding. We further show that Rsu-1 can be assembled, via PINCH-1-binding, into a tight hetero-pentamer complex comprising Rsu-1, PINCH-1, ILK, Parvin, and Kindlin-2 that constitute a major consensus integrin adhesome crucial for focal adhesion assembly. Consistently, our mutagenesis and cell biological data consolidate the significance of the Rsu-1/PINCH-1 interaction in focal adhesion assembly and cell spreading. Our results provide a crucial molecular insight into Rsu-1-mediated cell adhesion with implication on how it may regulate tumorigenic growth.

biochemistry↗

bioRxiv2023

Accumulation of TCR signaling from self-antigens in naive CD8 T cells mitigates early responsiveness

The cumulative effects of T cell receptor (TCR) signal transduction over extended periods of time influences T cell biology, such as the positive selection of immature thymocytes or the proliferative responses of naive T cells. Naive T cells experience recurrent TCR signaling in response to self-antigens in the steady state. However, how these signals influence the responsiveness of naive CD8+ T cells to subsequent agonist TCR stimulation remains incompletely understood. We investigated how naive CD8+ T cells that experienced relatively low or high levels of TCR signaling in response to self-antigens respond to stimulation with foreign antigens. A transcriptional reporter of Nr4a1 (Nur77-GFP) revealed substantial heterogeneity of the amount of TCR signaling naive CD8+ T cells accumulate in the steady state. Nur77-GFPHI cells exhibited diminished T cell activation and secretion of IFN{gamma} and IL-2 relative to Nur77-GFPLO cells in response to agonist TCR stimulation. Differential gene expression analyses revealed upregulation of genes associated with acutely stimulated T cells in Nur77-GFPHI cells but also increased expression of negative regulators such as the phosphatase Sts1. Responsiveness of Nur77-GFPHI cells to TCR stimulation was partially restored at the level of IFN{gamma} secretion by deficiency of Sts1 or the ubiquitin ligase Cbl-b. Our data suggest that extensive accumulation of TCR signaling during steady state conditions induces a recalibration of the responsiveness of naive CD8+ T cells through gene expression changes and negative regulation, at least in part, dependent on Sts1 and Cbl-b. This cell-intrinsic negative feedback loop may allow the immune system to limit the autoreactive potential of highly self-reactive naive CD8+ T cells.

immunology↗

bioRxiv2024

Microsporidian Nosema bombycis secretes serine protease inhibitor to suppress host cell apoptosis via caspase BmICE

Microsporidia are a group of intracellular pathogens that actively manipulate host cell biological processes to facilitate their intracellular niche. Apoptosis is an important defense mechanism by which host cell control intracellular pathogens. Microsporidia modulating host cell apoptosis has been reported previously, however the molecular mechanism is not yet clear. In this report, we describe that the microsporidia Nosema bombycis inhibits apoptosis of Bombyx mori cells through a secreted protein NbSPN14, which is a serine protease inhibitor (Serpin). An immunofluorescent assay demonstrated that upon infection with N. bombycis, NbSPN14 was initially found in the B. mori cell cytoplasm and then became enriched in the host cell nucleus. Overexpression and RNA-interference (RNAi) of NbSPN14 in B. mori embryo cells confirmed that NbSPN14 inhibited host cell apoptosis. Immunofluorescent and Co-IP assays verified the co-localization and interaction of NbSPN14 with the BmICE, the caspase 3 homolog in B. mori. Knocking out of BmICE or mutating the BmICE-interacting P1 site of NbSPN14, eliminated the localization of NbSPN14 into the host nucleus and prevented the apoptosis-inhibiting effect of NbSPN14, which also proved that the interaction between BmICE and NbSPN14 occurred in host cytoplasm and the NbSPN14 translocation into host cell nucleus is dependent on BmICE. These data elucidate that N. bombycis secretory protein NbSPN14 inhibits host cell apoptosis by directly inhibiting the caspase protease BmICE, which provides an important insight for understanding pathogen-host interactions and a potential therapeutic target for N. bombycis proliferation. Author SummaryMicrosporidia constitute a class of eukaryotic pathogens that exclusively reside within host cells. The species Nosema bombycis is the first microsporidian identified as the pathogen of silkworm Pebrine disease. In our research, we discovered how N. bombycis cleverly evades the hosts defenses. It has developed a strategy to survive inside host cells by manipulating host cell apoptosis, disarming the host cells self-destruct mechanism. In this study, we discovered that the N. bombycis secretes a serine protease inhibitor named NbSPN14, which infiltrates the cytoplasm of the host cell. The NbSPN14 interacts with the executioner Caspase protease BmICE within the silkworms apoptotic pathway, effectively neutralizing its apoptoic activity and thus curbing the apoptosis of the host cells.

microbiology↗