Biology subjects

Sunkara, V.

Publications and source records attributed to Sunkara, V..

8 recordsLinked to original sources

bioRxiv2025

Probabilistic migration events drive transient tissue residency of lymphocytes during homeostasis

Tissue-resident lymphocytes form a phenotypically and functionally distinct analog to the corresponding circulatory lymphocyte populations. Residential CD8 T cells, in particular, are identified as having prolonged residence in the tissues and key functions in recall responses at tissue-environmental interfaces, although the dwell time in individual tissues has yet to be resolved. Residential CD4 T cells, regulatory T cells, B cells, and NK cells have been demonstrated to share phenotypic properties with residential CD8 T cells, but the migratory kinetics are even more poorly defined. Here we used probabilistic modelling on a large parabiosis dataset, covering multiple time-points and tissues, to calculate migration kinetics and dwell times of multiple lymphocyte subsets across a diverse set of tissues. Markov chain modelling identified distinct cell type-specific and tissue-specific residency patterns. The liver and gut were prone to prolonged residency compared to other tissue types, and a hierarchy of residency was observed with CD8 T cells and NK cells demonstrating longer residency than CD4 conventional T cells and regulatory T cells, which in turn resided in tissues longer than B cells. With few exceptions, however, average residency was at least an order of magnitude shorter than the life-span of the mouse, indicating a more dynamic form of steady-state tissue residency than usually assumed. Together these data provide a comprehensive model of a pan-tissue shared program in lymphocyte tissue residence, as well as identifying cell type- and organ-specific modification of the migratory kinetics.

immunology↗

bioRxiv2022

Chimeric U-Net - Modifying the standard U-Net towards Explainability

Healthcare guided by semantic segmentation has the potential to improve our quality of life through early and accurate disease detection. Convolutional Neural Networks, especially the U-Net-based architectures, are currently the state-of-the-art learningbased segmentation methods and have given unprecedented performances. However, their decision-making processes are still an active field of research. In order to reliably utilize such methods in healthcare, explainability of how the segmentation was performed is mandated. To date, explainability is studied and applied heavily in classification tasks. In this work, we propose the Chimeric U-Net, a U-Net architecture with an invertible decoder unit, that inherently brings explainability into semantic segmentation tasks. We find that having the restriction of an invertible decoder does not hinder the performance of the segmentation task. However, the invertible decoder helps to disentangle the class information in the latent space embedding and to construct meaningful saliency maps. Furthermore, we found that with a simple k-Nearest-Neighbours classifier, we could predict the Intersection over Union scores of unseen data, demonstrating that the latent space, constructed by the Chimeric U-Net, encodes an interpretable representation of the segmentation quality. Explainability is an emerging field, and in this work, we propose an alternative approach, that is, rather than building tools for explaining a generic architecture, we propose constraints on the architecture which induce explainability. With this approach, we could peer into the architecture to reveal its class correlations and local contextual dependencies, taking an insightful step towards trustworthy and reliable A.I. Code to build and utilize the Chimeric U-Net is made available under: https://github.com/kenrickschulze/Chimeric-UNet---Half-invertible-UNet-in-Pytorch

bioinformatics↗

bioRxiv2021

Modelling altered signalling of G-protein coupled receptors in inflamed environment to advance drug design

We previously reported the successful design, synthesis and testing of the prototype opioid painkiller NFEPP that does not elicit adverse side effects. Uniquely, this design was based on mathematical modelling of extracellular interactions between G-protein coupled receptors (GPCRs) and ligands, recognizing that GPCRs function differently under pathological versus healthy conditions. We now present a novel stochastic model of GPCR function that includes intracellular dissociation of G-protein subunits and modulation of plasma membrane calcium channels associated with parameters of inflamed and healthy tissue (pH, radicals). The model is validated against in vitro experimental data for NFEPP and fentanyl ligands at different pH values. We found markedly reduced calcium channel inhibition induced by NFEPP at normal pH compared to lower pH, in contrast to the effect of fentanyl, and enhanced constitutive G-protein activation but lower probability of ligand binding with increasing radical concentrations. By means of molecular dynamics simulations, we also assessed qualitative changes of reaction rates due to additional disulfide bridges inside the GPCR binding pocket. The results suggest that, compared to radicals, low pH is a more important determinant of overall GPCR function in an inflamed environment. Future drug design efforts should take this into account.

systems biology↗

bioRxiv2021

Exhausted mature dendritic cells exhibit a slower and less persistent random motility but retain chemotaxis against CCL19

Dendritic cells (DCs), which are immune sentinels in the peripheral tissues, play a number of roles, including patrolling for pathogens, internalising antigens, transporting antigens to the lymph nodes (LNs), interacting with T cells, and secreting cytokines. The well-coordinated migration of DCs under various immunological or inflammatory conditions is therefore essential to ensure an effective immune response. Upon maturation, DCs migrate faster and more persistently than immature DCs (iDCs), which is believed to facilitate CCR7-dependent chemotaxis. It has been reported that lipopolysaccharide-activated DCs produce IL-12 only transiently, and become resistant to further stimulation through exhaustion. However, little is known about the influence of DC exhaustion on cellular motility. Here, we studied the cellular migration of exhausted DCs in tissue-mimicked confined environments. We found that the speed of exhausted matured DCs (xmDCs) decreased significantly compared to active matured DCs (amDCs) and iDCs. In contrast, the speed fluctuation increased compared to that of amDCs and was similar to that of iDCs. In addition, the diffusivity of the xmDCs was significantly lower than that of the amDCs, which implies that DC exhaustion reduces the space exploration ability. Interestingly, CCR7-dependent chemotaxis against CCL19 in xmDCs was not considerably different from that observed in amDCs. Taken together, we report a unique intrinsic cell migration behavior of xmDCs, which exhibit a slower, less persistent, and less diffusive random motility, which results in the DCs remaining at the site of infection, although a well-preserved CCR7-dependent chemotactic motility is maintained.

cell biology↗

bioRxiv2021

Inferring Gene Regulatory Networks from Single Cell RNA-seq Temporal Snapshot Data Requires Higher Order Moments

Single cell RNA-sequencing (scRNA-seq) has become ubiquitous in biology. Recently, there has been a push for using scRNA-seq snapshot data to infer the underlying gene regulatory networks (GRNs) steering cellular function. To date, this aspiration remains unrealised due to technical- and computational challenges. In this work, we focus on the latter, which is under-represented in the literature. We took a systemic approach by subdividing the GRN inference into three fundamental components: the data pre-processing, the feature extraction, and the inference. We saw that the regulatory signature is captured in the statistical moments of scRNA-seq data, and requires computationally intensive minimisation solvers to extract. Furthermore, current data pre-processing might not conserve these statistical moments. Though our moment-based approach is a didactic tool for understanding the different compartments of GRN inference, this line of thinking-finding computationally feasible multi-dimensional statistics of data-is imperative for designing GRN inference methods.

systems biology↗

bioRxiv2020

HIF-stabilization prevents delayed fracture healing

The initial phase of fracture healing decides on success of bone regeneration and is characterized by an inflammatory milieu and low oxygen tension (hypoxia). Negative interference with or prolongation of this fine-tuned initiation phase will ultimately lead to a delayed or incomplete healing such as non-unions which then requires an effective and gentle therapeutic intervention. Common reasons include a dysregulated immune response, immunosuppression or a failure in cellular adaptation to the inflammatory hypoxic milieu of the fracture gap and a reduction in vascularizing capacity by environmental noxious agents (e.g. rheumatoid arthritis, smoking). The hypoxia-inducible factor (HIF)-1α is responsible for the cellular adaptation to hypoxia, activating angiogenesis and supporting cell attraction and migration to the fracture gap. Here, we hypothesized that stabilizing HIF-1α could be a cost-effective and low-risk prevention strategy of fracture healing disorders. Therefore, we combined a well-known HIF-stabilizer – deferoxamine (DFO) – and a less known HIF-enhancer – macrophage migration inhibitory factor (MIF) – to synergistically induce improved fracture healing. Stabilization of HIF-1α enhanced calcification and osteogenic differentiation of MSCs in vitro. In vivo, the application of DFO with or without MIF during the initial healing phase accelerated callus mineralization and vessel formation in a clinically relevant mouse-osteotomy-model in a compromised healing setting. Our findings provide support for a promising preventive strategy towards bone healing disorders in patients with a higher risk due to e.g. delayed neovascularization by accelerating fracture healing using DFO and MIF to stabilize HIF-1α.Competing Interest StatementThe authors have declared no competing interest.View Full Text

bioengineering↗

bioRxiv2020

Combining segmental bulk- and single-cell RNA-sequencing to define the chondrocyte gene expression signature in the murine knee joint

ObjectiveDue to the small size of the murine knee joint, extracting the chondrocyte transcriptome from articular cartilage (AC) is a major technical challenge. In this study, we demonstrate a new and pragmatic approach of combining bulk RNA-sequencing (RNA-seq) and single cell (sc)RNA-seq to address this problem. DesignWe propose a new cutting strategy of the murine femur which produces three segments with a predictable mixed cell populations, where one segment contains AC and growth plate (GP) chondrocytes, another contains GP chondrocytes, and the last segment contains only bone and bone marrow. We analysed the bulk RNA-seq of the different segments to find common and distinct genes between the segments. Then, the segment containing AC chondrocytes was digested and analysed via scRNA-seq. ResultsDifferential expression analysis using bulk RNA-seq identified 350 candidate chondrocyte gene in the AC segment. Gene set enrichment analysis of these genes revealed biological processes related- and non-related to chondrocytes, including, cartilage development (adj. p-value: 3.45E-17) and endochondral bone growth (adj. p-value 1.22E-4), respectively. ScRNA-seq of the AC segment found a cluster of 131 cells containing mainly chondrocytes. This cluster had 759 differentially expressed genes which enriched for extracellular matrix organisation (adj. p-value 7.76E-40) and other joint development processes. The intersection of the gene sets of bulk- and scRNA-seq contained 75 genes, where all but ten genes were previously implicated in cartilage homeostasis or osteoarthritis (OA) progression. ConclusionsOur approach has the potential to detect the scarce disease phenotypes of chondrocytes in murine OA models.

molecular biology↗

bioRxiv2019

The relative contributions of infectious and mitotic spread to HTLV-1 persistence

Human T-lymphotropic virus type-1 (HTLV-1) persists within hosts via infectious spread (de novo infection) and mitotic spread (infected cell proliferation), creating a population structure of multiple clones (infected cell populations with identical genomic proviral integration sites). The relative contributions of infectious and mitotic spread to HTLV-1 persistence are unknown, and will determine the efficacy of different approaches to treatment.\n\nThe prevailing view is that infectious spread is negligible in HTLV-1 proviral load maintenance beyond early infection. However, in light of recent high-throughput data on the abundance of HTLV-1 clones, and recent estimates of HTLV-1 clonal diversity that are substantially higher than previously thought (typically between 104 and 105 HTLV-1+ T cell clones in the body of an asymptomatic carrier or patient with HAM/TSP), ongoing infectious spread during chronic infection remains possible.\n\nWe estimate the ratio of infectious to mitotic spread using a hybrid model of deterministic and stochastic processes, fitted to previously published HTLV-1 clonal diversity estimates. We investigate the robustness of our estimates using two alternative methods. We find that, contrary to previous belief, infectious spread persists during chronic infection, even after HTLV-1 proviral load has reached its set point, and we estimate that between 100 and 200 new HTLV-1 clones are created and killed every day. We find broad agreement between all three methods.\n\nThe risk of HTLV-1-associated malignancy and inflammatory disease is strongly correlated with proviral load, which in turn is correlated with the number of HTLV-1-infected clones, which are created by de novo infection. Our results therefore imply that suppression of de novo infection may reduce the risk of malignant transformation.\n\nAuthor SummaryThere are no effective antiretroviral treatments against Human T-lymphotropic virus type-1 (HTLV-1), which causes a range of inflammatory diseases and the aggressive malignancy Adult T-cell Leukaemia/Lymphoma (ATL) in approximately 10% of infected people. Within hosts the virus spreads via infectious spread (de novo infection) and mitotic spread (infected cell division). The relative contributions of each mechanism are unknown, and have major implications for drug development and clinical management of infection. We estimate the ratio of infectious to mitotic spread during the infections chronic phase using three methods. Each method indicates infectious spread at low but persistent levels after proviral load has reached set point, contrary to the prevailing view that infectious spread features in early infection only. Risk of disease in HTLV-1 infection is known to increase with proviral load, via mutations accrued from repeated infected cell division. Our analyses suggest that ongoing infectious spread may provide an additional mechanism whereby chronic infection becomes malignant. Further, because antiretroviral drugs against Human Immunodeficiency Virus (HIV) inhibit HTLV-1 infectious spread, they may reduce the risk of HTLV-1 malignancy.

immunology↗