Biology subjects

Ghosh, M.

Publications and source records attributed to Ghosh, M..

At least 19 recordsLinked to original sources

bioRxiv2026

Keratin 17 Defines an Epithelial-Mesenchymal Transition-Associated Migratory Cell State in Pancreatic Ductal Adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is characterized by early invasion, metastasis, and therapeutic resistance that contribute substantially to its poor clinical outcome. Keratin 17 (K17) is a recognized marker of the aggressive basal-like subtype of PDAC and an independent predictor of adverse prognosis; however, the biological basis for this association remains incompletely understood. To define K17-associated cellular programs, we integrated single-cell RNA sequencing (scRNA-seq) data from 42 patients, Clinical Proteomic Tumor Analysis Consortium (CPTAC) proteomic data from 140 tumors, MACSima multiplex spatial imaging of 63 patients, dual-plex immunohistochemistry of 60 cases, and three-dimensional spheroid migration assays. Single-cell transcriptomic analysis demonstrated significant enrichment of epithelial-mesenchymal transition (EMT)-associated gene programs among K17-positive malignant epithelial cells. Consistent with this finding, K17-positive tumors in the CPTAC cohort exhibited enrichment of the Hallmark EMT pathway at the protein level. Among six candidate markers identified through transcriptomic analyses, laminin subunit gamma-2 (LAMC2) was the only candidate associated with K17 at every validation step. Tumors positive for both K17 and LAMC2 had significantly worse overall survival than double-negative tumors (adjusted hazard ratio = 2.53). Multiplex spatial imaging localized K17-positive cells predominantly to poorly differentiated tumor regions and demonstrated frequent single-cell co-expression with LAMC2. Whole-slide dual-plex immunohistochemistry further confirmed the association between K17 and LAMC2 and showed progressive enrichment of K17/LAMC2-positive tumor cells with increasing intratumoral histologic grade. Functionally, gain- and loss-of-function studies in three-dimensional PDAC spheroids demonstrated that K17 promotes migratory outgrowth, and differences in proliferation did not account for this effect. Collectively, these findings identify K17 as a marker of a poorly differentiated, EMT-associated migratory cell state in PDAC and provide functional evidence that K17 contributes to tumor-cell migration.

cancer biology↗

bioRxiv2026

Dual Nanoparticle-Driven Therapeutics for Leishmaniasis: A Mathematical Model of Targeted Macrophage and Parasite Elimination

BackgroundTo control leishmaniasis, chemotherapy drugs are currently under development. However, these drugs often exhibit poor efficacy and are associated with toxicity, adverse effects, and drug resistance. At present, no specific drug is available for the treatment of leishmaniasis. Meanwhile, vaccine research is ongoing. Recent studies have analysed some experimental vaccines using mathematical models. AimIn previous work, drug targeting was focused on the entire human body rather than specifically addressing infected macrophages and parasites. In our current approach, we aim to eliminate infected macrophages and parasites through nano-drug design. Specifically, we utilise two types of nanoparticles: iron oxide and citric acid-coated iron oxide. Moving forward, we plan to advance this strategy using mathematical modelling of macrophage-parasite interactions. MethodsWe design PDE-based models of macrophages and parasites, incorporating cytokine dynamics, to support nano-drug development. Drug efficacy is estimated using posterior distributions to analyse phenotypic fluctuations of macrophages and parasites during the design phase. We investigate implicit and semi-implicit treatment schemes, focusing on energy decay properties. To model drug flow during treatment, we introduce a three-phase moving boundary problem. Comparative analyses are conducted to evaluate macrophage and parasite behaviour with and without treatment. Finally, the entire framework is implemented within a virtual lab environment. ResultsThe results show that the nano-drug exhibits better efficacy compared to combined drug doses. We analysed and compared two types of nano-drug particles: iron oxide and citric acid-coated iron oxide. We discuss how the drug effectively targets and eliminates infected macrophages and parasites. ConclusionOur models results and simulations will support researchers conducting further studies in nano-drug design for leishmaniasis. These simulations are performed within a virtual lab environment.

immunology↗

bioRxiv2025

Partial recurrence enables robust and efficient computation

AO_SCPLOWBSTRACTC_SCPLOWNeural circuits are sparse and bidirectional. Meaning that signals flow from early sensory areas to later regions and back. Yet, between connected areas there exist some but not all pathways. How does this structure, somewhere between feedforward and fully recurrent, shape circuit function? To address this question, we designed a recurrent neural network model in which a set of weight matrices (i.e. pathways) can be combined to generate every network structure between feedforward and fully recurrent. We term these architectures partially recurrent neural networks (pRNNs). We trained over 25,000 pRNNs on a novel set of reinforcement learning tasks, designed to mimic multisensory navigation, and compared their performance across multiple functional metrics. Our findings reveal three key insights. First, many architectures match or exceed the performance of fully recurrent networks, despite using as few as one-quarter the number of parameters; demonstrating that partial recurrence enables energy efficient, yet performant solutions. Second, each pathways functional impact is both task and circuit dependent. For instance, feedback connections enhance robustness to noise in some, but not all contexts. Third, different pRNN architectures learn solutions with distinct input sensitivities and memory dynamics, and these computational traits help to explain their functional capabilities. Overall, our results demonstrate that partial recurrence enables robust and efficient computation - a finding that helps to explain why neural circuits are sparse and bidirectional, and how these principles could inform the design of artificial systems. 1 Key PointsO_LIWe define a neural network model in which a set of weight matrices (i.e. pathways) can be combined to generate every network structure between feedforward and fully recurrent. C_LIO_LIMany of these partially recurrent (pRNN) architectures perform as well as, or even better than, fully recurrent networks, in terms of their task performance, sample efficiency and robustness to various perturbations. C_LIO_LIEach pathways functional impact is both task and circuit dependent. For example, input to output skip connections boost learning speed in some, but not all tasks or architectures. C_LIO_LIWe propose a conceptual model, whereby: networks with different structures learn solutions with distinct computational traits, which shape their function. C_LI

neuroscience↗

bioRxiv2025

GGA2-depletion in beta cells by Enteroviruses causes Golgi acidification, premature activation of cathepsins and alters the MHC class I immunopeptidome

Enteroviruses (EVs), such as Coxsackievirus B5 (CVB5), are linked to pancreatic beta cell autoimmunity in type 1 diabetes (T1D). CVB5 infection of beta cells blocks their cap-dependent translation but spares cap-independent translation of insulin secretory granule (SG) cargoes, including major T1D autoantigens. Despite this, insulin SG stores are depleted. We show that CVB5 protease 2A rapidly depletes Golgi-associated sorting factor GGA2 in beta cells due to its short half-life. Immunostaining of pancreas sections from recent-onset T1D donors confirmed reduced GGA2 in beta cells expressing EV capsid protein VP1. Both GGA2 depletion and CVB5 2A expression impair SG biogenesis at the TGN without affecting MHC class I trafficking. They also disrupt the sorting of vacuolar ATPase and cathepsins, leading to TGN acidification and premature lysosomal hydrolase activation, which typically generate peptides presented in HLA-II alleles. Notably, the unique immunopeptidome of EV-infected, GGA2-depleted ECN90 beta-like cells is mainly presented via HLA-B alleles with an average isoelectric point (pI) of [~]5, compared to a pI of [~]7 of the prevailing HLA-I A allele immunopeptidome of non-infected cells. We propose that delayed sorting at the acidified TGN promotes cathepsin-mediated processing of in-transit secretory proteins, including SG cargoes, thereby generating novel peptides that may replace HLA class I antigens due to the acidic environment. This novel cross-presentation mechanism could have implications for T1D pathogenesis and antigen presentation in general.

cell biology↗

bioRxiv2025

Neotenic expansion of adult-born dentate granule cells reconfigures GABAergic inhibition to enhance social memory consolidation

Adult-born dentate granule cells (abDGCs) contribute to hippocampal dentate gyrus (DG)-CA3/CA2 circuit functions in memory encoding, retrieval and consolidation. Heightened synaptic and structural plasticity of immature abDGCs is thought to govern their distinct contributions to circuit and network mechanisms of hippocampal-dependent memory operations. Protracted maturation or neoteny of abDGCs in higher mammals is hypothesized to offset decline in adult hippocampal neurogenesis by expanding the capacity for circuit and network plasticity underlying different memory operations. Here, we provide evidence for this hypothesis by genetically modelling the effective impact of neoteny of abDGCs on circuitry, network properties and social cognition in mice. We show that selective synchronous expansion of a single cohort of 4 weeks old immature, but not 8 weeks old mature abDGCs, increases functional recruitment of fast spiking parvalbumin expressing inhibitory interneurons (PV INs) in CA3/CA2, number of PV IN-CA3/CA2 synapses, and GABAergic inhibition of CA3/CA2. This transient increase in feed-forward inhibition in DG-CA2 decreased social memory interference and enhanced social memory consolidation. In vivo local field potential recordings revealed that the expansion of a single cohort of 4-week-old abDGCs increased baseline power, amplitude, and duration, as well as sensitivity to social investigation-dependent rate changes of sharp-wave ripples (SWRs) in CA1 and CA2, a neural substrate for memory consolidation. Inhibitory neuron-targeted chemogenetic manipulations implicate CA3/CA2 INs, including PV INs, as necessary and sufficient for social memory consolidation following neotenic expansion of the abDGC population and in wild-type mice, respectively. These studies suggest that neoteny of abDGCs may represent an evolutionary adaptation to support cognition by reconfiguring PV IN-CA3/CA2 circuitry and emergent network properties underlying memory consolidation.

neuroscience↗

bioRxiv2025

Identification of Evolutionary Trade-Offs Associated with High-Level Colistin Resistance in Acinetobacter baumannii

ABSTRACTColistin (COL) belongs to the polymyxin group of drugs which possesses a positive charge and interacts with lipopolysaccharide (LPS) of Gram-negative bacterial outer membrane. Additionally, it can penetrate the cell membrane and disrupt the integrity of the phospholipid leading to cell death. Resistance against COL has been reported in Acinetobacter baumannii, a bacteria in the ESKAPE group of priority pathogens of the World Health Organization. Multiple and pan-drug-resistant strains of A. baumannii are rising in many medical facilities and they are becoming resistant to last-resort antibiotics including colistin. Though plasmid-encoded acquisition of mcr-1 has been associated with clinical resistance, drug efflux, complete loss of LPS by inactivation of the biosynthetic pathway (lpxACD), and modifications of target LPS by-products of chromosomal pmrCAB genes has been ascribed with resistance evolution against COL. To further delve into the evolutionary dynamics of COL-resistance here we report experimental evolution of extreme COL-resistance by adaptive evolution of the reference strain A. baumannii ATCC19606. Phenotypic characterization has been performed for the evolved strains, which demonstrated hyperbiofilm phenotype and striking decrease in fitness. A comprehensive antibiotic susceptibility profiling has been performed on the evolved strains. Whole genome sequencing of the resistant strains led to the identification of mutations prospectively associated with COL resistance. Phenotypic characterization of three COL-resistant clinical isolates of A. baumannii revealed similarity with experimentally evolved resistant mutants at least in one of the isolates.

microbiology↗

bioRxiv2025

Silica-mediated exacerbation of inflammatory arthritis: A novel murine model

ObjectiveThe mucosal origin hypothesis in rheumatoid arthritis (RA) posits that inhalant exposures, such as cigarette smoke and crystalline silica (c-silica), trigger immune responses contributing to disease onset. Despite the established risk posed by these exposures, the mechanistic link between inhalants, lung inflammation, and inflammatory arthritis remains poorly understood, partly from the lack of a suitable experimental model. As c-silica accelerates autoimmune phenotypes in lupus models and is a recognized risk factor for several autoimmune diseases, we investigated whether c-silica exposure could induce RA-like inflammatory arthritis in mice. MethodsTwo arthritis-prone mouse strains, BXD2/TyJ and HLA-DR4 transgenic (DR4-Tg), were exposed to c-silica or PBS via oropharyngeal instillation. Arthritis was evaluated by clinical signs and histopathology. Autoimmunity was further evaluated by serological analysis, including autoantibodies and cytokines and chemokines. Lung pathology was evaluated by histopathology and immunofluorescent staining for lymphocyte and macrophages. ResultsC-silica exposure induced chronic pulmonary silicosis in all mice. In BXD2 mice, this was associated with rapid arthritis development, marked by synovitis, bone erosion, and elevated serum autoantibody levels targeting various antigens, including snRNP and citrullinated protein. Additionally, BXD2 mice exhibited inducible bronchus-associated lymphoid tissue (iBALT) formation and elevated autoantibodies in bronchoalveolar lavage fluid (BALF). Conversely, DR4-Tg mice had no significant arthritis, negligible autoantibody responses, and milder lung inflammation lacking iBALT. ConclusionWe introduce a novel model of c-silica-mediated inflammatory arthritis, creating a novel platform to unravel the molecular and cellular underpinnings of RA and advance understanding of the mucosal origin hypothesis. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=133 SRC="FIGDIR/small/631488v1_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@88080aorg.highwire.dtl.DTLVardef@c7d935org.highwire.dtl.DTLVardef@8548d3org.highwire.dtl.DTLVardef@703ceb_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2025

DANCE: An open-source analysis pipeline and low-cost hardware to quantify aggression and courtship in Drosophila

Quantifying animal behavior is pivotal for identifying the underlying neuronal and genetic mechanisms involved. Computational approaches have enabled automated analysis of complex behaviors such as aggression and courtship in Drosophila. However, existing approaches rely on rigid, rule-based algorithms and expensive hardware, limiting sensitivity to behavioral variations and accessibility. Here, we describe the Drosophila Aggression and Courtship Evaluator (DANCE), a low-cost, open-source platform that combines machine learning-based classifiers and inexpensive hardware to quantify aggression and courtship. DANCE consists of six novel behavioral classifiers trained using a supervised machine learning algorithm. DANCE classifiers address key limitations of rule-based algorithms, capturing dynamic behavioral variations more effectively. DANCE hardware is constructed using repurposed medicine blister packs and acrylic sheets, with recordings performed using smartphones, making it affordable and accessible. Benchmarking demonstrated that DANCE hardware performs comparably to sophisticated, high-cost setups. We validated DANCE in diverse contexts, including social isolation versus enrichment, which modulates aggression and courtship; RNAi-mediated downregulation of the neuropeptide Dsk; and optogenetic silencing of dopaminergic neurons, which promotes aggression. DANCE provides a cost-effective and portable solution for studying Drosophila behaviors in resource-limited settings or near natural habitats. Its accessibility and robust performance democratize behavioral neuroscience, enabling rapid screening of genes and neuronal circuits underlying complex social behaviors.

neuroscience↗

bioRxiv2024

Fusing multisensory signals across channels and time

Animals continuously combine information across sensory modalities and time, and use these combined signals to guide their behaviour. Picture a predator watching their prey sprint and screech through a field. To date, a range of multisensory algorithms have been proposed to model this process including linear and nonlinear fusion, which combine the inputs from multiple sensory channels via either a sum or nonlinear function. However, many multisensory algorithms treat successive observations independently, and so cannot leverage the temporal structure inherent to naturalistic stimuli. To investigate this, we introduce a novel multisensory task in which we provide the same number of task-relevant signals per trial but vary how this information is presented: from many short bursts to a few long sequences. We demonstrate that multisensory algorithms that treat different time steps as independent, perform sub-optimally on this task. However, simply augmenting these algorithms to integrate across sensory channels and short temporal windows allows them to perform surprisingly well, and comparably to fully recurrent neural networks. Overall, our work: highlights the benefits of fusing multisensory information across channels and time, shows that small increases in circuit/model complexity can lead to significant gains in performance, and provides a novel multisensory task for testing the relevance of this in biological systems. 1 Author summaryWe constantly detect sensory inputs, like sights and sounds, and use combinations of these signals to guide our actions. For example, by reading someones lips we can better converse with them in a noisy environment. Several mathematical models have been proposed to describe this process. However, these models are "blind" to time. That is, following the example above, if we took the audio and visual signals from our friend and mixed them up over time; current models would not notice any difference, but we would find the result incomprehensible. Motivated by this, we introduce a new set of models which describe how animals could fuse sensory signals across time. Surprisingly, we find that combining signals across senses and short periods of time, works as well as a more complex model. 2 Key PointsO_LIWe introduce a novel multisensory task in which we provide task relevant evidence via bursts of varying duration, amidst a noisy background. C_LIO_LIPrior multisensory algorithms perform sub-optimally on this task, as they cannot leverage temporal structure. C_LIO_LIHowever, they can perform better by integrating across sensory channels and short temporal windows. C_LIO_LISurprisingly, this allows for comparable performance to fully recurrent neural networks, while using less than one tenth the number of parameters. C_LI

neuroscience↗

bioRxiv2024

Integration of spatial transcriptomics with immunofluorescence staining reveals spatial heterogeneity and plasticity of astrocytes in experimental glioblastomas

Astrocytes comprise [~]50% of all brain cells and present distinct morphological, molecular and functional properties in different brain regions. In glioblastoma (GBM), an aggressive primary brain tumour, tumour-associated astrocytes (TAAs) become activated and exhibit different transcriptomic profiles, morphology and functions supporting disease progression. Heterogeneity and specific roles of TAAs within various regions of tumours are poorly known. Advancements of single-cell and spatial transcriptomics allow to profile tumours at unprecedented resolution revealing cell phenotypes, hidden functionalities and spatial architecture in disease-specific context. We combined spatial transcriptomics and multiple immunofluorescent staining to visualize TAAs heterogeneity and location of various subpopulations in intracranial murine gliomas. Using distinct gene expression profiles, we identified subtypes of TAAs with distinct localization and inferred their specialized functionalities. Gene signatures associated with TAAs reflected their reprograming in the tumour microenvironment (TME), revealed their multiple roles and potential contributing factors shaping the local milieu. Using spatial correlation analysis of the spots, we inferred the interactome of Slc1a2 (encoding a glutamate transporter) with the other markers of TAAs based on segregated areas of the tumour. The designer RGD peptide blocking tumour-microglia communications, alters the spatial distribution of TAAs in experimental gliomas providing insights into potential mechanisms. Spatial transcriptomics combined with multiple staining unveils multiple functional phenotypes of TAAs and interactions within TME. It shows their distinct morphology and unveils different roles in various regions of the tumour. We demonstrate the glioma-induced heterogeneity of TAAs and their adaption to the pharmacologically-induced modification of the immunosuppressive TME.

neuroscience↗

bioRxiv2024

Integrin blocking peptide reverses immunosuppression in experimental gliomas and improves anti-PD-1 therapy outcome

Immune checkpoint inhibitors (ICI) presented clinical benefits in many cancer patients but invariably fail in glioblastoma (GBM), the most common and deadly primary brain tumor. Lack of ICI efficacy in GBM is attributed to the accumulation of immunosuppressive myeloid cells that create the "cold" tumor microenvironment (TME) impeding infiltration and activation of effector T cells. We developed a designer RGD peptide that hindered glioma-instigated, integrin-mediated pro-tumoral reprogramming of myeloid cells and blocked microglia-dependent invasion of human and mouse glioma cells in co-cultures in vitro. Intratumorally-delivered RGD alone did not reduce glioma growth in syngeneic mice but prevented the emergence of immunosuppressive myeloid cells and led to peritumoral blood vessels normalization. Furthermore, combining RGD with immunotherapy using PD-1 blockade reduced tumor growth, led to upsurge of proliferating, interferon-{gamma} producing CD8+T cells and depleted regulatory T cells. Transcriptomic profiles of myeloid cells were altered by the combined treatment, consistently with the restored "hot" inflammatory TME and boosted immunotherapy responses. RGD modified the phenotypes of myeloid cells in human gliomas in nude mice. Thus, combining the integrin blockade with ICI reinvigorates antitumor immunity and paves the way to improve immunotherapy outcomes in GBM.

cancer biology↗

bioRxiv2024

Spiking neural network models of sound localisation via a massively collaborative process

AbstractNeuroscientists are increasingly initiating large-scale collaborations which bring together tens to hundreds of researchers. However, while these projects represent a step-change in scale, they retain a traditional structure with centralised funding, participating laboratories and data sharing on publication. Inspired by an open-source project in pure mathematics, we set out to test the feasibility of an alternative structure by running a grassroots, massively collaborative project in computational neuroscience. To do so, we launched a public Git repository, with code for training spiking neural networks to solve a sound localisation task via surrogate gradient descent. We then invited anyone, anywhere to use this code as a springboard for exploring questions of interest to them, and encouraged participants to share their work both asynchro-nously through Git and synchronously at monthly online workshops. At a scientific level, our work investigated how a range of biologically-relevant parameters, from time delays to mem-brane time constants and levels of inhibition, could impact sound localisation in networks of spiking units. At a more macro-level, our project brought together 31 researchers from multiple countries, provided hands-on research experience to early career participants, and opportunities for supervision and teaching to later career participants. Looking ahead, our project provides a glimpse of what open, collaborative science could look like and provides a necessary, tentative step towards it.

neuroscience↗

bioRxiv2024

Cell-type-specific cholinergic control of granular retrosplenial cortex with implications for angular velocity coding across brain states

Cholinergic receptor activation enables the persistent firing of cortical pyramidal neurons, providing a key cellular basis for theories of spatial navigation involving working memory, path integration, and head direction encoding. The granular retrosplenial cortex (RSG) is important for spatially-guided behaviors, but how acetylcholine impacts RSG neurons is unknown. Here, we show that a transcriptomically, morphologically, and biophysically distinct RSG cell-type - the low-rheobase (LR) neuron - has a very distinct expression profile of cholinergic muscarinic receptors compared to all other neighboring excitatory neuronal subtypes. LR neurons do not fire persistently in response to cholinergic agonists, in stark contrast to all other principal neuronal subtypes examined within the RSG and across midline cortex. This lack of persistence allows LR neuron models to rapidly compute angular head velocity (AHV), independent of cholinergic changes seen during navigation. Thus, LR neurons can consistently compute AHV across brain states, highlighting the specialized RSG neural codes supporting navigation.

neuroscience↗

bioRxiv2024

CD13 Activation Assembles Signaling Complexes that Promote the Formation of Tunneling Nanotubes in Endothelial Cells

Transmembrane CD13 assembles protein complexes at the plasma membrane to enable diverse cellular processes such as cell-cell adhesion, focal adhesion turnover, endocytosis and recycling of cell surface proteins. In this study, we demonstrate a novel CD13-dependent assembly platform that regulates phosphoinositide (PI) signal transduction during the formation of Tunneling Nanotubes (TNTs). TNTs are actin-based, membrane-delimited bridges that facilitate intercellular communication by connecting distant cells to physically transfer subcellular cargoes. TNTs form between various cell types under stress conditions, but few molecular TNT-inducers exist. Human Kaposis sarcoma-derived endothelial cells (KSECs) readily form stress-induced TNTs capable of transferring Ca2+ and membrane molecules between cells, with clear accumulation of CD13 and actin at the base of the protrusions. Alternatively, CD13-null KSECs form fewer TNTs and Ca2+ transfer is markedly reduced. Mechanistically, CD13-mediated TNT formation requires activation of CD13, Src, FAK and Cdc42 to allow tethering of the IQGAP1 and ARF6 complex at the membrane to activate the phosphatidylinositol-4-phosphate-5-kinase PI5K. This increases local phosphatidylinositol 4,5-bisphosphate (PI(4,5)P2) levels to promote the actin-polymerization and membrane protrusion necessary for TNT formation. Therefore, CD13 is a novel molecular PIP regulator and TNT trigger that will facilitate the dissection of downstream pathways and mechanisms regulating TNT formation.

cell biology↗

bioRxiv2023

An explainable machine learning data analytics method using TIGIT-linked genes for identifying biomarker signatures to clinical outcomes.

In the last decade, immunotherapies targeting immune checkpoint inhibitors have been extremely effective in eliminating subsets of some cancers in some patients. Multi-modal immune and non-immune factors that contribute to clinical outcomes have been utilized for predicting response to therapies and developing diagnostics. However, these data analytic methods involve a combination of complex mathematical data analytics, and even-more complex biological mechanistic pathways. In order to develop a method for data analytics of transcriptomics data sets, we have utilized an explainable machine learning (ML) model to investigate the genes involved in the signaling pathway of T-cell-immunoreceptor with immunoglobulin and ITIM domain (TIGIT). TIGIT is a receptor on T, NK, and T-regulatory cells, that has been classified as a checkpoint inhibitor due to its ability to inhibit innate and adaptive immune responses. We extracted gene whole genome sequencing data of 1029 early breast cancer patient tumors, and adjacent normal tissues from the TCGA and UCSC Xena Data Hub public databases. We followed a workflow which involved the following steps: i) data acquisition, processing, and visualization followed by ii) developed of a predictive prognostic model using input (gene expression data) and output (survival time) parameters iii) model interpretation was performed by calculating SHAP (Shapely-Additive-exPlanations); iv) the application of the model involved a Cox-regression model, trained with L-2 regularization and optimization using 5 fold cross validation. The model identified gene signatures associated with TIGIT that predicted survival outcome with a test set with a score of 0.601. In summary, we have utilized this case study of TIGIT-mediated signaling pathways to develop a roadmap for biologists to harness ML methods effectively.

cancer biology↗

bioRxiv2023

The Implant-Induced Foreign Body Response is Limited by CD13-Dependent Regulation of Ubiquitination of Fusogenic Proteins

Implanted medical devices from artificial heart valves, arthroscopic joints to implantable sensors often induce a Foreign Body Response (FBR), a form of chronic inflammation resulting from the inflammatory reaction to a persistent foreign stimulus. The FBR is characterized by a subset of multinucleated giant cells (MGCs) formed by macrophage fusion, the Foreign Body Giant cells (FBGCs), accompanied by inflammatory cytokines, matrix deposition and eventually, deleterious fibrotic implant encapsulation. Despite efforts to improve biocompatibility, implant-induced FBR persists, compromising the utility of devices and making efforts to control the FBR imperative for long-term function. Controlling macrophage fusion in FBGC formation presents a logical target to prevent implant failure, but the actual contribution of FBGCs to FBR-induced damage is controversial. CD13 is a molecular scaffold and in vitro induction of CD13KO bone-marrow progenitors generates many more MGCs than WT, suggesting CD13 regulates macrophage fusion. Moreover, in the mesh implant model of FBR, CD13KO mice produced significantly more peri-implant FBGCs with enhanced TGF{beta} expression and increased collagen deposition vs. WT. Pre-fusion, increased protrusion and microprojection formation accompanies hyperfusion in the absence of CD13. Expression of fusogenic proteins driving cell-cell fusion was aberrantly sustained at high levels in CD13KO MGCs, which we show is due to a novel CD13 function, regulating ubiquitin/proteasomal protein degradation. By controlling protein degradation, CD13 becomes a physiologic brake preventing aberrant macrophage fusion and may be a novel therapeutic target to improve success of implanted medical devices. Furthermore, our data directly implicates FBGCs in the detrimental fibrosis that characterizes the FBR.

cell biology↗

bioRxiv2023

Multimodal units fuse-then-accumulate evidence across channels

Animals continuously detect information via multiple sensory channels, like vision and hearing, and integrate these signals to realise faster and more accurate decisions; a fundamental neural computation known as multisensory integration. A widespread view of this process is that multimodal neurons linearly fuse information across sensory channels. However, does linear fusion generalise beyond the classical tasks used to explore multisensory integration? Here, we develop novel multisensory tasks, which focus on the underlying statistical relationships between channels, and deploy models at three levels of abstraction: from probabilistic ideal observers to artificial and spiking neural networks. Using these models, we demonstrate that when information provided by different channels is not independent, linear fusion performs sub-optimally and even fails in extreme cases. This leads us to propose a simple nonlinear algorithm for multisensory integration which is compatible with our current knowledge of multimodal circuits, excels in naturalistic settings and is optimal for a wide class of multisensory tasks. Thus, our work emphasises the role of nonlinear fusion in multisensory integration, and provides testable hypotheses for the field to explore at multiple levels: from single neurons to behaviour. Key PointsO_LIWe introduce a novel set of tasks, based on comodulating the signals from multiple channels. C_LIO_LIWe show that linear fusion performs sub-optimally on these tasks and even fails in extreme cases. C_LIO_LIIn contrast, nonlinear fusion is optimal and excels in naturalistic settings, like predator-prey interactions. C_LIO_LIWe demonstrate that networks of spiking neurons approximate the behaviour of this nonlinear algorithm, when trained on these tasks. C_LIO_LIFinally, we explore how single neuron properties allow networks to approximate nonlinear fusion. C_LI

neuroscience↗

bioRxiv2023

SALL3 mediates the loss of neuroectodermal differentiation potential in human embryonic stem cells with chromosome 18q loss

Human pluripotent stem cell (hPSC) cultures are prone to genetic drift, as cells that have acquired specific genetic abnormalities experience a selective advantage in vitro. These abnormalities are highly recurrent in hPSC lines worldwide, but currently their functional consequences in differentiating cells are scarcely described. An accurate assessment of the risk associated with these genetic variants in both research and clinical settings is therefore lacking. In this work, we established that one of these recurrent abnormalities, the loss of chromosome 18q, impairs neuroectoderm commitment and affects the cardiac progenitor differentiation of hESCs. We show that downregulation of SALL3, a gene located in the common 18q loss region, is responsible for failed neuroectodermal differentiation. Knockdown of SALL3 in control lines impaired differentiation in a manner similar to the loss of 18q, while transgenic overexpression of SALL3 in hESCs with 18q loss rescued the differentiation capacity of the cells. Finally, we show by gene expression analysis that loss of 18q and downregulation of SALL3 leads to changes in the expression of genes involved in pathways regulating pluripotency and differentiation, including the WNT, NOTCH, JAK-STAT, TGF-beta and NF-kB pathways, suggesting that these cells are in an altered state of pluripotency.

cell biology↗