Biology subjects

Pham, D.

Publications and source records attributed to Pham, D..

At least 19 recordsLinked to original sources

bioRxiv2026

The Human Gut Cell Atlas v1.0: A Unified, Community-BuiltReference of Cellular Diversity Across the Gut

Single-cell RNA-sequencing (scRNA-seq) studies have transformed our understanding of cellular diversity in the human intestine, but differences in anatomical sampling, experimental processing and cell-type nomenclature limit comparison across datasets. Here, we present the Human Gut Cell Atlas (HGCA) V1, a community-curated reference of 944,390 healthy and disease-adjacent intestinal cells from 502 samples, 265 donors and 24 landmark studies, linked to detailed anatomical, clinical, and technical metadata. As this metadata is of critical importance to human biology, we developed MetaManager, an open source data management framework for standardized metadata collection and tracking. A five-level consensus taxonomy resolves 94 homeostatic cell identities and enables transferrable, high-resolution annotation of new gut datasets. By assembling cell identity, sampling metadata, and independent spatial validation, intestinal segment and radial tissue depth were revealed to systematically shape cellular composition and transcriptional state, enabling inference of the presence of sampling-dependent tissue niches, including lymphoid follicles. Reference mapping accurately and rapidly recovered cohort-level Crohn's disease and treatment signatures without de novo annotation, while mapping human intestinal organoids quantified cell-type-specific identity, regional specification, maturation, and transcriptional fidelity to native tissue. The HGCA V1 provides a standardized cellular, anatomical coordinate, and metadata system for reproducible, high-quality analysis of intestinal biology across healthy tissue, disease-associated remodelling, and experimental models, providing a continuously evolving community reference for cumulative discovery in human intestinal biology.

systems biology↗

bioRxiv2026

Gp130 Orchestrates a Bidirectional Neuron-Microglia Circuit for neuroprotection

Acquired central nervous system (CNS) injury is one of the most common neurological conditions globally, yet effective treatment options are lacking. As the main tissue-resident macrophages of the CNS, microglia have emerged as key functional regulators of CNS repair. However, means to induce neuroprotective microglia and harness their intrinsic repair capabilities have remained elusive. Here we identify gp130 as a key receptor molecule for facilitating bidirectional microglia-neuron communication that improves outcomes from CNS injury. We show that activation of gp130 in CNS-resident microglia triggers the secretion of leukemia inhibitory factor (LIF), a neurotrophic cytokine. LIF induces neuronal IL-6 secretion that then acts back onto the microglial gp130 receptor, thus creating a neuroprotective loop. We demonstrate the broad therapeutic potential of acute gp130 activation across multiple models, including traumatic brain injury, stroke, and spinal cord injury, and that this pathway can be leveraged therapeutically with designer cytokines.

neuroscience↗

bioRxiv2026

Spatial lung niches shape Pseudomonas aeruginosa persistence

Chronic Pseudomonas aeruginosa lung infection is a major cause of morbidity and mortality in people with pre-existing lung disease. Once established, infection is rarely eradicated and often persists despite prolonged antimicrobial therapy, but the underlying mechanisms remain poorly understood. To define how P. aeruginosa occupies and adapts to diseased lung tissue, we applied host-pathogen spatial transcriptomics to profile over 23 million lung cells from explanted and resected lungs from people with Cystic Fibrosis (CF) and chronic obstructive pulmonary disease (COPD), mapping bacterial niches and transcriptional states in situ. We found that P. aeruginosa adopted distinct niche-linked states across chronically infected human lung tissue. Bacterial burden was highest in airway lumens, but bacteria also occupied submucosal glands, parenchyma and, unexpectedly, blood vessel lumens in CF tissue. Within airway lumens, two coupled host-pathogen states emerged: an alginate-rich biofilm-like state linked to PI3-positive neutrophil inflammation and host chemical-sensing programmes; and a motile, quorum-sensing state with activated type 6 secretion linked to human ciliary stress, epithelial remodelling and proteolytic injury. Intravascular bacteria co-localised with neutrophils, fibrin and vascular-remodelling signatures, suggesting local breach of barrier integrity and subsequent immune containment. P. aeruginosa also displayed disease-specific cellular associations, with neutrophil-dominated interactions in CF and greater association with macrophages and dendritic cells in COPD. Together, these data reveal chronic P. aeruginosa infection as a spatially partitioned ecosystem in which anatomical microenvironments impose distinct bacterial lifestyles and host inflammatory states. This niche-resolved framework helps explain how persistent infection can diversify within a single lung and suggests that eradication therapies may need to target multiple anatomical and cellular niches to be effective.

immunology↗

bioRxiv2025

Developmental benzopyrene exposure alters stress hormones, neurotransmitters and behavioral responses of mice dependent on Cyp1 genotype

Benzo[a]pyrene (BaP) is a prototypical polycyclic aromatic hydrocarbon (PAH) produced during combustion processes and when grilling foods. Epidemiological studies indicate exposure to PAHs during pregnancy lead to learning and memory deficits as well as behavioral problems that persist into adolescence. Studies in rodents and zebrafish have frequently reported anxiolytic effects of BaP exposure in adult animals and in developmental studies. We conducted sequential experiments of Cyp1a1(-/-) and Cyp1b1(-/-) knockout mice compared with wild type C57BL/6J mice to determine if genotype changes the response to developmental BaP exposure. We treated pregnant dams from gestational day 10 to postnatal day 25 (P25) with BaP in corn oil-soaked cereal or the corn oil vehicle and tested one male and one female offspring beginning at P60. We found increased exploratory behavior in the elevated zero maze for Cyp1a1(-/-) knockout mice, but no significant differences in Cyp1b1(-/-) knockouts. In contrast, Cyp1b1(-/-) knockout mice buried fewer marbles in a second test of anxiety-like behavior. There were no significant differences when Cyp1a1(-/-) knockout mice were tested. BaP decreased immobility time in Cyp1a1(-/-) knockouts in the forced swim test, but increased immobility time in wild type and Cyp1b1(-/-) knockout mice. We measured plasma corticosterone levels at baseline and following the forced swim test and monoamine neurotransmitters at the end of behavioral testing. BaP treatment increased corticosterone in wild type mice, but decreased it in Cyp1a1(-/-) knockout mice. Both BaP-exposed and corn oil control Cyp1b1(-/-) knockout mice had higher corticosterone levels compared with wild type mice. Dopamine and serotonin signaling were altered in the hypothalamus dependent on genotype, treatment and sex. Together, these data suggest that both CYP1A1 and CYP1B1 have a normal role in brain functioning or development, and that CYP1 genotype alters the response to developmental BaP exposure in behavioral and biochemical tests related to stress, anxiety and depression. HighlightsO_LIBaP-exposed Cyp1a1(-/-) mice had lower corticosterone and decreased immobility in the forced swim test C_LIO_LIBaP exposure increased FST immobility in wild type and Cyp1b1(-/-) mice C_LIO_LICyp1a1(-/-) and Cyp1b1(-/-) knockout mice showed less anxiety-like behavior C_LIO_LIDevelopmental BaP exposure altered corticosterone levels dependent on Cyp1 genotype C_LIO_LIGenotype, treatment and sex all impacted neurotransmitter levels in the hypothalamus C_LIO_LIGenetic differences in CYP enzymes altered susceptibility to developmental BaP exposure C_LI

pharmacology and toxicology↗

bioRxiv2025

LIPTER, a cardiomyocyte-enriched long noncoding RNA, controls cardiac cytoskeletal maturation and is regulated by a cardiomyocyte-specific enhancer.

Cardiac development is characterized by a complex series of molecular, cytoskeletal and electrophysiological changes that guarantee the proper functioning of adult cardiomyocytes (CMs). These changes are defined by cell-type-specific transcriptional rewiring of progenitor cells to form CMs, and are regulated by various epigenetic elements, such as long noncoding RNAs (lncRNAs). LncRNAs are versatile epigenetic regulators as they may act in cis or in trans to orchestrate important gene programs during cardiac development and may concurrently encode micropeptides. LIPTER is one such lncRNA, previously shown to regulate lipid droplet transport in cardiomyocytes and thus an important regulator of cardiomyocyte metabolism. Here we show that LIPTER also plays a role in the cytoskeletal maturation of CMs, as loss of LIPTER leads to persistent expression of fetal genes, changes in chromatin accessibility, disorganized sarcomeres and impaired calcium homeostasis in CMs. Furthermore, we have identified a cardiomyocyte-specific regulatory enhancer that regulates the expression of LIPTER in CMs. CRISPR-mediated inhibition of this enhancer led to reduced LIPTER expression in CMs and increased expression of fetal genes. This CM-specific enhancer could therefore be manipulated to control the expression of LIPTER for therapeutic benefit. In summary, we have unravelled a novel role of LIPTER in CMs cytoskeletal maturation and have identified a CM-specific enhancer for LIPTER.

genomics↗

bioRxiv2025

Integrating 12 Spatial and Single Cell Technologies to Characterise Tumour Neighbourhoods and Cellular Interactions in three Skin Cancer Types

Cutaneous squamous cell carcinoma (cSCC), basal cell carcinoma (BCC), and melanoma - the three major skin cancers - collectively comprise over 70% of all cancer cases. Despite their prevalence, much understanding of cellular interactions in the skin cancer microenvironment is needed, both in the outer skin layer where the cancer originates and at the deeper junctional and dermal layers into which it progresses. To address this gap, we integrated 12 complementary spatial and single-cell technologies to generate orthogonally-validated cell signatures, spatial maps, and interactomes for cSCC, BCC, and melanoma. Through comprehensive comparisons and integrating these spatial methods, we provided practical benchmarking guidelines for experimental design and analysis. By identifying keratinocyte cancer cells and melanomas, we found distinct signatures of these cells compared to non-cancer keratinocytes and melanocytes. Spatial integration of transcriptomics, proteomics and glycomics uncovered cancer niches enriched for cancer initiating cells (melanocytes or keratinocytes) and fibroblast and T-cell (MKFT) clusters, with altered tyrosine and pyrimidine metabolism. Ligand-receptor analysis across >700 cell-type combinations and >1.5 million interactions highlighted key roles for CD44, integrins, and collagens, with CD44-FGF2 emerging as a potential therapeutic target. Consistently, melanoma showed strong MFT interactions, validated by Opal Polaris, RNAScope, Proximal Ligation Assay and two additional single-cell spatial platforms (making a total of 14 technologies). For population-scale generalisation, genetic associations from >500,000 individuals were mapped onto spatial skin tissues, identifying SNPs enriched in domains containing melanocytes and T cells and their ligand-receptor pairs, shedding light on functional mechanisms linking genetic heritability to cells within cancer tissue. We built an interactive multiomics resource for exploring spatially-resolved molecular signatures and cellular crosstalk in skin cancer, available at https://skincanceratlas.com.

cancer biology↗

bioRxiv2025

An atlas of TF driven gene programs across human cells

Combinations of transcription factors (TFs) regulate gene expression and determine cell fate. Much effort has been devoted to understanding TF activity in different tissues and how tissue-specificity is achieved. However, ultimately gene regulation occurs at the single cell level and the recent explosion in the availability of single cell gene expression data now makes it possible to understand TF activity at this granular level of resolution. Here, we leverage a large collection of Human Cell Atlas (HCA) single cell data to explore TF activity by examining cell-type and tissue-specific sets of target genes, or regulons. We compile a regulon atlas, CellRegulon, and map the activity of TFs in an extensive set of healthy adult and foetal tissues spanning hundreds of cell types. Using CellRegulon, we describe dynamic patterns of co-regulation, associate TF-modules with different cellular functions and characterise the distribution of active TFs and TF families across cell types. We show that CellRegulon can link disease gene expression signatures to cell types and TFs relevant to the disease. Finally, using a newly generated multiome dataset of the adult lung, we show how CellRegulon can be extended into an enhancer-gene regulatory network (eGRN) to improve cell-type associations with genetic risk loci for diseases, such as childhood onset asthma, COPD and IPF, and to identify high risk gene modules. Our database for easy download and interactive exploration allows researchers to understand key gene modules activated at cell type transitions and will therefore be valuable for tasks such as cell type engineering (https://www.cellregulondb.org).

genetics↗

bioRxiv2024

inVAE: Conditionally invariant representation learning for generating multivariate single-cell reference maps

Single-cell data is driving new insights into the spatiotemporal dynamics of cells and individual disease susceptibility. However, accurately identifying cell states across diverse cohorts remains challenging, as both biological variation and technical biases cause distributional shifts in the data. Separating these effects is crucial for capturing cellular heterogeneity and ensuring interpretability. To address this, we developed inVAE, a conditionally invariant deep generative model based on variational autoencoders. inVAE models the latent space as a combination of invariant variables, encoding true biological signals, and spurious variables, capturing technical biases. By conditioning the prior distribution of cells on biological covariates, such as disease variants, inVAE identifies high-resolution cell states in the invariant representation. Enforcing independence between the two representations disentangles biological signals from noise, enabling a more interpretable and generalizable model with a causal semantic. inVAE outperformed existing methods across four human cellular atlases of the human heart and lung, while uncovering novel cell states. It precisely stratified cell atlas donors based on the genetic impact of pathogenic variants, and excelled in predicting cell types and disease in unseen data, proving its generalizability as a reference model for label transfer. Furthermore, inVAE accurately identified temporal cell states and trajectories from developmental datasets, and captured spatial cell states in a spatially-resolved atlas. In summary, inVAE provides a powerful method for integrating multivariate single-cell transcriptomics data. By leveraging prior knowledge such as metadata, it effectively accounts for biological variation and improves latent space interpretability by disentangling biological and technical sources of variation. These capabilities enable deeper insights into cellular heterogeneity and its role in disease progression.

bioinformatics↗

bioRxiv2024

Rebuilding the autoimmune-damaged corneal stroma through topical lubrication

Corneal lubrication is the most common treatment for relieving the signs and symptoms of dry eye and is considered to be largely palliative with no regenerative functions. Here we challenge this notion by demonstrating that wetting the desiccated cornea of an aqueous-deficient mouse model with the simplest form of lubrication, a saline-based solution, is sufficient to rescue the severely disrupted collagen-rich architecture of the stroma, the largest corneal compartment that is essential to transparency and vision. At the single cell level we show that stromal keratocytes responsible for maintaining stromal integrity are converted from an inflammatory state into unique reparative cell states by lubrication alone, thus revealing the extensive plasticity of these cells and the regenerative function of lubricating the surface. We further show that the generation of a reparative phenotype is due, in part, to disruption of an IL1{beta} autocrine amplification loop promoting chronic inflammation. Thus, our study uncovers the regenerative potential of topical lubrication in dry eye and represents a paradigm shift in our understanding of its therapeutic impact.

immunology↗

bioRxiv2024

Accurate quantitation of 16S gene copies in low biomass samples post-antibiotic treatment through deep sequencing with a balanced nucleotide synthetic spike-in approach

The microbiota significantly impacts health and treatment outcomes. While 16S rRNA gene sequencing reveals relative bacterial abundances, it does not provide absolute quantification. We developed a cost-effective solution incorporating synthetic DNA standards designed to ensure balanced nucleotide representation at each position. These standards are spiked into samples before DNA extraction, enabling simultaneous quantification of both relative and absolute bacterial abundances. We applied this method to samples collected from mice and patients, both before and after antibiotic treatment. Our approach showed a reduction in total bacterial density in mice and patients post-antibiotic treatment. This spike-in standard method can be adapted to samples with varying bacterial densities, allowing quantification of absolute taxonomical abundances without the need for an additional quantitative PCR assessment. Our approach also improves sequencing quality scores for low biomass samples.

microbiology↗

bioRxiv2024

Metabolic priming of GD2 TRAC-CAR T cellsduring manufacturing promotes memory phenotypes while enhancing persistence

Manufacturing Chimeric Antigen Receptor (CAR) T cell therapies is complex, with limited understanding of how media composition impact T-cell phenotypes. CRISPR/Cas9 ribonucleoproteins can precisely insert a CAR sequence while disrupting the endogenous T cell receptor alpha constant (TRAC) gene resulting in TRAC-CAR T cells with an enriched stem cell memory T-cell population, a process that could be further optimized through modifications to the media composition. In this study we generated anti-GD2 TRAC-CAR T cells using "metabolic priming" (MP), where the cells were activated in glucose/glutamine low media and then expanded in glucose/glutamine high media. T cell products were evaluated using spectral flow cytometry, metabolic assays, cytokine production, cytotoxicity assays in vitro and potency against human GD2+ xenograft neuroblastoma models in vivo. Compared to standard TRAC-CAR T cells, MP TRAC-CAR T cells showed less glycolysis, higher CCR7/CD62L expression, more bound NAD(P)H activity and reduced IFN-{gamma}, IL-2, IP-10, IL-1{beta}, IL-17, and TGF{beta} production at the end of manufacturing ex vivo, with increased central memory CAR T cells and better persistence observed in vivo. Metabolic priming with media during CAR T cell biomanufacturing can minimize glycolysis and enrich memory phenotypes ex vivo, which could lead to better responses against solid tumors in vivo.

bioengineering↗

bioRxiv2023

IL-2-induced Stat3 Signaling is Critical for Effector Treg Cell Programming

Maintenance of immune homeostasis to the intestinal mictrobiota is dependent on a population of effector regulatory T (eTreg) cells that develop from microbiota-reactive induced (i)Treg cells. A cardinal feature of eTreg cells is their production of IL-10, which plays a non-redundant role in immune tolerance of commensal microbes. Here, we identify an unexpected role for IL-2-induced Stat3 signaling to program iTreg cells for eTreg cell differentiation and Il10 transcriptional competency. IL-2 proved to be both necessary and sufficient for eTreg cell development - contingent on Stat3 output of the IL-2 receptor coordinate with IL-2 signaling during early Treg cell commitment. Induction of iTreg cell programming in absence of IL-2-induced Stat3 signaling resulted in impaired eTreg cell differentiation and a failure to produce IL-10. An IL-2 mutein with reduced affinity for the IL-2R{gamma} ({gamma}c) chain was found to have blunted IL-2R Stat3 output, resulting in a deficiency of Il10 transcriptional programming that could not be fully rescued by Stat3 signaling subsequent to an initial window of iTreg cell differentiation. These findings expose a heretofore unappreciated role of IL-2 signaling that acts early to program subsequent production of IL-10 by developing eTreg cells, with broad implications for IL-2-based therapeutic interventions in immune-mediated diseases.

immunology↗

bioRxiv2023

i-stLearn: An interactive platform for spatial transcriptomics analysis

SummaryEmerging spatial transcriptomics technologies (e.g. Visium, Slideseq, or MERFISH) have made it possible to keep the spatial information while profiling gene expression of every cell/spatial-spot. Integrating expression values, spatial coordinates, and imaging data type promises to bring more biological insights but is still technically challenging. A user-friendly software tool to enable interactive analysis of spatial transcriptomic data by the broader community is lacking. We present i-stLearn, an all-in-on web application with an analysis pipeline and interactive visualization for studying spatial heterogeneity using spatial transcriptomics data. i-stLearn can be used to gain biological insights from tissue through key analysis types cell-cell interaction analysis, clustering, and trajectory inference. Using functions, users can interactively segment the tissue and identify cellular state transition or cellular communications in a heterogeneous biological sample. Availabilityi-stLearn is freely available at https://github.com/BiomedicalMachineLearning/stlearn_interactive as local web application and we also provide a demo online web service available at https://i-stlearn-demo.web.app. Contactquan.nguyen@imb.uq.edu.au Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2022

Inferring cell diversity in single cell data using consortium-scale epigenetic data as a biological anchor for cell identity

Methods for cell clustering and gene expression from single-cell RNA sequencing (scRNA-seq) data are essential for biological interpretation of cell processes. Here we present TRIAGE-Cluster which uses genome-wide epigenetic data from diverse bio-samples to identify genes demarcating cell diversity in scRNA-seq data. TRIAGE-Cluster integrates patterns of repressive chromatin deposited across diverse cell types with weighted density estimation to determine cell type clusters in a 2D UMAP space. We then present TRIAGE-ParseR, a machine learning method that evaluates gene expression rank lists to define gene groups governing the identity and function of cell types. We demonstrate the utility of this two-step approach using atlases of in vivo and in vitro cell diversification and organogenesis. We also provide a web accessible dashboard for analysis and download of data and software. Collectively, genome-wide epigenetic repression provides a versatile strategy to define cell diversity and study gene regulation of scRNA-seq data.

bioinformatics↗

bioRxiv2022

An integrated cell barcoding and computational analysis pipeline for scalable analysis of differentiation at single-cell resolution

Pluripotent stem cells provide a scalable approach to analyse molecular regulation of cell differentiation across multiple developmental lineage trajectories. In this study, we engineered barcoded iPSCs to generate an atlas of multilineage differentiation from pluripotency, encompassing a time-course of WNT-induced differentiation perturbed using modulators of WNT, BMP, and VEGF signalling. Computational mapping of in vitro cell types to in vivo developmental lineages revealed a diversity of iPSC-derived cell types comprising mesendoderm lineage cell types including lateral plate and paraxial mesoderm, neural crest, and primitive gut. Coupling this atlas of in vitro differentiation with Summary data-based Mendelian Randomisation analysis of human complex traits, we identify the WNT-inhibitor protein TMEM88 as a putative regulator of mesendodermal cell types governing development of diverse cardiovascular and anthropometric traits. Using genetic loss of function models, we show that TMEM88 is required for differentiation of diverse endoderm and mesoderm cell lineages in vitro and that TMEM88 knockout in vivo results in a significant dysregulation of arterial blood pressure. This study provides an atlas of multilineage iPSC differentiation coupled with new molecular, computational, and statistical genetic tools to dissect genetic determinants of mammalian developmental physiology.

genomics↗

bioRxiv2022

Imputation and polygenic score performances of human genotyping arrays in diverse populations

Regardless of the overwhelming use of next-generation sequencing technologies, microarray-based genotyping combined with the imputation of untyped variants remains a cost-effective means to interrogate genetic variations across the human genome. This technology is widely used in genome-wide association studies (GWAS) at bio-bank scales, and more recently, in polygenic score (PGS) analysis to predict and to stratify disease risk. Over the last decade, human genotyping arrays have undergone a tremendous growth in both number, and content making a comprehensive evaluation of their performances became more important. Here, we performed a comprehensive performance assessment for 23 available human genotyping arrays in 6 ancestry groups using diverse public, and in-house datasets. The analyses focus on performance estimation of derived imputation (in terms of accuracy and coverage) and PGS (in term of concordance to PGS estimated from whole genome sequencing data) in three different traits and diseases. We found that the arrays with a higher number of SNPs are not necessarily the ones with higher imputation performance, but the arrays that are well-optimized for the targeted population could provide very good imputation performance. In addition, PGS estimated by imputed SNP array data is highly correlated to PGS estimated by whole genome sequencing data in most of cases. When optimal arrays are used, the correlations of key PGS metrics between two types of data can be higher than 0.97, but interestingly, arrays with high density can result in lower PGS performance. Our results suggest the importance of properly selecting a suitable genotyping array for PGS applications. Finally, we developed a web tool that provide interactive analyses of tag SNP contents and imputation performance based on population and genomic regions of interest. This study would act as a practical guide for researchers to design their genotyping arrays-based studies. The tool is available at: https://genome.vinbigdata.org/tools/saa/

genomics↗

bioRxiv2022

Incubation of palatable food craving is associated with brain-wide neuronal activation in mice

Studies using rodent models have shown that relapse to drug or food seeking increases progressively during abstinence, a phenomenon termed incubation of craving. Mechanistic studies of incubation of craving have focused on specific neurobiological targets within pre- selected brain areas. Recent methodological advances in whole-brain immunohistochemistry, clearing, and imaging now enable unbiased brain-wide cellular resolution mapping of regions and circuits engaged during learned behaviors. However, these whole brain imaging approaches were developed for mouse brains while incubation of drug craving has primarily been studied in rats and incubation of food craving has not been demonstrated in mice. Here, we established a mouse model of incubation of palatable food craving and examined food reward seeking after 1, 15, and 60 abstinence days. We then used the neuronal activity marker Fos with intact brain mapping procedures to identify corresponding patterns of brain-wide activation. Relapse to food seeking was significantly higher after 60 abstinence days than after 1 or 15 days. Using unbiased ClearMap analysis, we identified increased activation of multiple brain regions, particularly corticostriatal structures, following 60, but not 15 abstinence days. We used orthogonal SMART2 analysis to confirm these findings within corticostriatal and thalamocortical subvolumes and applied expert-guided registration to investigate subdivision and layer-specific activation patterns. Overall, we (1) identified novel brain-wide activity patterns during incubation of food seeking using complementary analytical approaches, and (2) provide a single-cell resolution whole-brain atlas that can be used to identify functional networks and global architecture underlying incubation of food craving. Significance StatementRelapse to reward seeking progressively increases during abstinence, a phenomenon termed incubation of craving. Mechanistic studies of incubation can lead to novel relapse treatments. However, previous studies have primarily used rat models and targeted region-by-region analyses and a brain-wide functional atlas of incubation of reward seeking is lacking. We established a behavioral procedure for incubation of palatable food seeking in mice and applied whole-brain activity mapping with Fos as a neuronal activity marker to identify the functional connectome of this incubation. Like rats, mice showed incubation of food seeking during abstinence. Using two complementary activity mapping approaches, we identified a brain-wide pattern of increased neural activation that mirrored incubation of food seeking after 60, but not 15, days of abstinence.

neuroscience↗

bioRxiv2022

Lagrangian Formalism in Biology: I. Standard Lagrangians and their Role in Population Dynamics

The Lagrangian formalism is developed for the population dynamics of interacting species that are described by several well-known models. The formalism is based on standard Lagrangians, which represent differences between the physical kinetic and potential energy-like terms. A method to derive these Lagrangians is presented and applied to selected theoretical models of the population dynamics. The role of the derived Lagrangians and the energy-like terms in the population dynamics is investigated and discussed. It is suggested that the obtained standard Lagrangians can be used to identify physical similarities between different population models.

ecology↗