Biology subjects

Pers, T. H.

Publications and source records attributed to Pers, T. H..

At least 19 recordsLinked to original sources

bioRxiv2026

A uniquely leptin sensitive hypothalamic neuron population limits hyperphagia and weight gain in diet-induced obesity

Despite widespread loss of leptin responsiveness in obesity, endogenous leptin continues to restrain feeding, yet the neural substrates that remain sensitive and mediate this effect are unknown. Combining spatial transcriptomics with single-nucleus RNA sequencing in mice with diet-induced obesity (DIO), we show that while most hypothalamic leptin receptor (Lepr) neurons minimally respond to elevated leptin, a single population defined by glucagon-like peptide-1 receptor (Glp1r) co-expression retains robust leptin sensitivity. These Lepr/Glp1r neurons project onto and restrain orexigenic Agrp neurons. Lepr deletion from Lepr/Glp1r neurons blocks the anorectic effect of exogenous leptin, reinstates hyperphagic responses normally suppressed in DIO, amplifies the obesogenic response to palatable diet, and unexpectedly attenuates hypothalamic microglial activation- a hallmark of DIO previously attributed to diet rather than leptin signaling. Hence, preserved leptin action through a single neuronal population governs downstream circuit activity to limit hyperphagia and weight gain during obesity.

molecular biology↗

bioRxiv2026

Semaglutide Attenuates Neuroinflammation in Mice

Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) have shown promise in preclinical models of neurodegeneration, with emerging evidence suggesting these effects may be driven by modulation of neuroinflammation. However, the cellular mechanisms underlying GLP-1 RA effects on neuroinflammation remain poorly understood. Here, using a mouse model of lipopolysaccharide-induced neuroinflammation, we investigated how semaglutide coordinates cellular responses to resolve neuroinflammation. We find that semaglutide prevents brain infiltration of neutrophils, excessive cytokine release, and suppresses neuroinflammation-associated transcriptional signatures specifically in microglia, endothelial cells, and a subset of pericytes. Mechanistically, we identify a subset of Glp1r-expressing neurons in the dorsal vagal complex that, upon semaglutide treatment, regulate genes involved in anti-inflammatory signaling. Semaglutide-modulated pathways overlap with inflammatory signatures found in human neurodegenerative diseases, including Alzheimers disease, suggesting broad relevance for conditions involving neuroinflammation. Together, these findings reveal how GLP-1R signaling orchestrates resolution of neuroinflammation through coordinated multi-cellular programs.

neuroscience↗

bioRxiv2025

A Cross-Species Atlas of the Dorsal Vagal Complex Reveals Neural Mediators of Cagrilintide's Effects on Energy Balance

Amylin analogs, including potential anti-obesity therapies like cagrilintide, act on neurons in the brainstem dorsal vagal complex (DVC) that express calcitonin receptors (CALCR). These receptors, often combined with receptor activity-modifying proteins (RAMPs), mediate the suppression of food intake and body weight. To understand the molecular and neural mechanisms of cagrilintide action, we used single-nucleus RNA sequencing to define 89 cell populations across the rat, mouse, and non-human primate caudal brainstem. We then integrated spatial profiling to reveal neuron distribution in the rat DVC. Furthermore, we compared the acute and long-term transcriptional responses to cagrilintide across DVC neurons of rats, which exhibit strong cagrilintide responsiveness, and mice, which respond poorly to cagrilintide over the long term. We found that cagrilintide promoted long-term transcriptional changes, including increased prolactin releasing hormone (Prlh) expression, in the nucleus of the solitary tract (NTS) Calcr/Prlh cells in rats, but not in mice, suggesting the importance of NTS Calcr/Prlh cells for sustained weight loss. Indeed, activating rat area postrema Calcr cells briefly reduced food intake but failed to decrease food intake or body weight over the long term. Overall, these results not only provide a cross-species and spatial atlas of DVC cell populations but also define the molecular and neural mediators of acute and long-term cagrilintide action.

bioinformatics↗

bioRxiv2024

Generation of human appetite-regulating neurons and tanycytes from stem cells

The balance between energy intake and expenditure is controlled by the hypothalamus, a small brain region characterised by high neuronal diversity. Specifically, the arcuate nucleus (ARC) and ventromedial hypothalamus (VMH) are key hypothalamic nuclei controlling appetite through behavioural response to circulating humoral signals. Yet, despite their physiological importance, the cellular and functional characteristics of this highly specialised neural region has been studied mainly in animals due to a lack of human models. Here, we fine-tuned the differentiation of human pluripotent stem cells toward the ARC and VMH hypothalamic nuclei and identified key subtype-specific progenitor markers of these subregions. We demonstrate that the timing for initiation and termination of bone morphogenetic protein (BMP) signalling is essential for controlling subregional specification of tuberal hypothalamic progenitors along the anterior-posterior axis, balancing VMH versus ARC fates. A particular population of SHH-/NKX2.1+/FGF10high/RAXhigh/TBX3high posterior tuberal progenitors was identified as the source for generation of ARC-associated agouti-related peptide (AGRP) neurons and tanycytes whilst anterior tuberal SHH+/NKX2.1+/FGF10low/RAXlow/TBX3low progenitors generated VMH phenotypes including NR5A1 neurons. Upon maturation in vitro and in xenografts, ARC-patterned progenitors gave rise to key appetite-regulating cell types including those producing AGRP, prepronociceptin (PNOC), growth hormone-releasing hormone (GHRH), thyrotropin-releasing hormone (TRH) and pro-opiomelanocortin (POMC), as well as tanycyte glial cells. Differentiated ARC cultures showed high transcriptomic similarity to the human ARC and displayed evidence of functionality by AGRP secretion and responsiveness to leptin and fibroblast growth factor 1 (FGF1). In summary, our work provides insights into the developmental lineages underlying hypothalamic subregional specification and enables access to highly characterised human ARC and VMH cultures, which will provide novel opportunities for investigating the cellular and molecular pathways triggered by obesity-associated genetic variants and weight-regulating stimuli.

neuroscience↗

bioRxiv2023

Molecular Connectomics Reveals a Glucagon-Like Peptide 1 Sensitive Neural Circuit for Satiety

Liraglutide and other agonists of the glucagon-like peptide 1 receptor (GLP-1RAs) are effective weight loss drugs, but how they suppress appetite remains unclear. One potential mechanism is by activating neurons which inhibit hunger-promoting Agouti-related peptide (AgRP) neurons of the arcuate hypothalamus (Arc). To identify these afferents, we developed a method combining rabies-based connectomics with single-nuclei transcriptomics. Applying this method to AgRP neurons predicted at least 21 afferent subtypes in the mouse mediobasal and paraventricular hypothalamus. Among these are Trh+ Arc neurons (TrhArc), inhibitory neurons which express the Glp1r gene and are activated by the GLP-1RA liraglutide. Activating TrhArc neurons inhibits AgRP neurons and feeding in an AgRP neuron-dependent manner. Silencing TrhArc neurons causes over-eating and weight gain and attenuates liraglutides effect on body weight. Our results demonstrate a widely applicable method for molecular connectomics, comprehensively identify local inputs to AgRP neurons, and reveal a circuit through which GLP-1RAs suppress appetite.

neuroscience↗

bioRxiv2023

Molecular Consequences of Peripheral Influenza A Infection on Cell Populations in the Murine Hypothalamus

Infection with Influenza A virus (IAV) causes the well-known symptoms of the flu, including fever, loss of appetite and excessive sleepiness. These responses, mediated by the brain, will normally disappear once the virus is cleared from the system, but a severe respiratory virus infection may cause long-lasting neurological disturbances. These include encephalitis lethargica and narcolepsy. The mechanisms behind such long lasting changes are unknown. The hypothalamus is a central regulator of the homeostatic response during a viral challenge. To gain insight into the neuronal and non-neuronal molecular changes during an IAV infection, we intranasally infected mice with an H1N1 virus and extracted the brain at different time points. Using single-nucleus RNA sequencing (snRNA-seq) of the hypothalamus, we identify transcriptional effects in all identified cell populations. The snRNA-seq data showed the most pronounced transcriptional response at 3 days past infection, with a strong downregulation of genes across all cell types. General immune processes were mainly impacted in microglia, the brain resident immune cells, where we found increased numbers of cells expressing pro-inflammatory gene networks. In addition, we found that most neuronal cell populations downregulated genes contributing to the energy homeostasis in mitochondria and protein translation in the cytosol, indicating potential reduced cellular and neuronal activity. This might be a preventive mechanism in neuronal cells to avoid intracellular viral replication and attack by phagocytosing cells. The change of microglia gene activity suggest that this is complemented by a shift in microglia activity to provide increased surveillance of their surroundings.

neuroscience↗

bioRxiv2023

A single-cell, time-resolved profiling of Xenopus mucociliary epithelium reveals non-hierarchical model of development

The specialized cell-types of the mucociliary epithelium (MCE) lining the respiratory tract enable continuous airway clearing, with its defects leading to chronic respiratory diseases. The molecular mechanisms driving cell-fate acquisition and temporal specialization during mucociliary epithelial development remain largely unknown. Here, we profile the developing Xenopus MCE from pluripotent to mature stages by single-cell transcriptomics, identifying novel, multipotent early epithelial progenitors that execute multi-lineage cues before specialising into late-stage ionocytes, goblet and basal cells. Combining in silico lineage inference, in situ hybridization and single-cell multiplexed RNA imaging, we capture the initial bifurcation into early epithelial and multiciliated progenitors, chart cell- type emergence and fate progression into specialized cell-types. Comparative analysis of nine airway atlases reveals an evolutionary conserved transcriptional module in ciliated cells, whereas secretory and basal types execute distinct function-specific programmes across vertebrates. We uncover a continuous non-hierarchical model of MCE development alongside a significant data resource for understanding respiratory biology.

developmental biology↗

bioRxiv2021

A single cell atlas of human and mouse white adipose tissue

White adipose tissue (WAT), once regarded as morphologically and functionally bland, is now recognized to be dynamic, plastic, heterogenous, and involved in a wide array of biological processes including energy homeostasis, glucose and lipid handling, blood pressure control, and host defense1. High fat feeding and other metabolic stressors cause dramatic changes in adipose morphology, physiology, and cellular composition1, and alterations in adiposity are associated with insulin resistance, dyslipidemia, and type 2 diabetes (T2D)2. Here, we provide detailed cellular atlases of human and murine subcutaneous and visceral white fat at single cell resolution across a range of body weight. We identify subpopulations of adipocytes, adipose stem and progenitor cells (ASPCs), vascular, and immune cells and demonstrate commonalities and differences across species and dietary conditions. We link specific cell types to increased risk of metabolic disease, and we provide an initial blueprint for a comprehensive set of interactions between individual cell types in the adipose niche in leanness and obesity. These data comprise an extensive resource for the exploration of genes, traits, and cell types in the function of WAT across species, depots, and nutritional conditions.

physiology↗

bioRxiv2021

Single-cell characterisation of mononuclear phagocytes in the human intestinal mucosa

Mononuclear phagocytes (MNP), including macrophages and classical dendritic cells (cDC), are highly heterogeneous cells with distinct functions. Understanding MNP complexity in the intestinal lamina propria (LP), particularly in humans, has proved difficult due to the expression of overlapping phenotypic markers and the inability to isolate these cells without contamination from gut-associated lymphoid tissues (GALT). Here, we exploited our novel method for isolation of human GALT-free LP to carry out single-cell (sc)RNA-seq, CITE-seq and flow cytometry analysis of human ileal and colonic LP MNPs. As well as classical monocytes, non-classical monocytes, mature macrophage subsets, cDC1s, and cDC2s, we identified a CD1c+ cDC subset with features of both cDC2 and monocytes, which were transcriptionally similar to the recently described cDC3. While similar MNP subsets were present in both ileal and colonic LP, the proportions and transcriptional profiles of these populations differed between these sites and in diseased states, indicating local specialization and environmental imprinting. Using computational trajectory tools, we identified putative early committed pre-cDC subsets and developmental intermediates of mature cDC1, cDC2 and cDC3, as well as monocyte-to-macrophage trajectories. Collectively, our results provide novel insights into the heterogeneity and development of intestinal LP MNP and an important framework for studying the role of these populations in intestinal homeostasis and disease. One sentence summaryFenton and Wulff et al. use single-cell methods to explore the complexity of the mononuclear phagocyte compartment of the human intestinal lamina propria, identifying distinct dendritic cell and macrophage subsets, site-specific transcriptional signatures, and lineage-specific precursors.

immunology↗

bioRxiv2020

Role of hypothalamic MAPK/ERK signaling in diabetes remission induced by the central action of fibroblast growth factor 1

The capacity of the brain to elicit sustained remission of hyperglycemia in rodent models of type 2 diabetes following intracerebroventricular (icv) injection of fibroblast growth factor 1 (FGF1) is well established. Here, we show that following icv FGF1 injection, hypothalamic signaling by extracellular signal-regulated kinases 1 and 2 (ERK1/2), members of the mitogen-activated protein kinase (MAPK) family is induced for at least 24h. Further, we show that in diabetic Lepob/ob mice, this prolonged response is required for the sustained antidiabetic action of FGF1, since it is abolished by sustained (but not acute) pharmacologic blockade of hypothalamic MAPK/ERK signaling. We also demonstrate that FGF1 R50E, a FGF1 mutant that activates FGF receptors but induces only transient hypothalamic MAPK/ERK signaling, fails to mimic the sustained glucose lowering induced by FGF1. These data identify sustained activation of hypothalamic MAPK/ERK signaling as playing an essential role in the mechanism underlying diabetes remission induced by icv FGF1 administration.

neuroscience↗

bioRxiv2020

Human skeletal muscle CD90+ fibro-adipogenic progenitors are associated with muscle degeneration in type 2 diabetes patients

Aging and type 2 diabetes mellitus (T2DM) are associated with impaired skeletal muscle function and degeneration of the skeletal muscle microenvironment. However, the origin and mechanisms underlying the degeneration are not well described in human skeletal muscle. Here we show that skeletal muscles of T2DM patients exhibit pathological degenerative remodeling of the extracellular matrix that was associated with a selective increase of a subpopulation of fibro-adipogenic progenitors (FAPs) marked by expression of THY1 (CD90) - the FAPCD90+. We identified Platelet-derived growth factor (PDGF) signaling as key regulator of human FAP biology, as it promotes proliferation and collagen production at the expense of adipogenesis, an effect accompanied with a metabolic shift towards glycolytic lactate fermentation. FAPsCD90+ showed a PDGF-mimetic phenotype, with high proliferative activity and clonogenicity, increased production of extracellular matrix production and enhanced glycolysis. Importantly, the pathogenic phenotype of T2DM FAPCD90+ was reduced by treatment with the anti-diabetic drug Metformin. These data identify PDGF-driven conversion of a sub-population of FAPs as a key event in the pathogenic accumulation of extracellular matrix in T2DM muscles.

cell biology↗

bioRxiv2020

Single-cell analysis reveals cellular heterogeneity and molecular determinants of hypothalamic leptin-receptor cells

Hypothalamic nuclei which regulate homeostatic functions express leptin receptor (LepR), the primary target of the satiety hormone leptin. Single-cell RNA sequencing (scRNA-seq) has facilitated the discovery of a variety of hypothalamic cell types. However, low abundance of LepR transcripts prevented further characterization of LepR cells. Therefore, we perform scRNA-seq on isolated LepR cells and identify eight neuronal clusters, including three uncharacterized Trh-expressing populations as well as 17 non-neuronal populations including tanycytes, oligodendrocytes and endothelial cells. Food restriction had a major impact on Agrp neurons and changed the expression of obesity-associated genes. Multiple cell clusters were enriched for GWAS signals of obesity. We further explored changes in the gene regulatory landscape of LepR cell types. We thus reveal the molecular signature of distinct populations with diverse neurochemical profiles, which will aid efforts to illuminate the multi-functional nature of leptins action in the hypothalamus.

neuroscience↗

bioRxiv2020

Identification of 370 loci for age at onset of sexual and reproductive behaviour, highlighting common aetiology with reproductive biology, externalizing behaviour and longevity

Age at first sexual intercourse (AFS) and age at first birth (AFB) have implications for health and evolutionary fitness. In the largest genome-wide association study to date (AFS, N=387,338; AFB, N=542,901), we identify 370 independent signals, 11 sex-specific, with a 5-6% polygenic score (PGS) prediction. Heritability of AFB shifted from 9% [CI=4-14] for women born in 1940 to 22% [CI=19-25] in 1965. Signals are driven by the genetics of reproductive biology and externalising behaviour, with key genes related to follicle stimulating hormone (FSHB), implantation (ESR1), infertility, and spermatid differentiation. Polycystic Ovarian Syndrome leads to later AFB, linking with infertility. Late AFB is protective against later-life disease and associated with parental longevity. Higher childhood socioeconomic circumstances and those in the highest PGS decile (90%+) experience markedly later reproductive onset. Results are relevant for improving teenage and late-life health, for understanding longevity, and guiding experimentation into mechanisms of infertility.

genetics↗

bioRxiv2020

Mapping heritability of obesity by brain cell types

The underlying cell types mediating predisposition to obesity remain largely obscure. Here we first integrated recently published single-cell RNA-sequencing (scRNA-seq) data from >380 peripheral and nervous system cell types spanning 19 mouse organs with body mass index (BMI) genome-wide association study (GWAS) data from >450,000 individuals. Leveraging a novel strategy for integrating scRNA-seq data with GWAS data, we identified 22, exclusively neuronal, cell types from the subthalamus, midbrain, hippocampus, thalamus, cortex, pons, medulla, pallidum that were significantly enriched for BMI heritability (P<1.6x10-4). Using genes harboring coding mutations leading to syndromic forms of obesity, we replicate four midbrain cell types from the anterior pretectal nucleus, superior nucleus, periaqueductal gray and pallidum (P<1.7x10-4). Testing an additional set of 347 hypothalamic cell types, ventromedial hypothalamic steroidogenic-factor 1 (SF1) and cholecystokinin b receptor (CCKBR)-expressing neurons (P=4.9x10-5) previously implicated in energy homeostasis and glucose control and three cell types from the preoptic area of the hypothalamus and the lateral hypothalamus enriched for BMI GWAS associations (P<4.9x10-5). Together, our results suggest brain nuclei regulating integration of sensory stimuli, learning and memory are likely to play a key role in obesity and provide testable hypotheses for mechanistic follow-up studies.

genetics↗

bioRxiv2019

Multi-trait genome-wide association meta-analysis of dietary intake identifies new loci and genetic and functional links with metabolic traits

Dietary intake, a major contributor to the global obesity epidemic1-5, is a complex phenotype partially affected by innate physiological processes.6-11 However, previous genome-wide association studies (GWAS) have only implicated a few loci in variability of dietary composition.12-14 Here, we present a multi-trait genome-wide association meta-analysis of inter-individual variation in dietary intake in 283,119 European-ancestry participants from UK Biobank and CHARGE consortium, and identify 96 genome-wide significant loci. Dietary intake signals map to different brain tissues and are enriched for genes expressed in {beta}1-tanycytes and serotonergic and GABAergic neurons. We also find enrichment of biological pathways related to neurogenesis. Integration of cell-line and brain-specific epigenomic annotations identify 15 additional loci. Clustering of genome-wide significant variants yields three main genetic clusters with distinct associations with obesity and type 2 diabetes (T2D). Overall, these results enhance biological understanding of dietary composition, highlight neural mechanisms, and support functional follow-up experiments.

genetics↗

bioRxiv2018

Benchmarker: an unbiased, association-data-driven strategy to evaluate gene prioritization algorithms

Genome-wide association studies (GWAS) are valuable for understanding human biology, but associated loci typically contain multiple associated variants and genes. Thus, algorithms that prioritize likely causal genes and variants for a given phenotype can provide biological interpretations of association data. However, a critical, currently missing capability is to objectively compare performance of such algorithms. Typical comparisons rely on \"gold standard\" genes harboring causal coding variants, but such gold standards may be biased and incomplete. To address this issue, we developed Benchmarker, an unbiased, data-driven benchmarking method that compares performance of prioritization strategies to each other (and to random chance) by leave-one-chromosome-out cross-validation with stratified linkage disequilibrium (LD) score regression. We first applied Benchmarker to twenty well-powered GWAS and compared gene prioritization based on strategies employing three different data sources, including annotated gene sets and gene expression. No individual strategy clearly outperformed the others, but genes prioritized by multiple strategies had higher per-SNP heritability than those prioritized by one strategy only. We also compared two gene prioritization methods, DEPICT and MAGMA; genes prioritized by both methods strongly outperformed genes prioritized by only one. Our results suggest that combining data sources and algorithms should pinpoint higher quality genes for follow-up. Benchmarker provides an unbiased approach to evaluate any method that provides genome-wide prioritization of gene sets, genes, or variants, and can determine the best such method for any particular GWAS. Our method addresses an important unmet need for rigorous tool assessment and can assist in mapping genetic associations to causal function.

genomics↗

bioRxiv2018

scVAE: Variational auto-encoders for single-cell gene expression data

MotivationModels for analysing and making relevant biological inferences from massive amounts of complex single-cell transcriptomic data typically require several individual data-processing steps, each with their own set of hyperparameter choices. With deep generative models one can work directly with count data, make likelihood-based model comparison, learn a latent representation of the cells and capture more of the variability in different cell populations.\n\nResultsWe propose a novel method based on variational auto-encoders (VAEs) for analysis of single-cell RNA sequencing (scRNA-seq) data. It avoids data preprocessing by using raw count data as input and can robustly estimate the expected gene expression levels and a latent representation for each cell. We tested several count likelihood functions and a variant of the VAE that has a priori clustering in the latent space. We show for several scRNA-seq data sets that our method outperforms recently proposed scRNA-seq methods in clustering cells and that the resulting clusters reflect cell types.\n\nAvailability and implementationOur method, called scVAE, is implemented in Python using the TensorFlow machine-learning library, and it is freely available at https://github.com/scvae/scvae.

bioinformatics↗

bioRxiv2017

PAIRUP-MS: Pathway Analysis and Imputation to Relate Unknowns in Profiles from Mass Spectrometry-based metabolite data

Metabolomics is a powerful approach for discovering biomarkers and metabolic quantitative trait loci. While untargeted profiling methods can measure up to thousands of metabolite signals in a single experiment, many signals cannot be readily identified as known metabolites or compared across datasets, making it difficult to infer biology and to conduct well-powered meta-analyses across studies. To deal with these challenges, we developed a suite of computational methods, PAIRUP-MS, to match metabolite signals across mass spectrometry-based profiling datasets using an imputation-based approach and to generate pathway annotations for these signals. We performed meta and pathway analyses for both known and unknown signals in multiple datasets and then validated the results using genetic associations. Finally, we applied the methods to detect metabolite signals and pathways associated with body mass index, demonstrating that our framework is useful for analyzing unknown signals in a robust and biologically meaningful manner and for improving the power of untargeted metabolomics studies.

bioinformatics↗