Biology subjects

Weber, L.

Publications and source records attributed to Weber, L..

15 recordsLinked to original sources

bioRxiv2026

NIK stabilization reprograms the pancreas toward chronic pancreatitis and pancreatic lipomatosis

Pancreatic lipomatosis, characterized by progressive adipose replacement of the exocrine pancreas, is a known consequence of chronic pancreatitis. Despite its clinical relevance, the mechanisms driving lipomatosis remain poorly understood. Here, we used a pancreas-specific NF-{kappa}B-inducing kinase (NIK) stabilization mouse model to investigate how sustained non-canonical NF-{kappa}B signaling contributes to chronic pancreatitis and pancreatic lipomatosis. NIK stabilization initiated a rapid and fully penetrant pancreatic remodeling program, leading to acinar cell death already by the end of the first week of age. Deep learning on high-definition spatial transcriptomics revealed a progressive loss of acinar identity resulting in acinar-to-ductal metaplasia (ADM) formation and identified GPA33 as a novel surface marker of ADMs. Over time, ADM lesions underwent apoptosis, leading to progressive loss of pancreatic parenchyma, organ atrophy, and extensive adipose replacement. Mechanistically, Relb deletion fully rescued the NIK-induced pancreatic phenotype, demonstrating that chronic pancreatitis and lipomatosis require RelB-dependent non-canonical NF-{kappa}B signaling in this model. Together, these findings define a RelB-dependent pathway linking sustained NIK stabilization to chronic pancreatitis and pancreatic lipomatosis. Our study provides a model for studying the development of pancreatic lipomatosis and for exploring potential therapeutic approaches.

cancer biology↗

bioRxiv2026

Cardiac belief updating from volatile physiological afferents

Cardiac interoception is commonly assessed by comparing subjective estimates of heart rate with physiological measurements. But beliefs can easily obscure these measures, such that they cannot readily distinguish sensitivity to afferent signals from the influence of prior expectations; this ultimately challenges the notion that behaviours under these tasks could reflect interoceptive processes at all. Here, we develop a computational framework that uses naturally occurring heart rate variability to quantify how strongly perceptual beliefs are updated by physiological evidence. We formalise interoception as weighted Bayesian updating under the joint influence of expectations and afferents, and derive a new measure, cardiac interoceptive sensitivity, that quantifies the extent to which beliefs move with incoming signals. Applying this to the largest Heart Rate Discrimination dataset to date (n=549), a task providing robust estimates of cardiac beliefs, we find that sensitivity is weak in the healthy population, but shows large interindividual differences, with 43% of the participants exhibiting behaviours at least minimally compatible with interoceptive processing. These findings challenge the interpretation of conventional measures of cardiac interoception. They also introduce a new framework to relate bodily signals that cannot be controlled experimentally to external signals that can, laying a computational foundation for embodied psychophysics.

neuroscience↗

bioRxiv2026

A human developmental and adult brain atlas benchmarks dopaminergic stem cell models and cell therapy candidates

Parkinsons disease (PD) is characterized by the progressive loss of midbrain dopaminergic (mDA) neurons1. Stem cell-derived mDA neurons hold promise for disease modelling2,3 and are currently in clinical trials for cell replacement therapy4-6. However, systematic benchmarking has been limited by the lack of a unified high-resolution reference and methods that quantify incomplete or mixed lineage specification in vitro7. We establish a single-cell and spatial atlas of the human developing diencephalon-midbrain-hindbrain axis resolving 93 cell subtypes, including 39 lacking prior single-cell characterization and 4 entirely novel populations. Using this atlas as a reference, we integrate 19 hPSC-derived mDA datasets, both published2,8-25 and unpublished, to build the Human Dopaminergic Neural Atlas (HDNA) spanning 2D, 3D, and graft models, including those used in clinical trials. To classify cells and quantify lineage fidelity, we develop CapybaraBrain, a marker-driven non-negative decomposition framework that assigns each cell continuous identity scores across all 93 developmental programs, enabling systematic discrimination of discrete, transitioning, and cross-lineage hybrid states26. We uncover a pervasive landscape of off-target populations reflecting relaxed transcriptional boundaries in vitro, including a previously unrecognized TH-PITX2 midbrain neuronal population, and we validate atlas-predicted latent lineage plasticity through inducible genetic fate mapping in mouse models. We further define maturation-associated transcriptional programs by harmonizing adult mDA subtype atlases, revealing that dopaminergic identity and maturation are partially decoupled across protocols. Finally, projecting PD patient-derived tri-cultures onto the HDNA uncovers genotype- and cell-type-specific transcriptional dysregulation. Together, these integrated atlases and computational framework establish a unified standard for benchmarking differentiation fidelity, exposing off-target states, and guiding next-generation PD models and cell therapies.

Developmental Biology↗

bioRxiv2026

Habitat and Hydrodynamics Influence Coral Reef and Seagrass Microbial and Exometabolite Dynamics

Coral reef and seagrass ecosystems provide critical storm protection and economic revenue to tropical coastal communities. Effective monitoring and restoration strategies are essential given increasing impacts from climate change and human development. Microorganisms, and the metabolites they produce and consume, are key drivers of coastal ecosystem function. However, microbially-mediated metabolite recycling remains poorly understood, limiting its inclusion in conservation and restoration strategies. Here we examine how seawater exometabolites and microorganisms in coastal ecosystems vary in relation to habitat (seagrass or coral reef), temporal scales and hydrodynamics. We characterized benthic seawater from two St. John, U.S. Virgin Islands coral reefs (Yawzi and Tektite) and one seagrass meadow at dawn and mid-day over four consecutive days. Using quantitative metabolomics and SSU rRNA gene amplicon sequencing, we found that habitat served as the primary driver of exometabolite and microbial community composition, while daily changes and hydrodynamics strongly influenced system variability. Hydrodynamic modeling indicated offshore water intrusion at mid-day at Yawzi reef, likely driving exometabolite and microbial shifts towards oligotrophic taxa (e.g., SAR11, SAR86). In contrast, a high percentage of coastal source water in the seagrass habitat maintained stable exometabolite pools and supported diverse microbial communities. These findings demonstrate that coastal habitat and hydrodynamics strongly influence exometabolite and microbial assemblages, with lesser contributions from temporal changes. Integrating exometabolites, microorganisms, and hydrodynamics provides new insights into coastal ecosystem functioning useful for habitat monitoring and restoration strategies.

ecology↗

bioRxiv2025

Does more data solve the problem of deep support? A phylogenomic approach to clarifying evolutionary relationships in the Lecanoraceae

The family Lecanoraceae is one of the most diverse groups within Lecanoromycetes, yet its internal relationships have remained unresolved despite previous multilocus studies. Here we present the first phylogenomic reconstruction of the family, based on 1003 nuclear orthologous genes from 65 genome assemblies representing the major species groups and genera. Phylogenetic trees inferred from coalescent-based analyses, together with Quartet Sampling (QS) evaluation, confirm the monophyly of Lecanoraceae and recover several well-supported lineages. The Lecanora subfusca group is resolved as polyphyletic, comprising three major clades together with Zeora and Vainionora. Additional strongly supported lineages correspond to Glaucomaria s. lat., the MPRPS clade, and Lecidella s. lat., while Bryonora and Frutidella represent early-diverging members of the family. The L. fuscescens group and Miriquidica form a distinct lineage sister to Lecanoraceae, whereas Ramboldia (Ramboldiaceae) constitutes another sister family. Backbone support was generally improved, and QS analyses showed counter-support for only a few deep nodes, indicating a complex evolutionary history likely shaped by incomplete lineage sorting, hybridization, or rapid radiation. Our results demonstrate that genome-scale data improve the resolution of Lecanoraceae, but increasing the number of genes alone can not fully resolve the backbone relationships of the family.

molecular biology↗

bioRxiv2025

CD8+ T cells regulate the bioenergetic reprogramming of lymphoid organs and the heart during viral infection

The activation of the immune system is a bioenergetically-costly process1. Yet, essential bodily functions require a continuous energy supply, imposing energy constraints and trade-offs between competing processes2. Our understanding of the underlying bioenergetic adaptations reconciling rapid immune activation with other vital processes remains scarce. 3-6 Here, by using experimental models of viral infections, we identified an unexpected CD8+ T cell-driven redistribution of energy substrates between lymphoid organs and the heart. Viral infection promoted systemic hypoglycaemia and ketogenesis, together with systemic reallocation of energy substrates. Across organs analysed, secondary lymphoid organs and the heart showed the most dramatic changes. The former increased glucose uptake and oxidation while the heart showed the opposite, switching to preferential fatty acid utilization. These bioenergetic adaptations were absent in infected mice lacking CD8+ T cells or with T cells lacking the glucose transporter GLUT1. Pharmacological inhibition of fatty acid oxidation forced a systemic switch to glucose oxidation. This was associated with metabolic decompensation, reduced cardiac energetics, left ventricular stress, and mortality in otherwise nonlethal viral infections. Our results reveal how the energetic cost of immune cell activation imposes bioenergetic adaptations on non-lymphoid organs, posing a major challenge for the heart by completely relying on fatty acids.

immunology↗

bioRxiv2025

Development of a novel alpha7-nicotinic acetylcholine receptor-selective cell-penetrating peptide for intracellular cargo transport

Drug delivery into the brain remains a critical barrier for the treatment of neurological diseases. While the brain is shielded from many toxins and viruses by the blood-brain barrier (BBB), therapeutics are being created that exploit natural bypass mechanisms by forming complexes with cell-penetrating peptides (CPPs) derived from viruses. Neurological diseases often impact specific proteins in the brain; however, current CPPs lack the ability to selectively target precise cellular macromolecules. As a result, they are distributed broadly throughout the brain and cause off-target side effects. Neurotropic CPPs derived from the rabies virus glycoprotein (RVG) can access the brain by binding to plasma membrane targets, including, but not exclusively, nicotinic acetylcholine receptors (nAChRs). To overcome this barrier of minimal target selectivity, we designed several chimeric peptides composed of regions from the RVG and -bungarotoxin, an 7 subtype-selective protein. Using human nAChRs expressed in Xenopus laevis oocytes, we screened the selectivity of our peptides using two-electrode voltage clamp electrophysiology. We identified a peptide with improved 7 nAChR subtype selectivity and apparent potency compared to the control RVG peptide. Using mammalian Neuro-2a cells, we demonstrated that our peptide depends on 7 nAChR plasma membrane expression to internalize and carry small-molecule payloads into the neuronal-like cells, without significant cytotoxic effects. Our novel 7 nAChR subtype-selective CPP may be useful in research applications requiring cargo delivery. Translationally, our 7 nAChR selective CPP holds potential to be a dual drug delivery system to transport cargo into the brain for the treatment of neurological diseases.

bioengineering↗

bioRxiv2025

Adaptive Clonal Expansion Shapes Brain Development

Embryonic neural stem/progenitor cells (NSPCs) exhibit remarkable proliferative plasticity, allowing them to fully recover neuronal populations even after substantial cell loss 1,2. However, it remains unclear whether all embryonic NSPCs respond to brain lesions. To address this, we developed a mouse model to investigate NSPC proliferation dynamics, hypothesizing that the loss of progenitor cells would induce fitness competition among NSPCs. In this model, half of the founder NSPCs were ablated using diphtheria toxin A at the onset of neurogenesis, yet the surviving cells regenerated a brain containing all neuronal types within five of the total twenty embryonic days. Analysis of allelic variants revealed overrepresented somatic variants, indicating that only a small fraction of NSPCs underwent significant clonal expansion during early neurogenesis. Modelling proliferation dynamics predicted that as few as 10% of NSPCs could produce 83% of neurons by the time of birth. Single nucleotide substitution analysis suggested a potential link to oxidative metabolism in some of the expanded clones. Moreover, single-cell transcriptomics showed delayed development and a reduced NSPC pool as consequences of adaptive clonal expansion. Our findings highlight that NSPC exhibit varying expansion potential and that adaptive clonal expansion indirectly altered neuronal cell composition in the brain.

developmental biology↗

bioRxiv2025

A single pathogen-secreted protein reprograms plants for drought resilience

Climate change-enforced drought stress conditions and diseases caused by pathogens often co-occur and represent one of the greatest challenges in plant science1-3. Wilt pathogens that colonize water-conducting plant tissues can aggravate the problem and affect a wide range of agricultural crops4,5. However, whilst fungal infections with the vascular pathogen Verticillium dahliae are typically associated with wilt symptoms due to occlusion of xylem tissues, the related V. longisporum induces de novo formation of tracheary elements6,7. This promotes not only its virulence but also enables elevated water storage capacity of the infected host plant and resilience against drought stress conditions6,7. Here, we identified a secreted Verticillium protein, TRANSDIFFERENTIATION EFFECTOR (TRADE), which triggers cell identity switches of bundle sheath cells into tracheary elements. We show that TRADE interacts with the intracellular plant protein VARICOSE (VCS), a conserved component of the mRNA turnover machinery and ortholog of the metazoan protein ENHANCER OF DECAPPING 4 (EDC4/HEDLS/Ge-1)8. The TRADE-VCS interaction induces SUCROSE NON-FERMENTING 1 (SNF1)-related protein kinase (SRK)-dependent phosphorylation and thus dysfunction of VCS. This affects the abundance of mRNAs encoding master regulators of xylem differentiation and demonstrates how a single pathogen effector protein triggers complex tissue-specific developmental reprogramming and thus promotes abiotic stress resilience.

plant biology↗

bioRxiv2024

A microglia clonal inflammatory disorder in Alzheimer's Disease

Somatic genetic heterogeneity resulting from post-zygotic DNA mutations is widespread in human tissues and can cause diseases, however few studies have investigated its role in neurodegenerative processes such as Alzheimers Disease (AD). Here we report the selective enrichment of microglia clones carrying pathogenic variants, that are not present in neuronal, glia/stromal cells, or blood, from patients with AD in comparison to age-matched controls. Notably, microglia-specific AD-associated variants preferentially target the MAPK pathway, including recurrent CBL ring-domain mutations. These variants activate ERK and drive a microglia transcriptional program characterized by a strong neuro-inflammatory response, both in vitro and in patients. Although the natural history of AD-associated microglial clones is difficult to establish in human, microglial expression of a MAPK pathway activating variant was previously shown to cause neurodegeneration in mice, suggesting that AD-associated neuroinflammatory microglial clones may contribute to the neurodegenerative process in patients. One-Sentence SummaryA subset of Alzheimer Disease patients carry mutant microglia somatic clones which promote neuro-inflammation.

neuroscience↗

bioRxiv2024

Digital Microbe: A Genome-Informed Data Integration Framework for Collaborative Research on Emerging Model Organisms

The remarkable pace of genomic data generation is rapidly transforming our understanding of life at the micron scale. Yet this data stream also creates challenges for team science. A single microbe can have multiple versions of genome architecture, functional gene annotations, and gene identifiers; additionally, the lack of mechanisms for collating and preserving advances in this knowledge raises barriers to community coalescence around shared datasets. "Digital Microbes" are frameworks for interoperable and reproducible collaborative science through open source, community-curated data packages built on a (pan)genomic foundation. Housed within an integrative software environment, Digital Microbes ensure real-time alignment of research efforts for collaborative teams and facilitate novel scientific insights as new layers of data are added. Here we describe two Digital Microbes: 1) the heterotrophic marine bacterium Ruegeria pomeroyi DSS-3 with >100 transcriptomic datasets from lab and field studies, and 2) the pangenome of the cosmopolitan marine heterotroph Alteromonas containing 339 genomes. Examples demonstrate how an integrated framework collating public (pan)genome-informed data can generate novel and reproducible findings.

bioinformatics↗

bioRxiv2023

Advancing Reef Monitoring Techniques through Exometabolomics: Quantification of Labile Dissolved Organic Metabolites on Coral Reefs

Extracellular chemical cues constitute much of the language of life among marine organisms from microbes to mammals. These chemical cues shape foraging and feeding strategies, symbiotic interactions, selection of mates and habitats, and the transfer of energy and nutrients within and among ecosystems. Changes in the chemical pool flux within these ecosystems serve as invisible signals of overall ecosystem health and, conversely, disruption to this finely tuned equilibrium. In coral reef systems, the full scope and magnitude of the chemicals involved in maintaining reef equilibria are largely unknown. In particular, processes involving small, polar molecules, which form the majority components of labile dissolved organic carbon (DOC), are often poorly captured using traditional techniques and therefore remain unconstrained. In this study, we employed a recently developed chemical derivatization method and mass spectrometry-based targeted exometabolomics to capture and quantify polar dissolved phase metabolites on five coral reefs in the U.S. Virgin Islands. We detected and quantified 45 polar exometabolites, and further demonstrate their variability across the investigated geographic landscape and contextualize these findings in terms of geographic and benthic cover differences. By comparing our results to previously published coral reef exometabolomes that did not employ chemical derivatization, we show the novel quantification of 23 metabolites which were previously undetected in the coral reef exometabolome, including compounds involved in central carbon metabolism (e.g., glutamate) and novel metabolites such as homoserine betaine. We highlight the immense potential of chemical derivatization-based exometabolomics for quantifying labile chemical cues on coral reefs and measuring molecular level responses to environmental stressors. Overall, improving our understanding of the composition and dynamics of reef exometabolites is vital for effective ecosystem monitoring and management strategies. SynopsisMinimal research exists on the polar exometabolites on coral reefs, an essential component of reef health and the majority component of labile DOC. This study reports 23 newly quantified polar metabolites on coral reefs and highlights significant differences in the exometabolome composition across geographically distinct reefs and bays.

systems biology↗

bioRxiv2020

PhyDOSE: Design of Follow-up Single-cell Sequencing Experiments of Tumors

The combination of bulk and single-cell DNA sequencing data of the same tumor enables the inference of high-fidelity phylogenies that form the input to many important downstream analyses in cancer genomics. While many studies simultaneously perform bulk and single-cell sequencing, some studies have analyzed initial bulk data to identify which mutations to target in a follow-up single-cell sequencing experiment, thereby decreasing cost. Bulk data provide an additional untapped source of valuable information, composed of candidate phylogenies and associated clonal prevalence. Here, we introduce PhyDOSE, a method that uses this information to strategically optimize the design of follow-up single cell experiments. Underpinning our method is the observation that only a small number of clones uniquely distinguish one candidate tree from all other trees. We incorporate distinguishing features into a probabilistic model that infers the number of cells to sequence so as to confidently reconstruct the phylogeny of the tumor. We validate PhyDOSE using simulations and a retrospective analysis of a leukemia patient, concluding that PhyDOSEs computed number of cells resolves tree ambiguity even in the presence of typical single-cell sequencing errors. We also conduct a retrospective analysis on an acute myeloid leukemia cohort, demonstrating the potential to achieve similar results with a significant reduction in the number of cells sequenced. In a prospective analysis, we demonstrate that only a small number of cells suffice to disambiguate the solution space of trees in a recent lung cancer cohort. In summary, PhyDOSE proposes cost-efficient single-cell sequencing experiments that yield high-fidelity phylogenies, which will improve downstream analyses aimed at deepening our understanding of cancer biology. Author summaryCancer development in a patient can be explained using a phylogeny -- a tree that describes the evolutionary history of a tumor and has therapeutic implications. A tumor phylogeny is constructed from sequencing data, commonly obtained using either bulk or single-cell DNA sequencing technology. The accuracy of tumor phylogeny inference increases when both types of data are used, but single-cell sequencing may become prohibitively costly with increasing number of cells. Here, we propose a method that uses bulk sequencing data to guide the design of a follow-up single-cell sequencing experiment. Our results suggest that PhyDOSE provides a significant decrease in the number of cells to sequence compared to the number of cells sequenced in existing studies. The ability to make informed decisions based on prior data can help reduce the cost of follow-up single cell sequencing experiments of tumors, improving accuracy of tumor phylogeny inference and ultimately getting us closer to understanding and treating cancer.

bioinformatics↗

bioRxiv2019

Ketamine Affects Prediction Errors About Statistical Regularities: A Computational Single-Trial Analysis of the Mismatch Negativity

The auditory mismatch negativity (MMN) is significantly reduced in schizophrenia. Notably, a similar MMN reduction can be achieved with NMDA receptor (NMDAR) antagonists. Both phenomena have been interpreted as reflecting an impairment of predictive coding or, more generally, the "Bayesian brain" notion that the brain continuously updates a hierarchical model to infer the causes of its sensory inputs. Specifically, predictive coding views perceptual inference as an NMDAR-dependent process of minimizing hierarchical precision-weighted prediction errors (PEs). Disturbances of this putative process play a key role in hierarchical Bayesian theories of schizophrenia. Here, we provide empirical evidence for this clinical theory, demonstrating the existence of multiple, hierarchically related PEs in a "roving MMN" paradigm. We applied a computational model, the Hierarchical Gaussian Filter (HGF), to single-trial EEG data from healthy volunteers that received the NMDAR antagonist S-ketamine in a placebo-controlled, double-blind, within-subject fashion. Using an unrestricted analysis of the entire time-sensor space, our computational trial-by-trial analysis indicated that low-level PEs (about stimulus transitions) are expressed early (102-207ms post-stimulus), while high-level PEs (about transition probability) are reflected by later components (152-199ms, 215-277ms) of single-trial responses. Furthermore, we find that ketamine significantly diminished the expression of high-level PE responses, implying that NMDAR antagonism disrupts inference on abstract statistical regularities. Our findings are consistent with long-standing notions that NMDAR dysfunction may cause positive symptoms in schizophrenia by impairing hierarchical Bayesian inference about the worlds statistical structure. Beyond their relevance for schizophrenia, our results illustrate the potential of computational single-trial analyses for assessing potential disease mechanisms.

neuroscience↗

bioRxiv2017

Identification of Gene Regulation Models from Single-Cell Data

In quantitative analyses of biological processes, one may use many different scales of models (e.g., spatial or non-spatial, deterministic or stochastic, time-varying or at steady-state) or many different approaches to match models to experimental data (e.g., model fitting or parameter uncertainty/sloppiness quantification with different experiment designs). These different analyses can lead to surprisingly different results, even when applied to the same data and the same model. We use a simplified gene regulation model to illustrate many of these concerns, especially for ODE analyses of deterministic processes, chemical master equation and finite state projection analyses of heterogeneous processes, and stochastic simulations. For each analysis, we employ MO_SCPLOWATLABC_SCPLOW and PO_SCPLOWYTHONC_SCPLOW software to consider a time-dependent input signal (e.g., a kinase nuclear translocation) and several model hypotheses, along with simulated single-cell data. We illustrate different approaches (e.g., deterministic and stochastic) to identify the mechanisms and parameters of the same model from the same simulated data. For each approach, we explore how uncertainty in parameter space varies with respect to the chosen analysis approach or specific experiment design. We conclude with a discussion of how our simulated results relate to the integration of experimental and computational investigations to explore signal-activated gene expression models in yeast [1] and human cells [2]{ddagger}.\n\nPACS numbers: 87.10.+e, 87.15.Aa, 05.10.Gg, 05.40.Ca,02.50.-r\n\nSubmitted to: Phys. Biol.

systems biology↗