Biology subjects

Meier, J.

Publications and source records attributed to Meier, J..

At least 19 recordsLinked to original sources

bioRxiv2026

Single shot low-dose radiation durably and focally increases cortical excitability: a potential therapy for neuronal circuit disorders?

Herein, we assess the potential of low-dose stereotactic radiosurgery (SRS) to modulate neuronal network states without apparent damage to cellular integrity. Using a small animal radiation research platform (SARRP), a 1 mm3 focal target in the mouse visual cortex was irradiated with doses of 5, 20, and 40 Gy. One-month later a significant dose-dependent increase in excitatory synapse numbers was observed, notably limited to the treated visual cortex and not the adjacent somatosensory cortex. Six months post-irradiation, cortical neuronal microcircuit activity was monitored in awake mice using high sensitivity two-photon calcium imaging. A single 5 Gy dose resulted in a significant microcircuit-wide increase of spontaneous neuronal activity, consistent with a lasting shift in the functional architecture of the irradiated nodal network. At higher SRS doses (40 Gy) this neuromodulatory window appears to close. In aggregate, these data suggest that low-dose radiation could, in some circumstances, be exploited by selected high precision SRS technologies to durably modulate neuronal circuit disorders. Some, or even all the clinical benefits reported in the companion article by Zhao et al. are likely attributable to the biological properties we sought to characterize in our research.

neuroscience↗

bioRxiv2026

Dopamine Depletion Drives Whole-Brain Oscillatory Disruptions via Cortico-Subcortical Resonance: A Multiscale Model of Parkinson's Disease in Mice

Parkinson's disease is defined by dopaminergic neuron loss in the substantia nigra, yet its hallmark, exaggerated beta-band synchrony, pervades motor cortex, thalamus, and cerebellum, implicating network dynamics far beyond any single circuit. How focal subcortical dopamine depletion translates into brain-wide oscillatory pathology remains unresolved. We use a connectome-constrained multiscale model of the mouse brain, embedding biophysically detailed spiking networks of basal ganglia and cerebellum within whole-brain corticothalamic dynamics grounded in the Allen Mouse Brain Connectivity Atlas. We show that confining dopamine depletion exclusively to subcortical circuits is sufficient to produce widespread beta hypersynchrony (10-30 Hz), accompanied by heterogeneous theta and gamma dysregulation. Virtual loop ablations reveal that cortical and cerebellar beta amplification strictly requires intact cortico-basal ganglia-thalamic feedback; severing this loop confines beta to subcortical generators. These results support resonance within closed large-scale loops, rather than local rhythmogenesis, as the mechanism underlying distributed Parkinsonian beta pathology.

neuroscience↗

bioRxiv2026

HuR Regulates GATA3-Driven Type 2 Inflammation in CD4⁺ T cells and ILC2 in Airway Inflammation

Type 2-high asthma is driven by coordinated GATA3-dependent programs in CD4 T cells and group 2 innate lymphoid cells (ILC2). Although biologics targeting Th2 cytokines benefit subsets of patients, many remain symptomatic, suggesting upstream regulatory mechanisms may sustain type 2 inflammation. We investigated whether the RNA-binding protein HuR (ELAVL1) functions as a post-transcriptional regulator of GATA3-driven type 2 inflammation in allergic asthma. Using a house dust mite (HDM) model in vivo, HuR inhibition with KH-3 reduced lung inflammation, suppressed Th2 cytokine expression, accelerated Gata3 mRNA decay in lung CD4 T cells, and attenuated airway hyperresponsiveness toward control levels. In ex vivo-activated human lung CD4 T cells, KH-3 accelerated GATA3 mRNA decay with minimal effects on RORC or TBX21 and selectively reduced Th2 cytokine secretion, while IL-10 and IL-2 were unchanged. Similarly, ILC2s isolated from PBMCs of type 2-high asthmatic donors showed reduced GATA3 mRNA stability and diminished Th2 cytokine production following KH-3 treatment. Single-cell transcriptomic analysis of bronchoalveolar lavage fluid after allergen challenge in asthmatic subjects demonstrated co-enrichment of ELAVL1 and GATA3 within Th2 clusters in human airways. Together, these findings identify HuR as a therapeutically targetable upstream regulator of GATA3-driven type 2 inflammation in allergic asthma.

immunology↗

bioRxiv2026

Genotype-phenotype mapping identifies fetal-like CD55+ immunoregulatory cancer cells as mediators of immune escape in colorectal cancer

Immune evasion is a defining feature of advanced colorectal cancer (CRC), yet the cellular mechanisms linking cancer cell states to immune suppression remain poorly understood. To systematically map tumour-immune interactions in advanced CRC, we modelled recurrent human CRC mutations using a multiplex CRISPR-based genetically engineered mouse model platform. The resulting 20 models recapitulate key stages, genetic routes and histopathological features of human CRC, while single-cell transcriptomics reveals extensive disease complexity across models. Profiling of the epithelial tumour compartment identified a population of fetal-like immunoregulatory cancer cells (IRCs) characterized by interferon-{gamma} and MAPK activity. IRCs are marked by expression of the membrane-bound complement regulatory protein CD55. Mechanistically, CD55 protects IRCs from immune surveillance and complement-mediated lysis. Together, these findings reveal a cancer cell-immune interface that promotes immune evasion in advanced CRC.

cancer biology↗

bioRxiv2026

WNT-driven immune evasion promotes malignant transformation of BRAF-mutant colorectal cancer

BRAF-mutant colorectal cancer (CRC) constitutes a molecularly and clinically distinct subtype with poor prognosis and resistance to standard therapies, representing a major unmet clinical need. Arising from the serrated pathway of colorectal carcinogenesis rather than the classical adenoma-carcinoma sequence, this subgroup remains relatively understudied yet displays a more aggressive disease course. To investigate the progression of serrated CRC, we generated multiple genetically engineered mouse models (GEMMs) of BRAF-mutant, microsatellite-stable (MSS) CRC that closely recapitulate human disease. Our findings demonstrate that WNT-pathway activation via APC- or CTNNB1-mutations, but not RNF43-loss, initiates serrated CRC by suppressing immune-mediated tumor surveillance. Mechanistically, WNT-signaling drives distinct alterations in T cell phenotypes within the tumor microenvironment, enabling tumor progression. Together, these data indicate that WNT-signaling mediates immune escape during the malignant transformation of BRAF-mutant CRC.

cancer biology↗

bioRxiv2025

An Interactive Brain Atlas of Knowledge

Biomedical knowledge about the brain increases every day, with a rapidly growing number of scientific publications, datasets, and software tools. While this informational plethora is not merely comprehensible by human beings, recent developments in information science and computational linguistics aim to make this knowledge programmatically accessible by literature mining. However, integrating these semantic methods into neuroimaging standards remains insufficient, hindering researchers from unraveling their full potential. Therefore, we developed the semantic meta-analysis platform The Virtual Brain adapter of semantics (TVBase) that projects biomedical knowledge preserved in over 36 million scientific articles onto a 3D standardized brain. The literature-mining platform SCAIView was used to extract ontologically defined biomedical entities and their associations with brain anatomy from the PubMed database. By querying a specific concept, the association strength with each anatomical term was calculated using entropy. To project the data onto a standardized brain, we created a unique transformation matrix that links over 800 anatomical terms to voxel coordinates of a parcellated standard brain. This novel method of knowledge projection extracts region-specific information about biomedical concepts from the literature to support translational multi-scale approaches to computational neuroscience. The multi-purpose software framework TVBase is openly available as a Python library. It aims for hypothesis-free neuroimaging pattern interpretation, hypothesis generation, and applications in personalized medicine.

neuroscience↗

bioRxiv2025

Drug-like antibody design against challenging targets with atomic precision

Computational antibody design has seen rapid progress, with high success rates enabling direct translation to characterization without any high-throughput screening required. In this work, we markedly expand the scope of de novo antibody design by applying our state-of-the-art Chai-2 platform to design drug-like antibodies in full-length monoclonal format. We find that >86% of these full-length mAbs have strong developability profiles on par with therapeutic antibodies. We further show that experimentally determined structures of Chai-2 designs closely match their in silico predictions, demonstrating that Chai-2 produces atomically accurate models of designed antibodies. Building on these foundational capabilities, we showcase two potential applications of Chai-2 against different targets: designing functional antibodies mediating GPCR agonism, and highly specific antibodies selectively binding tumor-specific neoepitopes. Taken together, this work brings new flexibility to modern discovery pipelines, accelerating the path from in silico design to functional validation across both conventional and challenging targets. Beyond reducing the cost and timelines associated with large screening campaigns, in silico design can now open new frontiers for creative, targeted therapeutics that address unmet clinical needs.

molecular biology↗

bioRxiv2025

Zero-shot antibody design in a 24-well plate

Despite breakthroughs in protein design enabled by artificial intelligence, reliably designing functional antibodies from scratch has remained an elusive challenge. Recent works show promise but still require large-scale experimental screening of thousands to millions of designs to reliably identify hits. In this work, we introduce Chai-2, a multimodal generative model that achieves a 16% hit rate in fully de novo antibody design, representing an over 100-fold improvement compared to previous computational methods. We prompt Chai-2 to design[≤] 20 antibodies or nanobodies to 52 diverse targets, completing the workflow from AI design to wet-lab validation in under two weeks. Crucially, none of these targets have a preexisting antibody or nanobody binder in the Protein Data Bank. Remarkably, in just a single round of experimental testing, we find at least one successful hit for 50% of targets, often with strong affinities and favorable drug-like profiles. Beyond antibody design, Chai-2 achieves a 68% wet-lab success rate in miniprotein design - routinely yielding picomolar binders. The high success rate of Chai-2 enables rapid experimental validation and characterization of novel antibodies in under two weeks, paving the way toward a new era of rapid and precise atomic-level molecular engineering. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=56 SRC="FIGDIR/small/663018v1_ufig1.gif" ALT="Figure 1"> View larger version (38K): org.highwire.dtl.DTLVardef@69655borg.highwire.dtl.DTLVardef@17d44dborg.highwire.dtl.DTLVardef@133b7ddorg.highwire.dtl.DTLVardef@6c4319_HPS_FORMAT_FIGEXP M_FIG 52 antigens targeted by Chai-2. Blue boxes indicate targets with at least one successful binder out of [≤]20 assayed designs, representing 50% of the tested targets. Date: June 30, 2025 C_FIG

synthetic biology↗

bioRxiv2024

Chai-1: Decoding the molecular interactions of life

We introduce Chai-1, a multi-modal foundation model for molecular structure prediction that performs at the state-of-the-art across a variety of tasks relevant to drug discovery. Chai-1 can optionally be prompted with experimental restraints (e.g. derived from wet-lab data) which boosts performance by double-digit percentage points. Chai-1 can also be run in single-sequence mode with-out MSAs while preserving most of its performance. We release Chai-1 model weights and inference code as a Python package for non-commercial use and via a web interface where it can be used for free including for commercial drug discovery purposes.

synthetic biology↗

bioRxiv2024

Mutualisms within light microhabitats drive sensory convergence in a mimetic butterfly community

Niche partitioning within variable habitats can expose species to distinct sensory information. Vision is the primary sensory modality used by many animals to interact with their habitat. However, within diurnal terrestrial ecosystems, little is known if, and how, variation in light environments impact species assemblages and visual system evolution. By studying a diverse, sympatric community of mimetic butterflies, we demonstrate that forest architecture creates a mosaic of light microhabitats that drive adaptive sensory convergence and divergence in both peripheral and central sensory systems. Our study provides insights into the dynamic response of visual systems when confronted with similar ecological challenges, and illustrates the wide-reaching consequences of interspecific mutualisms, such as mimicry, on organismal evolution.

evolutionary biology↗

bioRxiv2024

Connectomes, simultaneous EEG-fMRI resting-state data and brain simulation results from 50 healthy subjects

We present raw and processed multimodal empirical data as well as simulation results from a study with The Virtual Brain (TVB). Simultaneous electroencephalography (EEG) - functional magnetic resonance imaging (fMRI) resting-state data, diffusion-weighted MRI, and structural MRI were acquired for 50 healthy adult subjects (18 - 80 years of age) at the Charite University Medicine, Berlin, Germany. We constructed personalized models from this multimodal data with TVB by optimizing parameters on an individual basis that predict multiple empirical features in fMRI and EEG, e.g. dynamic functional connectivity and bimodality in the alpha band power. We annotated this large comprehensive empirical and simulated dataset according to the openMINDS metadata framework and structured it following Brain Imaging Data Structure (BIDS) standards for EEG and MRI as well as the BIDS Extension Proposal for computational modeling data. This dataset provides ready-to-use data for future research at various levels of processing including the thereof inferred brain simulation results for a large dataset of healthy subjects with a wide age range.

neuroscience↗

bioRxiv2024

Musicians' brains at rest: Multilayer network analysis of MEG data

The ability to proficiently play a musical instrument requires a fine-grained synchronisation between several sensorimotor and cognitive brain regions. Previous studies have demonstrated that the brain undergoes functional changes with musical training, identifiable also in resting-state data. These studies analysed fMRI or electrophysiological frequency-specific brain networks in isolation. While the analysis of such "mono-layer" networks has proven useful, it fails to capture the complexities of multiple interacting networks. To this end, we applied a multilayer network framework for analysing publicly available data (Open MEG Archive) obtained with magnetoencephalography (MEG). We investigated resting-state differences between participants with musical training (n=31) and those without (n=31). While single-layer analysis did not demonstrate any group differences, multilayer analysis revealed that musicians show a modular organisation that spans visuomotor and frontotemporal areas, known to be involved in musical performance execution, which is significantly different from non-musicians. Differences between the two groups are primarily observed in the theta (6.5-8Hz), alpha1 (8.5-10Hz) and beta1 (12.5-16Hz) frequency bands. We demonstrate that the multilayer method provides additional information that single-layer analysis cannot. Overall, the multilayer network method provides a unique opportunity to explore the pan-spectral nature of oscillatory networks, with studies of brain plasticity as a potential future application.

neuroscience↗

bioRxiv2023

In vitro validated antibody design against multiple therapeutic antigens using generative inverse folding

Deep learning approaches have demonstrated the ability to design protein sequences given backbone structures [1, 2, 3, 4, 5]. While these approaches have been applied in silico to designing antibody complementarity-determining regions (CDRs), they have yet to be validated in vitro for designing antibody binders, which is the true measure of success for antibody design. Here we describe IgDesign, a deep learning method for antibody CDR design, and demonstrate its robustness with successful binder design for 8 therapeutic antigens. The model is tasked with designing heavy chain CDR3 (HCDR3) or all three heavy chain CDRs (HCDR123) using native backbone structures of antibody-antigen complexes, along with the antigen and antibody framework (FWR) sequences as context. For each of the 8 antigens, we design 100 HCDR3s and 100 HCDR123s, scaffold them into the native antibodys variable region, and screen them for binding against the antigen using surface plasmon resonance (SPR). As a baseline, we screen 100 HCDR3s taken from the models training set and paired with the native HCDR1 and HCDR2. We observe that both HCDR3 design and HCDR123 design outperform this HCDR3-only baseline. IgDesign is the first experimentally validated antibody inverse folding model. It can design antibody binders to multiple therapeutic antigens with high success rates and, in some cases, improved affinities over clinically validated reference antibodies. Antibody inverse folding has applications to both de novo antibody design and lead optimization, making IgDesign a valuable tool for accelerating drug development and enabling therapeutic design. The data generated in this study serve as a useful benchmark of diverse antibody-antigen interactions. We use this data to benchmark self-consistency RMSD (scRMSD), using ABodyBuilder2 [6], ABodyBuilder3 [7], and ESMFold [8], as a metric for assessing binding. We open source the code for IgDesign and the SPR datasets. Code and datasets can be found at https://github.com/AbSciBio/igdesign.

synthetic biology↗

bioRxiv2023

Sex chromosome - autosome fusions in Heliconius butterflies

Sex chromosomes are evolutionarily labile in many animals and sometimes fuse with autosomes, creating so-called neo-sex chromosomes. Fusions between sex chromosomes and autosomes have been proposed to reduce sexual conflict and to promote adaptation and reproductive isolation among species. Recently, advances in genomics have fuelled the discovery of such fusions across the tree of life. Here, we discovered multiple fusions leading to neo-sex chromosomes in the sara/sapho clade of the classical adaptive radiation of Heliconius butterflies. Heliconius butterflies generally have 21 chromosomes with very high synteny. However, the five species in the sapho subclade show large variation in chromosome number ranging from 21 to 60. We found that the W chromosome is fused with chromosome 4 in all of them. Two sister species pairs showed subsequent fusions between the W and chromosomes 9 or 14, respectively. These sequential fusions between autosomes and sex chromosomes make Heliconius butterflies an ideal system for studying the role of neo-sex chromosomes in adaptive radiations and the degeneration of sex chromosomes over time. Our findings emphasize the capability of WGS technology to detect neo-sex chromosomes even when sex chromosomes are not explicitly assembled. Author SummaryFusions between sex chromosomes and autosomes are thought to have the potential to resolve sexual conflict and enhance local adaptation or reproductive isolation between species. Here, we discovered such fusions in Heliconius butterflies. Heliconius butterflies mostly have a very stable karyotype with 21 chromosomes and high synteny across species. However, the five species in the sapho subclade have up to 60 chromosomes, suggesting that they have undergone many chromosomal fissions. We document that in addition to the fissions, the sapho subclade also showed multiple fusions between the female-specific sex chromosome (W) and autosomes. We found a W-4 fusion shared by all five species and additional W fusions with chromosomes 9 and 14 shared by two species each. Even though in many sapho subclade species the autosomes have undergone fissions, the chromosomes fused with the W represent the full-length autosomes, as seen in other Heliconius species, suggesting that fusing to the W protected them from fissions. Our study reveals the power of short-read sequencing to discover neo-sex chromosomes and showcases Heliconius butterflies as a promising system for studying the causes and consequences of sex chromosome evolution.

evolutionary biology↗

bioRxiv2023

Deep learning-based codon optimization with large-scale synonymous variant datasets enables generalized tunable protein expression

Increasing recombinant protein expression is of broad interest in industrial biotechnology, synthetic biology, and basic research. Codon optimization is an important step in heterologous gene expression that can have dramatic effects on protein expression level. Several codon optimization strategies have been developed to enhance expression, but these are largely based on bulk usage of highly frequent codons in the host genome, and can produce unreliable results. Here, we develop deep contextual language models that learn the codon usage rules from natural protein coding sequences across members of the Enterobacterales order. We then fine-tune these models with over 150,000 functional expression measurements of synonymous coding sequences from three proteins to predict expression in E. coli. We find that our models recapitulate natural context-specific patterns of codon usage and can accurately predict expression levels across synonymous sequences. Finally, we show that expression predictions can generalize across proteins unseen during training, allowing for in silico design of gene sequences for optimal expression. Our approach provides a novel and reliable method for tuning gene expression with many potential applications in biotechnology and biomanufacturing.

synthetic biology↗

bioRxiv2023

Unlocking de novo antibody design with generative artificial intelligence

Generative AI has the potential to redefine the process of therapeutic antibody discovery. In this report, we describe and validate deep generative models for the de novo design of antibodies against human epidermal growth factor receptor (HER2) without additional optimization. The models enabled an efficient workflow that combined in silico design methods with high-throughput experimental techniques to rapidly identify binders from a library of [~]106 heavy chain complementarity-determining region (HCDR) variants. We demonstrated that the workflow achieves binding rates of 10.6% for HCDR3 and 1.8% for HCDR123 designs and is statistically superior to baselines. We further characterized 421 diverse binders using surface plasmon resonance (SPR), finding 71 with low nanomolar affinity similar to the therapeutic anti-HER2 antibody trastuzumab. A selected subset of 11 diverse high-affinity binders were functionally equivalent or superior to trastuzumab, with most demonstrating suitable developability features. We designed one binder with [~]3x higher cell-based potency compared to trastuzumab and another with improved cross-species reactivity1. Our generative AI approach unlocks an accelerated path to designing therapeutic antibodies against diverse targets.

synthetic biology↗

bioRxiv2022

Multiple paths towards repeated phenotypic evolution in the spiny-leg adaptive radiation (Tetragnatha; Hawaii)

The repeated evolution of phenotypes is ubiquitous in nature and offers some of the clearest evidence of the role of natural selection in evolution. The genomic basis of repeated phenotypic evolution is often complex and can arise from a combination of gene flow, shared ancestral polymorphism and de novo mutation. Here, we investigate the genomic basis of repeated ecomorph evolution in the adaptive radiation of the Hawaiian spiny-leg Tetragnatha. This radiation comprises four ecomorphs that are microhabitat-specialists, and differ in body pigmentation and size (Green, Large Brown, Maroon, and Small Brown). Using 76 newly generated low-coverage, whole-genome resequencing samples, coupled with population genomic and phylogenomic tools, we studied the evolutionary history of the radiation to understand the evolution of the spiny-leg lineage and the genetic underpinnings of ecomorph evolution. Congruent with previous works, we find that each ecomorph has evolved twice, with the exception of the Small Brown ecomorph, which has evolved three times. The evolution of the Maroon and the Small Brown ecomorphs likely involved ancestral hybridization events, whereas the Green and the Large Brown ecomorphs likely evolved because of either standing genetic variation or de novo mutation. Pairwise comparisons of ecomorphs based on the fixation index (FST) show that divergent genomic regions include genes with functions associated with pigmentation (melanization), learning, neuronal and synapse activity, and circadian rhythms. These results show that the repeated evolution of ecomorphs in the Hawaiian spiny-leg Tetragnatha is linked to multiple genomic regions and suggests a previously unknown role of learning and circadian rhythms in ecomorph.

evolutionary biology↗

bioRxiv2022

Antibody optimization enabled by artificial intelligence predictions of binding affinity and naturalness

Traditional antibody optimization approaches involve screening a small subset of the available sequence space, often resulting in drug candidates with suboptimal binding affinity, developability or immunogenicity. Based on two distinct antibodies, we demonstrate that deep contextual language models trained on high-throughput affinity data can quantitatively predict binding of unseen antibody sequence variants. These variants span a KD range of three orders of magnitude over a large mutational space. Our models reveal strong epistatic effects, which highlight the need for intelligent screening approaches. In addition, we introduce the modeling of "naturalness", a metric that scores antibody variants for similarity to natural immunoglobulins. We show that naturalness is associated with measures of drug developability and immunogenicity, and that it can be optimized alongside binding affinity using a genetic algorithm. This approach promises to accelerate and improve antibody engineering, and may increase the success rate in developing novel antibody and related drug candidates.

bioinformatics↗