Biology subjects

Vogt, J.

Publications and source records attributed to Vogt, J..

9 recordsLinked to original sources

bioRxiv2026

Large-scale, interpretable gene regulatory network inference through biologically informed matrix factorization

Gene regulatory networks (GRNs) provide a mechanistic framework for understanding how transcription factors coordinate gene expression to establish cellular identity and phenotype. Methods that integrate gene expression with motif-derived regulatory priors and other sources of biological information have substantially advanced gene regulatory network inference by reconstructing condition-specific regulatory architecture. These approaches estimate the evidence supporting regulatory interactions and have proven remarkably successful in a wide range of biological applications. A complementary view of regulatory networks, however, seeks to estimate the effect of those interactions on gene expression itself, providing a framework in which regulatory edges can be interpreted as activating or inhibitory influences on transcription. We developed Giraffe, a biologically informed matrix factorization framework that jointly estimates transcription factor activities and gene regulatory networks by integrating gene expression, motif-based regulatory priors, and transcription factor protein-protein interactions. Giraffe estimates signed partial regulatory effects whose magnitude and sign can be interpreted as the strength and direction of transcriptional regulation. Building directly on the biological framework established by methods such as PANDA, Giraffe provides a complementary representation of gene regulatory networks that emphasizes mechanistic interpretation while remaining scalable, flexible, and computationally efficient. Across synthetic benchmarks, six human tissues, yeast transcription factor perturbation experiments, and liver hepatocellular carcinoma, Giraffe accurately reconstructs regulatory interactions while distinguishing activating from inhibitory regulation with high accuracy. The inferred networks recover known features of tissue-specific regulation, correctly classify regulatory effects in transcription factor perturbation experiments, and identify biologically coherent changes in regulatory programs associated with liver cancer. Together, these results demonstrate that estimating the direction of transcriptional regulation provides a complementary perspective on gene regulatory networks that facilitates biological interpretation and hypothesis generation.

systems biology↗

bioRxiv2026

ProtAff: Protein Binding Affinity Prediction via LoRA-Finetuned ESM-2

Predicting the binding affinity of protein-protein interactions remains a central challenge in computational biology. Structure prediction models such as AlphaFold3 (AF3) and Boltz-2 can produce high-quality docking poses, and their confidence scores indicate structure quality, but these same scores fail to rank binding affinity among confirmed binders. Here we present ProtAff, a sequence-only affinity prediction model built on ESM-2 (650M parameters) with low-rank adaptation (LoRA) fine-tuning and a cross-attention module. ProtAff is trained using a margin ranking loss on 362,567 affinity measurements spanning 20 heterogeneous data sources, and we removed all training samples whose target sequence exceeds 50% similarity to the test target EGFR. On the AdaptyvBio EGFR benchmark (N =55), ProtAff achieves a Spearman correlation coefficient{rho} = 0.413, outperforming the best AF3 metric ({rho} = 0.054), the best Boltz-2 metric ({rho} = -0.046), and ML-based predictors MINT ({rho} = 0.242) and CrossAffinity ({rho} = 0.216). Applied to the AdaptyvBio Nipah virus binder design competition, a pipeline incorporating ProtAff for affinity ranking produced a design with KD = 0.132 nM (2 of 5 designs confirmed binding), a 2.8-fold improvement over the competition winner. On a cross-target discrimination benchmark of 91 VHH-antigen crystal structures, ProtAff underperforms structural methods for distinguishing cognate from non-cognate pairings, indicating that sequence-based affinity models are effective for within-target ranking but not for cross-target specificity.

bioinformatics↗

bioRxiv2025

ATRX Deficiency Drives Aberrant Type I Interferon Signalling Through cGAS-Dependent Transcriptional Dysregulation

The X-linked -thalassaemia intellectual disability syndrome (ATRX) protein is a chromatin remodeller involved in transcriptional regulation and genome stability. While the importance of ATRX in development and malignancy is well recognised, its role in innate immunity is less well defined. In two unrelated patients demonstrating cerebral white matter disease, learning difficulties and a persistent upregulation of interferon stimulated gene expression in whole blood, we identified the same Y1758C missense substitution in ATRX. Using patient-derived cells, engineered fibroblasts and neuronal models, we show that this substitution, and other loss of function mutations in ATRX, result in enhanced type I interferon signalling through a cGAS-dependent mechanism uncoupled from the DNA sensing activity of cGAS. Loss of ATRX function leads to alterations in the chromatin distribution of DAXX and H3.3, with cGAS essential for the changes in nucleosome composition and gene expression mediated by ATRX deficiency. Thus, our study highlights a previously unrecognized link between ATRX dysfunction and inflammation involving a non-canonical role of cGAS.

immunology↗

bioRxiv2025

mRNA-based tuberculosis vaccines BNT164a1 and BNT164b1 are immunogenic, well-tolerated and efficacious in rodent models

We designed and preclinically tested two mRNA-LNP-based vaccine candidates to protect against tuberculosis (TB). BNT164a1 and BNT164b1 encode the same eight Mycobacterium tuberculosis (Mtb) antigens expressed across different infection stages: Ag85A, Hrp1, ESAT-6, RpfD, RpfA, HbhA, M72, and VapB47. BNT164a1 utilizes nucleoside-unmodified mRNA, while BNT164b1 utilizes N1-methyl pseudouridine-modified mRNA. Prime-boost immunization with BNT164 candidates elicited antibody and/or T-cell responses against all antigens in three mouse strains (C57BL/6, BALB/c, and HLA-A2.1/DR1 humanized mice). The candidates demonstrated favorable safety profiles in a rat toxicity study and significantly reduced bacterial burdens of two Mtb strains in murine aerosol challenge models. BNT164 protection correlated with granuloma infiltration by CD8+ T cells with memory precursor phenotypes. In conclusion, BNT164a1 and BNT164b1 were immunogenic, well tolerated and efficacious in preclinical models and are the first mRNA-based TB vaccines to enter phase I/II clinical trials (NCT05537038, NCT05547464).

immunology↗

bioRxiv2024

Benchmarking Self-Supervised Learning for Single-Cell Data

Self-supervised learning (SSL) has emerged as a powerful approach for learning biologically meaningful representations of single-cell data. To establish best practices in this domain, we present a comprehensive benchmark evaluating eight SSL methods across three downstream tasks and eight datasets, with various data augmentation strategies. Our results demonstrate that SimCLR and VICReg consistently outperform other methods across different tasks. Furthermore, we identify random masking as the most effective augmentation technique. This benchmark provides valuable insights into the application of SSL to single-cell data analysis, bridging the gap between SSL and single-cell biology.

bioinformatics↗

bioRxiv2021

Climate, latitude, and land cover predict flying insect biomass across a German malaise trap network

O_LIAmong the many concerns for biodiversity in the Anthropocene, recent reports of flying insect loss are particularly alarming, given their importance as pollinators and as a food source for many predators. Few insect monitoring programs cover large spatial scales required to provide more generalizable estimates of insect responses to global change drivers. C_LIO_LIWe ask how climate and surrounding habitat affect flying insect biomass and day of peak biomass using data from the first year of a new standardized distributed monitoring network at 84 locations across Germany comprising spatial gradient of land-cover types from protected to urban areas. C_LIO_LIFlying insect biomass increased linearly with monthly temperature across Germany. However, the effect of temperature on flying insect biomass flipped to negative in the hot months of June and July when local temperatures most exceeded long-term averages. C_LIO_LILand-cover explained little variation in insect biomass, but biomass was lowest in forested sites. Grasslands, pastures and orchards harbored the highest insect biomass. The date of peak biomass was primarily driven by surrounding land-cover type, with grasslands especially having earlier insect biomass phenologies. C_LIO_LIStandardized, large-scale monitoring is pivotal to uncover underlying processes of insect decline and to develop climate-adapted strategies to promote insect diversity. In a temperate climate region, we find that the benefits of temperature on flying insect biomass diminish in a German summer at locations where temperatures most exceeded long-term averages. These results highlighting the importance of local adaptation in climate change-driven impacts on insect communities. C_LI

ecology↗

bioRxiv2019

Community driven dynamics of oscillatory network responses to threat

Physiological responses to threat stimuli involve neural synchronized oscillations in cerebral networks with distinct organization properties. Community architecture within these networks and its dynamic adaptation could play a critical role in achieving optimal physiological responses.\n\nHere we applied dynamic network analyses to address the early phases of threat processing at the millisecond level, describing multi-frequency (theta and alpha) integration and basic reorganization properties (flexibility and clustering) that drive physiological responses. We quantified cortical and subcortical network interactions and captured illustrative reconfigurations using community allegiance as essential fingerprints of large-scale adaptation.\n\nA theta band driven community reorganization of key anatomical regions forming the threat network (TN) along with transitions of nodes from the dorsal attention (DAN) and salience (SN) circuits predict the optimal physiological response to threat. We show that increase flexibility of the community network architecture drives the physiological responses during instructed threat processing. Nodal switches modulate the directionality of information flows in the involved circuits.\n\nThese results provide a captivating perspective of flexible network responses to threat and shed new light on basic physiological principles relevant for the development of stress- and threat-related mental disorders.

neuroscience↗

bioRxiv2018

Mutant Plasticity Related Gene 1 (PRG1) acts as a potential modifier in SCN1A related epilepsy

Plasticity related gene 1 encodes a cerebral neuron-specific synaptic transmembrane protein that modulates hippocampal excitatory transmission on glutamatergic neurons. In mice, homozygous Prg1-deficiency results in juvenile epilepsy. Screening a cohort of 18 patients with infantile spasms (West syndrome), we identified one patient with a heterozygous mutation in the highly conserved third extracellular phosphatase domain (p.T299S). The functional relevance of this mutation was verified by in-utero electroporation of a mutant Prg1 construct into neurons of Prg1-knockout embryos, and the subsequent inability of hippocampal neurons to rescue the knockout phenotype on the single cell level. Whole exome sequencing revealed the index patient to additionally harbor a novel heterozygous SCN1A variant (p.N541S) that was inherited from her healthy mother. Only the affected child carried both heterozygous PRG1 and SCN1A mutations. The aggravating effect of Prg1-haploinsufficiency on the epileptic phenotype was verified using the kainate-model of epilepsy. Double heterozygous Prg1-/+|Scn1awt/p.R1648Hmice exhibited higher seizure susceptibility than either wildtype, Prg1-/+, or Scn1awt/p.R1648H littermates. Our study provides evidence that PRG1-mutations have a potential modifying influence on SCN1A-related epilepsy in humans.

neuroscience↗

bioRxiv2018

Excitability regulation in the dorsomedial prefrontal cortex during sustained instructed fear responses: a TMS-EEG study

BackgroundThreat detection is essential for protecting individuals from precarious situations. Early studies suggested a network of amygdala, limbic regions and dorsomedial prefrontal cortex (dmPFC) involved in fear processing. Excitability regulation in the dmPFC might be crucial for physiological fear processing, while an abnormal excitability pattern could lead to mental illness. Non-invasive paradigms to measure excitability regulation during fear processing in humans are missing.\n\nMethodsWe adapted an experimental approach of excitability characterization using electroencephalography (EEG) recordings and transcranial magnetic stimulation (TMS) over the dmPFC during an instructed fear paradigm to dynamically dissect its role in fear processing. Event-related (ERP) and TMS-evoked potentials (TEP) were analyzed to trace dmPFC excitability in healthy young volunteers (n = 40, age = 27.6 {+/-} 5.7 years, 22 females). Moreover, we linked the excitability regulation patterns to individual structural MRI-derived properties of gray matter microstructural integrity of the fear network.\n\nResultsAn increased cortical excitability was demonstrated in the threat (T) condition in comparison to no-threat (NT) as showed by increased amplitude of evoked potentials. Furthermore, TMS over the dmPFC induced markedly increased evoked responses during T condition in relation to NT. Moreover, we found that the structural integrity of the dmPFC and the amygdala predict excitability regulation patterns as measured by ERP and TEP during fear processing.\n\nConclusionsWe describe the dynamic range of excitability regulation in dmPFC during fear processing. The applied paradigm can be used to non-invasively track response abnormalities to threat stimuli in healthy subjects or patients with mental disorders.

neuroscience↗