Biology subjects

Hagen, T.

Publications and source records attributed to Hagen, T..

8 recordsLinked to original sources

bioRxiv2026

A massively parallel synthetic gene atlas for learning compact cis-regulatory grammar across cellular contexts

Virtual-cell models increasingly learn from large perturbation atlases, but their view of cis regulation remains limited to endogenous genes embedded in broad native regulatory contexts. Here we introduce Therna Biosciences' Chronos platform and its first public dataset release, comprising two complementary modules of a massively parallel synthetic gene atlas: Penta-47x27K for 5' UTRs/internal-promoter elements and Tria-47x28K for 3' UTR stability elements. Together, these datasets measure ~60,000 compact cis-regulatory elements across approximately 50 cell lines in one pooled experiment. The choice of cell lines here, provided to us courtesy of Tahoe Tx, was rooted in our aim to make this dataset maximally useful for the virtual-cell modeling community. At Therna, we routinely apply Chronos -- part of our RNA-Logix(TM) platform -- beyond cancer cell-line pools, to systems such as primary cells and organoids, via LNP-formulated mRNA delivery. Chronos captures both transcriptional and post-transcriptional gene expression control. Episomal DNA delivery measures DNA-normalized mRNA output, while direct RNA delivery with longitudinal sampling reliably quantifies RNA decay. The RNA-delivery arm uses chemically modified synthetic mRNA incorporating N1-methylpseudouridine, a modification widely used in mRNA therapeutics. To resolve high-complexity libraries of up to 30,000 elements, Chronos pushes the sensitivity limits of single-cell RNA sequencing to quantify individual RNA molecules within single cells. By massively expanding gene regulatory networks with synthetic genes whose variable regulatory code is short and defined, Chronos provides an auxiliary cis-regulatory lens for virtual-cell modeling and enables context-specific cis-trans regulatory interactions to be learned directly.

systems biology↗

bioRxiv2026

Strong Reward Signals, Weak Transfer: Limits of Spatial Priority Map Plasticity Across Task Contexts

Reward learning can bias attentional selection, but whether spatially biased reinforcement produces durable, context-general changes in spatial priority over days, and what neurophysiological signals track such learning, remains uncertain. We combined electroencephalography (EEG) and pupillometry with a multi-session spatial reward-learning paradigm (Chelazzi et al., 2014) in which targets could appear at eight locations and reward probability was systematically biased across locations during two days of training. A separate baseline/test visual detection task was administered before training and again four days after training to assess delayed transfer under cross-target competition. Training produced strong reward signals across measures. Feedback-locked ERPs (FRN and P300) differentiated outcome valence and reward magnitude and varied systematically with time-on-task, while pupil dilation was larger following high- than low-reward feedback and overall task-evoked responses decreased across blocks. Reward history also modulated stimulus-locked target processing: targets at high- versus low-reward locations differed reliably across N1/N2 and a late positivity, indicating multi-stage value-dependent influences on visual processing during active learning. In contrast, transfer was weak in both behavior and ERPs: behavioral indices did not show a reliable advantage for highly rewarded locations at delayed test, and neural evidence for persistence was limited to an N2 modulation in the most diagnostic high-versus-low comparison, which should be interpreted cautiously given low trial counts. Accordingly, we did not replicate the robust long-term spatial priority effect reported in the original study. Together, these findings reveal strong reward-learning signals but weak cross-task transfer, suggesting limits on how readily spatial reward learning consolidates into persistent, task-general spatial priority-map plasticity across contexts.

neuroscience↗

bioRxiv2025

Neural correlates of image memorability: Combining large-scale 7T fMRI with machine learning-based predictions

We combine ultra-high-resolution fMRI with machine learning predictions to investigate the neural correlates of image memorability. Using ViTMem, a visual transformer-based tool, we estimated memorability scores for over 70,000 natural scenes from the NSD dataset. Our analyses reveal that higher memorability scores correlate with increased activation in ventral visual regions (fusiform gyrus, middle occipital cortex) and medial temporal areas (hippocampus, amygdala). Notably, we also observed significant engagement in fronto-parietal regions, indicating a broader network involved in modulating memory encoding. These novel findings demonstrate that ViTMem not only reliably predicts stimulus memorability but also provides a powerful framework for scalable investigations into the neural mechanisms underlying perceptual and mnemonic processes.

animal behavior and cognition↗

bioRxiv2024

A Suite of Foundation Models Captures the Contextual Interplay Between Codons

In the canonical genetic code, many amino acids are assigned more than one codon. Work by us and others has shown that the choice of these synonymous codon is not random, and carries regulatory and functional consequences. Existing protein foundation models ignore this context-dependent role of coding sequence in shaping the protein landscape of the cell. To address this gap, we introduce cdsFM, a suite of codon-resolution large language models, including both EnCodon and DeCodon models, with up to 1B parameters. Pre-trained on 60 million protein-coding sequences from more than 5,000 species, our models effectively learn the relationship between codons and amino acids, recapitualing the overall structure of the genetic code. In addition to outperforming state-of-the-art genomic foundation models in a variety of zero-shot and few-shot learning tasks, the larger pre-trained models were superior in predicting the choice of synonymous codons. To systematically assess the impact of synonymous codon choices on protein expression and our models ability to capture these effects, we generated a large dataset measuring overall and surface expression levels of three proteins as a function of changes in their synonymous codons. We showed that our EnCodon models could be readily fine-tuned to predict the contextual consequences of synonymous codon choices. Armed with this knowledge, we applied EnCodon to existing clinical datasets of synonymous variants, and we identified a large number of synonymous codons that are likely pathogenic, several of which we experimentally confirmed in a cellbased model. Together, our findings establish the cdsFM suite as a powerful tool for decoding the complex functional grammar underlying the choice of synonymous codons.

genomics↗

bioRxiv2023

Baseline Pupil Size Seems Unrelated to Fluid Intelligence, Working Memory Capacity, and Attentional Control

Over the past few years, several studies have explored the relationship between resting-state baseline pupil size and cognitive abilities, including fluid intelligence, working memory capacity, and attentional control. However, the results have been inconsistent. Here we present the findings from two experiments designed to replicate and expand previous research, with the aim of clarifying previous mixed findings. In both experiments, we measured baseline pupil size while participants were not engaged in any tasks, and assessed fluid intelligence using a matrix task. In one experiment we also measured working memory capacity (letter-number-sequencing task) and attentional control (attentional-capture task). We controlled for several personal and demographic variables known to influence pupil size, such as age and nicotine consumption. Our analyses revealed no relationship between resting-state pupil size (average or variability) and any of the measured constructs, neither before nor after controlling for confounding variables. Taken together, our results suggest that any relationship between resting-state pupil size and cognitive abilities is likely to be weak or non-existent.

neuroscience↗

bioRxiv2023

Novel keto-alkyl-pyridinium antifungal molecules active in models of in vivo Candida albicans vascular catheter infection and ex vivo Candida auris skin colonization

New antifungal therapies are needed for both systemic, invasive infections as well as superficial infections of mucosal and skin surfaces as well as biofilms associated with medical devices. The resistance of biofilm and biofilm-like growth phases of fungi contributes to the poor efficacy of systemic therapies to non-systemic infections. Here, we describe the identification and characterization of a novel keto-alkyl-pyridinium scaffold with broad spectrum activity (2-16 {micro}g/mL) against medically important yeasts and moulds, including clinical isolates resistant to azoles and/or echinocandins. Furthermore, these keto-alkyl-pyridinium agents retain substantial activity against biofilm phase yeast and have direct activity against hyphal A. fumigatus. Although their toxicity precludes use in systemic infections, we found that the keto-alkyl-pyridinium molecules reduce C. albicans fungal burden in a rat model of vascular catheter infection and reduce Candida auris colonization in a porcine ex vivo model. These initial pre-clinical data suggest that molecules of this class may warrant further study and development.

microbiology↗

bioRxiv2021

Predicting trait phenotypes from knowledge of the topology of gene networks

In many fields there is interest in manipulating genes and gene networks to realize improved trait phenotypes. The practicality of doing so, however, requires accepted theory on the properties of gene networks that is well-tested by empirical results. The extension of quantitative genetics to include models that incorporate properties of gene networks expands the long tradition of studying epistasis resulting from gene-gene interactions. Here we consider NK models of gene networks by applying concepts from graph theory and Boolean logic theory, motivated by a desire to model the parameters that influence predictive skill for trait phenotypes under the control of gene networks; N defines the number of graph nodes, the number of genes in the network, and K defines the number of edges per node in the graph, representing the gene-gene interactions. We define and consider the attractor period of an NK network as an emergent trait phenotype for our purposes. A long-standing theoretical treatment of the dynamical properties of random Boolean networks suggests a transition from long to short attractor periods as a function of the average node degree K and the bias probability P in the applied Boolean rules. In this paper we investigate the appropriateness of this theory for predicting trait phenotypes on random and real microorganism networks through numerical simulation. We show that: (i) the transition zone between long and short attractor periods depends on the number of network nodes for random networks; (ii) networks derived from metabolic reaction data on microorganisms also show a transition from long to short attractor periods, but at higher values of the bias probability than in random networks with similar numbers of network nodes and average node degree; (iii) the distribution of phenotypes measured on microorganism networks shows more variation than random networks when the bias probability in the Boolean rules is above 0.75; and (iv) the topological structure of networks built from metabolic reaction data is not random, being best approximated, in a statistical sense, by a lognormal distribution. The implications of these results for predicting trait phenotypes where the genetic architecture of a trait is a gene network are discussed.

genetics↗

bioRxiv2018

Distinct neural mechanisms meet challenges in dynamic visual attention due to either load or spatial interference

When solving dynamic visuo-spatial tasks, the brain copes with perceptual and cognitive processing challenges. In the multiple-object tracking (MOT) task, the number of objects to be tracked (i.e. load) imposes attentional demands, but so does spatial interference from irrelevant objects (i.e. crowding). Presently, it is not clear whether load and crowding activate separate cognitive and physiological mechanisms. Such knowledge would be important to understand the neurophysiology of visual attention. Furthermore, it would help resolve conflicting views between theories of visual cognition, particularly concerning sources of capacity limitations. To address this problem, we varied the degree of processing challenge in the MOT task in two ways: First, the number of objects to track, and second, the spatial proximity between targets and distractors. We first measured task-induced pupil dilations and saccades during MOT. In a separate cohort we measured fMRI brain activity during MOT. The behavioral results in both cohorts revealed that increased load and crowding led to reduced accuracy in an additive manner. Load was associated with pupil dilations, whereas crowding was not. Activity in dorsal attentional areas and frequency of saccades were proportionally larger both with higher levels of load and crowding. Higher crowding recruited additionally ventral attentional areas that may reflect orienting mechanisms. The activity in the brainstem nuclei ventral tegmental area/substantia nigra and locus coeruleus showed clearly dissociated patterns. Our results constitute convergent evidence from independent samples that processing challenges due to load and object spacing may rely on different mechanisms.

neuroscience↗