Biology subjects

Ort, J.

Publications and source records attributed to Ort, J..

3 recordsLinked to original sources

bioRxiv2026

Host type governs influenza evolutionary strategy across reservoir and spillover hosts

Despite its high propensity for host switching, the evolutionary mechanisms underlying influenza host adaptation remain unclear. H3Nx influenza viruses are uniquely generalist, with long-term lineages that circulate in avian, human, swine, equine, and canine hosts. Using 13,295 H3Nx sequences, we quantified host-specific adaptive evolution and developed a pipeline to map reassortment events onto trees with measures of statistical uncertainty. We find that while H3Nx viruses in mammals undergo adaptive evolution in HA and NA, viruses in birds experience very little directional selection. Instead, avian lineages exhibit high rates of reassortment, frequently generating novel reassortant lineages that persist transiently and turn over rapidly. 29.8-47.4% of all avian reassortant lineages are purged within the first year of circulation, and reassortment shows no fitness benefit in birds. In contrast, reassorted lineages in swine are more likely to persist long-term, suggesting that reassortment in swine may be broadly beneficial. Segment-specific reassortment patterns were also distinct between avian and mammalian viruses, with NA reassorting more frequently than expected in birds, but less frequently than expected in swine. Reassortment events are enriched between mammalian, but not avian, host switches, suggesting that reassortment may be most beneficial for mediating host switches among mammalian species. Together, our data suggest that host differences drive fundamentally different evolutionary outcomes for influenza viruses, transitioning from reassortment-dominant evolution in their avian reservoir, to varying degrees of adaptation upon establishment in mammals.

evolutionary biology↗

bioRxiv2026

Graph theory for the analysis of micro-electrode array recordings of human brain slices - framework and benchmarking

Micro-electrode array (MEA) recordings are widely used to characterize functional connectivity in neural cultures and have gained traction for the analysis of human brain slices. However, the impact of graph construction methodology on the resulting network topology has not been systematically quantified. Here, we benchmark three methods - shared spiking activity, Pearson cross-correlation, and the spike time tiling coefficient (STTC) - across 37 recordings from human cortical slice cultures classified into low, moderate, and high activity groups. We show that method choice alone produces large topological differences (Cohens d = 0.86-1.14 for clustering coefficient, d > 1.0 for node count), while higher-order features such as modularity remain stable. Each method exhibits a distinct sensitivity profile: shared spiking detects activity-dependent changes primarily through network size, correlation uniquely captures clustering differences, and STTC combines strong biological sensitivity with negligible parameter dependence across lag windows (all d < 0.1). Within shared spiking, z-score normalization dominates all other parameter choices (d > 1.0 versus bin size effects of d < 0.23), functioning as an implicit analytical null model that fundamentally reshapes the edge set rather than merely rescaling weights. Inter-method edge overlap is low (Jaccard index 0.08-0.45) and activity dependent, demonstrating that these methods identify substantially different connections from identical data. Our results reveal that methodological choices including construction method, threshold, and normalization introduce hidden degrees of freedom with effect sizes comparable to the biological signals being measured. We provide practical recommendations for parameter selection, reporting, and cross-method validation in MEA-based network neuroscience. Author SummaryWhen we record electrical activity from brain tissue using grids of electrodes, we can ask how different sites influence one another and map the tissue as a network of connections. Thanks to novel culturing methods, this approach is increasingly used to study human brain slices. However, deciding what is "connected" is not well defined. Researchers use several different methods, and it has never been clear how much this choice shapes the network they end up describing. Here we compared three widely used methods on 37 recordings from human cortical slices spanning a range of activity levels. We found that the method alone can change the apparent structure of the network as much as real biological differences do. The methods frequently disagreed about which connections exist and some technical choices, including normalization techniques, had surprisingly large effects. Because these hidden choices can rival the biological signal, we provide this benchmarking work with practical recommendations for selecting, reporting, and cross-checking methods, so that network studies of brain tissue become more transparent, comparable, and reproducible.

neuroscience↗

bioRxiv2023

Modulation of Giant Depolarizing Potentials (GDPs) in Human Large Basket Cells by Norepinephrine and Acetylcholine

Rhythmic brain activity has been implicated in many brain functions and it is sensible to neuromodulation, but so far very few studies have investigated this activity on the cellular level in vitro in human tissue samples. In this study we revealed and characterized a novel rhythmic network activity in human neocortex. Intracellular patch-clamp recordings showed that giant depolarizing potentials (GDPs) were frequently found in human cortical neurons. GDPs appeared in a low frequency band ([~] 0.3 Hz) similar to that described for slow oscillations in vivo and displayed large amplitudes and long decay times. Under the same experimental conditions, no rhythmic activity was found in L2/3 of the rat neocortex. GDPs were predominantly observed in a subset of L2/3 interneurons considered to be large basket cells based on previously described morphological features. In addition, GDPs are highly sensitive to norepinephrine (NE) and acetylcholine (ACh), two neuromodulators known to modulate low frequency oscillations. NE increased the frequency of the GDPs by enhancing {beta}-adrenergic receptor activity while ACh decreased GDP frequency through M4 muscarinic receptor-activation. Multi-electrode array (MEA) recordings demonstrated that NE promoted synchronous oscillatory network activity while the application of ACh led to a desynchronization of neuronal activity. Our data indicate that the human neocortex is more prone to generate slow wave activity, which was reflected by more pronounced GDPs in L2/3 large basket cells. The distinct modulation of GDPs and slow wave activity by NE and ACh exerts a specific modulatory control over the human neocortex.

neuroscience↗