Biology subjects

Lehmann, L.

Publications and source records attributed to Lehmann, L..

At least 19 recordsLinked to original sources

bioRxiv2026

Pregnancy History and Loss of Normal PSEN2 Function Impact Female Late-Life Cognitive Processing, Inhibitory Signaling, and Gliosis

Background. Alzheimer's disease (AD) is well-documented to be more progressive in affected females. It is thus critical to define how female-specific life experiences interact with AD-related risk factors to impact disease trajectory and functional burden. For example, pregnancy exerts dramatic influences on the maternal immune system, behavior, and neuroplasticity, specifically in hippocampal and limbic structures. The long-term clinical impacts from pregnancy history (parity) on late-life AD outcomes are, however, conflicting. Thus, we investigated the late-life functional impacts of parity on rodent cognitive function, inhibitory neuron density, and glial biology with loss of normal function of the AD-related risk factor, presenilin 2 (PSEN2). Methods. Female PSEN2 knockout (KO) and wild-type (WT) mice with (multiparous) and without (nulliparous) pregnancy history were challenged at 10-15 months-old in the T-maze (TM) and Open Field Test (OFT), followed by immunofluorescence for neuronal signaling and gliosis markers. Results. Neither parity history nor PSEN2 deficiency impacted hippocampally-mediated spatial working memory of aged female mice in the TM. However, both PSEN2 KO and multiparous mice exhibited decreased OFT locomotor behavior. Multiparous mice also showed reduced OFT center zone exploration (p=0.0045) suggesting a more anxious phenotype. Parity history generally increased inhibitory marker expression in dorsal hippocampus (p=0.017), prefrontal cortex (p=0.021), and amygdala (p=0.035). Parity generally blunted oligodendrocyte marker expression in CA1 (p<0.05), while boosting astrocyte and microglia marker expression in CA3 of dorsal hippocampus. Conclusion. This work suggests that although parity history and PSEN2 deficiency exert individual differing age-related effects on cognition and neurobiology, they do not act synergistically.

neuroscience↗

bioRxiv2026

When to learn from elders or peers: accessibility-knowledge trade-offs explain diversity in age-biased social learning

In many animal species, individuals acquire knowledge from others that enhances their survival and reproduction. However, among the many available cultural exemplars, not all provide reliable information. Consequently, individuals tend to choose their exemplars selectively. One widespread pattern is a preference for older individuals, who may have accumulated valuable knowledge through life. Yet empirical studies also show that individuals frequently learn from age peers, suggesting that copying elders is not universally optimal. The ecological and social conditions that favor learning from elders rather than peers, therefore, remain unclear. Here, we investigate the evolutionary drivers of age-biased exemplar choice in age-structured populations where individuals accumulate knowledge over their lifespan. We develop a continuous-age model that captures the coevolution of exemplar age choice and age-specific investments in social learning, individual learning, and the use of acquired knowledge for energy extraction. We show that selection promotes a progressive shift from social to individual learning and from learning to energy extraction with age. Exemplar age choice evolves through a trade-off between knowledgeable and accessible individuals. Young learners therefore favor relatively young exemplars because they are common and still provide substantial novel knowledge, although older individuals remain more knowledgeable. As individuals age, encountering exemplars with substantially novel knowledge becomes increasingly difficult. Consequently, as they age, individuals are expected to shift toward learning from older individuals, who possess more knowledge. Population, environment, and knowledge characteristics can shift this balance, generating a wide range of strategies from learning primarily from peers to consistently targeting the oldest individuals. In particular, learning from age peers is favored by strong within-cohort interactions, high mortality, or high encounter rates. It is also favored in unstable environments with rapid knowledge loss and when knowledge is easily acquired, transmitted, and is bounded.

evolutionary biology↗

bioRxiv2026

DModE: An end-to-end framework for Differential Modification and Expression Analysis of Nanopore direct RNA sequencing data

SummaryNanopore direct RNA sequencing (DRS) enables simultaneous quantification of transcript abundance and RNA modifications from native RNA molecules, providing a unique opportunity to study transcriptional and epitranscriptomic regulation within a single experiment. However, comprehensive analysis of DRS data remains challenging, as existing workflows typically focus on individual processing steps and often require manual integration of multiple software packages for expression analysis, modification detection, statistical testing, and visualization. Furthermore, integrated differential expression and differential RNA modification analysis at both gene and isoform resolution remains poorly supported by current workflows. Here, we present DModE (Differential Modification and Expression Analysis), an end-to-end framework for integrated analysis of Nanopore DRS data. DModE combines an Epi2ME-compatible Nextflow preprocessing workflow with a dedicated Python package for downstream statistical analysis, visualization, and reporting. The framework supports differential gene and isoform expression analysis, differential RNA modification analysis at genome and transcript level, metagene profiling, exploratory epitranscriptomic analyses, and integrated assessment of relationships between expression and modification dynamics. Results are automatically summarized in interactive HTML reports, facilitating reproducible and accessible data interpretation. By integrating transcriptomic and epitranscriptomic analyses within a single framework, DModE substantially simplifies comprehensive DRS data analysis and lowers the barrier for studying RNA modification biology using Nanopore sequencing. Availability and implementationThe DModE Preprocessing pipeline is available on GitHub (https://github.com/johannesmiedema/DModE-preprocessing). The Python package can be installed via pip and is also available on GitHub (https://github.com/johannesmiedema/dmode).

bioinformatics↗

bioRxiv2026

Darwinian fitness, its directional derivative, and Hamilton's rule for limited dispersal with class structure under within and between generation environmental stochasticity

Darwinian fitness is equated here with invasion fitness and defined as the quantity determining the fate--certain extinction or possible spread--of a single mutant type. We derive it, together with its phenotypic derivative, for evolution in group-structured populations under limited genetic mixing, where the demography of the focal species and its environment is modeled as a discrete-time stochastic process. Reproduction, physiological development, dispersal, and survival are influenced by interactions within and between groups and by environmental fluctuations within and across generations. Using multitype branching processes in random environments, we show that invasion fitness is predicted by a stochastic growth rate that can be represented biologically in two meaningful genealogical ways. First, as the long-term geometric mean of the expected per-capita number of mutant copies produced per time step by a representative member of the mutant lineage. Second, as the the long-term geometric mean of the expected reproductive-value-weighted per-capita number of mutant copies produced by such an individual. This latter representation is useful for computing the phenotypic directional derivative of invasion fitness. Moreover, this derivative can be written as an actor-centered inclusive-fitness effect derived from properties of the resident population process. This effect depends on class-specific fitness differentials, relatedness, reproductive values, and class frequencies. However, unless generation- and class-specific fitness defines a stochastic matrix, the derivative does not separate stochastic reproductive values from relatedness and class frequencies, and must be evaluated by simulations. In summary, we formalize invasion fitness biologically quite generally and show how Hamiltons marginal rule can be deduced from it.

evolutionary biology↗

bioRxiv2026

Cardiomyocytes execute pro- and anti-inflammatory signaling of IFNγ-induced GBP5 by differential regulation of the inflammasome

Infiltration of conventional immune cells has been ascribed as the fundamental drivers of innate immune signaling in the damaged myocardium. However, the emerging intrinsic immunoregulatory potential of cardiomyocytes still remains poorly understood. Interferon gamma (IFN{gamma}) is a pleiotropic cytokine with context-dependent detrimental as well protective role in regulating cardiac inflammatory circuits. The prevailing view of IFN{gamma} as a prime pro-inflammatory cytokine has been challenged due to its paradoxical actions both as an inducer as well as negative regulator of inflammation, but the players involved in these converse processes remains enigmatic. Here we show that cardiomyocytes exhibit a cell-autonomous immunocompetent response upregulating innate inflammatory signaling upon type I and type II IFN stimulus. Notably, hiPSC-derived cardiomyocytes display a robust increase in guanylate binding protein 5 (GBP5), one of the major IFN{gamma}-induced GTPase involved in inflammasome signaling, followed by upregulation of AIM2/CASP1 pathway whereas NLRP3 levels remain unaltered by IFN{gamma} stimulation. GBP5 knockdown and overexpression studies in hiPSC-derived cardiomyocytes identify GBP5/TGF{beta} axis as a non-canonical anti-inflammatory feedback regulation on the IFN{gamma}-induced inflammatory cascade.

cell biology↗

bioRxiv2026

Ketone body beta-hydroxybutyrate restores neuronal Tau proteostasis via ketolysis-independent mechanism

Metabolic interventions that induce ketosis, including ketogenic diets, caloric restriction, intermittent fasting, and exercise, show promise in the treatment of Alzheimers disease (AD) and related tauopathies. {beta}-hydroxybutyrate ({beta}HB), the primary ketone body produced during ketosis, reproduces key features of these metabolic interventions, but the molecular mechanism underlying its neuroprotective properties is not fully understood. Here, we demonstrate that a {beta}HB precursor diet is sufficient to ameliorate Tau pathophysiology in a tauopathy mouse model. Furthermore, across in vitro, ex vivo, and in vivo models, we find that {beta}HB enhances neuronal Tau proteostasis and reduces Tau aggregation and secretion. Importantly, these effects are independent of {beta}HBs oxidation for ATP production, as its ketolysis-resistant enantiomer reproduces these benefits, indicating that ketolysis is dispensable for these effects. Overall, these data position {beta}HB as a novel therapeutic avenue for AD and tauopathy and elucidate a novel mechanism of action of metabolic interventions in neurodegenerative disease.

neuroscience↗

bioRxiv2026

HDAC inhibition via suberoylanilide hydroxamic acid (SAHA) ameliorates Doxorubicin-induced cardiotoxicity

BackgroundAnthracycline-induced cardiotoxicity remains a major limitation of cancer therapy, and effective preventive strategies are lacking. Topoisomerase II{beta} (Topo IIb) has been implicated as a central driver of this toxicity, suggesting that epigenetic regulators may interfere with pathological cardiac response. Methods and ResultsHere, we show that doxorubicin promotes Topo IIb accumulation at cardiomyocyte gene promoters (e.g., Actc1, Myl2, and Myh7) overlapping myocyte enhancer factor 2 (MEF2) binding sites and enhances MEF2-dependent transcription. This response is attenuated by the pan-histone deacetylase (HDAC) inhibitor suberoylanilide hydroxamic acid (SAHA). SAHA-mediated cardioprotection requires class IIa HDACs, as genetic loss of HDAC4 abolishes its effect. Mechanistically, SAHA induces acetylation of the chaperone 14-3-3, disrupting its interaction with HDAC4/5, promoting their nuclear accumulation, and repressing MEF2-driven transcription. In vivo, SAHA mitigates doxorubicin-induced cardiotoxicity. ConclusionThese findings identify HDAC inhibition as a cardioprotective repurposing strategy and reveal a mechanistic link between epigenetic regulation and anthracycline-associated cardiotoxicity.

molecular biology↗

bioRxiv2025

Directional and disruptive selection in populations structured by class and continuous ontogeny under incomplete plasticity

Many organisms undergo ontogeny, whereby individuals change in state (e.g. in size, morphology, or condition) as they age. Understanding the evolution of traits influencing ontogeny is challenging because their fitness effects unfold across an individuals lifetime and may differ between classes such as sexes. Here, we analyse selection on non-plastic traits (e.g., fixed resource allocation strategies) that determine the development of dynamical states throughout life (e.g., body size), with consequences for fecundity and survival in age- and class-structured populations. Using invasion analysis, we derive expressions for directional and quadratic selection that decompose into age- and class-specific components. This allows us to identify convergence stable trait values, assess whether they are uninvadable or potentially experience evolutionary branching, and pinpoint the age and class pathways through which correlational and disruptive selection act. Applying our results to a model of growth under size-mediated sexual selection, we show how selection on growth rates distributes across ages and sexes, and how its relative strength depends on genetic correlations, individual ploidy and life-history. We also show how sex-specific developmental trade-offs and constraints can generate disruptive selection on male growth and favour the evolution of alternative male life histories. More broadly, our results highlight how adaptation is mediated by the interaction of development and demography, and provide tools to investigate how conflicts across ages and classes may influence senescence, sexual dimorphism, and the diversification of ontogenetic strategies. Author SummaryHow does natural selection shape traits that influence how organisms develop through their lifetime? This question is difficult because the effects of such traits unfold dynamically as individuals change in states such as size or condition, which in turn influence survival and reproduction throughout life. We formulate a mathematical model that follows these causal links and characterises the gradual evolution of non-plastic traits affecting development. Our results apply to populations with different classes, such as males and females, that follow distinct developmental and demographic paths. Our model reveals when selection is stabilizing, holding populations fixed for an optimal trait value, and when it is disruptive, favouring trait diversification and the emergence of alternative life-history strategies. As an illustration, we analyse the evolution of sex-specific growth under size-based sexual selection. We show how the evolution of male and female growth rates depends on genetics, such as individual ploidy or genetic correlation between traits, but also on life history, which determines the relative strength of selection acting on trait expression at different ages. We further show how developmental trade-offs and demographic differences can generate disruptive selection on male growth, leading to the coexistence of fast-growing short-lived males and slow-growing long-lived ones. More broadly, our results link development, demography, and adaptation, and show how their interplay shapes biological diversity both within and between populations.

evolutionary biology↗

bioRxiv2025

The genetical evolution of social preferences: where the categorical imperatives of Hamilton, Kant and Nash meet

This paper models the genetical evolution of individual behavioral rules that guide the choice of strategies in pairwise assortative interactions under incomplete information. Building on results at the crossroads of evolutionary theory and game theory, it is first shown that in an uninvadable population state of behavioral rule evolution, individuals are compelled to use strategies that are Nash equilibria of a lineage fitness game. Thus, choice behavior evolves to be representable as the maximization of a utility function, as if each individual holds a personal preference that orders both their own and their interaction partners strategies. Second, the paper contrasts two representations of personal utility that are found to be uninvadable. The first is semi-Kantian in form. This preference averages a fitness self-interest with a relatedness weighted Kantian interest. The latter interest evaluates the consequence of own behavior for own fitness, assuming the interaction partner adopts the same behavior as self. The second preference is a personal inclusive fitness. This preference combines a self-regarding interest with a relatedness weighted other-regarding interest. Each such interest takes the form of an average effect, which evaluates the consequence of expressing own behavior, instead of average population behavior, on a statistical average fitness to self and the interaction partner. Species following the model should tend to evolve behavior such that each organism appears to be attempting to maximize its inclusive fitness. W.D. Hamilton (1964)

evolutionary biology↗

bioRxiv2025

How does stochasticity in learning impact the accumulation of knowledge and the evolution of learning?

Learning is crucial for humans and other animals to acquire knowledge, enhancing survival and reproduction. In particular, individual and social learning allow populations to accumulate knowledge across generations. Here, we examine how stochasticity in the production and social acquisition of knowledge influences the evolution of learning strategies and cumulative knowledge. Using a mathematical model where learning is stochastic, we show that learning stochasticity enhances cumulative knowledge by generating variability in knowledge levels. This allows selection to enhance population knowledge: individuals who acquire more knowledge by chance are more likely to survive and reproduce, and therefore to transmit their knowledge to the next generation. As knowledge accumulates, social learning exemplars tend to possess more of it, favoring greater time investment in social learning. Because social learning provides access to substantially more knowledge when learning is stochastic, selection also favors the evolution of greater investment into learning, at the expense of a fecundity cost. Moreover, when knowledge enhances fecundity but not survival, learning stochasticity favors learning from parents rather than other adults, because learning stochasticity increases uncertainty about exemplar knowledge, making parenthood a cue for possessing fecundity-enhancing knowledge. Finally, when learning occurs predominantly from parents, learning stochasticity itself is favored by selection.

evolutionary biology↗

bioRxiv2025

How economic exchange can favour human genetic diversity

By allowing individuals to use goods they do not produce, economic exchange is recognised as driving the wide diversity of economic activities seen in human societies. Since productivity also depends on innate abilities, we ask whether economic exchange could have influenced human evolution and promoted adaptive genetic diversity. We model a system where individuals produce and exchange goods under a Walrasian equilibrium, with abilities determined by an evolving quantitative genetic trait. We then analyse how exchange shapes the evolutionary pressures on this trait. Our analysis demonstrates that exchange consistently promotes negative frequency-dependent selection, which favours the maintenance of genetic diversity. Exchange also generates stable long-term adaptive polymorphism when the production of goods requires different abilities. Importantly, we establish that the mode of exchange matters: markets, where individuals can switch trading partners, promote genetic diversity under broader conditions than when exchange occurs in isolated pairs. Finally, we show that genetic diversity and economic specialisation can facilitate the emergence of the other under a wider range of conditions. Our findings suggest that economic exchanges play a crucial role in fostering biological diversity and offer insights into how a culturally determined mode of organisation may have shaped human evolution.

evolutionary biology↗

bioRxiv2025

A lentiviral fluorescent reporter to study circadian rhythms in single cells

Circadian rhythms--self-sustained, [~]24-hour oscillations in transcript and protein levels--are generated by a cell-autonomous molecular clock. These rhythms shape how individual cells respond to external signals, influencing key decisions such as differentiation and apoptosis. However, current tools for visualizing circadian rhythms at the single-cell level often rely on genomic engineering and clonal expansion, limiting their accessibility and applicability. We present fluorescent circadian reporters based on the murine REVERB/Nr1d1 gene, delivered via lentiviral transduction and compatible with time-lapse single cell microscopy. These reporters produce oscillatory signals that depend on a functional circadian clock and can be used to determine a cells circadian dynamics parameters, such as phase and period. Their simple and efficient delivery makes them suitable for a wide variety of cell types, greatly expanding opportunities to study single-cell circadian dynamics and their impact across diverse biological processes and systems.

cell biology↗

bioRxiv2025

Evolution of quantitative traits: exploring the ecological, social and genetic bases of adaptive polymorphism

Adaptive polymorphisms in quantitative traits are characterised by the coexistence of distinct genetically encoded phenotypes maintained by selection. Such intraspecific genetic and phenotypic variation make a substantial contribution to biological diversity. Building on established results from invasion analysis, we analyse conditions under which adaptive polymorphism can emerge under long-term gradual evolution. We first note that, in general, fully characterising an adaptive polymorphism, including its number of morphs, trait values and frequencies, is a computationally hard problem. Nevertheless, standard invasion results reveal a unifying necessary condition for the gradual emergence of polymorphism: negative trait-dependent selection (NTDS), whereby the marginal fitness effect of increasing trait expression in a mutant depends negatively on the population-level trait expression, reflecting a negative interaction between mutant and population trait changes. We use NTDS as an organising principle to structure the pathways through which polymorphism can evolve. This is based on a decomposition of fitness that distinguishes production factors, the inputs to survival and reproduction that are endogenous to individuals, from regulating factors, the ecological, social or genetic inputs to fitness that are influenced in part by conspecifics. This decomposition allows us to clarify how NTDS arises across three broad classes of interactions: ecological, social and genetic; and to identify previously unrecognised adaptive polymorphisms in well-studied scenarios, including life-history, social preferences, and class-structured populations. More broadly, by integrating perspectives from population genetics, evolutionary ecology, adaptive dynamics, game theory and inclusive fitness theory, we aim to provide a common thread among the pathways through which selection favours intraspecific variation.

evolutionary biology↗

bioRxiv2025

DNA Flipping as Facile Mechanism for Transmembrane Signaling in Synthetic Cells

Transmembrane signaling is essential for cellular communication, yet reconstituting such mechanisms in synthetic systems remains challenging. Here, we report a simple and robust DNA-based mechanism for transmembrane signaling in synthetic cells using cholesterol-modified single-stranded DNA (Chol-ssDNA). We discovered that anchored Chol-ssDNA spontaneously flips across the membrane of giant unilamellar lipid vesicles (GUVs) in a nucleation-driven, defect-mediated process. This flipping enables internal signal processing through hybridization with encapsulated complementary DNA and activation of downstream processes such as RNA transcription. The phenomenon shows a high transduction efficiency, is generic across DNA sequences and lipid compositions, and can be enhanced by glycerol, which modulates membrane dynamics. Mechanistic insights using fluorescence microscopy, nuclease degradation assays, and membrane permeability assays reveal that flipping is dominated by transient membrane pores. Leveraging this facile translocation process, we demonstrate selective transcriptional activation inside synthetic cells, underscoring the potential of Chol-ssDNA flipping as a programmable tool for synthetic biology and bottom-up synthetic cell design.

synthetic biology↗

bioRxiv2025

Experimental Genetics Validation of Plasmodium falciparum Gametogenesis Essential Protein 1 (GEP1) as a Transmission Blocking Target

Transmission of Plasmodium parasites to the Anopheles vector critically depends on swift activation of mature gametocytes upon entry into the mosquito midgut. Induction of gametogenesis requires two simultaneous stimuli, a temperature drop and xanthurenic acid. Previous work in the murine malaria model Plasmodium yoelii identified a protein, termed gametogenesis essential protein (GEP1), with a suggested role in xanthurenic acid-dependent activation of gametes. Here, we present an experimental genetics characterization of GEP1 in the human pathogen Plasmodium falciparum. Using CRISPR-Cas9 gene editing we generated PfGEP1 loss-of-function lines and analyzed their progression until gametocyte maturation. We show a complete defect in both male and female gametogenesis caused by disruption of PfGEP1. Pfgep1(-) gametocytes do not produce gametes when activated with xan-thurenic acid or a drop in temperature. This defect could not be overcome by the phosphodiesterase inhibitor Zaprinast, which induces gametogenesis. We also explored GEP1 haplotypes in P. falciparum parasites circulating in endemic regions and show the presence of two non-synonymous SNPs, resulting in V241L and S263P mutations, in 12% and 20% of 49 sentinel samples, respectively. Together, our data indicate that GEP1 plays a central role in the gamete activation process independent of xanthurenic acid and validates PfGEP1 as a promising transmission blocking target.

molecular biology↗

bioRxiv2025

Redefining human pre-rRNA processing at single nucleotide resolution using long read Nanopore sequencing

Ribosome biogenesis requires the synthesis and processing of precursor rRNAs (pre-rRNAs) into mature rRNAs. Traditional methods like northern blotting and metabolic labeling offer limited resolution. We present NanoRibolyzer, a nanopore-based, long-read sequencing approach that enables ab initio identification and quantification of rRNA precursors. Using both supervised and unsupervised mapping, it detects known and novel pre-rRNA species and defines cleavage sites at single-nucleotide resolution. A simple cell fractionation protocol provides spatial separation of nuclear and cytoplasmic pre-rRNAs. Targeted knockdowns quantify intermediate accumulations, revealing condition-specific processing fingerprints with biomarker potential. Pseudouridine mapping shows that the primary 47S transcript is extensively modified, while aberrant products are not. With its high resolution and unique mapping strategy, NanoRibolyzer offers new insights into rRNA processing and modification, enhancing our understanding of ribosome biogenesis.

cell biology↗

bioRxiv2025

ModiDeC: a multi-RNA modification classifier for direct nanopore sequencing

RNA modifications play a crucial role in various cellular functions. Here, we present ModiDeC, a deep-learning-based classifier able to identify and distinguish multiple RNA modifications (N6-methyladenosine, inosine, pseudouridine, 2'-O-methylguanosine, and N1-methyladenosine) using direct RNA sequencing. Alongside ModiDeC, we provide an extensive database of in vitro-transcribed and synthetic sequences generated with both the new RNA004 chemistry and the old RNA002 kit. We show that RNA modifications can be accurately recognized and distinguished across different sequence motifs using synthetic data as well as in HEK293T cells and human blood samples. ModiDeC comes with a graphical user interface that allows easy customization and adaptation to specific research questions, such as learning and classifying additional RNA modifications and further sequence motifs. The reproducibility across samples, together with the low rate of false positives, underscores the potential of ModiDeC as a powerful tool for advancing the analysis of epitranscriptomes and RNA modification.

bioinformatics↗

bioRxiv2024

Alzheimer's disease-associated genotypes differentially influence chronic evoked seizure outcomes and antiseizure medicine activity in aged mice

Structured AbstractO_ST_ABSINTRODUCTIONC_ST_ABSAlzheimers disease (AD) patients are at greater risk of focal seizures than similarly aged adults; these seizures, left untreated, may worsen functional decline. Older people with epilepsy generally respond well to antiseizure medications (ASMs). However, whether specific ASMs can differentially control seizures in AD is unknown. The corneal kindled mouse model of acquired chronic secondarily generalized focal seizures allows for precisely timed drug administration studies to quantify the efficacy and tolerability of ASMs in an AD-associated genetic model. We hypothesized that mechanistically distinct ASMs would exert differential anticonvulsant activity and tolerability in aged AD mice (8-15 months) to define whether rational ASM selection may benefit specific AD genotypes. METHODSAged male and female PSEN2-N141I versus age-matched non-transgenic control (PSEN2 control) C57Bl/6J mice, and APPswe/PS1dE9 versus transgene negative (APP control) littermates underwent corneal kindling to quantify latency to fully kindled criterion. Dose-related ASM efficacy was then compared in each AD model versus matched control over 1-2 months using ASMs commonly prescribed in older adults with epilepsy: valproic acid, levetiracetam, lamotrigine, phenobarbital, and gabapentin. RESULTSSex and AD genotype differentially impacted seizure susceptibility. Male PSEN2-N141I mice required more stimulations to attain kindling criterion (X2=5.521; p<0.05). Male APP/PS1 mice did not differ in kindling rate versus APP control mice, but they did have more severe seizures. There were significant ASM class-specific differences in acute seizure control and dose-related tolerability. APP/PS1 mice were more sensitive than APP controls to valproic acid, levetiracetam, and gabapentin. PSEN2-N141I mice were more sensitive than PSEN2 controls to valproic acid and lamotrigine. DISCUSSIONAD genotypes may differentially impact ASMs activity and tolerability in vivo with advanced biological age. These findings highlight the heterogeneity of seizure risk in AD and suggest that precisely selected ASMs may beneficially control seizures in AD, thus reducing functional decline.

neuroscience↗