Biology subjects

Thompson, M.

Publications and source records attributed to Thompson, M..

At least 19 recordsLinked to original sources

bioRxiv2026

β-Hairpin Fold Peptide Brush Polymers

Peptide-brush polymers prepared via graft-through polymerization of peptide-based olefinic monomers form Protein-Like Polymers (PLPs) with demonstrated utility as chemical biology tools for modulating intracellular protein--protein interactions and are in development as a emerging class of therapeutic. This favorable bioactivity has been achieved in part by mimicking some of the structural and functional features of proteins, such as their dynamic, globular solution-phase structures, ability to insert into lipid membranes and enter cells, and tendency to enforce -helicity in peptide side chains. However, because structure leads to function, insight into the structure of these proteomimetic polymers, particularly their overall tertiary structure and the extent to which this influences the brush peptide structure, is incomplete. To further this understanding, we prepared a set of brush polymers incorporating the trpzip2 peptide sequence, commonly used as a model system for protein folding. We examine these polymers via circular dichroism spectroscopy, infrared spectroscopy, and small-angle X-ray scattering, coupled with insights from molecular dynamics simulations. Here, a loss of the {beta}-hairpin structure of the monomer is observed regardless of polymer composition, and simulations corroborate that the polymer backbone destabilizes local secondary structure, restricting peptide motion and altering the accessible free-energy landscape. This study provides insight into design rules governing these synthetic polymer-based proteomimetics.

biochemistry↗

bioRxiv2026

OpenSplice: the impact of half a million mutations on the alternative splicing of 600 human exons

Alternative splicing of mRNA precursors is an important step in gene regulation and a major mechanism by which genetic variants cause human disease. However, changes in splicing have only been quantified for a tiny fraction of possible variants in the human genome, limiting our ability to interpret clinical variants, evaluate and develop machine learning models, and understand the splicing regulatory code. Here, to address this data gap, we present OpenSplice, a well-calibrated experimental dataset that quantifies the impact of >590,000 variants on the splicing of >600 human alternatively spliced exons. OpenSplice increases the number of exons with site saturation mutagenesis data [~]28-fold, quantifying the impact of all possible exonic and proximal intronic single nucleotide (nt) substitutions, as well as all 1, 3, 6 and 21nt deletions. Hundreds of thousands of variants affect splicing and we use the data to evaluate machine learning models, to interpret clinical variants, and to map splicing regulatory architectures. Exons and introns exhibit a broad spectrum of regulatory landscapes, including configurations dominated by enhancers or silencers, checkerboard-like patterns with interleaved enhancers and silencers, and sparse architectures with minimal regulatory content. Silencers are particularly important for determining inclusion levels. OpenSplice provides a complete atlas of variants that impact splicing, a rich testing and training dataset for machine learning models, and a comprehensive map of regulatory elements for mechanistic studies.

genomics↗

bioRxiv2026

Elevated Expression of MALAT1 Contributes to the Survival of Drug-Tolerant Persister Cells Following Targeted Therapy in Lung Adenocarcinoma

Lung adenocarcinomas frequently harbour actionable oncogenic mutations that are vulnerable to treatment with targeted therapies. While responses to targeted therapies are often initially dramatic, relapse is almost inevitable and prevents durable responses in advanced-stage patients. Relapse is, in part, caused by drug tolerant persister cells (DTPs) which are able to survive treatment by entering a reversible, dormant state. Although long non-coding RNAs (lncRNAs) regulate processes thought to allow DTPs to survive and become stably resistant, the potential roles of lncRNAs in DTPs are largely unknown. In this study, we sought to investigate the expression of lncRNAs in in vitro DTP models of lung adenocarcinoma. We found that the lncRNAs Metastasis-Associated Lung Adenocarcinoma Transcript 1 (MALAT1) and Nuclear Paraspeckle Assembly Transcript 1 (NEAT1) were enriched in DTPs and that knocking down MALAT1 enhanced the effect of targeted therapies in both EGFR- and KRAS-mutant DTP models. To better understand pathways that MALAT1 might regulate in DTPs, bulk RNA-sequencing was performed and several pathways that may contribute to the actions of MALAT1 in DTPs were identified. Overall, our work describes a role for the lncRNA MALAT1 in DTPs in NSCLC and suggests that MALAT1 may be a novel target for the prevention of drug tolerance and subsequent resistance to targeted therapy in NSCLC.

cancer biology↗

bioRxiv2026

Microembossing Hydrogel Meso-Circuits for Patterning Dissociated Neurons Promotes Ensemble Formation

Functional networks of wired neurons comprise the basis for neuronal computation and processing. Within neuronal networks, activation of unique ensembles is an important identity of neuronal processing. However, dissociated neuronal networks form homogeneous functional structures with minimal variety in ensemble dynamics. To reintroduce such dynamics, we propose structuring the networks to follow multi-connectivity (micro- and meso-network) paradigms. Here, we use agarose microembossing to physically pattern dissociated neuronal networks across these scales. To perform agarose microembossing, we impress features with poly-dimethyl-siloxane (PDMS) stamps into liquid agarose to emboss features which hold under cold gelation. We validate the viability of primary neurons within the hydrogel patterns and interrogate circuit dynamics through calcium imaging. Patterned features presented with robust ensemble dynamics that are dependent on connectivity paradigms. Altogether, this work establishes a platform for investigating how engaging multi-scale features in the physical network informs neuronal ensemble dynamics. Clinical RelevanceThis work enables further dissociated studies to probe dynamics. We expect that this platform would be especially useful in early-stage drug development or personalized medicine pipelines that need to investigate circuit dynamics.

bioengineering↗

bioRxiv2026

Unfiltered: A rapid method for Agrobacterium-mediated transformation of Basidiomycete and Ascomycete yeasts

Agrobacterium-mediated transformation (AMT) is a critical method for genetic manipulation of non-model fungi, yet it remains a laborious and inefficient technique. When cocultured in acetosyringone-supplemented induction medium, Agrobacterium transfers DNA directly into yeast cells using its virulence machinery. Membrane filters are commonly used to support the co-culture of yeast and Agrobacterium on agar plates, however some reports demonstrate that these filters are unnecessary for specific yeast species. Here we confirm across diverse budding yeasts that membrane filters are not necessary for effective AMT. Concentrating the cells via centrifugation and "spotting" the cell pellet directly onto the induction medium proved effective. This reduces hands-on time to 15 minutes and eliminates filter cost. In the oleaginous yeast, Rhodotorula toruloides, this simplified method increases transformation efficiency by 66% to 2,500 transformants per 106 recipient cells. We further optimized the Agrobacterium:Rhodotorula cell ratio and culture resuspension volume to achieve more than 200,000 CFU per transformation representing a 2-3 fold improvement over previously implemented protocols. This spot-plating method was successfully applied to seven yeast species, including one for which genetic transformation has not previously been reported, Botryozyma nematodophila. This approach highlights the broad applicability of the spot-plating method across diverse yeast systems. Furthermore, this method could facilitate high-throughput transformation workflows that are critical for genome-scale functional studies.

microbiology↗

bioRxiv2025

MAGIC: Methylation Analysis with Genomic Inferred Contexts

MotivationWhole-genome bisulfite sequencing (WGBS) enables base-resolution methylation profiling but poses statistical challenges for differential methylation analysis. While existing methods have made important progress using uniform dispersion shrinkage across CpGs, there remains opportunity to better capture methylation heterogeneity across the genome. There is a need to account for context-specific methylation variability and to improve detection accuracy under realistic sequencing constraints. ResultsWe propose MAGIC (Methylation Analysis with Genomic Inferred Contexts), a beta-binomial mixture model that learns genomic contexts directly from methylation data. MAGIC models genomic heterogeneity through context-specific dispersion shrinkage and detects differential methylation via two complementary tests, including a Wald test and Bayes factor test. Simulations across various coverage and effect sizes demonstrate that MAGIC improves sensitivity and false-positive rate compared with DSS, providing a robust framework for differential methylation analysis in WGBS data. AvailabilityThe source code of MAGIC is available at https://github.com/Pickledzebra/MAGIC. ContactMatteoP@mcdb.ucla.edu Supplementary informationSupplementary data are available at bioRxiv.

bioinformatics↗

bioRxiv2025

Binary vector origin predictably determines Agrobacterium-mediated transformation outcome across eukaryotic kingdom

Agrobacterium-mediated transformation (AMT) is the primary means of genetic engineering in plants and many fungi, but the factors that control transformation outcomes--efficiency, transgene insertion number, and transgene integrity--remain poorly characterized. Although transformation outcomes dictate an events potential utility in both industrial and academic contexts, AMT remains largely unoptimized for these metrics. Here, we systematically analyze the impact of the transgene-harboring binary vector on transformation outcomes across plant and fungal species. Through a comparison of different plasmid origin of replication (ORI) families and engineered copy number variants, our results reveal that the ORI family--not plasmid copy number--dictates T-DNA insertion number, backbone inclusion, and transformation efficiency, while plasmid copy number tuning alters efficiency without changing ORI family-specific signatures. Independent of plasmid copy number across kingdoms, the most widely used pVS1 ORI-based vectors (e.g. pCambia) result in significantly more insertions per transformant and high levels of transgene silencing compared to the less-utilized pSa ORI family, which enriches for more uniform single insertion events. Furthermore, we demonstrate that ORI-dependent transformation outcomes in yeast predictably reflect those in Arabidopsis. Together, these results lay the foundation for future binary vector design aimed at achieving more predictable, controllable, and optimized transformation outcomes across diverse eukaryotic hosts.

plant biology↗

bioRxiv2025

Circadian rhythms in sexual behavior and their influence on reproductive outcomes in mice

Circadian clocks coordinate mammalian reproductive physiology, and circadian misalignment (resulting from shift work, jet lag, etc.) is known to impair fertility. Despite the well-established links between clock function and reproductive success, it remains unclear whether male and female mice maintained under standard ad libitum feeding conditions exhibit circadian rhythms in the propensity for sexual behavior, and to what degree such timing influences reproductive outcomes. Using standardized mating paradigms in C57BL/6J mice, we identified a circadian rhythm in sexual behavior in both sexes, with peak sexual activity most often occurring at circadian time (CT) 13-16 and a trough at CT4-7. To test the functional significance of these rhythms, we conducted 1-hour mating trials across four cohorts of C57BL/6J mice with pairs of mice having either aligned (e.g. male CT16 peak and female CT16 peak) or misaligned (e.g. male CT16 peak and female CT4 nadir) sexual behavior phases and monitored mating outcomes via ultrasound. While pregnancies were almost as frequent across all four cohorts, the numbers of live offspring were significantly more frequent when both the male and female mated at their peak phases than when both mice mated at their troughs. Notably, mating specifically during the males behavioral peak increased the likelihood of successful delivery of pups, suggesting that male circadian phase is a key determinant of miscarriage vs successful birth. These findings establish a circadian rhythm in the propensity for sexual behavior under standard feeding and housing conditions and indicate that mating time--particularly relative to the males circadian peak--can influence reproductive success. This insight provides a foundation for translational studies that explore whether intercourse timed to the circadian rhythms of the couple might help to increase fertility chances.

physiology↗

bioRxiv2025

A splice-switching antisense oligonucleotide approach for pediatric genetic epilepsies

Summary ParagraphVariants in ion channel genes are common causes of pediatric epilepsy, often leading to intractable seizures, developmental delay and other comorbidities, which increases risk of death. Pathogenic variants in the SCN8A gene, which encodes a voltage-gated sodium channel critical for action potential generation in the brain, account for [~]1% of genetic epilepsies. The voltage sensor in SCN8A domain 1 is encoded by one of two developmentally-regulated mutually exclusive alternative exons, 5N and 5A. We observe that variants in these exons are more likely to cause infantile spasms, a severe seizure type, than variants elsewhere in SCN8A, and that some pathogenic variants affect exon 5 splicing, impacting patient phenotype. Molecular and evolutionary analyses implicate the exon sequences of these and other voltage-gated ion channel alternative exons in splicing regulation. We identified antisense oligonucleotides (ASOs) that shift splicing of SCN8A exon 5N to 5A or vice versa. These ASOs normalize neuronal activity in patient-derived iPSC neurons, and reduce seizures and motor impairment and extend lifespan in a new exon 5N mutant mouse model. Our results demonstrate that splice-switching ASOs can effectively reduce the expression of pathogenic isoforms and rescue both seizure and non-seizure phenotypes. Similar approaches should be applicable to pediatric genetic epilepsies caused by mutations in other ion channel alternative exons.

neuroscience↗

bioRxiv2025

Loss of miRNA-153 promotes endothelial-to-mesenchymal transition and compromises lung vascular integrity

Endothelial-to-mesenchymal transition (EndMT) is a biological process through which lung vascular endothelial cells (ECs) transdifferentiate into mesenchymal-like cells. EndMT has recently been implicated in the development and progression of pulmonary vascular remodeling in pulmonary hypertension (PH); however, its underlying regulatory mechanisms remain incompletely understood. MicroRNAs (miRNAs) are key post-transcriptional regulators of EC gene expression and cellular responses to various stimuli. Notably, microRNA-153 (miR-153) has been shown to directly target SNAI1 to modulate epithelial-to-mesenchymal transition (EMT), a process closely related to EndMT and extensively studied in cancer. Whether miR-153 also participates in EndMT regulation, however, remains unknown. In this study, we demonstrate that 72-hour hypoxic exposure induces SNAI1-mediated EndMT in human lung vascular ECs. Hypoxia also increased cell proliferation and disrupted intercellular junctions, leading to enhanced endothelial permeability. Reduced miR-153 expression was observed in both hypoxia- and TGF-{beta}1-induced EndMT, as well as in ECs isolated from PH patients exhibiting an EndMT phenotype. Similar to hypoxia, TGF-{beta}1 promoted EC permeability. Loss of miR-153 enhanced SNAI1-mediated EndMT, endothelial survival, and permeability under normoxic conditions, whereas miR-153 overexpression attenuated EndMT induced by hypoxia or TGF-{beta}1. However, miR-153 restoration did not completely recover endothelial barrier integrity disrupted by these stimuli. In conclusion, miR-153 serves as a critical regulator of EndMT, maintaining endothelial identity and barrier function. Therapeutic delivery of miR-153 may therefore represent a novel strategy to inhibit EndMT and attenuate pulmonary vascular remodeling in PH.

physiology↗

bioRxiv2025

Under which circumstances do genomic neural networks learn motifs and their interactions?

A central challenge in biology is to understand, predict, and engineer the second genetic code: how sequence encodes gene expression. Two components of this challenge are: (1) accurate prediction (and design) of gene expression from sequence and (2) mechanistic understanding of how sequence-to-expression encoding actually works in cells. A powerful general approach to this problem is to combine large scale data generation with artificial intelligence. For example, massively parallel reporter assays (MPRAs) can quantify the expression of thousands of different sequences in pooled experiments and the resulting data can be used to train deep learning models. Unlike in the case of long-context genomic language models, where transformer-based architectures are a dominant paradigm, it remains contested whether for MPRA datasets other architectural components can lead to more useful, generalizable predictors, and whether they affect model interpretability, i.e. the ability to capture causal biological mechanisms (either inherently or when using downstream interpretability or explainability techniques, "xAI"). Ablation analyses may help elucidate important architectural components, but are almost always anecdotal, unable to describe generalizable tendencies, as they are done with a single training dataset or a few testing datasets. Here, we attempt to reconcile concerns and provide guidance for MPRA model design and xAI choice by simulating at scale 1,500 motif-based genetic architectures and evaluating the ability of different model architecture-xAI pairs to first predict an outcome given a sequence as input, and second, report involved motifs and their corresponding grammar. We find that attention-based models are efficient learners, and while we recommend their use in low-data regimes, their performance is surpassed by alternative models, like dilated CNNs, under larger sample sizes. We next show that across grammars and models, current methods for motif extraction converge toward reporting the same set of motifs, which is dominated by motifs with large effect sizes. We then perform in silico experiments across models and their discovered motifs and find that these methods accurately rank motifs based on learned effect size, but that their learned effect size is systematically miscalibrated, particularly in the presence of interactions (epistasis). Finally, we propose a novel metric for identifying motifs involved in epistasis and confirm our findings across three experimental datasets. Our work provides practical guidance for modeling and interpreting massively parallel reporter assay experiments from end to end.

bioinformatics↗

bioRxiv2025

Causes and consequences of experimental variation in Nicotiana benthamiana transient expression

Infiltration of Agrobacterium tumefaciens into Nicotiana benthamiana has become a foundational technique in plant biology, enabling efficient delivery of transgenes in planta with technical ease, robust signal, and relatively high throughput. Despite transient expressions prevalence in disciplines such as synthetic biology, little work has been done to describe and address the variability inherent in this system, a concern for experiments that rely on highly quantitative readouts. In a comprehensive analysis of N. benthamiana agroinfiltration experiments, we model sources of variability that affect transient expression. Our findings emphasize the need to validate normalization methods under the specific conditions of each study, as distinct normalization schemes do not always reduce variation either within or between experiments. Using a dataset of 1,915 plants collected over three years, we develop a model of variation in N. benthamiana transient expression, using power analysis to determine the number of individual plants required for a given effect size. Drawing on our longitudinal data, these findings inform practical guidelines for minimizing variability through strategic experimental design and power analysis, providing a foundation for more robust and reproducible use of N. benthamiana in quantitative plant biology and synthetic biology applications.

plant biology↗

bioRxiv2025

Consumer resilience suppresses the recovery of overgrazed ecosystems

O_LIMany heterotroph species perish when faced with severe food limitation, others can persist, adapt, and thrive. Sea urchins are emblematic of this paradox: they can overgraze kelp forests to form barren habitats, but can then survive for decades in these nutritionally depauperate seascapes. Understanding the mechanisms enabling persistence under starvation, and rapid recovery when food returns, provides insight into how consumer resilience shapes ecosystem dynamics. C_LIO_LIWe quantified how food abundance, quality, deprivation, and reintroduction influence bioenergetic performance in the red sea urchin (Mesocentrotus franciscanus), integrating field observations of kelp forest and barren populations with a controlled feeding experiment. We measured respiration, feeding rates, gonadal growth, and fatty acid biomarkers to test how habitat history and diet jointly govern metabolic plasticity and nutrient assimilation. C_LIO_LIResting metabolic rates (RMR) were nearly twofold higher in kelp forest urchins than barrens conspecifics, yet feeding rates were equivalent across habitats, indicating that metabolic depression does not constrain food intake. Reciprocal shifts emerged in the experiment: starvation reduced RMR and lipid reserves in kelp forest urchins, while feeding elevated both traits in barrens urchins to levels comparable with kelp forest conspecifics. These results demonstrate rapid physiological compensation in response to both food deprivation and reintroduction. C_LIO_LIDiet quality strongly modulated performance. Urchins fed nutritionally poor monospecific diets consumed more biomass and calories than those on diverse, polyunsaturated fatty acid (PUFA)-rich diets, but did so with markedly lower efficiency of conversion to gonadal tissue. Fatty acid assimilation revealed that starvation elevated bacterial and biofilm biomarkers in tissues, whereas algal diets enriched essential PUFA profiles, particularly when diets were diverse. These results highlight that both quantity and quality of food influence consumer recovery trajectories, with nutritional geometry shaping efficiency of energy and nutrient use. C_LIO_LITogether, our findings show that M. franciscanus exhibits pronounced metabolic resilience, allowing persistence in barren habitats and rapid reactivation of grazing and reproduction when food becomes available. This work links nutritional ecology to ecosystem feedbacks by showing how compensatory feeding and metabolic flexibility enable consumers to maintain pressure on primary producers, thereby influencing the stability, hysteresis, and recovery of degraded ecosystems. C_LI

ecology↗

bioRxiv2024

The clumped isotope signatures of multiple methanogenesis metabolisms

Methane is a potent greenhouse gas, an important energy source, and a potential biosignature on extraterrestrial planetary bodies. The relative abundances of doubly substituted ("clumped") methane isotopologues (13CH3D and 12CH2D2) offer important information on the sources and sinks of methane. However, the clumped isotope signatures of microbially produced methane from different methanogenic pathways lack a systematic investigation. In this study, we provide a dataset encompassing the relative isotopologue abundances produced by hydrogenotrophic, methylotrophic, acetoclastic, and methoxydotrophic methanogenesis. We find that a statistical "combinatorial effect" generates significant differences in 12CH2D2 compositions between hydrogenotrophic methanogenesis and other pathways. The thermodynamic drive of methanogenic reactions and phylogenetic affiliation may also influence the isotope compositions of methane. Our study provides new experimental constraints on the isotope signatures of different microbial methanogenic pathways, and evidence of the mechanisms responsible for the observed differences. TeaserA novel stable isotope tool to track and differentiate sources of biological methane.

microbiology↗

bioRxiv2024

Identifying novel genetic and phenotypic associations to genomic features by leveraging off-target reads in exome sequencing data

Upwards of 40% of reads in sequencing datasets may be unmapped and discarded by standard protocols. Recent work has shown the utility of re-analyzing these unmapped reads to construct meaningful features, such as immune diversity repertoires or copy number variation in mtDNA and rDNA. While previous analyses of these features have produced significant correlations with diverse traits, they have generally been limited to analyses of RNA-sequencing data in phenotype-specific cohorts. Here, we explore whether associations can be identified using population-scale, whole-exome sequencing data in the UK BioBank. Using recently developed tools, we constructed multiple features including T-cell receptor diversity metrics, microbial load, and mtDNA and rDNA copy numbers for nearly 50,000 individuals in the UK BioBank. We first verify the validity of our method by showing that GWAS on these constructed traits results in replication of associations from studies in which the phenotypes were explicitly measured. Next, across several GWAS, we identified 21 novel independent significant loci in 11 genes, most of them in genes implicated in the innate immune response. Finally, we further analyzed the read-constructed features by establishing correlations to other population-level biobank traits such as immune disorders, metabolic disorders, neuropsychiatric disorders, and blood cell counts. Our results suggest that existing tools for feature construction from unmapped reads can offer novel information at the population level, and that these features can be used to establish novel genetic associations.

bioinformatics↗

bioRxiv2024

Biomarkers of recovery: characterizing trophic flow following ecological restoration

Coastal kelp forests are important sources of primary productivity and provide essential habitat and ecosystem services. In many areas around the world, the formation and persistence of urchin barrens threatens kelp forest ecosystems. Over the past several decades, restoration efforts have emerged aiming to increase the abundance of foundation species like kelp in such systems. However, we lack a comprehensive understanding of how successful kelp restoration affects the nutritional landscape and the fitness of kelp forest herbivores. We bridge this knowledge gap with a Before-After-Control-Impact Paired Series (BACIPS) focused on kelp forest restoration where reductions of herbivorous sea urchins in Haida Gwaii resulted in substantial increases in kelp abundance in habitat previously characterized as barrens. Specifically, we document body size specific shifts in the fatty acid (FA) profiles of red sea urchins (Mesocentrotus franciscanus) and northern abalone (Haliotis kamtschatkana). FAs associated with bacteria and diatoms were elevated in tissues of urchins and abalone in barrens habitat while kelp biomarkers were elevated in restored kelp forest habitat. For urchins, these shifts tracked the increase in gonad mass following kelp forest recovery. For abalone, these results varied depending on animal body size. Specifically, abalone exhibited a continuous size-specific shift from biofilm-associated markers at small sizes to kelp-associated markers as animals increased in size. For both species, a marked increase in essential fatty acids was observed following kelp restoration. Our results demonstrate kelp restoration via sea urchin reduction enhances not only the quantity but also the quality and diversity of food in previously degraded habitats, and subsequently enhances the amount and nutritional quality of roe (i.e., gonads) in sea urchins therein.

ecology↗

bioRxiv2024

Interpretably deep learning amyloid nucleation by massive experimental quantification of random sequences

Protein aggregation is a pathological hallmark of more than fifty human diseases and a major problem for biotechnology. Methods have been proposed to predict aggregation from sequence, but these have been trained and evaluated on small and biased experimental datasets. Here we directly address this data shortage by experimentally quantifying the amyloid nucleation of >100,000 protein sequences. This unprecedented dataset reveals the limited performance of existing computational methods and allows us to train CANYA, a convolution-attention hybrid neural network that accurately predicts amyloid nucleation from sequence. We adapt genomic neural network interpretability analyses to reveal CANYAs decision-making process and learned grammar. Our results illustrate the power of massive experimental analysis of random sequence-spaces and provide an interpretable and robust neural network model to predict amyloid nucleation.

genomics↗

bioRxiv2024

Hippocampal ripples mediate motor learning during brief rest breaks in humans

Critical aspects of motor learning and memory happen offline, during both wake and sleep. When healthy young people learn a motor sequence task, most of their performance improvement happens not while typing, but offline, during interleaved rest breaks. In contrast, the performance of patients with dense amnesia due to hippocampal damage actually gets worse over the rest breaks and improves while typing. These findings indicate that an intact hippocampus is necessary for offline motor learning during wake, but do not specify its mechanism. Here, we studied epilepsy patients (n=17) undergoing direct intracranial EEG monitoring of the hippocampus as they learned the same motor sequence task. Like healthy young people, they showed greater speed gains across rest breaks than while typing. They also showed higher hippocampal ripple rates during these rest breaks that predicted offline gains in speed. This suggests that motor learning during brief rest breaks during wake is mediated by hippocampal ripples. These results expand our understanding of the role of hippocampal ripples beyond declarative memory to include enhancing motor procedural memory. Significance StatementIn patients with epilepsy undergoing direct intracranial EEG monitoring of the hippocampus, we found a higher rate of ripples during the brief rest breaks of a motor sequence task than during task execution. These offline hippocampal ripples predicted the amount of performance improvement over the break. We conclude that the hippocampus contributes to motor learning during brief rest breaks, and propose that this offline learning is mediated by ripples.

neuroscience↗