Biology subjects

Longtin, A.

Publications and source records attributed to Longtin, A..

13 recordsLinked to original sources

bioRxiv2026

Accelerated Evolution of Context-Dependent Gene Expression in Primate Immune Cells

Regulatory variation is widely recognized as a driver of evolution within and between species, including in the primate lineage. Evidence for potentially adaptive regulatory evolution in primates comes from both sequence-based tests for selection and cross-species comparisons of gene expression. However, comparative studies of gene expression have tended to focus on baseline cellular states, despite growing evidence that context-dependent gene regulation is important. Here, we investigate the evolution of gene expression in lymphoblastoid cell lines (LCLs) from 6 primate lineages (humans, chimpanzees, bonobos, western gorillas, Sumatran orangutans, and rhesus macaques) using data from both control and 5 perturbed cellular conditions (n=22-25 individuals per condition). Analyzing baseline expression data, we find that genes involved in immunity often exhibit lineage-specific transcriptional shifts, and that these genes occur near regions with lineage-specific sequence acceleration and lineage-specific epigenomic states. However, the subset of genes that respond to in vitro perturbations have evolved at significantly higher rates than environmentally-insensitive genes, a finding we replicate in an independent four-species dataset of primary white blood cells (n=26). Together, our findings support the argument that focusing on baseline states alone provides a biased view of gene regulatory evolution, and suggest that studying gene regulation in dynamic contexts can enrich our understanding of primate evolution.

evolutionary biology↗

bioRxiv2026

Clustered heterogeneity, spiking history and efficient silencing maximize mutual information in adaptive time encoding

Neurons involved in a computation are remarkably diverse. They display a range of thresholds, time scales and spiking history dependence, and often exhibit adaptation that enhances computational power by concentrating spikes at transient stimuli. We seek basic organizational principles for coding with cellular heterogeneity by focusing on the encoding and retrieval of inter-event time interval sequences by adaptive neurons. We formulate the general input-output mutual information maximization problem for parallel neurons with a fading memory of past intervals. The solution reveals an unexpected multi-variate heterogeneity that is tailored to encode information efficiently. Gradient ascent on mutual information produces a number of parameterized clusters bounded by the sequence length. Correlations between parameters emerge naturally. The predicted covariation of adaptation time with spiking history strength, and the optimal combination of history and non-history cells, agree with experiments. Threshold optimization yields a continuum of time constants and thresholds within each cluster. Strikingly, division of labour between neurons arises where subsets respond selectively to different interval ranges. This creates cell-specific interval ranges that tile reasonably well a uniform prior of intervals, and almost perfectly a scale-free prior, producing a code that is efficient in cell number and reduces metabolic cost by limiting spike rates.

neuroscience↗

bioRxiv2024

Cost-effective solutions for high-throughput enzymatic DNA methylation sequencing

Characterizing DNA methylation patterns is important for addressing key questions in evolutionary biology, geroscience, and medical genomics. While costs are decreasing, whole-genome DNA methylation profiling remains prohibitively expensive for most population-scale studies, creating a need for cost-effective, reduced representation approaches (i.e., assays that rely on microarrays, enzyme digests, or sequence capture to target a subset of the genome). Most common whole genome and reduced representation techniques rely on bisulfite conversion, which can damage DNA resulting in DNA loss and sequencing biases. Enzymatic methyl sequencing (EM-seq) was recently proposed to overcome these issues, but thorough benchmarking of EM-seq combined with cost-effective, reduced representation strategies has not yet been performed. To do so, we optimized Targeted Methylation Sequencing protocol (TMS)--which profiles [~]4 million CpG sites--for miniaturization, flexibility, and multispecies use at a cost of [~]$80. First, we tested modifications to increase throughput and reduce cost, including increasing multiplexing, decreasing DNA input, and using enzymatic rather than mechanical fragmentation to prepare DNA. Second, we compared our optimized TMS protocol to commonly used techniques, specifically the Infinium MethylationEPIC BeadChip (n=55 paired samples) and whole genome bisulfite sequencing (n=6 paired samples). In both cases, we found strong agreement between technologies (R{superscript 2} = 0.97 and 0.99, respectively). Third, we tested the optimized TMS protocol in three non-human primate species (rhesus macaques, geladas, and capuchins). We captured a high percentage (mean=77.1%) of targeted CpG sites and produced methylation level estimates that agreed with those generated from reduced representation bisulfite sequencing (R{superscript 2} = 0.98). Finally, we applied our protocol to profile age-associated DNA methylation variation in two subsistence-level populations--the Tsimane of lowland Bolivia and the Orang Asli of Peninsular Malaysia--and found age-methylation patterns that were strikingly similar to those reported in high income cohorts, despite known differences in age-health relationships between lifestyle contexts. Altogether, our optimized TMS protocol will enable cost-effective, population-scale studies of genome-wide DNA methylation levels across human and non-human primate species.

genomics↗

bioRxiv2023

Mice can learn a cognitive map based on a stable start location and self-motion signals

Animals navigate by learning the spatial layout of their environment. We investigated spatial learning of mice in an open maze where food was hidden in one of a hundred holes. Mice leaving from a stable entrance learned to efficiently navigate to the food without the need for landmarks. We developed a quantitative framework to reveal how the mice estimate the food location based on analyses of trajectories and active hole checks. After learning, the computed "target estimation vector" (TEV) closely approximated the mices route and its hole check distribution. The TEV required learning both the direction and distance of the start to food vector, and our data suggests that different learning dynamics underlie these estimates. We propose that the TEV can be precisely connected to the properties of hippocampal place cells. Finally, we provide the first demonstration that, after learning the location of two food sites, the mice took a shortcut between the sites, demonstrating that they had generated a cognitive map.

neuroscience↗

bioRxiv2022

Separation of NaV Subtypes in the Axon InitialSegment can Impede or Promote Backpropagation

In a variety of neurons, action potentials (APs) initiate at the proximal axon, within a region called the axon initial segment (AIS), which has a high density of voltage-gated sodium channels (NaVs) on its membrane. In pyramidal neurons, the proximal AIS has been reported to exhibit a higher proportion of NaVs with gating properties that are "right-shifted" to more depolarized voltages, compared to the distal AIS. Further, recent experiments have revealed that as neurons develop, the spatial distribution of NaV subtypes along the AIS can change substantially, suggesting that neurons tune their excitability by modifying said distribution. When neurons are stimulated axonally, computational modelling has shown that this spatial separation of gating properties in the AIS enhances the backpropagation of APs into the dendrites. In contrast, in the more natural scenario of somatic stimulation, our simulations show that the same distribution can impede backpropagation. We implemented a range of hypothetical NaV distributions in the AIS of three multicompartmental pyramidal cell models and investigated the precise kinetic mechanisms underlying such effects, as the spatial distribution of NaV subtypes is varied. With axonal stimulation, proximal NaV availability dominates, such that concentrating right-shifted NaVs in the proximal AIS promotes backpropagation. However, with somatic stimulation, the models are insensitive to availability. Instead, the higher activation threshold of right-shifted NaVs in the AIS impedes backpropagation. Therefore, recently observed developmental changes to the spatial separation and relative proportions of NaV1.2 and NaV1.6 in the AIS differentially impact activation and availability. The effects on backpropagation, and potentially learning, are opposite for orthodromic versus antidromic stimulation. Author SummaryNeurons use sodium ion currents, controlled by a neurons voltage, to trigger signals called action potentials (APs). These APs typically result from synaptic input from other neurons onto the dendrites and soma. An AP is generated at the axon initial segment (AIS) just beyond the soma. From there, it travels down the axon to other cells, but can also propagate "backwards" towards the soma and dendrites. This "backpropagation" allows a comparison at synapses of the timing of outgoing and incoming signals, a feedback process that modifies synaptic connection strengths linked to learning. It is puzzling that in many neurons, sodium ion channels come in two types: high-voltage threshold channels clustered near the soma where the AIS begins, and low-voltage ones further away towards the axon. This separation changes in the early development of the animal, which raises the question of its role in backpropagation. We constructed a detailed mathematical model to explore how separation affects backpropagation. Separation either impedes or enhances learning, depending on whether the AP results from synaptic inputs or, less typically, currents moving backwards from the axon. This is explained by the different effects the separation has on two key kinetic processes that govern sodium currents.

neuroscience↗

bioRxiv2022

Dentate gyrus mossy cells exhibit sparse coding via adaptive spike threshold dynamics

Hilar mossy cells (hMCs) are glutamatergic neurons in the dentate gyrus (DG) that receive inputs primarily from DG granule cells (GCs), CA3 pyramidal cells and local inhibitory interneurons. The hMCs then provide direct excitatory and disynaptic inhibitory feedback input to GCs. Behavioral and in vivo single unit recording experiments have implicated hMCs in pattern separation as well as is in spatial navigation and learning. It has, however, been difficult to mechanistically link the in vivo physiological behavior of hMCs with their intrinsic excitability properties that convert their synaptic inputs into spiking output. Here, we carried out electrophysiological recordings from the main cell types in the DG and found that hMCs displayed a highly adaptive threshold acting over a remarkably protracted time-scale. The hMC spike threshold increased linearly with increasing current stimulation and saturated at high current intensities. This threshold also increased in response to spiking and this effect also decayed over a long timescale, allowing for activity-dependent summation that limited hMC firing rates. This mechanism operates in parallel with a prominent medium after-hyperpolarizing potential (AHP) generated by the small conductance K+ channel. Based on experimentally derived parameters, we developed a phenomenological exponential integrate-and-fire model that closely mimics the hMC adaptive threshold. This lightweight model is amenable to its incorporation into large network models of the DG that will be conducive to deepen our understanding of the neural bases of pattern separation, spatial learning and navigation in the hippocampus. Statement of significanceRecent studies on hilar mossy cells have revealed that they are implicated in spatial navigation and mnemonic functions. Yet, the basic intrinsic characterization of these hMCs is still too superficial to explain their spiking behavior in vivo. Here, we describe novel biophysical properties of hMCs, including an independent relationship between spike latency and spike threshold as well as a slowly adapting spike threshold. These findings complement several other biophysical and connectivity similarities between hMCs and CA3 pyramidal cells, while emphasizing the contrast with hilar interneurons. Additionally, our results are well captured by a phenomenological model of the hMC which provides a useful framework to study the neural substrate of spatial navigation and learning in the dentate gyrus.

neuroscience↗

bioRxiv2021

Temporal derivative computation in the dorsal raphe network revealed by an experimentally-driven augmented integrate-and-fire modeling framework

By means of an expansive innervation, the serotonin (5-HT) neurons of the dorsal raphe nucleus (DRN) are positioned to enact coordinated modulation of circuits distributed across the entire brain in order to adaptively regulate behavior. Yet the network computations that emerge from the excitability and connectivity features of the DRN are still poorly understood. To gain insight into these computations, we began by carrying out a detailed electrophysiological characterization of genetically-identified mouse 5-HT and somatostatin (SOM) neurons. We next developed a single-neuron modeling framework that combines the realism of Hodgkin-Huxley models with the simplicity and predictive power of generalized integrate-and-fire (GIF) models. We found that feedforward inhibition of 5-HT neurons by heterogeneous SOM neurons implemented divisive inhibition, while endocannabinoid-mediated modulation of excitatory drive to the DRN increased the gain of 5-HT output. Our most striking finding was that the output of the DRN encodes a mixture of the intensity and temporal derivative of its input, and that the temporal derivative component dominates this mixture precisely when the input is increasing rapidly. This network computation primarily emerged from prominent adaptation mechanisms found in 5-HT neurons, including a previously undescribed dynamic threshold. By applying a bottom-up neural network modeling approach, our results suggest that the DRN is particularly apt to encode input changes over short timescales, reflecting one of the salient emerging computations that dominate its output to regulate behavior.

neuroscience↗

bioRxiv2021

Mixed selectivity coding of sensory and motor social signals in the thalamus of a weakly electric fish

Recent studies have shown that high-level neural activity often exhibits mixed selectivity to multivariate signals. How such representations arise and how they modulate natural behavior is poorly understood. The social behavior of weakly electric fish is relatively low-dimensional and easily reproduced in the laboratory. Here we show how electrosensory signals related to courtship and rivalry in Apteronotus leptorhynchus are represented in the preglomerular nucleus, the thalamic region exclusively connecting the midbrain with the pallium. We show that preglomerular cells convert their midbrain inputs into a mixed selectivity code that includes corollary discharge of outgoing communication signals. We discuss how the preglomerular pallial targets might use these inputs to control social behavior and determine dominance in male-male competition and female mate selection during courtship. Our results showcase the potential of the electrocommunication system as an accessible model for studying the neural substrates of social behavior and principles of multi-dimensional neural representation.

neuroscience↗

bioRxiv2020

The hybrid nature of task-evoked activity: Inside-out neural dynamics in intracranial EEG and Deep Learning

A.The standard approach in neuroscience research infers from the external stimulus (outside) to the brain (inside) through stimulus-evoked activity. Recently challenged by Buzsaki, he advocates the reverse; an inside-out approach inferring from the brains activity to the neural effects of the stimulus. If so, stimulus-evoked activity should be a hybrid of internal and external components. Providing direct evidence for this hybrid nature, we measured human intracranial stereo-electroencephalography (sEEG) to investigate how prestimulus variability, i.e., standard deviation, shapes poststimulus activity through trial-to-trial variability. We first observed greater poststimulus variability quenching in trials exhibiting high prestimulus variability. Next, we found that the relative effect of the stimulus was higher in the later (300-600ms) than the earlier (0-300ms) poststimulus period. These results were extended by our Deep Learning LSTM network models at the single trial level. The accuracy to classify single trials (prestimulus low/high) increased greatly when the models were trained and tested with real trials compared to trials that exclude the effects of the prestimulus-related ongoing dynamics (corrected trials). Lastly, we replicated our findings showing that trials with high prestimulus variability in theta and alpha bands exhibits faster reaction times. Together, our results support the inside-out approach by demonstrating that stimulus-related activity is a hybrid of two factors: 1) the effects of the external stimulus itself, and 2) the effects of the ongoing dynamics spilling over from the prestimulus period, with the second, i.e., the inside, dwarfing the influence of the first, i.e., the outside. B. Significance StatementOur findings signify a significant conceptual advance in the relationship between pre- and poststimulus dynamics in humans. These findings are important as they show that we miss an essential component - the impact of the ongoing dynamics - when restricting our analyses to the effects of the external stimulus alone. Consequently, these findings may be crucial to fully understand higher cognitive functions and their impairments, as can be seen in psychiatric illnesses. In addition, our Deep Learning LSTM models show a second conceptual advance: high classification accuracy of a single trial to its prestimulus state. Finally, our replicated results in an independent dataset and task showed that this relationship between pre- and poststimulus dynamics exists across tasks and is behaviorally relevant.

neuroscience↗

bioRxiv2019

Parsing out the variability of transmission at central synapses using optical quantal analysis

Properties of synaptic release dictates the core of information transfer in neural circuits. Despite decades of technical and theoretical advances, distinguishing bona fide information content from the multiple sources of synaptic variability remains a challenging problem. Here, we employed a combination of computational approaches with cellular electrophysiology, two-photon uncaging of MNI-Glutamate and imaging at single synapses. We describe and calibrate the use of the fluorescent glutamate sensor iGluSnFR and found that its kinetic profile is close to that of AMPA receptors, therefore providing several distinct advantages over slower methods relying on NMDA receptor activation (i.e., chemical or genetically encoded Calcium indicators). Using an array of statistical methods, we further developed, and validated on surrogate data, an expectation-maximization algorithm that, by biophysically constraining release variability, extracts the quantal parameters n (maximum number of released vesicles) and p (unitary probability of release) from single-synapse iGluSnFR-mediated transients. Together, we present a generalizable mathematical formalism which, when applied to optical recordings, paves the way to an increasingly precise investigation of information transfer at central synapses.

neuroscience↗

bioRxiv2018

Spike-Diffuse-Spike Modelling of AxonalPropagation Links Demyelination to CompoundAction Potential Dispersion

To establish and exploit novel biomarkers of demyelinating diseases requires a mechanistic understanding of axonal propagation. Here, we present a novel computational framework called the stochastic spike-diffuse-spike (SSDS) model for assessing the effects of demyelination on axonal transmission. It models transmission through nodal and internodal compartments with two types of operations: a stochastic integrate-and-fire operation captures nodal excitability and a linear filtering operation describes internodal propagation. The effects of demyelinated segments on the probability of transmission, transmission delay and spike time jitter are explored. We argue that demyelination induced impedance mismatch prevents propagation in most part when the action potential leaves a demyelinated region, not when it enters a demyelinated region. In addition, we model sodium channel remodeling as a homeostatic control of nodal excitability. We find that the effects of mild demyelination on transmission probability and delay can be largely counterbalanced by an increase in excitability at the nodes surrounding the demyelination. The spike timing jitter, however, reflects the level of demyelination whether excitability is fixed or is allowed to change in compensation. This jitter can accumulate over long axons and leads to a broadening of the compound action potential, linking microscopic defects to a mesoscopic observable. Our findings articulate why action potential jitter and compound action potential dispersion can serve as potential markers of weak and sporadic demyelination.

neuroscience↗

bioRxiv2018

A novel time-stamp mechanism transforms egocentric encounters into an allocentric spatial representation

Learning the spatial organization of the environment is essential for most animals survival. This often requires the animal to derive allocentric information about the environment from egocentric sensory and motor experience. The neural circuits and mechanisms underlying this transformation are currently unknown. We addressed this problem in electric fish, which can precisely navigate in complete darkness and whose requisite brain circuitry is relatively simple. We conducted the first neural recordings in the preglomerular complex, the thalamic region exclusively connecting the optic tectum with the spatial learning circuits in the dorsolateral pallium. While tectal egocentric information was eliminated in preglomerular neurons, the time-intervals between object encounters were precisely encoded. We show that this highly-reliable temporal information, combined with a speed signal, can permit accurate path-integration that then enables computing allocentric spatial relations. Our results suggest that similar mechanisms are involved in spatial learning via sequential encounters in all vertebrates.

neuroscience↗

bioRxiv2017

Noise Gated by Dendrosomatic Interactions Increases Information Transmission

We study how noise in active dendrites affects information transmission. A mismatch of both noise and refractoriness between a dendritic compartment and a somatic compartment is shown to lead to an input-dependent exchange of leadership, where the dendrite entrains the soma for weak stimuli and the soma entrains the dendrite for strong stimuli. Using this simple mechanism, the noise in the dendritic compartment can boost weak signals without affecting the output of the neuron for strong stimuli. We show that these mechanisms give rise to a noise-induced increase of information transmission by neural populations.

neuroscience↗