Biology subjects

Bernard, C.

Publications and source records attributed to Bernard, C..

At least 37 records · Page 2Linked to original sources

bioRxiv2024

Has AlphaFold~3 reached its success for RNAs?

Predicting the 3D structure of RNA is a significant challenge despite ongoing advancements in the field. Although AlphaFold has successfully addressed this problem for proteins, RNA structure prediction raises difficulties due to fundamental differences between proteins and RNAs, which hinder direct adaptation. The latest release of AlphaFold, AlphaFold 3, has broadened its scope to include multiple different molecules like DNA, ligands and RNA. While the article discusses the results of the last CASP-RNA dataset, the scope of performances and the limitations for RNAs are unclear. In this article, we provide a comprehensive analysis of the performances of AlphaFold 3 in the prediction of RNA 3D structures. Through an extensive benchmark over five different test sets, we discuss the performances and limitations of AlphaFold 3. We also compare its performances with ten existing state-of-the-art ab initio, template-based and deep-learning approaches. Our results are freely available on the EvryRNA platform: https://evryrna.ibisc.univ-evry.fr/evryrna/alphafold3/.

bioinformatics↗

bioRxiv2024

RNA-TorsionBERT: leveraging language models for RNA 3D torsion angles prediction

Predicting the 3D structure of RNA is an ongoing challenge that has yet to be completely addressed despite continuous advancements. RNA 3D structures rely on distances between residues and base interactions but also backbone torsional angles. Knowing the torsional angles for each residue could help reconstruct its global folding, which is what we tackle in this work. This paper presents a novel approach for directly predicting RNA torsional angles from raw sequence data. Our method draws inspiration from the successful application of language models in various domains and adapts them to RNA. We have developed a language-based model, RNA-TorsionBERT, incorporating better sequential interactions for predicting RNA torsional and pseudo-torsional angles from the sequence only. Through extensive benchmarking, we demonstrate that our method improves the prediction of torsional angles compared to state-of-the-art methods. In addition, by using our predictive model, we have inferred a torsion angle-dependent scoring function, called TB-MCQ, that replaces the true reference angles by our model prediction. We show that it accurately evaluates the quality of near-native predicted structures, in terms of RNA back-bone torsion angle values. Our work demonstrates promising results, suggesting the potential utility of language models in advancing RNA 3D structure prediction. Source code is freely available on the EvryRNA platform: https://evryrna.ibisc.univevry.fr/evryrna/RNA-TorsionBERT.

bioinformatics↗

bioRxiv2024

A summer in the Greater Paris: trophic status of peri-urban lakes shapes prokaryotic community structure and functional potential

With more than 12 million inhabitants, the Greater Paris offers a "natural laboratory" to explore the effects of eutrophication on freshwater lakes within a relatively restricted area. Here, a four-month time-series was carried out during summertime to monitor planktonic microbial communities of nine lakes located within a [~]70 km radius around Paris (Ile-de-France, France) of comparable morphologies, yet distinct trophic statuses (mesotrophic to hypereutrophic). The contribution of the trophic status and intra-summer variations were investigated on prokaryotic community structures (16S rRNA gene sequencing) and functions (shotgun metagenomics). Sampled lakes harbored highly distinct and diverse prokaryotic communities. Although their gene pool was quite stable and shared among lakes, taxonomical and functional changes were correlated. The trophic status appears as the main driver of both community structure and functional potential. When focusing on function involved in biogeochemical cycles, responses to phosphorus limitation (mostly polyphosphate-related processes) were highlighted between trophic statuses. Hypereutrophic lakes communities displayed the highest contrast and heterogeneity over time, suggesting a regime shift compared to lakes of lower trophic status. This study explores the influence of eutrophication on lakes microbiome ecology, by comparing for the first time the planktonic microbial communities and their functional potential of lakes of distinct trophic status in close vicinity over a summer season.

microbiology↗

bioRxiv2024

Limnospira (Cyanobacteria) chemical fingerprint reveals local molecular adaptation

Limnospira can colonize a wide variety of environments (e.g., freshwater, brackish, alkaline or alkaline-saline water) and develop dominant and even permanent blooms that limit over-shadowed adjacent phototrophs diversity, especially in alkaline and saline environments. Previous phylogenomic analysis of Limnospira allowed us to distinguish two major phylogenetic clades (I and II) but failed to clearly segregate strains according to their respective habitats in terms of salinity or biogeography. In the present work, we attempt to determine whether Limnospira displays metabolic signatures specific to its different habitats, particularly brackish or alkaline-saline ecosystems, and question the impact of accessory gene repertoires on respective chemical adaptations. The study of the metabolomic diversity of 93 strains of Limnospira from the Paris Museum Collection, grown under standardized lab culture conditions, showed clearly distinct chemical fingerprints that were correlated with the respective biogeographic origins of the strains. The molecules that most distinguish the different Limnospira geographic groups are sugars, lipids, peptides, photosynthetic pigments, and antioxidant molecules. Interestingly, these molecule enrichments might represent adaptation traits to the local conditions encountered in their respective sampling environments concerning salinity, light and oxidative stress. We hypothesize that within extreme environments, such as those colonized by Limnospira, a large set of flexible genes can provide remarkable adaptation to specific local environmental conditions (e.g., salinity, light, and oxidative pressure). Thus, the occurrence within Limnospira population genomes of a specific set of flexible genes potentially involved in the production of certain metabolites may provide valuable adaptative traits that may support the bloom persistence beyond environmental condition variations. ImportanceLimnospira are ubiquitous cyanobacteria able to colonize and dominate a wide range of alkaline-saline environments around the world according to remarkable adaptative strategies. Phylogenomic analysis of Limnospira platensis allowed to distinguish two major phylogenetic clades (I and II) but failed to clearly segregate strains following their habitats in terms of salinity or biogeography. One can presume that the genes found within this variable portion of the genome of these clades could be involved in L. platensis adaptation to local environmental conditions. In the present paper, we attempt to determine whether Limnospira platensis displays metabolic signatures specific to its different habitats, particularly brackish or alkalinesaline ecosystems, and question the impact of accessory gene repertoire on respective chemical adaptation.

microbiology↗

bioRxiv2024

A postnatal molecular switch drives the activity-dependent maturation of parvalbumin interneurons

Cortical neurons are specified during embryonic development but often only acquire their mature properties at relatively late stages of postnatal development. This delay in terminal differentiation is particularly prominent for fast-spiking parvalbumin-expressing (PV+) interneurons, which play critical roles in regulating the function of the cerebral cortex. We found that the maturation of PV+ interneurons is triggered by neuronal activity and mediated by the transcriptional cofactor peroxisome proliferator-activated receptor-gamma coactivator 1-alpha (PGC-1). Developmental loss of PGC-1 prevents PV+ interneurons from acquiring unique structural, electrophysiological, synaptic, and metabolic features and disrupts their diversification into distinct subtypes. PGC-1 exerts its function as a master regulator of the differentiation of PV+ interneurons by directly controlling gene expression through a transcriptional complex that includes ERR{gamma} and Mef2c. Our results uncover a molecular switch that translates neural activity into a specific transcriptional program promoting the maturation of PV+ interneurons at the appropriate developmental stage.

neuroscience↗

bioRxiv2024

Circadian dynamics of the Zbtb14 protein in the ventral hippocampus are disrupted in epileptic mice

Our previous in silico data indicated an overrepresentation of the ZF5 motif in the promoters of genes in which circadian oscillations are altered in the ventral hippocampus in the pilocarpine model of temporal lobe epilepsy in mice. In this study, we test the hypothesis that the Zbtb14 protein oscillates in the hippocampus in a circadian manner and that this oscillation is disrupted by epilepsy. We found that Zbtb14 immunostaining is present in the cytoplasm and cell nuclei. Western blot data indicate that the cytoplasmic and nuclear levels of Zbtb14 protein oscillate, but the phase is shifted. The densities of the Zbtb14-immunopositive express circadian dynamics in the ventral hilus and Ca3 but not in the dorsal hilus, Ca3, or the somatosensory cortex. In the pilocarpine model of epilepsy increase in the level of Zbtb14 protein was found at 11 PM, but not at 3 PM compared to controls. Finally, in silico analysis revealed the presence of the ZF5 motif in the promoters of 21 out of 24 genes down-regulated by epileptiform discharges in vitro, many of which are involved in neuronal plasticity. Our data suggest that Zbtb14 may be involved in the circadian dynamic of seizure regulation or brain response to seizure rhythmicity. HighlightsO_LIthe Zbtb14 protein is expressed in neurons in the mouse brain; C_LIO_LIZbtb14 protein levels oscillate through the circadian cycle in the ventral hippocampus but not in the dorsal hippocampus; C_LIO_LIthe oscillations of the Zbtb14 protein occur in both the cytoplasm and nucleus but in a different temporal pattern; C_LIO_LIthe circadian dynamics of the Zbtb14 protein are perturbed in epilepsy in an in vivo model of epilepsy; C_LIO_LInumerous genes that are downregulated in the in vitro model of epileptiform discharges have a ZF5 motif in their promoters C_LI

neuroscience↗

bioRxiv2024

Spatial patterns of Hyalomma marginatum-borne pathogens in the Occitanie region (France), a focus on the intriguing dynamics of Rickettsia aeschlimannii

Hyalomma marginatum is an invasive tick species recently established in mainland southern France. This tick is known to host a diverse range of human and animal pathogens such as Rickettsia aeschlimannii, Theileria equi, Anaplasma phagocytophilum, Anaplasma marginale, Ehrlichia minasensis, Crimean-Congo hemorrhagic fever and West Nile virus. While information about the dynamics of these pathogens is crucial to assess disease risk and develop effective monitoring strategies, few data on the spatial dynamics of these pathogens are currently available. We thus collected ticks in 27 sites in the Occitanie region to characterize spatial patterns of H. marginatum- borne pathogens. Several pathogens have been detected: Theileria equi (9.2%), Theileria orientalis (0.2%), Anaplasma phagocytophilum (1.6%), Anaplasma marginale (0.8%) and Rickettsia aeschlimannii (87.3%). Interestingly, we found a spatial clustered distribution for the pathogen R. aeschlimannii between two geographically isolated areas with infection rates and bacterial loads significantly lower in Herault/Gard departments (infection rate 78.6% in average) compared to Aude/Pyrenees-Orientales departments (infection rate 92.3% in average). At a smaller scale, R. aeschlimannii infection rates varied from one site to another, ranging from 29% to 100%. Overall, such high infection rates (87.3% in average) and the effective maternal transmission of R. aeschlimannii might suggest a role as a tick symbiont in H. marginatum. Moreover, currently identified as a human pathogen, such results also question about its pathogenic status in humans given the low number of human cases. Further studies are thus needed to understand both the status and the role of R. aeschlimannii in H. marginatum ticks. IMPORTANCETicks are obligatory hematophagous arthropods which transmit pathogens of medical and veterinary importance. Their infections cause serious health issues in humans and considerable economic loss in domestic animals. Information about the presence of pathogens in ticks and their dynamics is crucial to assess disease risk for public and animal health. Analysing tick-borne pathogens in ticks collected in 27 sites in the regions Occitanie, our results highlight clear spatial patterns in the Hyalomma marginatum-borne pathogen distribution and strengthen the postulate that it is essential to develop effective monitoring strategies and consider the spatial scale to better characterize the circulation of tick-borne pathogens.

microbiology↗

bioRxiv2024

Tick-borne pathogens identified in Hyalomma marginatum (Acari: Ixodidae) collected from various vertebrate hosts in the South of France

Hyalomma marginatum is a common ectoparasitic tick of ungulates, lagomorphs, insectivores, ground-foraging birds, observed in Corsica for decades, but whose permanent establishment in mainland France is very recent. This species is known to be one of the main vectors of the Crimean-Congo Hemorrhagic Fever virus, but also of various parasitic, bacterial or viral pathogens. In this study, we investigated the molecular infection rates of numerous tick-borne pathogens in H. marginatum ticks mainly sampled on horses, and occasionally on other animal species, from the French Mediterranean rim and Corsica between 2016 and 2020. In total, 1, 195 DNA and RNA purified from individual ticks or pools of ticks were screened for 26 microbial genera or species (viruses, bacteria and parasites), using a high-throughput microfluidic real-time PCR system (BioMark dynamic array system, Standard Biotools). For individual ticks and pooled ones, respectively, the most prevalent tick-borne microorganisms were Francisella-like endosymbionts at 97.0% and 96.8%, followed by Rickettsia aeschlimannii (76.4% and 96.4%), Theileria spp. and Theileria equi (3,5% and 0%; 1,9% and 5,8%), Anaplasma phagocytophilum (3.7% and 6.7%), and West Nile virus (0.1% and 0.4%). Babesia occultans (0.9%), Ehrlichia minasensis (0.3%), and Coxiella-like endosymbionts (0.1%) were only detected in individual ticks. Our study provides an overview of the diversity of microorganisms and tick-borne pathogens detected in the invasive tick H. marginatum in Corsica and the continental departments of the Mediterranean rim. Our study opens up new research perspectives on the epidemiology of tick-borne pathogens carried by H. marginatum and on the associated public and veterinary health risks.

microbiology↗

bioRxiv2024

How cancer-associated fibroblasts promote T cell exclusion in human lung tumors: a physical perspective

The tumor stroma is a tissue composed primarily of extracellular matrix, fibroblasts, immune cells, and vasculature. Its structure and functions, such as nutrient support and waste removal, are altered during malignancy. Tumor cells transform fibroblasts into cancer-associated fibroblasts, which have important immunosuppressive activity on which growth, invasion, and metastasis depend. These activated fibroblasts prevent immune cell infiltration into the tumor nest, thereby promoting cancer progression and inhibiting T-cell-based immunotherapy. To understand these complex interactions, we measure the density of different cell types in the stroma using immunohistochemistry techniques on tumor samples from lung cancer patients. We incorporate these data, and also known information on cell proliferation rates and relevant biochemical interactions, into a minimal dynamical system with few parameters. A spatio-temporal approach to the inhomogeneous environment explains the cell distribution and fate of lung carcinomas. The model reproduces that cancer-associated fibroblasts act as a barrier to tumor growth, but also reduce the efficiency of the immune response. The final outcome depends on the parameter values for each patient and leads to either tumor invasion, persistence, or eradication as a result of the interplay between cancer cell growth, T-cell cytotoxic activity, and fibroblast attraction, activation, and spatial dynamics. Our conclusion is that a wide spectrum of scenarios exists as a result of the competition between the characteristic times of cancer cell growth and the activity rates of the other species. Nevertheless, distinct trajectories and patterns allow quantitative predictions that may help in the selection of new therapies and personalized protocols. We conclude with different options for further modeling. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=118 SRC="FIGDIR/small/575824v3_ufig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@156ac4forg.highwire.dtl.DTLVardef@14a9a19org.highwire.dtl.DTLVardef@cafebeorg.highwire.dtl.DTLVardef@11aa3bf_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical AbstractC_FLOATNO C_FIG

biophysics↗

bioRxiv2023

State-of-the-RNArt: benchmarking current methods for RNA 3D structure prediction

RNAs are essential molecules involved in numerous biological functions. Understanding RNA functions requires the knowledge of their 3D structures. Computational methods have been developed for over two decades to predict the 3D conformations from RNA sequences. These computational methods have been widely used and are usually categorised as either ab initio or template-based. The performances remain to be improved. Recently, the rise of deep learning has changed the sight of novel approaches. Deep learning methods are promising, but their adaptation to RNA 3D structure prediction remains difficult. In this paper, we give a brief review of the ab initio, template-based and novel deep learning approaches. We highlight the different available tools and provide a benchmark on nine methods using the RNA-Puzzles dataset. We provide an online dashboard that shows the predictions made by benchmarked methods, freely available on the EvryRNA platform: https://evryrna.ibisc.univ-evry.fr/evryrna/state_of_the_rnart/

bioinformatics↗

bioRxiv2023

RNAdvisor: a comprehensive benchmarking tool for the measure and prediction of RNA structural model quality

RNA is a complex macromolecule that plays central roles in the cell. While it is well-known that its structure is directly related to its functions, understanding and predicting RNA structures is challenging. Assessing the real or predictive quality of a structure is also at stake with the complex 3D possible conformations of RNAs. Metrics have been developed to measure model quality while scoring functions aim at assigning quality to guide the discrimination of structures without a known and solved reference. Throughout the years, many metrics and scoring functions have been developed, and no unique assessment is used nowadays. Each developed assessment method has its specificity and might be complementary to understanding structure quality. Therefore, to evaluate RNA 3D structure predictions, it would be important to calculate different metrics and/or scoring functions. For this purpose, we developed RNAdvisor, a comprehensive automated software that integrates and enhances the accessibility of existing metrics and scoring functions. In this paper, we present our RNAdvisor tool, as well as state-of-the-art existing metrics, scoring functions and a set of benchmarks we conducted for evaluating them. Source code is freely available on the EvryRNA platform: https://evryrna.ibisc.univ-evry.fr.

bioinformatics↗

bioRxiv2023

Structural phylogenetics unravels the evolutionary diversification of communication systems in gram-positive bacteria and their viruses

Recent advances in AI-based protein structure modeling have yielded remarkable progress in predicting protein structures. Since structures are constrained by their biological function, their geometry tends to evolve more slowly than the underlying amino acids sequences. This feature of structures could in principle be used to reconstruct phylogenetic trees over longer evolutionary timescales than sequence-based approaches, but until now a reliable structure-based tree building method has been elusive. Here, we introduce a rigorous framework for empirical tree accuracy evaluation and tested multiple approaches using sequence and structure information. The best results were obtained by inferring trees from sequences aligned using a local structural alphabet--an approach robust to conformational changes that confound traditional structural distance measures. We illustrate the power of structure-informed phylogenetics by deciphering the evolutionary diversification of a particularly challenging family: the fast-evolving RRNPPA quorum sensing receptors. We were able to propose a more parsimonious evolutionary history for this critical protein family which enables gram-positive bacteria, plasmids and bacteriophages to communicate and coordinate key behaviors. The advent of high-accuracy structural phylogenetics enables a myriad of applications across biology, such as uncovering deeper evolutionary relationships, elucidating unknown protein functions, or refining the design of bioengineered molecules.

bioinformatics↗

bioRxiv2023

Probing the Mechanisms of Global Brain Reconfiguration after Local Manipulations

Understanding how localized brain interventions translate into whole-brain dynamics is crucial for deciphering neural function and tailoring therapeutic strategies. Combining mouse experimental datasets of focal interventions (thalamic lesion and chemogenetic silencing of cortical hubs), we demonstrate both local and global effects. Using whole-brain simulations of experimental data, we not only confirm the distributed nature of local manipulations but also offer mechanistic insights into these processes. Our simulations predict specific alterations in firing rates and spectral characteristics across specific brain networks, leading to structured changes in functional connectivity patterns. Some of these predictions have been empirically validated. Notably, the affected brain subnetworks--and their resultant signatures of change--are contingent on the original intervention site, suggesting a method to accurately localize the source of alteration. Our results provide a general framework for interpreting localized intervention effects, offering insights that could refine clinical interventions for focal brain disorders by enabling targeted circuit-level neuromodulation strategies.

neuroscience↗

bioRxiv2023

Long-term near-continuous recording with Neuropixels probes in healthy and epileptic rats

Neuropixels probes have become a crucial tool for high-density electrophysiological recordings. Although most research involving these electrodes is in acute preparations, some scientific inquiries require long-term recordings in freely moving animals. Recent reports have presented prosthesis designs for chronic recordings, but some of them do not allow for probe recovery, which is desirable given their cost. Others appear to be fragile, as these articles describe numerous broken implants. This fragility presents a challenge for recordings in rats, particularly in epilepsy models where strong mechanical stress impinges upon the prosthesis. To overcome these limitations, we present a new prosthesis specifically designed to protect the probes from strong shocks and enable the safe retrieval of electrodes after experiments. This implant was successfully used to record from healthy and epileptic rats for up to three weeks almost continuously, with a probe retrieval and reuse success rate of 91%, improving previously described recycling performances.

neuroscience↗

bioRxiv2023

Regulation of parvalbumin interneuron plasticity by neuropeptide-encoding genes

Neuronal activity is regulated in a narrow permissive band for the proper operation of neural networks. Changes in synaptic connectivity and network activity, for example, during learning, might disturb this balance, eliciting compensatory mechanisms to maintain network function. In the neocortex, excitatory pyramidal cells and inhibitory interneurons exhibit robust forms of stabilising plasticity. However, while neuronal plasticity has been thoroughly studied in pyramidal cells, little is known about how interneurons adapt to persistent changes in their activity. Here we uncover the critical cellular and molecular mechanisms through which cortical parvalbumin-expressing (PV+) interneurons adapt to changes in their activity levels. We found that changes in the activity of PV+ interneurons drive cell-autonomous, bi-directional compensatory adjustments of the number and strength of inhibitory synapses received by these cells, specifically from other PV+ interneurons. High-throughput profiling of ribosome-associated mRNA revealed that increasing the activity of PV+ interneurons leads to the cell-autonomous upregulation of two genes encoding multiple secreted neuropeptides, Vgf and Scg2. Functional experiments demonstrated that VGF is critically required for the activity-dependent scaling of inhibitory PV+ synapses onto PV+ interneurons. Our findings reveal an instructive role for neuropeptide-encoding genes in regulating synaptic connections among PV+ interneurons in the adult mouse neocortex.

neuroscience↗

bioRxiv2023

A standard protocol to report discrete stage-structured demographic information

O_LIStage-based demographic methods, such as matrix population models (MPMs), are powerful tools used to address a broad range of fundamental questions in ecology, evolutionary biology, and conservation science. Accordingly, MPMs now exist for over 3,000 species worldwide. These data are being digitised as an ongoing process and periodically released into two large open-access online repositories: the COMPADRE Plant Matrix Database and the COMADRE Animal Matrix Database. During the last decade, data archiving and curation of COMPADRE and COMADRE, and subsequent comparative research, have revealed pronounced variation in how MPMs are parameterized and reported. C_LIO_LIHere, we summarise current issues related to the parameterisation and reporting of MPMs that arise most frequently and outline how they affect MPM construction, analysis, and interpretation. To quantify variation in how MPMs are reported, we present results from a survey identifying key aspects of MPMs that are frequently unreported in manuscripts. We then screen COMPADRE and COMADRE to quantify how often key pieces of information are omitted from manuscripts using MPMs. C_LIO_LIOver 80% of surveyed researchers (n=60) state a clear benefit to adopting more standardised methodologies for reporting MPMs. Furthermore, over 85% of the 300 MPMs assessed from COMPADRE and COMADRE omitted one or more elements that are key to their accurate interpretation. Based on these insights, we identify fundamental issues that can arise from MPM construction and communication and provide suggestions to improve clarity, reproducibility, and future research utilising MPMs and their required metadata. To fortify reproducibility and empower researchers to take full advantage of their demographic data, we introduce a standardized protocol to present MPMs in publications. This standard is linked to www.compadre-db.org, so that authors wishing to archive their MPMs can do so prior to submission of publications, following examples from other open-access repositories such as DRYAD, Figshare, and Zenodo. C_LIO_LICombining and standardising MPMs parameterized from populations around the globe and across the tree of life opens up powerful research opportunities in evolutionary biology, ecology, and conservation research. However, this potential can only be fully realised by adopting standardised methods to ensure reproducibility. C_LI

ecology↗

bioRxiv2022

Hippocampal ripples coincide with ''up-state'' and cortical spindles in Retrosplenial Cortex

During NREM sleep hippocampal Sharp-wave ripples (SWR) events are thought to stabilize memory traces for long-term storage in downstream neocortical structures. Within the neocortex, Default Mode Network (DMN) areas interact preferentially with the hippocampus purportedly to consolidate those traces. Transient bouts of slow oscillations and sleep spindles in DMN areas are often observed around SWRs, suggesting that these two activities are related and that their interplay possibly contributes to memory consolidation. To investigate how SWRs interact with the DMN and spindles, we combined cortical wide-field voltage imaging, ECoG, and hippocampal LFP recordings in anesthetized and sleeping mice. Here we show that, during SWR, "up-states" and spindles reliably co-occur in a cortical subnetwork centered around the Retrosplenial cortex. Furthermore, Retrosplenial transient activations and spindles predict Slow Gamma oscillations in CA1 during SWRs. Together, our results suggest that Retrosplenial-hippocampal interaction may be a central source of information exchange between cortex and hippocampus.

neuroscience↗

bioRxiv2022

Tactile perception of auditory roughness

Auditory roughness resulting of fast temporal beatings is often studied by summing two pure tones with close frequencies. Interestingly, the tactile counterpart of auditory roughness can be provided through touch with vibrotactile actuators. However, whether auditory roughness could also be perceived through touch, and whether they exhibit similar characteristics is unclear. Here, auditory roughness perception and its tactile counterpart were evaluated using similar two pure tones stimuli. Results revealed similar roughness curves in both modalities suggesting similar sensory processing. This study attests of the relevance of such a paradigm for investing auditory and tactile roughness in a multisensory fashion.

neuroscience↗