Biology subjects

Bena, G.

Publications and source records attributed to Bena, G..

6 recordsLinked to original sources

bioRxiv2024

Spiking neural network models of sound localisation via a massively collaborative process

AbstractNeuroscientists are increasingly initiating large-scale collaborations which bring together tens to hundreds of researchers. However, while these projects represent a step-change in scale, they retain a traditional structure with centralised funding, participating laboratories and data sharing on publication. Inspired by an open-source project in pure mathematics, we set out to test the feasibility of an alternative structure by running a grassroots, massively collaborative project in computational neuroscience. To do so, we launched a public Git repository, with code for training spiking neural networks to solve a sound localisation task via surrogate gradient descent. We then invited anyone, anywhere to use this code as a springboard for exploring questions of interest to them, and encouraged participants to share their work both asynchro-nously through Git and synchronously at monthly online workshops. At a scientific level, our work investigated how a range of biologically-relevant parameters, from time delays to mem-brane time constants and levels of inhibition, could impact sound localisation in networks of spiking units. At a more macro-level, our project brought together 31 researchers from multiple countries, provided hands-on research experience to early career participants, and opportunities for supervision and teaching to later career participants. Looking ahead, our project provides a glimpse of what open, collaborative science could look like and provides a necessary, tentative step towards it.

neuroscience↗

bioRxiv2023

Multimodal units fuse-then-accumulate evidence across channels

Animals continuously detect information via multiple sensory channels, like vision and hearing, and integrate these signals to realise faster and more accurate decisions; a fundamental neural computation known as multisensory integration. A widespread view of this process is that multimodal neurons linearly fuse information across sensory channels. However, does linear fusion generalise beyond the classical tasks used to explore multisensory integration? Here, we develop novel multisensory tasks, which focus on the underlying statistical relationships between channels, and deploy models at three levels of abstraction: from probabilistic ideal observers to artificial and spiking neural networks. Using these models, we demonstrate that when information provided by different channels is not independent, linear fusion performs sub-optimally and even fails in extreme cases. This leads us to propose a simple nonlinear algorithm for multisensory integration which is compatible with our current knowledge of multimodal circuits, excels in naturalistic settings and is optimal for a wide class of multisensory tasks. Thus, our work emphasises the role of nonlinear fusion in multisensory integration, and provides testable hypotheses for the field to explore at multiple levels: from single neurons to behaviour. Key PointsO_LIWe introduce a novel set of tasks, based on comodulating the signals from multiple channels. C_LIO_LIWe show that linear fusion performs sub-optimally on these tasks and even fails in extreme cases. C_LIO_LIIn contrast, nonlinear fusion is optimal and excels in naturalistic settings, like predator-prey interactions. C_LIO_LIWe demonstrate that networks of spiking neurons approximate the behaviour of this nonlinear algorithm, when trained on these tasks. C_LIO_LIFinally, we explore how single neuron properties allow networks to approximate nonlinear fusion. C_LI

neuroscience↗

bioRxiv2022

Comparative genomics and transcriptomic response to root exudates of six rice root-associated Burkholderia sensu lato species

Beyond being a reliable nutrient provider, some bacteria will perceive the plant as a potential host and undertake root colonization leading to mutualistic or parasitic interactions. Bacteria of the Burkholderia and Paraburkholderia genera are frequently found in the rhizosphere of rice. While the latter are often described as plant growth promoting species, Burkholderia are often studied for their human opportunistic traits. Here, we used root exudate stimulation on three Burkholderia and three Paraburkholderia strains isolated from rice roots to characterize their preliminary adaptation to the rice host at the transcriptomic level. Instead of the awaited genus-dependent adaptation, we observed a strongly species-specific response for all tested strains. While all bacteria originate from the rice environment, there are great disparities in their levels of adaptation following the sensing of root exudates. We further report the shared major functions that were differentially regulated in this early step of bacterial adaptation to plant colonization, including amino acids and putrescine metabolism, the Entner-Doudoroff (ED) pathway as well as cyclic diguanylate monophosphate (c-di-GMP) cycling.

microbiology↗

bioRxiv2022

Differential genetic strategies of Burkholderia vietnamiensis and Paraburkholderia kururiensis for root colonization of Oryza sativa ssp. japonica and ssp. indica, as revealed by Tn-seq

Burkholderia vietnamiensis LMG10929 (Bv) and Paraburkholderia kururiensis M130 (Pk) are bacterial rice growth-promoting models. Besides this common ecological niche, species of the Burkholderia genus are also found as opportunistic human pathogens while Paraburkholderia are mostly environmental and plant-associated species. Here, we compared the genetic strategies used by Bv and Pk to colonize two subspecies of their common host, Oryza sativa ssp. japonica (cv. Nipponbare) and ssp. indica (cv. IR64). We used high-throughput screening of transposon insertional mutant libraries (Tn-seq) to infer which genetic elements have the highest fitness contribution during root surface colonization at 7 days post inoculation. Overall, we detected twice more genes in Bv involved in rice roots colonization compared to Pk, including genes contributing to the tolerance of plant defenses, which suggests a stronger adverse reaction of rice towards Bv compared to Pk. For both strains, the bacterial fitness depends on a higher number of genes when colonizing indica rice compared to japonica. These divergences in host pressure on bacterial adaptation could be partly linked to the cultivars differences in nitrogen assimilation. We detected several functions commonly enhancing root colonization in both bacterial strains e.g., Entner-Doudoroff (ED) glycolysis. Less frequently and more strain-specifically, we detected functions limiting root colonization such as biofilm production in Bv and quorum sensing in Pk. The involvement of genes identified through the Tn-seq procedure as contributing to root colonization i.e., ED pathway, c-di-GMP cycling and cobalamin synthesis, was validated by directed mutagenesis and competition with WT strains in rice root colonization assays. ImportanceBurkholderiaceae are frequent and abundant colonizers of the rice rhizosphere and interesting candidates to investigate for growth promotion. Species of Paraburkholderia have repeatedly been described to stimulate plant growth. However, the closely related Burkholderia genus hosts both beneficial and phytopathogenic species, as well as species able to colonize animal hosts and cause disease in humans. We need to understand to what extent the bacterial strategies used for the different biotic interactions differ depending on the host and if strains with agricultural potential could also pose a threat towards other plant hosts or humans. To start answering these questions, we used here transposon sequencing to identify genetic traits in Burkholderia vietnamiensis and Paraburkholderia kururiensis that contribute to the colonization of two different rice varieties. Our results revealed large differences in the fitness gene sets between the two strains and between the host plants, suggesting a strong specificity in each bacterium-plant interaction.

microbiology↗

bioRxiv2022

The impact of the rice production system (irrigated vs lowland) on root-associated microbiome from farmer's fields in western Burkina Faso

As a consequence of its potential applications for food safety, there is a growing interest in rice root-associated microbial communities, but some systems remain understudied. Here, we compare the assemblage of root-associated microbiota in rice sampled in 19 small farmers fields from irrigated and rainfed lowlands in western Burkina Faso, using an amplicon metabarcoding approach 16S (Prokaryotes, three plant sample per field) and ITS (fungi, one sample per field). In addition to the expected structure according to the root compartment (root vs. rhizosphere) and geographical zones, we show that the rice production system is a major driver of microbiome structure, both for prokaryotes and fungi. In irrigated systems, we found a higher diversity of prokaryotic communities from rhizosphere and more complex co-occurrence networks, compared to rainfed lowlands. Core taxa were different between the two systems, and indicator species were identified: mostly within Bacillaceae and Bradyrhizobiaceae families in rainfed lowlands, and within Burkholderiaceae and Moraxellaceae in irrigated areas. Finally, phylotypes assigned to putative phytobeneficial and pathogen species were found. Mycorrhizal fungi Glomeromycetes abundance was higher in rainfed lowlands. Our results highlight deep microbiome differences induced by contrasted rice production systems that should consequently be considered for potential microbial engineering applications.

microbiology↗

bioRxiv2019

Influence of plant genotype and soil on the wheat rhizosphere microbiome: identification of a core microbiome across eight African and European soils

Here, we assessed the relative influence of wheat genotype, agricultural practices (conventional vs organic) and soil type on the rhizosphere microbiome. We characterized the prokaryotic (archaea, bacteria) and eukaryotic (fungi, protists) communities in soils from four different countries (Cameroon, France, Italy, Senegal) and determined if a rhizosphere core microbiome existed across these different countries. The wheat genotype had a limited effect on the rhizosphere microbiome (2% of variance) as the majority of the microbial taxa were consistently associated to multiple wheat genotypes grown in the same soil. Large differences in taxa richness and in community structure were observed between the eight soils studied (57% variance) and the two agricultural practices (10% variance). Despite these differences between soils, we observed that 179 taxa (2 archaea, 104 bacteria, 41 fungi, 32 protists) were consistently detected in the rhizosphere, constituting a core microbiome. In addition to being prevalent, these core taxa were highly abundant and collectively represented 50% of the reads in our dataset. Based on these results, we identify a list of key taxa as future targets of culturomics, metagenomics and wheat synthetic microbiomes. Additionally, we show that protists are an integral part of the wheat holobiont that is currently overlooked. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=126 SRC="FIGDIR/small/777383v2_ufig1.gif" ALT="Figure 1"> View larger version (40K): org.highwire.dtl.DTLVardef@dd56a6org.highwire.dtl.DTLVardef@163b5f7org.highwire.dtl.DTLVardef@13f6bd7org.highwire.dtl.DTLVardef@c10d9d_HPS_FORMAT_FIGEXP M_FIG C_FIG

ecology↗