Through data interpolation, the olivine matrix reveals high fluid concentrations where shear strain is localized. These concentrations far exceed the predicted amount of H2O that grain boundaries can contain, excluding grain size reduction as a unique source of water storage. Instead, we show that H2O increases per unit of grain boundary across the shear zone, suggesting that cavitation and “healing” processes compete with each other to produce a larger pore volume with increasing strain rate. This provides an alternative process for fluids to be collected where strain rate is the highest in deep shear zones.Strain localization in viscously deformed rocks commonly results in fine-grained shear zones where massive fluid circulation is regularly observed. Recently attributed to strain-induced pumping, this phenomenon may have major implications for the distribution of ores deposits and rock rheology. However, although grain size reduction and/or creep cavitation have been proposed as important processes, the source mechanism of fluid concentration remains unresolved, particularly at high pressure. Here we use secondary ion mass spectrometry to document the H Although the source process of such fluid flow localization is unknown at present, it may have critical implications for rock mechanics and distribution of ore deposits in deep Earth environments5.On Earth, both the crust and mantle incorporate aqueous fluids that interact with solid rock materials in many ways. Commonly referred to as fluid-rock interactions, they strongly affect rock deformation and petrogenetic processes, giving rise, for instance, to hydrolytic weakening, pressure-solution creep or metamorphic reactions, including partial melting. During strain localization, this type of interactions may involve one or several chemo-physical processes that help to channelize fluid flow in ductile shear zones where grain size is substantially reduced. When rocks deform by sub-solidus viscous creep, strain indeed partitions into fine-grained shear bands that recurrently develop in the presence of massive fluid circulation, as revealed by the enrichment of hydrous phases1, fluid infiltration is commonly inferred using evidence for dissolution-precipitation, which suggests a long-term process rather than a co-seismic one7. Rutter and Brodie8 and Wark and Watson9 proposed that high fluid contents could occur in ductile shear zones as a result of fluid permeation in response to grain size reduction; because of high pressure and high temperature, supercritical fluids may distribute along grain boundaries as a uniform boundary film9. However, recent observations of syn-tectonic water accumulation along mantle shear bands4 do not support such a “passive” process for attracting fluids towards ductile shear zones. Instead, they suggest that fluids are dynamically driven by the deformation itself.Primarily attributed to seismic pumping of a pre-existing fault15 and during deformation experiments on ceramics, metals and natural rocks20. Also referred to as creep cavitation, the production of these micro-pores results either from grain boundary sliding (GBS) or, in a minor extent, from Zener-Stroh cracking if dislocations interact with grain boundaries17. In both cases, the micro-cavities arise from limitations of the material to flow, particularly when diffusive mass transfer is slow at low temperature. This led several authors to propose that strain-induced cavities in fine-grained shear zones may fill with fluids during deformation, constituting a potential alternative for surrounding fluids to be pumped14.There is abundant documentation of micro-pores produced during deformation of fine-grained material, including within natural shear zones in the middle/lower crust15. Furthermore, as creep cavitation is a process producing pressure-dependent dilatancy21, could we expect cavitation to occur if lithostatic pressure increases and limits dilatancy? The role of creep cavitation in fluid infiltration within ductile shear zones therefore remains uncertain, particularly at high pressure.However, evidence of fluid pumping induced by creep cavitation is rather circumstantial. As direct observation of fluid infiltration is still lacking, such interaction only relies on the coexistence of micro-pores and tracks of fluid circulation in some crustal shear zones2O content and grain size across a shear zone produced experimentally at 1.2 GPa and 900 °C. This deformation experiment highlights strain-related fluid concentration that arises with an increase of H2O per unit of grain boundary. Providing the first experimental evidence of excess water storage in a ductile shear zone, our dataset is here described and discussed in the view of current hypotheses.In this study, we used Secondary Ion Mass Spectrometry (SIMS) and Electron Backscatter Diffraction (EBSD) analyses to determine the respective distributions of H24 . The data interpolation indicates ~1500 ppm and locally more than 2000 ppm where strain is highest. These values are far above the 1330 ppm of bulk water added before the experiment, particularly if we consider the loss of hydrogen during hot-pressing and deformation, here estimated at ~550 ppm H2O from a hot-pressed, undeformed sample  maps, we further document the olivine grain size across the strain gradient. Along a cross-section that extends from the low-strain (α) to high-strain (β) zones, we used 30 maps of ~30 × 30 µmH2O Fig. . From α H2O Fig. . By apprH2O Fig. . The min27 26. For comparison, we also estimate the amount of fluid that grain boundaries can contain for a given interface density and grain boundary thickness  of 0.3, whereas a graph of the same dataset for H2O with respect to the shortest distance from the high-strain zone yields a positive correlation with a linear R2 of 0.59  minus 700 ppm , we confirm that grain boundaries are characterized by a deficit and then an excess of H2O with decreasing distance from the high-strain zone  with a good and positive linear correlation (R2 = 0.58) and a second one with a few data (3) out of trend and far above the linear correlation. Together with an excess water storage in the high-strain zone, our dataset therefore highlights 1) an increase of H2O per unit of grain boundary with increasing strain, and 2) a few local anomalies of high fluid content with respect to the first-order trend.In Fig. 2O in excess with respect to the volume deduced from the interface density may be expected. However, a continuous increase with strain as described in Fig. Because of the high confining pressure during the experiment, some pores have been produced while the sample was quenched and decompressed. These pores may still contain some fluid if they were not connected to the sample surface during SIMS analysis, and hence, documenting a few anomalies of H9, excess water storage in ductile shear zones was attributed to grain size reduction leading to fluid permeation into new grain boundaries. Although grain size reduction is documented here, the grain size is not correlated with the full distribution of H2O content across the shear zone  an overall expansion of grain boundaries with increasing strain, so that more H2O can be included per unit of grain boundary; or (2) creep cavitation forming a volume fraction of cavities in excess of the interface density.In previous studiesone Fig. . The cal9. A thickness of more than 2 nm in the high-strain zone does not either match available data from high-pressure experiments28 . In contrast, many experiments on metals indicate that creep cavitation is a direct function of strain rate, and hence, a function of strain acquired during a given period of time17. Indeed, uniaxial tensile experiments on aluminium, copper and steel have been performed at various temperatures that involve viscous strain32. They all describe a time-to-rupture that decreases with increasing strain rate, giving rise to the so-called Monkman-Grant relationship33. This suggests that the creep rate primarily controls both the nucleation rate and subsequent growth of cavities, such as required for the material to fracture through void coalescence32.The occurrence of a non-uniform boundary film along grain boundaries is very unlikely to occur considering viscous flow at high confining pressure14. Thus, the occurrence of ductile failure in metals, which typically describes a non-steady-state flow, differs from natural samples where the rupture of the material is prevented by closing of the micro-pores. This requires the occurrence of “healing” processes that may include grain boundary migration and/or phase nucleation34, both of which depend on fluid-assisted temperature-dependent diffusional processes. The confining pressure may also help to suppress failure by cavitation. In our experiment, deformation occurred at high confining pressure and at stabilized conditions of flow stress and temperature25. The olivine matrix deformed by grain-size-sensitive creep with evidence of phase nucleation and grain boundary sliding, which were both active across the shear zone. Together with observations of strain-induced micro-cavities in the high-strain zone25, these features support creep cavitation to occur coevally with healing processes during steady-state flow. Considering our SIMS dataset as a proxy of the volume fraction of cavities, we therefore attribute fluid concentration to creep cavitation, either related to a production of more cavities in the high-strain zone, or owing to larger pore sizes as the strain rate increases.In nature, the production of strain-induced micro-cavities is depicted as a non-cumulative, transient phenomenon related to steady-state grain boundary sliding35: the smaller is the grain size, the higher is the contribution of GBS to deformation with respect to other deformation processes. Hence, a higher production of pores may be expected if the contribution of GBS increases with decreasing grain size. Nevertheless, our distribution of H2O content is better correlated with strain (or strain rate) than with grain size  and diopside (Wo51En48Fs1) in an agate mortar, and sorted the grains according to their size using a decantation method with distilled water. For olivine, we used a grain size ranging from 1 to 10 µm with a mean of 2 µm. For clinopyroxene (CPx), we used coarser CPx grains between 40 and 125 µm. Olivine and pyroxene powders were mixed at a ratio of 70 to 30 wt% in a solution of ethanol following the procedure described in de Ronde et al.36. The powder was then placed between two alumina shear pistons cut at 45° to the piston axis, wrapped in a nickel foil to apply Ni/NiO buffering and enclosed in a platinum jacket welded at both ends. The shear piston surfaces have been previously roughened using 120-grit corundum paper. Before welding, 0.2 wt.% of distilled water was added. These amounts exceed the solubility of water in olivine and pyroxene at our experimental conditions38. Such wet conditions and grain size were chosen to ensure that (1) olivine deformed in the diffusion creep regime41, and (2) initial grains were large enough to then observe significant grain size reduction, if applicable. Few grains of orthopyroxene  and amphibole  were present in the CPx powder despite initial handpicking.The deformation experiment and hot-pressed sample are part of a study recently published by Précigout and Stünitz43 at Tromsø University (Norway). Temperature and pressure were alternately increased over several hours up to the chosen experimental conditions (900 °C and 1.2 GPa), which were applied for 24 h before deformation to hot-press the sample in situ. The deformation piston (σ1-piston) was subsequently advanced at a constant rate, first through the top lead piece, and then touching the alumina piston .Both the deformation and hot-pressing were conducted in a solid medium (NaCl) Griggs-type apparatus−8 torr. A liquid nitrogen cold trap and a sublimation pump were used to reduce the H2O background and keep the vacuum below 3 × 10−9 torr in the sample chamber (except for very few spots (11) that were between 3 and 6 × 10−9 torr; see Supplementary Table 133Cs+ focused to a ~10 μm spot. A raster of 10 × 10 µm was used during the 90 seconds of presputtering and of 5 × 5 µm during the analysis. The field aperture was closed to 1,000 µm to eliminate any secondary ion signal from the spot margin. The energy slit closed to 30 eV was manually shifted by ~4,000 digits (corresponding to an offset of ~40 eV) in order to keep ~5% of the initial signal. By doing this, we remove secondary ions with low energy and thus we eliminated potential hydrid contamination. Since variations of secondary ionization yields due to matrix effects preferentially affect low energy ions, this setting allows to keep it negligible compared to the analytical uncertainties. A mass-resolving power of ~7000, enough to resolve 17O from 16OH and 30Si from 29SiH was applied. 16O− (3 s counting time), 17O− (4 s), 16OH− (12 s), 28Si− (3 s) and 30Si− (3 s) ions were counted on the axial electron multiplier or Faraday cup during 10 cycles. A set of reference glasses  and pyrope (MON 9) with SiO2 contents varying from 42.2 to 76.45% and H2O contents varying from 56 ppm to 1.17% were used to create the calibration line, which we used to calibrate H2O contents of unknown samples. Using the calibration line, the accuracy of SIMS analyses  was estimated to be 5% relative. The background concentration of H2O estimated from the calibration line is ~60 ppm. For data interpolation, we then used the MATLAB software with the griddata function .SIMS measurements are available in Supplementary Table 2  with an EDAX Pegasus system at 20 kV and using a working distance of 15 mm. All maps were acquired using a step size between 0.1 and 0.2 μm over areas that averaged ~100 × 100 μmSupplementary information


The identification of drug targets is highly challenging, particularly for diseases of the brain. To address this problem, we developed and experimentally validated a general computational framework for drug target discovery that combines gene regulatory information with causal reasoning . Using a systems genetics approach and starting from gene expression data from the target tissue, CRAFT provides a predictive framework for identifying cell membrane receptors with a direction-specified influence over disease-related gene expression profiles. As proof of concept, we applied CRAFT to epilepsy and predicted the tyrosine kinase receptor Csf1R as a potential therapeutic target. The predicted effect of Csf1R blockade in attenuating epilepsy seizures was validated in three pre-clinical models of epilepsy. These results highlight CRAFT as a systems-level framework for target discovery and suggest Csf1R blockade as a novel therapeutic strategy in epilepsy. CRAFT is applicable to disease settings other than epilepsy. The identification of new drug targets is highly challenging, particularly for diseases of the brain. This study describes a general computational gene regulatory framework called CRAFT for drug target discovery, and the authors use CRAFT to identify the microglial membrane receptor Csf1R as a potential therapeutic target for epilepsy. One reason for the high rate of attrition in drug development, particularly for diseases of the central nervous system, is inadequate target validation in early-stage drug discovery2. Optimism that advances in gene discovery would facilitate the validation of mechanistically novel drug targets has yet to materialize, and there is a requirement for new approaches to target discovery and validation.Despite advances in our understanding of disease processes at the molecular and cellular levels, modern drug discovery has failed to deliver improved rates of approval for mechanistically novel drugs7. The power of the gene network approach comes from the analysis of multiple genes in functionally enriched pathways, as opposed to traditional single gene approaches that examine only one component of a complex system at a time. Using genome-wide transcriptional profiling in tissues relevant to the disease under investigation, gene co-expression network analysis can identify modules  as candidate regulators and drivers of disease states. Network-based drug discovery aims to harness this knowledge to identify drugs capable of restoring the expression of disease modules toward health9. At this systems level, therapeutic compounds are judged not by their binding affinity to a particular protein, but by their ability to induce a transcriptional response  that is anti-correlated to the coordinated transcriptional program underpinning the disease state. This systems approach to disease modification is loosely termed the “signature reversion paradigm” and is orthogonal to traditional concepts of drug discovery.Network-based systems analyses provide powerful techniques for elucidating molecular processes and pathways underlying disease10. Whilst successful examples for the use of such “perturbation databases” have emerged12, new targets are not identified by this route and the method’s reliance on chance overlap in expression profiles means a very large number of drugs may need to be profiled to find one with a suitable profile13. A second approach has therefore emerged that aims to map the underlying drivers and regulators of disease-related gene expression signatures as candidate drug targets7. Successful examples include mapping the upstream regulators of disease modules using expression quantitative trait loci mapping15, and approaches that make use of regulatory interactions between genes (“regulomes”)16. As currently formulated, however, these approaches also have limitations. For example, expression quantitative trait loci mapping of networks may identify only large genomic regions in which several candidate genes could be equally implicated whilst regulome approaches are not currently formulated to specifically identify regulators that have tractability as drug targets.Currently, drugs capable of reversing disease-related transcriptional signatures are identified using two main strategies. One approach uses public databases of transcriptomic profiles of cell lines treated with chemical compounds and seeks a chance anti-correlated overlap between a drug’s gene expression profile and a disease’s gene expression signature17, thus maximizing the opportunity for drug repositioning and rapid experimental medicine proofs of principle. Although in this study we applied CRAFT to epilepsy, CRAFT is equally applicable to any disease for which an underlying disease expression signature can be identified.Given these constraints we aimed to develop a new framework for drug target discovery based on identifying the regulators of disease-associated gene co-expression modules. Our method, “Causal Reasoning Analytical Framework for Target discovery” or CRAFT, combines gene regulatory information with a causal reasoning framework to computationally predict cell surface receptors with a direction-specified influence over module activity. We specifically chose to develop a method connecting module expression to membrane receptors because more than half of all approved drugs target receptors18. Secondly, epilepsy is a disease benefiting from well-characterized pre-clinical models with proven relevance to the human disease, and indeed, pre-clinical testing in rodent models of epilepsy remains the mainstay for determining efficacy of candidate antiepilepsy drugs19. This attribute allowed the development and validation of CRAFT in a controlled experimental framework.We chose to study epilepsy using CRAFT for two main reasons. Firstly, epilepsy is a highly debilitating disease of the brain for which there is a global unmet need—approximately one in three epilepsy patients are resistant to all currently available antiepileptic drugs (AEDs) and none of the current drugs are disease modifying or curative20. The causes of epilepsy can be broadly divided into cases that arise through no cause other than a genetic predisposition (“genetic epilepsy”), and epilepsy which develops secondary to an acquired brain injury such as following status epilepticus (SE) or head injury (“acquired epilepsy”)21. Here, focusing on acquired epilepsy, we set ourselves the challenge of developing, implementing, and validating a computational method for inferring drug targets for epilepsy from disease-related gene expression data.Epilepsy itself is a disease characterized by recurrent unprovoked epileptic seizures, but is also associated with additional features including cognitive and behavioral impairments and a heightened risk of death22. In this model of epilepsy the mice develop spontaneous recurrent seizures approximately 4 weeks after pilocarpine-induced SE. As well as manifesting spontaneous epileptic seizures, these mice also reflect several of the behavioral and cognitive disturbances associated with TLE in humans, and their response to AED therapy has been shown to be predictive of drug efficacy in human epilepsy23.A summary and description of the study workflow is shown in Fig. 2 FPKM >0) in at least 5% of samples, and of these, 9013 genes showed significant  <0.05) differential expression (DE) between epileptic and healthy control mice (Supplementary Data High-throughput sequencing of mRNA (RNA-sequencing (RNA-seq)) was performed on whole hippocampus samples from 100 outbred epileptic mice and 100 control  littermate mice (see Methods). In total, 14,188 genes were expressed , and modules 10, 14, 26, and 29 were enriched for neuronal functions including “synaptic transmission” .Analysis of the biological terms and canonical pathways enriched among the 28 co-expression modules in the mouse epileptic hippocampus revealed that the modules were generally enriched for specific functions—the top Gene Ontology (GO) biological processes enriched in each module are shown in Supplementary Figure 26. The individual modules demonstrated notable cell-type specificity 27 (see Methods), 12 modules were found to be significantly (FDR <0.05) differentially co-expressed between the epileptic and control hippocampus, whilst 16 modules displayed conservation of co-expression  and calculated the correlation between each module’s eigengene and seizure frequency. Of the 12 differentially co-expressed modules, nine  had an eigengene that significantly (FDR <0.05) correlated with seizure frequency . This revealed a diurnal variation of seizure occurrence in mice as well as clustering of seizures reflective of the classical patterns of human TLE  correlated with seizure frequency (hereon termed “QTT genes”)  analysis14, we found that all nine modules were conserved in the human epileptic hippocampus (FDR <0.05) , we then assessed whether the module was conserved in the human epileptic hippocampus. Using human orthologs of mouse module genes and genome-wide gene expression data from 122 human epileptic hippocampus samples surgically ascertained from TLE patients31. Among the nine mouse modules differentially co-expressed in epilepsy and correlated with seizures, seven  were also differentially co-expressed in human TLE Before proceeding to mapping the upstream regulators of these modules as candidate drug targets for epilepsy, since an important goal of our study was to identify mechanistically new drugs for epilepsy, we asked whether any of the seven modules could be considered to have a “known” relationship to epilepsy based on the published biomedical literature (see Methods). Briefly, we first extracted published Abstracts for every gene in the genome using SCAIview webserver (www.scaiview.com) . The weight of evidence relating a particular gene to epilepsy was then quantified by determining if that gene’s co-citation with epilepsy  was more frequent than expected by chance  as candidate modules for epilepsy by virtue of being (a) differentially co-expressed in mouse and human TLE, (b) conserved across mouse and human TLE, and (c) correlated with seizure frequency. From the pragmatic perspective of drug discovery, we set out to identify regulators of each of these modules as potential antiepilepsy drug targets.17, we decided to focus our search on finding membrane receptors exerting a regulatory influence over module activity. To this aim, we developed and implemented a computational approach that combines “causal reasoning” with gene regulatory information to rank receptors based on the strength of their predicted effect on module expression (see Methods). Briefly, using Clarivate Analytics MetaBase® (version 6.15.62452), we first extracted information relating to known interactions between membrane receptors and transcription factors (TFs) via linear canonical pathways and then between TFs and their target genes. To provide context to this “interactome,” only membrane receptors, TFs, and target genes expressed in the mouse hippocampus were considered (resulting in a list of 1624 expressed TFs and 307 expressed receptors). In a causal reasoning framework  target genes, allowing membrane receptors to be visualized in terms of their predicted directionality on the genes in a module which are over-expressed or under-expressed in disease, as well as the specificity and magnitude of the predicted effect  were significantly (FDR <0.05) enriched for one or more direction-specified receptor effect on module expression (Supplementary Data Csf1R gene in the mouse) was predicted to be a regulator of two of the seven prioritized candidate epilepsy modules . For both these modules, Csf1R was predicted by CRAFT to “activate” the subset of genes in the module that are over-expressed in epilepsy. According to the CRAFT causal reasoning framework  receptor  on the basis that these genes (a) belonged to either module 18 or 22 and were among the set of genes predicted by CRAFT to be positively regulated by Csf1R, (b) are over-expressed in epileptic cases compared to controls, and (c) are not predicted to be regulated by c-Kit 34. Epileptic mice were treated with PLX3397 at 3 or 30 mg/kg per day or vehicle for 7 days (see Methods). Hippocampus RNA was extracted on day 7 and gene expression was measured by reverse transcriptase quantitative PCR (qPCR). Csf1R blockade with PLX3397 at 30 mg/kg per day was associated with a significant decrease in the marker genes of module 18 but not module 22  and dose-dependent (P = 3.6x10−14) restoration of module 18 expression toward health following treatment with PLX3397 . In keeping with CRAFT prediction, we observed a significant  is both a component of module 18 and a predicted regulatory target of Csf1R and therefore Iba1 staining alone cannot distinguish between depletion of microglia cells by PLX3397 or a more focused down-regulation in Aif1 gene expression. To distinguish between these two possibilities we undertook a series of further analyses.These results are consistent with a shift in the expression of module 18 in epilepsy toward the healthy state following PLX3397 exposure. However, module 18 is predicted to be highly expressed in microglia .First, since module 18 is only one of three microglial modules (the others being modules 16 and 24), if the measured change in expression of module 18 following PLX3397 treatment is a consequence of microglia cell loss rather than a focused transcriptional change, then all three modules should be enriched for genes down-regulated by PLX3397. Of these three modules, however, only module 18 was enriched (FDR <0.01) for genes down-regulated by PLX3397 . Supplementary Figure P = < 1.0 × 10−5) and activated (P < 1.0 × 10−5) states  basal microglia, untreated, (2) basal microglia, PLX3397 treated, (3) activated microglia, untreated, and (4) activated microglia, PLX3397 treated. The full list of microglia genes differentially expressed following treatment with PLX3397 in basal and activated microglia are reported in Supplementary Data Taken together, these analyses point to a PLX3397-dependent reversion of module 18 expression in epilepsy toward health via Csf1R inhibition. Under the signature reversion paradigm, if module 18 has been correctly assigned as a driver of epileptic seizures then PLX3397 treatment should exert a therapeutic effect in epilepsy. We decided to test this prediction by investigating the therapeutic effect of PLX3397 in pre-clinical models of epilepsy.P < 0.01) decrease seizure frequency . Epileptic mice underwent 14 days of continuous motion sensing 3D accelerometry synchronized with continuous video monitoring to determine their baseline seizure frequency. This was followed by daily treatment with PLX3397 (3 or 30 mg/kg per day) or vehicle alone (20 mice in each group) for 14 days with continuous monitoring of seizures during the treatment phase. Treatment of epileptic mice with PLX3397 30 mg/kg per day resulted in a significant  recordings23. The primary clinical outcome was reduction in the duration of hippocampal paroxysmal discharges (HPDs) in response to PLX3397 treatment, which is a standard efficacy outcome measure in this epilepsy model. In the kainate model, treatment with standard AEDs usually only achieves a reduction in HPD duration at supra-therapeutic doses, suggesting the kainate model is a model of drug-resistant epilepsy37. Epileptic mice were EEG monitored at baseline (day 0) for 2 h prior to daily administration of PLX3397 at 30 mg/kg per day for 4 days and then EEG monitored again on day 5 for 2 h to assess drug efficacy. Treatment with PLX3397 was associated with a significant (P < 0.05) reduction in the duration of HPDs PDs Fig. .38. Whilst pre-clinical testing in animal models remains the mainstay for determining efficacy of candidate antiepilepsy drugs19, ex vivo OHSCs retain many of the key phenotypic features of acquired epilepsy including a latent period prior to the occurrence of spontaneous ictal events39, and the developmental sequence of interictal spikes to spontaneous ictal events in OHSCs closely mimics the temporal progression observed in the kainate model of epilepsy40. The ex vivo OHSC model of epilepsy therefore allows a multidimensional assessment of PLX3397 efficacy and mechanism beyond the in vivo models considered above. For example, because OHSCs represent tissue isolated from the systemic blood supply or wider brain, the development of epilepsy in OHSC’s reflects the intrinsic properties of the hippocampal slice isolated from potential infiltration of exogenous inflammatory (or other) cell types. In Fig. P < 0.05) reduction in the duration of ictal events. Concurrent assessment of lactate dehydrogenase (LDH) in the OHSC culture supernatants at baseline before PLX3397 exposure and after the first and second week of PLX3397 treatment revealed no evidence that the anti-seizure effect of PLX3397 was dependent on microglial (or other) cell death  model of epilepsyath Fig. .33. In cellular assays of PLX3397 selectivity41, the half maximal inhibitory concentration (IC50) of PLX3397 for inhibiting Csf1R is 20 nM, compared to IC50s for c-Kit and Flt3 of 120 nM and 1.7 μM, respectively  of PLX3397 in mice (see Methods). First, we wanted to confirm sufficient duration of exposure of PLX3397 following oral gavage. Following treatment of epileptic mice (pilocarpine model) at 3 and 30 mg/kg per day (per os (p.o.)) for 1 week (8 mice in each group), we analyzed PLX3397 levels in the plasma and brain . Supplementary Figure Taken together, these drug efficacy, cell viability, and gene expression analyses are consistent with microglial Csf1R inhibition exerting a therapeutic effect in acquired epilepsy in the absence of microglial depletion. We therefore evaluated whether PLX3397 has a detectable effect on the microglial phenotype (see Methods).We first assessed the microglia phenotype using ex vivo brain slices from epileptic mice (pilocarpine model) treated with vehicle or PLX3397 at 30 mg/kg (the therapeutic dosage in epileptic mice). Analysis of phagocytic function assessed by pH-sensitive rhodamine-labeled zymosan particle uptake revealed no significant differences in the number of particles taken up by microglia in the brain slices of vehicle or PLX3397-treated mice , we investigated whether PLX3397 has anti-seizure effects in normal (non-epileptic) mice induced to have seizures (see Methods). For these studies, we used three different acute seizure models, the maximal electroshock seizure (MES) model, the electrical 6 Hz psychomotor model and the chemical pentylenetetrazol (PTZ) model. In Supplementary Figure In this study, we used a gene network perspective of disease as a landscape for drug discovery. Under this framework, restoration of disease-related module expression toward health is considered to be predictive of therapeutic benefit, allowing “target” validation at the earliest stage of the drug discovery process. Based on this premise, we set out to develop and validate a predictive gene regulatory framework for target discovery. Given the tractability of cell membrane receptors as drug targets, and the large number of drugs that already target cell surface receptors, we aimed to connect module expression to cell membrane receptors.Starting from genome-wide gene expression profiling of the epileptic mouse hippocampus, we first identified co-expression networks (modules) associated with the epileptic condition. The cell-type specificity of these modules and their functional processes was assessed using enrichment analyses, and the regulatory influence of cell membrane receptors over the selected modules was then inferred using gene regulatory information in a causal reasoning framework (CRAFT).43.Of the cell surface receptors predicted by CRAFT to influence the expression one or more candidate epilepsy module, we chose to validate Csf1R because of an absence of prior information connecting Csf1R to epilepsy and the availability of a tool compound (PLX3397) by which to test CRAFT’s predictions related to Csf1R’s regulation of module 18 (and by extension module 18's relationship to epilepsy). Analysis of module 18 expression in the epileptic mouse brain revealed a PLX3397-dependent restoration of module expression toward health. The predicted therapeutic effect of PLX3397 on epilepsy was then confirmed in three independent models of epilepsy, including a mouse model of pharmacoresistant epilepsy—a model in which traditional AEDs at standard doses are ineffective. In addition to validating CRAFT as a predictive framework for drug target discovery, these results identify Csf1R inhibition as a potential novel therapeutic strategy in epilepsy and provide further evidence to support the role of innate immunity in the occurrence and maintenance of seizures in acquired epilepsy44. It has been suggested that microglia are dependent on Csf1R signaling for their survival such that brain microglia are reported to be depleted from naïve mice following prolonged high dose treatment with PLX3397 (5–6 times higher exposure than in our study)33. In our study, we found no evidence for microglial depletion by Csf1R in a series of ex vivo and in vitro experiments and further we identified that the microglial marker Iba1 (encoded by Aif1) is a predicted target of Csf1R and expected to be down-regulated by Csf1R blockade. These results highlight the dangers of interpreting reduced Iba1 expression as microglial cell depletion. In our study, we present multiple layers of evidence through genomic and microglial functional readouts to suggest that PLX3397 has an effect on module 18 expression and microglial phenotpye in the absence of microglial cell death.Csf1R is a membrane receptor expressed by myeloid lineage cells including monocytes, macrophages, and microglia® have limitations in terms of the accuracy and completeness of this information, which places restrictions on the scope and accuracy of our target predictions. For example, the direction of effect of an interaction is often not specified in a database, and the relationship between membrane receptors and TFs is currently determined using linear pathways where knowledge is still incomplete. However, as the completeness of our knowledge of these regulatory relationships improves, including more detailed knowledge of cell-type-specific interactions between TFs and target genes, so the accuracy and scope of CRAFT is also expected to improve. At present, the major challenge was to establish proof of concept that gene regulatory knowledge combined with causal reasoning offers a valid framework for discovering mechanistically novel membrane receptors as drug targets, and this is what the framework described here makes possible. Although our causal reasoning framework was implemented using regulatory interactions from Clarivate Analytics MetaBase®, several other databases provide similar sources of information that can be adapted to the CRAFT framework. For example, biological pathway databases such as the Reactome pathway Knowledgebase45 and Pathway Commons46 provide well-characterized linear signaling pathways which can be used to connect membrane receptors to TFs, whilst TF target databases such as TRRUST47 provide information relating TFs to target genes. As well as having utility in prioritizing novel drug targets for disease, CRAFT’s causal reasoning framework may ultimately have broader biological value in terms of understanding and modulating maladaptive transcriptional responses to environmental perturbations.The ability to map the landscape of a disease in terms of its gene regulatory relationships offers considerable opportunities to accelerate the drug discovery process. Although we took advantage of experimentally validated interactions between TFs and target genes and between membrane receptors and TFs, meta-databases such as MetaBase48.From a clinical perspective, our study identifies Csf1R as a novel drug target for acquired epilepsy, and highlights and further supports the use of immunomodulatory therapies as a valid therapeutic approach in acquired epilepsyFor this study, we chose to develop CRAFT using an established mouse model of acquired epilepsy. This allowed for a standardization of epilepsy cases and controls not possible with human samples due to the substantial batch differences between surgically resected hippocampi from living patients and control post-mortem samples ascertained after death.n = 100).Unlike traditional gene expression analyses where power is most often considered in terms of power to detect differentially expressed genes (DEGs), for our study, power related to the sample size required to detect differentially co-expressed modules. To investigate the effect of sample size on our ability to detect modules which are differentially co-expressed between the epileptic and control mouse hippocampus, we implemented a post hoc permutation-based framework that preserves the real data structure and employs random sub-sampling to determine the minimum sample size necessary to identify differentially co-expressed modules. Our simulations .In conclusion, CRAFT provides a gene regulatory and causal reasoning framework to identify membrane receptors as novel drug targets from gene expression data. As well revealing Csf1R as a mechanistically novel target for acquired epilepsy, CRAFT highlighted many other candidate regulators of epileptic networks that may warrant further investigation as potential novel antiepilepsy drug targets. We therefore make our causal reasoning framework in epilepsy and all its results available to the epilepsy scientific community for unrestricted interrogation via a web interface  with 1 mg/kg of N-methylscopolamine bromide 30 min prior to pilocarpine treatment . Ten to forty-five minutes after pilocarpine injection the animals displayed generalized clonic–tonic seizures that progressed to continuous convulsive activity, that is, SE. The SE was allowed to persist for 3 h and was then interrupted by i.p. injection of diazepam (10 mg/kg). Mice surviving SE typically show spontaneous recurrent seizures  within days to weeks and continue to have spontaneous seizures for several weeks49. All mice underwent continuous monitoring for seizures for 14 consecutive days beginning 28 days following SE prior to sampling the hippocampus and extraction of RNA. Seizure monitoring was performed with a proprietary system (UCB Pharma) using simultaneous recording of locomotor activity with 3D accelerometer and video. This system allows for automated detection of behavioral seizures by analysis of the accelerometry signal, which is then reviewed manually using the time-locked video recordings. All behavioral seizures were scored according to the Racine’s50 method after careful review of corresponding video clips by experienced technical personnel. Only secondary generalized seizures Racine’s score 3–5 were quantified and used to calculate total seizure counts. Data from 100 epileptic and age-matched and gender-matched control were included in the study. All in vivo experiments were performed according to the National Rules on Animal Experiments in Belgium and to the guidelines of the European Community Council Directive 2010/63/EU. Analyses were conducted under Imperial College Research Ethics Committee approval ICREC_14_2_11. All efforts were made to minimize animal suffering.SE was induced in male Crl:NMRI(Han)-FR mice (each mouse weighing 28–32 g at the beginning of the study) by a single injection of pilocarpine as previously describeddifferentially co-expressed modules. To investigate the effect of sample size on our ability to detect modules which are differentially co-expressed between the epileptic and control mouse hippocampus, we implemented a post hoc permutation-based framework that preserves the real data structure and employs random sub-sampling to determine the minimum sample size necessary to detect significantly differential co-expressed modules. This framework was implemented in three steps: (i) sample size was randomly reduced in steps of 10% , (ii) the empirical significance of differential co-expression was calculated at each sample size, (iii) steps (i–ii) were performed 50 times to assess sampling variation. We then selected three representative differential co-expression modules associated with epilepsy in our study , which were each of similar size in terms of the number of genes but which varied in terms of the mean gene–gene correlation of each module. Using this permutation-based framework, for each module, differential co-expression was assessed in terms of the empirical significance  of the difference between the mean correlation of module genes in epileptic versus control mice.Unlike traditional gene expression analyses where power is most often considered in terms of power to detect DEGs, for our study, power relates to the samples size required to detect n = 100. These results show that some epilepsy modules would not have been reliably detected as differentially co-expressed with a sample size less than n = 100.Our simulations revealed that as the sample size decreases there is an increase in noise . Sample preparation for RNA-seq was performed according to the protocols recommended by the manufacturers . Sequencing was done using Illumina HiSeq 2000 sequencer, with paired-end 75 bp nucleotide reads according to the protocol recommended by the vendor. Raw reads were mapped to the reference mouse genome (mm10) using TopHat version 2.0.851. Reads were annotated using “union” gene model from HTSeq package version 0.6.Total RNA was extracted from the left hippocampus of each mouse  using the Ward’s clustering method57 and organized into a dendrogram. Briefly, starting from the matrix of Spearman distances, Ward’s method uses a recursive clustering procedure to form partitions (clusters) of genes. At each step of the procedure, the Ward clustering minimizes the loss of information (measured as the error of sum of squares in the Spearman distances) associated with each grouping of genes. To identify discrete clusters, we recursively cut the dendrogram to generate 299 clustering configurations that included from K = 2 to K = 300 clusters. In order to identify the optimal and stable number of clusters (Kx), we calculated the percentage of the variance explained (R2) by each clustering configuration  as follows:Kx for which the variance explained reaches a plateau, that is, there is no additional gain in information (R2) when using the next clustering Kx + 1  the “Elbow” (or “knee”) method25 and (2) the pseudo F-index58. Both criteria indicated an optimal and stable number of clusters, Kx = 29  as a distance metric between the expression of any two genes1 Figure . The cri9 Figure .27. This empirical P value of significance was estimated for each cluster and then corrected for the number of clusters tested for differential co-expression using BH correction (BH-adjusted). A cluster was considered to be significantly differentially co-expressed if its BH-adjusted empirical P value was <0.05.For each cluster the correlation between gene expression profiles was computed in healthy and in epileptic animals separately and the difference in co-expression measure was based on Euclidian distance between the two distributions . The statistical significance for the difference in co-expression was assessed according to the null distribution generated by performing 10,000 permutations of genes in the module14. For non-epileptic controls (n = 63), we used genome-wide gene expression data generated from 63 human hippocampus samples with no history of neurological or psychiatric disease, downloaded from GSE4564231. The differential co-expression test was performed as described above.Using the lists of genes from the co-expression clusters in the mouse epileptic hippocampus, we ascertained “one2one” human orthologs from the biomart Ensembl database. For epileptic cases, we used genome-wide expression from whole human hippocampus samples from 122 epilepsy patients who had undergone selective amydalohippocampectomy for mesial temporal epilepsy (mTLE) with hippocampus sclerosis (HS), downloaded from GSE6380829), and then tested the enrichment of each module for genes individually correlated with seizure frequency (at FDR <0.05).First, the frequency of behavioral seizures in each epileptic mouse was recorded by 14 days of continuous motion sensing 3D accelerometry synchronized with continuous video monitoring starting on day 28 post SE (as described above). Next, the relationship between module expression and seizures was explored in two ways. First, we explored the correlation between each module’s eigengene  and seizure frequency using Spearman’s correlation. Second, the relationship between the expression of a module and seizures we explored by first calculating the Spearman’s correlation of expression of each individual gene in a module with seizure frequency . BH correction for multiple testing was done and significance threshold was at FDR ≤0.05.Cell-type enrichment analysis was performed for nine major cell types  using marker gene signatures obtained from single-cell RNA-seq of the mouse hippocampusTo extract epilepsy-associated genes from the scientific literature, gene–epilepsy co-citation was searched in PubMed abstracts  using SCAIview webserver as available on 28 May 2014. The weight of evidence relating a particular gene to epilepsy was then quantified by determining if that gene’s co-citation with epilepsy  was more frequent than expected by chance (hypergeometric test). The list of epilepsy-associated genes was established by applying a threshold of FDR ≤0.05. Then, by considering gene–epilepsy pairs significant at FDR <0.05, the modules were ranked according to their enrichment of gene–epilepsy pairs (hypergeometric test).® , which is a meta-database of manually curated literature-based contextual biological interactions. Prior to the causal reasoning analysis, we removed all membrane receptors and TFs that were not expressed in the mouse hippocampus, resulting in a list of 1624 TFs and 307 receptors, and we also considered only those target genes that were expressed in the mouse hippocampus. Next, for each TF, we identified the set of genes targeted by that TF and considered the activity of the TF on the target genes in one or more of three possible categories: (a) genes activated by the TF (act), (b) genes inhibited by the TF (inh), and (c) genes reported to be regulated by the TF in MetaBase® but where the direction of the interaction was unknown (unk). Next, the effect of each receptor on a TF was assessed using MetaBase® defined canonical linear pathways, with the direction of the effect of the membrane receptor on the TF again considered as either activating (act), inhibitory (inh), or unknown (unk). Note, hereon the term “regulator” can refer to either a TF or a membrane receptor. The transcriptional effect of a regulator is defined by the set of target genes whose expression is predicted to be influenced by that regulator. For regulators that are membrane receptors, the directionality of the receptors on network expression was defined by the causal reasoning rules set out in Table To predict drug targets, we aimed to connect disease-associated co-expression modules to regulatory TFs that in turn can be modulated by membrane receptors. To implement this analysis, we designed a causal reasoning framework that takes into account the direction of effects between the three components of the system, that is, membrane receptor > TF > target genes. The interactions between these three components and the direction of these interactions were obtained from the Clarivate Analytics MetaBaseP values, taking into account the total number of enrichment tests performed. Regulators were considered to exert a significant effect on sub-module expression at FDR <0.05 and reported graphically in Supplementary Figure Next, to assign directionality to the activity of a regulator in terms of disease context, we considered the effect of the regulator on gene expression in the context of whether the target genes in the co-expression module were over-expressed (“o”) or under-expressed (“u”) in epilepsy. The set of genes in a module over-expressed or under-expressed in epilepsy were termed “sub-modules” . The significance of effect of a regulator on a sub-module was then assessed by testing the overlap between genes under the control of the regulator  and the genes belonging to a sub-module (hypergeometric test), taking all genes under the control of the regulator as the universe. We report the effect of membrane receptors and TFs on sub-module gene expression separately in Supplementary Data The proportion of genes regulated by the “regulator” which are in the sub-module = activity.The proportion of genes in the sub-module that are under the control of the regulator = weight.The impact of a regulator on sub-module expression was then quantified according to the following two statistics:(A)Absolute ranking: Regulators with a defined impact, that is, “act” or “inh”, were considered to have a higher priority over regulators with an unspecified effect. The regulators were then sorted according to the sum of the ranks of their activity and weight.(B)Relative ranking: Because modules may be regulated by more than one regulator, and because the target genes in a sub-module for each regulator may overlap, where more than one regulator was identified, for each we re-calculated the absolute rank based on the target genes not shared with the previous regulator (based on the absolute rank list) in order to maximize sub-module coverage and to identify potential synergistic combinations of regulators. The first regulator is the one with the highest absolute rank.Using these two values, regulators were ranked as follows:n = 64 chronic epileptic mice (Racine’s score 3–5) were video monitored (as described above) for a further 14 consecutive days to establish their baseline seizure daily frequency, which was calculated by dividing the total number of seizures by the exact duration of monitoring period. Subsequently, the mice were randomly assigned into three separate groups (n = 21–22) to ensure comparable seizure frequency between the groups at baseline. One group then received daily injections  with vehicle, while the two other groups received the Csf1R inhibitor PLX3397 at 3 and 30 mg/kg per day for 14 days. All three groups were continuously monitored and their daily seizure frequency was calculated and compared to baseline. To analyze the effect of PLX3397 treatment on the daily seizure frequency, Wilcoxon's signed-rank test was applied between the baseline and the treatment monitoring periods within each treatment group. Mice were sacrificed for sampling of the brain on the day after the last injection with either vehicle or PLX3397. Since in the pilocarpine model symmetrical and bilateral pathology is observed in the brain, one hemisphere of the brain was processed for immunohistochemistry (see below), while the hippocampus from the other hemisphere was used for RNA extraction and microarray quantification of mRNA expression.To evaluate the effect of Csf1R blockade on gene network expression and epilepsy, we first used a separate cohort of chronic epileptic NMRI mice (pilocarpine model as above). All mice underwent screening consisting of 7 continuous days (starting 6–7 weeks after pilocarpine-induced SE) to confirm the presence of spontaneous recurrent seizures . During the eighth post SE week, Following brain dissection, right brain hemispheres were immediately fixed by immersion in 4% paraformaldehyde overnight at 4 °C. Samples were rinsed in several changes of cold 1× phosphate-buffered saline (PBS), cryo-protected by overnight immersion in 15% sucrose solution at 4 °C, and then embedded in OCT (Tissue-Tek) and quickly frozen over liquid nitrogen. Hemispheres were serially cut with a cryostat microtome into 12 µm coronal sections that were mounted on slides and stored at –40 °C until treatment. For immunohistochemistry, sections were rinsed twice in PBS, and then incubated in PBS containing 0.3% Triton X-100 and 5% normal goat serum (PBS-T) for 1 h at room temperature to block non-specific binding sites. Anti-Iba1 primary antibody  was diluted in PBS-T and applied on sections overnight at room temperature in a humidified chamber. Labeling was visualized using Alexa Fluor secondary antibodies 488  and sections were counterstained with 4′,6-diamidino-2-phenylindole (DAPI). Whole slide imaging was performed by fluorescence microscopy using NanoZoomer-XR with ×40 objective (Hamamatsu). Automatic quantification of cell density in specific brain regions was performed using the VisioPharm 5 software (VisioPharm).® system in CK14 tubes following the manufacturer’s instructions. Total RNA was obtained from mouse hippocampi using RNeasy Plus Mini Kit  following the manufacturer’s instructions. RNA concentration was measured with a NanoDrop ND-1000 Spectrophotometer. RNA quality was assessed using the Bio-Rad Experion Automated Electrophoresis System (RQI ≥7).Snap-frozen hippocampi were homogenized in RLT buffer  using Precellys® Gene Expression Assays (ThermoFisher Scientific) and TaqMan® Universal PCR Master Mix (ThermoFisher Scientific) in a final volume of 10 µl according to the manufacturer’s recommendations. Cq values were obtained from the Bio-Rad CFX Manager 3.1 software using regression determination mode. Normalized relative expression levels were calculated using qbase + software59 . Among eight genes, Brap and Bcl2l13 were identified with the geNormplus60 module in qbase+ as the most suitable reference genes and were used for normalization. One-tailed Welch’s t test was performed on Log 2-transformed normalized expressions.Complementary DNA (cDNA) was synthesized from 1 µg total RNA using Applied Biosystems High-Capacity cDNA Reverse Transcription Kit in a total volume of 100 µl following the manufacturer’s protocol. qPCR reactions were performed using a CFX384 Real-Time System. Two microliters of 2× diluted cDNA were analyzed in triplicate for genes of interest (see Supplementary methods) expression using inventoried TaqMan61 in R-Bioconductor62. Briefly, samples that passed QC were Log 2 transformed and normalized by quantiles between chips. DE was calculated between the different groups using “Limma” package63 in R-Bioconductor.The quality control (QC), RNA labeling, hybridization, and data extraction were performed at GenomeScan/ServiceXS B.V. . RNA concentration was measured using the Nanodrop ND-1000 spectrophotometer . The RNA quality and integrity was determined using Lab-on-Chip analysis on the Agilent 2100 Bioanalyzer . Biotinylated cRNA was prepared using the Illumina TotalPrep RNA Amplification Kit  according to the manufacturer’s specifications with an input of 200 ng total RNA. Per sample, 750 ng of the obtained biotinylated cRNA samples was hybridized onto the Illumina MouseRef-8 v2 . Hybridization and washing were performed according to the Illumina Manual “Direct Hybridization Assay Guide.” Scanning was performed on the Illumina iScan . Image analysis and extraction of raw expression data was performed with Illumina GenomeStudio v2011.1 Gene Expression software with default settings . QC and normalization of microarray data were performed using “Lumi” packageKainate-induced mTLE model experiments were performed at Synapcell SAS . Animal procedures were approved by the ethical committee of the High Technology Animal Platform and performed in accordance with the European Community Council Directive (2010/63/EU). Briefly, C57/BL6 mice underwent surgery under general anesthesia using isoflurane (3% in oxygen). Stereotaxic injection of 50 nl of a kainic acid solution (1nmol) was performed in the right dorsal hippocampus . After KA injection, mice were implanted with a bipolar electrode in the ipsilateral dorsal hippocampus and a reference electrode over the cerebellum. After surgery, animals were housed in individual cages with access to food and water ad libitum under a 12/12 h light and dark cycle. Eight mTLE mice underwent 2 h baseline EEG recording 4 weeks after KA injection. Chronic treatment started after the baseline monitoring (D0), where animals were injected daily p.o. (10 ml/kg) with 32.6 mg/kg of PLX3397 for four consecutive days. PLX3397 was formulated in 1% (w/v) methylcellulose (400 cps), 0.1% (w/v) Tween-80, and 0.1% (w/v) silicone antifoam 1510 US in water. An efficacy EEG recording was performed for 2 h on the fifth day (D5). EEG recordings were analyzed for identification/quantification of HPDs. Individual data were expressed as mean cumulated duration and mean number of HPDs per h over the 2-h EEG recording period. The effect of the tested compound was compared using one-way analysis of variance (ANOVA) for repeated measures followed by paired comparison versus baseline period (D0).Slope test was performed to test the relationship between PLX3397 dose and restoration of network gene expression back to “normal.” The test was performed using “smartr” version 3.4.3, R package, with null hypothesis that there is no difference in the slopes obtained from two doses of PLX3397.64. Briefly, hippocampi were dissected from 6- to 9-day-old Sprague–Dawley rats and cut into 350-µm-thick slices using a McIlwain tissue chopper. Slices were kept in ice-cold Gey’s balanced salt solution containing 0.6% d-glucose and 300 µM kynurenic acid  for 30 min. After recovery, slices were washed three times with the culture medium containing Neurobasal A, B27 supplement, 0.5 mM Glutamax , and 30 µg/ml gentamicin (Sigma-Aldrich). Only slices from the middle part of the hippocampus were selected and placed in the center of the mini-wells, which allowed a good positioning over the MEA assembly . For immunostaining experiments, slices were cultured on glass coverslips containing the mini-wells in a 6-well culture plate. OHSCs were maintained in a humidified CO2 incubator at 35 °C and the medium was refreshed 2–3 times a week.All animals used in this study were performed according to the guidelines of the European Community Council Directive 2010/63/EU. All experimental protocols using animals were reviewed and approved by the ethical committee at UCB Biopharma. OHSCs were prepared using polydimethylsiloxane mini-wells as described previously64. Field potential recordings were performed once per slice at days in vitro (DIV) 8 (baseline before PLX3397 exposure) and then on DIV 15 and DIV 22, that is, after first and second week of exposure to 1 µM of PLX3397. Extracellular field potential recording and stimulation were performed using the MEA2100 system . OHSCs grown on MEAs at different DIVs were quickly moved to the MEA recording chamber and spontaneous activity was recorded for 40 min using the MC Rack software (Multi Channel Systems). The recording pads were pre-heated at 35 °C and 95% O2/5% CO2 gas was continuously flowing into the chamber while recording. At the end of the recording, OHSCs showing no activity or only interictal activity were further examined for viability and synaptic response. Extracellular field potentials were evoked in the CA1, CA3, and dentate gyrus (DG) regions followed by biphasic current pulse stimulation (±30 μA) to Schaffer collaterals, stratum lucidum, and perforant path, respectively. Only slices showing >500 μV of peak-to-peak field excitatory postsynaptic potential amplitude in each region were selected for data analysis. In the study, 9 of 60 slices showing only interictal-like activity did not meet the QC criteria described above and therefore were not included in the data analysis. Data were collected at 1 kHz sampling frequency. Since epileptiform activity is highly synchronized in all subregions of the hippocampus64, we chose one electrode in the DG granular cell layer to analyze epileptic activity. Since cultured slices were required to adjust to the recording chamber, we excluded signals from first 10 min and only last 30 min of recorded data were used for the data analysis. Automatic detection of ictal events was performed using Labview . Interictal epileptiform discharges were defined as paroxysmal discharges that are clearly distinguished from background activity, with an abrupt change in polarity occurring at low frequency (<2 Hz). Ictal epileptiform discharges were defined as paroxysmal discharges lasting more than 10 s occurring at higher frequency (≥2 Hz). If the next ictal event occurs within 10 s after the previous one, we considered the two as one ictal event.An assessment of spontaneous epileptiform activity was performed as described previously65 using the LDH Assay Kit (BioVision). The culture supernatants were diluted 2–3-folds with the assay buffer (BioVision) and colorimetric signals were measured at 450 nm by a microplate reader FlexStation 3 (Molecular Devices). LDH concentrations were calculated from the level of LDH activity that converts nicotinamide adenine dinucleotide (NAD) to NAD reduced (NADH). The concentration was expressed as mU/ml, where one unit of LDH generates 1 µM NADH per minute. Statistical differences were assessed using the GraphPad Prism v.7 software  by two-way ANOVA with Fisher's least significant difference post hoc test. P < 0.05 was considered significant.Culture supernatants were collected on DIV 7 (baseline before PLX3397 exposure) and then on DIV 14 and DIV 21, that is, after first and second week of exposure to 1 µM of PLX3397. Immediately after collection, the culture supernatants were kept at −80 °C until the assay was performed. Experiment was performed according to the manufacturer’s protocol. LDH was measured as a maker of cell death3). All animals had free access to standard pellet food and water before random assignment to experimental groups consisting of 10 mice each. All in vivo experiments were performed according to the guidelines of the National Rules on Animal Experiments in Belgium, ARRIVE, and the European Community Council Directive 2010/63/EU. All efforts were made to minimize animal suffering.Animals: Male Crl:NMRI(Han)-FR mice weighing 20–30 g were used in all experiments. The animals were kept on a 12/12-h light/dark cycle with lights on at 0600 hours and were housed at a temperature maintained at 20–21 °C and at humidity of about 40%. The mice were housed in groups of 10 per cage (38 × 26 × 14 cmn = 10 per group). Mice received either a single drug dose (acute treatment) or daily doses over 5 days (chronic treatment). Seizure tests were performed 4 h after the single dose or the fifth day of treatment, 4 h after the last dose.Drug treatment: PLX3397 (3 and 30 mg/kg) was administered by oral gavage (p.o.) in separate groups of animals  was placed on the eyes before electrical stimulation. The mice were observed for 10 s following stimulation and the incidence of tonic hindlimb extension was noted.MES model: MES was produced by a stimulator  using a current of 50 mA delivered with a pulse frequency of 50 Hz for 0.2 s through corneal electrodes66. Before infusion mice were briefly restrained and a 30-gauge needle attached to a 0.3-m-long polyethylene tubing (PE-10) was inserted into the lateral tail vein. The needle was gently secured to the tail with plastic tape. The tubing was connected to a syringe mounted on infusion pump . PTZ (5 mg/ml)  was infused at the rate of 0.25 ml/min in freely moving mice. The time from the start of the infusion to the onset of myoclonic, clonic, and tonic convulsions was recorded. The threshold doses of PTZ were calculated according to the following formula: threshold dose (mg/kg) = (PTZ concentration (mg/ml) × infusion rate (ml/s) × infusion duration (s) × 1000)/weight of mouse (g)).PTZ seizure threshold model: PTZ (intravenous) infusion was performed as previously described66. Briefly, corneal stimulation  was delivered by a constant-current device . A drop of 0.4% oxybuprocaine hydrochloride  was placed on the eyes before electrical stimulation. During the stimulation, mice were manually restrained and released into the observation cage (38 × 26 × 14 cm3) immediately after the current application. The seizures were often preceded by a brief period (~2–3 s) of intense locomotor agitation (wild running and jumping). The animals then exhibited a “stunned” posture associated with rearing, forelimb automatic movements and clonus, twitching of the vibrissae, and strub tail. At the end of the seizure, animals resumed their normal exploratory behavior. The animal was considered as having a seizure if it did not resume its normal exploratory behavior within 7 s from the stimulation. The duration and severity (Racine’s score) of seizures was also noted.6 Hz model: The 6 Hz model was carried out according to a previously described protocolStatistics: Seizure incidence data in the MES and 6 Hz models were compared with Fisher’s exact test. Seizure duration and seizure severity data in the 6 Hz model, as well as the threshold doses of PTZ for induction of myoclonic, clonic, and tonic seizures were expressed as means ± SD and compared with one-way ANOVA.5 cells/ml was plated in 12-well culture plates. For immunofluorescence, 250 μL of cell suspension at 3 × 105 cells/ml was plated in μ-Slide 8 Well Glass Bottom . The medium was replaced 24 h later, and experiments started 24 h after replacement of medium. Cells were treated with PLX 3397 1 µM (Selleckchem) or DMSO  for 30 min before adding PBS or mouse interleukin-1β (50 ng/ml) + interferon-γ (20 ng/ml) (R&D Systems). Twenty-four hours later, media were removed and plates frozen at −80 °C for RNA-seq experiment. For immunocytofluorescence one positive control was added to other conditions using 4 h of incubation with staurosporine 1 µM (Abcam). Cells were fixed at room temperature with 4% formaldehyde for 20 min.Primary microglia culture and treatment: Primary microglia were collected from P7 brain of OF1 strain mice . Microglia was obtained after cell dissociation and CD11B+ cell separation using Neural Tissue Dissociation Kit (P) and anti-CD11B microbeads (Microglia) (MACS Technology), according to the manufacturer’s protocol . Microglia were pelleted in macrophage-serum-free medium (Gibco) supplemented with 1% P/S (Gibco). For RNA-seq experiment, 1 ml/well of cell suspension at 6 × 10RNA extraction, quantification, and quality: Total RNA from primary microglia was extracted using NucleoSpin RNA XS according to the manufacturer’s instructions . RNA concentration and quality were assessed by spectrophotometry with the NanodropTM apparatus  and Experion RNA StdSens Analysis Kit (Bio-Rad).RNA-seq: mRNA was sequenced on Illumina HiSeq 4000 at an average read depth of 40 million reads per sample.Immunocytofluorescence and quantification: Immunofluorescence staining on ibidi chambers was performed at restroom temperature and started by 30 min of incubation in a blocking buffer /0.3% Triton X-100) followed by 2 h of incubation with biotinylated lycopersicon esculentum (Tomato) lectin  and rabbit anti-cleaved caspase-3 (Asp175) antibody  diluted in PBS/1% BSA/0.3%Triton X-100. After rinsing with PBS, cells were incubated 1 h with streptavidin Cy3  and donkey anti-rabbit Alexa 488  followed by a counterstaining with DAPI.2.Fives pictures per well were made using Nikon Eclipse Ti-E microscope (×10) and fields were automatically defined by the software not by the experimenter. An experimenter blind to treatment group performed all analyses. All cells were Tomato lectin positive (TL+). The number DAPI+ nucleus was counted as well as the cleaved caspase-3+ cells using the Fiji Software. The number of DAPI+ nucleus and cleaved caspase-3+ cells were expressed per mm2 for 1 week. Microglia were isolated from mixed glial cell cultures by shaking flasks for 1 h at 200 rpm at 37 °C, re-suspended in complete medium with 20 ng/ml M-CSF (R&D Systems) and grown for 7 days in 2-well culture insert 24-well (Ibidi) or 96-well (Greiner) plates.Primary microglia cell culture: Forebrains were isolated from post-natal day 7–8 (P7–8) mice (C57BL/6J) and meninges were carefully removed. Brains were dissociated using the Papain Dissociation System (Worthington) according to the manufacturer’s instructions. Homogenates were filtered through a 40 µm cell strainer  and re-suspended in complete medium: DMEM GlutaMAX (ThermoFisher Scientific) supplemented with 10% fetal bovine serum and 1% penicillin/streptomycin (P/S) (ThermoFisher Scientific). Single-cell suspensions were then transferred into T75 flasks and incubated at 37 °C in 5% CO2 until reaching approximately 80% confluence. Culture inserts were then carefully removed followed by washing of the cell monolayer with fresh complete medium and imaging of the scratch area using an EVOS digital inverted light microscope. Primary microglia were treated with 1 µM of PLX3397 for 24 h and the scratch area re-imaged. Microglia cell migration into the scratch area was quantified using ImageJ.Scratch wound migration assay: Primary microglia were seeded at a density of 30,000 cells per insert in 2-well culture insert 24-well plates (Ibidi). Cells were incubated at 37 °C in 5% COLDH assay: Cell death activity in brain slice culture was measured in slice-bathing fluids collected 4 h after exposure to zymosan beads. The LDH Activity Colorimetric Assay Kit (BioVision) was used following the manufacturer’s instructions.2. Cell viability was measured using a LIVE/DEAD Viability/Cytotoxicity Kit as per the manufacturer’s instructions (ThermoFisher Scientific). Labeled cells were fixed and imaged using a Zeiss AxioObserver Z1 fluorescent microscope (Zeiss).Live/dead assay: Primary mouse microglia isolated and cultured as described above were exposed to vehicle (medium + 0.01% DMSO) or 1 µM PLX3397 for 24 h at 37 °C at 4% CO2, 5% O2)-bubbled artificial cerebrospinal fluid solution (aCSF) consisting of 126 mM choline chloride, 3 mM KCl, 2.4 mM CaCl2, 1.3 mM MgCl2, 26 mM NaHCO3, 1.24 mM NaH2PO4, and 10 mM glucose. Slices were immediately placed for 1 h at 35 °C in incubation chambers  filled with carbogen-bubbled aCSF (with choline chloride replaced by a stoichiometric amount of NaCl). Slices were then incubated for 1 h at 37 °C followed by incubation with pH Rodo-conjugated zymosan bioparticles and prepared as per the manufacturer’s instructions (ThermoFisher Scientific) at 37 °C for 1 h. After washing slices with PBS, they were fixed in 4% paraformaldehyde for 1 h at room temperature before immunostaining. Fixed slices were incubated for 3 h with blocking solution  and incubated for 48 h with primary antibody: anti-Iba1 . Slices were washed 3× for 15 min in PBS and incubated with secondary antibody  for 3 h at room temperature. After washing in 1× PBS, slices were counterstained with DAPI and mounted. Images were acquired using the Zeiss LSM880 confocal microscope (Zeiss) and the number of particles engulfed by Iba1 + microglia and the number of phagocytic microglia counted.Ex vivo phagocytosis assay using acute brain slices: After exposure to isoflurane, PLX3397-treated pilocarpine mice were decapitated and brains rapidly dissected. Sagittal sections were generated at 300 µm thickness using a Leica VT 1200S vibratome (Leica) in ice-cold carbogen  were captured from random areas of vehicle-treated and PLX3397-treated pilocarpine brain sections and processed using the Zeiss ZEN 2.3 software (Zeiss). These images were then used for microglia morphology analysis using the ImageJ plugin t test.Statistical analysis: Results are presented as means ± standard error of the mean (SEM). Statistics were calculated using the GraphPad Prism 7 using two-tailed Student’s n = 2–4). Plasma samples were taken at 0.25, 0.5, 1, 2, and 6 h after dosing, while terminal plasma and brain samples were collected 24 h after dosing. In addition, pilocarpine NMRI mice (n = 8) were dosed at 3 and 30 mg/kg (p.o.), once a day for 1 week, and terminal plasma and brain samples were taken 24 h after the last administration and analyzed by liquid chromatography with tandem mass spectrometry bioanalytical method. The relationships between peak area ratio  and plasma concentration was linear over the range of 20–50,000 ng/ml for PLX3397. The relationships between peak area ratio  and brain concentration was linear over the range of 100–250,000 ng/g for PLX3397. Data are submitted to a 1/x2 weighing factor and UCB30542 is used as an internal standard for PLX3397. Raw data were smoothed (no. of smooths: 3), peaks were automatically integrated and integrations were checked individually.PLX3397 at 30 mg/kg (p.o.) was administered in naïve male NMRI  mice (Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9Supplementary Data 10Supplementary Data 11Supplementary Data 12Supplementary Data 13Supplementary Data 14Supplementary Data 15Supplementary Data 16


Salmonella in food environments is crucial. The role of food processing conditions such as cleaning and disinfection procedures on antimicrobial resistance emergence should especially be investigated. In this work, the prevalence and antimicrobial resistance of Salmonella and the microbial ecology of associated surfaces communities were investigated in a pig slaughterhouse before and after cleaning and disinfection procedures. Salmonella were detected in 67% of samples and isolates characterization revealed the presence of 15 PFGE-patterns belonging to five serotypes: S.4,5,12:i:-, Rissen, Typhimurium, Infantis and Derby. Resistance to ampicillin, sulfamethoxazole, tetracycline and/or chloramphenicol was detected depending on serotypes. 16S rRNA-based bacterial diversity analyses showed that Salmonella surface associated communities were highly dominated by the Moraxellaceae family with a clear site-specific composition suggesting a persistent colonization of the pig slaughterhouse. Cleaning and disinfection procedures did not lead to a modification of Salmonella susceptibility to antimicrobials in this short-term study but they tended to significantly reduce bacterial diversity and favored some genera such as Rothia and Psychrobacter. Such data participate to the construction of a comprehensive view of Salmonella ecology and antimicrobial resistance emergence in food environments in relation with cleaning and disinfection procedures.To guarantee food safety, a better deciphering of ecology and adaptation strategies of bacterial pathogens such as  In France, between 2008 and 2013, Salmonella was the leading cause of death related to contaminated food (26.2%)2. Pig products constitute common sources of human infections involving Salmonella and slaughtering line represent a critical stage for controlling its dissemination on the food chain4. The continuous introduction of bacteria, potentially including Salmonella, with each new pig band and the presence of a resident flora associated to the slaughter environment may indeed constitute an important risk for carcass contamination during the slaughtering or meat-cutting processes5 despite good hygiene practices. In order to guarantee food safety, a better understanding of Salmonella ecology and persistence strategies in such food environment constitutes a prerequisite. In this perspective, the role of interactions with resident flora in pathogenic Salmonella survival on the food chain6 require a particular interest. Indeed, several studies have showed that formation of biofilm and persistence of Salmonella could be promoted by the presence of other bacteria including strains directly isolated from food industries8. However, data about microbial ecology in pig slaughterhouses are lacking in the literature, thus stressing the need to improve our knowledge on microbial diversity and population dynamics in such food environments.Salmonellosis remains one of the main foodborne zoonosis in Europe with 94.530 cases in 2016Salmonella is becoming one of the most preoccupying issues for public health. Being a continuum between environment, animals and human health, food chain constitutes a privileged area for occurrence and spreading of antimicrobial resistance10. Food processing industries as slaughterhouses for instance can act as connecting paths including substantial selection for resistance due to procedures inherent to the industrial process such as cleaning and disinfection (C&D). In the last few years, an increasing number of studies have indeed highlighted the potential role of biocides in the selection of cross-resistance towards antibiotics in bacteria13. Nevertheless, the vast majority of them were conducted using pure culture models in laboratory conditions, and the current lack of data makes it difficult to evaluate the substantial risk to select bacteria with higher resistance to antibiotics through the use of biocides in industries and to determine which biocides is associated with the highest risk of antibiotic resistance cross-selection. The need to multiply environmental studies focusing on the characterization of cross-resistance to antibiotics in pathogens following use of biocides and including considerations about complex microbial ecology associated was therefore highlighted14.Alongside, antimicrobial resistance (AMR) in bacteria including foodborne pathogen as Salmonella prevalence and anti-microbial resistance (AMR) levels in a pig slaughterhouse, gaining an overview of associated bacterial community diversity and to examine how C&D steps impact microbial communities and potentially select isolates with reduced susceptibility to biocides and/or antibiotics.In this context, this project aimed at collecting data about p < 0.05). One aliquot of 10 ml was also directly frozen at −80 °C for sample conservation.The study was carrying out in a French pig slaughterhouse. The slaughterhouse was visited four times between March and May 2017. Samples were collected from the 6 following areas: 2 were located from the “dirty” zone at the dehairing machine (DH) and whips from the scraping machine (WH), and 4 were taken from the “clean” zone (after a singeing step) on the neck clipper machine (NC), carcass opener circular saw (CO), white offals gutter (WOG) and platform used for red offal removing (ROP). Each area was sampled twice at the same location for each sampling date, before and after C&D procedures. While the cleaning and disinfection procedures are highly dependent from the slaughterhouse, the pig slaughterhouse sampled in this work was chosen because it used biocide formualtions among the most used. Concretely, C&D procedures consisted here in daily application of a chlorinated alkaline solution along chain using foamer after slaughtering following by a rinsing step. An ethanol-based solution was additionally sprayed on cutting blades in contact with meat at NC and CO areas. In addition, an acid foaming solution was applied weekly along the slaughter chain. Samples were collected by swabbing 1 m² of surfaces using sterile wipes and placed individually in sterile Stomacher bags with filters  which were then transported to the laboratory in refrigerated conditions and processed within 12 h. Upon arrival, bags containing wipes were filled using 225 ml of One broth medium  and introduced in a stomacher  for blending during 1 min at high speed. Two aliquots of 20 ml of stomached suspension were directly sampled from bags and centrifuged at 10,000 g during 5 min and pellets were frozen at −80 °C for further DNA extraction and 16s rRNA sequencing. An additional aliquot of 1 ml was used to enumerate total bacterial flora after serial 10-fold dilutions in peptone water and plating on tryptone soya agar . Differences in bacterial counts between sampling area or before and after disinfection were analyzed statistically using analysis of variance  were transferred at 42 °C for enrichment. After 20–24 h, 10 µl were plated on Brilliance Salmonella agar  and incubated 24 h at 37 °C before identification of positive Salmonella colonies according to manufacturer instructions. Salmonella were then re-isolated on TSAye and incubated 20 h at 37 °C before being stocked at −80 °C in cryoprotective solution or used for PCR confirmation. PCR was performed using forward primer ST11 5′-GCCAACCATTGCTAAATTGGCGCA-3′ and reverse primer ST15 5′-GGTAGAAATTCCCAGCGGGTACTGG-3′15 and a LightCycler 480 thermocycler  as follows: 95 °C for 5 min for initial melting; 40 cycles at 95 °C for 30 s, 59 °C for 30 s, 72 °C for 30 s; and 72 °C for 10 min following by incubation at 4 °C.The Serotyping was performed according to the White-Kauffmann-Le Minor scheme (refs. 17).18. Salmonella enterica serotype Braenderup H9812 was used as the molecular size marker in the PFGE experiment19. Gels were stained with ethidium bromide and banding patterns were visualized under UV light, using the Gel Doc XR and Quantity One software (Bio-Rad). DNA patterns were analyzed with BioNumerics software . Algorithms available within the program were used to compare patterns. Dendrograms were produced, using the Dice coefficient and the unweighted pair group method with arithmetic averages (UPGMA), with a 1% tolerance limit and 1% optimization20. Each PFGE pattern differing by at least one band from a previously recognized type was considered to be a new pattern. Each new pattern was given a unique designation, as suggested by Peters et al.21, and added to the PFGE patterns library. The recommendations of Barrett et al.22 were also followed for the interpretation of PFGE patterns.PFGE using XbaI restriction enzyme was carried out with a CHEF-DR III system (Bio-Rad), according to the PulseNet Protocol5 CFU/well in presence of various concentrations of biocides in a 96-wells microtiter plate . Plates were then incubated at 37 °C for 24 h and MIC was determined as the lowest concentration of biocide that prevents bacterial growth. All determinations of MIC were repeated twice. Statistical differences between MIC distribution for biocides between strains isolated before and after C&D procedures or between serotypes were assessed using a Student test (significance for p < 0.05).Minimal inhibitory concentrations (MIC) were determined using a standard microdilution method for the 3 biocide solutions used in the slaughterhouse: a chlorinated alkaline solution, an ethanol-based solution and an acid foaming solution. Bacterial inocula were prepared from overnight cultures diluted in Muller-Hinton broth to reach from 1 to 3.10http://mic.eucast.org).Antibiotic susceptibility tests were performed using a standard microdilution method with the Sensititre® system on EUVSEC plates  using a panel of 14 antimicrobial substances according to manufacturer instructions. The strains were interpreted as resistant to antibiotics according to the epidemiological resistance cut-off determined from EUCAST  and 5 µl of adequate 10 X PCR buffer , 1 µl of 10 mM dNTP (Sigma), 1.25 µM of each primer (20 µM) and 10 ng of DNA template. PCR was performed using a LightCycler 480 thermocycler as follows: 94 °C for 60 s for initial melting; 30 cycles at 94°c for 60 s, 65 °C for 60 s, 72 °C for 60 s; and 72 °C for 10 min following by incubation at 4 °C. Products were then verified on a 1.5% agarose gel before being sent to GeT-PLaGe Genotoul plateform  for paired-end sequencing on Illumina MiSeq platform  at a read length of 2 × 250 pb.Frozen pellet obtained from 20 ml sample extract after stomaching were centrifuged and DNA extraction was performed on the pellet. Nucleospin® tissue kit (Macherey-Nagel) was used for the extraction according to manufacturer instructions. Concentration and quality of extracted DNA were checked using a BioSpec-nano spectrophotometer (Shimadzu). The bacterial V3-V4 region of the 16S rRNA gene was then amplified using the forward primer 5′-CTTTCCCTACACGACGCTCTTCCGATCTACGGRAGGCAGCAG-3′ and the reverse primer 5′-GGAGTTCAGACGTGTGCTCTTCCGATCTTACCAGGGTATCTAATCCT-3′24 implemented on a galaxy instance . Bacterial 16S rRNA paired-end reads were merged with a maximum of 10% mismatches in the overlap region using FLASH25. Denoising procedures consisted in discarding reads with no expected length and the ones containing ambiguous bases (N). After dereplication, the clusterisation tool ran with SWARM26 with an aggregation distance equal to 3. Chimeras were then removed using VSEARCH27 and sequences were filtered to keep OTUs  accounting for at least 0.005% of all sequences28. Taxonomic affiliation was performed with both RDP Classifier29 and Blastn+30 against the SILVA 132 pintail 100 database31.Illumina sequences were processed using the FROGS pipeline 32. Principal coordinate analysis (PCoA) were performed on weighted unifrac dissimilarity matrices calculated using the R package phyloseq. Using non-parametric permutation-based multivariate analysis of variance  on unifrac and weighted unifrac-based distance matrices, we tested for significant differences in community structure. Assumptions of the adonis test were verified using the betadisper function in the R package vegan, which tests the multivariate homogeneity of group dispersions. Pairwise comparisons were performed on weighted unifrac distance matrix using pairwise.adonis function from “vegan” package and Bonferroni p-value correction (significance for p < 0.05). Figures were prepared with R and the package ggplot2.Data set was rarefied and alpha diversity indexes  were calculated and further plotted using the R “phyloseq” package33. Relative abundances of all features were first compared by using the nonparametric Kruskal-Wallis rank sum test (significance for p < 0.05), and each statistically significant feature was further subjected to effect size estimation using LDA.Differentially abundant taxa in bacterial populations depending on various factors were identified using the linear discriminant analysis (LDA) effect size (LEfSe)p < 0.05) from 6.15 ± 0.26 to 4.66 ± 0.79 log (CFU)/cm² after C&D.A total of 48 wipes taken before (24) and after (24) disinfection at six different sites in a pig slaughterhouse and at four different dates were collected. Total flora enumeration showed similar bacterial counts for the different areas sampled with values comprised between 3.34 ± 1,87 and 4.68 ± 0.56 log (CFU)/cm² except for the dehairing area (DH) before C&D procedures which exhibited significant higher bacterial counts with 6.15 ± 0.26 log (CFU)/cm²  with 18 and 20 strains isolated before and after C&D procedures respectively. There was no significant difference between numbers of positive samples before and after C&D procedures. All sites investigated were positive for Salmonella except the neck clipper for which Salmonella was never isolated , serotype Infantis reached 10.5% of the total number of isolates with a unique PFGE patterns (B12) and serotype Derby represented 8% of strains isolated and was subdivided in 3 PFGE patterns . As showed in Table Salmonella-positive sampling area and for various dates, serotype Typhimurium was only isolated at a single date (04/24) at DH, WOG and ROP areas. Interestingly, serotype Infantis was exclusively found at CO area but repeatedly for three of the four dates investigated. Serotypes Rissen and Derby were occasionally isolated both at two different dates (03/22 and 04/03) and sampling areas (WH and WOG for Derby and WOG and ROP areas for Rissen).A total of 38 Salmonella strains were obtained for a total of 14 antibiotics. MIC values obtained are available in Table Antibiotic susceptibilities of the 38 isolated Salmonella isolated before and after C&D procedures are displayed in Fig. MICs of the three biocide solutions used in the slaughterhouse were determined using a broth microdilution method. MIC distributions for the A total of 47 samples were sequenced successfully (the sample from the 03/22 before C&D at the carcass opener area (CO) did not yield sufficient DNA for sequencing). After filtering for quality, length and removal of chimera, a total of 1 175,969 sequences were obtained corresponding to an average read number per sample of 25,020 ± 11,073 reads. A total of 441 operational taxonomic units (OTUs) were obtained after bioinformatics analysis.gamma-proteobacteria in the slaughterhouse with an average relative abundance of 75.8 ± 5.3%  level  were significantly more abundant in Salmonella-negative populations. Conversely, Acinetobacter, Hydrogenophilus, Corynebacterium, Prevotella, Flavobacterium, Lactobacillus, and Comamonas (from higher to lower LDA scores) were significantly more abundant in samples harboring Salmonella.LEfSE analysis was then used to identify differentially abundant bacterial taxa between ons Fig. . Flavobap = 0.9905 and 0.1336 for unifrac and weighted unifrac distance respectively, Fig. p < 0.0001, Fig. p < 0.05) between all sampling areas except between WH and WOG, ROP, DH for unifrac distance and between WH andWOG, ROP, DH and CO for weighted unifrac distance, stressing the heterogeneity of WH populations depending on sampling date compared to the other sampling areas. The homogeneity of dispersion between each sampling area was checked using the betadisper function and revealed that NC area demonstrated significantly (p < 0.05) a different dispersion compared with CO and WOG for unifrac distance and only with WOG for weighted unifrac distance.A principal coordinate analysis (PCoA) using Unifrac and weighted Unifrac distances was performed to visualize differences in microbial population structures among samples depending on sampling areas, sampling dates or between samples before and after C&D Fig. . While Pp < 0.0001 and R² = 0.23319) and weighted unifrac  distances , Rothia  and Psychrobacter were significantly more abundant (p < 0.05) in samples after C&D procedures with LDA score >3. Interestingly the significant increase of Psychrobacter relative abundance after C&D is only due to the increase in relative abundance of one dominant OTU  which accounted for 58% of total Salmonella isolates. Such observations are consistent with its fast emergence reported since a decade and its increasing role in human infections37 and highlight the importance to pay specific attention to this serotype. Some PFGE-patterns were recurrently isolated at various locations in the slaughterhouse and for different dates. In one hand, our results could reflect a persistent colonization of the slaughterhouse, especially as some recurrent strains (such as B01 or B12) were also found before and after C&D for some sampling areas at a given date, underlining their ability to persist despite hygienic procedures. The potential cross-contamination of incoming pig carcasses during the slaughtering process throughout contact with Salmonella-positive surfaces in the slaughterhouse may occur at some sites and therefore represent a risk for food safety39. On the other hand, it could not be excluded that some strains are also highly prevalent in pigs and were thus continuously introduced in the slaughterhouse with new pig bands to be slaughtered. Strains with PFGE-patterns B04, B09 and B10 have been especially very frequently isolated in French pig slaughterhouses from meat, carcass or feces .In the present work, Salmonella was not isolated from NC area could be related to the location of this area, just after a singeing step that likely enabled the removal of Salmonella from carcasses and thus limited the contamination of the NC area. In addition, the fact that NC was also disinfected using ethanol-based solution should participate to maintain a low level of Salmonella contamination at this area. The carcass opening at the next step (CO) and the potentially associated intestinal perforation could then enable Salmonella to transfer from intestinal tract to CO surfaces, explaining why Salmonella strains were isolated at CO and then at following sampling areas. An increase of Salmonella prevalence on pig carcasses was indeed mostly observed after carcass opening and evisceration step41. Together, these observations again pointed out the criticality of the carcass opening step in controlling Salmonella risk at slaughterhouses as already mentioned42.The fact that Moraxellaceae family and especially four genera: Acinetobacter, Moraxella, Psychrobacter and Enhydrobacter. From our knowledge, it is the first 16s rDNA analysis of microbial diversity from pig slaughterhouse environment, therefore there is no available data for direct comparison in the literature. Nevertheless, various published analyses of pig gut microbiome showed the dominance of Firmicutes, Bacteroidetes and Proteobacteria phyla in agreement with the data reported here46. Such observations are indeed coherent with the fact that bacterial populations found on the slaughterhouse surfaces are initially originating from pig carcasses and then colonize the slaughterhouse environment. In line with this observation, by evaluating the nasal microbiota composition in slaughter-aged pigs using 16s rDNA sequencing, Weese et al.47 found that Moraxella, Psychrobacter and Acinetobacter were among the most dominant genera in healthy pigs with relative abundance of 35.4, 21.1 and 4.8% respectively, in consistency with data presented here.The sequencing and analysis of the v3-v4 region of the rDNA 16S sequences revealed that bacterial communities in the slaughterhouse were clearly dominated by the Moraxellaceae family. These observations were supported by the analysis of composition of these genera at the OTUs level . Gantzhorn et al.49 found similar results while reporting no difference in MICs toward antibiotics nor in the number of resistances of Salmonella isolates obtained before and after C&D in six Danish pig slaughterhouses. However, by analyzing susceptibilities of Pseudomonas spp. isolated from goat and lamb slaughterhouses to biocides and antibiotics, Lavilla-Lerma et al.50 found statistical correlations between both antimicrobials revealing a co- or cross-resistance between antimicrobials especially between polyhexamethylene guanidine hydrochloride or triclosan and different antibiotics. First, the relatively short observation time of two months in the present study may not enable to observe an evolution of Salmonella antimicrobials susceptibilities. It could thus be interesting to go back in the same slaughterhouse and to sample the same areas to compare the antimicrobial resistance profiles of Salmonella strain on a longer period. Furthermore, it would be interesting to also collect data about antimicrobial resistance phenotypes of the associated dominant and persistent genera identified here in the slaughterhouse . Indeed, these resident populations undergo repeatedly C&D procedures and are more likely to adapt in the long term. Even though they are not necessarily pathogenic strains, they can represent a potential risk throughout interactions with pathogens as they could play a role in pathogen persistence and/or constitute a potential reservoir for antimicrobial resistances for example.In one hand, comparison of MIC values obtained for 52, chlorine53, dibromonitrilopropionamide54, and other molecules and mixed disinfectants55. Analyzing the impact of the additional ethanol-based disinfection on cutting blades, we also found that some genera as Moraxella, Enhydrobacter and Paracoccus were significantly more abundant at sampling areas treated with ethanol suggesting a potential role of ethanol disinfection step in shaping bacterial populations at NC and CO areas.On the other hand, C&D procedures applied here modified bacterial community composition and led to a loss of richness and evenness in bacterial populations as revealed by the significant decrease of all α-diversity indexes. Likewise, different studies highlighted a loss of diversity in microbial populations after exposure to various disinfectants and active substances including quaternary ammonium-based solutionsBergeyella, Brevundimonas, and especially Rothia and Psychrobacter. Interestingly, Psychrobacter was also identified as one of the dominant genus in the slaughterhouse. Furthermore, one single dominant OTU . Interestingly, C&D procedures did not lead to the selection of Salmonella with higher resistance toward antibiotics and biocides but markedly impacted the composition of bacterial populations associated to slaughterhouse surfaces. From these observations, it seems essential to study antimicrobial resistance dynamics in industries by integrating the whole bacterial community using global approach as shotgun metagenomics. Indeed, resident flora (as Psychrobacter here) constitutes the best illustration of bacterial adaptation in a given area and could participate to the dissemination of antimicrobial resistance throughout interactions with pathogens. Overall, data collected here are important for the construction of a realistic vision of Salmonella ecology and AMR emergence in food industries with a focus on the potential role of C&D procedures.In this contribution, original data on Supplementary material